ScaDaMaLe Course site and book

Introduction

  • Course Name: Scalable Data Science and Distributed Machine Learning
  • Course Acronym: ScaDaMaLe or sds-3.x.

The course was designed to be the fifth and final mandatory course in the AI-Track of the WASP Graduate School in 2021. From 2022 ScaDaMaLe is an optional course for WASP students who have successfully completed the mandatory courses. It is given in three modules. In addition to academic lectures there are invited guest speakers from industry.

The course can also be taken by select post-graduate students at Uppsala University as a Special Topics Course from the Department of Mathematics.

This site provides course contents for the three modules. This content is referred to as sds-3.x here.

Module 1 – Introduction to Data Science: Introduction to fault-tolerant distributed file systems and computing.

The whole data science process illustrated with industrial case-studies. Practical introduction to scalable data processing to ingest, extract, load, transform, and explore (un)structured datasets. Scalable machine learning pipelines to model, train/fit, validate, select, tune, test and predict or estimate in an unsupervised and a supervised setting using nonparametric and partitioning methods such as random forests. Introduction to distributed vertex-programming.

Module 2 – Distributed Deep Learning: Introduction to the theory and implementation of distributed deep learning.

Classification and regression using generalised linear models, including different learning, regularization, and hyperparameters tuning techniques. The feedforward deep network as a fundamental network, and the advanced techniques to overcome its main challenges, such as overfitting, vanishing/exploding gradient, and training speed. Various deep neural networks for various kinds of data. For example, the CNN for scaling up neural networks to process large images, RNN to scale up deep neural models to long temporal sequences, and autoencoder and GANs.

Module 3 – Decision-making with Scalable Algorithms

Theoretical foundations of distributed systems and analysis of their scalable algorithms for sorting, joining, streaming, sketching, optimising and computing in numerical linear algebra with applications in scalable machine learning pipelines for typical decision problems (eg. prediction, A/B testing, anomaly detection) with various types of data (eg. time-indexed, space-time-indexed and network-indexed). Privacy-aware decisions with sanitized (cleaned, imputed, anonymised) datasets and datastreams. Practical applications of these algorithms on real-world examples (eg. mobility, social media, machine sensors and logs). Illustration via industrial use-cases.

Expected Reference Readings

Note that you need to be logged into your library with access to these publishers:

Course Contents

The databricks notebooks will be made available as the course progresses in the : - course site at: - [site](https://lamastex.github.io/scalable-data-science/sds/3/x/) and [book](https://lamastex.github.io/ScaDaMaLe/index.html) - and course book at: - https://lamastex.github.io/ScaDaMaLe/index.html

  • You may upload Course Content into Databricks Community Edition from:

Course Assessment

There will be minimal reading and coding exercises that will not be graded. The main assessment will be based on a peer-reviewed group project. The group project will include notebooks/codes along with a video of the project presentation. Each group cannot have more than four members and should be seen as an opportunity to do something you are passionate about or interested in, as opposed to completing and auto-gradeable programming assessment in the shortest amount of time.

Detailed instructions will be given in the sequel.

Course Sponsors

The course builds on contents developed since 2016 with support from New Zealand's Data Industry. The 2017-2019 versions were academically sponsored by Uppsala University's Inter-Faculty Course grant, Department of Mathematics and The Centre for Interdisciplinary Mathematics and industrially sponsored by databricks, AWS and Swedish data industry via Combient AB, SEB and Combient Mix AB. This 2021 version is academically sponsored by AI-Track of the WASP Graduate School and Centre for Interdisciplinary Mathematics and industrially sponsored by databricks and AWS via databricks University Alliance and Combient Mix AB via industrial mentorships.

Course Instructor

I, Raazesh Sainudiin or Raaz, will be an instructor for the course.

I have

  • more than 15 years of academic research experience in applied mathematics and statistics and
  • over 3 and 5 years of full-time and part-time experience in the data industry.

I currently (2020) have an effective joint appointment as:

Quick links on Raaz's background:

Industrial Case Study

We will see an industrial case-study that will illustrate a concrete data science process in action in the sequel.

What is the Data Science Process

The Data Science Process in one picture

what is sds?

What is scalable data science and distributed machine learning?

Scalability merely refers to the ability of the data science process to scale to massive datasets (popularly known as big data).

For this we need distributed fault-tolerant computing typically over large clusters of commodity computers -- the core infrastructure in a public cloud today.

Distributed Machine Learning allows the models in the data science process to be scalably trained and extract value from big data.

What is Data Science?

It is increasingly accepted that Data Science

is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.

Data science is a "concept to unify statistics, data analysis and their related methods" in order to "understand and analyze actual phenomena" with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, domain knowledge and information science. Turing award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge.

Now, let us look at two industrially-informed academic papers that influence the above quote on what is Data Science, but with a view towards the contents and syllabus of this course.

Source: Vasant Dhar, Data Science and Prediction, Communications of the ACM, Vol. 56 (1). p. 64, DOI:10.1145/2500499

key insights in the above paper

  • Data Science is the study of the generalizabile extraction of knowledge from data.
  • A common epistemic requirement in assessing whether new knowledge is actionable for decision making is its predictive power, not just its ability to explain the past.
  • A data scientist requires an integrated skill set spanning
    • mathematics,
    • machine learning,
    • artificial intelligence,
    • statistics,
    • databases, and
    • optimization,
    • along with a deep understanding of the craft of problem formulation to engineer effective solutions.

Source: Machine learning: Trends, perspectives, and prospects, M. I. Jordan, T. M. Mitchell, Science 17 Jul 2015: Vol. 349, Issue 6245, pp. 255-260, DOI: 10.1126/science.aaa8415

key insights in the above paper

  • ML is concerned with the building of computers that improve automatically through experience
  • ML lies at the intersection of computer science and statistics and at the core of artificial intelligence and data science
  • Recent progress in ML is due to:
    • development of new algorithms and theory
    • ongoing explosion in the availability of online data
    • availability of low-cost computation (*through clusters of commodity hardware in the *cloud* )
  • The adoption of data science and ML methods is leading to more evidence-based decision-making across:
    • health sciences (neuroscience research, )
    • manufacturing
    • robotics (autonomous vehicle)
    • vision, speech processing, natural language processing
    • education
    • financial modeling
    • policing
    • marketing

But what is Data Engineering (including Machine Learning Engineering and Operations) and how does it relate to Data Science?

Data Engineering

There are several views on what a data engineer is supposed to do:

Some views are rather narrow and emphasise division of labour between data engineers and data scientists:

  • https://www.oreilly.com/ideas/data-engineering-a-quick-and-simple-definition
    • Let's check out what skills a data engineer is expected to have according to the link above.

"Ian Buss, principal solutions architect at Cloudera, notes that data scientists focus on finding new insights from a data set, while data engineers are concerned with the production readiness of that data and all that comes with it: formats, scaling, resilience, security, and more."

What skills do data engineers need? Those “10-30 different big data technologies” Anderson references in “Data engineers vs. data scientists” can fall under numerous areas, such as file formats, > ingestion engines, stream processing, batch processing, batch SQL, data storage, cluster management, transaction databases, web frameworks, data visualizations, and machine learning. And that’s just the tip of the iceberg.

Buss says data engineers should have the following skills and knowledge:

  • They need to know Linux and they should be comfortable using the command line.
  • They should have experience programming in at least Python or Scala/Java.
  • They need to know SQL.
  • They need some understanding of distributed systems in general and how they are different from traditional storage and processing systems.
  • They need a deep understanding of the ecosystem, including ingestion (e.g. Kafka, Kinesis), processing frameworks (e.g. Spark, Flink) and storage engines (e.g. S3, HDFS, HBase, Kudu). They should know the strengths and weaknesses of each tool and what it's best used for.
  • They need to know how to access and process data.

Let's dive deeper into such highly compartmentalised views of data engineers and data scientists and the so-called "machine learning engineers" according the following view:

  • https://www.oreilly.com/ideas/data-engineers-vs-data-scientists

embedded below.

The Data Engineering Scientist as "The Middle Way"

Here are some basic axioms that should be self-evident.

  • Yes, there are differences in skillsets across humans
    • some humans will be better and have inclinations for engineering and others for pure mathematics by nature and nurture
    • one human cannot easily be a master of everything needed for innovating a new data-based product or service (very very rarely though this happens)
  • Skills can be gained by any human who wants to learn to the extent s/he is able to expend time, energy, etc.

For the Scalable Data Engineering Science Process: towards Production-Ready and Productisable Prototyping for the Data-based Factory we need to allow each data engineer to be more of a data scientist and each data scientist to be more of a data engineer, up to each individual's comfort zones in technical and mathematical/conceptual and time-availability planes, but with some minimal expectations of mutual appreciation.

This course is designed to help you take the first minimal steps towards such a data engineering science.

In the sequel it will become apparent why a team of data engineering scientists with skills across the conventional (2021) spectrum of data engineer versus data scientist is crucial for Production-Ready and Productisable Prototyping for the Data-based Factory, whose outputs include standard AI products today.

Standing on shoulders of giants!

This course was originally structured from two other edX courses from 2015. Unfortunately, these courses and their content,including video lectures and slides, are not available openly any longer.

  • BerkeleyX/CS100-1x, Introduction to Big Data Using Apache Spark by Anthony A Joseph, Chancellor's Professor, UC Berkeley
  • BerkeleyX/CS190-1x, Scalable Machine Learning by Ameet Talwalkar, Ass. Prof., UC Los Angeles

This course will be an expanded and up-to-date scala version with an emphasis on individualized course project as opposed to completing labs that test sytactic skills that are auto-gradeable.

We will also be borrowing more theoretical aspects from the following course:

Note the Expected Reference Readings above for this course.

A Brief Tour of Data Science

History of Data Analysis and Where Does "Big Data" Come From?

  • A Brief History and Timeline of Data Analysis and Big Data

  • https://en.wikipedia.org/wiki/Big_data

  • https://whatis.techtarget.com/feature/A-history-and-timeline-of-big-data

  • Where does Data Come From?

  • Some of the sources of big data.

    • online click-streams (a lot of it is recorded but a tiny amount is analyzed):
      • record every click
      • every ad you view
      • every billing event,
      • every transaction, every network message, and every fault.
    • User-generated content (on web and mobile devices):
      • every post that you make on Facebook
      • every picture sent on Instagram
      • every review you write for Yelp or TripAdvisor
      • every tweet you send on Twitter
      • every video that you post to YouTube.
    • Science (for scientific computing):
      • data from various repositories for natural language processing:
        • Wikipedia,
        • the Library of Congress,
        • twitter firehose and google ngrams and digital archives,
      • data from scientific instruments/sensors/computers:
        • the Large Hadron Collider (more data in a year than all the other data sources combined!)
        • genome sequencing data (sequencing cost is dropping much faster than Moore's Law!)
        • output of high-performance computers (super-computers) for data fusion, estimation/prediction and exploratory data analysis
    • Graphs are also an interesting source of big data (network science).
      • social networks (collaborations, followers, fb-friends or other relationships),
      • telecommunication networks,
      • computer networks,
      • road networks
    • machine logs:
      • by servers around the internet (hundreds of millions of machines out there!)
      • internet of things.

Data Science with Cloud Computing and What's Hard about it?

Cloud computing is the on-demand availability of computer system resources, especially data storage (cloud storage) and computing power, without direct active management by the user. Large clouds often have functions distributed over multiple locations, each location being a data center. Cloud computing relies on sharing of resources to achieve coherence and economies of scale, typically using a "pay-as-you-go" model which can help in reducing capital expenses but may also lead to unexpected operating expenses for unaware users.

  • In fact, if you are logged into https://*.databricks.com/* you are computing in the cloud! So the computations are actually running in an instance of the hardware available at a data center like the following:

  • Here is a data center used by CERN in 2010.

  • What's hard about scalable data science in the cloud?

    • To analyse datasets that are big, say more than a few TBs, we need to split the data and put it in several computers that are networked - *a typical cloud *
    • However, as the number of computer nodes in such a network increases, the probability of hardware failure or fault (say the hard-disk or memory or CPU or switch breaking down) also increases and can happen while the computation is being performed
    • Therefore for scalable data science, i.e., data science that can scale with the size of the input data by adding more computer nodes, we need fault-tolerant computing and storage framework at the software level to ensure the computations finish even if there are hardware faults.

Here is a recommended light reading on What is "Big Data" -- Understanding the History (18 minutes): - https://towardsdatascience.com/what-is-big-data-understanding-the-history-32078f3b53ce

What should you be able to do at the end of this course?

By following these online interactions in the form of lab/lectures, asking questions, engaging in discussions, doing HOMEWORK assignments and completing the group project, you should be able to:

  • Understand the principles of fault-tolerant scalable computing in Spark
    • in-memory and generic DAG extensions of Map-reduce
    • resilient distributed datasets for fault-tolerance
    • skills to process today's big data using state-of-the art techniques in Apache Spark 3.0, in terms of:
      • hands-on coding with realistic datasets
      • an intuitive understanding of the ideas behind the technology and methods
      • pointers to academic papers in the literature, technical blogs and video streams for you to futher your theoretical understanding.
  • More concretely, you will be able to:
    • Extract, Transform, Load, Interact, Explore and Analyze Data
    • Build Scalable Machine Learning Pipelines (or help build them) using Distributed Algorithms and Optimization
  • How to keep up?
    • This is a fast-changing world.
    • Recent videos around Apache Spark are archived here (these videos are a great way to learn the latest happenings in industrial R&D today!):
  • What is mathematically stable in the world of 'big data'?
    • There is a growing body of work on the analysis of parallel and distributed algorithms, the work-horse of big data and AI.
    • We will see some of this in a theoretical module later, but the immediate focus here is on how to write programs and analyze data.

ScaDaMaLe Course site and book

Why Apache Spark?

  • Apache Spark: A Unified Engine for Big Data Processing By Matei Zaharia, Reynold S. Xin, Patrick Wendell, Tathagata Das, Michael Armbrust, Ankur Dave, Xiangrui Meng, Josh Rosen, Shivaram Venkataraman, Michael J. Franklin, Ali Ghodsi, Joseph Gonzalez, Scott Shenker, Ion Stoica Communications of the ACM, Vol. 59 No. 11, Pages 56-65 10.1145/2934664

Apache Spark ACM Video

Right-click the above image-link, open in a new tab and watch the video (4 minutes) or read about it in the Communications of the ACM in the frame below or from the link above.

**Key Insights from Apache Spark: A Unified Engine for Big Data Processing **

  • A simple programming model can capture streaming, batch, and interactive workloads and enable new applications that combine them.
  • Apache Spark applications range from finance to scientific data processing and combine libraries for SQL, machine learning, and graphs.
  • In six years, Apache Spark has grown to 1,000 contributors and thousands of deployments.

Key Insights

Spark 3.0 is the latest version now (20200918) and it should be seen as the latest step in the evolution of tools in the big data ecosystem as summarized in https://towardsdatascience.com/what-is-big-data-understanding-the-history-32078f3b53ce:

Spark in context

Alternatives to Apache Spark

There are several alternatives to Apache Spark, but none of them have the penetration and community of Spark as of 2021.

For real-time streaming operations Apache Flink is competitive. See Apache Flink vs Spark – Will one overtake the other? for a July 2021 comparison. Most scalable data science and engineering problems faced by several major industries in Sweden today are routinely solved using tools in the ecosystem around Apache Spark. Therefore, we will focus on Apache Spark here which still holds the world record for 10TB or 10,000 GB sort by Alibaba cloud in 06/17/2020.

The big data problem

Hardware, distributing work, handling failed and slow machines

Let us recall and appreciate the following:

  • The Big Data Problem
    • Many routine problems today involve dealing with "big data", operationally, this is a dataset that is larger than a few TBs and thus won't fit into a single commodity computer like a powerful desktop or laptop computer.
  • Hardware for Big Data
  • The best single commodity computer can not handle big data as it has limited hard-disk and memory
  • Thus, we need to break the data up into lots of commodity computers that are networked together via cables to communicate instructions and data between them - this can be thought of as a cloud
  • How to distribute work across a cluster of commodity machines?
    • We need a software-level framework for this.
  • How to deal with failures or slow machines?
    • We also need a software-level framework for this.

Key Papers

MapReduce and Apache Spark.

MapReduce as we will see shortly in action is a framework for distributed fault-tolerant computing over a fault-tolerant distributed file-system, such as Google File System or open-source Hadoop for storage.

  • Unfortunately, Map Reduce is bounded by Disk I/O and can be slow
    • especially when doing a sequence of MapReduce operations requirinr multiple Disk I/O operations
  • Apache Spark can use Memory instead of Disk to speed-up MapReduce Operations
    • Spark Versus MapReduce - the speed-up is orders of magnitude faster
  • SUMMARY
    • Spark uses memory instead of disk alone and is thus fater than Hadoop MapReduce
    • Spark's resilience abstraction is by RDD (resilient distributed dataset)
    • RDDs can be recovered upon failures from their lineage graphs, the recipes to make them starting from raw data
    • Spark supports a lot more than MapReduce, including streaming, interactive in-memory querying, etc.
    • Spark demonstrated an unprecedented sort of 1 petabyte (1,000 terabytes) worth of data in 234 minutes running on 190 Amazon EC2 instances (in 2015).
    • Spark expertise corresponds to the highest Median Salary in the US (~ 150K)

Next let us get everyone to login to databricks (or another Spark platform) to get our hands dirty with some Spark code!

ScaDaMaLe Course site and book

Login to databricks

We will use databricks community edition and later on the databricks project shard granted for this course under the databricks university alliance with cloud computing grants from databricks for waived DBU units and AWS.

Please go here for a relaxed and detailed-enough tour (later):

databricks community edition

  1. First obtain a free Obtain a databricks community edition account at:
  1. Let's get an overview of the databricks managed cloud for processing big data with Apache Spark

DBC Essentials: What is Databricks Cloud?

DB workspace, spark, platform

DBC Essentials: Shard, Cluster, Notebook and Dashboard

DB workspace, spark, platform

DBC Essentials: Team, State, Collaboration, Elastic Resources in one picture

DB workspace, spark, platform

You Should All Have databricks community edition account by now! and have successfully logged in to it.

Import Course Content Now!

Two Steps:

  1. Create a folder named scalable-data-science in your Workspace (NO Typos due to hard-coding of paths in the sequel!)

Cloud-free Computing Environment

(Optional but strongly recommended)

Before we dive into Scala crash course in a notebook, let's take a look at TASK 2 of the first step in the instructions to set up a local and "cloud-free" computing environment, say on your laptop computer here:

This can be handy for prototyping quickly and may even be necessary due to sensitivity of data in certain projects that mandate the data to be confined to some on-premise cluster, etc.

NOTE: This can be done as an optional exercise as it heavily depends on your local computing environment and your software skills or willingness to acquire them.

CAVEAT: The docker-compose prepared for your local environment uses Spark 2.x instead of 3.x, but most of the contents here would run in either version of Spark. - Feel free to make PR with latest versions of Spark :)

ScaDaMaLe Course site and book

Please go here for a relaxed and detailed-enough tour (later):

Multi-lingual Notebooks

Write Spark code for processing your data in notebooks.

Note that there are several open-sourced notebook servers including:

Here, we are mainly focused on using databricks notebooks due to its effeciently managed engineering layers over AWS (or Azure public clouds).

NOTE: You should have already cloned this notebook and attached it to a cluster that you started in the Community Edition of databricks by now.

Databricks Notebook

Next we delve into the mechanics of working with databricks notebooks. But many of the details also apply to other notebook environments with minor differences.

Notebooks can be written in Python, Scala, R, or SQL.

  • This is a Scala notebook - which is indicated next to the title above by (Scala).
  • One can choose the default language of the notebook when it is created.

Creating a new Notebook

Change Name

  • Click the tiangle on the right side of a folder to open the folder menu.
  • Select Create > Notebook.
  • Enter the name of the notebook, the language (Python, Scala, R or SQL) for the notebook, and a cluster to run it on.

Cloning a Notebook

  • You can clone a notebook to create a copy of it, for example if you want to edit or run an Example notebook like this one.
  • Click File > Clone in the notebook context bar above.
  • Enter a new name and location for your notebook. If Access Control is enabled, you can only clone to folders that you have Manage permissions on.

Clone Or Import This Notebook

  • From the File menu at the top left of this notebook, choose Clone or click Import Notebook on the top right. This will allow you to interactively execute code cells as you proceed through the notebook.

Menu Bar Clone Notebook * Enter a name and a desired location for your cloned notebook (i.e. Perhaps clone to your own user directory or the "Shared" directory.) * Navigate to the location you selected (e.g. click Menu > Workspace > Your cloned location)

Attach the Notebook to a cluster

  • A Cluster is a group of machines which can run commands in cells.
  • Check the upper left corner of your notebook to see if it is Attached or Detached.
  • If Detached, click on the right arrow and select a cluster to attach your notebook to.

Attach Notebook

Deep-dive into databricks notebooks

Let's take a deeper dive into a databricks notebook next.

Quick Note Cells are units that make up notebooks

A Cell

Cells each have a type - including scala, python, sql, R, markdown, filesystem, and shell.

  • While cells default to the type of the Notebook, other cell types are supported as well.
  • This cell is in markdown and is used for documentation. Markdown is a simple text formatting syntax.

Create and Edit a New Markdown Cell in this Notebook

  • When you mouse between cells, a + sign will pop up in the center that you can click on to create a new cell.

New Cell * Type %md Hello, world! into your new cell (%md indicates the cell is markdown).

  • Click out of the cell to see the cell contents update.

    Run cell

Hello, world!

Running a cell in your notebook.

You Try Now! Just double-click the cell below, modify the text following %md and press Ctrl+Enter to evaluate it and see it's mark-down'd output.

> %md Hello, world!

Hello, world!

Quick Note Markdown Cell Tips

  • To change a non-markdown cell to markdown, add %md to very start of the cell.
  • After updating the contents of a markdown cell, click out of the cell to update the formatted contents of a markdown cell.
  • To edit an existing markdown cell, doubleclick the cell.

Learn more about markdown:

Note that there are flavours or minor variants and enhancements of markdown, including those specific to databricks, github, pandoc, etc.

It will be future-proof to remain in the syntactic zone of pure markdown (at the intersection of various flavours) as much as possible and go with pandoc-compatible style if choices are necessary. ***

Run a Scala Cell

  • Run the following scala cell.
  • Note: There is no need for any special indicator (such as %md) necessary to create a Scala cell in a Scala notebook.
  • You know it is a scala notebook because of the (Scala) appended to the name of this notebook.
  • Make sure the cell contents updates before moving on.
  • Press Shift+Enter when in the cell to run it and proceed to the next cell.
    • The cells contents should update.
    • Alternately, press Ctrl+Enter when in a cell to run it, but not proceed to the next cell.
  • characters following // are comments in scala. ***
1+1

res0: Int = 2

println(System.currentTimeMillis) // press Ctrl+Enter to evaluate println that prints its argument as a line

1610582284328

1+1

res2: Int = 2

Spark is written in Scala, but ...

For this reason Scala will be the primary language for this course is Scala.

However, let us use the best language for the job! as each cell can be written in a specific language in the same notebook. Such multi-lingual notebooks are the norm in any realistic data science process today!

The beginning of each cells has a language type if it is not the default language of the notebook. Such cell-specific language types include the following with the prefix %:

  • %scala for Scala,

  • %py for Python,

  • %r for R,

  • %sql for SQL,

  • %fs for databricks' filesystem,

  • %sh for BASH SHELL and

  • %md for markdown.

  • While cells default to the language type of the Notebook (scala, python, r or sql), other cell types are supported as well in a cell-specific manner.

  • For example, Python Notebooks can contain python, sql, markdown, and even scala cells. This lets you write notebooks that do use multiple languages.

  • This cell is in markdown as it begins with %mdand is used for documentation purposes.

Thus, all language-typed cells can be created in any notebook, regardless of the the default language of the notebook itself.

Cross-language cells can be used to mix commands from other languages.

Examples:

print("For example, this is a scala notebook, but we can use %py to run python commands inline.")

For example, this is a scala notebook, but we can use %py to run python commands inline.

print("We can also access other languages such as R.")

// you can be explicit about the language even if the notebook's default language is the same
println("We can access Scala like this.")

We can access Scala like this.

Command line cells can be used to work with local files on the Spark driver node. * Start a cell with %sh to run a command line command

# This is a command line cell. Commands you write here will be executed as if they were run on the command line.
# For example, in this cell we access the help pages for the bash shell.
ls

conf
derby.log
eventlogs
ganglia
logs

whoami

root

Filesystem cells allow access to the Databricks File System (DBFS).

  • Start a cell with %fs to run DBFS commands
  • Type %fs help for a list of commands

Notebooks can be run from other notebooks using %run

  • Syntax: %run /full/path/to/notebook
  • This is commonly used to import functions you defined in other notebooks.

Further Pointers

Here are some useful links to bookmark as you will need to use them for Reference.

These links provide a relaxed and detailed-enough tour (that you are strongly encouraged to take later):

ScaDaMaLe Course site and book

Scala Crash Course

Here we take a minimalist approach to learning just enough Scala, the language that Apache Spark is written in, to be able to use Spark effectively.

In the sequel we can learn more Scala concepts as they arise. This learning can be done by chasing the pointers in this crash course for a detailed deeper dive on your own time.

There are two basic ways in which we can learn Scala:

1. Learn Scala in a notebook environment

For convenience we use databricks Scala notebooks like this one here.

You can learn Scala locally on your own computer using Scala REPL (and Spark using Spark-Shell).

2. Learn Scala in your own computer

The most easy way to get Scala locally is through sbt, the Scala Build Tool. You can also use an IDE that integrates sbt.

See: https://docs.scala-lang.org/getting-started/index.html to set up Scala in your own computer.

Software Engineering NOTE: If you completed TASK 2 for Cloud-free Computing Environment in the notebook prefixed 002_00 using dockerCompose (optional exercise) then you will have Scala 2.11 with sbt and Spark 2.4 inside the docker services you can start and stop locally. Using docker volume binds you can also connect the docker container and its services (including local zeppelin or jupyter notebook servers as well as hadoop file system) to IDEs on your machine, etc.

Scala Resources

You will not be learning scala systematically and thoroughly in this course. You will learn to use Scala by doing various Spark jobs.

If you are interested in learning scala properly, then there are various resources, including:

The main sources for the following content are (you are encouraged to read them for more background):

What is Scala?

"Scala smoothly integrates object-oriented and functional programming. It is designed to express common programming patterns in a concise, elegant, and type-safe way." by Matrin Odersky.

  • High-level language for the Java Virtual Machine (JVM)
  • Object oriented + functional programming
  • Statically typed
  • Comparable in speed to Java
  • Type inference saves us from having to write explicit types most of the time Interoperates with Java
  • Can use any Java class (inherit from, etc.)
  • Can be called from Java code

See a quick tour here:

Why Scala?

  • Spark was originally written in Scala, which allows concise function syntax and interactive use
  • Spark APIs for other languages include:
    • Java API for standalone use
    • Python API added to reach a wider user community of programmes
    • R API added more recently to reach a wider community of data analyststs
    • Unfortunately, Python and R APIs are generally behind Spark's native Scala (for eg. GraphX is only available in Scala currently and datasets are only available in Scala as of 20200918).
  • See Darren Wilkinson's 11 reasons for scala as a platform for statistical computing and data science. It is embedded in-place below for your convenience.

Learn Scala in Notebook Environment

Run a Scala Cell

  • Run the following scala cell.
  • Note: There is no need for any special indicator (such as %md) necessary to create a Scala cell in a Scala notebook.
  • You know it is a scala notebook because of the (Scala) appended to the name of this notebook.
  • Make sure the cell contents updates before moving on.
  • Press Shift+Enter when in the cell to run it and proceed to the next cell.
    • The cells contents should update.
    • Alternately, press Ctrl+Enter when in a cell to run it, but not proceed to the next cell.
  • characters following // are comments in scala. ***
1+1

res0: Int = 2

println(System.currentTimeMillis) // press Ctrl+Enter to evaluate println that prints its argument as a line

1610582084465

frameIt: (u: String, h: Int)String

See Scala as a platform for statistical computing and data science.

Let's get our hands dirty in Scala

We will go through the following programming concepts and tasks by building on https://docs.scala-lang.org/tour/basics.html.

  • Scala Types
  • Expressions and Printing
  • Naming and Assignments
  • Functions and Methods in Scala
  • Classes and Case Classes
  • Methods and Tab-completion
  • Objects and Traits
  • Collections in Scala and Type Hierarchy
  • Functional Programming and MapReduce
  • Lazy Evaluations and Recursions

Remark: You need to take a computer science course (from CourseEra, for example) to properly learn Scala. Here, we will learn to use Scala by example to accomplish our data science tasks at hand. You can learn more Scala as needed from various sources pointed out above in Scala Resources.

Scala Types

In Scala, all values have a type, including numerical values and functions. The diagram below illustrates a subset of the type hierarchy.

For now, notice some common types we will be usinf including Int, String, Double, Unit, Boolean, List, etc. For more details see https://docs.scala-lang.org/tour/unified-types.html. We will return to this at the end of the notebook after seeing a brief tour of Scala now.

Expressions

Expressions are computable statements such as the 1+1 we have seen before.

1+1

res3: Int = 2

We can print the output of a computed or evaluated expressions as a line using println:

println(1+1) // printing 2

2

println("hej hej!") // printing a string

hej hej!

Naming and Assignments

value and variable as val and var

You can name the results of expressions using keywords val and var.

Let us assign the integer value 5 to x as follows:

val x : Int = 5 // <Ctrl+Enter> to declare a value x to be integer 5. 

x: Int = 5

x is a named result and it is a value since we used the keyword val when naming it.

Scala is statically typed, but it uses built-in type inference machinery to automatically figure out that x is an integer or Int type as follows. Let's declare a value x to be Int 5 next without explictly using Int.

val x = 5    // <Ctrl+Enter> to declare a value x as Int 5 (type automatically inferred)

x: Int = 5

Let's declare x as a Double or double-precision floating-point type using decimal such as 5.0 (a digit has to follow the decimal point!)

val x = 5.0   // <Ctrl+Enter> to declare a value x as Double 5

x: Double = 5.0

Alternatively, we can assign x as a Double explicitly. Note that the decimal point is not needed in this case due to explicit typing as Double.

val x :  Double = 5    // <Ctrl+Enter> to declare a value x as Double 5 (type automatically inferred)

x: Double = 5.0

Next note that labels need to be declared on first use. We have declared x to be a val which is short for value. This makes x immutable (cannot be changed).

Thus, x cannot be just re-assigned, as the following code illustrates in the resulting error: ... error: reassignment to val.

//x = 10    //  uncomment and <Ctrl+Enter> to try to reassign val x to 10

Scala allows declaration of mutable variables as well using var, as follows:

var y = 2    // <Shift+Enter> to declare a variable y to be integer 2 and go to next cell

y: Int = 2

y = 3    // <Shift+Enter> to change the value of y to 3

y: Int = 3

y = y+1 // adds 1 to y

y: Int = 4

y += 2 // adds 2 to y

println(y) // the var y is 6 now

6

Blocks

Just combine expressions by surrounding them with { and } called a block.

println({
  val x = 1+1
  x+2 // expression in last line is returned for the block
})// prints 4

4

println({ val x=22; x+2})

24

Functions

Functions are expressions that have parameters. A function takes arguments as input and returns expressions as output.

A function can be nameless or anonymous and simply return an output from a given input. For example, the following annymous function returns the square of the input integer.

(x: Int) => x*x

res11: Int => Int = line186c28489fff404184da2d59bd09a90463.$read$$Lambda$5065/1820207503@597d20b

On the left of => is a list of parameters with name and type. On the right is an expression involving the parameters.

You can also name functions:

val multiplyByItself = (x: Int) => x*x

multiplyByItself: Int => Int = line186c28489fff404184da2d59bd09a90465.$read$$Lambda$5067/2036039718@12f273c8

println(multiplyByItself(10))

100

A function can have no parameters:

val howManyAmI = () => 1

howManyAmI: () => Int = line186c28489fff404184da2d59bd09a90469.$read$$Lambda$5070/1826556511@56f9e3f2

println(howManyAmI()) // 1

1

A function can have more than one parameter:

val multiplyTheseTwoIntegers = (a: Int, b: Int) => a*b

multiplyTheseTwoIntegers: (Int, Int) => Int = line186c28489fff404184da2d59bd09a90473.$read$$Lambda$5071/161461748@62178cca

println(multiplyTheseTwoIntegers(2,4)) // 8

8

Methods

Methods are very similar to functions, but a few key differences exist.

Methods use the def keyword followed by a name, parameter list(s), a return type, and a body.

def square(x: Int): Int = x*x    // <Shitf+Enter> to define a function named square

square: (x: Int)Int

Note that the return type Int is specified after the parameter list and a :.

square(5)    // <Shitf+Enter> to call this function on argument 5

res15: Int = 25

val y = 3    // <Shitf+Enter> make val y as Int 3

y: Int = 3

square(y) // <Shitf+Enter> to call the function on val y of the right argument type Int

res16: Int = 9

val x = 5.0     // let x be Double 5.0

x: Double = 5.0

//square(x) // <Shift+Enter> to call the function on val x of type Double will give type mismatch error

def square(x: Int): Int = { // <Shitf+Enter> to declare function in a block
  val answer = x*x
  answer // the last line of the function block is returned
}

square: (x: Int)Int

square(5000)    // <Shift+Enter> to call the function

res18: Int = 25000000

// <Shift+Enter> to define function with input and output type as String
def announceAndEmit(text: String): String = 
{
  println(text)
  text // the last line of the function block is returned
}

announceAndEmit: (text: String)String

Scala has a return keyword but it is rarely used as the expression in the last line of the multi-line block is the method's return value.

// <Ctrl+Enter> to call function which prints as line and returns as String
announceAndEmit("roger  roger")

roger  roger
res19: String = roger  roger

A method can have output expressions involving multiple parameter lists:

def multiplyAndTranslate(x: Int, y: Int)(translateBy: Int): Int = (x * y) + translateBy

multiplyAndTranslate: (x: Int, y: Int)(translateBy: Int)Int

println(multiplyAndTranslate(2, 3)(4))  // (2*3)+4 = 10

10

A method can have no parameter lists at all:

def time: Long = System.currentTimeMillis

time: Long

println("Current time in milliseconds is " + time)

Current time in milliseconds is 1610582096790

println("Current time in milliseconds is " + time)

Current time in milliseconds is 1610582097046

Classes

The class keyword followed by the name and constructor parameters is used to define a class.

class Box(h: Int, w: Int, d: Int) {
  def printVolume(): Unit = println(h*w*d)
}

defined class Box
  • The return type of the method printVolume is Unit.
  • When the return type is Unit it indicates that there is nothing meaningful to return, similar to void in Java and C, but with a difference.
  • Because every Scala expression must have some value, there is actually a singleton value of type Unit, written () and carrying no information.

We can make an instance of the class with the new keyword.

val my1Cube = new Box(1,1,1)

my1Cube: Box = line186c28489fff404184da2d59bd09a904107.$read$Box@6c4cbb75

And call the method on the instance.

my1Cube.printVolume() // 1

1

Our named instance my1Cube of the Box class is immutable due to val.

You can have mutable instances of the class using var.

var myVaryingCuboid = new Box(1,3,2)

myVaryingCuboid: Box = line186c28489fff404184da2d59bd09a904107.$read$Box@77404a48

myVaryingCuboid.printVolume()

6

myVaryingCuboid = new Box(1,1,1)

myVaryingCuboid: Box = line186c28489fff404184da2d59bd09a904107.$read$Box@748cdfd1

myVaryingCuboid.printVolume()

1

See https://docs.scala-lang.org/tour/classes.html for more details as needed.

Case Classes

Scala has a special type of class called a case class that can be defined with the case class keyword.

Unlike classes, whose instances are compared by reference, instances of case classes are immutable by default and compared by value. This makes them useful for defining rows of typed values in Spark.

case class Point(x: Int, y: Int, z: Int)

defined class Point

Case classes can be instantiated without the new keyword.

val point = Point(1, 2, 3)
val anotherPoint = Point(1, 2, 3)
val yetAnotherPoint = Point(2, 2, 2)

point: Point = Point(1,2,3)
anotherPoint: Point = Point(1,2,3)
yetAnotherPoint: Point = Point(2,2,2)

Instances of case classes are compared by value and not by reference.

if (point == anotherPoint) {
  println(point + " and " + anotherPoint + " are the same.")
} else {
  println(point + " and " + anotherPoint + " are different.")
} // Point(1,2,3) and Point(1,2,3) are the same.

if (point == yetAnotherPoint) {
  println(point + " and " + yetAnotherPoint + " are the same.")
} else {
  println(point + " and " + yetAnotherPoint + " are different.")
} // Point(1,2,3) and Point(2,2,2) are different.

Point(1,2,3) and Point(1,2,3) are the same.
Point(1,2,3) and Point(2,2,2) are different.

By contrast, instances of classes are compared by reference.

myVaryingCuboid.printVolume() // should be 1 x 1 x 1

1

my1Cube.printVolume()  // should be 1 x 1 x 1

1

if (myVaryingCuboid == my1Cube) {
  println("myVaryingCuboid and my1Cube are the same.")
} else {
  println("myVaryingCuboid and my1Cube are different.")
} // they are compared by reference and are not the same.

myVaryingCuboid and my1Cube are different.

More about case classes here: https://docs.scala-lang.org/tour/case-classes.html.

Methods and Tab-completion

Many methods of a class can be accessed by ..

val s  = "hi"    // <Ctrl+Enter> to declare val s to String "hi"

s: String = hi

You can place the cursor after . following a declared object and find out the methods available for it as shown in the image below.

tabCompletionAfterSDot PNG image

You Try doing this next.

//s.  // place cursor after the '.' and press Tab to see all available methods for s

For example,

  • scroll down to contains and double-click on it.
  • This should lead to s.contains in your cell.
  • Now add an argument String to see if s contains the argument, for example, try:
    • s.contains("f")
    • s.contains("") and
    • s.contains("i")
//s    // <Shift-Enter> recall the value of String s

s.contains("f")     // <Shift-Enter> returns Boolean false since s does not contain the string "f"

res32: Boolean = false

s.contains("")    // <Shift-Enter> returns Boolean true since s contains the empty string ""

res33: Boolean = true

s.contains("i")    // <Ctrl+Enter> returns Boolean true since s contains the string "i"

res34: Boolean = true

Objects

Objects are single instances of their own definitions using the object keyword. You can think of them as singletons of their own classes.

object IdGenerator {
  private var currentId = 0
  def make(): Int = {
    currentId += 1
    currentId
  }
}

defined object IdGenerator

You can access an object through its name:

val newId: Int = IdGenerator.make()
val newerId: Int = IdGenerator.make()

newId: Int = 1
newerId: Int = 2

println(newId) // 1
println(newerId) // 2

1
2

For details see https://docs.scala-lang.org/tour/singleton-objects.html

Traits

Traits are abstract data types containing certain fields and methods. They can be defined using the trait keyword.

In Scala inheritance, a class can only extend one other class, but it can extend multiple traits.

trait Greeter {
  def greet(name: String): Unit
}

defined trait Greeter

Traits can have default implementations also.

trait Greeter {
  def greet(name: String): Unit =
    println("Hello, " + name + "!")
}

defined trait Greeter

You can extend traits with the extends keyword and override an implementation with the override keyword:

class DefaultGreeter extends Greeter

class SwedishGreeter extends Greeter {
  override def greet(name: String): Unit = {
    println("Hej hej, " + name + "!")
  }
}

class CustomizableGreeter(prefix: String, postfix: String) extends Greeter {
  override def greet(name: String): Unit = {
    println(prefix + name + postfix)
  }
}

defined class DefaultGreeter
defined class SwedishGreeter
defined class CustomizableGreeter

Instantiate the classes.

val greeter = new DefaultGreeter()
val swedishGreeter = new SwedishGreeter()
val customGreeter = new CustomizableGreeter("How are you, ", "?")

greeter: DefaultGreeter = line186c28489fff404184da2d59bd09a904155.$read$DefaultGreeter@5d7c7786
swedishGreeter: SwedishGreeter = line186c28489fff404184da2d59bd09a904155.$read$SwedishGreeter@a1c1128
customGreeter: CustomizableGreeter = line186c28489fff404184da2d59bd09a904155.$read$CustomizableGreeter@7c2dc867

Call the greet method in each case.

greeter.greet("Scala developer") // Hello, Scala developer!
swedishGreeter.greet("Scala developer") // Hej hej, Scala developer!
customGreeter.greet("Scala developer") // How are you, Scala developer?

Hello, Scala developer!
Hej hej, Scala developer!
How are you, Scala developer?

A class can also be made to extend multiple traits.

For more details see: https://docs.scala-lang.org/tour/traits.html.

Main Method

The main method is the entry point of a Scala program.

The Java Virtual Machine requires a main method, named main, that takes an array of strings as its only argument.

Using an object, you can define the main method as follows:

object Main {
  def main(args: Array[String]): Unit =
    println("Hello, Scala developer!")
}

defined object Main

What I try not do while learning a new language?

  1. I don't immediately try to ask questions like: how can I do this particular variation of some small thing I just learned so I can use patterns I am used to from another language I am hooked-on right now?
  2. first go through the detailed Scala Tour on your own and then through the 50 odd lessons in the Scala Book
  3. then return to 1. and ask detailed cross-language comparison questions by diving deep as needed with the source and scala docs as needed (google or duck-duck-go search!).

ScaDaMaLe Course site and book

Scala Crash Course Continued

Recall!

Scala Resources

You will not be learning scala systematically and thoroughly in this course. You will learn to use Scala by doing various Spark jobs.

If you are interested in learning scala properly, then there are various resources, including:

The main sources for the following content are (you are encouraged to read them for more background):

//%run "/scalable-data-science/xtraResources/support/sdsFunctions"
//This allows easy embedding of publicly available information into any other notebook
//when viewing in git-book just ignore this block - you may have to manually chase the URL in frameIt("URL").
//Example usage:
// displayHTML(frameIt("https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation#Topics_in_LDA",250))
def frameIt( u:String, h:Int ) : String = {
      """<iframe 
 src=""""+ u+""""
 width="95%" height="""" + h + """"
 sandbox>
  <p>
    <a href="http://spark.apache.org/docs/latest/index.html">
      Fallback link for browsers that, unlikely, don't support frames
    </a>
  </p>
</iframe>"""
   }

frameIt: (u: String, h: Int)String

Let's continue getting our hands dirty in Scala

We will go through the remaining programming concepts and tasks by building on https://docs.scala-lang.org/tour/basics.html.

  • Scala Types
  • Expressions and Printing
  • Naming and Assignments
  • Functions and Methods in Scala
  • Classes and Case Classes
  • Methods and Tab-completion
  • Objects and Traits
  • Collections in Scala and Type Hierarchy
  • Functional Programming and MapReduce
  • Lazy Evaluations and Recursions

Remark: You need to take a computer science course (from CourseEra, for example) to properly learn Scala. Here, we will learn to use Scala by example to accomplish our data science tasks at hand. You can learn more Scala as needed from various sources pointed out above in Scala Resources.

Scala Type Hierarchy

In Scala, all values have a type, including numerical values and functions. The diagram below illustrates a subset of the type hierarchy.

For now, notice some common types we will be usinf including Int, String, Double, Unit, Boolean, List, etc. For more details see https://docs.scala-lang.org/tour/unified-types.html.

Let us take a closer look at Scala Type Hierarchy now.

displayHTML(frameIt("https://docs.scala-lang.org/tour/unified-types.html",550))

Scala Collections

Familiarize yourself with the main Scala collections classes here:

displayHTML(frameIt("https://docs.scala-lang.org/overviews/scala-book/collections-101.html",550))

List

Lists are one of the most basic data structures.

There are several other Scala collections and we will introduce them as needed. The other most common ones are Vector, Array and Seq and the ArrayBuffer.

For details on list see: - https://docs.scala-lang.org/overviews/scala-book/list-class.html

// <Ctrl+Enter> to declare (an immutable) val lst as List of Int's 1,2,3
val lst = List(1, 2, 3)

lst: List[Int] = List(1, 2, 3)

Vectors

The Vector class is an indexed, immutable sequence. The “indexed” part of the description means that you can access Vector elements very rapidly by their index value, such as accessing listOfPeople(999999).

In general, except for the difference that Vector is indexed and List is not, the two classes work the same, so we’ll run through these examples quickly.

For details see: - https://docs.scala-lang.org/overviews/scala-book/vector-class.html

val vec = Vector(1,2,3)

vec: scala.collection.immutable.Vector[Int] = Vector(1, 2, 3)

Arrays, Sequences and Tuples

See https://www.scala-lang.org/api/current/scala/collection/index.html for docs.

val arr = Array(1,2,3) // <Shift-Enter> to declare an Array

arr: Array[Int] = Array(1, 2, 3)

val seq = Seq(1,2,3)    // <Shift-Enter> to declare a Seq

seq: Seq[Int] = List(1, 2, 3)

A tuple is a neat class that gives you a simple way to store heterogeneous (different) items in the same container. We will use tuples for key-value pairs in Spark.

See https://docs.scala-lang.org/overviews/scala-book/tuples.html

val myTuple = ('a',1) // a 2-tuple

myTuple: (Char, Int) = (a,1)

myTuple._1 // accessing the first element of the tuple. NOTE index starts at 1 not 0 for tuples

res2: Char = a

myTuple._2 // accessing the second element of the tuple

res3: Int = 1

Functional Programming and MapReduce

"Functional programming is a style of programming that emphasizes writing applications using only pure functions and immutable values. As Alvin Alexander wrote in Functional Programming, Simplified, rather than using that description, it can be helpful to say that functional programmers have an extremely strong desire to see their code as math — to see the combination of their functions as a series of algebraic equations. In that regard, you could say that functional programmers like to think of themselves as mathematicians. That’s the driving desire that leads them to use only pure functions and immutable values, because that’s what you use in algebra and other forms of math."

See https://docs.scala-lang.org/overviews/scala-book/functional-programming.html for short lessons in functional programming.

We will apply functions for processing elements of a scala collection to quickly demonstrate functional programming.

Five ways of adding 1

The first four use anonymous functions and the last one uses a named method.

  1. explicit version:
(x: Int) => x + 1
  1. type-inferred more intuitive version:
x => x + 1
  1. placeholder syntax (each argument must be used exactly once):
_ + 1
  1. type-inferred more intuitive version with code-block for larger function body:
x => { 
      // body is a block of code
      val integerToAdd = 1
      x + integerToAdd
}
  1. as methods using def:
def addOne(x: Int): Int = x + 1

displayHTML(frameIt("https://superruzafa.github.io/visual-scala-reference/map/",500))

Now, let's do some functional programming over scala collection (List) using some of their methods: map, filter and reduce. In the end we will write our first mapReduce program!

For more details see:

displayHTML(frameIt("https://superruzafa.github.io/visual-scala-reference/map/",500))

// <Shift+Enter> to map each value x of lst with x+10 to return a new List(11, 12, 13)
lst.map(x => x + 10)  

res6: List[Int] = List(11, 12, 13)

// <Shift+Enter> for the same as above using place-holder syntax
lst.map( _ + 10)  

res7: List[Int] = List(11, 12, 13)

displayHTML(frameIt("https://superruzafa.github.io/visual-scala-reference/filter/",600))

// <Shift+Enter> to return a new List(1, 3) after filtering x's from lst if (x % 2 == 1) is true
lst.filter(x => (x % 2 == 1) )

res9: List[Int] = List(1, 3)

// <Shift+Enter> for the same as above using place-holder syntax
lst.filter( _ % 2 == 1 )

res10: List[Int] = List(1, 3)

displayHTML(frameIt("https://superruzafa.github.io/visual-scala-reference/reduce/",600))

// <Shift+Enter> to use reduce to add elements of lst two at a time to return Int 6
lst.reduce( (x, y) => x + y )

res12: Int = 6

// <Ctrl+Enter> for the same as above but using place-holder syntax
lst.reduce( _ + _ )

res13: Int = 6

Let's combine map and reduce programs above to find the sum of after 10 has been added to every element of the original List lst as follows:

lst.map(x => x+10)
   .reduce((x,y) => x+y) // <Ctrl-Enter> to get Int 36 = sum(1+10,2+10,3+10)

res14: Int = 36

Exercise in Functional Programming

You should spend an hour or so going through the Functional Programming Section of the Scala Book:

displayHTML(frameIt("https://docs.scala-lang.org/overviews/scala-book/functional-programming.html",700))

There are lots of methods in Scala Collections. And much more in this scalable language. See for example http://docs.scala-lang.org/cheatsheets/index.html.

Lazy Evaluation

Another powerful programming concept we will need is lazy evaluation -- a form of delayed evaluation. So the value of an expression that is lazily evaluated is only available when it is actually needed.

This is to be contrasted with eager evaluation that we have seen so far -- an expression is immediately evaluated.

val eagerImmutableInt = 1 // eagerly evaluated as 1

eagerImmutableInt: Int = 1

var eagerMutableInt = 2 // eagerly evaluated as 2

eagerMutableInt: Int = 2

Let's demonstrate lazy evaluation using a getTime method and the keyword lazy.

import java.util.Calendar

import java.util.Calendar

lazy val lazyImmutableTime = Calendar.getInstance.getTime // lazily defined and not evaluated immediately

lazyImmutableTime: java.util.Date = <lazy>

val eagerImmutableTime = Calendar.getInstance.getTime // egaerly evaluated immediately

eagerImmutableTime: java.util.Date = Wed Jan 13 23:54:53 UTC 2021

println(lazyImmutableTime) // evaluated when actully needed by println

Wed Jan 13 23:54:53 UTC 2021

println(eagerImmutableTime) // prints what was already evaluated eagerly

Wed Jan 13 23:54:53 UTC 2021

def lazyDefinedInt = 5 // you can also use method to lazily define 

lazyDefinedInt: Int

lazyDefinedInt // only evaluated now

res18: Int = 5

See https://www.scala-exercises.org/scalatutorial/lazyevaluation for more details including the following example with StringBuilder.

val builder = new StringBuilder

builder: StringBuilder =

val x = { builder += 'x'; 1 } // eagerly evaluates x as 1 after appending 'x' to builder. NOTE: ';' is used to separate multiple expressions on the same line

x: Int = 1

builder.result()

res19: String = x

x

res20: Int = 1

builder.result() // calling x again should not append x again to builder

res21: String = x

lazy val y = { builder += 'y'; 2 } // lazily evaluate y later when it is called

y: Int = <lazy>

builder.result() // builder should remain unchanged

res22: String = x

def z = { builder += 'z'; 3 } // lazily evaluate z later when the method is called

z: Int

builder.result() // builder should remain unchanged

res23: String = x

What should builder.result() be after the following arithmetic expression involving x,y and z is evaluated?

z + y + x + z + y + x

res24: Int = 12

Lazy Evaluation Exercise - You try Now!

Understand why the output above is what it is!

  • Why is z different in its appearance in the final builder string when compared to x and y as we evaluated?
z + y + x + z + y + x

builder.result() 

res25: String = xzyz

Why Lazy?

Imagine a more complex expression involving the evaluation of millions of values. Lazy evaluation will allow us to actually compute with big data when it may become impossible to hold all the values in memory. This is exactly what Apache Spark does as we will see.

Recursions

Recursion is a powerful framework when a function calls another function, including itself, until some terminal condition is reached.

Here we want to distinguish between two ways of implementing a recursion using a simple example of factorial.

Recall that for any natural number \(n\), its factorial is denoted and defined as follows:

\[ n! := n \times (n-1) \times (n-2) \times \cdots \times 2 \times 1 \]

which has the following recursive expression:

\[ n! = n*(n-1)! , , \qquad 0! = 1 \]

Let us implement it using two approaches: a naive approach that can run out of memory and another tail-recursive approach that uses constant memory. Read https://www.scala-exercises.org/scalatutorial/tailrecursion for details.

def factorialNaive(n: Int): Int =
  if (n == 0) 1 else n * factorialNaive(n - 1)

factorialNaive: (n: Int)Int

factorialNaive(4)

res26: Int = 24

When factorialNaive(4) was evaluated above the following steps were actually done:

factorial(4)
if (4 == 0) 1 else 4 * factorial(4 - 1)
4 * factorial(3)
4 * (3 * factorial(2))
4 * (3 * (2 * factorial(1)))
4 * (3 * (2 * (1 * factorial(0)))
4 * (3 * (2 * (1 * 1)))
24

Notice how we add one more element to our expression at each recursive call. Our expressions becomes bigger and bigger until we end by reducing it to the final value. So the final expression given by a directed acyclic graph (DAG) of the pairwise multiplications given by the right-branching binary tree, whose leaves are input integers and internal nodes are the bianry * operator, can get very large when the input n is large.

Tail recursion is a sophisticated way of implementing certain recursions so that memory requirements can be kept constant, as opposed to naive recursions.

Tail Recursion

That difference in the rewriting rules actually translates directly to a difference in the actual execution on a computer. In fact, it turns out that if you have a recursive function that calls itself as its last action, then you can reuse the stack frame of that function. This is called tail recursion.

And by applying that trick, a tail recursive function can execute in constant stack space, so it's really just another formulation of an iterative process. We could say a tail recursive function is the functional form of a loop, and it executes just as efficiently as a loop.

Implementation of tail recursion in the Exercise below uses Scala annotation, which is a way to associate meta-information with definitions. In our case, the annotation @tailrec ensures that a method is indeed tail-recursive. See the last link to understand how memory requirements can be kept constant in tail recursions.

We mainly want you to know that tail recursions are an important functional programming concept.

Tail Recursion Exercise - You Try Now

Replace ??? with the correct values to make this a tail recursion for factorial.

import scala.annotation.tailrec

// replace ??? with the right values to make this a tail recursion for factorial
def factorialTail(n: Int): Int = {
  @tailrec
  def iter(x: Int, result: Int): Int =
    if ( x == ??? ) result
    else iter(x - 1, result * x)

  iter( n, ??? )
}

factorialTail(3) //shouldBe 6

factorialTail(4) //shouldBe 24

Functional Programming is a vast subject and we are merely covering the fewest core ideas to get started with Apache Spark asap.

We will return to more concepts as we need them in the sequel.

ScaDaMaLe Course site and book

Introduction to Spark

Spark Essentials: RDDs, Transformations and Actions

  • This introductory notebook describes how to get started running Spark (Scala) code in Notebooks.
  • Working with Spark's Resilient Distributed Datasets (RDDs)
    • creating RDDs
    • performing basic transformations on RDDs
    • performing basic actions on RDDs

RECOLLECT from 001_WhySpark notebook and AJ's videos that Spark does fault-tolerant, distributed, in-memory computing

THEORY CAVEAT This module is focused on getting you to quickly write Spark programs with a high-level appreciation of the underlying concepts.

In the last module, we will spend more time on analyzing the core algorithms in parallel and distributed setting of a typical Spark cluster today -- where several multi-core parallel computers (Spark workers) are networked together to provide a fault-tolerant distributed computing platform.

Spark Cluster Overview:

Driver Program, Cluster Manager and Worker Nodes

The driver does the following:

  1. connects to a cluster manager to allocate resources across applications
  • acquire executors on cluster nodes
    • executor processs run compute tasks and cache data in memory or disk on a worker node
  • sends application (user program built on Spark) to the executors
  • sends tasks for the executors to run
    • task is a unit of work that will be sent to one executor

See http://spark.apache.org/docs/latest/cluster-overview.html for an overview of the spark cluster.

The Abstraction of Resilient Distributed Dataset (RDD)

RDD is a fault-tolerant collection of elements that can be operated on in parallel.

Two types of Operations are possible on an RDD:

  • Transformations
  • Actions

(watch now 2:26):

RDD in Spark by Anthony Joseph in BerkeleyX/CS100.1x

Transformations

(watch now 1:18):

Spark Transformations by Anthony Joseph in BerkeleyX/CS100.1x

Actions

(watch now 0:48):

Spark Actions by Anthony Joseph in BerkeleyX/CS100.1x

Key Points

  • Resilient distributed datasets (RDDs) are the primary abstraction in Spark.
  • RDDs are immutable once created:
    • can transform it.
    • can perform actions on it.
    • but cannot change an RDD once you construct it.
  • Spark tracks each RDD's lineage information or recipe to enable its efficient recomputation if a machine fails.
  • RDDs enable operations on collections of elements in parallel.
  • We can construct RDDs by:
    • parallelizing Scala collections such as lists or arrays
    • by transforming an existing RDD,
    • from files in distributed file systems such as (HDFS, S3, etc.).
  • We can specify the number of partitions for an RDD
  • The more partitions in an RDD, the more opportunities for parallelism
  • There are two types of operations you can perform on an RDD:
    • transformations (are lazily evaluated)
      • map
      • flatMap
      • filter
      • distinct
      • ...
    • actions (actual evaluation happens)
      • count
      • reduce
      • take
      • collect
      • takeOrdered
      • ...
  • Spark transformations enable us to create new RDDs from an existing RDD.
  • RDD transformations are lazy evaluations (results are not computed right away)
  • Spark remembers the set of transformations that are applied to a base data set (this is the lineage graph of RDD)
  • The allows Spark to automatically recover RDDs from failures and slow workers.
  • The lineage graph is a recipe for creating a result and it can be optimized before execution.
  • A transformed RDD is executed only when an action runs on it.
  • You can also persist, or cache, RDDs in memory or on disk (this speeds up iterative ML algorithms that transforms the initial RDD iteratively).
  • Here is a great reference URL for programming guides for Spark that one should try to cover first

Let's get our hands dirty in Spark!

DO NOW!

In your databricks community edition:

  1. In your WorkSpace create a Folder named scalable-data-science
  2. Import the databricks archive file at the following URL:
  3. This should open a structure of directories in with path: /Workspace/scalable-data-science/xtraResources/

Let us look at the legend and overview of the visual RDD Api by doing the following first:

Running Spark

The variable sc allows you to access a Spark Context to run your Spark programs. Recall SparkContext is in the Driver Program.

**NOTE: Do not create the sc variable - it is already initialized for you in spark-shell REPL, that includes notebook environments like databricks, Jupyter, zeppelin, etc. **

We will do the following next:

  1. Create an RDD using sc.parallelize
  • Perform the collect action on the RDD and find the number of partitions it is made of using getNumPartitions action
  • Perform the take action on the RDD
  • Transform the RDD by map to make another RDD
  • Transform the RDD by filter to make another RDD
  • Perform the reduce action on the RDD
  • Transform the RDD by flatMap to make another RDD
  • Create a Pair RDD
  • Perform some transformations on a Pair RDD
  • Where in the cluster is your computation running?
  • Shipping Closures, Broadcast Variables and Accumulator Variables
  • Spark Essentials: Summary
  • HOMEWORK
  • Importing Standard Scala and Java libraries

Entry Point

Now we are ready to start programming in Spark!

Our entry point for Spark 2.x applications is the class SparkSession. An instance of this object is already instantiated for us which can be easily demonstrated by running the next cell

We will need these docs!

println(spark)

org.apache.spark.sql.SparkSession@69141846

NOTE that since Spark 2.0 SparkSession is a replacement for the other entry points: * SparkContext, available in our notebook as sc. * SQLContext, or more specifically its subclass HiveContext, available in our notebook as sqlContext.

println(sc)
println(sqlContext)

org.apache.spark.SparkContext@517c9049
org.apache.spark.sql.hive.HiveContext@6c5b5052

We will be using the pre-made SparkContext sc when learning about RDDs.

1. Create an RDD using sc.parallelize

First, let us create an RDD of three elements (of integer type Int) from a Scala Seq (or List or Array) with two partitions by using the parallelize method of the available Spark Context sc as follows:

val x = sc.parallelize(Array(1, 2, 3), 2)    // <Ctrl+Enter> to evaluate this cell (using 2 partitions)

x: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[33] at parallelize at command-685894176422457:1

//x.  // place the cursor after 'x.' and hit Tab to see the methods available for the RDD x we created

2. Perform the collect action on the RDD and find the number of partitions in it using getNumPartitions action

No action has been taken by sc.parallelize above. To see what is "cooked" by the recipe for RDD x we need to take an action.

The simplest is the collect action which returns all of the elements of the RDD as an Array to the driver program and displays it.

So you have to make sure that all of that data will fit in the driver program if you call collect action!

Let us look at the collect action in detail and return here to try out the example codes.

Let us perform a collect action on RDD x as follows:

x.collect()    // <Ctrl+Enter> to collect (action) elements of rdd; should be (1, 2, 3)

res44: Array[Int] = Array(1, 2, 3)

CAUTION: collect can crash the driver when called upon an RDD with massively many elements. So, it is better to use other diplaying actions like take or takeOrdered as follows:

Let us look at the getNumPartitions action in detail and return here to try out the example codes.

// <Ctrl+Enter> to evaluate this cell and find the number of partitions in RDD x
x.getNumPartitions 

res45: Int = 2

We can see which elements of the RDD are in which parition by calling glom() before collect().

glom() flattens elements of the same partition into an Array.

x.glom().collect() // glom() flattens elements on the same partition

res46: Array[Array[Int]] = Array(Array(1), Array(2, 3))

val a = x.glom().collect()

a: Array[Array[Int]] = Array(Array(1), Array(2, 3))

Thus from the output above, Array[Array[Int]] = Array(Array(1), Array(2, 3)), we know that 1 is in one partition while 2 and 3 are in another partition.

You Try!

Crate an RDD x with three elements, 1,2,3, and this time do not specifiy the number of partitions. Then the default number of partitions will be used. Find out what this is for the cluster you are attached to.

The default number of partitions for an RDD depends on the cluster this notebook is attached to among others - see programming-guide.

val x = sc.parallelize(Seq(1, 2, 3))    // <Shift+Enter> to evaluate this cell (using default number of partitions)

x: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[36] at parallelize at command-685894176422471:1

x.getNumPartitions // <Shift+Enter> to evaluate this cell

res47: Int = 8

x.glom().collect() // <Ctrl+Enter> to evaluate this cell

res48: Array[Array[Int]] = Array(Array(), Array(), Array(1), Array(), Array(), Array(2), Array(), Array(3))

3. Perform the take action on the RDD

The .take(n) action returns an array with the first n elements of the RDD.

x.take(2) // Ctrl+Enter to take two elements from the RDD x

res49: Array[Int] = Array(1, 2)
You Try!

Fill in the parenthes ( ) below in order to take just one element from RDD x.

//x.take(1) // uncomment by removing '//' before x in the cell and fill in the parenthesis to take just one element from RDD x and Cntrl+Enter

4. Transform the RDD by map to make another RDD

The map transformation returns a new RDD that's formed by passing each element of the source RDD through a function (closure). The closure is automatically passed on to the workers for evaluation (when an action is called later).

Let us look at the map transformation in detail and return here to try out the example codes.

// Shift+Enter to make RDD x and RDD y that is mapped from x
val x = sc.parallelize(Array("b", "a", "c")) // make RDD x: [b, a, c]
val y = x.map(z => (z,1))                    // map x into RDD y: [(b, 1), (a, 1), (c, 1)]

x: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[38] at parallelize at command-685894176422480:2
y: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[39] at map at command-685894176422480:3

// Cntrl+Enter to collect and print the two RDDs
println(x.collect().mkString(", "))
println(y.collect().mkString(", "))

b, a, c
(b,1), (a,1), (c,1)

5. Transform the RDD by filter to make another RDD

The filter transformation returns a new RDD that's formed by selecting those elements of the source RDD on which the function returns true.

Let us look at the filter transformation in detail and return here to try out the example codes.

//Shift+Enter to make RDD x and filter it by (n => n%2 == 1) to make RDD y
val x = sc.parallelize(Array(1,2,3))
// the closure (n => n%2 == 1) in the filter will 
// return True if element n in RDD x has remainder 1 when divided by 2 (i.e., if n is odd)
val y = x.filter(n => n%2 == 1) 

x: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[40] at parallelize at command-685894176422484:2
y: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[41] at filter at command-685894176422484:5

// Cntrl+Enter to collect and print the two RDDs
println(x.collect().mkString(", "))
println(y.collect().mkString(", "))
//y.collect()

1, 2, 3
1, 3

6. Perform the reduce action on the RDD

Reduce aggregates a data set element using a function (closure). This function takes two arguments and returns one and can often be seen as a binary operator. This operator has to be commutative and associative so that it can be computed correctly in parallel (where we have little control over the order of the operations!).

Let us look at the reduce action in detail and return here to try out the example codes.

//Shift+Enter to make RDD x of inteegrs 1,2,3,4 and reduce it to sum
val x = sc.parallelize(Array(1,2,3,4))
val y = x.reduce((a,b) => a+b)

x: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[42] at parallelize at command-685894176422488:2
y: Int = 10

//Cntrl+Enter to collect and print RDD x and the Int y, sum of x
println(x.collect.mkString(", "))
println(y)

1, 2, 3, 4
10

7. Transform an RDD by flatMap to make another RDD

flatMap is similar to map but each element from input RDD can be mapped to zero or more output elements. Therefore your function should return a sequential collection such as an Array rather than a single element as shown below.

Let us look at the flatMap transformation in detail and return here to try out the example codes.

//Shift+Enter to make RDD x and flatMap it into RDD by closure (n => Array(n, n*100, 42))
val x = sc.parallelize(Array(1,2,3))
val y = x.flatMap(n => Array(n, n*100, 42))

x: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[43] at parallelize at command-685894176422492:2
y: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[44] at flatMap at command-685894176422492:3

//Cntrl+Enter to collect and print RDDs x and y
println(x.collect().mkString(", "))
println(y.collect().mkString(", "))
sc.parallelize(Array(1,2,3)).map(n => Array(n,n*100,42)).collect()

1, 2, 3
1, 100, 42, 2, 200, 42, 3, 300, 42
res54: Array[Array[Int]] = Array(Array(1, 100, 42), Array(2, 200, 42), Array(3, 300, 42))

8. Create a Pair RDD

Let's next work with RDD of (key,value) pairs called a Pair RDD or Key-Value RDD.

// Cntrl+Enter to make RDD words and display it by collect
val words = sc.parallelize(Array("a", "b", "a", "a", "b", "b", "a", "a", "a", "b", "b"))
words.collect()

words: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[47] at parallelize at command-685894176422495:2
res55: Array[String] = Array(a, b, a, a, b, b, a, a, a, b, b)

Let's make a Pair RDD called wordCountPairRDD that is made of (key,value) pairs with key=word and value=1 in order to encode each occurrence of each word in the RDD words, as follows:

// Cntrl+Enter to make and collect Pair RDD wordCountPairRDD
val wordCountPairRDD = words.map(s => (s, 1))
wordCountPairRDD.collect()

wordCountPairRDD: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[48] at map at command-685894176422497:2
res56: Array[(String, Int)] = Array((a,1), (b,1), (a,1), (a,1), (b,1), (b,1), (a,1), (a,1), (a,1), (b,1), (b,1))

Wide Transformations and Shuffles

So far we have seen transformations that are narrow -- with no data transfer between partitions. Think of map.

ReduceByKey and GroupByKey are wide transformations as data has to be shuffled across the partitions in different executors -- this is generally very expensive operation.

READ the Background about Shuffles in the programming guide below.

In Spark, data is generally not distributed across partitions to be in the necessary place for a specific operation. During computations, a single task will operate on a single partition - thus, to organize all the data for a single reduceByKey reduce task to execute, Spark needs to perform an all-to-all operation. It must read from all partitions to find all the values for all keys, and then bring together values across partitions to compute the final result for each key - this is called the shuffle

READ the Performance Impact about Shuffles in the programming guide below.

The Shuffle is an expensive operation since it involves disk I/O, data serialization, and network I/O. To organize data for the shuffle, Spark generates sets of tasks - map tasks to organize the data, and a set of reduce tasks to aggregate it. This nomenclature comes from MapReduce and does not directly relate to Spark’s map and reduce operations.

Internally, results from individual map tasks are kept in memory until they can’t fit. Then, these are sorted based on the target partition and written to a single file. On the reduce side, tasks read the relevant sorted blocks.

https://spark.apache.org/docs/latest/rdd-programming-guide.html#shuffle-operations

9. Perform some transformations on a Pair RDD

Let's next work with RDD of (key,value) pairs called a Pair RDD or Key-Value RDD.

Now some of the Key-Value transformations that we could perform include the following.

  • reduceByKey transformation
    • which takes an RDD and returns a new RDD of key-value pairs, such that:
      • the values for each key are aggregated using the given reduced function
      • and the reduce function has to be of the type that takes two values and returns one value.
  • sortByKey transformation
    • this returns a new RDD of key-value pairs that's sorted by keys in ascending order
  • groupByKey transformation
    • this returns a new RDD consisting of key and iterable-valued pairs.

Let's see some concrete examples next.

// Cntrl+Enter to reduceByKey and collect wordcounts RDD
//val wordcounts = wordCountPairRDD.reduceByKey( _ + _ )
val wordcounts = wordCountPairRDD.reduceByKey( (value1, value2) => value1 + value2 )
wordcounts.collect()

wordcounts: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[49] at reduceByKey at command-685894176422504:3
res58: Array[(String, Int)] = Array((a,6), (b,5))

Now, let us do just the crucial steps and avoid collecting intermediate RDDs (something we should avoid for large datasets anyways, as they may not fit in the driver program).

//Cntrl+Enter to make words RDD and do the word count in two lines
val words = sc.parallelize(Array("a", "b", "a", "a", "b", "b", "a", "a", "a", "b", "b"))
val wordcounts = words
                    .map(s => (s, 1))
                    .reduceByKey(_ + _)
                    .collect() 

words: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[50] at parallelize at command-685894176422506:2
wordcounts: Array[(String, Int)] = Array((a,6), (b,5))
You Try!

You try evaluating sortByKey() which will make a new RDD that consists of the elements of the original pair RDD that are sorted by Keys.

// Shift+Enter and comprehend code
val words = sc.parallelize(Array("a", "b", "a", "a", "b", "b", "a", "a", "a", "b", "b"))
val wordCountPairRDD = words.map(s => (s, 1))
val wordCountPairRDDSortedByKey = wordCountPairRDD.sortByKey()

words: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[53] at parallelize at command-685894176422508:2
wordCountPairRDD: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[54] at map at command-685894176422508:3
wordCountPairRDDSortedByKey: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[57] at sortByKey at command-685894176422508:4

wordCountPairRDD.collect() // Shift+Enter and comprehend code

res59: Array[(String, Int)] = Array((a,1), (b,1), (a,1), (a,1), (b,1), (b,1), (a,1), (a,1), (a,1), (b,1), (b,1))

wordCountPairRDDSortedByKey.collect() // Cntrl+Enter and comprehend code

res60: Array[(String, Int)] = Array((a,1), (a,1), (a,1), (a,1), (a,1), (a,1), (b,1), (b,1), (b,1), (b,1), (b,1))

The next key value transformation we will see is groupByKey

When we apply the groupByKey transformation to wordCountPairRDD we end up with a new RDD that contains two elements. The first element is the tuple b and an iterable CompactBuffer(1,1,1,1,1) obtained by grouping the value 1 for each of the five key value pairs (b,1). Similarly the second element is the key a and an iterable CompactBuffer(1,1,1,1,1,1) obtained by grouping the value 1 for each of the six key value pairs (a,1).

CAUTION: groupByKey can cause a large amount of data movement across the network. It also can create very large iterables at a worker. Imagine you have an RDD where you have 1 billion pairs that have the key a. All of the values will have to fit in a single worker if you use group by key. So instead of a group by key, consider using reduced by key.

val wordCountPairRDDGroupByKey = wordCountPairRDD.groupByKey() // <Shift+Enter> CAUTION: this transformation can be very wide!

wordCountPairRDDGroupByKey: org.apache.spark.rdd.RDD[(String, Iterable[Int])] = ShuffledRDD[58] at groupByKey at command-685894176422513:1

wordCountPairRDDGroupByKey.collect()  // Cntrl+Enter

res61: Array[(String, Iterable[Int])] = Array((a,CompactBuffer(1, 1, 1, 1, 1, 1)), (b,CompactBuffer(1, 1, 1, 1, 1)))

10. Understanding Closures - Where in the cluster is your computation running?

One of the harder things about Spark is understanding the scope and life cycle of variables and methods when executing code across a cluster. RDD operations that modify variables outside of their scope can be a frequent source of confusion. In the example below we’ll look at code that uses foreach() to increment a counter, but similar issues can occur for other operations as well.

https://spark.apache.org/docs/latest/rdd-programming-guide.html#understanding-closures-

val data = Array(1, 2, 3, 4, 5)
var counter = 0
var rdd = sc.parallelize(data)

// Wrong: Don't do this!!
rdd.foreach(x => counter += x)

println("Counter value: " + counter)

Counter value: 0
data: Array[Int] = Array(1, 2, 3, 4, 5)
counter: Int = 0
rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[59] at parallelize at command-685894176422517:3

From RDD programming guide:

The behavior of the above code is undefined, and may not work as intended. To execute jobs, Spark breaks up the processing of RDD operations into tasks, each of which is executed by an executor. Prior to execution, Spark computes the task’s closure. The closure is those variables and methods which must be visible for the executor to perform its computations on the RDD (in this case foreach()). This closure is serialized and sent to each executor.

The variables within the closure sent to each executor are now copies and thus, when counter is referenced within the foreach function, it’s no longer the counter on the driver node. There is still a counter in the memory of the driver node but this is no longer visible to the executors! The executors only see the copy from the serialized closure. Thus, the final value of counter will still be zero since all operations on counter were referencing the value within the serialized closure.

11. Shipping Closures, Broadcast Variables and Accumulator Variables

Closures, Broadcast and Accumulator Variables

(watch now 2:06):

Closures, Broadcast and Accumulators by Anthony Joseph in BerkeleyX/CS100.1x

We will use these variables in the sequel.

SUMMARY

Spark automatically creates closures

  • for functions that run on RDDs at workers,
  • and for any global variables that are used by those workers
  • one closure per worker is sent with every task
  • and there's no communication between workers
  • closures are one way from the driver to the worker
  • any changes that you make to the global variables at the workers
    • are not sent to the driver or
    • are not sent to other workers.

The problem we have is that these closures

  • are automatically created are sent or re-sent with every job
  • with a large global variable it gets inefficient to send/resend lots of data to each worker
  • we cannot communicate that back to the driver

To do this, Spark provides shared variables in two different types.

  • broadcast variables
    • lets us to efficiently send large read-only values to all of the workers
    • these are saved at the workers for use in one or more Spark operations.
  • accumulator variables
    • These allow us to aggregate values from workers back to the driver.
    • only the driver can access the value of the accumulator
    • for the tasks, the accumulators are basically write-only
Accumulators

Accumulators are variables that are only “added” to through an associative and commutative operation and can therefore be efficiently supported in parallel. They can be used to implement counters (as in MapReduce) or sums. Spark natively supports accumulators of numeric types, and programmers can add support for new types.

Read: https://spark.apache.org/docs/latest/rdd-programming-guide.html#accumulators.

A numeric accumulator can be created by calling SparkContext.longAccumulator() or SparkContext.doubleAccumulator() to accumulate values of type Long or Double, respectively. Tasks running on a cluster can then add to it using the add method. However, they cannot read its value. Only the driver program can read the accumulator’s value, using its value method.

The code below shows an accumulator being used to add up the elements of an array:

val accum = sc.longAccumulator("My Accumulator")

accum: org.apache.spark.util.LongAccumulator = LongAccumulator(id: 1891, name: Some(My Accumulator), value: 0)

sc.parallelize(Array(1, 2, 3, 4)).foreach(x => accum.add(x))

accum.value

res66: Long = 10
Broadcast Variables

From https://spark.apache.org/docs/latest/rdd-programming-guide.html#broadcast-variables:

Broadcast variables allow the programmer to keep a read-only variable cached on each machine rather than shipping a copy of it with tasks. They can be used, for example, to give every node a copy of a large input dataset in an efficient manner. Spark also attempts to distribute broadcast variables using efficient broadcast algorithms to reduce communication cost.

Spark actions are executed through a set of stages, separated by distributed “shuffle” operations. Spark automatically broadcasts the common data needed by tasks within each stage. The data broadcasted this way is cached in serialized form and deserialized before running each task. This means that explicitly creating broadcast variables is only useful when tasks across multiple stages need the same data or when caching the data in deserialized form is important.

Broadcast variables are created from a variable v by calling SparkContext.broadcast(v). The broadcast variable is a wrapper around v, and its value can be accessed by calling the value method. The code below shows this in action.

val broadcastVar = sc.broadcast(Array(1, 2, 3))

broadcastVar: org.apache.spark.broadcast.Broadcast[Array[Int]] = Broadcast(67)

broadcastVar.value

res68: Array[Int] = Array(1, 2, 3)

broadcastVar.value(0)

res69: Int = 1

val rdd = sc.parallelize(1 to 10)

rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[61] at parallelize at command-685894176422531:1

rdd.collect

res70: Array[Int] = Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)

rdd.map(x => x%3).collect

res71: Array[Int] = Array(1, 2, 0, 1, 2, 0, 1, 2, 0, 1)

rdd.map(x => x+broadcastVar.value(x%3)).collect

res72: Array[Int] = Array(3, 5, 4, 6, 8, 7, 9, 11, 10, 12)

After the broadcast variable is created, it should be used instead of the value v in any functions run on the cluster so that v is not shipped to the nodes more than once. In addition, the object v should not be modified after it is broadcast in order to ensure that all nodes get the same value of the broadcast variable (e.g. if the variable is shipped to a new node later).

To release the resources that the broadcast variable copied onto executors, call .unpersist(). If the broadcast is used again afterwards, it will be re-broadcast. To permanently release all resources used by the broadcast variable, call .destroy(). The broadcast variable can’t be used after that. Note that these methods do not block by default. To block until resources are freed, specify blocking=true when calling them.

broadcastVar.unpersist()
A more interesting example of broadcast variable

Let us broadcast maps and use them to lookup the values at each executor. This example is taken from: - https://sparkbyexamples.com/spark/spark-broadcast-variables/

val states = Map(("NY","New York"),("CA","California"),("FL","Florida"))
val countries = Map(("USA","United States of America"),("IN","India"))

val broadcastStates = spark.sparkContext.broadcast(states)
val broadcastCountries = spark.sparkContext.broadcast(countries)

val data = Seq(("James","Smith","USA","CA"),
    ("Michael","Rose","USA","NY"),
    ("Robert","Williams","USA","CA"),
    ("Maria","Jones","USA","FL"))

val rdd = spark.sparkContext.parallelize(data) // spark.sparkContext is the same as sc.parallelize in spark-shell/notebook

  val rdd2 = rdd.map(f=>{
    val country = f._3
    val state = f._4
    val fullCountry = broadcastCountries.value.get(country).get
    val fullState = broadcastStates.value.get(state).get
    (f._1,f._2,fullCountry,fullState)
  })

states: scala.collection.immutable.Map[String,String] = Map(NY -> New York, CA -> California, FL -> Florida)
countries: scala.collection.immutable.Map[String,String] = Map(USA -> United States of America, IN -> India)
broadcastStates: org.apache.spark.broadcast.Broadcast[scala.collection.immutable.Map[String,String]] = Broadcast(71)
broadcastCountries: org.apache.spark.broadcast.Broadcast[scala.collection.immutable.Map[String,String]] = Broadcast(72)
data: Seq[(String, String, String, String)] = List((James,Smith,USA,CA), (Michael,Rose,USA,NY), (Robert,Williams,USA,CA), (Maria,Jones,USA,FL))
rdd: org.apache.spark.rdd.RDD[(String, String, String, String)] = ParallelCollectionRDD[64] at parallelize at command-685894176422538:12
rdd2: org.apache.spark.rdd.RDD[(String, String, String, String)] = MapPartitionsRDD[65] at map at command-685894176422538:14

println(rdd2.collect().mkString("\n"))

(James,Smith,United States of America,California)
(Michael,Rose,United States of America,New York)
(Robert,Williams,United States of America,California)
(Maria,Jones,United States of America,Florida)

12. Spark Essentials: Summary

(watch now: 0:29)

Spark Essentials Summary by Anthony Joseph in BerkeleyX/CS100.1x

NOTE: In databricks cluster, we (the course coordinator/administrators) set the number of workers for you.

13. HOMEWORK

See the notebook in this folder named 005_RDDsTransformationsActionsHOMEWORK. This notebook will give you more examples of the operations above as well as others we will be using later, including:

  • Perform the takeOrdered action on the RDD
  • Transform the RDD by distinct to make another RDD and
  • Doing a bunch of transformations to our RDD and performing an action in a single cell.

14. Importing Standard Scala and Java libraries

  • For other libraries that are not available by default, you can upload other libraries to the Workspace.
  • Refer to the Libraries guide for more details.
import scala.math._
val x = min(1, 10)

import scala.math._
x: Int = 1

import java.util.HashMap
val map = new HashMap[String, Int]()
map.put("a", 1)
map.put("b", 2)
map.put("c", 3)
map.put("d", 4)
map.put("e", 5)

import java.util.HashMap
map: java.util.HashMap[String,Int] = {a=1, b=2, c=3, d=4, e=5}
res75: Int = 0

ScaDaMaLe Course site and book

HOMEWORK on RDD Transformations and Actions

Just go through the notebook and familiarize yourself with these transformations and actions.

  1. Perform the takeOrdered action on the RDD**

To illustrate take and takeOrdered actions, let's create a bigger RDD named rdd0_1000000 that is made up of a million integers from 0 to 1000000. We will sc.parallelize the Seq Scala collection by using its .range(startInteger,stopInteger) method.

val rdd0_1000000 = sc.parallelize(Seq.range(0, 1000000)) // <Shift+Enter> to create an RDD of million integers: 0,1,2,...,10^6

rdd0_1000000.take(5) // <Ctrl+Enter> gives the first 5 elements of the RDD, (0, 1, 2, 3, 4)

takeordered(n) returns n elements ordered in ascending order (by default) or as specified by the optional key function, as shown below.

rdd0_1000000.takeOrdered(5) // <Shift+Enter> is same as rdd0_1000000.take(5) 

rdd0_1000000.takeOrdered(5)(Ordering[Int].reverse) // <Ctrl+Enter> to get the last 5 elements of the RDD 999999, 999998, ..., 999995

// HOMEWORK: edit the numbers below to get the last 20 elements of an RDD made of a sequence of integers from 669966 to 969696
sc.parallelize(Seq.range(0, 10)).takeOrdered(5)(Ordering[Int].reverse) // <Ctrl+Enter> evaluate this cell after editing it for the right answer
  1. More examples of map
val rdd = sc.parallelize(Seq(1, 2, 3, 4))    // <Shift+Enter> to evaluate this cell (using default number of partitions)

rdd.map( x => x*2) // <Ctrl+Enter> to transform rdd by map that doubles each element

To see what's in the transformed RDD, let's perform the actions of count and collect on the rdd.map( x => x*2), the transformation of rdd by the map given by the closure x => x*2.

rdd.map( x => x*2).count()    // <Shift+Enter> to perform count (action) the element of the RDD = 4

rdd.map( x => x*2).collect()    // <Shift+Enter> to perform collect (action) to show 2, 4, 6, 8

// HOMEWORK: uncomment the last line in this cell and modify the '<Fill-In-Here>' in the code below to collect and display the square (x*x) of each element of the RDD
// the answer should be Array[Int] = Array(1, 4, 9, 16) Press <Cntrl+Enter> to evaluate the cell after modifying '???'

//sc.parallelize(Seq(1, 2, 3, 4)).map( x => <Fill-In-Here> ).collect()
  1. More examples of filter

Let's declare another val RDD named rddFiltered by transforming our first RDD named rdd via the filter transformation x%2==0 (of being even).

This filter transformation based on the closure x => x%2==0 will return true if the element, modulo two, equals zero. The closure is automatically passed on to the workers for evaluation (when an action is called later). So this will take our RDD of (1,2,3,4) and return RDD of (2, 4).

val rddFiltered = rdd.filter( x => x%2==0 )    // <Ctrl+Enter> to declare rddFiltered from transforming rdd

rddFiltered.collect()    // <Ctrl+Enter> to collect (action) elements of rddFiltered; should be (2, 4)
  1. More examples of reduce
val rdd = sc.parallelize(Array(1,2,3,4,5))

rdd.reduce( (x,y)=>x+y ) // <Shift+Enter> to do reduce (action) to sum and return Int = 15

rdd.reduce( _ + _ )    // <Shift+Enter> to do same sum as above and return Int = 15 (undescore syntax)

rdd.reduce( (x,y)=>x*y ) // <Shift+Enter> to do reduce (action) to multiply and return Int = 120

val rdd0_1000000 = sc.parallelize(Seq.range(0, 1000000)) // <Shift+Enter> to create an RDD of million integers: 0,1,2,...,10^6

rdd0_1000000.reduce( (x,y)=>x+y ) // <Ctrl+Enter> to do reduce (action) to sum and return Int 1783293664

// the following correctly returns Int = 0 although for wrong reason 
// we have flowed out of Int's numeric limits!!! (but got lucky with 0*x=0 for any Int x)
// <Shift+Enter> to do reduce (action) to multiply and return Int = 0
rdd0_1000000.reduce( (x,y)=>x*y ) 

// <Ctrl+Enter> to do reduce (action) to multiply 1*2*...*9*10 and return correct answer Int = 3628800
sc.parallelize(Seq.range(1, 11)).reduce( (x,y)=>x*y )

CAUTION: Know the limits of your numeric types!

The minimum and maximum value of Int and Long types are as follows:

(Int.MinValue , Int.MaxValue)

(Long.MinValue, Long.MaxValue)

// <Ctrl+Enter> to do reduce (action) to multiply 1*2*...*20 and return wrong answer as Int = -2102132736
//  we have overflowed out of Int's in a circle back to negative Ints!!! (rigorous distributed numerics, anyone?)
sc.parallelize(Seq.range(1, 21)).reduce( (x,y)=>x*y ) 

//<Ctrl+Enter> we can accomplish the multiplication using Long Integer types 
// by adding 'L' ro integer values, Scala infers that it is type Long
sc.parallelize(Seq.range(1L, 21L)).reduce( (x,y)=>x*y )

As the following products over Long Integers indicate, they are limited too!

 // <Shift+Enter> for wrong answer Long = -8718968878589280256 (due to Long's numeric limits)
sc.parallelize(Seq.range(1L, 61L)).reduce( (x,y)=>x*y )

// <Cntrl+Enter> for wrong answer Long = 0 (due to Long's numeric limits)
sc.parallelize(Seq.range(1L, 100L)).reduce( (x,y)=>x*y )
  1. Let us do a bunch of transformations to our RDD and perform an action
  • start from a Scala Seq,
  • sc.parallelize the list to create an RDD,
  • filter that RDD, creating a new filtered RDD,
  • do a map transformation that maps that RDD to a new mapped RDD,
  • and finally, perform a reduce action to sum the elements in the RDD.

This last reduce action causes the parallelize, the filter, and the map transformations to actually be executed, and return a result back to the driver machine.

sc.parallelize(Seq(1, 2, 3, 4))    // <Ctrl+Enter> will return Array(4, 8)
  .filter(x => x%2==0)             // (2, 4) is the filtered RDD
  .map(x => x*2)                   // (4, 8) is the mapped RDD
  .reduce(_+_)                     // 4+8=12 is the final result from reduce
  1. Transform the RDD by distinct to make another RDD

Let's declare another RDD named rdd2 that has some repeated elements to apply the distinct transformation to it. That would give us a new RDD that only contains the distinct elements of the input RDD.

val rdd2 = sc.parallelize(Seq(4, 1, 3, 2, 2, 2, 3, 4))    // <Ctrl+Enter> to declare rdd2

Let's apply the distinct transformation to rdd2 and have it return a new RDD named rdd2Distinct that contains the distinct elements of the source RDD rdd2.

val rdd2Distinct = rdd2.distinct() // <Ctrl+Enter> transformation: distinct gives distinct elements of rdd2

rdd2Distinct.collect()    // <Ctrl+Enter> to collect (action) as Array(4, 2, 1, 3)
  1. more flatMap
val rdd = sc. parallelize(Array(1,2,3)) // <Shift+Enter> to create an RDD of three Int elements 1,2,3

Let us pass the rdd above to a map with a closure that will take in each element x and return Array(x, x+5). So each element of the mapped RDD named rddOfArrays is an Array[Int], an array of integers.

// <Shift+Enter> to make RDD of Arrays, i.e., RDD[Array[int]]
val rddOfArrays = rdd.map( x => Array(x, x+5) ) 

rddOfArrays.collect() // <Ctrl+Enter> to see it is RDD[Array[int]] = (Array(1, 6), Array(2, 7), Array(3, 8))

Now let's observer what happens when we use flatMap to transform the same rdd and create another RDD called rddfM.

Interestingly, flatMap flattens our rdd by taking each Array (or sequence in general) and truning it into individual elements.

Thus, we end up with the RDD rddfM consisting of the elements (1, 6, 2, 7, 3, 8) as shown from the output of rddfM.collect below.

val rddfM = rdd.flatMap(x => Array(x, x+5))    // <Shift+Enter> to flatMap the rdd using closure (x => Array(x, x+5))

rddfM.collect    // <Ctrl+Enter> to collect rddfM = (1, 6, 2, 7, 3, 8)

ScaDaMaLe Course site and book

Piped RDDs and Bayesian AB Testing

Here we will first take excerpts with minor modifications from the end of Chapter 12. Resilient Distributed Datasets (RDDs) of Spark: The Definitive Guide:

  • https://learning.oreilly.com/library/view/spark-the-definitive/9781491912201/ch12.html

Next, we will do Bayesian AB Testing using PipedRDDs.

First, we create the toy RDDs as in The Definitive Guide:

From a Local Collection

To create an RDD from a collection, you will need to use the parallelize method on a SparkContext (within a SparkSession). This turns a single node collection into a parallel collection. When creating this parallel collection, you can also explicitly state the number of partitions into which you would like to distribute this array. In this case, we are creating two partitions:

// in Scala
val myCollection = "Spark The Definitive Guide : Big Data Processing Made Simple"  .split(" ")
val words = spark.sparkContext.parallelize(myCollection, 2)

# in Python
myCollection = "Spark The Definitive Guide : Big Data Processing Made Simple"\
  .split(" ")
words = spark.sparkContext.parallelize(myCollection, 2)
words

glom from The Definitive Guide

glom is an interesting function that takes every partition in your dataset and converts them to arrays. This can be useful if you’re going to collect the data to the driver and want to have an array for each partition. However, this can cause serious stability issues because if you have large partitions or a large number of partitions, it’s simple to crash the driver.

Let's use glom to see how our words are distributed among the two partitions we used explicitly.

words.glom.collect 

words.glom().collect()

Checkpointing from The Definitive Guide

One feature not available in the DataFrame API is the concept of checkpointing. Checkpointing is the act of saving an RDD to disk so that future references to this RDD point to those intermediate partitions on disk rather than recomputing the RDD from its original source. This is similar to caching except that it’s not stored in memory, only disk. This can be helpful when performing iterative computation, similar to the use cases for caching:

Let's create a directory in dbfs:/// for checkpointing of RDDs in the sequel. The following %fs mkdirs /path_to_dir is a shortcut to create a directory in dbfs:///

mkdirs /datasets/ScaDaMaLe/checkpointing/

spark.sparkContext.setCheckpointDir("dbfs:///datasets/ScaDaMaLe/checkpointing")
words.checkpoint()

Now, when we reference this RDD, it will derive from the checkpoint instead of the source data. This can be a helpful optimization.

YouTry

Just some more words in haha_words with \n, the End-Of-Line (EOL) characters, in-place.

val haha_words = sc.parallelize(Seq("ha\nha", "he\nhe\nhe", "ho\nho\nho\nho"),3)

Let's use glom to see how our haha_words are distributed among the partitions

haha_words.glom.collect

Pipe RDDs to System Commands

The pipe method is probably one of Spark’s more interesting methods. With pipe, you can return an RDD created by piping elements to a forked external process. The resulting RDD is computed by executing the given process once per partition. All elements of each input partition are written to a process’s stdin as lines of input separated by a newline. The resulting partition consists of the process’s stdout output, with each line of stdout resulting in one element of the output partition. A process is invoked even for empty partitions.

The print behavior can be customized by providing two functions.

We can use a simple example and pipe each partition to the command wc. Each row will be passed in as a new line, so if we perform a line count, we will get the number of lines, one per partition:

The following produces a PipedRDD:

val wc_l_PipedRDD = words.pipe("wc -l")

wc_l_PipedRDD = words.pipe("wc -l")
wc_l_PipedRDD

Now, we take an action via collect to bring the results to the Driver.

NOTE: Be careful what you collect! You can always write the output to parquet of binary files in dbfs:/// if the returned output is large.

wc_l_PipedRDD.collect

wc_l_PipedRDD.collect()

In this case, we got the number of lines returned by wc -l per partition.

YouTry

Try to make sense of the next few cells where we do NOT specifiy the number of partitions explicitly and let Spark decide on the number of partitions automatically.

val haha_words = sc.parallelize(Seq("ha\nha", "he\nhe\nhe", "ho\nho\nho\nho"),3)
haha_words.glom.collect
val wc_l_PipedRDD_haha_words = haha_words.pipe("wc -l")
wc_l_PipedRDD_haha_words.collect()

Do you understand why the above collect statement returns what it does?

val haha_words_again = sc.parallelize(Seq("ha\nha", "he\nhe\nhe", "ho\nho\nho\nho"))
haha_words_again.glom.collect
val wc_l_PipedRDD_haha_words_again = haha_words_again.pipe("wc -l")
wc_l_PipedRDD_haha_words_again.collect()

Did you understand why some of the results are 0 in the last collect statement?

mapPartitions

The previous command revealed that Spark operates on a per-partition basis when it comes to actually executing code. You also might have noticed earlier that the return signature of a map function on an RDD is actually MapPartitionsRDD.

Or ParallelCollectionRDD in our case.

This is because map is just a row-wise alias for mapPartitions, which makes it possible for you to map an individual partition (represented as an iterator). That’s because physically on the cluster we operate on each partition individually (and not a specific row). A simple example creates the value “1” for every partition in our data, and the sum of the following expression will count the number of partitions we have:

// in Scala
words.mapPartitions(part => Iterator[Int](1)).sum() // 2.0

# in Python
words.mapPartitions(lambda part: [1]).sum() # 2

Naturally, this means that we operate on a per-partition basis and therefore it allows us to perform an operation on that entire partition. This is valuable for performing something on an entire subdataset of your RDD. You can gather all values of a partition class or group into one partition and then operate on that entire group using arbitrary functions and controls. An example use case of this would be that you could pipe this through some custom machine learning algorithm and train an individual model for that company’s portion of the dataset. A Facebook engineer has an interesting demonstration of their particular implementation of the pipe operator with a similar use case demonstrated at Spark Summit East 2017.

Other functions similar to mapPartitions include mapPartitionsWithIndex. With this you specify a function that accepts an index (within the partition) and an iterator that goes through all items within the partition. The partition index is the partition number in your RDD, which identifies where each record in our dataset sits (and potentially allows you to debug). You might use this to test whether your map functions are behaving correctly:

// in Scala
def indexedFunc(partitionIndex:Int, withinPartIterator: Iterator[String]) = {  withinPartIterator.toList.map(    
  value => s"Partition: $partitionIndex => $value").iterator
                                                                            }
words.mapPartitionsWithIndex(indexedFunc).collect()

# in Python
def indexedFunc(partitionIndex, withinPartIterator):  
  return ["partition: {} => {}".format(partitionIndex,    x) for x in withinPartIterator]
words.mapPartitionsWithIndex(indexedFunc).collect()

foreachPartition

Although mapPartitions needs a return value to work properly, this next function does not. foreachPartition simply iterates over all the partitions of the data. The difference is that the function has no return value. This makes it great for doing something with each partition like writing it out to a database. In fact, this is how many data source connectors are written. You can create

your

own text file source if you want by specifying outputs to the temp directory with a random ID:

words.foreachPartition { iter =>  
  import java.io._  
  import scala.util.Random  
  val randomFileName = new Random().nextInt()  
  val pw = new PrintWriter(new File(s"/tmp/random-file-${randomFileName}.txt"))  
  while (iter.hasNext) {
    pw.write(iter.next())  
  }  
  pw.close()
}

You’ll find these two files if you scan your /tmp directory.

You need to scan for the file across all the nodes. As the file may not be in the Driver node's /tmp/ directory but in those of the executors that hosted the partition.

pwd

ls /tmp/random-file-*.txt

Numerically Rigorous Bayesian AB Testing

This is an example of Bayesian AB Testing with computer-aided proofs for the posterior samples.

The main learning goal for you is to use pipedRDDs to distribute, in an embarassingly paralle way, across all the worker nodes in the Spark cluster an executible IsIt1or2Coins.

What does IsIt1or2Coins do?

At a very high-level, to understand what IsIt1or2Coins does, imagine the following simple experiment.

We are given

  • the number of heads that result from a first sequence of independent and identical tosses of a coin and then
  • we are given the number of heads that result from a second sequence of independent and identical tosses of a coin

Our decision problem is to do help shed light on whether both sequence of tosses came from the same coin or not (whatever the bias may be).

IsIt1or2Coins tries to help us decide if the two sequence of coin-tosses are based on one coin with an unknown bias or two coins with different biases.

If you are curious about details feel free to see:

  • Exact Bayesian A/B testing using distributed fault-tolerant Moore rejection sampler, Benny Avelin and Raazesh Sainudiin, Extended Abstract, 2 pages, 2018 (PDF 104KB).
  • which builds on: An auto-validating, trans-dimensional, universal rejection sampler for locally Lipschitz arithmetical expressions, Raazesh Sainudiin and Thomas York, Reliable Computing, vol.18, pp.15-54, 2013 (preprint: PDF 2612KB)

See first about PipedRDDs excerpt from Spark The Definitive Guide earlier.

Getting the executible IsIt1or2Coins into our Spark Cluster

This has already been done in the project-shard. You need not do it again for this executible!

You need to upload the C++ executible IsIt1or2Coins from: - https://github.com/lamastex/mrs2

Here, suppose you have an executible for linux x86 64 bit processor with all dependencies pre-compiled into one executibe.

Say this executible is IsIt10r2Coins.

This executible comes from the following dockerised build:

  • https://github.com/lamastex/mrs2/tree/master/docker
  • by statically compiling inside the docerised environment for mrs2:
    • https://github.com/lamastex/mrs2/tree/master/mrs-2.0/examples/MooreRejSam/IsIt1or2Coins

You can replace the executible with any other executible with appropriate I/O to it.

Then you upload the executible to databricks' FileStore.

Just note the path to the file and DO NOT click Create Table or other buttons!

creenShotOfUploadingStaticExecutibleIsIt1or2CoinsViaFileStore

ls "/FileStore/tables/IsIt1or2Coins"

Now copy the file from dbfs://FileStore that you just uploaded into the local file system of the Driver.

dbutils.fs.cp("dbfs:/FileStore/tables/IsIt1or2Coins", "file:/tmp/IsIt1or2Coins")

ls -al /tmp/IsIt1or2Coins

Note it is a big static executible with all dependencies inbuilt (it uses GNU Scientific Library and a specialized C++ Library called C-XSC or C Extended for Scientific Computing to do hard-ware optimized rigorous numerical proofs using Interval-Extended Hessian Differentiation Arithmetics over Rounding-Controlled Hardware-Specified Machine Intervals).

Just note it is over 6.5MB. Also we need to change the permissions so it is indeed executible.

chmod +x /tmp/IsIt1or2Coins

Usage instructions for IsIt1or2Coins

./IsIt1or2Coins numboxes numiter seed numtosses1 heads1 numtosses2 heads2 logScale - numboxes = Number of boxes for Moore Rejection Sampling (Rigorous von Neumann Rejection Sampler) - numiter = Number of samples drawn from posterior distribution to estimate the model probabilities - seed = a random number seed - numtosses1 = number of tosses for the first coin - heads1 = number of heads shown up on the first coin - numtosses2 = number of tosses for the second coin - heads2 = number of heads shown up on the second coin - logscale = True/False as Int

Don't worry about the details of what the executible IsIt1or2Coins is doing for now. Just realise that this executible takes some input on command-line and gives some output.

Let's make sure the executible takes input and returns output string on the Driver node.

/tmp/IsIt1or2Coins 1000 100 234565432 1000 500 1200 600 1

# You can also do it like this

/dbfs/FileStore/tables/IsIt1or2Coins 1000 100 234565432 1000 500 1200 600 1

Moving the executables to the worker nodes

To copy the executible from dbfs to the local drive of each executor you can use the following helper function.

import scala.sys.process._
import scala.concurrent.duration._
// from Ivan Sadikov

def copyFile(): Unit = {
  "mkdir -p /tmp/executor/bin".!!
  "cp /dbfs/FileStore/tables/IsIt1or2Coins /tmp/executor/bin/".!!
}

sc.runOnEachExecutor(copyFile, new FiniteDuration(1, HOURS))

Now, let us use piped RDDs via bash to execute the given command in each partition as follows:

val input = Seq("/tmp/executor/bin/IsIt1or2Coins 1000 100 234565432 1000 500 1200 600 1", "/tmp/executor/bin/IsIt1or2Coins 1000 100 234565432 1000 500 1200 600 1")

val output = sc
  .parallelize(input)
  .repartition(2)
  .pipe("bash")
  .collect()

In fact, you can just use DBFS FUSE to run the commands without any file copy in databricks-provisioned Spark clusters we are on here:

val isIt1or2StaticExecutible = "/dbfs/FileStore/tables/IsIt1or2Coins"
val same_input = Seq(s"$isIt1or2StaticExecutible 1000 100 234565432 1000 500 1200 600 1", 
                     s"$isIt1or2StaticExecutible 1000 100 234565432 1000 500 1200 600 1")

val same_output = sc
  .parallelize(same_input)
  .repartition(2)
  .pipe("bash")
  .collect()

Thus by mixing several different executibles that are statically compiled for linux 64 bit machine, we can mix and match multiple executibles with appropriate inputs.

The resulting outputs can themselves be re-processed in Spark to feed into toher pipedRDDs or normal RDDs or DataFrames and DataSets.

Finally, we can have more than one command per partition and then use mapPartitions to send all the executible commands within the input partition that is to be run by the executor in which that partition resides as follows:

val isIt1or2StaticExecutible = "/dbfs/FileStore/tables/IsIt1or2Coins"

// let us make 2 commands in each of the 2 input partitions
val same_input_mp = Seq(s"$isIt1or2StaticExecutible 1000 100 234565432 1000 500 1200 600 1", 
                        s"$isIt1or2StaticExecutible 1000 100 123456789 1000 500 1200 600 1",
                        s"$isIt1or2StaticExecutible 1000 100 123456789 1000 500 1200 600 1",
                        s"$isIt1or2StaticExecutible 1000 100 234565432 1000 500 1200 600 1")

val same_output_mp = sc
  .parallelize(same_input)
  .repartition(2)
  .pipe("bash")
  .mapPartitions(x => Seq(x.mkString("\n")).iterator)
  .collect()

allCatch is a useful tool to use as a filtering function when testing if a command will work without error.

import scala.util.control.Exception.allCatch
(allCatch opt " 12 ".trim.toLong).isDefined

ScaDaMaLe Course site and book

Word Count on US State of the Union (SoU) Addresses

  • Word Count in big data is the equivalent of Hello World in programming
  • We count the number of occurences of each word in the first and last (2016) SoU addresses.

prerequisite see DO NOW below. You should have loaded data as instructed in scalable-data-science/xtraResources/sdsDatasets.

DO NOW (if not done already)

In your databricks community edition:

  1. In your WorkSpace create a Folder named scalable-data-science
  2. Import the databricks archive file at the following URL:
  3. This should open a structure of directories in with path: /Workspace/scalable-data-science/xtraResources/

An interesting analysis of the textual content of the State of the Union (SoU) addresses by all US presidents was done in:

Fig. 5. A river network captures the flow across history of US political discourse, as perceived by contemporaries. Time moves along the x axis. Clusters on semantic networks of 300 most frequent terms for each of 10 historical periods are displayed as vertical bars. Relations between clusters of adjacent periods are indexed by gray flows, whose density reflects their degree of connection. Streams that connect at any point in history may be considered to be part of the same system, indicated with a single color.

Let us investigate this dataset ourselves!

  1. We first get the source text data by scraping and parsing from http://stateoftheunion.onetwothree.net/texts/index.html as explained in scraping and parsing SoU addresses.
  • This data is already made available in DBFS, our distributed file system.
  • We only do the simplest word count with this data in this notebook and will do more sophisticated analyses in the sequel (including topic modeling, etc).

Key Data Management Concepts

The Structure Spectrum

(watch now 1:10):

Structure Spectrum by Anthony Joseph in BerkeleyX/CS100.1x

Here we will be working with unstructured or schema-never data (plain text files). ***

Files

(watch later 1:43):

Files by Anthony Joseph in BerkeleyX/CS100.1x

DBFS and dbutils - where is this dataset in our distributed file system?

  • Since we are on the databricks cloud, it has a file system called DBFS
  • DBFS is similar to HDFS, the Hadoop distributed file system
  • dbutils allows us to interact with dbfs.
  • The 'display' command displays the list of files in a given directory in the file system.
display(dbutils.fs.ls("dbfs:/"))

display(dbutils.fs.ls("dbfs:/datasets/sou")) 

display(dbutils.fs.ls("dbfs:/datasets/sou")) // Cntrl+Enter to display the files in dbfs:/datasets/sou

Let us display the head or the first few lines of the file dbfs:/datasets/sou/17900108.txt to see what it contains using dbutils.fs.head method. head(file: String, maxBytes: int = 65536): String -> Returns up to the first 'maxBytes' bytes of the given file as a String encoded in UTF-8 as follows:

dbutils.fs.head("dbfs:/datasets/sou/17900108.txt",673) // Cntrl+Enter to get the first 673 bytes of the file (which corresponds to the first five lines)
You Try!

Uncomment and modify xxxx in the cell below to read the first 1000 bytes from the file.

//dbutils.fs.head("dbfs:/datasets/sou/17900108.txt", xxxx) // Cntrl+Enter to get the first 1000 bytes of the file

Read the file into Spark Context as an RDD of Strings

  • The textFile method on the available SparkContext sc can read the text file dbfs:/datasets/sou/17900108.txt into Spark and create an RDD of Strings
    • but this is done lazily until an action is taken on the RDD sou17900108!
val sou17900108 = sc.textFile("dbfs:/datasets/sou/17900108.txt") // Cntrl+Enter to read in the textfile as RDD[String]

Perform some actions on the RDD

  • Each String in the RDD sou17900108 represents one line of data from the file and can be made to perform one of the following actions:
    • count the number of elements in the RDD sou17900108 (i.e., the number of lines in the text file dbfs:/datasets/sou/17900108.txt) using sou17900108.count()
    • display the contents of the RDD using take or collect.
sou17900108.count() // <Shift+Enter> to count the number of elements in the RDD

sou17900108.take(5) // <Shift+Enter> to display the first 5 elements of RDD

sou17900108.take(5).foreach(println) // <Shift+Enter> to display the first 5 elements of RDD line by line

sou17900108.collect // <Cntrl+Enter> to display all the elements of RDD

Cache the RDD in (distributed) memory to avoid recreating it for each action

  • Above, every time we took an action on the same RDD, the RDD was reconstructed from the textfile.
    • Spark's advantage compared to Hadoop MapReduce is the ability to cache or store the RDD in distributed memory across the nodes.
  • Let's use .cache() after creating an RDD so that it is in memory after the first action (and thus avoid reconstruction for subsequent actions).
    • count the number of elements in the RDD sou17900108 (i.e., the number of lines in the text file dbfs:/datasets/sou/17900108.txt) using sou17900108.count()
    • display the contents of the RDD using take or collect.
// Shift+Enter to read in the textfile as RDD[String] and cache it in distributed memory
val sou17900108 = sc.textFile("dbfs:/datasets/sou/17900108.txt")
sou17900108.cache() // cache the RDD in memory

sou17900108.count() // Shift+Enter during this count action the RDD is constructed from texfile and cached

sou17900108.count() // Shift+Enter during this count action the cached RDD is used (notice less time taken by the same command)

sou17900108.take(5) // <Cntrl+Enter> to display the first 5 elements of the cached RDD

Lifecycle of a Spark Program

(watch now 0:23):

Spark Program Lifecycle by Anthony Joseph in BerkeleyX/CS100.1x

Summary
  • create RDDs from:
    • some external data source (such as a distributed file system)
    • parallelized collection in your driver program
  • lazily transform these RDDs into new RDDs
  • cache some of those RDDs for future reuse
  • you perform actions to execute parallel computation to produce results

Transform lines to words

  • We need to loop through each line and split the line into words
  • For now, let us split using whitespace
  • More sophisticated regular expressions can be used to split the line (as we will see soon)
sou17900108
.flatMap(line => line.split(" "))
.take(100)

Naive word count

At a first glace, to do a word count of George Washingtons SoU address, we are templed to do the following:

  • just break each line by the whitespace character " " and find the words using a flatMap
  • then do the map with the closure word => (word, 1) to initialize each word with a integer count of 1
    • ie., transform each word to a (key, value) pair or Tuple such as (word, 1)
  • then count all values with the same key (word is the Key in our case) by doing a
    • reduceByKey(_+_)
  • and finally collect() to display the results.
sou17900108
.flatMap( line => line.split(" ") )
.map( word => (word, 1) )
.reduceByKey(_+_)
.collect()

Unfortunately, as you can see from the collect above:

  • the words have punctuations at the end which means that the same words are being counted as different words. Eg: importance
  • empty words are being counted

So we need a bit of regex'ing or regular-expression matching (all readily available from Scala via Java String types).

We will cover the three things we want to do with a simple example from Middle Earth!

  • replace all multiple whitespace characters with one white space character " "
  • replace all punction characters we specify within [ and ] such as [,?.!:;] by the empty string "" (i.e., remove these punctuation characters)
  • convert everything to lower-case.
val example = "Master, Master!   It's me, Sméagol... mhrhm*%* But they took away our precious, they wronged us. Gollum will protect us..., Master, it's me Sméagol."

example
  .replaceAll("\\s+", " ") //replace multiple whitespace characters (including space, tab, new line, etc.) with one whitespace " "
  .replaceAll("""([,?.!:;])""", "") // replace the following punctions characters: , ? . ! : ; . with the empty string ""
  .toLowerCase() // converting to lower-case

More sophisticated word count

We are now ready to do a word count of George Washington's SoU on January 8th 1790 as follows:

val wordCount_sou17900108 = 
 sou17900108
    .flatMap(line => 
         line.replaceAll("\\s+", " ") //replace multiple whitespace characters (including space, tab, new line, etc.) with one whitespace " "
             .replaceAll("""([,?.!:;])""", "") // replace the following punctions characters: , ? . ! : ; . with the empty string ""
             .toLowerCase() // converting to lower-case
             .split(" "))
    .map(x => (x, 1))
    .reduceByKey(_+_)
    
wordCount_sou17900108.collect()

val top10 = wordCount_sou17900108.sortBy(_._2, false).collect()

Doing it all together for George Washington and Barrack Obama

//sc.textFile("dbfs:/datasets/sou/17900108.txt") // George Washington's first SoU
sc.textFile("dbfs:/datasets/sou/20160112.txt")   // Barrack Obama's second SoU
    .flatMap(line => 
         line.replaceAll("\\s+", " ") //replace multiple whitespace characters (including space, tab, new line, etc.) with one whitespace " "
             .replaceAll("""([,?.!:;])""", "") // replace the following punctions characters: , ? . ! : ; . with the empty string ""
             .toLowerCase() // converting to lower-case
             .split(" "))
    .map(x => (x,1))
    .reduceByKey(_+_)
    .sortBy(_._2, false)
    .collect()

Reading all SoUs at once using wholetextFiles

Let us next read all text files (ending with .txt) in the directory dbfs:/datasets/sou/ at once!

SparkContext.wholeTextFiles lets you read a directory containing multiple small text files, and returns each of them as (filename, content) pairs of strings.

This is in contrast with textFile, which would return one record per line in each file.

val souAll = sc.wholeTextFiles("dbfs:/datasets/sou/*.txt") // Shift+Enter to read all text files in dbfs:/datasets/sou/
souAll.cache() // let's cache this RDD for efficient reuse

souAll.count() // Shift+enter to count the number of entries in RDD[(String,String)]

souAll.count() // Cntrl+Enter to count the number of entries in cached RDD[(String,String)] again (much faster!)

Let's examine the first two elements of the RDD souAll.

souAll.take(2) // Cntr+Enter to see the first two elements of souAll

Clearly, the elements are a pair of Strings, where the first String gives the filename and the second String gives the contents in the file.

this can be very helpful to simply loop through the files and take an action, such as counting the number of words per address, as folows:

// this just collects the file names which is the first element of the tuple given by "._1" 
souAll.map( fileContentsPair => fileContentsPair._1).collect()

Let us find the number of words in each of the SoU addresses next (we need to work with Strings inside the closure!).

val wcs = souAll.map( fileContentsPair => 
  {
    val wc = fileContentsPair._2
                             .replaceAll("\\s+", " ") //replace multiple whitespace characters (including space, tab, new line, etc.) with one whitespace " "
                             .replaceAll("""([,?.!:;])""", "") // replace the following punctions characters: , ? . ! : ; . with the empty string ""
                             .toLowerCase() // converting to lower-case
                             .split(" ") // split each word separated by white space
                             .size // find the length of array
    wc
  }    
)      

wcs.collect()

YouTry: HOMEWORK

  • HOWEWORK WordCount 1: sortBy
  • HOMEWROK WordCount 2: dbutils.fs
HOMEWORK WordCount 1. sortBy

Let's understand sortBy a bit more carefully.

val example = "Master, Master!   It's me, Sméagol... mhrhm*%* But they took away our precious, they wronged us. Gollum will protect us..., Master, it's me Sméagol."

val words = example.replaceAll("\\s+", " ") //replace multiple whitespace characters (including space, tab, new line, etc.) with one whitespace " "
       .replaceAll("""([,?.!:;])""", "") // replace the following punctions characters: , ? . ! : ; . with the empty string ""
       .toLowerCase() // converting to lower-case
       .split(" ")

val rddWords = sc.parallelize(words)

rddWords.take(10)

val wordCounts = rddWords
                  .map(x => (x,1))
                  .reduceByKey(_+_)

val top10 = wordCounts.sortBy(_._2, false).take(10)

Make your code easy to read for other developers ;) Use 'case classes' with well defined variable names that everyone can understand

val top10 = wordCounts.sortBy({
  case (word, count) => count
}, false).take(10)

If you just want a total count of all words in the file

rddWords.count
YoutTry: HOMEWORK WordCount 2: dbutils.fs

Have a brief look at what other commands dbutils.fs supports. We will introduce them as needed.

dbutils.fs.help // some of these were used to ETL this data into dbfs:/datasets/sou

Exercise 2: SouWordCount

Count the number of each word across all the "dbfs:/datasets/sou/*.txt" files and output the result as an Array of (word,count) tuples from the most frequent to the least frequent word.

This is the same as Exercise 2 in the local environment.

// code in this cell the solution to the above exercise in the notebook environment
//
//

ScaDaMaLe Course site and book

This and the next sequence of notebooks are an elaboration of the http://spark.apache.org/docs/latest/sql-programming-guide.html by Ivan Sadikov and Raazesh Sainudiin.

Spark Sql Programming Guide

  • Overview
    • SQL
    • DataFrames
    • Datasets
  • Getting Started
    • Starting Point: SQLContext
    • Creating DataFrames
    • DataFrame Operations
    • Running SQL Queries Programmatically
    • Creating Datasets
    • Interoperating with RDDs
      • Inferring the Schema Using Reflection
      • Programmatically Specifying the Schema
  • Data Sources
    • Generic Load/Save Functions
      • Manually Specifying Options
      • Run SQL on files directly
      • Save Modes
      • Saving to Persistent Tables
    • Parquet Files
      • Loading Data Programmatically
      • Partition Discovery
      • Schema Merging
      • Hive metastore Parquet table conversion
        • Hive/Parquet Schema Reconciliation
        • Metadata Refreshing
      • Configuration
    • JSON Datasets
    • Hive Tables
      • Interacting with Different Versions of Hive Metastore
    • JDBC To Other Databases
    • Troubleshooting
  • Performance Tuning
    • Caching Data In Memory
    • Other Configuration Options
  • Distributed SQL Engine
    • Running the Thrift JDBC/ODBC server
    • Running the Spark SQL CLI
  • SQL Reference

What could one do with these notebooks?

One could read the Spark SQL Programming Guide that is embedded below and also linked above while going through the cells and doing the YouTrys in the following notebooks.

Why might one do it?

This homework/self-study will help you solve the assigned lab and theory exercises in the sequel, much faster by introducing you to some basic knowledge you need about Spark SQL.

NOTE on intra-iframe html navigation within a notebook:

  • When navigating in the html-page embedded as an iframe, as in the cell below, you can:
    • click on a link in the displayed html page to see the content of the clicked link and
    • then right-click on the page and click on the arrow keys <- and -> to go back or forward.
//This allows easy embedding of publicly available information into any other notebook
//Example usage:
// displayHTML(frameIt("https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation#Topics_in_LDA",250))
def frameIt( u:String, h:Int ) : String = {
      """<iframe 
 src=""""+ u+""""
 width="95%" height="""" + h + """"
 sandbox>
  <p>
    <a href="http://spark.apache.org/docs/latest/index.html">
      Fallback link for browsers that, unlikely, don't support frames
    </a>
  </p>
</iframe>"""
   }
displayHTML(frameIt("https://spark.apache.org/docs/latest/sql-programming-guide.html",750))

Let's go through the programming guide in databricks now

This is an elaboration of the http://spark.apache.org/docs/latest/sql-programming-guide.html by Ivan Sadikov and Raazesh Sainudiin.

Spark SQL, DataFrames and Datasets Guide

Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. Internally, Spark SQL uses this extra information to perform extra optimizations. There are several ways to interact with Spark SQL including SQL and the Dataset API. When computing a result, the same execution engine is used, independent of which API/language you are using to express the computation. This unification means that developers can easily switch back and forth between different APIs based on which provides the most natural way to express a given transformation.

All of the examples on this page use sample data included in the Spark distribution and can be run in the spark-shell, pyspark shell, or sparkR shell.

SQL

One use of Spark SQL is to execute SQL queries. Spark SQL can also be used to read data from an existing Hive installation. For more on how to configure this feature, please refer to the Hive Tables section. When running SQL from within another programming language the results will be returned as a Dataset/DataFrame. You can also interact with the SQL interface using the command-line or over JDBC/ODBC.

Datasets and DataFrames

A Dataset is a distributed collection of data. Dataset is a new interface added in Spark 1.6 that provides the benefits of RDDs (strong typing, ability to use powerful lambda functions) with the benefits of Spark SQL’s optimized execution engine. A Dataset can be constructed from JVM objects and then manipulated using functional transformations (map, flatMap, filter, etc.). The Dataset API is available in Scala and Java. Python does not have the support for the Dataset API. But due to Python’s dynamic nature, many of the benefits of the Dataset API are already available (i.e. you can access the field of a row by name naturally row.columnName). The case for R is similar.

A DataFrame is a Dataset organized into named columns. It is conceptually equivalent to a table in a relational database or a data frame in R/Python, but with richer optimizations under the hood. DataFrames can be constructed from a wide array of sources such as: structured data files, tables in Hive, external databases, or existing RDDs. The DataFrame API is available in Scala, Java, Python, and R. In Scala and Java, a DataFrame is represented by a Dataset of Rows. In the Scala API, DataFrame is simply a type alias of Dataset[Row]. While, in Java API, users need to use Dataset<Row> to represent a DataFrame.

Throughout this document, we will often refer to Scala/Java Datasets of Rows as DataFrames.

Background and Preparation

  • If you are unfamiliar with SQL please brush-up from the basic links below.
  • SQL allows one to systematically explore any structured data (i.e., tables) using queries. This is necessary part of the data science process.

One can use the SQL Reference at https://spark.apache.org/docs/latest/sql-ref.html to learn SQL quickly.

displayHTML(frameIt("https://en.wikipedia.org/wiki/SQL",500))

displayHTML(frameIt("https://en.wikipedia.org/wiki/Apache_Hive#HiveQL",175))

displayHTML(frameIt("https://spark.apache.org/docs/latest/sql-ref.html",700))

ScaDaMaLe Course site and book

This is an elaboration of the http://spark.apache.org/docs/latest/sql-programming-guide.html by Ivan Sadikov and Raazesh Sainudiin.

Getting Started

Spark Sql Programming Guide

  • Starting Point: SparkSession
  • Creating DataFrames
  • Untyped Dataset Operations (aka DataFrame Operations)
  • Running SQL Queries Programmatically
  • Global Temporary View
  • Creating Datasets
  • Interoperating with RDDs
    • Inferring the Schema Using Reflection
    • Programmatically Specifying the Schema
  • Scalar Functions
  • Aggregate Functions

Getting Started

Starting Point: SparkSession

The entry point into all functionality in Spark is the SparkSession class and/or SQLContext/HiveContext. SparkSession is created for you as spark when you start spark-shell on command-line REPL or through a notebook server (databricks, zeppelin, jupyter, etc.). You will need to create SparkSession usually when building an application for submission to a Spark cluster. To create a basic SparkSession, just use SparkSession.builder():

import org.apache.spark.sql.SparkSession

val spark = SparkSession
  .builder()
  .appName("Spark SQL basic example")
  .config("spark.some.config.option", "some-value")
  .getOrCreate()

// For implicit conversions like converting RDDs to DataFrames
import spark.implicits._

Find full example code in the Spark repo at:

SparkSession in Spark 2.0 provides builtin support for Hive features including the ability to write queries using HiveQL, access to Hive UDFs, and the ability to read data from Hive tables. To use these features, you do not need to have an existing Hive setup.

// You could get SparkContext and SQLContext from SparkSession
val sc = spark.sparkContext
val sqlContext = spark.sqlContext

But in Databricks notebook (similar to spark-shell) SparkSession is already created for you and is available as spark (similarly, sc and sqlContext are also available).

// Evaluation of the cell by Ctrl+Enter will print spark session available in notebook
spark

After evaluation you should see something like this, i.e., a reference to the SparkSession you just created:

res0: org.apache.spark.sql.SparkSession = org.apache.spark.sql.SparkSession@5a289bf5

Creating DataFrames

With a SparkSessions, applications can create Dataset or DataFrame from an existing RDD, from a Hive table, or from various datasources.

Just to recap, a DataFrame is a distributed collection of data organized into named columns. You can think of it as an organized into table RDD of case class Row (which is not exactly true). DataFrames, in comparison to RDDs, are backed by rich optimizations, including tracking their own schema, adaptive query execution, code generation including whole stage codegen, extensible Catalyst optimizer, and project Tungsten.

Dataset provides type-safety when working with SQL, since Row is mapped to a case class, so that each column can be referenced by property of that class.

Note that performance for Dataset/DataFrames is the same across languages Scala, Java, Python, and R. This is due to the fact that the planning phase is just language-specific, only logical plan is constructed in Python, and all the physical execution is compiled and executed as JVM bytecode.

As an example, the following creates a DataFrame based on the content of a JSON file:

val df = spark.read.json("examples/src/main/resources/people.json")

// Displays the content of the DataFrame to stdout
df.show()
// +----+-------+
// | age|   name|
// +----+-------+
// |null|Michael|
// |  30|   Andy|
// |  19| Justin|
// +----+-------+

Find full example code at - https://raw.githubusercontent.com/apache/spark/master/examples/src/main/scala/org/apache/spark/examples/sql/SparkSQLExample.scala in the Spark repo.

To be able to try this example in databricks we need to load the people.json file into dbfs. Let us do this programmatically next.

// the following lines merely fetch the file from the URL and load it into the dbfs for us to try in databricks
// getLines from the file at the URL
val peopleJsonLinesFromURL = scala.io.Source.fromURL("https://raw.githubusercontent.com/apache/spark/master/examples/src/main/resources/people.json").getLines
// remove any pre-existing file at the dbfs location
dbutils.fs.rm("dbfs:///datasets/spark-examples/people.json",recurse=true)
// convert the lines fetched from the URL to a Seq, then make it a RDD of String and finally save it as textfile to dbfs
sc.parallelize(peopleJsonLinesFromURL.toSeq).saveAsTextFile("dbfs:///datasets/spark-examples/people.json")
// read the text file we just saved and see what it has
sc.textFile("dbfs:///datasets/spark-examples/people.json").collect.mkString("\n")

val df = spark.read.json("dbfs:///datasets/spark-examples/people.json")

// you can also read into df like this
val df = spark.read.format("json").load("dbfs:///datasets/spark-examples/people.json")

df.show()

Untyped Dataset Operations (aka DataFrame Operations)

DataFrames provide a domain-specific language for structured data manipulation in Scala, Java, Python and R.

As mentioned above, in Spark 2.0, DataFrames are just Dataset of Rows in Scala and Java API. These operations are also referred as “untyped transformations” in contrast to “typed transformations” come with strongly typed Scala/Java Datasets.

Here we include some basic examples of structured data processing using Datasets:

// This import is needed to use the $-notation
import spark.implicits._
// Print the schema in a tree format
df.printSchema()

// Select only the "name" column
df.select("name").show()

// Select everybody, but increment the age by 1
df.select($"name", $"age" + 1).show()

// Select people older than 21
df.filter($"age" > 21).show()

// Count people by age
df.groupBy("age").count().show()

Find full example code at - https://raw.githubusercontent.com/apache/spark/master/examples/src/main/scala/org/apache/spark/examples/sql/SparkSQLExample.scala in the Spark repo.

For a complete list of the types of operations that can be performed on a Dataset, refer to the API Documentation.

In addition to simple column references and expressions, Datasets also have a rich library of functions including string manipulation, date arithmetic, common math operations and more. The complete list is available in the DataFrame Function Reference.

Running SQL Queries Programmatically

The sql function on a SparkSession enables applications to run SQL queries programmatically and returns the result as a DataFrame.

// Register the DataFrame as a SQL temporary view
df.createOrReplaceTempView("people")

val sqlDF = spark.sql("SELECT * FROM people")
sqlDF.show()

Global Temporary View

Temporary views in Spark SQL are session-scoped and will disappear if the session that creates it terminates. If you want to have a temporary view that is shared among all sessions and keep alive until the Spark application terminates, you can create a global temporary view. Global temporary view is tied to a system preserved database global_temp, and we must use the qualified name to refer it, e.g. SELECT * FROM global_temp.view1.

// Register the DataFrame as a global temporary view
df.createGlobalTempView("people")

// Global temporary view is tied to a system preserved database `global_temp`
spark.sql("SELECT * FROM global_temp.people").show()

// Global temporary view is cross-session
spark.newSession().sql("SELECT * FROM global_temp.people").show()

Creating Datasets

Datasets are similar to RDDs, however, instead of using Java serialization or Kryo they use a specialized Encoder to serialize the objects for processing or transmitting over the network. While both encoders and standard serialization are responsible for turning an object into bytes, encoders are code generated dynamically and use a format that allows Spark to perform many operations like filtering, sorting and hashing without deserializing the bytes back into an object.

case class Person(name: String, age: Long)

// Encoders are created for case classes
val caseClassDS = Seq(Person("Andy", 32)).toDS()
caseClassDS.show()

// Encoders for most common types are automatically provided by importing spark.implicits._
val primitiveDS = Seq(1, 2, 3).toDS()
primitiveDS.map(_ + 1).collect() // Returns: Array(2, 3, 4)

// DataFrames can be converted to a Dataset by providing a class. Mapping will be done by name
val path = "dbfs:///datasets/spark-examples/people.json"
val peopleDS = spark.read.json(path).as[Person]
peopleDS.show()

Dataset is not available directly in PySpark or SparkR.

Interoperating with RDDs

Spark SQL supports two different methods for converting existing RDDs into Datasets. The first method uses reflection to infer the schema of an RDD that contains specific types of objects. This reflection-based approach leads to more concise code and works well when you already know the schema while writing your Spark application.

The second method for creating Datasets is through a programmatic interface that allows you to construct a schema and then apply it to an existing RDD. While this method is more verbose, it allows you to construct Datasets when the columns and their types are not known until runtime.

Inferring the Schema Using Reflection

The Scala interface for Spark SQL supports automatically converting an RDD containing case classes to a DataFrame. The case class defines the schema of the table. The names of the arguments to the case class are read using reflection and become the names of the columns. Case classes can also be nested or contain complex types such as Seqs or Arrays. This RDD can be implicitly converted to a DataFrame and then be registered as a table. Tables can be used in subsequent SQL statements.

// the following lines merely fetch the file from the URL and load it into the dbfs for us to try in databricks
// getLines from the file at the URL
val peopleTxtLinesFromURL = scala.io.Source.fromURL("https://raw.githubusercontent.com/apache/spark/master/examples/src/main/resources/people.txt").getLines
// remove any pre-existing file at the dbfs location
dbutils.fs.rm("dbfs:///datasets/spark-examples/people.txt",recurse=true)
// convert the lines fetched from the URL to a Seq, then make it a RDD of String and finally save it as textfile to dbfs
sc.parallelize(peopleTxtLinesFromURL.toSeq).saveAsTextFile("dbfs:///datasets/spark-examples/people.txt")
// read the text file we just saved and see what it has
sc.textFile("dbfs:///datasets/spark-examples/people.txt").collect.mkString("\n")

sc.textFile("dbfs:///datasets/spark-examples/people.txt").collect.mkString("\n")

// For implicit conversions from RDDs to DataFrames
import spark.implicits._

// make a case class
case class Person(name: String, age: Long)

// Create an RDD of Person objects from a text file, convert it to a Dataframe
val peopleDF = spark.sparkContext
  .textFile("dbfs:///datasets/spark-examples/people.txt")
  .map(_.split(","))
  .map(attributes => Person(attributes(0), attributes(1).trim.toLong))
  //.map(attributes => Person(attributes(0), attributes(1).trim.toLong))
  .toDF()

peopleDF.show

// Register the DataFrame as a temporary view
peopleDF.createOrReplaceTempView("people")

// SQL statements can be run by using the sql methods provided by Spark
val teenagersDF = spark.sql("SELECT name, age FROM people WHERE age BETWEEN 13 AND 19")

teenagersDF.show()

// The columns of a row in the result can be accessed by field index
teenagersDF.map(teenager => "Name: " + teenager(0)).show()

// or by field name
teenagersDF.map(teenager => "Name: " + teenager.getAs[String]("name")).show()

// No pre-defined encoders for Dataset[Map[K,V]], define explicitly
implicit val mapEncoder = org.apache.spark.sql.Encoders.kryo[Map[String, Any]]

// Primitive types and case classes can be also defined as
// import more classes here...
//implicit val stringIntMapEncoder: Encoder[Map[String, Any]] = ExpressionEncoder()

// row.getValuesMap[T] retrieves multiple columns at once into a Map[String, T]
teenagersDF.map(teenager => teenager.getValuesMap[Any](List("name", "age"))).collect()

Programmatically Specifying the Schema

When case classes cannot be defined ahead of time (for example, the structure of records is encoded in a string, or a text dataset will be parsed and fields will be projected differently for different users), a DataFrame can be created programmatically with three steps.

  1. Create an RDD of Rows from the original RDD;
  2. Create the schema represented by a StructType matching the structure of Rows in the RDD created in Step 1.
  3. Apply the schema to the RDD of Rows via createDataFrame method provided by SparkSession.

For example:

import org.apache.spark.sql.Row

import org.apache.spark.sql.types._

// Create an RDD
val peopleRDD = spark.sparkContext.textFile("dbfs:///datasets/spark-examples/people.txt")

// The schema is encoded in a string
val schemaString = "name age"

// Generate the schema based on the string of schema
val fields = schemaString.split(" ")
  .map(fieldName => StructField(fieldName, StringType, nullable = true))

val schema = StructType(fields)

// Convert records of the RDD (people) to Rows
val rowRDD = peopleRDD
  .map(_.split(","))
  .map(attributes => Row(attributes(0), attributes(1).trim))

// Apply the schema to the RDD
val peopleDF = spark.createDataFrame(rowRDD, schema)

peopleDF.show

// Creates a temporary view using the DataFrame
peopleDF.createOrReplaceTempView("people")

// SQL can be run over a temporary view created using DataFrames
val results = spark.sql("SELECT name FROM people")

results.show

// The results of SQL queries are DataFrames and support all the normal RDD operations
// The columns of a row in the result can be accessed by field index or by field name
results.map(attributes => "Name: " + attributes(0)).show()

Find full example code at - https://raw.githubusercontent.com/apache/spark/master/examples/src/main/scala/org/apache/spark/examples/sql/SparkSQLExample.scala in the Spark repo.

Scalar Functions

Scalar functions are functions that return a single value per row, as opposed to aggregation functions, which return a value for a group of rows. Spark SQL supports a variety of Built-in Scalar Functions. It also supports User Defined Scalar Functions.

Aggregate Functions

Aggregate functions are functions that return a single value on a group of rows. The Built-in Aggregation Functions provide common aggregations such as count(), countDistinct(), avg(), max(), min(), etc. Users are not limited to the predefined aggregate functions and can create their own. For more details about user defined aggregate functions, please refer to the documentation of User Defined Aggregate Functions.

ScaDaMaLe Course site and book

This is an elaboration of the http://spark.apache.org/docs/latest/sql-programming-guide.html by Ivan Sadikov and Raazesh Sainudiin.

Getting Started - Exercise

After having gone through the simple example dataset in the programming guide, let's try a slightly larger dataset next.

Let us first create a table of social media usage from NYC

See the Load Data section to create this social_media_usage table from raw data.

First let's make sure this table is available for us. If you don't see social_media_usage as a named table in the output of the next cell then we first need to ingest this dataset. Let's do it using the databricks' GUI for creating Data as done next.

// Let's find out what tables are already available for loading
spark.catalog.listTables.show(50)

NYC Social Media Usage Data

This dataset is from https://datahub.io/JohnSnowLabs/nyc-social-media-usage#readme

The Demographic Reports are produced by the Economic, Demographic and Statistical Research unit within the Countywide Service Integration and Planning Management (CSIPM) Division of the Fairfax County Department of Neighborhood and Community Services. Information produced by the Economic, Demographic and Statistical Research unit is used by every county department, board, authority and the Fairfax County Public Schools. In addition to the small area estimates and forecasts, state and federal data on Fairfax County are collected and summarized, and special studies and Quantitative research are conducted by the unit.

We are going to fetch this data, with slightly simplified column names, from the following URL:

  • http://lamastex.org/datasets/public/NYCUSA/social-media-usage.csv

To turn the dataset into a registered table we will load it using the GUI as follows:

  • Download it to your local machine / laptop and then use the 'Data' button on the left to upload it (we will try this method now).
    • This will put your data in the Filestore in databricks' distributed file system.

Overview

Below we will show you how to create and query a table or DataFrame that you uploaded to DBFS. DBFS is a Databricks File System (their distributed file system) that allows you to store data for querying inside of Databricks. This notebook assumes that you have a file already inside of DBFS that you would like to read from.

In other setups, you can have the data in s3 (say in AWS) or in hdfs in your hadoop cluster, etc.

Alternatively, you can use curl or wget to download it to the local file system in /databricks/driver and then load it into dbfs, after this you can use read it via spark session into a dataframe and register it as a hive table.

You can also get the data directly from here (but in this case you need to change the column names in the databricks Data upload GUI or programmatically to follow this notebook):

  • http://datahub.io/JohnSnowLabs/nyc-social-media-usage

Load Data

How to uoload csv file and make a table in databricks

Okay, so how did we actually make table social_media_usage? Databricks allows us to upload/link external data and make it available as registerd SQL table. It involves several steps:

  1. Dowload this social-media-usage.csv file from the following URL to your laptop:

  • http://lamastex.org/datasets/public/NYCUSA/social-media-usage.csv

  • Go to Databricks cloud (where you log in to use Databricks notebooks) and open tab Data on the left panel

  • On the very top of the left sub-menu you will see button +Add Data, click on it

  • Choose Upload File for Data Sources by Browse or Drag and Drop, where File means any file (Parquet, Avro, CSV), but it works the best with CSV format

  • Upload social-media-usage.csv file you just downloaded to databricks

  • Just note the path to the uploaded file, for example in my case:

    File uploaded to /FileStore/tables/social_media_usage.csv

// File location and type
// You may need to change the file_location "social_media_usage-5dbee.csv" depending on your location given by
// File uploaded to /FileStore/tables/social_media_usage.csv
val file_location = "/FileStore/tables/social_media_usage.csv"
val file_type = "csv"

// CSV options
val infer_schema = "true"
val first_row_is_header = "true"
val delimiter = ","

// The applied options are for CSV files. For other file types, these will be ignored.
val socialMediaDF = spark.read.format(file_type) 
  .option("inferSchema", infer_schema) 
  .option("header", first_row_is_header) 
  .option("sep", delimiter) 
  .load(file_location)

socialMediaDF.show(10)

// Let's create a view or table

val temp_table_name = "social_media_usage"

socialMediaDF.createOrReplaceTempView(temp_table_name)

// Let's find out what tables are already available for loading
spark.catalog.listTables.show(100)

With this registered as a temporary view, social_media_usage will only be available to this particular notebook.

If you'd like other users to be able to query this table (in the databricks professional shard - not the free community edition; or in a managed on-premise cluster), you can also create a table from the DataFrame.

Once saved, this table will persist across cluster restarts as well as allow various users across different notebooks to query this data. To do so, choose your table name and use saveAsTable as done in the next cell.

val permanent_table_name = "social_media_usage"
socialMediaDF.write.format("parquet").saveAsTable(permanent_table_name)

// Let's find out what tables are already available for loading
// spark.catalog.listTables.show(100)

It looks like the table social_media_usage is available as a permanent table (isTemporary set as false), if you have not uncommented the last line in the previous cell (otherwise it will be available from a parquet file as a permanent table - we will see more about parquet in the sequel).

Next let us do the following:

  • load this table as a DataFrame (yes, the dataframe already exists as socialMediaDF, but we want to make a new DataFrame directly from the table)
  • print its schema and
  • show the first 20 rows.
spark.catalog.listTables.show(100)

val df = spark.table("social_media_usage") // Ctrl+Enter

As you can see the immutable value df is a DataFrame and more specifically it is:

org.apache.spark.sql.DataFrame = [agency: string, platform: string, url: string, date: timestamp, visits: integer].

Now let us print schema of the DataFrame df and have a look at the actual data:

// Ctrl+Enter
df.printSchema() // prints schema of the DataFrame
df.show() // shows first n (default is 20) rows

Note that (nullable = true) simply means if the value is allowed to be null.

Let us count the number of rows in df.

df.count() // Ctrl+Enter to get 5898

So there are 5899 records or rows in the DataFrame df. Pretty good! You can also select individual columns using so-called DataFrame API, as follows:

val platforms = df.select("platform") // Shift+Enter

platforms.count() // Shift+Enter to count the number of rows

platforms.show(5) // Ctrl+Enter to show top 5 rows

You can also apply .distinct() to extract only unique entries as follows:

val uniquePlatforms = df.select("platform").distinct() // Shift+Enter

uniquePlatforms.count() // Ctrl+Enter to count the number of distinct platforms

Let's see all the rows of the DataFrame uniquePlatforms.

Note that display(uniquePlatforms) unlike uniquePlatforms.show() displays all rows of the DataFrame + gives you ability to select different view, e.g. charts.

display(uniquePlatforms) // Ctrl+Enter to show all rows; use the scroll-bar on the right of the display to see all platforms

Spark SQL and DataFrame API

Spark SQL provides DataFrame API that can perform relational operations on both external data sources and internal collections, which is similar to widely used data frame concept in R, but evaluates operations support lazily (remember RDDs?), so that it can perform relational optimizations. This API is also available in Java, Python and R, but some functionality may not be available, although with every release of Spark people minimize this gap.

So we give some examples how to query data in Python and R, but continue with Scala. You can do all DataFrame operations in this notebook using Python or R.

# Ctrl+Enter to evaluate this python cell, recall '#' is the pre-comment character in python
# Using Python to query our "social_media_usage" table
pythonDF = spark.table("social_media_usage").select("platform").distinct()
pythonDF.show(3)

-- Ctrl+Enter to achieve the same result using standard SQL syntax!
select distinct platform from social_media_usage

Now it is time for some tips around how you use select and what the difference is between $"a", col("a"), df("a").

As you probably have noticed by now, you can specify individual columns to select by providing String values in select statement. But sometimes you need to: - distinguish between columns with the same name - use it to filter (actually you can still filter using full String expression) - do some "magic" with joins and user-defined functions (this will be shown later)

So Spark gives you ability to actually specify columns when you select. Now the difference between all those three notations is ... none, those things are just aliases for a Column in Spark SQL, which means following expressions yield the same result:

// Using string expressions
df.select("agency", "visits")

// Using "$" alias for column
df.select($"agency", $"visits")

// Using "col" alias for column
df.select(col("agency"), col("visits"))

// Using DataFrame name for column
df.select(df("agency"), df("visits"))

This "same-difference" applies to filtering, i.e. you can either use full expression to filter, or column as shown in the following example:

// Using column to filter
df.select("visits").filter($"visits" > 100)

// Or you can use full expression as string
df.select("visits").filter("visits > 100")

Note that $"visits" > 100 expression looks amazing, but under the hood it is just another column, and it equals to df("visits").>(100), where, thanks to Scala paradigm > is just another function that you can define.

val sms = df.select($"agency", $"platform", $"visits").filter($"platform" === "SMS")
sms.show() // Ctrl+Enter

Again you could have written the query above using any column aliases or String names or even writing the query directly.

For example, we can do it using String names, as follows:

// Ctrl+Enter Note that we are using "platform = 'SMS'" since it will be evaluated as actual SQL
val sms = df.select(df("agency"), df("platform"), df("visits")).filter("platform = 'SMS'")
sms.show(5)

Refer to the DataFrame API for more detailed API. In addition to simple column references and expressions, DataFrames also have a rich library of functions including string manipulation, date arithmetic, common math operations and more. The complete list is available in the DataFrame Function Reference.

Let's next explore some of the functionality that is available by transforming this DataFrame df into a new DataFrame called fixedDF.

  • First, note that some columns are not exactly what we want them to be.
    • visits should not contain null values, but 0s instead.
  • Let us fix it using some code that is briefly explained here (don't worry if you don't get it completely now, you will get the hang of it by playing more)
    • The coalesce function is similar to if-else statement, i.e., if first column in expression is null, then the value of the second column is used and so on.
    • lit just means column of constant value (literally speaking!).
    • we also remove TOTAL value from platform column.
// Ctrl+Enter to make fixedDF

// import the needed sql functions
import org.apache.spark.sql.functions.{coalesce, lit}

// make the fixedDF DataFrame
val fixedDF = df.
   select(
     $"agency", 
     $"platform", 
     $"url", 
     $"date", 
     coalesce($"visits", lit(0)).as("visits"))
    .filter($"platform" =!= "TOTAL")

fixedDF.printSchema() // print its schema 
// and show the top 20 records fully
fixedDF.show(false) // the false argument does not truncate the rows, so you will not see something like this "anot..."

Okay, this is better, but urls are still inconsistent.

Let's fix this by writing our own UDF (user-defined function) to deal with special cases.

Note that if you CAN USE Spark functions library, do it. But for the sake of the example, custom UDF is shown below.

We take value of a column as String type and return the same String type, but ignore values that do not start with http.

// Ctrl+Enter to evaluate this UDF which takes a input String called "value"
// and converts it into lower-case if it begins with http and otherwise leaves it as null, so we sort of remove non valid web-urls
val cleanUrl = udf((value: String) => if (value != null && value.startsWith("http")) value.toLowerCase() else null)

Let us apply our UDF on fixedDF to create a new DataFrame called cleanedDF as follows:

// Ctrl+Enter
val cleanedDF = fixedDF.select($"agency", $"platform", cleanUrl($"url").as("url"), $"date", $"visits")

Now, let's check that it actually worked by seeing the first 5 rows of the cleanedDF whose url isNull and isNotNull, as follows:

// Shift+Enter
cleanedDF.filter($"url".isNull).show(5)

// Ctrl+Enter
cleanedDF.filter($"url".isNotNull).show(5, false) // false in .show(5, false) shows rows untruncated

Now there is a suggestion from you manager's manager's manager that due to some perceived privacy concerns we want to replace agency with some unique identifier.

So we need to do the following:

  • create unique list of agencies with ids and
  • join them with main DataFrame.

Sounds easy, right? Let's do it.

// Crtl+Enter
// Import Spark SQL function that will give us unique id across all the records in this DataFrame
import org.apache.spark.sql.functions.monotonically_increasing_id

// We append column as SQL function that creates unique ids across all records in DataFrames 
val agencies = cleanedDF.select(cleanedDF("agency"))
                        .distinct()
                        .withColumn("id", monotonically_increasing_id())
agencies.show(5)

Those who want to understand left/right inner/outer joins can see the video lectures in Module 3 of Anthony Joseph's Introduction to Big data edX course.

// Ctrl+Enter
// And join with the rest of the data, note how join condition is specified 
val anonym = cleanedDF.join(agencies, cleanedDF("agency") === agencies("agency"), "inner").select("id", "platform", "url", "date", "visits")

// We also cache DataFrame since it can be quite expensive to recompute join
anonym.cache()

// Display result
anonym.show(5)

spark.catalog.listTables().show() // look at the available tables

-- to remove a TempTable if it exists already
drop table if exists anonym

// Register table for Spark SQL, we also import "month" function 
import org.apache.spark.sql.functions.month

anonym.createOrReplaceTempView("anonym")

-- Interesting. Now let's do some aggregation. Display platform, month, visits
-- Date column allows us to extract month directly

select platform, month(date) as month, sum(visits) as visits from anonym group by platform, month(date)

Note, that we could have done aggregation using DataFrame API instead of Spark SQL.

Alright, now let's see some cool operations with window functions.

Our next task is to compute (daily visits / monthly average) for all platforms.

import org.apache.spark.sql.functions.{dayofmonth, month, row_number, sum}
import org.apache.spark.sql.expressions.Window

val coolDF = anonym.select($"id", $"platform", dayofmonth($"date").as("day"), month($"date").as("month"), $"visits").
  groupBy($"id", $"platform", $"day", $"month").agg(sum("visits").as("visits"))

// Run window aggregation on visits per month and platform
val window = coolDF.select($"id", $"day", $"visits", sum($"visits").over(Window.partitionBy("platform", "month")).as("monthly_visits"))

// Create and register percent table
val percent = window.select($"id", $"day", ($"visits" / $"monthly_visits").as("percent"))

percent.createOrReplaceTempView("percent")

-- A little bit of visualization as result of our efforts
select id, day, `percent` from percent where `percent` > 0.3 and day = 2

-- You also could just use plain SQL to write query above, note that you might need to group by id and day as well.
with aggr as (
  select id, dayofmonth(date) as day, visits / sum(visits) over (partition by (platform, month(date))) as percent
  from anonym
)
select * from aggr where day = 2 and percent > 0.3

Interoperating with RDDs

Spark SQL supports two different methods for converting existing RDDs into DataFrames. The first method uses reflection to infer the schema of an RDD that contains specific types of objects. This reflection based approach leads to more concise code and works well when you already know the schema.

The second method for creating DataFrames is through a programmatic interface that allows you to construct a schema and then apply it to an existing RDD. While this method is more verbose, it allows you to construct DataFrames when the columns and their types are not known until runtime.

Inferring the Schema Using Reflection

The Scala interface for Spark SQL supports automatically converting an RDD containing case classes to a DataFrame. The case class defines the schema of the table. The names of the arguments to the case class are read using reflection and become the names of the columns. Case classes can also be nested or contain complex types such as Sequences or Arrays. This RDD can be implicitly converted to a DataFrame and then be registered as a table.

// Define case class that will be our schema for DataFrame
case class Hubot(name: String, year: Int, manufacturer: String, version: Array[Int], details: Map[String, String])

// You can process a text file, for example, to convert every row to our Hubot, but we will create RDD manually
val rdd = sc.parallelize(
  Array(
    Hubot("Jerry", 2015, "LCorp", Array(1, 2, 3), Map("eat" -> "yes", "sleep" -> "yes", "drink" -> "yes")),
    Hubot("Mozart", 2010, "LCorp", Array(1, 2), Map("eat" -> "no", "sleep" -> "no", "drink" -> "no")),
    Hubot("Einstein", 2012, "LCorp", Array(1, 2, 3), Map("eat" -> "yes", "sleep" -> "yes", "drink" -> "no"))
  )
)

// In order to convert RDD into DataFrame you need to do this:
val hubots = rdd.toDF()

// Display DataFrame, note how array and map fields are displayed
hubots.printSchema()
hubots.show()

// You can query complex type the same as you query any other column
// By the way you can use `sql` function to invoke Spark SQL to create DataFrame
hubots.createOrReplaceTempView("hubots")

val onesThatEat = sqlContext.sql("select name, details.eat from hubots where details.eat = 'yes'")

onesThatEat.show()

Programmatically Specifying the Schema

When case classes cannot be defined ahead of time (for example, the structure of records is encoded in a string, or a text dataset will be parsed and fields will be projected differently for different users), a DataFrame can be created programmatically with three steps.

  1. Create an RDD of Rows from the original RDD
  2. Create the schema represented by a StructType and StructField classes matching the structure of Rows in the RDD created in Step 1.
  3. Apply the schema to the RDD of Rows via createDataFrame method provided by SQLContext.
import org.apache.spark.sql.types._

// Let's say we have an RDD of String and we need to convert it into a DataFrame with schema "name", "year", and "manufacturer"
// As you can see every record is space-separated.
val rdd = sc.parallelize(
  Array(
    "Jerry 2015 LCorp",
    "Mozart 2010 LCorp",
    "Einstein 2012 LCorp"
  )
)

// Create schema as StructType //
val schema = StructType(
  StructField("name", StringType, false) :: 
  StructField("year", IntegerType, false) :: 
  StructField("manufacturer", StringType, false) :: 
  Nil
)

// Prepare RDD[Row]
val rows = rdd.map { entry => 
  val arr = entry.split("\\s+")
  val name = arr(0)
  val year = arr(1).toInt
  val manufacturer = arr(2)
  
  Row(name, year, manufacturer)
}

// Create DataFrame
val bots = sqlContext.createDataFrame(rows, schema)
bots.printSchema()
bots.show()

Creating Datasets

A Dataset is a strongly-typed, immutable collection of objects that are mapped to a relational schema. At the core of the Dataset API is a new concept called an encoder, which is responsible for converting between JVM objects and tabular representation. The tabular representation is stored using Spark’s internal Tungsten binary format, allowing for operations on serialized data and improved memory utilization. Spark 2.2 comes with support for automatically generating encoders for a wide variety of types, including primitive types (e.g. String, Integer, Long), and Scala case classes.

Simply put, you will get all the benefits of DataFrames with fair amount of flexibility of RDD API.

// We can start working with Datasets by using our "hubots" table

// To create Dataset from DataFrame do this (assuming that case class Hubot exists):
val ds = hubots.as[Hubot]
ds.show()

Side-note: Dataset API is first-class citizen in Spark, and DataFrame is an alias for Dataset[Row]. Note that Python and R use DataFrames (since they are dynamically typed), but it is essentially a Dataset.

Finally

DataFrames and Datasets can simplify and improve most of the applications pipelines by bringing concise syntax and performance optimizations. We would highly recommend you to check out the official API documentation, specifically around

Unfortunately, this is just a getting started quickly course, and we skip features like custom aggregations, types, pivoting, etc., but if you are keen to know then start from the links above and this notebook and others in this directory.

ScaDaMaLe Course site and book

This is an elaboration of the http://spark.apache.org/docs/latest/sql-programming-guide.html by Ivan Sadikov and Raazesh Sainudiin.

Data Sources

Spark Sql Programming Guide

  • Data Sources
    • Generic Load/Save Functions
      • Manually Specifying Options
      • Run SQL on files directly
      • Save Modes
      • Saving to Persistent Tables
    • Parquet Files
      • Loading Data Programmatically
      • Partition Discovery
      • Schema Merging
      • Hive metastore Parquet table conversion
        • Hive/Parquet Schema Reconciliation
        • Metadata Refreshing
      • Configuration
    • JSON Datasets
    • Hive Tables
      • Interacting with Different Versions of Hive Metastore
    • JDBC To Other Databases
    • Troubleshooting

Data Sources

Spark SQL supports operating on a variety of data sources through the DataFrame or DataFrame interfaces. A Dataset can be operated on as normal RDDs and can also be registered as a temporary table. Registering a Dataset as a table allows you to run SQL queries over its data. But from time to time you would need to either load or save Dataset. Spark SQL provides built-in data sources as well as Data Source API to define your own data source and use it read / write data into Spark.

Overview

Spark provides some built-in datasources that you can use straight out of the box, such as Parquet, JSON, JDBC, ORC (available with enabled Hive Support, but this is changing, and ORC will not require Hive support and will work with default Spark session starting from next release), and Text (since Spark 1.6) and CSV (since Spark 2.0, before that it is accessible as a package).

Third-party datasource packages

Community also have built quite a few datasource packages to provide easy access to the data from other formats. You can find list of those packages on http://spark-packages.org/, e.g. Avro, CSV, Amazon Redshit (for Spark < 2.0), XML, NetFlow and many others.

Generic Load/Save functions

In order to load or save DataFrame you have to call either read or write. This will return DataFrameReader or DataFrameWriter depending on what you are trying to achieve. Essentially these classes are entry points to the reading / writing actions. They allow you to specify writing mode or provide additional options to read data source.

// This will return DataFrameReader to read data source
println(spark.read)

val df = spark.range(0, 10)

// This will return DataFrameWriter to save DataFrame
println(df.write)

// Saving Parquet table in Scala
// DataFrames and tables can be saved as Parquet files, maintaining the schema information
val df_save = spark.table("social_media_usage").select("platform", "visits") // assuming you made the social_media_usage table permanent in previous notebook
df_save.write.mode("overwrite").parquet("/tmp/platforms.parquet")

// Read in the parquet file created above
// Parquet files are self-describing so the schema is preserved
// The result of loading a Parquet file is also a DataFrame
val df = spark.read.parquet("/tmp/platforms.parquet")
df.show(5)

// in databricks '/tmp/...' is the same as 'dbfs:///tmp/...'
display(dbutils.fs.ls("/tmp/"))

display(dbutils.fs.ls("/tmp/platforms.parquet/")) // note this is a directory with many files in it... files beginning with part have content in possibly many partitions

# Loading Parquet table in Python
dfPy = spark.read.parquet("/tmp/platforms.parquet")
dfPy.show(5)

// Saving JSON dataset in Scala
val df_save = spark.table("social_media_usage").select("platform", "visits")
df_save.write.mode("overwrite").json("/tmp/platforms.json")

// Loading JSON dataset in Scala
val df = spark.read.json("/tmp/platforms.json")
df.show(5)

# Loading JSON dataset in Python
dfPy = spark.read.json("/tmp/platforms.json")
dfPy.show(5)

Manually Specifying Options

You can also manually specify the data source that will be used along with any extra options that you would like to pass to the data source. Data sources are specified by their fully qualified name (i.e., org.apache.spark.sql.parquet), but for built-in sources you can also use their short names (json, parquet, jdbc). DataFrames of any type can be converted into other types using this syntax.

val json = sqlContext.read.format("json").load("/tmp/platforms.json")
json.select("platform").show(10)

val parquet = sqlContext.read.format("parquet").load("/tmp/platforms.parquet")
parquet.select("platform").show(10)

Run SQL on files directly

Instead of using read API to load a file into DataFrame and query it, you can also query that file directly with SQL.

val df = sqlContext.sql("SELECT * FROM parquet.`/tmp/platforms.parquet`")
df.printSchema()

Save Modes

Save operations can optionally take a SaveMode, that specifies how to handle existing data if present. It is important to realize that these save modes do not utilize any locking and are not atomic. Additionally, when performing a Overwrite, the data will be deleted before writing out the new data.

Scala/JavaAny languageMeaning
SaveMode.ErrorIfExists (default)"error" (default)When saving a DataFrame to a data source, if data already exists, an exception is expected to be thrown.
SaveMode.Append"append"When saving a DataFrame to a data source, if data/table already exists, contents of the DataFrame are expected to be appended to existing data.
SaveMode.Overwrite"overwrite"Overwrite mode means that when saving a DataFrame to a data source, if data/table already exists, existing data is expected to be overwritten by the contents of the DataFrame.
SaveMode.Ignore"ignore"Ignore mode means that when saving a DataFrame to a data source, if data already exists, the save operation is expected to not save the contents of the DataFrame and to not change the existing data. This is similar to a CREATE TABLE IF NOT EXISTS in SQL.

Saving to Persistent Tables

DataFrame and Dataset can also be saved as persistent tables using the saveAsTable command. Unlike the createOrReplaceTempView command, saveAsTable will materialize the contents of the dataframe and create a pointer to the data in the metastore. Persistent tables will still exist even after your Spark program has restarted, as long as you maintain your connection to the same metastore. A DataFrame for a persistent table can be created by calling the table method on a SparkSession with the name of the table.

By default saveAsTable will create a “managed table”, meaning that the location of the data will be controlled by the metastore. Managed tables will also have their data deleted automatically when a table is dropped.

// First of all list tables to see that table we are about to create does not exist
spark.catalog.listTables.show()

drop table if exists simple_range

val df = spark.range(0, 100)
df.write.saveAsTable("simple_range")

// Verify that table is saved and it is marked as persistent ("isTemporary" value should be "false")
spark.catalog.listTables.show()

Parquet Files

Parquet is a columnar format that is supported by many other data processing systems. Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema of the original data. When writing Parquet files, all columns are automatically converted to be nullable for compatibility reasons.

More on Parquet

Apache Parquet is a columnar storage format available to any project in the Hadoop ecosystem, regardless of the choice of data processing framework, data model or programming language. It is a more efficient way to store data frames.

//This allows easy embedding of publicly available information into any other notebook
//when viewing in git-book just ignore this block - you may have to manually chase the URL in frameIt("URL").
//Example usage:
// displayHTML(frameIt("https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation#Topics_in_LDA",250))
def frameIt( u:String, h:Int ) : String = {
      """<iframe 
 src=""""+ u+""""
 width="95%" height="""" + h + """"
 sandbox>
  <p>
    <a href="http://spark.apache.org/docs/latest/index.html">
      Fallback link for browsers that, unlikely, don't support frames
    </a>
  </p>
</iframe>"""
   }
displayHTML(frameIt("https://parquet.apache.org/documentation/latest/",500))

Loading Data Programmatically

// Read in the parquet file created above. Parquet files are self-describing so the schema is preserved.
// The result of loading a Parquet file is also a DataFrame.
val parquetFile = sqlContext.read.parquet("/tmp/platforms.parquet")

// Parquet files can also be registered as tables and then used in SQL statements.
parquetFile.createOrReplaceTempView("parquetFile")
val platforms = sqlContext.sql("SELECT platform FROM parquetFile WHERE visits > 0")
platforms.distinct.map(t => "Name: " + t(0)).collect().foreach(println)

Bucketing, Sorting and Partitioning

For file-based data source, it is also possible to bucket and sort or partition the output. Bucketing and sorting are applicable only to persistent tables:

val social_media_usage_DF = spark.table("social_media_usage")

Find full example code at - https://raw.githubusercontent.com/apache/spark/master/examples/src/main/scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala in the Spark repo.

Note that partitioning can be used with both save and saveAsTable when using the Dataset APIs.

partitionBy creates a directory structure as described in the Partition Discovery section. Thus, it has limited applicability to columns with high cardinality. In contrast bucketBy distributes data across a fixed number of buckets and can be used when the number of unique values is unbounded. One can use partitionBy by itself or along with `bucketBy.

social_media_usage_DF.write.mode("overwrite").parquet("/tmp/social_media_usage.parquet") // write to parquet

display(dbutils.fs.ls("/tmp/social_media_usage.parquet")) // there is one part-00000 file inside the parquet folder

val social_media_usage_readFromParquet_DF = spark.read.parquet("/tmp/social_media_usage.parquet")

social_media_usage_readFromParquet_DF.count

social_media_usage_readFromParquet_DF.rdd.getNumPartitions

social_media_usage_readFromParquet_DF.printSchema

social_media_usage_readFromParquet_DF.select("platform").distinct.count

social_media_usage_readFromParquet_DF
  .write
  .partitionBy("platform")
  .mode("overwrite").parquet("/tmp/social_media_usage_partitionedByPlatform.parquet") 

display(dbutils.fs.ls("/tmp/social_media_usage_partitionedByPlatform.parquet")) // there are many platform=* folders inside the parquet folder

display(dbutils.fs.ls("/tmp/social_media_usage_partitionedByPlatform.parquet/platform=Android")) // threre are part-00000- files with contents inside each platform=* folder in the parquet folder

spark.read.parquet("/tmp/social_media_usage_partitionedByPlatform.parquet").rdd.getNumPartitions

We can also use a fixed number of buckets and sort by a column within each partition. Such finer control of the dataframe written as a parquet file can help with optimizing downstream operations on the dataframe.

social_media_usage_readFromParquet_DF
  .write
  .partitionBy("platform")
  .bucketBy(10, "date")
  .sortBy("date")
  .mode("overwrite")
  .saveAsTable("social_media_usage_table_partitionedByPlatformBucketedByDate") 

spark.catalog.listTables.show()

val df = spark.table("social_media_usage_table_partitionedByPlatformBucketedByDate")

df.rdd.getNumPartitions

Partition Discovery

Table partitioning is a common optimization approach used in systems like Hive. In a partitioned table, data are usually stored in different directories, with partitioning column values encoded in the path of each partition directory. The Parquet data source is now able to discover and infer partitioning information automatically. For example, we can store all our previously used population data (from the programming guide example!) into a partitioned table using the following directory structure, with two extra columns, gender and country as partitioning columns:

    path
    └── to
        └── table
            ├── gender=male
            │   ├── ...
            │   │
            │   ├── country=US
            │   │   └── data.parquet
            │   ├── country=CN
            │   │   └── data.parquet
            │   └── ...
            └── gender=female
                ├── ...
                │
                ├── country=US
                │   └── data.parquet
                ├── country=CN
                │   └── data.parquet
                └── ...

By passing path/to/table to either SparkSession.read.parquet or SparkSession.read.load, Spark SQL will automatically extract the partitioning information from the paths. Now the schema of the returned DataFrame becomes:

    root
    |-- name: string (nullable = true)
    |-- age: long (nullable = true)
    |-- gender: string (nullable = true)
    |-- country: string (nullable = true)

Notice that the data types of the partitioning columns are automatically inferred. Currently, numeric data types and string type are supported. Sometimes users may not want to automatically infer the data types of the partitioning columns. For these use cases, the automatic type inference can be configured by spark.sql.sources.partitionColumnTypeInference.enabled, which is default to true. When type inference is disabled, string type will be used for the partitioning columns.

Starting from Spark 1.6.0, partition discovery only finds partitions under the given paths by default. For the above example, if users pass path/to/table/gender=male to either SparkSession.read.parquet or SparkSession.read.load, gender will not be considered as a partitioning column. If users need to specify the base path that partition discovery should start with, they can set basePath in the data source options. For example, when path/to/table/gender=male is the path of the data and users set basePath to path/to/table/, gender will be a partitioning column.

Schema Merging

Like ProtocolBuffer, Avro, and Thrift, Parquet also supports schema evolution. Users can start with a simple schema, and gradually add more columns to the schema as needed. In this way, users may end up with multiple Parquet files with different but mutually compatible schemas. The Parquet data source is now able to automatically detect this case and merge schemas of all these files.

Since schema merging is a relatively expensive operation, and is not a necessity in most cases, we turned it off by default starting from 1.5.0. You may enable it by:

  1. setting data source option mergeSchema to true when reading Parquet files (as shown in the examples below), or
  2. setting the global SQL option spark.sql.parquet.mergeSchema to true.
// Create a simple DataFrame, stored into a partition directory
val df1 = sc.parallelize(1 to 5).map(i => (i, i * 2)).toDF("single", "double")
df1.write.mode("overwrite").parquet("/tmp/data/test_table/key=1")

// Create another DataFrame in a new partition directory, adding a new column and dropping an existing column
val df2 = sc.parallelize(6 to 10).map(i => (i, i * 3)).toDF("single", "triple")
df2.write.mode("overwrite").parquet("/tmp/data/test_table/key=2")

// Read the partitioned table
val df3 = spark.read.option("mergeSchema", "true").parquet("/tmp/data/test_table")
df3.printSchema()

// The final schema consists of all 3 columns in the Parquet files together
// with the partitioning column appeared in the partition directory paths.
// root
//  |-- single: integer (nullable = true)
//  |-- double: integer (nullable = true)
//  |-- triple: integer (nullable = true)
//  |-- key: integer (nullable = true))

df3.show

Hive metastore Parquet table conversion

When reading from and writing to Hive metastore Parquet tables, Spark SQL will try to use its own Parquet support instead of Hive SerDe for better performance. This behavior is controlled by the spark.sql.hive.convertMetastoreParquet configuration, and is turned on by default.

Hive/Parquet Schema Reconciliation

There are two key differences between Hive and Parquet from the perspective of table schema processing.

  1. Hive is case insensitive, while Parquet is not
  2. Hive considers all columns nullable, while nullability in Parquet is significant

Due to this reason, we must reconcile Hive metastore schema with Parquet schema when converting a Hive metastore Parquet table to a Spark SQL Parquet table. The reconciliation rules are:

  1. Fields that have the same name in both schema must have the same data type regardless of nullability. The reconciled field should have the data type of the Parquet side, so that nullability is respected.
  2. The reconciled schema contains exactly those fields defined in Hive metastore schema.
  • Any fields that only appear in the Parquet schema are dropped in the reconciled schema.
  • Any fileds that only appear in the Hive metastore schema are added as nullable field in the reconciled schema.

Metadata Refreshing

Spark SQL caches Parquet metadata for better performance. When Hive metastore Parquet table conversion is enabled, metadata of those converted tables are also cached. If these tables are updated by Hive or other external tools, you need to refresh them manually to ensure consistent metadata.

// should refresh table metadata
spark.catalog.refreshTable("simple_range")

-- Or you can use SQL to refresh table
REFRESH TABLE simple_range;

Configuration

Configuration of Parquet can be done using the setConf method on SQLContext or by running SET key=value commands using SQL.

Property NameDefaultMeaning
spark.sql.parquet.binaryAsStringfalseSome other Parquet-producing systems, in particular Impala, Hive, and older versions of Spark SQL, do not differentiate between binary data and strings when writing out the Parquet schema. This flag tells Spark SQL to interpret binary data as a string to provide compatibility with these systems.
spark.sql.parquet.int96AsTimestamptrueSome Parquet-producing systems, in particular Impala and Hive, store Timestamp into INT96. This flag tells Spark SQL to interpret INT96 data as a timestamp to provide compatibility with these systems.
spark.sql.parquet.cacheMetadatatrueTurns on caching of Parquet schema metadata. Can speed up querying of static data.
spark.sql.parquet.compression.codecgzipSets the compression codec use when writing Parquet files. Acceptable values include: uncompressed, snappy, gzip, lzo.
spark.sql.parquet.filterPushdowntrueEnables Parquet filter push-down optimization when set to true.
spark.sql.hive.convertMetastoreParquettrueWhen set to false, Spark SQL will use the Hive SerDe for parquet tables instead of the built in support.
spark.sql.parquet.output.committer.classorg.apache.parquet.hadoop.ParquetOutputCommitterThe output committer class used by Parquet. The specified class needs to be a subclass of org.apache.hadoop.mapreduce.OutputCommitter. Typically, it's also a subclass of org.apache.parquet.hadoop.ParquetOutputCommitter. Spark SQL comes with a builtin org.apache.spark.sql.parquet.DirectParquetOutputCommitter, which can be more efficient then the default Parquet output committer when writing data to S3.
spark.sql.parquet.mergeSchemafalseWhen true, the Parquet data source merges schemas collected from all data files, otherwise the schema is picked from the summary file or a random data file if no summary file is available.

JSON Datasets

Spark SQL can automatically infer the schema of a JSON dataset and load it as a DataFrame. This conversion can be done using SparkSession.read.json() on either an RDD of String, or a JSON file.

Note that the file that is offered as a json file is not a typical JSON file. Each line must contain a separate, self-contained valid JSON object. As a consequence, a regular multi-line JSON file will most often fail.

// A JSON dataset is pointed to by path.
// The path can be either a single text file or a directory storing text files.
val path = "/tmp/platforms.json"
val platforms = spark.read.json(path)

// The inferred schema can be visualized using the printSchema() method.
platforms.printSchema()
// root
//  |-- platform: string (nullable = true)
//  |-- visits: long (nullable = true)

// Register this DataFrame as a table.
platforms.createOrReplaceTempView("platforms")

// SQL statements can be run by using the sql methods provided by sqlContext.
val facebook = spark.sql("SELECT platform, visits FROM platforms WHERE platform like 'Face%k'")
facebook.show()

// Alternatively, a DataFrame can be created for a JSON dataset represented by
// an RDD[String] storing one JSON object per string.
val rdd = sc.parallelize("""{"name":"IWyn","address":{"city":"Columbus","state":"Ohio"}}""" :: Nil)
val anotherPlatforms = spark.read.json(rdd)
anotherPlatforms.show()

Hive Tables

Spark SQL also supports reading and writing data stored in Apache Hive. However, since Hive has a large number of dependencies, it is not included in the default Spark assembly. Hive support is enabled by adding the -Phive and -Phive-thriftserver flags to Spark’s build. This command builds a new assembly jar that includes Hive. Note that this Hive assembly jar must also be present on all of the worker nodes, as they will need access to the Hive serialization and deserialization libraries (SerDes) in order to access data stored in Hive.

Configuration of Hive is done by placing your hive-site.xml, core-site.xml (for security configuration), hdfs-site.xml (for HDFS configuration) file in conf/. Please note when running the query on a YARN cluster (cluster mode), the datanucleus jars under the lib_managed/jars directory and hive-site.xml under conf/ directory need to be available on the driver and all executors launched by the YARN cluster. The convenient way to do this is adding them through the --jars option and --file option of the spark-submit command.

When working with Hive one must construct a HiveContext, which inherits from SQLContext, and adds support for finding tables in the MetaStore and writing queries using HiveQL. Users who do not have an existing Hive deployment can still create a HiveContext. When not configured by the hive-site.xml, the context automatically creates metastore_db in the current directory and creates warehouse directory indicated by HiveConf, which defaults to /user/hive/warehouse. Note that you may need to grant write privilege on /user/hive/warehouse to the user who starts the spark application.

val spark = SparkSession.builder.enableHiveSupport().getOrCreate()

spark.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)")
spark.sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")

// Queries are expressed in HiveQL
spark.sql("FROM src SELECT key, value").collect().foreach(println)

Interacting with Different Versions of Hive Metastore

One of the most important pieces of Spark SQL’s Hive support is interaction with Hive metastore, which enables Spark SQL to access metadata of Hive tables. Starting from Spark 1.4.0, a single binary build of Spark SQL can be used to query different versions of Hive metastores, using the configuration described below. Note that independent of the version of Hive that is being used to talk to the metastore, internally Spark SQL will compile against Hive 1.2.1 and use those classes for internal execution (serdes, UDFs, UDAFs, etc).

The following options can be used to configure the version of Hive that is used to retrieve metadata:

Property NameDefaultMeaning
spark.sql.hive.metastore.version1.2.1Version of the Hive metastore. Available options are 0.12.0 through 1.2.1.
spark.sql.hive.metastore.jarsbuiltinLocation of the jars that should be used to instantiate the HiveMetastoreClient. This property can be one of three options: builtin, maven, a classpath in the standard format for the JVM. This classpath must include all of Hive and its dependencies, including the correct version of Hadoop. These jars only need to be present on the driver, but if you are running in yarn cluster mode then you must ensure they are packaged with you application.
spark.sql.hive.metastore.sharedPrefixescom.mysql.jdbc,org.postgresql,com.microsoft.sqlserver,oracle.jdbcA comma separated list of class prefixes that should be loaded using the classloader that is shared between Spark SQL and a specific version of Hive. An example of classes that should be shared is JDBC drivers that are needed to talk to the metastore. Other classes that need to be shared are those that interact with classes that are already shared. For example, custom appenders that are used by log4j.
spark.sql.hive.metastore.barrierPrefixes(empty)A comma separated list of class prefixes that should explicitly be reloaded for each version of Hive that Spark SQL is communicating with. For example, Hive UDFs that are declared in a prefix that typically would be shared (i.e. org.apache.spark.*).

JDBC To Other Databases

Spark SQL also includes a data source that can read data from other databases using JDBC. This functionality should be preferred over using JdbcRDD. This is because the results are returned as a DataFrame and they can easily be processed in Spark SQL or joined with other data sources. The JDBC data source is also easier to use from Java or Python as it does not require the user to provide a ClassTag. (Note that this is different than the Spark SQL JDBC server, which allows other applications to run queries using Spark SQL).

To get started you will need to include the JDBC driver for you particular database on the spark classpath. For example, to connect to postgres from the Spark Shell you would run the following command:

SPARK_CLASSPATH=postgresql-9.3-1102-jdbc41.jar bin/spark-shell

Tables from the remote database can be loaded as a DataFrame or Spark SQL Temporary table using the Data Sources API. The following options are supported:

Property NameMeaning
urlThe JDBC URL to connect to.
dbtableThe JDBC table that should be read. Note that anything that is valid in a FROM clause of a SQL query can be used. For example, instead of a full table you could also use a subquery in parentheses.
driverThe class name of the JDBC driver needed to connect to this URL. This class will be loaded on the master and workers before running an JDBC commands to allow the driver to register itself with the JDBC subsystem.
partitionColumn, lowerBound, upperBound, numPartitionsThese options must all be specified if any of them is specified. They describe how to partition the table when reading in parallel from multiple workers. partitionColumn must be a numeric column from the table in question. Notice that lowerBound and upperBound are just used to decide the partition stride, not for filtering the rows in table. So all rows in the table will be partitioned and returned.
fetchSizeThe JDBC fetch size, which determines how many rows to fetch per round trip. This can help performance on JDBC drivers which default to low fetch size (eg. Oracle with 10 rows).
// Example of using JDBC datasource
val jdbcDF = spark.read.format("jdbc").options(Map("url" -> "jdbc:postgresql:dbserver", "dbtable" -> "schema.tablename")).load()

-- Or using JDBC datasource in SQL
CREATE TEMPORARY TABLE jdbcTable
USING org.apache.spark.sql.jdbc
OPTIONS (
  url "jdbc:postgresql:dbserver",
  dbtable "schema.tablename"
)

Troubleshooting

  • The JDBC driver class must be visible to the primordial class loader on the client session and on all executors. This is because Java’s DriverManager class does a security check that results in it ignoring all drivers not visible to the primordial class loader when one goes to open a connection. One convenient way to do this is to modify compute_classpath.sh on all worker nodes to include your driver JARs.
  • Some databases, such as H2, convert all names to upper case. You’ll need to use upper case to refer to those names in Spark SQL.

ScaDaMaLe Course site and book

Performance Tuning

Spark Sql Programming Guide

If you have read the spark-SQL paper and have some idea of how distributed sorting and joining work then you will need to know the following part of the programming guide to tune the performance of Spark SQL queries:

ScaDaMaLe Course site and book

This is an elaboration of the http://spark.apache.org/docs/latest/sql-programming-guide.html by Ivan Sadikov and Raazesh Sainudiin.

Distributed SQL Engine

Spark Sql Programming Guide

  • Distributed SQL Engine
    • Running the Thrift JDBC/ODBC server
    • Running the Spark SQL CLI

Distributed SQL Engine

Spark SQL can also act as a distributed query engine using its JDBC/ODBC or command-line interface. In this mode, end-users or applications can interact with Spark SQL directly to run SQL queries, without the need to write any code.

Running the Thrift JDBC/ODBC server

The Thrift JDBC/ODBC server implemented here corresponds to the HiveServer2 in Hive 1.2.1 You can test the JDBC server with the beeline script that comes with either Spark or Hive 1.2.1.

To start the JDBC/ODBC server, run the following in the Spark directory:

./sbin/start-thriftserver.sh

This script accepts all bin/spark-submit command line options, plus a --hiveconf option to specify Hive properties. You may run ./sbin/start-thriftserver.sh --help for a complete list of all available options. By default, the server listens on localhost:10000. You may override this behaviour via either environment variables, i.e.:

export HIVE_SERVER2_THRIFT_PORT=<listening-port>
export HIVE_SERVER2_THRIFT_BIND_HOST=<listening-host>
./sbin/start-thriftserver.sh \
  --master <master-uri> \
  ...

or system properties:

./sbin/start-thriftserver.sh \
  --hiveconf hive.server2.thrift.port=<listening-port> \
  --hiveconf hive.server2.thrift.bind.host=<listening-host> \
  --master <master-uri>
  ...

Now you can use beeline to test the Thrift JDBC/ODBC server:

./bin/beeline

Connect to the JDBC/ODBC server in beeline with:

beeline> !connect jdbc:hive2://localhost:10000

Beeline will ask you for a username and password. In non-secure mode, simply enter the username on your machine and a blank password. For secure mode, please follow the instructions given in the beeline documentation.

Configuration of Hive is done by placing your hive-site.xml, core-site.xml and hdfs-site.xml files in conf/.

You may also use the beeline script that comes with Hive.

Thrift JDBC server also supports sending thrift RPC messages over HTTP transport. Use the following setting to enable HTTP mode as system property or in hive-site.xml file in conf/:

hive.server2.transport.mode - Set this to value: http
hive.server2.thrift.http.port - HTTP port number fo listen on; default is 10001
hive.server2.http.endpoint - HTTP endpoint; default is cliservice

To test, use beeline to connect to the JDBC/ODBC server in http mode with:

beeline> !connect jdbc:hive2://<host>:<port>/<database>?hive.server2.transport.mode=http;hive.server2.thrift.http.path=<http_endpoint>

Running the Spark SQL CLI

The Spark SQL CLI is a convenient tool to run the Hive metastore service in local mode and execute queries input from the command line. Note that the Spark SQL CLI cannot talk to the Thrift JDBC server.

To start the Spark SQL CLI, run the following in the Spark directory:

./bin/spark-sql

Configuration of Hive is done by placing your hive-site.xml, core-site.xml and hdfs-site.xml files in conf/. You may run ./bin/spark-sql --help for a complete list of all available options.

ScaDaMaLe Course site and book

SQL Pivoting since Spark 2.4

SQL Pivot: Converting Rows to Columns

This is from the following blogpost: - https://databricks.com/blog/2018/11/01/sql-pivot-converting-rows-to-columns.html

This is a useful trick to know when having to do ETL before exploring datasets that need row to column conversions.

Load Data

Create tables and load temperature data

CREATE OR REPLACE TEMPORARY VIEW high_temps
  USING csv
  OPTIONS (path "/databricks-datasets/weather/high_temps", header "true", mode "FAILFAST")

CREATE OR REPLACE TEMPORARY VIEW low_temps
  USING csv
  OPTIONS (path "/databricks-datasets/weather/low_temps", header "true", mode "FAILFAST")

Pivoting in SQL

Getting the monthly average high temperatures with month as columns and year as rows.

SELECT * FROM (
  SELECT year(date) year, month(date) month, temp
  FROM high_temps
  WHERE date BETWEEN DATE '2015-01-01' AND DATE '2018-08-31'
)
PIVOT (
  CAST(avg(temp) AS DECIMAL(4, 1))
  FOR month in (
    1 JAN, 2 FEB, 3 MAR, 4 APR, 5 MAY, 6 JUN,
    7 JUL, 8 AUG, 9 SEP, 10 OCT, 11 NOV, 12 DEC
  )
)
ORDER BY year DESC
year JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
2018.0 49.7 45.8 54.0 58.6 70.8 71.9 82.8 79.1 null null null null
2017.0 43.7 46.6 51.5 57.3 67.0 72.1 78.3 81.5 73.8 61.1 51.3 45.5
2016.0 49.1 53.6 56.4 65.9 68.8 73.1 76.0 79.5 69.6 60.5 56.0 41.9
2015.0 50.3 54.5 57.9 59.9 68.0 78.9 82.6 79.0 68.5 63.6 49.4 47.1

Pivoting with Multiple Aggregate Expressions

Getting monthly average and maximum high temperatures with month as columns and year as rows.

SELECT * FROM (
  SELECT year(date) year, month(date) month, temp
  FROM high_temps
  WHERE date BETWEEN DATE '2015-01-01' AND DATE '2018-08-31'
)
PIVOT (
  CAST(avg(temp) AS DECIMAL(4, 1)) avg, max(temp) max
  FOR month in (6 JUN, 7 JUL, 8 AUG, 9 SEP)
)
ORDER BY year DESC
year JUN_avg JUN_max JUL_avg JUL_max AUG_avg AUG_max SEP_avg SEP_max
2018.0 71.9 88 82.8 94 79.1 94 null null
2017.0 72.1 96 78.3 87 81.5 94 73.8 90
2016.0 73.1 93 76.0 89 79.5 95 69.6 78
2015.0 78.9 92 82.6 95 79.0 92 68.5 81

Pivoting with Multiple Grouping Columns

Getting monthly average high and average low temperatures with month as columns and (year, hi/lo) as rows.

SELECT * FROM (
  SELECT year(date) year, month(date) month, temp, flag `H/L`
  FROM (
    SELECT date, temp, 'H' as flag
    FROM high_temps
    UNION ALL
    SELECT date, temp, 'L' as flag
    FROM low_temps
  )
  WHERE date BETWEEN DATE '2015-01-01' AND DATE '2018-08-31'
)
PIVOT (
  CAST(avg(temp) AS DECIMAL(4, 1))
  FOR month in (6 JUN, 7 JUL, 8 AUG, 9 SEP)
)
ORDER BY year DESC, `H/L` ASC
year H/L JUN JUL AUG SEP
2018.0 H 71.9 82.8 79.1 null
2018.0 L 53.4 58.5 58.5 null
2017.0 H 72.1 78.3 81.5 73.8
2017.0 L 53.7 56.3 59.0 55.6
2016.0 H 73.1 76.0 79.5 69.6
2016.0 L 53.9 57.6 57.9 52.9
2015.0 H 78.9 82.6 79.0 68.5
2015.0 L 56.4 59.9 58.5 52.5

Pivoting with Multiple Pivot Columns

Getting monthly average high and average low temperatures with (month, hi/lo) as columns and year as rows.

SELECT * FROM (
  SELECT year(date) year, month(date) month, temp, flag
  FROM (
    SELECT date, temp, 'H' as flag
    FROM high_temps
    UNION ALL
    SELECT date, temp, 'L' as flag
    FROM low_temps
  )
  WHERE date BETWEEN DATE '2015-01-01' AND DATE '2018-08-31'
)
PIVOT (
  CAST(avg(temp) AS DECIMAL(4, 1))
  FOR (month, flag) in (
    (6, 'H') JUN_hi, (6, 'L') JUN_lo,
    (7, 'H') JUL_hi, (7, 'L') JUL_lo,
    (8, 'H') AUG_hi, (8, 'L') AUG_lo,
    (9, 'H') SEP_hi, (9, 'L') SEP_lo
  )
)
ORDER BY year DESC
year JUN_hi JUN_lo JUL_hi JUL_lo AUG_hi AUG_lo SEP_hi SEP_lo
2018.0 71.9 53.4 82.8 58.5 79.1 58.5 null null
2017.0 72.1 53.7 78.3 56.3 81.5 59.0 73.8 55.6
2016.0 73.1 53.9 76.0 57.6 79.5 57.9 69.6 52.9
2015.0 78.9 56.4 82.6 59.9 79.0 58.5 68.5 52.5

ScaDaMaLe Course site and book

Introduction to Spark SQL

  • This notebook explains the motivation behind Spark SQL
  • It introduces interactive SparkSQL queries and visualizations
  • This notebook uses content from Databricks SparkSQL notebook and SparkSQL programming guide

Some resources on SQL

Some of them are embedded below in-place for your convenience.

READ - https://databricks.com/blog/2016/07/14/a-tale-of-three-apache-spark-apis-rdds-dataframes-and-datasets.html

This is an elaboration of the Apache Spark 2.2 sql-progamming-guide.

Overview

Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. Internally, Spark SQL uses this extra information to perform extra optimizations. There are several ways to interact with Spark SQL including SQL and the Dataset API. When computing a result the same execution engine is used, independent of which API/language you are using to express the computation. This unification means that developers can easily switch back and forth between different APIs based on which provides the most natural way to express a given transformation.

All of the examples on this page use sample data included in the Spark distribution and can be run in the spark-shell, pyspark shell, or sparkR shell.

Datasets and DataFrames

A Dataset is a distributed collection of data. Dataset is a new interface added in Spark 1.6 that provides the benefits of RDDs (strong typing, ability to use powerful lambda functions) with the benefits of Spark SQL’s optimized execution engine. A Dataset can be constructed from JVM objects and then manipulated using functional transformations (map, flatMap, filter, etc.). The Dataset API is available in Scala and Java. Python does not have the support for the Dataset API. But due to Python’s dynamic nature, many of the benefits of the Dataset API are already available (i.e. you can access the field of a row by name naturally row.columnName). The case for R is similar.

A DataFrame is a Dataset organized into named columns. It is conceptually equivalent to a table in a relational database or a data frame in R/Python, but with richer optimizations under the hood. DataFrames can be constructed from a wide array of sources such as: structured data files, tables in Hive, external databases, or existing RDDs. The DataFrame API is available in Scala, Java, Python, and R. In Scala and Java, a DataFrame is represented by a Dataset of Rows. In the Scala API, DataFrame is simply a type alias of Dataset[Row]. While, in Java API, users need to use Dataset<Row> to represent a DataFrame.

Throughout this document, we will often refer to Scala/Java Datasets of Rows as DataFrames.

Getting Started in Spark 2.x

Starting Point: SparkSession

The entry point into all functionality in Spark is the SparkSession. To create a basic SparkSession in your scala Spark code, just use SparkSession.builder():

import org.apache.spark.sql.SparkSession

val spark = SparkSession
  .builder()
  .appName("Spark SQL basic example")
  .config("spark.some.config.option", "some-value")
  .getOrCreate()

// For implicit conversions like converting RDDs to DataFrames
import spark.implicits._

Conveniently, in Databricks notebook (similar to spark-shell) SparkSession is already created for you and is available as spark.

spark // ready-made Spark-Session

res2: org.apache.spark.sql.SparkSession = org.apache.spark.sql.SparkSession@393e6c25

Creating DataFrames

With a SparkSession or SQLContext, applications can create DataFrame

  • from an existing RDD,
  • from a Hive table, or
  • from various other data sources.

Just to recap:

  • A DataFrame is a distributed collection of data organized into named columns (it is not strogly typed).
  • You can think of it as being organized into table RDD of case class Row (which is not exactly true).
  • DataFrames, in comparison to RDDs, are backed by rich optimizations, including:
    • tracking their own schema,
    • adaptive query execution,
    • code generation including whole stage codegen,
    • extensible Catalyst optimizer, and
    • project Tungsten for optimized storage.

Note that performance for DataFrames is the same across languages Scala, Java, Python, and R. This is due to the fact that the only planning phase is language-specific (logical + physical SQL plan), not the actual execution of the SQL plan.

DataFrame Basics

1. An empty DataFrame

2. DataFrame from a range

3. DataFrame from an RDD

4. DataFrame Operations (aka Untyped Dataset Operations)

5. Running SQL Queries Programmatically

6. Creating Datasets

1. Making an empty DataFrame

Spark has some of the pre-built methods to create simple DataFrames

  • let us make an Empty DataFrame
val emptyDF = spark.emptyDataFrame // Ctrl+Enter to make an empty DataFrame

Not really interesting, or is it?

You Try!

Uncomment the following cell, put your cursor after emptyDF. below and hit Tab to see what can be done with emptyDF.

//emptyDF.

2. Making a DataFrame from a range

Let us make a DataFrame next

  • from a range of numbers, as follows:
val rangeDF = spark.range(0, 3).toDF() // Ctrl+Enter to make DataFrame with 0,1,2
// sc.parallelize(1 to 3).toDF()

Note that Spark automatically names column as id and casts integers to type bigint for big integer or Long.

In order to get a preview of data in DataFrame use show() as follows:

rangeDF.show() // Ctrl+Enter

3. Making a DataFrame from an RDD

  • Make an RDD
  • Conver the RDD into a DataFrame using the defualt .toDF() method
  • Conver the RDD into a DataFrame using the non-default .toDF(...) method
  • Do it all in one line

Let's first make an RDD using the sc.parallelize method, transform it by a map and perform the collect action to display it, as follows:

val rdd1 = sc.parallelize(1 to 5).map(i => (i, i*2))
rdd1.collect() // Ctrl+Enter

Next, let us convert the RDD into DataFrame using the .toDF() method, as follows:

val df1 = rdd1.toDF() // Ctrl+Enter

As it is clear, the DataFrame has columns named _1 and _2, each of type int. Let us see its content using the .show() method next.

df1.show() // Ctrl+Enter

Note that by default, i.e. without specifying any options as in toDF(), the column names are given by _1 and _2.

We can easily specify column names as follows:

val df1 = rdd1.toDF("once", "twice") // Ctrl+Enter
df1.show()

Of course, we can do all of the above steps to make the DataFrame df1 in one line and then show it, as follows:

val df1 = sc.parallelize(1 to 5)
            .map(i => (i, i*2))
            .toDF("once", "twice") //Ctrl+enter
df1.show()

4. DataFrame Operations (aka Untyped Dataset Operations)

DataFrames provide a domain-specific language for structured data manipulation in Scala, Java, Python and R.

As mentioned above, in Spark 2.0, DataFrames are just Dataset of Rows in Scala and Java API. These operations are also referred as “untyped transformations” in contrast to “typed transformations” come with strongly typed Scala/Java Datasets.

Here we include some basic examples of structured data processing using Datasets:

// This import is needed to use the $-notation
import spark.implicits._
// Print the schema in a tree format
df1.printSchema()

// Select only the "name" column
df1.select("once").show()

// Select both columns, but increment the double column by 1
df1.select($"once", $"once" + 1).show()

// Select both columns, but increment the double column by 1 and rename it as "oncemore"
df1.select($"once", ($"once" * 1).as("oncemore")).show()

df1.filter($"once" > 2).show()

// Count the number of distinct singles -  a bit boring
df1.groupBy("once").count().show()

Let's make a more interesting DataFrame for groupBy with repeated elements so that the count will be more than 1.

df1.show()

val df11 = sc.parallelize(3 to 5).map(i => (i, i*2)).toDF("once", "twice") // just make a small one
df11.show()

val df111 = df1.union(df11) // let's take the unionAll of df1 and df11 into df111
df111.show() // df111 is obtained by simply appending the rows of df11 to df1

// Count the number of distinct singles -  a bit less boring
df111.groupBy("once").count().show()

For a complete list of the types of operations that can be performed on a Dataset refer to the API Documentation.

In addition to simple column references and expressions, Datasets also have a rich library of functions including string manipulation, date arithmetic, common math operations and more. The complete list is available in the DataFrame Function Reference.

You Try!

Uncomment the two lines in the next cell, and then fill in the ??? below to get a DataFrame df2 whose first two columns are the same as df1 and whose third column named triple has values that are three times the values in the first column.

//val df2 = sc.parallelize(1 to 5).map(i => (i, i*2, i????)).toDF("single", "double", "triple") // Ctrl+enter after editing ???
//df2.show()

5. Running SQL Queries Programmatically

The sql function on a SparkSession enables applications to run SQL queries programmatically and returns the result as a DataFrame.

df1

// Register the DataFrame as a SQL temporary view
df1.createOrReplaceTempView("sdtable")

val sqlDF = spark.sql("SELECT * FROM sdtable")
sqlDF.show()

spark.sql("SELECT * FROM SDTable WHERE once>2").show()

5. Using SQL for interactively querying a table is very powerful!

Note -- comments are how you add comments in SQL cells beginning with %sql.

  • You can run SQL select * statement to see all columns of the table, as follows:
    • This is equivalent to the above `display(diamondsDF)' with the DataFrame
-- Ctrl+Enter to select all columns of the table
select * from SDTable

-- Ctrl+Enter to select all columns of the table
-- note table names of registered tables are case-insensitive
select * from sdtable

Global Temporary View

Temporary views in Spark SQL are session-scoped and will disappear if the session that creates it terminates. If you want to have a temporary view that is shared among all sessions and keep alive until the Spark application terminates, you can create a global temporary view. Global temporary view is tied to a system preserved database global_temp, and we must use the qualified name to refer it, e.g. SELECT * FROM global_temp.view1. See http://spark.apache.org/docs/latest/sql-programming-guide.html#global-temporary-view for details.

  1. Creating Datasets

Datasets are similar to RDDs, however, instead of using Java serialization or Kryo they use a specialized Encoder to serialize the objects for processing or transmitting over the network. While both encoders and standard serialization are responsible for turning an object into bytes, encoders are code generated dynamically and use a format that allows Spark to perform many operations like filtering, sorting and hashing without deserializing the bytes back into an object.

val rangeDS = spark.range(0, 3) // Ctrl+Enter to make DataSet with 0,1,2; Note we added '.toDF()' to this to create a DataFrame

rangeDS.show() // the column name 'id' is made by default here

We can have more complicated objects in a DataSet too.

// Note: Case classes in Scala 2.10 can support only up to 22 fields. To work around this limit,
// you can use custom classes that implement the Product interface
case class Person(name: String, age: Long)

// Encoders are created for case classes
val caseClassDS = Seq(Person("Andy", 32), Person("Erik",44), Person("Anna", 15)).toDS()
caseClassDS.show()

// Encoders for most common types are automatically provided by importing spark.implicits._
val primitiveDS = Seq(1, 2, 3).toDS()
primitiveDS.map(_ + 1).collect() // Returns: Array(2, 3, 4)

df1

df1.show

// let's make a case class for our DF so we can convert it to Dataset
case class singleAndDoubleIntegers(once: Integer, twice: Integer)

val ds1 = df1.as[singleAndDoubleIntegers]

ds1.show()

Next we will play with data

The data here is semi-structured tabular data (Tab-delimited text file in dbfs). Let us see what Anthony Joseph in BerkeleyX/CS100.1x had to say about such data.

Key Data Management Concepts: Semi-Structured Tabular Data

(watch now 1:26):

Semi-Structured Tabular Data by Anthony Joseph in BerkeleyX/CS100.1x

Go through the databricks Introductions Now

This week's recommended homework is a deep dive into the SparkSQL programming guide.

ScaDaMaLe Course site and book

Diamonds ML Pipeline Workflow - DataFrame ETL and EDA Part

This is the Spark SQL parts that are focussed on extract-transform-Load (ETL) and exploratory-data-analysis (EDA) parts of an end-to-end example of a Machine Learning (ML) workflow.

Why are we using DataFrames? This is because of the Announcement in the Spark MLlib Main Guide for Spark 2.2 https://spark.apache.org/docs/latest/ml-guide.html that "DataFrame-based API is primary API".

This notebook is a scalarific break-down of the pythonic 'Diamonds ML Pipeline Workflow' from the Databricks Guide.

We will see this example again in the sequel

For this example, we analyze the Diamonds dataset from the R Datasets hosted on DBC.

Later on, we will use the DecisionTree algorithm to predict the price of a diamond from its characteristics.

Here is an outline of our pipeline:

  • Step 1. Load data: Load data as DataFrame
  • Step 2. Understand the data: Compute statistics and create visualizations to get a better understanding of the data.
  • Step 3. Hold out data: Split the data randomly into training and test sets. We will not look at the test data until after learning.
  • Step 4. On the training dataset:
    • Extract features: We will index categorical (String-valued) features so that DecisionTree can handle them.
    • Learn a model: Run DecisionTree to learn how to predict a diamond's price from a description of the diamond.
    • Tune the model: Tune the tree depth (complexity) using the training data. (This process is also called model selection.)
  • Step 5. Evaluate the model: Now look at the test dataset. Compare the initial model with the tuned model to see the benefit of tuning parameters.
  • Step 6. Understand the model: We will examine the learned model and results to gain further insight.

In this notebook, we will only cover Step 1 and Step 2. above. The other Steps will be revisited in the sequel.

Step 1. Load data as DataFrame

This section loads a dataset as a DataFrame and examines a few rows of it to understand the schema.

For more info, see the DB guide on importing data.

// We'll use the Diamonds dataset from the R datasets hosted on DBC.
val diamondsFilePath = "dbfs:/databricks-datasets/Rdatasets/data-001/csv/ggplot2/diamonds.csv"

diamondsFilePath: String = dbfs:/databricks-datasets/Rdatasets/data-001/csv/ggplot2/diamonds.csv

sc.textFile(diamondsFilePath).take(2) // looks like a csv file as it should

res0: Array[String] = Array("","carat","cut","color","clarity","depth","table","price","x","y","z", "1",0.23,"Ideal","E","SI2",61.5,55,326,3.95,3.98,2.43)

val diamondsRawDF = sqlContext.read    // we can use sqlContext instead of SparkSession for backwards compatibility to 1.x
    .format("com.databricks.spark.csv") // use spark.csv package
    .option("header", "true") // Use first line of all files as header
    .option("inferSchema", "true") // Automatically infer data types
    //.option("delimiter", ",") // Specify the delimiter as comma or ',' DEFAULT
    .load(diamondsFilePath)

diamondsRawDF: org.apache.spark.sql.DataFrame = [_c0: int, carat: double ... 9 more fields]

//There are 10 columns.  We will try to predict the price of diamonds, treating the other 9 columns as features.
diamondsRawDF.printSchema()

root
 |-- _c0: integer (nullable = true)
 |-- carat: double (nullable = true)
 |-- cut: string (nullable = true)
 |-- color: string (nullable = true)
 |-- clarity: string (nullable = true)
 |-- depth: double (nullable = true)
 |-- table: double (nullable = true)
 |-- price: integer (nullable = true)
 |-- x: double (nullable = true)
 |-- y: double (nullable = true)
 |-- z: double (nullable = true)

Note: (nullable = true) simply means if the value is allowed to be null.

Let us count the number of rows in diamondsDF.

diamondsRawDF.count() // Ctrl+Enter

res3: Long = 53940

So there are 53940 records or rows in the DataFrame.

Use the show(n) method to see the first n (default is 20) rows of the DataFrame, as folows:

diamondsRawDF.show(10)

+---+-----+---------+-----+-------+-----+-----+-----+----+----+----+
|_c0|carat|      cut|color|clarity|depth|table|price|   x|   y|   z|
+---+-----+---------+-----+-------+-----+-----+-----+----+----+----+
|  1| 0.23|    Ideal|    E|    SI2| 61.5| 55.0|  326|3.95|3.98|2.43|
|  2| 0.21|  Premium|    E|    SI1| 59.8| 61.0|  326|3.89|3.84|2.31|
|  3| 0.23|     Good|    E|    VS1| 56.9| 65.0|  327|4.05|4.07|2.31|
|  4| 0.29|  Premium|    I|    VS2| 62.4| 58.0|  334| 4.2|4.23|2.63|
|  5| 0.31|     Good|    J|    SI2| 63.3| 58.0|  335|4.34|4.35|2.75|
|  6| 0.24|Very Good|    J|   VVS2| 62.8| 57.0|  336|3.94|3.96|2.48|
|  7| 0.24|Very Good|    I|   VVS1| 62.3| 57.0|  336|3.95|3.98|2.47|
|  8| 0.26|Very Good|    H|    SI1| 61.9| 55.0|  337|4.07|4.11|2.53|
|  9| 0.22|     Fair|    E|    VS2| 65.1| 61.0|  337|3.87|3.78|2.49|
| 10| 0.23|Very Good|    H|    VS1| 59.4| 61.0|  338| 4.0|4.05|2.39|
+---+-----+---------+-----+-------+-----+-----+-----+----+----+----+
only showing top 10 rows

If you notice the schema of diamondsRawDF you will see that the automatic schema inference of SqlContext.read method has cast the values in the column price as integer.

To cleanup:

  • let's recast the column price as double for downstream ML tasks later and
  • let's also get rid of the first column of row indices.
import org.apache.spark.sql.types.DoubleType
//we will convert price column from int to double for being able to model, fit and predict in downstream ML task
val diamondsDF = diamondsRawDF.select($"carat", $"cut", $"color", $"clarity", $"depth", $"table",$"price".cast(DoubleType).as("price"), $"x", $"y", $"z")
diamondsDF.cache() // let's cache it for reuse
diamondsDF.printSchema // print schema

root
 |-- carat: double (nullable = true)
 |-- cut: string (nullable = true)
 |-- color: string (nullable = true)
 |-- clarity: string (nullable = true)
 |-- depth: double (nullable = true)
 |-- table: double (nullable = true)
 |-- price: double (nullable = true)
 |-- x: double (nullable = true)
 |-- y: double (nullable = true)
 |-- z: double (nullable = true)

import org.apache.spark.sql.types.DoubleType
diamondsDF: org.apache.spark.sql.DataFrame = [carat: double, cut: string ... 8 more fields]

diamondsDF.show(10,false) // notice that price column has Double values that end in '.0' now

+-----+---------+-----+-------+-----+-----+-----+----+----+----+
|carat|cut      |color|clarity|depth|table|price|x   |y   |z   |
+-----+---------+-----+-------+-----+-----+-----+----+----+----+
|0.23 |Ideal    |E    |SI2    |61.5 |55.0 |326.0|3.95|3.98|2.43|
|0.21 |Premium  |E    |SI1    |59.8 |61.0 |326.0|3.89|3.84|2.31|
|0.23 |Good     |E    |VS1    |56.9 |65.0 |327.0|4.05|4.07|2.31|
|0.29 |Premium  |I    |VS2    |62.4 |58.0 |334.0|4.2 |4.23|2.63|
|0.31 |Good     |J    |SI2    |63.3 |58.0 |335.0|4.34|4.35|2.75|
|0.24 |Very Good|J    |VVS2   |62.8 |57.0 |336.0|3.94|3.96|2.48|
|0.24 |Very Good|I    |VVS1   |62.3 |57.0 |336.0|3.95|3.98|2.47|
|0.26 |Very Good|H    |SI1    |61.9 |55.0 |337.0|4.07|4.11|2.53|
|0.22 |Fair     |E    |VS2    |65.1 |61.0 |337.0|3.87|3.78|2.49|
|0.23 |Very Good|H    |VS1    |59.4 |61.0 |338.0|4.0 |4.05|2.39|
+-----+---------+-----+-------+-----+-----+-----+----+----+----+
only showing top 10 rows

//View DataFrame in databricks
// note this 'display' is a databricks notebook specific command that is quite powerful for visual interaction with the data
// other notebooks like zeppelin have similar commands for interactive visualisation
display(diamondsDF)
carat cut color clarity depth table price x y z
0.23 Ideal E SI2 61.5 55.0 326.0 3.95 3.98 2.43
0.21 Premium E SI1 59.8 61.0 326.0 3.89 3.84 2.31
0.23 Good E VS1 56.9 65.0 327.0 4.05 4.07 2.31
0.29 Premium I VS2 62.4 58.0 334.0 4.2 4.23 2.63
0.31 Good J SI2 63.3 58.0 335.0 4.34 4.35 2.75
0.24 Very Good J VVS2 62.8 57.0 336.0 3.94 3.96 2.48
0.24 Very Good I VVS1 62.3 57.0 336.0 3.95 3.98 2.47
0.26 Very Good H SI1 61.9 55.0 337.0 4.07 4.11 2.53
0.22 Fair E VS2 65.1 61.0 337.0 3.87 3.78 2.49
0.23 Very Good H VS1 59.4 61.0 338.0 4.0 4.05 2.39
0.3 Good J SI1 64.0 55.0 339.0 4.25 4.28 2.73
0.23 Ideal J VS1 62.8 56.0 340.0 3.93 3.9 2.46
0.22 Premium F SI1 60.4 61.0 342.0 3.88 3.84 2.33
0.31 Ideal J SI2 62.2 54.0 344.0 4.35 4.37 2.71
0.2 Premium E SI2 60.2 62.0 345.0 3.79 3.75 2.27
0.32 Premium E I1 60.9 58.0 345.0 4.38 4.42 2.68
0.3 Ideal I SI2 62.0 54.0 348.0 4.31 4.34 2.68
0.3 Good J SI1 63.4 54.0 351.0 4.23 4.29 2.7
0.3 Good J SI1 63.8 56.0 351.0 4.23 4.26 2.71
0.3 Very Good J SI1 62.7 59.0 351.0 4.21 4.27 2.66
0.3 Good I SI2 63.3 56.0 351.0 4.26 4.3 2.71
0.23 Very Good E VS2 63.8 55.0 352.0 3.85 3.92 2.48
0.23 Very Good H VS1 61.0 57.0 353.0 3.94 3.96 2.41
0.31 Very Good J SI1 59.4 62.0 353.0 4.39 4.43 2.62
0.31 Very Good J SI1 58.1 62.0 353.0 4.44 4.47 2.59
0.23 Very Good G VVS2 60.4 58.0 354.0 3.97 4.01 2.41
0.24 Premium I VS1 62.5 57.0 355.0 3.97 3.94 2.47
0.3 Very Good J VS2 62.2 57.0 357.0 4.28 4.3 2.67
0.23 Very Good D VS2 60.5 61.0 357.0 3.96 3.97 2.4
0.23 Very Good F VS1 60.9 57.0 357.0 3.96 3.99 2.42
0.23 Very Good F VS1 60.0 57.0 402.0 4.0 4.03 2.41
0.23 Very Good F VS1 59.8 57.0 402.0 4.04 4.06 2.42
0.23 Very Good E VS1 60.7 59.0 402.0 3.97 4.01 2.42
0.23 Very Good E VS1 59.5 58.0 402.0 4.01 4.06 2.4
0.23 Very Good D VS1 61.9 58.0 402.0 3.92 3.96 2.44
0.23 Good F VS1 58.2 59.0 402.0 4.06 4.08 2.37
0.23 Good E VS1 64.1 59.0 402.0 3.83 3.85 2.46
0.31 Good H SI1 64.0 54.0 402.0 4.29 4.31 2.75
0.26 Very Good D VS2 60.8 59.0 403.0 4.13 4.16 2.52
0.33 Ideal I SI2 61.8 55.0 403.0 4.49 4.51 2.78
0.33 Ideal I SI2 61.2 56.0 403.0 4.49 4.5 2.75
0.33 Ideal J SI1 61.1 56.0 403.0 4.49 4.55 2.76
0.26 Good D VS2 65.2 56.0 403.0 3.99 4.02 2.61
0.26 Good D VS1 58.4 63.0 403.0 4.19 4.24 2.46
0.32 Good H SI2 63.1 56.0 403.0 4.34 4.37 2.75
0.29 Premium F SI1 62.4 58.0 403.0 4.24 4.26 2.65
0.32 Very Good H SI2 61.8 55.0 403.0 4.35 4.42 2.71
0.32 Good H SI2 63.8 56.0 403.0 4.36 4.38 2.79
0.25 Very Good E VS2 63.3 60.0 404.0 4.0 4.03 2.54
0.29 Very Good H SI2 60.7 60.0 404.0 4.33 4.37 2.64
0.24 Very Good F SI1 60.9 61.0 404.0 4.02 4.03 2.45
0.23 Ideal G VS1 61.9 54.0 404.0 3.93 3.95 2.44
0.32 Ideal I SI1 60.9 55.0 404.0 4.45 4.48 2.72
0.22 Premium E VS2 61.6 58.0 404.0 3.93 3.89 2.41
0.22 Premium D VS2 59.3 62.0 404.0 3.91 3.88 2.31
0.3 Ideal I SI2 61.0 59.0 405.0 4.3 4.33 2.63
0.3 Premium J SI2 59.3 61.0 405.0 4.43 4.38 2.61
0.3 Very Good I SI1 62.6 57.0 405.0 4.25 4.28 2.67
0.3 Very Good I SI1 63.0 57.0 405.0 4.28 4.32 2.71
0.3 Good I SI1 63.2 55.0 405.0 4.25 4.29 2.7
0.35 Ideal I VS1 60.9 57.0 552.0 4.54 4.59 2.78
0.3 Premium D SI1 62.6 59.0 552.0 4.23 4.27 2.66
0.3 Ideal D SI1 62.5 57.0 552.0 4.29 4.32 2.69
0.3 Ideal D SI1 62.1 56.0 552.0 4.3 4.33 2.68
0.42 Premium I SI2 61.5 59.0 552.0 4.78 4.84 2.96
0.28 Ideal G VVS2 61.4 56.0 553.0 4.19 4.22 2.58
0.32 Ideal I VVS1 62.0 55.3 553.0 4.39 4.42 2.73
0.31 Very Good G SI1 63.3 57.0 553.0 4.33 4.3 2.73
0.31 Premium G SI1 61.8 58.0 553.0 4.35 4.32 2.68
0.24 Premium E VVS1 60.7 58.0 553.0 4.01 4.03 2.44
0.24 Very Good D VVS1 61.5 60.0 553.0 3.97 4.0 2.45
0.3 Very Good H SI1 63.1 56.0 554.0 4.29 4.27 2.7
0.3 Premium H SI1 62.9 59.0 554.0 4.28 4.24 2.68
0.3 Premium H SI1 62.5 57.0 554.0 4.29 4.25 2.67
0.3 Good H SI1 63.7 57.0 554.0 4.28 4.26 2.72
0.26 Very Good F VVS2 59.2 60.0 554.0 4.19 4.22 2.49
0.26 Very Good E VVS2 59.9 58.0 554.0 4.15 4.23 2.51
0.26 Very Good D VVS2 62.4 54.0 554.0 4.08 4.13 2.56
0.26 Very Good D VVS2 62.8 60.0 554.0 4.01 4.05 2.53
0.26 Very Good E VVS1 62.6 59.0 554.0 4.06 4.09 2.55
0.26 Very Good E VVS1 63.4 59.0 554.0 4.0 4.04 2.55
0.26 Very Good D VVS1 62.1 60.0 554.0 4.03 4.12 2.53
0.26 Ideal E VVS2 62.9 58.0 554.0 4.02 4.06 2.54
0.38 Ideal I SI2 61.6 56.0 554.0 4.65 4.67 2.87
0.26 Good E VVS1 57.9 60.0 554.0 4.22 4.25 2.45
0.24 Premium G VVS1 62.3 59.0 554.0 3.95 3.92 2.45
0.24 Premium H VVS1 61.2 58.0 554.0 4.01 3.96 2.44
0.24 Premium H VVS1 60.8 59.0 554.0 4.02 4.0 2.44
0.24 Premium H VVS2 60.7 58.0 554.0 4.07 4.04 2.46
0.32 Premium I SI1 62.9 58.0 554.0 4.35 4.33 2.73
0.7 Ideal E SI1 62.5 57.0 2757.0 5.7 5.72 3.57
0.86 Fair E SI2 55.1 69.0 2757.0 6.45 6.33 3.52
0.7 Ideal G VS2 61.6 56.0 2757.0 5.7 5.67 3.5
0.71 Very Good E VS2 62.4 57.0 2759.0 5.68 5.73 3.56
0.78 Very Good G SI2 63.8 56.0 2759.0 5.81 5.85 3.72
0.7 Good E VS2 57.5 58.0 2759.0 5.85 5.9 3.38
0.7 Good F VS1 59.4 62.0 2759.0 5.71 5.76 3.4
0.96 Fair F SI2 66.3 62.0 2759.0 6.27 5.95 4.07
0.73 Very Good E SI1 61.6 59.0 2760.0 5.77 5.78 3.56
0.8 Premium H SI1 61.5 58.0 2760.0 5.97 5.93 3.66
0.75 Very Good D SI1 63.2 56.0 2760.0 5.8 5.75 3.65
0.75 Premium E SI1 59.9 54.0 2760.0 6.0 5.96 3.58
0.74 Ideal G SI1 61.6 55.0 2760.0 5.8 5.85 3.59
0.75 Premium G VS2 61.7 58.0 2760.0 5.85 5.79 3.59
0.8 Ideal I VS1 62.9 56.0 2760.0 5.94 5.87 3.72
0.75 Ideal G SI1 62.2 55.0 2760.0 5.87 5.8 3.63
0.8 Premium G SI1 63.0 59.0 2760.0 5.9 5.81 3.69
0.74 Ideal I VVS2 62.3 55.0 2761.0 5.77 5.81 3.61
0.81 Ideal F SI2 58.8 57.0 2761.0 6.14 6.11 3.6
0.59 Ideal E VVS2 62.0 55.0 2761.0 5.38 5.43 3.35
0.8 Ideal F SI2 61.4 57.0 2761.0 5.96 6.0 3.67
0.74 Ideal E SI2 62.2 56.0 2761.0 5.8 5.84 3.62
0.9 Premium I VS2 63.0 58.0 2761.0 6.16 6.12 3.87
0.74 Very Good G SI1 62.2 59.0 2762.0 5.73 5.82 3.59
0.73 Ideal F VS2 62.6 56.0 2762.0 5.77 5.74 3.6
0.73 Ideal F VS2 62.7 53.0 2762.0 5.8 5.75 3.62
0.8 Premium F SI2 61.7 58.0 2762.0 5.98 5.94 3.68
0.71 Ideal G VS2 62.4 54.0 2762.0 5.72 5.76 3.58
0.7 Ideal E VS2 60.7 58.0 2762.0 5.73 5.76 3.49
0.8 Ideal F SI2 59.9 59.0 2762.0 6.01 6.07 3.62
0.71 Ideal D SI2 62.3 56.0 2762.0 5.73 5.69 3.56
0.74 Ideal E SI1 62.3 54.0 2762.0 5.8 5.83 3.62
0.7 Very Good F VS2 61.7 63.0 2762.0 5.64 5.61 3.47
0.7 Fair F VS2 64.5 57.0 2762.0 5.57 5.53 3.58
0.7 Fair F VS2 65.3 55.0 2762.0 5.63 5.58 3.66
0.7 Premium F VS2 61.6 60.0 2762.0 5.65 5.59 3.46
0.91 Premium H SI1 61.4 56.0 2763.0 6.09 5.97 3.7
0.61 Very Good D VVS2 59.6 57.0 2763.0 5.56 5.58 3.32
0.91 Fair H SI2 64.4 57.0 2763.0 6.11 6.09 3.93
0.91 Fair H SI2 65.7 60.0 2763.0 6.03 5.99 3.95
0.77 Ideal H VS2 62.0 56.0 2763.0 5.89 5.86 3.64
0.71 Very Good D SI1 63.6 58.0 2764.0 5.64 5.68 3.6
0.71 Ideal D SI1 61.9 59.0 2764.0 5.69 5.72 3.53
0.7 Very Good E VS2 62.6 60.0 2765.0 5.62 5.65 3.53
0.77 Very Good H VS1 61.3 60.0 2765.0 5.88 5.9 3.61
0.63 Premium E VVS1 60.9 60.0 2765.0 5.52 5.55 3.37
0.71 Very Good F VS1 60.1 62.0 2765.0 5.74 5.77 3.46
0.71 Premium F VS1 61.8 59.0 2765.0 5.69 5.73 3.53
0.76 Ideal H SI1 61.2 57.0 2765.0 5.88 5.91 3.61
0.64 Ideal G VVS1 61.9 56.0 2766.0 5.53 5.56 3.43
0.71 Premium G VS2 60.9 57.0 2766.0 5.78 5.75 3.51
0.71 Premium G VS2 59.8 56.0 2766.0 5.89 5.81 3.5
0.7 Very Good D VS2 61.8 55.0 2767.0 5.68 5.72 3.52
0.7 Very Good F VS1 60.0 57.0 2767.0 5.8 5.87 3.5
0.71 Ideal D SI2 61.6 55.0 2767.0 5.74 5.76 3.54
0.7 Good H VVS2 62.1 64.0 2767.0 5.62 5.65 3.5
0.71 Very Good G VS1 63.3 59.0 2768.0 5.52 5.61 3.52
0.73 Very Good D SI1 60.2 56.0 2768.0 5.83 5.87 3.52
0.7 Very Good D SI1 61.1 58.0 2768.0 5.66 5.73 3.48
0.7 Ideal E SI1 60.9 57.0 2768.0 5.73 5.76 3.5
0.71 Premium D SI2 61.7 59.0 2768.0 5.71 5.67 3.51
0.74 Ideal I SI1 61.3 56.0 2769.0 5.82 5.86 3.57
0.71 Premium D VS2 62.5 60.0 2770.0 5.65 5.61 3.52
0.73 Premium G VS2 61.4 59.0 2770.0 5.83 5.76 3.56
0.76 Very Good F SI1 62.9 57.0 2770.0 5.79 5.81 3.65
0.76 Ideal D SI2 62.4 57.0 2770.0 5.78 5.83 3.62
0.71 Ideal F SI1 60.7 56.0 2770.0 5.77 5.8 3.51
0.73 Premium G VS2 60.7 58.0 2770.0 5.87 5.82 3.55
0.73 Premium G VS1 61.5 58.0 2770.0 5.79 5.75 3.55
0.73 Ideal D SI2 59.9 57.0 2770.0 5.92 5.89 3.54
0.73 Premium G VS2 59.2 59.0 2770.0 5.92 5.87 3.49
0.72 Very Good H VVS2 60.3 56.0 2771.0 5.81 5.83 3.51
0.73 Very Good F SI1 61.7 60.0 2771.0 5.79 5.82 3.58
0.71 Ideal G VS2 61.9 57.0 2771.0 5.73 5.77 3.56
0.79 Ideal F SI2 61.9 55.0 2771.0 5.97 5.92 3.68
0.73 Very Good H VVS1 60.4 59.0 2772.0 5.83 5.89 3.54
0.8 Very Good F SI2 61.0 57.0 2772.0 6.01 6.03 3.67
0.58 Ideal G VVS1 61.5 55.0 2772.0 5.39 5.44 3.33
0.58 Ideal F VVS1 61.7 56.0 2772.0 5.33 5.37 3.3
0.71 Good E VS2 59.2 61.0 2772.0 5.8 5.88 3.46
0.75 Ideal D SI2 61.3 56.0 2773.0 5.85 5.89 3.6
0.7 Premium D VS2 58.0 62.0 2773.0 5.87 5.78 3.38
1.17 Very Good J I1 60.2 61.0 2774.0 6.83 6.9 4.13
0.6 Ideal E VS1 61.7 55.0 2774.0 5.41 5.44 3.35
0.7 Ideal E SI1 62.7 55.0 2774.0 5.68 5.74 3.58
0.83 Good I VS2 64.6 54.0 2774.0 5.85 5.88 3.79
0.74 Very Good F VS2 61.3 61.0 2775.0 5.8 5.84 3.57
0.72 Very Good G VS2 63.7 56.4 2776.0 5.62 5.69 3.61
0.71 Premium E VS2 62.7 58.0 2776.0 5.74 5.68 3.58
0.71 Ideal E VS2 62.2 57.0 2776.0 5.79 5.62 3.55
0.54 Ideal E VVS2 61.6 56.0 2776.0 5.25 5.27 3.24
0.54 Ideal E VVS2 61.5 57.0 2776.0 5.24 5.26 3.23
0.72 Ideal G SI1 61.8 56.0 2776.0 5.72 5.75 3.55
0.72 Ideal G SI1 60.7 56.0 2776.0 5.79 5.82 3.53
0.72 Good G VS2 59.7 60.5 2776.0 5.8 5.84 3.47
0.71 Ideal G SI1 60.5 56.0 2776.0 5.8 5.76 3.5
0.7 Very Good D VS1 62.7 58.0 2777.0 5.66 5.73 3.57
0.71 Premium F VS2 62.1 58.0 2777.0 5.67 5.7 3.53
0.71 Very Good F VS2 62.8 57.0 2777.0 5.64 5.69 3.56
0.71 Good F VS2 63.8 58.0 2777.0 5.61 5.64 3.59
0.71 Good F VS2 57.8 60.0 2777.0 5.87 5.9 3.4
0.7 Ideal E VS2 62.1 55.0 2777.0 5.7 5.67 3.53
0.7 Premium E VS2 61.1 60.0 2777.0 5.71 5.64 3.47
0.7 Premium E SI1 60.0 59.0 2777.0 5.79 5.75 3.46
0.7 Premium E SI1 61.2 57.0 2777.0 5.73 5.68 3.49
0.7 Premium E SI1 62.7 59.0 2777.0 5.67 5.63 3.54
0.7 Premium E SI1 61.0 57.0 2777.0 5.73 5.68 3.48
0.7 Premium E SI1 61.0 58.0 2777.0 5.78 5.72 3.51
0.7 Ideal E SI1 61.4 57.0 2777.0 5.76 5.7 3.52
0.72 Premium F SI1 61.8 61.0 2777.0 5.82 5.71 3.56
0.7 Very Good E SI1 59.9 63.0 2777.0 5.76 5.7 3.43
0.7 Premium E SI1 61.3 58.0 2777.0 5.71 5.68 3.49
0.7 Premium E SI1 60.5 58.0 2777.0 5.77 5.74 3.48
0.7 Good E VS2 64.1 59.0 2777.0 5.64 5.59 3.6
0.98 Fair H SI2 67.9 60.0 2777.0 6.05 5.97 4.08
0.78 Premium F SI1 62.4 58.0 2777.0 5.83 5.8 3.63
0.7 Very Good E SI1 63.2 60.0 2777.0 5.6 5.51 3.51
0.52 Ideal F VVS1 61.3 55.0 2778.0 5.19 5.22 3.19
0.73 Very Good H VS2 60.8 56.0 2779.0 5.82 5.84 3.55
0.74 Ideal E SI1 61.7 56.0 2779.0 5.84 5.8 3.59
0.7 Very Good F VS2 63.6 57.0 2780.0 5.61 5.65 3.58
0.77 Premium G VS2 61.2 58.0 2780.0 5.9 5.93 3.62
0.71 Ideal F VS2 62.1 54.0 2780.0 5.68 5.72 3.54
0.74 Ideal G VS1 61.5 55.0 2780.0 5.81 5.86 3.59
0.7 Ideal G VS1 61.4 59.0 2780.0 5.64 5.73 3.49
1.01 Premium F I1 61.8 60.0 2781.0 6.39 6.36 3.94
0.77 Ideal H SI1 62.2 56.0 2781.0 5.83 5.88 3.64
0.78 Ideal H SI1 61.2 56.0 2781.0 5.92 5.99 3.64
0.72 Very Good H VS1 60.6 63.0 2782.0 5.83 5.76 3.51
0.53 Very Good D VVS2 57.5 64.0 2782.0 5.34 5.37 3.08
0.76 Ideal G VS2 61.3 56.0 2782.0 5.9 5.94 3.63
0.7 Good E VS1 57.2 62.0 2782.0 5.81 5.77 3.31
0.7 Premium E VS1 62.9 60.0 2782.0 5.62 5.54 3.51
0.75 Very Good D SI2 63.1 58.0 2782.0 5.78 5.73 3.63
0.72 Ideal D SI1 60.8 57.0 2782.0 5.76 5.75 3.5
0.72 Premium D SI1 62.7 59.0 2782.0 5.73 5.69 3.58
0.7 Premium D SI1 62.8 60.0 2782.0 5.68 5.66 3.56
0.84 Fair G SI1 55.1 67.0 2782.0 6.39 6.2 3.47
0.75 Premium F SI1 61.4 59.0 2782.0 5.88 5.85 3.6
0.52 Ideal F IF 62.2 55.0 2783.0 5.14 5.18 3.21
0.72 Very Good F VS2 63.0 54.0 2784.0 5.69 5.73 3.6
0.79 Very Good H VS1 63.7 56.0 2784.0 5.85 5.92 3.75
0.72 Very Good F VS2 63.6 58.0 2787.0 5.66 5.69 3.61
0.51 Ideal F VVS1 62.0 57.0 2787.0 5.11 5.15 3.18
0.64 Ideal D VS1 61.5 56.0 2787.0 5.54 5.55 3.41
0.7 Very Good H VVS1 60.5 60.0 2788.0 5.74 5.77 3.48
0.83 Very Good I VS1 61.1 60.0 2788.0 6.07 6.1 3.72
0.76 Ideal I VVS2 61.8 56.0 2788.0 5.85 5.87 3.62
0.71 Good D VS2 63.3 56.0 2788.0 5.64 5.68 3.58
0.77 Good G VS1 59.4 64.0 2788.0 5.97 5.92 3.53
0.71 Ideal F SI1 62.5 55.0 2788.0 5.71 5.65 3.55
1.01 Fair E I1 64.5 58.0 2788.0 6.29 6.21 4.03
1.01 Premium H SI2 62.7 59.0 2788.0 6.31 6.22 3.93
0.77 Good F SI1 64.2 52.0 2789.0 5.81 5.77 3.72
0.76 Good E SI1 63.7 54.0 2789.0 5.76 5.85 3.7
0.76 Premium E SI1 60.4 58.0 2789.0 5.92 5.94 3.58
0.76 Premium E SI1 61.8 58.0 2789.0 5.82 5.86 3.61
1.05 Very Good J SI2 63.2 56.0 2789.0 6.49 6.45 4.09
0.81 Ideal G SI2 61.6 56.0 2789.0 5.97 6.01 3.69
0.7 Ideal E SI1 61.6 56.0 2789.0 5.72 5.75 3.53
0.55 Ideal G IF 60.9 57.0 2789.0 5.28 5.3 3.22
0.81 Good G SI2 61.0 61.0 2789.0 5.94 5.99 3.64
0.63 Premium E VVS2 62.1 57.0 2789.0 5.48 5.41 3.38
0.63 Premium E VVS1 60.9 60.0 2789.0 5.55 5.52 3.37
0.77 Premium H VS1 61.3 60.0 2789.0 5.9 5.88 3.61
1.05 Fair J SI2 65.8 59.0 2789.0 6.41 6.27 4.18
0.64 Ideal G IF 61.3 56.0 2790.0 5.54 5.58 3.41
0.76 Premium I VVS1 58.8 59.0 2790.0 6.0 5.94 3.51
0.83 Ideal F SI2 62.3 55.0 2790.0 6.02 6.05 3.76
0.71 Premium F VS1 60.1 62.0 2790.0 5.77 5.74 3.46
0.71 Premium F VS1 61.8 59.0 2790.0 5.73 5.69 3.53
0.87 Very Good I SI1 63.6 55.8 2791.0 6.07 6.1 3.87
0.73 Ideal E SI1 62.2 56.0 2791.0 5.74 5.78 3.58
0.71 Premium E SI1 59.2 59.0 2792.0 5.83 5.86 3.46
0.71 Premium E SI1 61.8 59.0 2792.0 5.7 5.75 3.54
0.71 Ideal E SI1 61.3 55.0 2792.0 5.72 5.77 3.52
0.7 Premium F VS1 62.1 60.0 2792.0 5.71 5.65 3.53
0.7 Premium F VS1 60.7 60.0 2792.0 5.78 5.75 3.5
0.76 Premium H VVS2 59.6 57.0 2792.0 5.91 5.86 3.51
0.7 Ideal F VS1 62.2 56.0 2792.0 5.73 5.68 3.55
0.79 Very Good G SI1 60.6 57.0 2793.0 5.98 6.06 3.65
0.7 Very Good E VS2 62.9 57.0 2793.0 5.66 5.69 3.57
0.7 Good E VS2 64.1 55.0 2793.0 5.6 5.66 3.61
0.76 Ideal I VS2 61.3 56.0 2793.0 5.87 5.91 3.61
0.73 Ideal H VS2 62.7 55.0 2793.0 5.72 5.76 3.6
0.79 Very Good E SI1 63.2 56.0 2794.0 5.91 5.86 3.72
0.71 Very Good E VS2 60.7 56.0 2795.0 5.81 5.82 3.53
0.81 Premium I VVS2 61.9 60.0 2795.0 5.91 5.86 3.64
0.81 Ideal F SI2 62.6 55.0 2795.0 5.92 5.96 3.72
0.72 Good F VS1 60.7 60.0 2795.0 5.74 5.72 3.48
0.72 Premium D SI2 62.0 60.0 2795.0 5.73 5.69 3.54
0.72 Premium I IF 63.0 57.0 2795.0 5.72 5.7 3.6
0.81 Premium H VS2 58.0 59.0 2795.0 6.17 6.13 3.57
0.72 Premium G VS2 62.9 57.0 2795.0 5.73 5.65 3.58
1.0 Premium I SI2 58.2 60.0 2795.0 6.61 6.55 3.83
0.73 Good E SI1 63.2 58.0 2796.0 5.7 5.76 3.62
0.81 Very Good H SI2 61.3 59.0 2797.0 5.94 6.01 3.66
0.81 Very Good E SI1 60.3 60.0 2797.0 6.07 6.1 3.67
0.71 Premium D SI1 62.7 60.0 2797.0 5.67 5.71 3.57
0.71 Premium D SI1 61.3 58.0 2797.0 5.73 5.75 3.52
0.71 Premium D SI1 61.6 60.0 2797.0 5.74 5.69 3.52
0.57 Ideal F VVS2 61.9 55.0 2797.0 5.34 5.35 3.31
0.51 Ideal D VVS1 61.7 56.0 2797.0 5.12 5.16 3.17
0.72 Ideal G VS2 61.9 58.0 2797.0 5.72 5.75 3.55
0.74 Ideal H VS1 61.8 58.0 2797.0 5.77 5.81 3.58
0.74 Ideal H VS1 61.6 56.0 2797.0 5.81 5.82 3.58
0.7 Fair G VVS1 58.8 66.0 2797.0 5.81 5.9 3.44
0.8 Premium F SI2 61.0 57.0 2797.0 6.03 6.01 3.67
1.01 Fair E SI2 67.4 60.0 2797.0 6.19 6.05 4.13
0.8 Very Good H VS2 63.4 60.0 2797.0 5.92 5.82 3.72
0.77 Ideal I VS1 61.5 59.0 2798.0 5.87 5.91 3.62
0.83 Very Good E SI2 58.0 62.0 2799.0 6.19 6.25 3.61
0.82 Ideal F SI2 62.4 54.0 2799.0 5.97 6.02 3.74
0.78 Ideal D SI1 61.9 57.0 2799.0 5.91 5.86 3.64
0.6 Very Good G IF 61.6 56.0 2800.0 5.43 5.46 3.35
0.9 Good I SI2 62.2 59.0 2800.0 6.07 6.11 3.79
0.7 Premium E VS1 62.2 58.0 2800.0 5.6 5.66 3.5
0.9 Very Good I SI2 61.3 56.0 2800.0 6.17 6.23 3.8
0.83 Ideal G SI1 62.3 57.0 2800.0 5.99 6.08 3.76
0.83 Ideal G SI1 61.8 57.0 2800.0 6.03 6.07 3.74
0.83 Very Good H SI1 62.5 59.0 2800.0 5.95 6.02 3.74
0.74 Premium G VS1 62.9 60.0 2800.0 5.74 5.68 3.59
0.79 Ideal I VS1 61.8 59.0 2800.0 5.92 5.95 3.67
0.61 Ideal G IF 62.3 56.0 2800.0 5.43 5.45 3.39
0.76 Fair G VS1 59.0 70.0 2800.0 5.89 5.8 3.46
0.96 Ideal F I1 60.7 55.0 2801.0 6.37 6.41 3.88
0.73 Ideal F VS2 62.5 55.0 2801.0 5.8 5.76 3.61
0.73 Premium F VS2 62.7 58.0 2801.0 5.76 5.7 3.59
0.75 Ideal H SI1 60.4 57.0 2801.0 5.93 5.96 3.59
0.71 Premium F VS2 62.1 58.0 2801.0 5.7 5.67 3.53
0.71 Good F VS2 57.8 60.0 2801.0 5.9 5.87 3.4
0.71 Good F VS2 63.8 58.0 2801.0 5.64 5.61 3.59
0.71 Premium F VS2 62.8 57.0 2801.0 5.69 5.64 3.56
1.04 Premium G I1 62.2 58.0 2801.0 6.46 6.41 4.0
1.0 Premium J SI2 62.3 58.0 2801.0 6.45 6.34 3.98
0.87 Very Good G SI2 59.9 58.0 2802.0 6.19 6.23 3.72
0.53 Ideal F IF 61.9 54.0 2802.0 5.22 5.25 3.24
0.72 Premium E VS2 63.0 55.0 2802.0 5.79 5.61 3.59
0.72 Premium F VS1 62.4 58.0 2802.0 5.83 5.7 3.6
0.7 Very Good F VS2 62.9 58.0 2803.0 5.63 5.65 3.55
0.74 Very Good E SI1 63.5 56.0 2803.0 5.74 5.79 3.66
0.71 Ideal G VS2 61.3 56.0 2803.0 5.75 5.71 3.51
0.73 Ideal E SI1 60.6 54.0 2803.0 5.84 5.89 3.55
0.7 Good G VS1 65.1 58.0 2803.0 5.56 5.59 3.63
0.71 Premium F VS2 62.6 58.0 2803.0 5.7 5.67 3.56
0.71 Premium F VS2 58.0 62.0 2803.0 5.85 5.81 3.38
0.71 Premium G VS1 62.4 61.0 2803.0 5.7 5.65 3.54
0.77 Premium G VS2 61.3 57.0 2803.0 5.93 5.88 3.62
0.71 Premium G VS2 59.9 60.0 2803.0 5.81 5.77 3.47
0.78 Premium G VS2 60.8 58.0 2803.0 6.03 5.95 3.64
0.71 Very Good G VS1 63.5 55.0 2803.0 5.66 5.64 3.59
0.91 Ideal D SI2 62.2 57.0 2803.0 6.21 6.15 3.85
0.71 Very Good E VS2 63.8 58.0 2804.0 5.62 5.66 3.6
0.71 Very Good E VS2 64.0 57.0 2804.0 5.66 5.68 3.63
0.8 Very Good E SI2 62.5 56.0 2804.0 5.88 5.96 3.7
0.7 Very Good D SI1 62.3 58.0 2804.0 5.69 5.73 3.56
0.72 Ideal F VS1 61.7 57.0 2804.0 5.74 5.77 3.55
0.72 Very Good F VS1 62.2 58.0 2804.0 5.75 5.7 3.56
0.82 Ideal H VS2 61.5 56.0 2804.0 6.01 6.08 3.72
0.7 Ideal D SI1 61.0 59.0 2804.0 5.68 5.7 3.47
0.72 Ideal D SI1 62.2 56.0 2804.0 5.74 5.77 3.58
0.72 Ideal D SI1 61.5 54.0 2804.0 5.77 5.8 3.56
0.9 Fair I SI1 67.3 59.0 2804.0 5.93 5.84 3.96
0.74 Premium F VS2 61.7 58.0 2805.0 5.85 5.78 3.59
0.74 Premium F VS2 61.9 56.0 2805.0 5.8 5.77 3.58
0.73 Ideal E SI2 61.8 58.0 2805.0 5.77 5.81 3.58
0.57 Fair E VVS1 58.7 66.0 2805.0 5.34 5.43 3.16
0.73 Premium F VS2 62.5 57.0 2805.0 5.75 5.7 3.58
0.72 Ideal G VS2 62.8 56.0 2805.0 5.74 5.7 3.59
0.74 Fair F VS2 61.1 68.0 2805.0 5.82 5.75 3.53
0.82 Good G VS2 64.0 57.0 2805.0 5.92 5.89 3.78
0.81 Very Good G SI1 62.5 60.0 2806.0 5.89 5.94 3.69
0.75 Very Good H VVS1 60.6 58.0 2806.0 5.85 5.9 3.56
0.7 Ideal F SI1 61.6 55.0 2806.0 5.72 5.74 3.53
0.71 Very Good F VS1 62.2 58.0 2807.0 5.66 5.72 3.54
0.71 Very Good F VS1 60.0 57.0 2807.0 5.84 5.9 3.52
0.93 Premium J SI2 61.9 57.0 2807.0 6.21 6.19 3.84
0.8 Very Good H VS2 62.8 57.0 2808.0 5.87 5.91 3.7
0.7 Very Good F VS1 62.0 57.0 2808.0 5.64 5.71 3.52
1.0 Fair G I1 66.4 59.0 2808.0 6.16 6.09 4.07
0.75 Very Good G VS2 63.4 56.0 2808.0 5.78 5.74 3.65
0.58 Ideal E VVS2 60.9 56.0 2808.0 5.41 5.43 3.3
0.73 Very Good D SI1 63.1 57.0 2808.0 5.74 5.7 3.61
0.81 Very Good F SI1 63.1 59.0 2809.0 5.85 5.79 3.67
0.81 Premium D SI2 59.2 57.0 2809.0 6.15 6.05 3.61
0.71 Premium F SI1 60.7 54.0 2809.0 5.84 5.8 3.53
1.2 Fair F I1 64.6 56.0 2809.0 6.73 6.66 4.33
0.7 Very Good F VS1 61.8 56.0 2810.0 5.63 5.7 3.5
0.7 Very Good F VS1 59.9 60.0 2810.0 5.77 5.84 3.48
0.74 Ideal D SI2 61.7 55.0 2810.0 5.81 5.85 3.6
0.7 Good F VS1 62.8 61.0 2810.0 5.57 5.61 3.51
0.8 Good G SI1 62.7 57.0 2810.0 5.84 5.93 3.69
0.75 Very Good F SI1 63.4 58.0 2811.0 5.72 5.76 3.64
0.83 Very Good D SI1 63.5 54.0 2811.0 5.98 5.95 3.79
1.0 Fair J VS2 65.7 59.0 2811.0 6.14 6.07 4.01
0.99 Fair I SI2 68.1 56.0 2811.0 6.21 6.06 4.18
0.7 Very Good G VS1 63.0 60.0 2812.0 5.57 5.64 3.53
0.7 Very Good F VS2 59.5 58.0 2812.0 5.75 5.85 3.45
0.7 Good E SI1 63.5 59.0 2812.0 5.49 5.53 3.5
0.7 Very Good F VS2 61.7 58.0 2812.0 5.63 5.69 3.49
0.32 Premium I SI1 62.7 58.0 554.0 4.37 4.34 2.73
0.32 Premium I SI1 62.8 58.0 554.0 4.39 4.34 2.74
0.32 Ideal I SI1 62.4 57.0 554.0 4.37 4.35 2.72
0.32 Premium I SI1 61.0 59.0 554.0 4.39 4.36 2.67
0.32 Very Good I SI1 63.1 56.0 554.0 4.39 4.36 2.76
0.32 Ideal I SI1 60.7 57.0 554.0 4.47 4.42 2.7
0.3 Premium H SI1 60.9 59.0 554.0 4.31 4.29 2.62
0.3 Premium H SI1 60.1 55.0 554.0 4.41 4.38 2.64
0.3 Premium H SI1 62.9 58.0 554.0 4.28 4.24 2.68
0.3 Very Good H SI1 63.3 56.0 554.0 4.29 4.27 2.71
0.3 Good H SI1 63.8 55.0 554.0 4.26 4.2 2.7
0.3 Ideal H SI1 62.9 57.0 554.0 4.27 4.22 2.67
0.3 Very Good H SI1 63.4 60.0 554.0 4.25 4.23 2.69
0.32 Good I SI1 63.9 55.0 554.0 4.36 4.34 2.78
0.33 Ideal H SI2 61.4 56.0 554.0 4.85 4.79 2.95
0.29 Very Good E VS1 61.9 55.0 555.0 4.28 4.33 2.66
0.29 Very Good E VS1 62.4 55.0 555.0 4.2 4.25 2.63
0.31 Very Good F SI1 61.8 58.0 555.0 4.32 4.35 2.68
0.34 Ideal H VS2 61.5 56.0 555.0 4.47 4.5 2.76
0.34 Ideal H VS2 60.4 57.0 555.0 4.54 4.57 2.75
0.34 Ideal I VS1 61.8 55.0 555.0 4.48 4.52 2.78
0.34 Ideal I VS1 62.0 56.0 555.0 4.5 4.53 2.8
0.3 Ideal G VS1 62.3 56.0 555.0 4.29 4.31 2.68
0.29 Ideal F VS1 61.6 56.0 555.0 4.26 4.31 2.64
0.35 Ideal G SI1 60.6 56.0 555.0 4.56 4.58 2.77
0.43 Very Good E I1 58.4 62.0 555.0 4.94 5.0 2.9
0.32 Very Good F VS2 61.4 58.0 556.0 4.37 4.42 2.7
0.36 Ideal I VS2 61.9 56.0 556.0 4.54 4.57 2.82
0.3 Ideal G VS2 62.0 56.0 556.0 4.28 4.3 2.66
0.26 Ideal E VS1 61.5 57.0 556.0 4.09 4.12 2.52
0.7 Very Good F VS2 62.3 58.0 2812.0 5.64 5.72 3.54
0.7 Very Good F VS2 60.9 61.0 2812.0 5.66 5.71 3.46
0.71 Ideal D SI1 62.4 57.0 2812.0 5.69 5.72 3.56
0.99 Fair J SI1 55.0 61.0 2812.0 6.72 6.67 3.68
0.73 Premium E VS2 58.6 60.0 2812.0 5.92 5.89 3.46
0.51 Ideal F VVS1 62.0 57.0 2812.0 5.15 5.11 3.18
0.91 Premium G SI2 59.8 58.0 2813.0 6.3 6.29 3.77
0.84 Very Good E SI1 63.4 55.0 2813.0 6.0 5.95 3.79
0.91 Good I VS2 64.3 58.0 2813.0 6.09 6.05 3.9
0.76 Premium E SI1 62.2 59.0 2814.0 5.86 5.81 3.63
0.76 Ideal E SI1 61.7 57.0 2814.0 5.88 5.85 3.62
0.75 Premium E SI1 61.1 59.0 2814.0 5.86 5.83 3.57
0.55 Very Good D VVS1 61.5 56.0 2815.0 5.23 5.27 3.23
0.76 Very Good F SI2 58.5 62.0 2815.0 5.93 6.01 3.49
0.74 Premium G VS1 61.7 58.0 2815.0 5.79 5.81 3.58
0.7 Ideal H SI1 60.4 56.0 2815.0 5.75 5.81 3.49
0.7 Ideal H SI1 61.4 56.0 2815.0 5.7 5.76 3.52
0.7 Ideal H SI1 61.5 55.0 2815.0 5.73 5.79 3.54
0.7 Ideal H SI1 61.4 56.0 2815.0 5.72 5.77 3.53
0.9 Fair J VS2 65.0 56.0 2815.0 6.08 6.04 3.94
0.95 Fair F SI2 56.0 60.0 2815.0 6.62 6.53 3.68
0.89 Premium H SI2 60.2 59.0 2815.0 6.26 6.23 3.76
0.72 Premium E VS2 58.3 58.0 2815.0 5.99 5.92 3.47
0.96 Fair E SI2 53.1 63.0 2815.0 6.73 6.65 3.55
1.02 Premium G I1 60.3 58.0 2815.0 6.55 6.5 3.94
0.78 Very Good I VVS2 61.4 56.0 2816.0 5.91 5.95 3.64
0.61 Ideal G VVS2 60.1 57.0 2816.0 5.52 5.54 3.32
0.71 Good D VS1 63.4 55.0 2816.0 5.61 5.69 3.58
0.78 Premium F SI1 61.5 59.0 2816.0 5.96 5.88 3.64
0.87 Ideal H SI2 62.7 56.0 2816.0 6.16 6.13 3.85
0.83 Ideal H SI1 62.5 55.0 2816.0 6.04 6.0 3.76
0.71 Premium E SI1 61.3 56.0 2817.0 5.78 5.73 3.53
0.71 Ideal I VVS2 60.2 56.0 2817.0 5.84 5.89 3.53
0.71 Ideal E VS2 62.7 57.0 2817.0 5.66 5.64 3.54
0.71 Premium E VS2 62.3 58.0 2817.0 5.69 5.65 3.53
0.63 Ideal F VVS2 61.5 56.0 2817.0 5.48 5.52 3.38
0.71 Premium E SI1 59.2 59.0 2817.0 5.86 5.83 3.46
0.71 Premium E SI1 61.8 59.0 2817.0 5.75 5.7 3.54
0.71 Ideal E SI1 61.3 55.0 2817.0 5.77 5.72 3.52
0.71 Premium E SI1 61.4 58.0 2817.0 5.77 5.73 3.53
0.9 Ideal J VS2 62.8 55.0 2817.0 6.2 6.16 3.88
0.71 Good E SI1 62.8 64.0 2817.0 5.6 5.54 3.5
0.7 Premium E VS2 62.4 61.0 2818.0 5.66 5.63 3.52
0.7 Premium E VS2 59.3 60.0 2818.0 5.78 5.73 3.41
0.7 Premium E VS2 63.0 60.0 2818.0 5.64 5.6 3.54
1.0 Premium H I1 61.3 60.0 2818.0 6.43 6.39 3.93
0.86 Premium F SI2 59.3 62.0 2818.0 6.36 6.22 3.73
0.8 Ideal H SI1 61.0 57.0 2818.0 6.07 6.0 3.68
0.7 Ideal E VS1 62.9 57.0 2818.0 5.66 5.61 3.54
0.7 Premium E VS1 59.6 57.0 2818.0 5.91 5.83 3.5
0.7 Premium F VS2 61.8 60.0 2818.0 5.69 5.64 3.5
0.7 Premium E VS1 62.7 57.0 2818.0 5.68 5.64 3.55
1.0 Fair H SI2 65.3 62.0 2818.0 6.34 6.12 4.08
0.72 Very Good G VS1 63.8 58.0 2819.0 5.64 5.68 3.61
0.72 Ideal H VS1 62.3 56.0 2819.0 5.73 5.77 3.58
0.7 Good F VS1 59.7 63.0 2819.0 5.76 5.79 3.45
0.86 Good F SI2 64.3 60.0 2819.0 5.97 5.95 3.83
0.71 Ideal G VS1 62.9 58.0 2820.0 5.66 5.69 3.57
0.75 Ideal E SI1 62.0 57.0 2821.0 5.8 5.78 3.59
0.73 Premium E VS2 61.6 59.0 2821.0 5.77 5.73 3.54
0.53 Ideal E VVS1 61.9 55.0 2821.0 5.2 5.21 3.22
0.73 Premium E SI1 61.3 58.0 2821.0 5.83 5.76 3.55
0.73 Good E SI1 63.6 57.0 2821.0 5.72 5.7 3.63
0.73 Premium E SI1 59.6 61.0 2821.0 5.92 5.85 3.51
0.73 Premium E SI1 62.2 59.0 2821.0 5.77 5.68 3.56
0.73 Premium D SI1 61.7 55.0 2821.0 5.84 5.82 3.6
0.73 Very Good E SI1 63.2 58.0 2821.0 5.76 5.7 3.62
0.7 Premium E VS1 60.8 60.0 2822.0 5.74 5.71 3.48
0.72 Premium E VS2 60.3 59.0 2822.0 5.84 5.8 3.51
0.72 Premium E VS2 60.9 60.0 2822.0 5.8 5.76 3.52
0.72 Premium E VS2 62.4 59.0 2822.0 5.77 5.7 3.58
0.7 Premium E VS2 60.2 60.0 2822.0 5.73 5.7 3.44
0.6 Ideal F VVS2 62.0 55.0 2822.0 5.37 5.4 3.34
0.74 Ideal I VVS1 60.8 57.0 2822.0 5.85 5.89 3.57
0.73 Ideal F SI1 62.1 55.0 2822.0 5.75 5.78 3.58
0.71 Premium D SI1 62.7 60.0 2822.0 5.71 5.67 3.57
0.71 Premium D SI1 61.3 58.0 2822.0 5.75 5.73 3.52
0.7 Premium D SI1 60.2 60.0 2822.0 5.82 5.75 3.48
0.7 Ideal D SI1 60.7 56.0 2822.0 5.75 5.72 3.48
0.9 Good J VS2 64.0 61.0 2822.0 6.04 6.03 3.86
0.71 Ideal D SI1 60.2 56.0 2822.0 5.86 5.83 3.52
0.7 Premium E VS2 61.5 59.0 2822.0 5.73 5.68 3.51
0.7 Premium E VS2 62.6 56.0 2822.0 5.71 5.66 3.56
0.7 Ideal D SI1 59.7 58.0 2822.0 5.82 5.77 3.46
0.7 Good E SI1 61.4 64.0 2822.0 5.71 5.66 3.49
0.7 Ideal D SI1 62.5 57.0 2822.0 5.62 5.59 3.51
0.7 Ideal D SI1 61.8 56.0 2822.0 5.73 5.63 3.51
0.7 Premium E VS2 60.7 62.0 2822.0 5.72 5.68 3.46
0.7 Premium F VS2 60.6 58.0 2822.0 5.8 5.72 3.49
0.7 Ideal D SI1 61.4 54.0 2822.0 5.75 5.71 3.52
0.79 Very Good D SI2 62.8 59.0 2823.0 5.86 5.9 3.69
0.9 Good I SI1 63.8 57.0 2823.0 6.06 6.13 3.89
0.71 Premium E VS2 62.3 58.0 2823.0 5.71 5.66 3.54
0.61 Ideal E VVS2 61.3 54.0 2823.0 5.51 5.59 3.4
0.9 Fair H SI2 65.8 54.0 2823.0 6.05 5.98 3.96
0.71 Ideal E SI1 60.5 56.0 2823.0 5.77 5.73 3.47
0.71 Premium D VS2 61.2 59.0 2824.0 5.74 5.69 3.5
0.77 Ideal I VVS2 62.1 57.0 2824.0 5.84 5.86 3.63
0.74 Good E VS1 63.1 58.0 2824.0 5.73 5.75 3.62
0.82 Ideal F SI2 62.4 54.0 2824.0 6.02 5.97 3.74
0.82 Premium E SI2 60.8 60.0 2824.0 6.05 6.03 3.67
0.71 Premium G VS1 62.2 59.0 2825.0 5.73 5.66 3.54
0.83 Premium H SI1 60.0 59.0 2825.0 6.08 6.05 3.64
0.73 Very Good G VS1 62.0 57.0 2825.0 5.75 5.8 3.58
0.83 Premium H SI1 62.5 59.0 2825.0 6.02 5.95 3.74
1.17 Premium J I1 60.2 61.0 2825.0 6.9 6.83 4.13
0.91 Fair H SI2 61.3 67.0 2825.0 6.24 6.19 3.81
0.73 Premium E VS1 62.6 60.0 2826.0 5.75 5.68 3.58
0.7 Good E VS1 57.2 59.0 2826.0 5.94 5.88 3.38
0.9 Premium I SI2 62.2 59.0 2826.0 6.11 6.07 3.79
0.7 Premium E VS1 62.2 58.0 2826.0 5.66 5.6 3.5
0.7 Very Good D VS2 63.3 56.0 2826.0 5.6 5.58 3.54
0.7 Premium E VS1 59.4 61.0 2826.0 5.78 5.74 3.42
0.9 Very Good I SI2 63.5 56.0 2826.0 6.17 6.07 3.88
0.78 Premium F SI1 60.8 60.0 2826.0 5.97 5.94 3.62
0.96 Ideal F I1 60.7 55.0 2826.0 6.41 6.37 3.88
0.7 Very Good D SI1 62.3 59.0 2827.0 5.67 5.7 3.54
0.72 Good D VS2 64.0 54.0 2827.0 5.68 5.7 3.64
0.79 Premium H VVS2 62.6 58.0 2827.0 5.96 5.9 3.71
0.7 Ideal H VVS1 61.6 57.0 2827.0 5.69 5.74 3.52
0.7 Ideal H VVS1 62.3 55.0 2827.0 5.66 5.7 3.54
0.7 Ideal D SI2 60.6 57.0 2828.0 5.74 5.77 3.49
1.01 Premium H SI2 61.6 61.0 2828.0 6.39 6.31 3.91
0.72 Premium F VS1 62.2 58.0 2829.0 5.75 5.7 3.56
0.8 Good E SI2 63.7 54.0 2829.0 5.91 5.87 3.75
0.59 Ideal E VVS1 62.0 56.0 2829.0 5.36 5.38 3.33
0.72 Ideal F VS1 61.7 57.0 2829.0 5.77 5.74 3.55
0.75 Premium E SI2 61.9 57.0 2829.0 5.88 5.82 3.62
0.8 Premium E SI2 60.2 57.0 2829.0 6.05 6.01 3.63
0.71 Very Good E VS2 62.7 59.0 2830.0 5.65 5.7 3.56
0.77 Very Good H SI1 61.7 56.0 2830.0 5.84 5.89 3.62
0.97 Ideal F I1 60.7 56.0 2830.0 6.41 6.43 3.9
0.53 Ideal F VVS1 60.9 57.0 2830.0 5.23 5.29 3.19
0.53 Ideal F VVS1 61.8 57.0 2830.0 5.16 5.19 3.2
0.8 Ideal I VS2 62.1 54.4 2830.0 5.94 5.99 3.7
0.9 Premium G SI1 60.6 62.0 2830.0 6.21 6.13 3.74
0.76 Very Good E SI2 60.8 60.0 2831.0 5.89 5.98 3.61
0.72 Ideal E SI1 62.3 57.0 2831.0 5.7 5.76 3.57
0.75 Ideal E SI1 61.4 57.0 2831.0 5.82 5.87 3.59
0.72 Premium E SI1 62.1 58.0 2831.0 5.73 5.76 3.57
0.79 Ideal G SI1 61.8 56.0 2831.0 5.93 5.91 3.66
0.72 Very Good F VS2 62.5 58.0 2832.0 5.71 5.75 3.58
0.91 Very Good I SI2 62.8 61.0 2832.0 6.15 6.18 3.87
0.71 Premium G VVS2 62.1 57.0 2832.0 5.75 5.65 3.54
0.81 Premium G SI1 63.0 60.0 2832.0 5.87 5.81 3.68
0.82 Ideal H SI1 62.5 57.0 2832.0 5.91 5.97 3.71
0.71 Premium F VS1 62.2 58.0 2832.0 5.72 5.66 3.54
0.9 Good J SI1 64.3 63.0 2832.0 6.05 6.01 3.88
0.8 Very Good I VS2 62.0 58.0 2833.0 5.86 5.95 3.66
0.56 Very Good E IF 61.0 59.0 2833.0 5.28 5.34 3.24
0.7 Very Good D VS2 59.6 61.0 2833.0 5.77 5.8 3.45
0.7 Ideal D VS2 61.0 57.0 2833.0 5.74 5.76 3.51
0.61 Ideal F VVS2 61.7 55.0 2833.0 5.45 5.48 3.37
0.85 Ideal H SI2 62.5 57.0 2833.0 6.02 6.07 3.78
0.7 Ideal F SI1 60.7 57.0 2833.0 5.73 5.75 3.49
0.8 Ideal G VS2 62.2 56.0 2834.0 5.94 5.87 3.67
0.8 Ideal H VS2 62.8 57.0 2834.0 5.91 5.87 3.7
0.51 Very Good D VVS1 59.9 58.0 2834.0 5.16 5.19 3.1
0.53 Ideal F VVS1 61.4 57.0 2834.0 5.2 5.23 3.2
0.78 Ideal I VS2 61.8 55.0 2834.0 5.92 5.95 3.67
0.9 Very Good J SI1 63.4 54.0 2834.0 6.17 6.14 3.9
0.9 Fair G SI2 65.3 59.0 2834.0 6.07 6.0 3.94
0.77 Ideal E SI2 60.7 55.0 2834.0 6.01 5.95 3.63
0.73 Ideal F VS1 61.2 56.0 2835.0 5.89 5.81 3.58
0.63 Ideal F VVS2 61.9 57.0 2835.0 5.47 5.51 3.4
0.7 Ideal E VS2 61.5 54.0 2835.0 5.7 5.75 3.52
0.72 Ideal E VS2 62.8 57.0 2835.0 5.71 5.73 3.59
0.72 Ideal E SI1 61.0 57.0 2835.0 5.78 5.8 3.53
0.75 Premium F VS2 59.6 59.0 2835.0 6.04 5.94 3.57
0.82 Very Good H SI1 60.7 56.0 2836.0 6.04 6.06 3.67
0.71 Good E VS2 62.8 60.0 2836.0 5.6 5.65 3.53
0.7 Premium E VS1 62.6 59.0 2837.0 5.69 5.66 3.55
0.7 Ideal E VS1 61.8 56.0 2837.0 5.74 5.69 3.53
0.71 Ideal F SI1 59.8 53.0 2838.0 5.86 5.82 3.49
0.76 Very Good H SI1 60.9 55.0 2838.0 5.92 5.94 3.61
0.82 Fair F SI1 64.9 58.0 2838.0 5.83 5.79 3.77
0.72 Premium F VS1 58.8 60.0 2838.0 5.91 5.89 3.47
0.7 Premium F VS2 62.3 58.0 2838.0 5.72 5.64 3.54
0.7 Premium F VS2 61.7 58.0 2838.0 5.69 5.63 3.49
0.7 Premium G VS1 62.6 55.0 2838.0 5.73 5.64 3.56
0.7 Premium F VS2 59.4 61.0 2838.0 5.83 5.79 3.45
0.7 Very Good E SI1 63.5 59.0 2838.0 5.53 5.49 3.5
0.7 Premium F VS2 60.9 61.0 2838.0 5.71 5.66 3.46
0.7 Premium F VS2 59.5 58.0 2838.0 5.85 5.75 3.45
0.7 Premium G VS1 63.0 60.0 2838.0 5.64 5.57 3.53
0.74 Very Good E SI1 60.0 57.0 2839.0 5.85 5.89 3.52
0.71 Ideal F VS1 61.5 57.0 2839.0 5.74 5.71 3.52
0.7 Ideal F VS1 61.6 54.0 2839.0 5.75 5.72 3.53
0.71 Ideal F VS1 62.1 55.0 2839.0 5.82 5.68 3.57
0.71 Premium F VS1 59.1 61.0 2839.0 5.84 5.81 3.44
0.71 Premium F VS1 59.0 60.0 2839.0 5.82 5.8 3.43
0.71 Premium F VS1 60.5 58.0 2839.0 5.75 5.72 3.47
0.7 Ideal F VS1 62.4 53.0 2839.0 5.73 5.71 3.57
0.73 Ideal G VS2 61.8 54.0 2839.0 5.8 5.82 3.59
0.7 Ideal E VS2 62.1 54.0 2839.0 5.69 5.72 3.54
0.7 Ideal G VS1 61.3 57.0 2839.0 5.71 5.74 3.51
0.71 Premium G VVS2 60.3 58.0 2839.0 5.82 5.78 3.5
0.71 Premium F VS1 59.2 58.0 2839.0 5.87 5.82 3.46
0.79 Premium G VS2 59.3 62.0 2839.0 6.09 6.01 3.59
0.71 Premium F VS1 62.7 59.0 2839.0 5.7 5.62 3.55
0.77 Very Good H VS1 61.0 60.0 2840.0 5.9 5.87 3.59
0.75 Very Good F SI2 59.8 56.0 2840.0 5.85 5.92 3.52
0.7 Ideal F SI1 61.0 56.0 2840.0 5.75 5.8 3.52
0.71 Premium F VS2 59.3 56.0 2840.0 5.88 5.82 3.47
0.92 Ideal D SI2 61.9 56.0 2840.0 6.27 6.2 3.86
0.83 Premium F SI2 61.4 59.0 2840.0 6.08 6.04 3.72
0.7 Premium H VVS1 59.2 60.0 2840.0 5.87 5.78 3.45
0.73 Premium F VS2 60.3 59.0 2841.0 5.9 5.87 3.55
0.71 Very Good D VS1 63.4 55.0 2841.0 5.69 5.61 3.58
0.73 Very Good D SI1 63.9 57.0 2841.0 5.66 5.71 3.63
0.82 Ideal F SI2 61.7 53.0 2841.0 6.0 6.12 3.74
0.82 Ideal F SI2 62.3 56.0 2841.0 5.96 6.02 3.73
0.82 Very Good F SI2 59.7 57.0 2841.0 6.12 6.14 3.66
0.52 Ideal F VVS1 61.2 56.0 2841.0 5.19 5.21 3.18
1.0 Premium F I1 58.9 60.0 2841.0 6.6 6.55 3.87
0.95 Fair G SI1 66.7 56.0 2841.0 6.16 6.03 4.06
0.73 Ideal D SI1 61.4 57.0 2841.0 5.76 5.8 3.55
0.73 Premium F VS2 59.9 59.0 2841.0 5.87 5.77 3.5
0.73 Premium G VS1 61.4 58.0 2841.0 5.82 5.77 3.56
0.8 Ideal I VS1 62.6 54.0 2842.0 5.92 5.96 3.72
0.7 Premium F VS2 58.7 61.0 2842.0 5.8 5.72 3.38
0.7 Very Good E VS2 60.2 62.0 2843.0 5.71 5.75 3.45
0.7 Very Good E VS2 62.7 58.0 2843.0 5.65 5.67 3.55
0.71 Very Good E VS2 59.4 58.0 2843.0 5.76 5.82 3.44
0.81 Very Good F SI2 63.2 58.0 2843.0 5.91 5.92 3.74
0.71 Very Good D SI1 61.5 58.0 2843.0 5.73 5.79 3.54
0.73 Ideal G VVS2 61.3 57.0 2843.0 5.81 5.84 3.57
0.73 Very Good F VS1 61.8 59.0 2843.0 5.73 5.79 3.56
0.72 Ideal E VS2 62.0 57.0 2843.0 5.71 5.74 3.55
0.81 Ideal F SI2 62.1 57.0 2843.0 5.91 5.95 3.68
0.71 Ideal G VVS2 60.7 57.0 2843.0 5.81 5.78 3.52
0.73 Very Good E SI1 57.7 61.0 2844.0 5.92 5.96 3.43
0.7 Very Good E VS1 62.0 59.0 2844.0 5.65 5.68 3.51
1.01 Ideal I I1 61.5 57.0 2844.0 6.45 6.46 3.97
1.01 Good I I1 63.1 57.0 2844.0 6.35 6.39 4.02
0.79 Ideal H VS2 62.5 57.0 2844.0 5.91 5.93 3.7
0.7 Very Good E VS2 61.8 59.0 2845.0 5.65 5.68 3.5
0.7 Very Good E VS2 58.9 60.0 2845.0 5.83 5.85 3.44
0.8 Good H VS2 63.4 60.0 2845.0 5.92 5.82 3.72
1.27 Premium H SI2 59.3 61.0 2845.0 7.12 7.05 4.2
0.79 Ideal D SI1 61.5 56.0 2846.0 5.96 5.91 3.65
0.72 Very Good F VS1 60.2 59.0 2846.0 5.79 5.84 3.5
0.73 Ideal H VVS2 61.6 56.0 2846.0 5.79 5.84 3.58
1.01 Fair H SI2 65.4 59.0 2846.0 6.3 6.26 4.11
1.01 Good H I1 64.2 61.0 2846.0 6.25 6.18 3.99
0.73 Ideal E SI1 59.1 59.0 2846.0 5.92 5.95 3.51
0.7 Ideal E SI1 61.6 57.0 2846.0 5.71 5.76 3.53
0.7 Good F VS2 59.1 61.0 2846.0 5.76 5.84 3.43
0.77 Premium E SI1 62.9 59.0 2846.0 5.84 5.79 3.66
0.77 Premium G VS2 61.3 60.0 2846.0 5.91 5.81 3.59
0.77 Premium G VS1 61.4 58.0 2846.0 5.94 5.89 3.63
0.84 Very Good H SI1 61.2 57.0 2847.0 6.1 6.12 3.74
0.72 Ideal E SI1 60.3 57.0 2847.0 5.83 5.85 3.52
0.76 Premium D SI1 61.1 59.0 2847.0 5.93 5.88 3.61
0.7 Very Good G VVS2 62.9 59.0 2848.0 5.61 5.68 3.55
0.54 Ideal D VVS2 61.5 55.0 2848.0 5.25 5.29 3.24
0.75 Fair D SI2 64.6 57.0 2848.0 5.74 5.72 3.7
0.79 Good E SI1 64.1 54.0 2849.0 5.86 5.84 3.75
0.74 Very Good E VS1 63.1 58.0 2849.0 5.75 5.73 3.62
0.7 Very Good E VS2 61.0 60.0 2850.0 5.74 5.77 3.51
0.7 Ideal F VS2 60.8 59.0 2850.0 5.69 5.79 3.49
0.75 Ideal J SI1 61.5 56.0 2850.0 5.83 5.87 3.6
1.2 Very Good H I1 63.1 60.0 2850.0 6.75 6.67 4.23
0.8 Very Good F SI1 63.4 57.0 2851.0 5.89 5.82 3.71
0.66 Ideal D VS1 62.1 56.0 2851.0 5.54 5.57 3.45
0.87 Very Good F SI2 61.0 63.0 2851.0 6.22 6.07 3.75
0.86 Premium H SI1 62.7 59.0 2851.0 6.04 5.98 3.77
0.74 Ideal F SI1 61.0 57.0 2851.0 5.85 5.81 3.56
0.58 Very Good E IF 60.6 59.0 2852.0 5.37 5.43 3.27
0.78 Ideal I VS1 61.5 57.0 2852.0 5.88 5.92 3.63
0.74 Ideal G SI1 61.3 55.0 2852.0 5.85 5.86 3.59
0.73 Ideal E SI1 62.7 55.0 2852.0 5.7 5.79 3.6
0.91 Very Good I SI1 63.5 57.0 2852.0 6.12 6.07 3.87
0.71 Premium F VS2 62.6 58.0 2853.0 5.67 5.7 3.56
0.71 Good G VS1 63.5 55.0 2853.0 5.64 5.66 3.59
0.79 Ideal D SI2 62.8 57.0 2853.0 5.9 5.85 3.69
0.79 Premium D SI2 60.0 60.0 2853.0 6.07 6.03 3.63
0.71 Premium E SI1 62.7 58.0 2853.0 5.73 5.66 3.57
0.82 Premium I VS1 61.9 58.0 2853.0 5.99 5.97 3.7
0.78 Very Good H VS1 61.9 57.1 2854.0 5.87 5.95 3.66
0.7 Very Good E VS1 62.4 56.0 2854.0 5.64 5.7 3.54
1.12 Premium H I1 59.1 61.0 2854.0 6.78 6.75 4.0
0.73 Premium E VS2 62.0 57.0 2854.0 5.86 5.76 3.6
0.91 Fair J VS2 64.4 62.0 2854.0 6.06 6.03 3.89
0.91 Fair J VS2 65.4 60.0 2854.0 6.04 6.0 3.94
0.91 Good J VS2 64.2 58.0 2854.0 6.12 6.09 3.92
0.91 Fair H SI1 65.8 58.0 2854.0 6.04 6.01 3.96
0.7 Premium E VS1 58.4 59.0 2854.0 5.91 5.83 3.43
0.68 Premium F VVS2 61.7 57.0 2854.0 5.67 5.64 3.49
0.73 Very Good F VS2 62.5 57.0 2855.0 5.7 5.75 3.58
1.03 Good J SI1 63.6 57.0 2855.0 6.38 6.29 4.03
0.74 Premium D VS2 62.4 57.0 2855.0 5.8 5.74 3.6
0.98 Fair E SI2 53.3 67.0 2855.0 6.82 6.74 3.61
1.02 Fair I SI1 53.0 63.0 2856.0 6.84 6.77 3.66
1.0 Fair G SI2 67.8 61.0 2856.0 5.96 5.9 4.02
1.02 Ideal H SI2 61.6 55.0 2856.0 6.49 6.43 3.98
0.6 Ideal F VVS2 60.8 57.0 2856.0 5.44 5.49 3.32
0.8 Ideal G SI2 61.6 56.0 2856.0 5.97 6.01 3.69
0.97 Ideal F I1 60.7 56.0 2856.0 6.43 6.41 3.9
1.0 Fair I SI1 67.9 62.0 2856.0 6.19 6.03 4.15
0.26 Ideal E VS1 62.3 57.0 556.0 4.05 4.08 2.53
0.26 Ideal E VS1 62.1 56.0 556.0 4.09 4.12 2.55
0.36 Ideal H SI1 61.9 55.0 556.0 4.57 4.59 2.83
0.34 Good G VS2 57.5 61.0 556.0 4.6 4.66 2.66
0.34 Good E SI1 63.3 57.0 556.0 4.44 4.47 2.82
0.34 Good E SI1 63.5 55.0 556.0 4.44 4.47 2.83
0.34 Good E SI1 63.4 55.0 556.0 4.44 4.46 2.82
0.34 Very Good G VS2 59.6 62.0 556.0 4.54 4.56 2.71
0.34 Ideal E SI1 62.2 54.0 556.0 4.47 4.5 2.79
0.32 Good E VS2 64.1 54.0 556.0 4.34 4.37 2.79
0.31 Ideal I VVS1 61.6 55.0 557.0 4.36 4.41 2.7
0.31 Ideal I VVS1 61.3 56.0 557.0 4.36 4.38 2.68
0.31 Ideal I VVS1 62.3 54.0 557.0 4.37 4.4 2.73
0.31 Ideal I VVS1 62.0 54.0 557.0 4.37 4.4 2.72
0.31 Ideal I VVS1 62.7 53.0 557.0 4.33 4.35 2.72
0.31 Ideal I VVS1 62.2 53.0 557.0 4.36 4.38 2.72
0.31 Ideal G VS2 62.2 53.6 557.0 4.32 4.35 2.7
0.31 Ideal H VS1 61.6 54.8 557.0 4.35 4.37 2.69
0.31 Ideal H VS1 61.8 54.2 557.0 4.33 4.37 2.69
0.33 Premium G SI2 59.4 59.0 557.0 4.52 4.5 2.68
0.33 Premium F SI2 62.3 58.0 557.0 4.43 4.4 2.75
0.33 Premium G SI2 62.6 58.0 557.0 4.42 4.4 2.76
0.33 Ideal G SI2 61.9 56.0 557.0 4.45 4.41 2.74
0.33 Premium F SI2 63.0 58.0 557.0 4.42 4.4 2.78
0.33 Premium J VS1 62.8 58.0 557.0 4.41 4.38 2.76
0.33 Premium J VS1 61.5 61.0 557.0 4.46 4.39 2.72
0.33 Ideal J VS1 62.1 55.0 557.0 4.44 4.41 2.75
0.33 Ideal I SI1 63.0 57.0 557.0 4.39 4.37 2.76
0.33 Good I SI1 63.6 53.0 557.0 4.43 4.4 2.81
0.33 Premium I SI1 60.4 59.0 557.0 4.54 4.5 2.73
1.0 Fair H SI2 66.1 56.0 2856.0 6.21 5.97 4.04
0.77 Premium F SI1 60.8 59.0 2856.0 5.92 5.86 3.58
0.77 Premium F SI1 61.0 58.0 2856.0 5.94 5.9 3.61
0.7 Good E VVS2 60.1 63.0 2857.0 5.68 5.71 3.42
0.9 Very Good G SI2 63.1 58.0 2857.0 6.08 6.02 3.82
0.72 Ideal E SI1 62.3 57.0 2857.0 5.76 5.7 3.57
0.9 Premium I VS2 61.9 59.0 2857.0 6.2 6.14 3.82
0.72 Premium E SI1 62.1 58.0 2857.0 5.76 5.73 3.57
0.7 Ideal G VVS2 62.1 56.0 2858.0 5.63 5.71 3.52
0.81 Very Good F SI1 61.3 57.0 2858.0 6.02 6.05 3.7
0.81 Very Good F SI1 61.7 57.0 2858.0 6.0 6.05 3.72
0.71 Premium E VS2 61.0 60.0 2858.0 5.76 5.69 3.49
0.7 Premium E VS2 61.4 59.0 2858.0 5.73 5.7 3.51
0.71 Premium E VS2 61.5 60.0 2858.0 5.76 5.68 3.52
0.71 Very Good E VS2 63.5 59.0 2858.0 5.68 5.59 3.58
0.92 Premium J SI1 62.9 58.0 2858.0 6.22 6.18 3.9
0.76 Ideal E SI1 62.7 54.0 2858.0 5.88 5.83 3.67
0.73 Ideal D SI1 61.5 56.0 2858.0 5.84 5.8 3.58
0.71 Premium D VS2 60.4 62.0 2858.0 5.74 5.72 3.46
0.7 Good E VVS2 63.6 62.0 2858.0 5.61 5.58 3.56
0.9 Fair G SI2 64.5 56.0 2858.0 6.06 6.0 3.89
0.71 Fair D VS2 56.9 65.0 2858.0 5.89 5.84 3.34
0.7 Ideal D VS2 61.0 57.0 2859.0 5.76 5.74 3.51
0.7 Premium D VS2 62.4 56.0 2859.0 5.72 5.66 3.55
0.77 Premium F VS1 60.9 60.0 2859.0 5.91 5.88 3.59
0.71 Ideal G VS1 61.5 56.0 2859.0 5.74 5.78 3.54
0.7 Premium D VS2 59.6 61.0 2859.0 5.8 5.77 3.45
0.75 Fair F VS1 55.8 70.0 2859.0 6.09 5.98 3.37
0.83 Premium E SI2 59.2 60.0 2859.0 6.17 6.12 3.64
0.71 Very Good F VS2 61.3 61.0 2860.0 5.68 5.73 3.5
0.9 Very Good J SI2 63.6 58.0 2860.0 6.07 6.1 3.87
0.6 Ideal E VVS2 61.9 54.9 2860.0 5.41 5.44 3.35
0.71 Premium D VS1 62.9 57.0 2860.0 5.66 5.6 3.54
0.53 Ideal F VVS1 61.4 57.0 2860.0 5.23 5.2 3.2
0.71 Premium D SI1 60.7 58.0 2861.0 5.95 5.78 3.56
0.62 Ideal G VVS2 61.6 56.0 2861.0 5.45 5.48 3.37
0.62 Ideal G VVS2 61.6 56.0 2861.0 5.48 5.51 3.38
0.9 Premium I SI1 63.0 58.0 2861.0 6.09 6.01 3.81
0.62 Fair F IF 60.1 61.0 2861.0 5.53 5.56 3.33
0.82 Premium E SI2 61.7 59.0 2861.0 6.01 5.98 3.7
0.66 Premium D VS1 61.0 58.0 2861.0 5.67 5.57 3.43
0.7 Very Good D SI1 62.5 55.0 2862.0 5.67 5.72 3.56
0.8 Very Good F SI1 62.6 58.0 2862.0 5.9 5.92 3.7
0.8 Very Good D SI2 62.5 59.0 2862.0 5.88 5.92 3.69
0.79 Premium F SI1 62.3 54.0 2862.0 5.97 5.91 3.7
0.71 Very Good F VVS1 63.2 60.0 2862.0 5.65 5.61 3.56
0.7 Ideal H VS2 61.1 57.0 2862.0 5.71 5.74 3.5
0.7 Very Good E VS2 58.7 63.0 2862.0 5.73 5.69 3.35
0.79 Premium H VS1 60.0 60.0 2862.0 6.07 5.99 3.64
0.7 Premium E VS2 59.5 59.0 2862.0 5.82 5.77 3.45
1.22 Premium E I1 60.9 57.0 2862.0 6.93 6.88 4.21
1.01 Fair E SI2 67.6 57.0 2862.0 6.21 6.11 4.18
0.73 Premium E VS2 62.5 61.0 2862.0 5.78 5.64 3.59
0.91 Good I VS2 64.3 58.0 2863.0 6.05 6.09 3.9
0.71 Ideal D SI1 60.8 56.0 2863.0 5.8 5.77 3.52
0.83 Premium G SI1 62.3 58.0 2863.0 6.01 5.97 3.73
0.84 Premium F SI2 62.3 59.0 2863.0 6.06 6.01 3.76
0.71 Premium D SI1 61.0 61.0 2863.0 5.82 5.75 3.53
0.71 Premium D SI1 59.7 59.0 2863.0 5.82 5.8 3.47
0.71 Premium D SI1 61.7 56.0 2863.0 5.8 5.68 3.54
0.71 Ideal D SI1 61.7 57.0 2863.0 5.75 5.7 3.53
0.71 Premium D SI1 61.4 58.0 2863.0 5.79 5.75 3.54
0.71 Premium D SI1 60.6 58.0 2863.0 5.79 5.77 3.5
0.91 Premium J SI1 59.5 62.0 2863.0 6.4 6.18 3.74
0.9 Premium J VS2 59.8 62.0 2863.0 6.24 6.21 3.72
0.71 Premium H VVS2 61.5 62.0 2863.0 5.74 5.68 3.51
0.71 Premium E SI1 59.1 61.0 2863.0 5.84 5.8 3.44
0.72 Ideal F VS2 59.5 57.0 2863.0 5.91 5.86 3.5
0.72 Premium E SI1 60.9 60.0 2863.0 5.78 5.74 3.51
0.71 Ideal E VS2 61.0 55.0 2863.0 5.79 5.75 3.52
0.81 Ideal E SI2 60.3 57.0 2864.0 6.07 6.04 3.65
0.83 Very Good I VS2 61.6 58.0 2865.0 6.05 6.07 3.73
0.73 Premium D SI1 60.8 55.0 2865.0 5.87 5.81 3.55
0.56 Very Good D VVS1 62.0 56.0 2866.0 5.25 5.3 3.27
0.56 Very Good D VVS1 61.8 55.0 2866.0 5.27 5.31 3.27
0.71 Ideal E VS1 62.2 55.0 2866.0 5.74 5.7 3.56
0.7 Ideal H VVS1 62.3 58.0 2866.0 5.66 5.7 3.54
0.96 Premium I SI1 61.3 58.0 2866.0 6.39 6.3 3.89
0.71 Very Good H VVS1 62.9 57.0 2867.0 5.67 5.69 3.57
0.7 Ideal D VS2 62.4 57.0 2867.0 5.68 5.61 3.52
0.71 Ideal H VVS1 60.4 57.0 2867.0 5.78 5.81 3.5
0.8 Premium H VS2 61.2 53.0 2867.0 6.05 5.98 3.68
0.95 Premium F SI2 58.4 57.0 2867.0 6.49 6.41 3.77
0.82 Ideal F SI2 62.3 56.0 2867.0 5.99 5.95 3.72
0.52 Ideal F VVS1 61.2 56.0 2867.0 5.21 5.19 3.18
0.82 Ideal F SI2 61.7 53.0 2867.0 6.12 6.0 3.74
0.82 Ideal F SI2 62.3 56.0 2867.0 6.02 5.96 3.73
0.82 Premium F SI2 59.7 57.0 2867.0 6.14 6.12 3.66
0.8 Ideal G SI1 61.3 57.0 2867.0 5.96 5.91 3.64
0.96 Fair F SI2 68.2 61.0 2867.0 6.07 5.88 4.1
0.72 Ideal I VS1 62.4 55.0 2868.0 5.72 5.75 3.58
0.62 Ideal G IF 60.5 57.0 2868.0 5.52 5.56 3.35
0.79 Premium E SI2 61.0 58.0 2868.0 5.96 5.9 3.62
0.75 Very Good E SI1 63.1 56.0 2868.0 5.78 5.7 3.62
1.08 Premium D I1 61.9 60.0 2869.0 6.55 6.48 4.03
0.72 Ideal E SI1 60.8 55.0 2869.0 5.77 5.84 3.53
0.62 Ideal G IF 61.8 56.0 2869.0 5.43 5.47 3.37
0.73 Ideal G VVS2 61.3 57.0 2869.0 5.84 5.81 3.57
0.72 Ideal H VVS2 60.9 57.0 2869.0 5.79 5.77 3.52
0.52 Premium F VVS2 61.8 60.0 2870.0 5.16 5.13 3.18
0.83 Ideal E SI2 62.2 57.0 2870.0 6.0 6.05 3.75
0.64 Premium E VVS2 62.1 58.0 2870.0 5.56 5.51 3.44
0.8 Ideal G SI1 62.5 57.0 2870.0 5.94 5.9 3.7
0.74 Ideal H SI1 62.1 56.0 2870.0 5.77 5.83 3.6
0.72 Ideal F SI1 61.5 56.0 2870.0 5.72 5.79 3.54
0.82 Ideal H VS2 59.5 57.0 2870.0 6.12 6.09 3.63
0.73 Premium E VS1 61.3 59.0 2870.0 5.81 5.78 3.55
1.04 Premium I I1 61.6 61.0 2870.0 6.47 6.45 3.98
0.73 Very Good E SI1 61.3 58.0 2871.0 5.76 5.83 3.55
0.73 Good E SI1 63.6 57.0 2871.0 5.7 5.72 3.63
0.9 Premium J SI1 62.8 59.0 2871.0 6.13 6.03 3.82
0.75 Ideal I SI1 61.8 55.0 2871.0 5.83 5.85 3.61
0.79 Ideal G SI1 62.6 55.0 2871.0 5.91 5.95 3.71
0.7 Good D SI1 62.5 56.7 2872.0 5.59 5.62 3.51
0.75 Very Good D SI1 60.7 55.0 2872.0 5.87 5.92 3.58
1.02 Ideal I I1 61.7 56.0 2872.0 6.44 6.49 3.99
0.7 Very Good G SI2 59.0 62.0 2872.0 5.79 5.81 3.42
0.7 Ideal D SI1 61.8 56.0 2872.0 5.63 5.73 3.51
0.7 Good E SI1 61.4 64.0 2872.0 5.66 5.71 3.49
0.7 Ideal D SI1 61.4 54.0 2872.0 5.71 5.75 3.52
0.7 Ideal D SI1 60.7 56.0 2872.0 5.72 5.75 3.48
0.7 Very Good D SI1 60.2 60.0 2872.0 5.75 5.82 3.48
0.72 Very Good E VS2 58.3 57.0 2872.0 5.89 5.94 3.45
0.74 Ideal E SI1 62.3 58.0 2872.0 5.74 5.78 3.59
0.84 Good G SI1 65.1 55.0 2872.0 5.88 5.97 3.86
0.76 Very Good F VS2 62.0 58.0 2873.0 5.8 5.86 3.62
0.77 Very Good E SI1 63.2 58.0 2873.0 5.8 5.84 3.68
0.76 Ideal E SI2 62.8 56.0 2873.0 5.78 5.82 3.64
1.0 Ideal I SI2 61.7 56.0 2873.0 6.45 6.41 3.97
1.0 Fair H SI1 65.5 62.0 2873.0 6.14 6.07 4.0
0.9 Fair I SI1 65.7 58.0 2873.0 6.03 6.0 3.95
0.9 Premium J SI1 61.8 58.0 2873.0 6.16 6.13 3.8
0.9 Good J SI1 64.0 61.0 2873.0 6.0 5.96 3.83
0.9 Fair I SI1 65.3 61.0 2873.0 5.98 5.94 3.89
0.9 Fair I SI1 65.8 56.0 2873.0 6.01 5.96 3.94
0.9 Premium J SI1 60.9 61.0 2873.0 6.26 6.22 3.8
0.78 Premium F VS2 62.6 58.0 2874.0 5.91 5.82 3.67
0.71 Premium D VS2 61.2 59.0 2874.0 5.69 5.74 3.5
0.7 Premium F VS1 59.0 59.0 2874.0 5.79 5.77 3.41
0.7 Premium F VS1 60.8 62.0 2874.0 5.71 5.67 3.46
0.7 Premium G VVS2 61.8 58.0 2874.0 5.67 5.63 3.49
0.7 Ideal F VS1 61.0 55.0 2874.0 5.77 5.73 3.51
0.7 Ideal F VS1 61.6 55.0 2874.0 5.75 5.71 3.53
0.7 Ideal F VS1 62.4 56.0 2874.0 5.69 5.65 3.54
0.7 Premium G VVS2 62.9 59.0 2874.0 5.68 5.61 3.55
1.0 Fair H SI2 67.7 60.0 2875.0 6.11 5.98 4.09
0.77 Ideal H SI1 62.4 56.0 2875.0 5.84 5.9 3.66
1.0 Fair J VS1 65.5 55.0 2875.0 6.3 6.25 4.11
1.0 Fair I SI1 66.3 61.0 2875.0 6.08 6.03 4.01
1.0 Fair H SI2 69.5 55.0 2875.0 6.17 6.1 4.26
0.73 Premium E VS1 62.6 60.0 2876.0 5.68 5.75 3.58
0.79 Premium E VS2 60.6 53.0 2876.0 6.04 5.98 3.64
0.72 Very Good H VS1 62.2 54.0 2877.0 5.74 5.76 3.57
0.71 Ideal E VS1 62.4 56.0 2877.0 5.75 5.7 3.57
0.74 Ideal G VS2 62.3 55.0 2877.0 5.8 5.83 3.62
0.7 Good H VVS1 62.7 56.0 2877.0 5.6 5.66 3.53
0.7 Good F VS1 59.1 62.0 2877.0 5.82 5.86 3.44
0.79 Very Good F SI1 62.8 59.0 2878.0 5.86 5.89 3.69
0.79 Very Good F SI1 62.7 60.0 2878.0 5.82 5.89 3.67
0.79 Very Good D SI2 59.7 58.0 2878.0 6.0 6.07 3.6
0.71 Ideal I VS2 61.5 55.0 2878.0 5.76 5.78 3.55
0.79 Ideal F SI1 62.8 56.0 2878.0 5.88 5.9 3.7
0.73 Very Good F SI1 61.4 56.0 2879.0 5.81 5.86 3.58
0.63 Premium E IF 60.3 62.0 2879.0 5.55 5.53 3.34
0.7 Premium F VS1 60.4 60.0 2879.0 5.73 5.7 3.45
0.71 Premium F VS1 62.7 58.0 2879.0 5.71 5.67 3.57
0.84 Ideal G SI2 61.0 56.0 2879.0 6.13 6.1 3.73
0.84 Ideal G SI2 62.3 55.0 2879.0 6.08 6.03 3.77
1.02 Ideal J SI2 60.3 54.0 2879.0 6.53 6.5 3.93
0.72 Fair F VS1 56.9 69.0 2879.0 5.93 5.77 3.33
0.72 Ideal F VS1 62.0 56.0 2879.0 5.76 5.73 3.56
0.92 Very Good J SI2 58.7 61.0 2880.0 6.34 6.43 3.75
0.74 Very Good D SI1 63.9 57.0 2880.0 5.72 5.74 3.66
0.7 Ideal H VVS1 62.0 55.0 2881.0 5.74 5.71 3.55
0.71 Very Good E VS2 60.0 59.0 2881.0 5.84 5.83 3.5
1.05 Premium H I1 62.0 59.0 2881.0 6.5 6.47 4.02
0.7 Very Good H IF 62.8 56.0 2882.0 5.62 5.65 3.54
0.54 Ideal F VVS1 61.8 56.0 2882.0 5.23 5.26 3.24
0.73 Premium F VS2 59.9 58.0 2882.0 5.87 5.84 3.51
0.88 Fair F SI1 56.6 65.0 2882.0 6.39 6.32 3.6
0.73 Premium F VS2 58.7 57.0 2882.0 5.97 5.92 3.49
0.72 Ideal D SI1 61.8 56.0 2883.0 5.75 5.81 3.57
0.9 Good H SI2 62.7 64.0 2883.0 6.09 6.0 3.79
0.9 Fair H SI2 65.0 61.0 2883.0 6.01 5.96 3.89
1.03 Fair I SI2 65.3 55.0 2884.0 6.32 6.27 4.11
0.84 Very Good F SI1 63.8 57.0 2885.0 5.95 6.0 3.81
1.01 Premium I SI1 62.7 60.0 2885.0 6.36 6.27 3.96
0.77 Ideal D SI2 61.5 55.0 2885.0 5.9 5.93 3.64
0.8 Fair E SI1 56.3 63.0 2885.0 6.22 6.14 3.48
0.9 Fair D SI2 66.9 57.0 2885.0 6.02 5.9 3.99
0.73 Ideal E SI1 61.4 56.0 2886.0 5.79 5.81 3.56
0.72 Ideal E SI1 62.7 55.0 2886.0 5.64 5.69 3.55
0.71 Very Good D SI1 62.4 54.0 2887.0 5.71 5.79 3.59
0.7 Premium E VS1 62.6 59.0 2887.0 5.66 5.69 3.55
0.79 Ideal I VS1 61.7 59.0 2888.0 5.93 5.96 3.67
0.72 Very Good G VVS2 62.5 58.0 2889.0 5.68 5.72 3.56
0.7 Very Good E VS2 63.5 54.0 2889.0 5.62 5.66 3.58
0.7 Very Good F VS1 62.2 58.0 2889.0 5.64 5.75 3.54
0.9 Good H SI2 63.5 58.0 2889.0 6.09 6.14 3.88
0.71 Very Good F VS1 62.8 56.0 2889.0 5.69 5.72 3.58
0.5 Ideal E VVS2 62.2 54.0 2889.0 5.08 5.12 3.17
0.5 Ideal E VVS2 62.2 54.0 2889.0 5.09 5.11 3.17
0.74 Ideal F SI1 61.2 56.0 2889.0 5.83 5.87 3.58
0.77 Premium F VS2 61.8 56.0 2889.0 5.94 5.9 3.66
0.77 Premium E SI1 59.8 61.0 2889.0 5.99 5.95 3.57
0.8 Ideal F SI1 61.5 54.0 2890.0 6.07 6.0 3.71
0.8 Ideal F SI1 62.4 57.0 2890.0 5.9 5.87 3.67
0.8 Premium F SI1 61.5 60.0 2890.0 5.97 5.94 3.66
0.8 Good F SI1 63.8 59.0 2890.0 5.87 5.83 3.73
0.66 Ideal G VVS1 61.5 56.0 2890.0 5.61 5.58 3.44
0.71 Very Good E VS2 61.2 58.0 2891.0 5.71 5.79 3.52
0.71 Ideal F VS2 61.2 56.0 2891.0 5.73 5.77 3.52
0.71 Ideal E VS2 61.6 56.0 2891.0 5.74 5.76 3.54
0.71 Ideal E VS2 62.7 56.0 2891.0 5.71 5.75 3.59
0.72 Ideal D SI1 61.1 56.0 2891.0 5.78 5.81 3.54
0.71 Good D VS2 62.3 61.0 2891.0 5.7 5.73 3.56
0.86 Ideal H SI2 61.8 55.0 2892.0 6.12 6.14 3.79
1.19 Fair H I1 65.1 59.0 2892.0 6.62 6.55 4.29
0.71 Very Good F VS1 62.6 55.0 2893.0 5.66 5.71 3.56
0.82 Very Good G SI2 62.5 56.0 2893.0 5.99 6.04 3.76
0.71 Ideal G VVS2 61.5 57.0 2893.0 5.73 5.75 3.53
0.75 Ideal F VS2 62.5 57.0 2893.0 5.78 5.83 3.63
0.7 Very Good H VVS1 59.2 60.0 2893.0 5.87 5.78 3.45
0.8 Ideal G SI2 62.5 55.0 2893.0 5.89 5.92 3.69
0.82 Good G SI2 59.9 62.0 2893.0 6.02 6.04 3.61
0.82 Very Good G SI1 63.4 55.0 2893.0 6.0 5.93 3.78
0.82 Premium G SI1 59.9 59.0 2893.0 6.09 6.06 3.64
0.81 Very Good E SI2 62.4 57.0 2894.0 5.91 5.99 3.71
0.81 Ideal G SI2 62.2 57.0 2894.0 5.96 6.0 3.72
0.76 Ideal F SI1 61.4 56.0 2894.0 5.88 5.92 3.62
0.71 Very Good G VS2 60.9 56.0 2895.0 5.75 5.78 3.51
0.7 Very Good F VS1 61.8 59.0 2895.0 5.66 5.76 3.53
0.7 Ideal G VVS2 62.1 53.0 2895.0 5.71 5.75 3.56
0.74 Very Good G VS1 59.8 58.0 2896.0 5.85 5.89 3.51
0.77 Very Good G VS2 61.3 60.0 2896.0 5.81 5.91 3.59
0.77 Very Good G VS2 58.3 63.0 2896.0 6.0 6.05 3.51
0.53 Ideal F VVS1 61.6 56.0 2896.0 5.18 5.24 3.21
0.79 Ideal D SI1 61.5 56.0 2896.0 5.91 5.96 3.65
0.73 Ideal E SI2 61.5 55.0 2896.0 5.82 5.86 3.59
0.77 Ideal D SI2 62.1 56.0 2896.0 5.83 5.89 3.64
0.77 Premium E SI1 60.9 58.0 2896.0 5.94 5.88 3.6
1.01 Very Good I I1 63.1 57.0 2896.0 6.39 6.35 4.02
1.01 Ideal I I1 61.5 57.0 2896.0 6.46 6.45 3.97
0.6 Very Good D VVS2 60.6 57.0 2897.0 5.48 5.51 3.33
0.76 Premium E SI1 61.1 58.0 2897.0 5.91 5.85 3.59
0.54 Ideal D VVS2 61.4 52.0 2897.0 5.3 5.34 3.26
0.72 Ideal E SI1 62.5 55.0 2897.0 5.69 5.74 3.57
0.72 Good F VS1 59.4 61.0 2897.0 5.82 5.89 3.48
0.74 Premium D VS2 61.8 58.0 2897.0 5.81 5.77 3.58
1.12 Premium J SI2 60.6 59.0 2898.0 6.68 6.61 4.03

Step 2. Understand the data

Let's examine the data to get a better understanding of what is there. We only examine a couple of features (columns), but it gives an idea of the type of exploration you might do to understand a new dataset.

For more examples of using Databricks's visualization (even across languages) see https://docs.databricks.com/user-guide/visualizations/index.html NOW.

We can see that we have a mix of

  • categorical features (cut, color, clarity) and
  • continuous features (depth, x, y, z).
Let's first look at the categorical features.

You can also select one or more individual columns using so-called DataFrame API.

Let us select the column cut from diamondsDF and create a new DataFrame called cutsDF and then display it as follows:

val cutsDF = diamondsDF.select("cut") // Shift+Enter

cutsDF: org.apache.spark.sql.DataFrame = [cut: string]

cutsDF.show(10) // Ctrl+Enter

+---------+
|      cut|
+---------+
|    Ideal|
|  Premium|
|     Good|
|  Premium|
|     Good|
|Very Good|
|Very Good|
|Very Good|
|     Fair|
|Very Good|
+---------+
only showing top 10 rows

Let us use distinct to find the distinct types of cut's in the dataset.

// View distinct diamond cuts in dataset
val cutsDistinctDF = diamondsDF.select("cut").distinct()

cutsDistinctDF: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [cut: string]

cutsDistinctDF.show()

+---------+
|      cut|
+---------+
|  Premium|
|    Ideal|
|     Good|
|     Fair|
|Very Good|
+---------+

Clearly, there are just 5 kinds of cuts.

// View distinct diamond colors in dataset
val colorsDistinctDF = diamondsDF.select("color").distinct() //.collect()
colorsDistinctDF.show()

+-----+
|color|
+-----+
|    F|
|    E|
|    D|
|    J|
|    G|
|    I|
|    H|
+-----+

colorsDistinctDF: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [color: string]

// View distinct diamond clarities in dataset
val claritiesDistinctDF = diamondsDF.select("clarity").distinct() // .collect()
claritiesDistinctDF.show()

+-------+
|clarity|
+-------+
|   VVS2|
|    SI1|
|     IF|
|     I1|
|   VVS1|
|    VS2|
|    SI2|
|    VS1|
+-------+

claritiesDistinctDF: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [clarity: string]

We can examine the distribution of a particular feature by using display(),

You Try!

  1. Click on the chart icon and Plot Options, and setting:
  • Value=<id>
  • Series groupings='cut'
  • and Aggregation=COUNT.
  1. You can also try this using columns "color" and "clarity"
display(diamondsDF.select("cut"))
cut
Ideal
Premium
Good
Premium
Good
Very Good
Very Good
Very Good
Fair
Very Good
Good
Ideal
Premium
Ideal
Premium
Premium
Ideal
Good
Good
Very Good
Good
Very Good
Very Good
Very Good
Very Good
Very Good
Premium
Very Good
Very Good
Very Good
Very Good
Very Good
Very Good
Very Good
Very Good
Good
Good
Good
Very Good
Ideal
Ideal
Ideal
Good
Good
Good
Premium
Very Good
Good
Very Good
Very Good
Very Good
Ideal
Ideal
Premium
Premium
Ideal
Premium
Very Good
Very Good
Good
Ideal
Premium
Ideal
Ideal
Premium
Ideal
Ideal
Very Good
Premium
Premium
Very Good
Very Good
Premium
Premium
Good
Very Good
Very Good
Very Good
Very Good
Very Good
Very Good
Very Good
Ideal
Ideal
Good
Premium
Premium
Premium
Premium
Premium
Ideal
Fair
Ideal
Very Good
Very Good
Good
Good
Fair
Very Good
Premium
Very Good
Premium
Ideal
Premium
Ideal
Ideal
Premium
Ideal
Ideal
Ideal
Ideal
Ideal
Premium
Very Good
Ideal
Ideal
Premium
Ideal
Ideal
Ideal
Ideal
Ideal
Very Good
Fair
Fair
Premium
Premium
Very Good
Fair
Fair
Ideal
Very Good
Ideal
Very Good
Very Good
Premium
Very Good
Premium
Ideal
Ideal
Premium
Premium
Very Good
Very Good
Ideal
Good
Very Good
Very Good
Very Good
Ideal
Premium
Ideal
Premium
Premium
Very Good
Ideal
Ideal
Premium
Premium
Ideal
Premium
Very Good
Very Good
Ideal
Ideal
Very Good
Very Good
Ideal
Ideal
Good
Ideal
Premium
Very Good
Ideal
Ideal
Good
Very Good
Very Good
Premium
Ideal
Ideal
Ideal
Ideal
Ideal
Good
Ideal
Very Good
Premium
Very Good
Good
Good
Ideal
Premium
Premium
Premium
Premium
Premium
Premium
Ideal
Premium
Very Good
Premium
Premium
Good
Fair
Premium
Very Good
Ideal
Very Good
Ideal
Very Good
Premium
Ideal
Ideal
Ideal
Premium
Ideal
Ideal
Very Good
Very Good
Ideal
Good
Premium
Very Good
Ideal
Premium
Premium
Fair
Premium
Ideal
Very Good
Very Good
Very Good
Ideal
Ideal
Very Good
Very Good
Ideal
Good
Good
Ideal
Fair
Premium
Good
Good
Premium
Premium
Very Good
Ideal
Ideal
Ideal
Good
Premium
Premium
Premium
Fair
Ideal
Premium
Ideal
Premium
Premium
Very Good
Ideal
Premium
Premium
Ideal
Premium
Premium
Premium
Ideal
Very Good
Very Good
Good
Ideal
Ideal
Very Good
Very Good
Premium
Ideal
Good
Premium
Premium
Premium
Premium
Premium
Good
Very Good
Very Good
Premium
Premium
Premium
Ideal
Ideal
Ideal
Ideal
Ideal
Fair
Premium
Fair
Very Good
Ideal
Very Good
Ideal
Ideal
Very Good
Good
Premium
Very Good
Ideal
Ideal
Very Good
Premium
Ideal
Ideal
Fair
Ideal
Ideal
Premium
Ideal
Premium
Good
Good
Premium
Premium
Premium
Very Good
Ideal
Premium
Premium
Very Good
Very Good
Ideal
Ideal
Good
Premium
Premium
Premium
Premium
Premium
Premium
Very Good
Ideal
Very Good
Very Good
Very Good
Very Good
Ideal
Very Good
Ideal
Ideal
Ideal
Ideal
Fair
Premium
Premium
Ideal
Fair
Premium
Ideal
Fair
Good
Very Good
Very Good
Ideal
Very Good
Very Good
Premium
Very Good
Very Good
Fair
Very Good
Ideal
Very Good
Very Good
Premium
Premium
Fair
Very Good
Very Good
Ideal
Good
Good
Very Good
Very Good
Fair
Fair
Very Good
Very Good
Good
Very Good
Premium
Premium
Ideal
Premium
Very Good
Ideal
Premium
Premium
Premium
Very Good
Good
Ideal
Very Good
Good
Ideal
Very Good
Very Good
Very Good
Ideal
Ideal
Ideal
Ideal
Ideal
Ideal
Ideal
Very Good
Very Good
Ideal
Ideal
Ideal
Very Good
Very Good
Ideal
Fair
Premium
Ideal
Premium
Very Good
Good
Premium
Ideal
Premium
Very Good
Very Good
Premium
Ideal
Ideal
Ideal
Ideal
Fair
Fair
Premium
Premium
Fair
Premium
Very Good
Ideal
Good
Premium
Ideal
Ideal
Premium
Ideal
Ideal
Premium
Ideal
Premium
Premium
Ideal
Premium
Ideal
Good
Premium
Premium
Premium
Premium
Premium
Ideal
Ideal
Premium
Premium
Premium
Fair
Very Good
Ideal
Good
Good
Ideal
Ideal
Premium
Ideal
Premium
Good
Premium
Premium
Premium
Very Good
Premium
Premium
Premium
Premium
Premium
Ideal
Ideal
Ideal
Premium
Premium
Premium
Ideal
Good
Ideal
Premium
Premium
Ideal
Good
Ideal
Ideal
Premium
Premium
Ideal
Very Good
Good
Premium
Ideal
Fair
Ideal
Premium
Ideal
Good
Ideal
Premium
Premium
Premium
Very Good
Premium
Premium
Fair
Premium
Good
Premium
Premium
Very Good
Premium
Very Good
Premium
Ideal
Very Good
Good
Premium
Ideal
Ideal
Ideal
Premium
Premium
Good
Ideal
Ideal
Premium
Premium
Very Good
Very Good
Ideal
Ideal
Ideal
Ideal
Premium
Very Good
Ideal
Ideal
Premium
Ideal
Very Good
Very Good
Premium
Premium
Ideal
Premium
Good
Very Good
Very Good
Very Good
Ideal
Ideal
Ideal
Ideal
Ideal
Ideal
Very Good
Ideal
Ideal
Very Good
Fair
Ideal
Ideal
Ideal
Ideal
Ideal
Ideal
Premium
Very Good
Good
Premium
Ideal
Ideal
Very Good
Fair
Premium
Premium
Premium
Premium
Premium
Very Good
Premium
Premium
Premium
Very Good
Ideal
Ideal
Ideal
Premium
Premium
Premium
Ideal
Ideal
Ideal
Ideal
Premium
Premium
Premium
Premium
Very Good
Very Good
Ideal
Premium
Ideal
Premium
Premium
Premium
Very Good
Very Good
Ideal
Ideal
Very Good
Ideal
Premium
Fair
Ideal
Premium
Premium
Ideal
Premium
Very Good
Very Good
Very Good
Very Good
Very Good
Ideal
Very Good
Ideal
Ideal
Ideal
Very Good
Very Good
Ideal
Good
Ideal
Very Good
Very Good
Good
Premium
Ideal
Very Good
Ideal
Fair
Good
Ideal
Ideal
Good
Premium
Premium
Premium
Very Good
Ideal
Premium
Very Good
Ideal
Fair
Good
Very Good
Very Good
Ideal
Ideal
Very Good
Very Good
Ideal
Very Good
Premium
Ideal
Very Good
Ideal
Ideal
Ideal
Very Good
Premium
Good
Ideal
Premium
Premium
Premium
Very Good
Very Good
Premium
Premium
Fair
Fair
Good
Fair
Premium
Premium
Very Good
Good
Premium
Fair
Fair
Fair
Ideal
Ideal
Ideal
Ideal
Fair
Ideal
Ideal
Ideal
Good
Good
Good
Good
Very Good
Ideal
Good
Ideal
Ideal
Ideal
Ideal
Ideal
Ideal
Ideal
Ideal
Ideal
Premium
Premium
Premium
Ideal
Premium
Premium
Premium
Ideal
Ideal
Good
Premium
Fair
Premium
Premium
Good
Very Good
Ideal
Premium
Premium
Ideal
Very Good
Very Good
Premium
Premium
Premium
Very Good
Premium
Ideal
Ideal
Premium
Good
Fair
Fair
Ideal
Premium
Premium
Ideal
Premium
Fair
Premium
Very Good
Very Good
Ideal
Premium
Ideal
Premium
Ideal
Ideal
Premium
Fair
Premium
Premium
Very Good
Very Good
Very Good
Premium
Very Good
Ideal
Very Good
Premium
Premium
Premium
Fair
Premium
Good
Ideal
Premium
Premium
Premium
Premium
Premium
Ideal
Premium
Premium
Premium
Premium
Premium
Premium
Ideal
Premium
Ideal
Ideal
Very Good
Premium
Very Good
Very Good
Ideal
Ideal
Premium
Very Good
Ideal
Ideal
Premium
Premium
Ideal
Ideal
Ideal
Ideal
Premium
Ideal
Fair
Ideal
Ideal
Premium
Very Good
Premium
Ideal
Ideal
Ideal
Ideal
Premium
Ideal
Premium
Ideal
Ideal
Ideal
Ideal
Premium
Premium
Very Good
Good
Premium
Ideal
Ideal
Good
Very Good
Ideal
Very Good
Ideal
Good
Ideal
Ideal
Very Good
Very Good
Ideal
Good
Very Good
Very Good
Ideal
Ideal
Fair
Fair
Premium
Good
Fair
Fair
Premium
Premium
Premium
Premium
Premium
Premium
Ideal
Ideal
Ideal
Premium
Fair
Ideal
Fair
Fair
Fair
Premium
Premium
Very Good
Ideal
Ideal
Good
Good
Very Good
Very Good
Very Good
Ideal
Ideal
Very Good
Premium
Premium
Premium
Ideal
Ideal
Ideal
Fair
Ideal
Very Good
Very Good
Ideal
Very Good
Premium
Very Good
Ideal
Premium
Fair
Premium
Ideal
Good
Fair
Fair
Very Good
Premium
Ideal
Fair
Fair
Ideal
Ideal
Very Good
Premium
Ideal
Very Good
Very Good
Very Good
Good
Very Good
Ideal
Ideal
Ideal
Premium
Premium
Ideal
Ideal
Premium
Good
Ideal
Very Good
Ideal
Ideal
Ideal
Ideal
Good
Ideal
Fair
Very Good
Very Good
Ideal
Ideal
Very Good
Ideal
Good
Very Good
Premium
Very Good
Ideal
Ideal
Very Good
Very Good
Ideal
Very Good
Very Good
Very Good
Ideal
Ideal
Ideal
Ideal
Premium
Very Good
Ideal
Very Good
Premium
Ideal
Ideal
Good
Premium
Premium 
// come on do the same for color NOW!

// and clarity too...

** You Try!**

Now play around with display of the entire DF and choosing what you want in the GUI as opposed to a .select(...) statement earlier.

For instance, the following display(diamondsDF) shows the counts of the colors by choosing in the Plot Options a bar-chart that is grouped with Series Grouping as color, values as <id> and Aggregation as COUNT. You can click on Plot Options to see these settings and can change them as you wish by dragging and dropping.

 display(diamondsDF)
carat cut color clarity depth table price x y z
0.23 Ideal E SI2 61.5 55.0 326.0 3.95 3.98 2.43
0.21 Premium E SI1 59.8 61.0 326.0 3.89 3.84 2.31
0.23 Good E VS1 56.9 65.0 327.0 4.05 4.07 2.31
0.29 Premium I VS2 62.4 58.0 334.0 4.2 4.23 2.63
0.31 Good J SI2 63.3 58.0 335.0 4.34 4.35 2.75
0.24 Very Good J VVS2 62.8 57.0 336.0 3.94 3.96 2.48
0.24 Very Good I VVS1 62.3 57.0 336.0 3.95 3.98 2.47
0.26 Very Good H SI1 61.9 55.0 337.0 4.07 4.11 2.53
0.22 Fair E VS2 65.1 61.0 337.0 3.87 3.78 2.49
0.23 Very Good H VS1 59.4 61.0 338.0 4.0 4.05 2.39
0.3 Good J SI1 64.0 55.0 339.0 4.25 4.28 2.73
0.23 Ideal J VS1 62.8 56.0 340.0 3.93 3.9 2.46
0.22 Premium F SI1 60.4 61.0 342.0 3.88 3.84 2.33
0.31 Ideal J SI2 62.2 54.0 344.0 4.35 4.37 2.71
0.2 Premium E SI2 60.2 62.0 345.0 3.79 3.75 2.27
0.32 Premium E I1 60.9 58.0 345.0 4.38 4.42 2.68
0.3 Ideal I SI2 62.0 54.0 348.0 4.31 4.34 2.68
0.3 Good J SI1 63.4 54.0 351.0 4.23 4.29 2.7
0.3 Good J SI1 63.8 56.0 351.0 4.23 4.26 2.71
0.3 Very Good J SI1 62.7 59.0 351.0 4.21 4.27 2.66
0.3 Good I SI2 63.3 56.0 351.0 4.26 4.3 2.71
0.23 Very Good E VS2 63.8 55.0 352.0 3.85 3.92 2.48
0.23 Very Good H VS1 61.0 57.0 353.0 3.94 3.96 2.41
0.31 Very Good J SI1 59.4 62.0 353.0 4.39 4.43 2.62
0.31 Very Good J SI1 58.1 62.0 353.0 4.44 4.47 2.59
0.23 Very Good G VVS2 60.4 58.0 354.0 3.97 4.01 2.41
0.24 Premium I VS1 62.5 57.0 355.0 3.97 3.94 2.47
0.3 Very Good J VS2 62.2 57.0 357.0 4.28 4.3 2.67
0.23 Very Good D VS2 60.5 61.0 357.0 3.96 3.97 2.4
0.23 Very Good F VS1 60.9 57.0 357.0 3.96 3.99 2.42
0.23 Very Good F VS1 60.0 57.0 402.0 4.0 4.03 2.41
0.23 Very Good F VS1 59.8 57.0 402.0 4.04 4.06 2.42
0.23 Very Good E VS1 60.7 59.0 402.0 3.97 4.01 2.42
0.23 Very Good E VS1 59.5 58.0 402.0 4.01 4.06 2.4
0.23 Very Good D VS1 61.9 58.0 402.0 3.92 3.96 2.44
0.23 Good F VS1 58.2 59.0 402.0 4.06 4.08 2.37
0.23 Good E VS1 64.1 59.0 402.0 3.83 3.85 2.46
0.31 Good H SI1 64.0 54.0 402.0 4.29 4.31 2.75
0.26 Very Good D VS2 60.8 59.0 403.0 4.13 4.16 2.52
0.33 Ideal I SI2 61.8 55.0 403.0 4.49 4.51 2.78
0.33 Ideal I SI2 61.2 56.0 403.0 4.49 4.5 2.75
0.33 Ideal J SI1 61.1 56.0 403.0 4.49 4.55 2.76
0.26 Good D VS2 65.2 56.0 403.0 3.99 4.02 2.61
0.26 Good D VS1 58.4 63.0 403.0 4.19 4.24 2.46
0.32 Good H SI2 63.1 56.0 403.0 4.34 4.37 2.75
0.29 Premium F SI1 62.4 58.0 403.0 4.24 4.26 2.65
0.32 Very Good H SI2 61.8 55.0 403.0 4.35 4.42 2.71
0.32 Good H SI2 63.8 56.0 403.0 4.36 4.38 2.79
0.25 Very Good E VS2 63.3 60.0 404.0 4.0 4.03 2.54
0.29 Very Good H SI2 60.7 60.0 404.0 4.33 4.37 2.64
0.24 Very Good F SI1 60.9 61.0 404.0 4.02 4.03 2.45
0.23 Ideal G VS1 61.9 54.0 404.0 3.93 3.95 2.44
0.32 Ideal I SI1 60.9 55.0 404.0 4.45 4.48 2.72
0.22 Premium E VS2 61.6 58.0 404.0 3.93 3.89 2.41
0.22 Premium D VS2 59.3 62.0 404.0 3.91 3.88 2.31
0.3 Ideal I SI2 61.0 59.0 405.0 4.3 4.33 2.63
0.3 Premium J SI2 59.3 61.0 405.0 4.43 4.38 2.61
0.3 Very Good I SI1 62.6 57.0 405.0 4.25 4.28 2.67
0.3 Very Good I SI1 63.0 57.0 405.0 4.28 4.32 2.71
0.3 Good I SI1 63.2 55.0 405.0 4.25 4.29 2.7
0.35 Ideal I VS1 60.9 57.0 552.0 4.54 4.59 2.78
0.3 Premium D SI1 62.6 59.0 552.0 4.23 4.27 2.66
0.3 Ideal D SI1 62.5 57.0 552.0 4.29 4.32 2.69
0.3 Ideal D SI1 62.1 56.0 552.0 4.3 4.33 2.68
0.42 Premium I SI2 61.5 59.0 552.0 4.78 4.84 2.96
0.28 Ideal G VVS2 61.4 56.0 553.0 4.19 4.22 2.58
0.32 Ideal I VVS1 62.0 55.3 553.0 4.39 4.42 2.73
0.31 Very Good G SI1 63.3 57.0 553.0 4.33 4.3 2.73
0.31 Premium G SI1 61.8 58.0 553.0 4.35 4.32 2.68
0.24 Premium E VVS1 60.7 58.0 553.0 4.01 4.03 2.44
0.24 Very Good D VVS1 61.5 60.0 553.0 3.97 4.0 2.45
0.3 Very Good H SI1 63.1 56.0 554.0 4.29 4.27 2.7
0.3 Premium H SI1 62.9 59.0 554.0 4.28 4.24 2.68
0.3 Premium H SI1 62.5 57.0 554.0 4.29 4.25 2.67
0.3 Good H SI1 63.7 57.0 554.0 4.28 4.26 2.72
0.26 Very Good F VVS2 59.2 60.0 554.0 4.19 4.22 2.49
0.26 Very Good E VVS2 59.9 58.0 554.0 4.15 4.23 2.51
0.26 Very Good D VVS2 62.4 54.0 554.0 4.08 4.13 2.56
0.26 Very Good D VVS2 62.8 60.0 554.0 4.01 4.05 2.53
0.26 Very Good E VVS1 62.6 59.0 554.0 4.06 4.09 2.55
0.26 Very Good E VVS1 63.4 59.0 554.0 4.0 4.04 2.55
0.26 Very Good D VVS1 62.1 60.0 554.0 4.03 4.12 2.53
0.26 Ideal E VVS2 62.9 58.0 554.0 4.02 4.06 2.54
0.38 Ideal I SI2 61.6 56.0 554.0 4.65 4.67 2.87
0.26 Good E VVS1 57.9 60.0 554.0 4.22 4.25 2.45
0.24 Premium G VVS1 62.3 59.0 554.0 3.95 3.92 2.45
0.24 Premium H VVS1 61.2 58.0 554.0 4.01 3.96 2.44
0.24 Premium H VVS1 60.8 59.0 554.0 4.02 4.0 2.44
0.24 Premium H VVS2 60.7 58.0 554.0 4.07 4.04 2.46
0.32 Premium I SI1 62.9 58.0 554.0 4.35 4.33 2.73
0.7 Ideal E SI1 62.5 57.0 2757.0 5.7 5.72 3.57
0.86 Fair E SI2 55.1 69.0 2757.0 6.45 6.33 3.52
0.7 Ideal G VS2 61.6 56.0 2757.0 5.7 5.67 3.5
0.71 Very Good E VS2 62.4 57.0 2759.0 5.68 5.73 3.56
0.78 Very Good G SI2 63.8 56.0 2759.0 5.81 5.85 3.72
0.7 Good E VS2 57.5 58.0 2759.0 5.85 5.9 3.38
0.7 Good F VS1 59.4 62.0 2759.0 5.71 5.76 3.4
0.96 Fair F SI2 66.3 62.0 2759.0 6.27 5.95 4.07
0.73 Very Good E SI1 61.6 59.0 2760.0 5.77 5.78 3.56
0.8 Premium H SI1 61.5 58.0 2760.0 5.97 5.93 3.66
0.75 Very Good D SI1 63.2 56.0 2760.0 5.8 5.75 3.65
0.75 Premium E SI1 59.9 54.0 2760.0 6.0 5.96 3.58
0.74 Ideal G SI1 61.6 55.0 2760.0 5.8 5.85 3.59
0.75 Premium G VS2 61.7 58.0 2760.0 5.85 5.79 3.59
0.8 Ideal I VS1 62.9 56.0 2760.0 5.94 5.87 3.72
0.75 Ideal G SI1 62.2 55.0 2760.0 5.87 5.8 3.63
0.8 Premium G SI1 63.0 59.0 2760.0 5.9 5.81 3.69
0.74 Ideal I VVS2 62.3 55.0 2761.0 5.77 5.81 3.61
0.81 Ideal F SI2 58.8 57.0 2761.0 6.14 6.11 3.6
0.59 Ideal E VVS2 62.0 55.0 2761.0 5.38 5.43 3.35
0.8 Ideal F SI2 61.4 57.0 2761.0 5.96 6.0 3.67
0.74 Ideal E SI2 62.2 56.0 2761.0 5.8 5.84 3.62
0.9 Premium I VS2 63.0 58.0 2761.0 6.16 6.12 3.87
0.74 Very Good G SI1 62.2 59.0 2762.0 5.73 5.82 3.59
0.73 Ideal F VS2 62.6 56.0 2762.0 5.77 5.74 3.6
0.73 Ideal F VS2 62.7 53.0 2762.0 5.8 5.75 3.62
0.8 Premium F SI2 61.7 58.0 2762.0 5.98 5.94 3.68
0.71 Ideal G VS2 62.4 54.0 2762.0 5.72 5.76 3.58
0.7 Ideal E VS2 60.7 58.0 2762.0 5.73 5.76 3.49
0.8 Ideal F SI2 59.9 59.0 2762.0 6.01 6.07 3.62
0.71 Ideal D SI2 62.3 56.0 2762.0 5.73 5.69 3.56
0.74 Ideal E SI1 62.3 54.0 2762.0 5.8 5.83 3.62
0.7 Very Good F VS2 61.7 63.0 2762.0 5.64 5.61 3.47
0.7 Fair F VS2 64.5 57.0 2762.0 5.57 5.53 3.58
0.7 Fair F VS2 65.3 55.0 2762.0 5.63 5.58 3.66
0.7 Premium F VS2 61.6 60.0 2762.0 5.65 5.59 3.46
0.91 Premium H SI1 61.4 56.0 2763.0 6.09 5.97 3.7
0.61 Very Good D VVS2 59.6 57.0 2763.0 5.56 5.58 3.32
0.91 Fair H SI2 64.4 57.0 2763.0 6.11 6.09 3.93
0.91 Fair H SI2 65.7 60.0 2763.0 6.03 5.99 3.95
0.77 Ideal H VS2 62.0 56.0 2763.0 5.89 5.86 3.64
0.71 Very Good D SI1 63.6 58.0 2764.0 5.64 5.68 3.6
0.71 Ideal D SI1 61.9 59.0 2764.0 5.69 5.72 3.53
0.7 Very Good E VS2 62.6 60.0 2765.0 5.62 5.65 3.53
0.77 Very Good H VS1 61.3 60.0 2765.0 5.88 5.9 3.61
0.63 Premium E VVS1 60.9 60.0 2765.0 5.52 5.55 3.37
0.71 Very Good F VS1 60.1 62.0 2765.0 5.74 5.77 3.46
0.71 Premium F VS1 61.8 59.0 2765.0 5.69 5.73 3.53
0.76 Ideal H SI1 61.2 57.0 2765.0 5.88 5.91 3.61
0.64 Ideal G VVS1 61.9 56.0 2766.0 5.53 5.56 3.43
0.71 Premium G VS2 60.9 57.0 2766.0 5.78 5.75 3.51
0.71 Premium G VS2 59.8 56.0 2766.0 5.89 5.81 3.5
0.7 Very Good D VS2 61.8 55.0 2767.0 5.68 5.72 3.52
0.7 Very Good F VS1 60.0 57.0 2767.0 5.8 5.87 3.5
0.71 Ideal D SI2 61.6 55.0 2767.0 5.74 5.76 3.54
0.7 Good H VVS2 62.1 64.0 2767.0 5.62 5.65 3.5
0.71 Very Good G VS1 63.3 59.0 2768.0 5.52 5.61 3.52
0.73 Very Good D SI1 60.2 56.0 2768.0 5.83 5.87 3.52
0.7 Very Good D SI1 61.1 58.0 2768.0 5.66 5.73 3.48
0.7 Ideal E SI1 60.9 57.0 2768.0 5.73 5.76 3.5
0.71 Premium D SI2 61.7 59.0 2768.0 5.71 5.67 3.51
0.74 Ideal I SI1 61.3 56.0 2769.0 5.82 5.86 3.57
0.71 Premium D VS2 62.5 60.0 2770.0 5.65 5.61 3.52
0.73 Premium G VS2 61.4 59.0 2770.0 5.83 5.76 3.56
0.76 Very Good F SI1 62.9 57.0 2770.0 5.79 5.81 3.65
0.76 Ideal D SI2 62.4 57.0 2770.0 5.78 5.83 3.62
0.71 Ideal F SI1 60.7 56.0 2770.0 5.77 5.8 3.51
0.73 Premium G VS2 60.7 58.0 2770.0 5.87 5.82 3.55
0.73 Premium G VS1 61.5 58.0 2770.0 5.79 5.75 3.55
0.73 Ideal D SI2 59.9 57.0 2770.0 5.92 5.89 3.54
0.73 Premium G VS2 59.2 59.0 2770.0 5.92 5.87 3.49
0.72 Very Good H VVS2 60.3 56.0 2771.0 5.81 5.83 3.51
0.73 Very Good F SI1 61.7 60.0 2771.0 5.79 5.82 3.58
0.71 Ideal G VS2 61.9 57.0 2771.0 5.73 5.77 3.56
0.79 Ideal F SI2 61.9 55.0 2771.0 5.97 5.92 3.68
0.73 Very Good H VVS1 60.4 59.0 2772.0 5.83 5.89 3.54
0.8 Very Good F SI2 61.0 57.0 2772.0 6.01 6.03 3.67
0.58 Ideal G VVS1 61.5 55.0 2772.0 5.39 5.44 3.33
0.58 Ideal F VVS1 61.7 56.0 2772.0 5.33 5.37 3.3
0.71 Good E VS2 59.2 61.0 2772.0 5.8 5.88 3.46
0.75 Ideal D SI2 61.3 56.0 2773.0 5.85 5.89 3.6
0.7 Premium D VS2 58.0 62.0 2773.0 5.87 5.78 3.38
1.17 Very Good J I1 60.2 61.0 2774.0 6.83 6.9 4.13
0.6 Ideal E VS1 61.7 55.0 2774.0 5.41 5.44 3.35
0.7 Ideal E SI1 62.7 55.0 2774.0 5.68 5.74 3.58
0.83 Good I VS2 64.6 54.0 2774.0 5.85 5.88 3.79
0.74 Very Good F VS2 61.3 61.0 2775.0 5.8 5.84 3.57
0.72 Very Good G VS2 63.7 56.4 2776.0 5.62 5.69 3.61
0.71 Premium E VS2 62.7 58.0 2776.0 5.74 5.68 3.58
0.71 Ideal E VS2 62.2 57.0 2776.0 5.79 5.62 3.55
0.54 Ideal E VVS2 61.6 56.0 2776.0 5.25 5.27 3.24
0.54 Ideal E VVS2 61.5 57.0 2776.0 5.24 5.26 3.23
0.72 Ideal G SI1 61.8 56.0 2776.0 5.72 5.75 3.55
0.72 Ideal G SI1 60.7 56.0 2776.0 5.79 5.82 3.53
0.72 Good G VS2 59.7 60.5 2776.0 5.8 5.84 3.47
0.71 Ideal G SI1 60.5 56.0 2776.0 5.8 5.76 3.5
0.7 Very Good D VS1 62.7 58.0 2777.0 5.66 5.73 3.57
0.71 Premium F VS2 62.1 58.0 2777.0 5.67 5.7 3.53
0.71 Very Good F VS2 62.8 57.0 2777.0 5.64 5.69 3.56
0.71 Good F VS2 63.8 58.0 2777.0 5.61 5.64 3.59
0.71 Good F VS2 57.8 60.0 2777.0 5.87 5.9 3.4
0.7 Ideal E VS2 62.1 55.0 2777.0 5.7 5.67 3.53
0.7 Premium E VS2 61.1 60.0 2777.0 5.71 5.64 3.47
0.7 Premium E SI1 60.0 59.0 2777.0 5.79 5.75 3.46
0.7 Premium E SI1 61.2 57.0 2777.0 5.73 5.68 3.49
0.7 Premium E SI1 62.7 59.0 2777.0 5.67 5.63 3.54
0.7 Premium E SI1 61.0 57.0 2777.0 5.73 5.68 3.48
0.7 Premium E SI1 61.0 58.0 2777.0 5.78 5.72 3.51
0.7 Ideal E SI1 61.4 57.0 2777.0 5.76 5.7 3.52
0.72 Premium F SI1 61.8 61.0 2777.0 5.82 5.71 3.56
0.7 Very Good E SI1 59.9 63.0 2777.0 5.76 5.7 3.43
0.7 Premium E SI1 61.3 58.0 2777.0 5.71 5.68 3.49
0.7 Premium E SI1 60.5 58.0 2777.0 5.77 5.74 3.48
0.7 Good E VS2 64.1 59.0 2777.0 5.64 5.59 3.6
0.98 Fair H SI2 67.9 60.0 2777.0 6.05 5.97 4.08
0.78 Premium F SI1 62.4 58.0 2777.0 5.83 5.8 3.63
0.7 Very Good E SI1 63.2 60.0 2777.0 5.6 5.51 3.51
0.52 Ideal F VVS1 61.3 55.0 2778.0 5.19 5.22 3.19
0.73 Very Good H VS2 60.8 56.0 2779.0 5.82 5.84 3.55
0.74 Ideal E SI1 61.7 56.0 2779.0 5.84 5.8 3.59
0.7 Very Good F VS2 63.6 57.0 2780.0 5.61 5.65 3.58
0.77 Premium G VS2 61.2 58.0 2780.0 5.9 5.93 3.62
0.71 Ideal F VS2 62.1 54.0 2780.0 5.68 5.72 3.54
0.74 Ideal G VS1 61.5 55.0 2780.0 5.81 5.86 3.59
0.7 Ideal G VS1 61.4 59.0 2780.0 5.64 5.73 3.49
1.01 Premium F I1 61.8 60.0 2781.0 6.39 6.36 3.94
0.77 Ideal H SI1 62.2 56.0 2781.0 5.83 5.88 3.64
0.78 Ideal H SI1 61.2 56.0 2781.0 5.92 5.99 3.64
0.72 Very Good H VS1 60.6 63.0 2782.0 5.83 5.76 3.51
0.53 Very Good D VVS2 57.5 64.0 2782.0 5.34 5.37 3.08
0.76 Ideal G VS2 61.3 56.0 2782.0 5.9 5.94 3.63
0.7 Good E VS1 57.2 62.0 2782.0 5.81 5.77 3.31
0.7 Premium E VS1 62.9 60.0 2782.0 5.62 5.54 3.51
0.75 Very Good D SI2 63.1 58.0 2782.0 5.78 5.73 3.63
0.72 Ideal D SI1 60.8 57.0 2782.0 5.76 5.75 3.5
0.72 Premium D SI1 62.7 59.0 2782.0 5.73 5.69 3.58
0.7 Premium D SI1 62.8 60.0 2782.0 5.68 5.66 3.56
0.84 Fair G SI1 55.1 67.0 2782.0 6.39 6.2 3.47
0.75 Premium F SI1 61.4 59.0 2782.0 5.88 5.85 3.6
0.52 Ideal F IF 62.2 55.0 2783.0 5.14 5.18 3.21
0.72 Very Good F VS2 63.0 54.0 2784.0 5.69 5.73 3.6
0.79 Very Good H VS1 63.7 56.0 2784.0 5.85 5.92 3.75
0.72 Very Good F VS2 63.6 58.0 2787.0 5.66 5.69 3.61
0.51 Ideal F VVS1 62.0 57.0 2787.0 5.11 5.15 3.18
0.64 Ideal D VS1 61.5 56.0 2787.0 5.54 5.55 3.41
0.7 Very Good H VVS1 60.5 60.0 2788.0 5.74 5.77 3.48
0.83 Very Good I VS1 61.1 60.0 2788.0 6.07 6.1 3.72
0.76 Ideal I VVS2 61.8 56.0 2788.0 5.85 5.87 3.62
0.71 Good D VS2 63.3 56.0 2788.0 5.64 5.68 3.58
0.77 Good G VS1 59.4 64.0 2788.0 5.97 5.92 3.53
0.71 Ideal F SI1 62.5 55.0 2788.0 5.71 5.65 3.55
1.01 Fair E I1 64.5 58.0 2788.0 6.29 6.21 4.03
1.01 Premium H SI2 62.7 59.0 2788.0 6.31 6.22 3.93
0.77 Good F SI1 64.2 52.0 2789.0 5.81 5.77 3.72
0.76 Good E SI1 63.7 54.0 2789.0 5.76 5.85 3.7
0.76 Premium E SI1 60.4 58.0 2789.0 5.92 5.94 3.58
0.76 Premium E SI1 61.8 58.0 2789.0 5.82 5.86 3.61
1.05 Very Good J SI2 63.2 56.0 2789.0 6.49 6.45 4.09
0.81 Ideal G SI2 61.6 56.0 2789.0 5.97 6.01 3.69
0.7 Ideal E SI1 61.6 56.0 2789.0 5.72 5.75 3.53
0.55 Ideal G IF 60.9 57.0 2789.0 5.28 5.3 3.22
0.81 Good G SI2 61.0 61.0 2789.0 5.94 5.99 3.64
0.63 Premium E VVS2 62.1 57.0 2789.0 5.48 5.41 3.38
0.63 Premium E VVS1 60.9 60.0 2789.0 5.55 5.52 3.37
0.77 Premium H VS1 61.3 60.0 2789.0 5.9 5.88 3.61
1.05 Fair J SI2 65.8 59.0 2789.0 6.41 6.27 4.18
0.64 Ideal G IF 61.3 56.0 2790.0 5.54 5.58 3.41
0.76 Premium I VVS1 58.8 59.0 2790.0 6.0 5.94 3.51
0.83 Ideal F SI2 62.3 55.0 2790.0 6.02 6.05 3.76
0.71 Premium F VS1 60.1 62.0 2790.0 5.77 5.74 3.46
0.71 Premium F VS1 61.8 59.0 2790.0 5.73 5.69 3.53
0.87 Very Good I SI1 63.6 55.8 2791.0 6.07 6.1 3.87
0.73 Ideal E SI1 62.2 56.0 2791.0 5.74 5.78 3.58
0.71 Premium E SI1 59.2 59.0 2792.0 5.83 5.86 3.46
0.71 Premium E SI1 61.8 59.0 2792.0 5.7 5.75 3.54
0.71 Ideal E SI1 61.3 55.0 2792.0 5.72 5.77 3.52
0.7 Premium F VS1 62.1 60.0 2792.0 5.71 5.65 3.53
0.7 Premium F VS1 60.7 60.0 2792.0 5.78 5.75 3.5
0.76 Premium H VVS2 59.6 57.0 2792.0 5.91 5.86 3.51
0.7 Ideal F VS1 62.2 56.0 2792.0 5.73 5.68 3.55
0.79 Very Good G SI1 60.6 57.0 2793.0 5.98 6.06 3.65
0.7 Very Good E VS2 62.9 57.0 2793.0 5.66 5.69 3.57
0.7 Good E VS2 64.1 55.0 2793.0 5.6 5.66 3.61
0.76 Ideal I VS2 61.3 56.0 2793.0 5.87 5.91 3.61
0.73 Ideal H VS2 62.7 55.0 2793.0 5.72 5.76 3.6
0.79 Very Good E SI1 63.2 56.0 2794.0 5.91 5.86 3.72
0.71 Very Good E VS2 60.7 56.0 2795.0 5.81 5.82 3.53
0.81 Premium I VVS2 61.9 60.0 2795.0 5.91 5.86 3.64
0.81 Ideal F SI2 62.6 55.0 2795.0 5.92 5.96 3.72
0.72 Good F VS1 60.7 60.0 2795.0 5.74 5.72 3.48
0.72 Premium D SI2 62.0 60.0 2795.0 5.73 5.69 3.54
0.72 Premium I IF 63.0 57.0 2795.0 5.72 5.7 3.6
0.81 Premium H VS2 58.0 59.0 2795.0 6.17 6.13 3.57
0.72 Premium G VS2 62.9 57.0 2795.0 5.73 5.65 3.58
1.0 Premium I SI2 58.2 60.0 2795.0 6.61 6.55 3.83
0.73 Good E SI1 63.2 58.0 2796.0 5.7 5.76 3.62
0.81 Very Good H SI2 61.3 59.0 2797.0 5.94 6.01 3.66
0.81 Very Good E SI1 60.3 60.0 2797.0 6.07 6.1 3.67
0.71 Premium D SI1 62.7 60.0 2797.0 5.67 5.71 3.57
0.71 Premium D SI1 61.3 58.0 2797.0 5.73 5.75 3.52
0.71 Premium D SI1 61.6 60.0 2797.0 5.74 5.69 3.52
0.57 Ideal F VVS2 61.9 55.0 2797.0 5.34 5.35 3.31
0.51 Ideal D VVS1 61.7 56.0 2797.0 5.12 5.16 3.17
0.72 Ideal G VS2 61.9 58.0 2797.0 5.72 5.75 3.55
0.74 Ideal H VS1 61.8 58.0 2797.0 5.77 5.81 3.58
0.74 Ideal H VS1 61.6 56.0 2797.0 5.81 5.82 3.58
0.7 Fair G VVS1 58.8 66.0 2797.0 5.81 5.9 3.44
0.8 Premium F SI2 61.0 57.0 2797.0 6.03 6.01 3.67
1.01 Fair E SI2 67.4 60.0 2797.0 6.19 6.05 4.13
0.8 Very Good H VS2 63.4 60.0 2797.0 5.92 5.82 3.72
0.77 Ideal I VS1 61.5 59.0 2798.0 5.87 5.91 3.62
0.83 Very Good E SI2 58.0 62.0 2799.0 6.19 6.25 3.61
0.82 Ideal F SI2 62.4 54.0 2799.0 5.97 6.02 3.74
0.78 Ideal D SI1 61.9 57.0 2799.0 5.91 5.86 3.64
0.6 Very Good G IF 61.6 56.0 2800.0 5.43 5.46 3.35
0.9 Good I SI2 62.2 59.0 2800.0 6.07 6.11 3.79
0.7 Premium E VS1 62.2 58.0 2800.0 5.6 5.66 3.5
0.9 Very Good I SI2 61.3 56.0 2800.0 6.17 6.23 3.8
0.83 Ideal G SI1 62.3 57.0 2800.0 5.99 6.08 3.76
0.83 Ideal G SI1 61.8 57.0 2800.0 6.03 6.07 3.74
0.83 Very Good H SI1 62.5 59.0 2800.0 5.95 6.02 3.74
0.74 Premium G VS1 62.9 60.0 2800.0 5.74 5.68 3.59
0.79 Ideal I VS1 61.8 59.0 2800.0 5.92 5.95 3.67
0.61 Ideal G IF 62.3 56.0 2800.0 5.43 5.45 3.39
0.76 Fair G VS1 59.0 70.0 2800.0 5.89 5.8 3.46
0.96 Ideal F I1 60.7 55.0 2801.0 6.37 6.41 3.88
0.73 Ideal F VS2 62.5 55.0 2801.0 5.8 5.76 3.61
0.73 Premium F VS2 62.7 58.0 2801.0 5.76 5.7 3.59
0.75 Ideal H SI1 60.4 57.0 2801.0 5.93 5.96 3.59
0.71 Premium F VS2 62.1 58.0 2801.0 5.7 5.67 3.53
0.71 Good F VS2 57.8 60.0 2801.0 5.9 5.87 3.4
0.71 Good F VS2 63.8 58.0 2801.0 5.64 5.61 3.59
0.71 Premium F VS2 62.8 57.0 2801.0 5.69 5.64 3.56
1.04 Premium G I1 62.2 58.0 2801.0 6.46 6.41 4.0
1.0 Premium J SI2 62.3 58.0 2801.0 6.45 6.34 3.98
0.87 Very Good G SI2 59.9 58.0 2802.0 6.19 6.23 3.72
0.53 Ideal F IF 61.9 54.0 2802.0 5.22 5.25 3.24
0.72 Premium E VS2 63.0 55.0 2802.0 5.79 5.61 3.59
0.72 Premium F VS1 62.4 58.0 2802.0 5.83 5.7 3.6
0.7 Very Good F VS2 62.9 58.0 2803.0 5.63 5.65 3.55
0.74 Very Good E SI1 63.5 56.0 2803.0 5.74 5.79 3.66
0.71 Ideal G VS2 61.3 56.0 2803.0 5.75 5.71 3.51
0.73 Ideal E SI1 60.6 54.0 2803.0 5.84 5.89 3.55
0.7 Good G VS1 65.1 58.0 2803.0 5.56 5.59 3.63
0.71 Premium F VS2 62.6 58.0 2803.0 5.7 5.67 3.56
0.71 Premium F VS2 58.0 62.0 2803.0 5.85 5.81 3.38
0.71 Premium G VS1 62.4 61.0 2803.0 5.7 5.65 3.54
0.77 Premium G VS2 61.3 57.0 2803.0 5.93 5.88 3.62
0.71 Premium G VS2 59.9 60.0 2803.0 5.81 5.77 3.47
0.78 Premium G VS2 60.8 58.0 2803.0 6.03 5.95 3.64
0.71 Very Good G VS1 63.5 55.0 2803.0 5.66 5.64 3.59
0.91 Ideal D SI2 62.2 57.0 2803.0 6.21 6.15 3.85
0.71 Very Good E VS2 63.8 58.0 2804.0 5.62 5.66 3.6
0.71 Very Good E VS2 64.0 57.0 2804.0 5.66 5.68 3.63
0.8 Very Good E SI2 62.5 56.0 2804.0 5.88 5.96 3.7
0.7 Very Good D SI1 62.3 58.0 2804.0 5.69 5.73 3.56
0.72 Ideal F VS1 61.7 57.0 2804.0 5.74 5.77 3.55
0.72 Very Good F VS1 62.2 58.0 2804.0 5.75 5.7 3.56
0.82 Ideal H VS2 61.5 56.0 2804.0 6.01 6.08 3.72
0.7 Ideal D SI1 61.0 59.0 2804.0 5.68 5.7 3.47
0.72 Ideal D SI1 62.2 56.0 2804.0 5.74 5.77 3.58
0.72 Ideal D SI1 61.5 54.0 2804.0 5.77 5.8 3.56
0.9 Fair I SI1 67.3 59.0 2804.0 5.93 5.84 3.96
0.74 Premium F VS2 61.7 58.0 2805.0 5.85 5.78 3.59
0.74 Premium F VS2 61.9 56.0 2805.0 5.8 5.77 3.58
0.73 Ideal E SI2 61.8 58.0 2805.0 5.77 5.81 3.58
0.57 Fair E VVS1 58.7 66.0 2805.0 5.34 5.43 3.16
0.73 Premium F VS2 62.5 57.0 2805.0 5.75 5.7 3.58
0.72 Ideal G VS2 62.8 56.0 2805.0 5.74 5.7 3.59
0.74 Fair F VS2 61.1 68.0 2805.0 5.82 5.75 3.53
0.82 Good G VS2 64.0 57.0 2805.0 5.92 5.89 3.78
0.81 Very Good G SI1 62.5 60.0 2806.0 5.89 5.94 3.69
0.75 Very Good H VVS1 60.6 58.0 2806.0 5.85 5.9 3.56
0.7 Ideal F SI1 61.6 55.0 2806.0 5.72 5.74 3.53
0.71 Very Good F VS1 62.2 58.0 2807.0 5.66 5.72 3.54
0.71 Very Good F VS1 60.0 57.0 2807.0 5.84 5.9 3.52
0.93 Premium J SI2 61.9 57.0 2807.0 6.21 6.19 3.84
0.8 Very Good H VS2 62.8 57.0 2808.0 5.87 5.91 3.7
0.7 Very Good F VS1 62.0 57.0 2808.0 5.64 5.71 3.52
1.0 Fair G I1 66.4 59.0 2808.0 6.16 6.09 4.07
0.75 Very Good G VS2 63.4 56.0 2808.0 5.78 5.74 3.65
0.58 Ideal E VVS2 60.9 56.0 2808.0 5.41 5.43 3.3
0.73 Very Good D SI1 63.1 57.0 2808.0 5.74 5.7 3.61
0.81 Very Good F SI1 63.1 59.0 2809.0 5.85 5.79 3.67
0.81 Premium D SI2 59.2 57.0 2809.0 6.15 6.05 3.61
0.71 Premium F SI1 60.7 54.0 2809.0 5.84 5.8 3.53
1.2 Fair F I1 64.6 56.0 2809.0 6.73 6.66 4.33
0.7 Very Good F VS1 61.8 56.0 2810.0 5.63 5.7 3.5
0.7 Very Good F VS1 59.9 60.0 2810.0 5.77 5.84 3.48
0.74 Ideal D SI2 61.7 55.0 2810.0 5.81 5.85 3.6
0.7 Good F VS1 62.8 61.0 2810.0 5.57 5.61 3.51
0.8 Good G SI1 62.7 57.0 2810.0 5.84 5.93 3.69
0.75 Very Good F SI1 63.4 58.0 2811.0 5.72 5.76 3.64
0.83 Very Good D SI1 63.5 54.0 2811.0 5.98 5.95 3.79
1.0 Fair J VS2 65.7 59.0 2811.0 6.14 6.07 4.01
0.99 Fair I SI2 68.1 56.0 2811.0 6.21 6.06 4.18
0.7 Very Good G VS1 63.0 60.0 2812.0 5.57 5.64 3.53
0.7 Very Good F VS2 59.5 58.0 2812.0 5.75 5.85 3.45
0.7 Good E SI1 63.5 59.0 2812.0 5.49 5.53 3.5
0.7 Very Good F VS2 61.7 58.0 2812.0 5.63 5.69 3.49
0.32 Premium I SI1 62.7 58.0 554.0 4.37 4.34 2.73
0.32 Premium I SI1 62.8 58.0 554.0 4.39 4.34 2.74
0.32 Ideal I SI1 62.4 57.0 554.0 4.37 4.35 2.72
0.32 Premium I SI1 61.0 59.0 554.0 4.39 4.36 2.67
0.32 Very Good I SI1 63.1 56.0 554.0 4.39 4.36 2.76
0.32 Ideal I SI1 60.7 57.0 554.0 4.47 4.42 2.7
0.3 Premium H SI1 60.9 59.0 554.0 4.31 4.29 2.62
0.3 Premium H SI1 60.1 55.0 554.0 4.41 4.38 2.64
0.3 Premium H SI1 62.9 58.0 554.0 4.28 4.24 2.68
0.3 Very Good H SI1 63.3 56.0 554.0 4.29 4.27 2.71
0.3 Good H SI1 63.8 55.0 554.0 4.26 4.2 2.7
0.3 Ideal H SI1 62.9 57.0 554.0 4.27 4.22 2.67
0.3 Very Good H SI1 63.4 60.0 554.0 4.25 4.23 2.69
0.32 Good I SI1 63.9 55.0 554.0 4.36 4.34 2.78
0.33 Ideal H SI2 61.4 56.0 554.0 4.85 4.79 2.95
0.29 Very Good E VS1 61.9 55.0 555.0 4.28 4.33 2.66
0.29 Very Good E VS1 62.4 55.0 555.0 4.2 4.25 2.63
0.31 Very Good F SI1 61.8 58.0 555.0 4.32 4.35 2.68
0.34 Ideal H VS2 61.5 56.0 555.0 4.47 4.5 2.76
0.34 Ideal H VS2 60.4 57.0 555.0 4.54 4.57 2.75
0.34 Ideal I VS1 61.8 55.0 555.0 4.48 4.52 2.78
0.34 Ideal I VS1 62.0 56.0 555.0 4.5 4.53 2.8
0.3 Ideal G VS1 62.3 56.0 555.0 4.29 4.31 2.68
0.29 Ideal F VS1 61.6 56.0 555.0 4.26 4.31 2.64
0.35 Ideal G SI1 60.6 56.0 555.0 4.56 4.58 2.77
0.43 Very Good E I1 58.4 62.0 555.0 4.94 5.0 2.9
0.32 Very Good F VS2 61.4 58.0 556.0 4.37 4.42 2.7
0.36 Ideal I VS2 61.9 56.0 556.0 4.54 4.57 2.82
0.3 Ideal G VS2 62.0 56.0 556.0 4.28 4.3 2.66
0.26 Ideal E VS1 61.5 57.0 556.0 4.09 4.12 2.52
0.7 Very Good F VS2 62.3 58.0 2812.0 5.64 5.72 3.54
0.7 Very Good F VS2 60.9 61.0 2812.0 5.66 5.71 3.46
0.71 Ideal D SI1 62.4 57.0 2812.0 5.69 5.72 3.56
0.99 Fair J SI1 55.0 61.0 2812.0 6.72 6.67 3.68
0.73 Premium E VS2 58.6 60.0 2812.0 5.92 5.89 3.46
0.51 Ideal F VVS1 62.0 57.0 2812.0 5.15 5.11 3.18
0.91 Premium G SI2 59.8 58.0 2813.0 6.3 6.29 3.77
0.84 Very Good E SI1 63.4 55.0 2813.0 6.0 5.95 3.79
0.91 Good I VS2 64.3 58.0 2813.0 6.09 6.05 3.9
0.76 Premium E SI1 62.2 59.0 2814.0 5.86 5.81 3.63
0.76 Ideal E SI1 61.7 57.0 2814.0 5.88 5.85 3.62
0.75 Premium E SI1 61.1 59.0 2814.0 5.86 5.83 3.57
0.55 Very Good D VVS1 61.5 56.0 2815.0 5.23 5.27 3.23
0.76 Very Good F SI2 58.5 62.0 2815.0 5.93 6.01 3.49
0.74 Premium G VS1 61.7 58.0 2815.0 5.79 5.81 3.58
0.7 Ideal H SI1 60.4 56.0 2815.0 5.75 5.81 3.49
0.7 Ideal H SI1 61.4 56.0 2815.0 5.7 5.76 3.52
0.7 Ideal H SI1 61.5 55.0 2815.0 5.73 5.79 3.54
0.7 Ideal H SI1 61.4 56.0 2815.0 5.72 5.77 3.53
0.9 Fair J VS2 65.0 56.0 2815.0 6.08 6.04 3.94
0.95 Fair F SI2 56.0 60.0 2815.0 6.62 6.53 3.68
0.89 Premium H SI2 60.2 59.0 2815.0 6.26 6.23 3.76
0.72 Premium E VS2 58.3 58.0 2815.0 5.99 5.92 3.47
0.96 Fair E SI2 53.1 63.0 2815.0 6.73 6.65 3.55
1.02 Premium G I1 60.3 58.0 2815.0 6.55 6.5 3.94
0.78 Very Good I VVS2 61.4 56.0 2816.0 5.91 5.95 3.64
0.61 Ideal G VVS2 60.1 57.0 2816.0 5.52 5.54 3.32
0.71 Good D VS1 63.4 55.0 2816.0 5.61 5.69 3.58
0.78 Premium F SI1 61.5 59.0 2816.0 5.96 5.88 3.64
0.87 Ideal H SI2 62.7 56.0 2816.0 6.16 6.13 3.85
0.83 Ideal H SI1 62.5 55.0 2816.0 6.04 6.0 3.76
0.71 Premium E SI1 61.3 56.0 2817.0 5.78 5.73 3.53
0.71 Ideal I VVS2 60.2 56.0 2817.0 5.84 5.89 3.53
0.71 Ideal E VS2 62.7 57.0 2817.0 5.66 5.64 3.54
0.71 Premium E VS2 62.3 58.0 2817.0 5.69 5.65 3.53
0.63 Ideal F VVS2 61.5 56.0 2817.0 5.48 5.52 3.38
0.71 Premium E SI1 59.2 59.0 2817.0 5.86 5.83 3.46
0.71 Premium E SI1 61.8 59.0 2817.0 5.75 5.7 3.54
0.71 Ideal E SI1 61.3 55.0 2817.0 5.77 5.72 3.52
0.71 Premium E SI1 61.4 58.0 2817.0 5.77 5.73 3.53
0.9 Ideal J VS2 62.8 55.0 2817.0 6.2 6.16 3.88
0.71 Good E SI1 62.8 64.0 2817.0 5.6 5.54 3.5
0.7 Premium E VS2 62.4 61.0 2818.0 5.66 5.63 3.52
0.7 Premium E VS2 59.3 60.0 2818.0 5.78 5.73 3.41
0.7 Premium E VS2 63.0 60.0 2818.0 5.64 5.6 3.54
1.0 Premium H I1 61.3 60.0 2818.0 6.43 6.39 3.93
0.86 Premium F SI2 59.3 62.0 2818.0 6.36 6.22 3.73
0.8 Ideal H SI1 61.0 57.0 2818.0 6.07 6.0 3.68
0.7 Ideal E VS1 62.9 57.0 2818.0 5.66 5.61 3.54
0.7 Premium E VS1 59.6 57.0 2818.0 5.91 5.83 3.5
0.7 Premium F VS2 61.8 60.0 2818.0 5.69 5.64 3.5
0.7 Premium E VS1 62.7 57.0 2818.0 5.68 5.64 3.55
1.0 Fair H SI2 65.3 62.0 2818.0 6.34 6.12 4.08
0.72 Very Good G VS1 63.8 58.0 2819.0 5.64 5.68 3.61
0.72 Ideal H VS1 62.3 56.0 2819.0 5.73 5.77 3.58
0.7 Good F VS1 59.7 63.0 2819.0 5.76 5.79 3.45
0.86 Good F SI2 64.3 60.0 2819.0 5.97 5.95 3.83
0.71 Ideal G VS1 62.9 58.0 2820.0 5.66 5.69 3.57
0.75 Ideal E SI1 62.0 57.0 2821.0 5.8 5.78 3.59
0.73 Premium E VS2 61.6 59.0 2821.0 5.77 5.73 3.54
0.53 Ideal E VVS1 61.9 55.0 2821.0 5.2 5.21 3.22
0.73 Premium E SI1 61.3 58.0 2821.0 5.83 5.76 3.55
0.73 Good E SI1 63.6 57.0 2821.0 5.72 5.7 3.63
0.73 Premium E SI1 59.6 61.0 2821.0 5.92 5.85 3.51
0.73 Premium E SI1 62.2 59.0 2821.0 5.77 5.68 3.56
0.73 Premium D SI1 61.7 55.0 2821.0 5.84 5.82 3.6
0.73 Very Good E SI1 63.2 58.0 2821.0 5.76 5.7 3.62
0.7 Premium E VS1 60.8 60.0 2822.0 5.74 5.71 3.48
0.72 Premium E VS2 60.3 59.0 2822.0 5.84 5.8 3.51
0.72 Premium E VS2 60.9 60.0 2822.0 5.8 5.76 3.52
0.72 Premium E VS2 62.4 59.0 2822.0 5.77 5.7 3.58
0.7 Premium E VS2 60.2 60.0 2822.0 5.73 5.7 3.44
0.6 Ideal F VVS2 62.0 55.0 2822.0 5.37 5.4 3.34
0.74 Ideal I VVS1 60.8 57.0 2822.0 5.85 5.89 3.57
0.73 Ideal F SI1 62.1 55.0 2822.0 5.75 5.78 3.58
0.71 Premium D SI1 62.7 60.0 2822.0 5.71 5.67 3.57
0.71 Premium D SI1 61.3 58.0 2822.0 5.75 5.73 3.52
0.7 Premium D SI1 60.2 60.0 2822.0 5.82 5.75 3.48
0.7 Ideal D SI1 60.7 56.0 2822.0 5.75 5.72 3.48
0.9 Good J VS2 64.0 61.0 2822.0 6.04 6.03 3.86
0.71 Ideal D SI1 60.2 56.0 2822.0 5.86 5.83 3.52
0.7 Premium E VS2 61.5 59.0 2822.0 5.73 5.68 3.51
0.7 Premium E VS2 62.6 56.0 2822.0 5.71 5.66 3.56
0.7 Ideal D SI1 59.7 58.0 2822.0 5.82 5.77 3.46
0.7 Good E SI1 61.4 64.0 2822.0 5.71 5.66 3.49
0.7 Ideal D SI1 62.5 57.0 2822.0 5.62 5.59 3.51
0.7 Ideal D SI1 61.8 56.0 2822.0 5.73 5.63 3.51
0.7 Premium E VS2 60.7 62.0 2822.0 5.72 5.68 3.46
0.7 Premium F VS2 60.6 58.0 2822.0 5.8 5.72 3.49
0.7 Ideal D SI1 61.4 54.0 2822.0 5.75 5.71 3.52
0.79 Very Good D SI2 62.8 59.0 2823.0 5.86 5.9 3.69
0.9 Good I SI1 63.8 57.0 2823.0 6.06 6.13 3.89
0.71 Premium E VS2 62.3 58.0 2823.0 5.71 5.66 3.54
0.61 Ideal E VVS2 61.3 54.0 2823.0 5.51 5.59 3.4
0.9 Fair H SI2 65.8 54.0 2823.0 6.05 5.98 3.96
0.71 Ideal E SI1 60.5 56.0 2823.0 5.77 5.73 3.47
0.71 Premium D VS2 61.2 59.0 2824.0 5.74 5.69 3.5
0.77 Ideal I VVS2 62.1 57.0 2824.0 5.84 5.86 3.63
0.74 Good E VS1 63.1 58.0 2824.0 5.73 5.75 3.62
0.82 Ideal F SI2 62.4 54.0 2824.0 6.02 5.97 3.74
0.82 Premium E SI2 60.8 60.0 2824.0 6.05 6.03 3.67
0.71 Premium G VS1 62.2 59.0 2825.0 5.73 5.66 3.54
0.83 Premium H SI1 60.0 59.0 2825.0 6.08 6.05 3.64
0.73 Very Good G VS1 62.0 57.0 2825.0 5.75 5.8 3.58
0.83 Premium H SI1 62.5 59.0 2825.0 6.02 5.95 3.74
1.17 Premium J I1 60.2 61.0 2825.0 6.9 6.83 4.13
0.91 Fair H SI2 61.3 67.0 2825.0 6.24 6.19 3.81
0.73 Premium E VS1 62.6 60.0 2826.0 5.75 5.68 3.58
0.7 Good E VS1 57.2 59.0 2826.0 5.94 5.88 3.38
0.9 Premium I SI2 62.2 59.0 2826.0 6.11 6.07 3.79
0.7 Premium E VS1 62.2 58.0 2826.0 5.66 5.6 3.5
0.7 Very Good D VS2 63.3 56.0 2826.0 5.6 5.58 3.54
0.7 Premium E VS1 59.4 61.0 2826.0 5.78 5.74 3.42
0.9 Very Good I SI2 63.5 56.0 2826.0 6.17 6.07 3.88
0.78 Premium F SI1 60.8 60.0 2826.0 5.97 5.94 3.62
0.96 Ideal F I1 60.7 55.0 2826.0 6.41 6.37 3.88
0.7 Very Good D SI1 62.3 59.0 2827.0 5.67 5.7 3.54
0.72 Good D VS2 64.0 54.0 2827.0 5.68 5.7 3.64
0.79 Premium H VVS2 62.6 58.0 2827.0 5.96 5.9 3.71
0.7 Ideal H VVS1 61.6 57.0 2827.0 5.69 5.74 3.52
0.7 Ideal H VVS1 62.3 55.0 2827.0 5.66 5.7 3.54
0.7 Ideal D SI2 60.6 57.0 2828.0 5.74 5.77 3.49
1.01 Premium H SI2 61.6 61.0 2828.0 6.39 6.31 3.91
0.72 Premium F VS1 62.2 58.0 2829.0 5.75 5.7 3.56
0.8 Good E SI2 63.7 54.0 2829.0 5.91 5.87 3.75
0.59 Ideal E VVS1 62.0 56.0 2829.0 5.36 5.38 3.33
0.72 Ideal F VS1 61.7 57.0 2829.0 5.77 5.74 3.55
0.75 Premium E SI2 61.9 57.0 2829.0 5.88 5.82 3.62
0.8 Premium E SI2 60.2 57.0 2829.0 6.05 6.01 3.63
0.71 Very Good E VS2 62.7 59.0 2830.0 5.65 5.7 3.56
0.77 Very Good H SI1 61.7 56.0 2830.0 5.84 5.89 3.62
0.97 Ideal F I1 60.7 56.0 2830.0 6.41 6.43 3.9
0.53 Ideal F VVS1 60.9 57.0 2830.0 5.23 5.29 3.19
0.53 Ideal F VVS1 61.8 57.0 2830.0 5.16 5.19 3.2
0.8 Ideal I VS2 62.1 54.4 2830.0 5.94 5.99 3.7
0.9 Premium G SI1 60.6 62.0 2830.0 6.21 6.13 3.74
0.76 Very Good E SI2 60.8 60.0 2831.0 5.89 5.98 3.61
0.72 Ideal E SI1 62.3 57.0 2831.0 5.7 5.76 3.57
0.75 Ideal E SI1 61.4 57.0 2831.0 5.82 5.87 3.59
0.72 Premium E SI1 62.1 58.0 2831.0 5.73 5.76 3.57
0.79 Ideal G SI1 61.8 56.0 2831.0 5.93 5.91 3.66
0.72 Very Good F VS2 62.5 58.0 2832.0 5.71 5.75 3.58
0.91 Very Good I SI2 62.8 61.0 2832.0 6.15 6.18 3.87
0.71 Premium G VVS2 62.1 57.0 2832.0 5.75 5.65 3.54
0.81 Premium G SI1 63.0 60.0 2832.0 5.87 5.81 3.68
0.82 Ideal H SI1 62.5 57.0 2832.0 5.91 5.97 3.71
0.71 Premium F VS1 62.2 58.0 2832.0 5.72 5.66 3.54
0.9 Good J SI1 64.3 63.0 2832.0 6.05 6.01 3.88
0.8 Very Good I VS2 62.0 58.0 2833.0 5.86 5.95 3.66
0.56 Very Good E IF 61.0 59.0 2833.0 5.28 5.34 3.24
0.7 Very Good D VS2 59.6 61.0 2833.0 5.77 5.8 3.45
0.7 Ideal D VS2 61.0 57.0 2833.0 5.74 5.76 3.51
0.61 Ideal F VVS2 61.7 55.0 2833.0 5.45 5.48 3.37
0.85 Ideal H SI2 62.5 57.0 2833.0 6.02 6.07 3.78
0.7 Ideal F SI1 60.7 57.0 2833.0 5.73 5.75 3.49
0.8 Ideal G VS2 62.2 56.0 2834.0 5.94 5.87 3.67
0.8 Ideal H VS2 62.8 57.0 2834.0 5.91 5.87 3.7
0.51 Very Good D VVS1 59.9 58.0 2834.0 5.16 5.19 3.1
0.53 Ideal F VVS1 61.4 57.0 2834.0 5.2 5.23 3.2
0.78 Ideal I VS2 61.8 55.0 2834.0 5.92 5.95 3.67
0.9 Very Good J SI1 63.4 54.0 2834.0 6.17 6.14 3.9
0.9 Fair G SI2 65.3 59.0 2834.0 6.07 6.0 3.94
0.77 Ideal E SI2 60.7 55.0 2834.0 6.01 5.95 3.63
0.73 Ideal F VS1 61.2 56.0 2835.0 5.89 5.81 3.58
0.63 Ideal F VVS2 61.9 57.0 2835.0 5.47 5.51 3.4
0.7 Ideal E VS2 61.5 54.0 2835.0 5.7 5.75 3.52
0.72 Ideal E VS2 62.8 57.0 2835.0 5.71 5.73 3.59
0.72 Ideal E SI1 61.0 57.0 2835.0 5.78 5.8 3.53
0.75 Premium F VS2 59.6 59.0 2835.0 6.04 5.94 3.57
0.82 Very Good H SI1 60.7 56.0 2836.0 6.04 6.06 3.67
0.71 Good E VS2 62.8 60.0 2836.0 5.6 5.65 3.53
0.7 Premium E VS1 62.6 59.0 2837.0 5.69 5.66 3.55
0.7 Ideal E VS1 61.8 56.0 2837.0 5.74 5.69 3.53
0.71 Ideal F SI1 59.8 53.0 2838.0 5.86 5.82 3.49
0.76 Very Good H SI1 60.9 55.0 2838.0 5.92 5.94 3.61
0.82 Fair F SI1 64.9 58.0 2838.0 5.83 5.79 3.77
0.72 Premium F VS1 58.8 60.0 2838.0 5.91 5.89 3.47
0.7 Premium F VS2 62.3 58.0 2838.0 5.72 5.64 3.54
0.7 Premium F VS2 61.7 58.0 2838.0 5.69 5.63 3.49
0.7 Premium G VS1 62.6 55.0 2838.0 5.73 5.64 3.56
0.7 Premium F VS2 59.4 61.0 2838.0 5.83 5.79 3.45
0.7 Very Good E SI1 63.5 59.0 2838.0 5.53 5.49 3.5
0.7 Premium F VS2 60.9 61.0 2838.0 5.71 5.66 3.46
0.7 Premium F VS2 59.5 58.0 2838.0 5.85 5.75 3.45
0.7 Premium G VS1 63.0 60.0 2838.0 5.64 5.57 3.53
0.74 Very Good E SI1 60.0 57.0 2839.0 5.85 5.89 3.52
0.71 Ideal F VS1 61.5 57.0 2839.0 5.74 5.71 3.52
0.7 Ideal F VS1 61.6 54.0 2839.0 5.75 5.72 3.53
0.71 Ideal F VS1 62.1 55.0 2839.0 5.82 5.68 3.57
0.71 Premium F VS1 59.1 61.0 2839.0 5.84 5.81 3.44
0.71 Premium F VS1 59.0 60.0 2839.0 5.82 5.8 3.43
0.71 Premium F VS1 60.5 58.0 2839.0 5.75 5.72 3.47
0.7 Ideal F VS1 62.4 53.0 2839.0 5.73 5.71 3.57
0.73 Ideal G VS2 61.8 54.0 2839.0 5.8 5.82 3.59
0.7 Ideal E VS2 62.1 54.0 2839.0 5.69 5.72 3.54
0.7 Ideal G VS1 61.3 57.0 2839.0 5.71 5.74 3.51
0.71 Premium G VVS2 60.3 58.0 2839.0 5.82 5.78 3.5
0.71 Premium F VS1 59.2 58.0 2839.0 5.87 5.82 3.46
0.79 Premium G VS2 59.3 62.0 2839.0 6.09 6.01 3.59
0.71 Premium F VS1 62.7 59.0 2839.0 5.7 5.62 3.55
0.77 Very Good H VS1 61.0 60.0 2840.0 5.9 5.87 3.59
0.75 Very Good F SI2 59.8 56.0 2840.0 5.85 5.92 3.52
0.7 Ideal F SI1 61.0 56.0 2840.0 5.75 5.8 3.52
0.71 Premium F VS2 59.3 56.0 2840.0 5.88 5.82 3.47
0.92 Ideal D SI2 61.9 56.0 2840.0 6.27 6.2 3.86
0.83 Premium F SI2 61.4 59.0 2840.0 6.08 6.04 3.72
0.7 Premium H VVS1 59.2 60.0 2840.0 5.87 5.78 3.45
0.73 Premium F VS2 60.3 59.0 2841.0 5.9 5.87 3.55
0.71 Very Good D VS1 63.4 55.0 2841.0 5.69 5.61 3.58
0.73 Very Good D SI1 63.9 57.0 2841.0 5.66 5.71 3.63
0.82 Ideal F SI2 61.7 53.0 2841.0 6.0 6.12 3.74
0.82 Ideal F SI2 62.3 56.0 2841.0 5.96 6.02 3.73
0.82 Very Good F SI2 59.7 57.0 2841.0 6.12 6.14 3.66
0.52 Ideal F VVS1 61.2 56.0 2841.0 5.19 5.21 3.18
1.0 Premium F I1 58.9 60.0 2841.0 6.6 6.55 3.87
0.95 Fair G SI1 66.7 56.0 2841.0 6.16 6.03 4.06
0.73 Ideal D SI1 61.4 57.0 2841.0 5.76 5.8 3.55
0.73 Premium F VS2 59.9 59.0 2841.0 5.87 5.77 3.5
0.73 Premium G VS1 61.4 58.0 2841.0 5.82 5.77 3.56
0.8 Ideal I VS1 62.6 54.0 2842.0 5.92 5.96 3.72
0.7 Premium F VS2 58.7 61.0 2842.0 5.8 5.72 3.38
0.7 Very Good E VS2 60.2 62.0 2843.0 5.71 5.75 3.45
0.7 Very Good E VS2 62.7 58.0 2843.0 5.65 5.67 3.55
0.71 Very Good E VS2 59.4 58.0 2843.0 5.76 5.82 3.44
0.81 Very Good F SI2 63.2 58.0 2843.0 5.91 5.92 3.74
0.71 Very Good D SI1 61.5 58.0 2843.0 5.73 5.79 3.54
0.73 Ideal G VVS2 61.3 57.0 2843.0 5.81 5.84 3.57
0.73 Very Good F VS1 61.8 59.0 2843.0 5.73 5.79 3.56
0.72 Ideal E VS2 62.0 57.0 2843.0 5.71 5.74 3.55
0.81 Ideal F SI2 62.1 57.0 2843.0 5.91 5.95 3.68
0.71 Ideal G VVS2 60.7 57.0 2843.0 5.81 5.78 3.52
0.73 Very Good E SI1 57.7 61.0 2844.0 5.92 5.96 3.43
0.7 Very Good E VS1 62.0 59.0 2844.0 5.65 5.68 3.51
1.01 Ideal I I1 61.5 57.0 2844.0 6.45 6.46 3.97
1.01 Good I I1 63.1 57.0 2844.0 6.35 6.39 4.02
0.79 Ideal H VS2 62.5 57.0 2844.0 5.91 5.93 3.7
0.7 Very Good E VS2 61.8 59.0 2845.0 5.65 5.68 3.5
0.7 Very Good E VS2 58.9 60.0 2845.0 5.83 5.85 3.44
0.8 Good H VS2 63.4 60.0 2845.0 5.92 5.82 3.72
1.27 Premium H SI2 59.3 61.0 2845.0 7.12 7.05 4.2
0.79 Ideal D SI1 61.5 56.0 2846.0 5.96 5.91 3.65
0.72 Very Good F VS1 60.2 59.0 2846.0 5.79 5.84 3.5
0.73 Ideal H VVS2 61.6 56.0 2846.0 5.79 5.84 3.58
1.01 Fair H SI2 65.4 59.0 2846.0 6.3 6.26 4.11
1.01 Good H I1 64.2 61.0 2846.0 6.25 6.18 3.99
0.73 Ideal E SI1 59.1 59.0 2846.0 5.92 5.95 3.51
0.7 Ideal E SI1 61.6 57.0 2846.0 5.71 5.76 3.53
0.7 Good F VS2 59.1 61.0 2846.0 5.76 5.84 3.43
0.77 Premium E SI1 62.9 59.0 2846.0 5.84 5.79 3.66
0.77 Premium G VS2 61.3 60.0 2846.0 5.91 5.81 3.59
0.77 Premium G VS1 61.4 58.0 2846.0 5.94 5.89 3.63
0.84 Very Good H SI1 61.2 57.0 2847.0 6.1 6.12 3.74
0.72 Ideal E SI1 60.3 57.0 2847.0 5.83 5.85 3.52
0.76 Premium D SI1 61.1 59.0 2847.0 5.93 5.88 3.61
0.7 Very Good G VVS2 62.9 59.0 2848.0 5.61 5.68 3.55
0.54 Ideal D VVS2 61.5 55.0 2848.0 5.25 5.29 3.24
0.75 Fair D SI2 64.6 57.0 2848.0 5.74 5.72 3.7
0.79 Good E SI1 64.1 54.0 2849.0 5.86 5.84 3.75
0.74 Very Good E VS1 63.1 58.0 2849.0 5.75 5.73 3.62
0.7 Very Good E VS2 61.0 60.0 2850.0 5.74 5.77 3.51
0.7 Ideal F VS2 60.8 59.0 2850.0 5.69 5.79 3.49
0.75 Ideal J SI1 61.5 56.0 2850.0 5.83 5.87 3.6
1.2 Very Good H I1 63.1 60.0 2850.0 6.75 6.67 4.23
0.8 Very Good F SI1 63.4 57.0 2851.0 5.89 5.82 3.71
0.66 Ideal D VS1 62.1 56.0 2851.0 5.54 5.57 3.45
0.87 Very Good F SI2 61.0 63.0 2851.0 6.22 6.07 3.75
0.86 Premium H SI1 62.7 59.0 2851.0 6.04 5.98 3.77
0.74 Ideal F SI1 61.0 57.0 2851.0 5.85 5.81 3.56
0.58 Very Good E IF 60.6 59.0 2852.0 5.37 5.43 3.27
0.78 Ideal I VS1 61.5 57.0 2852.0 5.88 5.92 3.63
0.74 Ideal G SI1 61.3 55.0 2852.0 5.85 5.86 3.59
0.73 Ideal E SI1 62.7 55.0 2852.0 5.7 5.79 3.6
0.91 Very Good I SI1 63.5 57.0 2852.0 6.12 6.07 3.87
0.71 Premium F VS2 62.6 58.0 2853.0 5.67 5.7 3.56
0.71 Good G VS1 63.5 55.0 2853.0 5.64 5.66 3.59
0.79 Ideal D SI2 62.8 57.0 2853.0 5.9 5.85 3.69
0.79 Premium D SI2 60.0 60.0 2853.0 6.07 6.03 3.63
0.71 Premium E SI1 62.7 58.0 2853.0 5.73 5.66 3.57
0.82 Premium I VS1 61.9 58.0 2853.0 5.99 5.97 3.7
0.78 Very Good H VS1 61.9 57.1 2854.0 5.87 5.95 3.66
0.7 Very Good E VS1 62.4 56.0 2854.0 5.64 5.7 3.54
1.12 Premium H I1 59.1 61.0 2854.0 6.78 6.75 4.0
0.73 Premium E VS2 62.0 57.0 2854.0 5.86 5.76 3.6
0.91 Fair J VS2 64.4 62.0 2854.0 6.06 6.03 3.89
0.91 Fair J VS2 65.4 60.0 2854.0 6.04 6.0 3.94
0.91 Good J VS2 64.2 58.0 2854.0 6.12 6.09 3.92
0.91 Fair H SI1 65.8 58.0 2854.0 6.04 6.01 3.96
0.7 Premium E VS1 58.4 59.0 2854.0 5.91 5.83 3.43
0.68 Premium F VVS2 61.7 57.0 2854.0 5.67 5.64 3.49
0.73 Very Good F VS2 62.5 57.0 2855.0 5.7 5.75 3.58
1.03 Good J SI1 63.6 57.0 2855.0 6.38 6.29 4.03
0.74 Premium D VS2 62.4 57.0 2855.0 5.8 5.74 3.6
0.98 Fair E SI2 53.3 67.0 2855.0 6.82 6.74 3.61
1.02 Fair I SI1 53.0 63.0 2856.0 6.84 6.77 3.66
1.0 Fair G SI2 67.8 61.0 2856.0 5.96 5.9 4.02
1.02 Ideal H SI2 61.6 55.0 2856.0 6.49 6.43 3.98
0.6 Ideal F VVS2 60.8 57.0 2856.0 5.44 5.49 3.32
0.8 Ideal G SI2 61.6 56.0 2856.0 5.97 6.01 3.69
0.97 Ideal F I1 60.7 56.0 2856.0 6.43 6.41 3.9
1.0 Fair I SI1 67.9 62.0 2856.0 6.19 6.03 4.15
0.26 Ideal E VS1 62.3 57.0 556.0 4.05 4.08 2.53
0.26 Ideal E VS1 62.1 56.0 556.0 4.09 4.12 2.55
0.36 Ideal H SI1 61.9 55.0 556.0 4.57 4.59 2.83
0.34 Good G VS2 57.5 61.0 556.0 4.6 4.66 2.66
0.34 Good E SI1 63.3 57.0 556.0 4.44 4.47 2.82
0.34 Good E SI1 63.5 55.0 556.0 4.44 4.47 2.83
0.34 Good E SI1 63.4 55.0 556.0 4.44 4.46 2.82
0.34 Very Good G VS2 59.6 62.0 556.0 4.54 4.56 2.71
0.34 Ideal E SI1 62.2 54.0 556.0 4.47 4.5 2.79
0.32 Good E VS2 64.1 54.0 556.0 4.34 4.37 2.79
0.31 Ideal I VVS1 61.6 55.0 557.0 4.36 4.41 2.7
0.31 Ideal I VVS1 61.3 56.0 557.0 4.36 4.38 2.68
0.31 Ideal I VVS1 62.3 54.0 557.0 4.37 4.4 2.73
0.31 Ideal I VVS1 62.0 54.0 557.0 4.37 4.4 2.72
0.31 Ideal I VVS1 62.7 53.0 557.0 4.33 4.35 2.72
0.31 Ideal I VVS1 62.2 53.0 557.0 4.36 4.38 2.72
0.31 Ideal G VS2 62.2 53.6 557.0 4.32 4.35 2.7
0.31 Ideal H VS1 61.6 54.8 557.0 4.35 4.37 2.69
0.31 Ideal H VS1 61.8 54.2 557.0 4.33 4.37 2.69
0.33 Premium G SI2 59.4 59.0 557.0 4.52 4.5 2.68
0.33 Premium F SI2 62.3 58.0 557.0 4.43 4.4 2.75
0.33 Premium G SI2 62.6 58.0 557.0 4.42 4.4 2.76
0.33 Ideal G SI2 61.9 56.0 557.0 4.45 4.41 2.74
0.33 Premium F SI2 63.0 58.0 557.0 4.42 4.4 2.78
0.33 Premium J VS1 62.8 58.0 557.0 4.41 4.38 2.76
0.33 Premium J VS1 61.5 61.0 557.0 4.46 4.39 2.72
0.33 Ideal J VS1 62.1 55.0 557.0 4.44 4.41 2.75
0.33 Ideal I SI1 63.0 57.0 557.0 4.39 4.37 2.76
0.33 Good I SI1 63.6 53.0 557.0 4.43 4.4 2.81
0.33 Premium I SI1 60.4 59.0 557.0 4.54 4.5 2.73
1.0 Fair H SI2 66.1 56.0 2856.0 6.21 5.97 4.04
0.77 Premium F SI1 60.8 59.0 2856.0 5.92 5.86 3.58
0.77 Premium F SI1 61.0 58.0 2856.0 5.94 5.9 3.61
0.7 Good E VVS2 60.1 63.0 2857.0 5.68 5.71 3.42
0.9 Very Good G SI2 63.1 58.0 2857.0 6.08 6.02 3.82
0.72 Ideal E SI1 62.3 57.0 2857.0 5.76 5.7 3.57
0.9 Premium I VS2 61.9 59.0 2857.0 6.2 6.14 3.82
0.72 Premium E SI1 62.1 58.0 2857.0 5.76 5.73 3.57
0.7 Ideal G VVS2 62.1 56.0 2858.0 5.63 5.71 3.52
0.81 Very Good F SI1 61.3 57.0 2858.0 6.02 6.05 3.7
0.81 Very Good F SI1 61.7 57.0 2858.0 6.0 6.05 3.72
0.71 Premium E VS2 61.0 60.0 2858.0 5.76 5.69 3.49
0.7 Premium E VS2 61.4 59.0 2858.0 5.73 5.7 3.51
0.71 Premium E VS2 61.5 60.0 2858.0 5.76 5.68 3.52
0.71 Very Good E VS2 63.5 59.0 2858.0 5.68 5.59 3.58
0.92 Premium J SI1 62.9 58.0 2858.0 6.22 6.18 3.9
0.76 Ideal E SI1 62.7 54.0 2858.0 5.88 5.83 3.67
0.73 Ideal D SI1 61.5 56.0 2858.0 5.84 5.8 3.58
0.71 Premium D VS2 60.4 62.0 2858.0 5.74 5.72 3.46
0.7 Good E VVS2 63.6 62.0 2858.0 5.61 5.58 3.56
0.9 Fair G SI2 64.5 56.0 2858.0 6.06 6.0 3.89
0.71 Fair D VS2 56.9 65.0 2858.0 5.89 5.84 3.34
0.7 Ideal D VS2 61.0 57.0 2859.0 5.76 5.74 3.51
0.7 Premium D VS2 62.4 56.0 2859.0 5.72 5.66 3.55
0.77 Premium F VS1 60.9 60.0 2859.0 5.91 5.88 3.59
0.71 Ideal G VS1 61.5 56.0 2859.0 5.74 5.78 3.54
0.7 Premium D VS2 59.6 61.0 2859.0 5.8 5.77 3.45
0.75 Fair F VS1 55.8 70.0 2859.0 6.09 5.98 3.37
0.83 Premium E SI2 59.2 60.0 2859.0 6.17 6.12 3.64
0.71 Very Good F VS2 61.3 61.0 2860.0 5.68 5.73 3.5
0.9 Very Good J SI2 63.6 58.0 2860.0 6.07 6.1 3.87
0.6 Ideal E VVS2 61.9 54.9 2860.0 5.41 5.44 3.35
0.71 Premium D VS1 62.9 57.0 2860.0 5.66 5.6 3.54
0.53 Ideal F VVS1 61.4 57.0 2860.0 5.23 5.2 3.2
0.71 Premium D SI1 60.7 58.0 2861.0 5.95 5.78 3.56
0.62 Ideal G VVS2 61.6 56.0 2861.0 5.45 5.48 3.37
0.62 Ideal G VVS2 61.6 56.0 2861.0 5.48 5.51 3.38
0.9 Premium I SI1 63.0 58.0 2861.0 6.09 6.01 3.81
0.62 Fair F IF 60.1 61.0 2861.0 5.53 5.56 3.33
0.82 Premium E SI2 61.7 59.0 2861.0 6.01 5.98 3.7
0.66 Premium D VS1 61.0 58.0 2861.0 5.67 5.57 3.43
0.7 Very Good D SI1 62.5 55.0 2862.0 5.67 5.72 3.56
0.8 Very Good F SI1 62.6 58.0 2862.0 5.9 5.92 3.7
0.8 Very Good D SI2 62.5 59.0 2862.0 5.88 5.92 3.69
0.79 Premium F SI1 62.3 54.0 2862.0 5.97 5.91 3.7
0.71 Very Good F VVS1 63.2 60.0 2862.0 5.65 5.61 3.56
0.7 Ideal H VS2 61.1 57.0 2862.0 5.71 5.74 3.5
0.7 Very Good E VS2 58.7 63.0 2862.0 5.73 5.69 3.35
0.79 Premium H VS1 60.0 60.0 2862.0 6.07 5.99 3.64
0.7 Premium E VS2 59.5 59.0 2862.0 5.82 5.77 3.45
1.22 Premium E I1 60.9 57.0 2862.0 6.93 6.88 4.21
1.01 Fair E SI2 67.6 57.0 2862.0 6.21 6.11 4.18
0.73 Premium E VS2 62.5 61.0 2862.0 5.78 5.64 3.59
0.91 Good I VS2 64.3 58.0 2863.0 6.05 6.09 3.9
0.71 Ideal D SI1 60.8 56.0 2863.0 5.8 5.77 3.52
0.83 Premium G SI1 62.3 58.0 2863.0 6.01 5.97 3.73
0.84 Premium F SI2 62.3 59.0 2863.0 6.06 6.01 3.76
0.71 Premium D SI1 61.0 61.0 2863.0 5.82 5.75 3.53
0.71 Premium D SI1 59.7 59.0 2863.0 5.82 5.8 3.47
0.71 Premium D SI1 61.7 56.0 2863.0 5.8 5.68 3.54
0.71 Ideal D SI1 61.7 57.0 2863.0 5.75 5.7 3.53
0.71 Premium D SI1 61.4 58.0 2863.0 5.79 5.75 3.54
0.71 Premium D SI1 60.6 58.0 2863.0 5.79 5.77 3.5
0.91 Premium J SI1 59.5 62.0 2863.0 6.4 6.18 3.74
0.9 Premium J VS2 59.8 62.0 2863.0 6.24 6.21 3.72
0.71 Premium H VVS2 61.5 62.0 2863.0 5.74 5.68 3.51
0.71 Premium E SI1 59.1 61.0 2863.0 5.84 5.8 3.44
0.72 Ideal F VS2 59.5 57.0 2863.0 5.91 5.86 3.5
0.72 Premium E SI1 60.9 60.0 2863.0 5.78 5.74 3.51
0.71 Ideal E VS2 61.0 55.0 2863.0 5.79 5.75 3.52
0.81 Ideal E SI2 60.3 57.0 2864.0 6.07 6.04 3.65
0.83 Very Good I VS2 61.6 58.0 2865.0 6.05 6.07 3.73
0.73 Premium D SI1 60.8 55.0 2865.0 5.87 5.81 3.55
0.56 Very Good D VVS1 62.0 56.0 2866.0 5.25 5.3 3.27
0.56 Very Good D VVS1 61.8 55.0 2866.0 5.27 5.31 3.27
0.71 Ideal E VS1 62.2 55.0 2866.0 5.74 5.7 3.56
0.7 Ideal H VVS1 62.3 58.0 2866.0 5.66 5.7 3.54
0.96 Premium I SI1 61.3 58.0 2866.0 6.39 6.3 3.89
0.71 Very Good H VVS1 62.9 57.0 2867.0 5.67 5.69 3.57
0.7 Ideal D VS2 62.4 57.0 2867.0 5.68 5.61 3.52
0.71 Ideal H VVS1 60.4 57.0 2867.0 5.78 5.81 3.5
0.8 Premium H VS2 61.2 53.0 2867.0 6.05 5.98 3.68
0.95 Premium F SI2 58.4 57.0 2867.0 6.49 6.41 3.77
0.82 Ideal F SI2 62.3 56.0 2867.0 5.99 5.95 3.72
0.52 Ideal F VVS1 61.2 56.0 2867.0 5.21 5.19 3.18
0.82 Ideal F SI2 61.7 53.0 2867.0 6.12 6.0 3.74
0.82 Ideal F SI2 62.3 56.0 2867.0 6.02 5.96 3.73
0.82 Premium F SI2 59.7 57.0 2867.0 6.14 6.12 3.66
0.8 Ideal G SI1 61.3 57.0 2867.0 5.96 5.91 3.64
0.96 Fair F SI2 68.2 61.0 2867.0 6.07 5.88 4.1
0.72 Ideal I VS1 62.4 55.0 2868.0 5.72 5.75 3.58
0.62 Ideal G IF 60.5 57.0 2868.0 5.52 5.56 3.35
0.79 Premium E SI2 61.0 58.0 2868.0 5.96 5.9 3.62
0.75 Very Good E SI1 63.1 56.0 2868.0 5.78 5.7 3.62
1.08 Premium D I1 61.9 60.0 2869.0 6.55 6.48 4.03
0.72 Ideal E SI1 60.8 55.0 2869.0 5.77 5.84 3.53
0.62 Ideal G IF 61.8 56.0 2869.0 5.43 5.47 3.37
0.73 Ideal G VVS2 61.3 57.0 2869.0 5.84 5.81 3.57
0.72 Ideal H VVS2 60.9 57.0 2869.0 5.79 5.77 3.52
0.52 Premium F VVS2 61.8 60.0 2870.0 5.16 5.13 3.18
0.83 Ideal E SI2 62.2 57.0 2870.0 6.0 6.05 3.75
0.64 Premium E VVS2 62.1 58.0 2870.0 5.56 5.51 3.44
0.8 Ideal G SI1 62.5 57.0 2870.0 5.94 5.9 3.7
0.74 Ideal H SI1 62.1 56.0 2870.0 5.77 5.83 3.6
0.72 Ideal F SI1 61.5 56.0 2870.0 5.72 5.79 3.54
0.82 Ideal H VS2 59.5 57.0 2870.0 6.12 6.09 3.63
0.73 Premium E VS1 61.3 59.0 2870.0 5.81 5.78 3.55
1.04 Premium I I1 61.6 61.0 2870.0 6.47 6.45 3.98
0.73 Very Good E SI1 61.3 58.0 2871.0 5.76 5.83 3.55
0.73 Good E SI1 63.6 57.0 2871.0 5.7 5.72 3.63
0.9 Premium J SI1 62.8 59.0 2871.0 6.13 6.03 3.82
0.75 Ideal I SI1 61.8 55.0 2871.0 5.83 5.85 3.61
0.79 Ideal G SI1 62.6 55.0 2871.0 5.91 5.95 3.71
0.7 Good D SI1 62.5 56.7 2872.0 5.59 5.62 3.51
0.75 Very Good D SI1 60.7 55.0 2872.0 5.87 5.92 3.58
1.02 Ideal I I1 61.7 56.0 2872.0 6.44 6.49 3.99
0.7 Very Good G SI2 59.0 62.0 2872.0 5.79 5.81 3.42
0.7 Ideal D SI1 61.8 56.0 2872.0 5.63 5.73 3.51
0.7 Good E SI1 61.4 64.0 2872.0 5.66 5.71 3.49
0.7 Ideal D SI1 61.4 54.0 2872.0 5.71 5.75 3.52
0.7 Ideal D SI1 60.7 56.0 2872.0 5.72 5.75 3.48
0.7 Very Good D SI1 60.2 60.0 2872.0 5.75 5.82 3.48
0.72 Very Good E VS2 58.3 57.0 2872.0 5.89 5.94 3.45
0.74 Ideal E SI1 62.3 58.0 2872.0 5.74 5.78 3.59
0.84 Good G SI1 65.1 55.0 2872.0 5.88 5.97 3.86
0.76 Very Good F VS2 62.0 58.0 2873.0 5.8 5.86 3.62
0.77 Very Good E SI1 63.2 58.0 2873.0 5.8 5.84 3.68
0.76 Ideal E SI2 62.8 56.0 2873.0 5.78 5.82 3.64
1.0 Ideal I SI2 61.7 56.0 2873.0 6.45 6.41 3.97
1.0 Fair H SI1 65.5 62.0 2873.0 6.14 6.07 4.0
0.9 Fair I SI1 65.7 58.0 2873.0 6.03 6.0 3.95
0.9 Premium J SI1 61.8 58.0 2873.0 6.16 6.13 3.8
0.9 Good J SI1 64.0 61.0 2873.0 6.0 5.96 3.83
0.9 Fair I SI1 65.3 61.0 2873.0 5.98 5.94 3.89
0.9 Fair I SI1 65.8 56.0 2873.0 6.01 5.96 3.94
0.9 Premium J SI1 60.9 61.0 2873.0 6.26 6.22 3.8
0.78 Premium F VS2 62.6 58.0 2874.0 5.91 5.82 3.67
0.71 Premium D VS2 61.2 59.0 2874.0 5.69 5.74 3.5
0.7 Premium F VS1 59.0 59.0 2874.0 5.79 5.77 3.41
0.7 Premium F VS1 60.8 62.0 2874.0 5.71 5.67 3.46
0.7 Premium G VVS2 61.8 58.0 2874.0 5.67 5.63 3.49
0.7 Ideal F VS1 61.0 55.0 2874.0 5.77 5.73 3.51
0.7 Ideal F VS1 61.6 55.0 2874.0 5.75 5.71 3.53
0.7 Ideal F VS1 62.4 56.0 2874.0 5.69 5.65 3.54
0.7 Premium G VVS2 62.9 59.0 2874.0 5.68 5.61 3.55
1.0 Fair H SI2 67.7 60.0 2875.0 6.11 5.98 4.09
0.77 Ideal H SI1 62.4 56.0 2875.0 5.84 5.9 3.66
1.0 Fair J VS1 65.5 55.0 2875.0 6.3 6.25 4.11
1.0 Fair I SI1 66.3 61.0 2875.0 6.08 6.03 4.01
1.0 Fair H SI2 69.5 55.0 2875.0 6.17 6.1 4.26
0.73 Premium E VS1 62.6 60.0 2876.0 5.68 5.75 3.58
0.79 Premium E VS2 60.6 53.0 2876.0 6.04 5.98 3.64
0.72 Very Good H VS1 62.2 54.0 2877.0 5.74 5.76 3.57
0.71 Ideal E VS1 62.4 56.0 2877.0 5.75 5.7 3.57
0.74 Ideal G VS2 62.3 55.0 2877.0 5.8 5.83 3.62
0.7 Good H VVS1 62.7 56.0 2877.0 5.6 5.66 3.53
0.7 Good F VS1 59.1 62.0 2877.0 5.82 5.86 3.44
0.79 Very Good F SI1 62.8 59.0 2878.0 5.86 5.89 3.69
0.79 Very Good F SI1 62.7 60.0 2878.0 5.82 5.89 3.67
0.79 Very Good D SI2 59.7 58.0 2878.0 6.0 6.07 3.6
0.71 Ideal I VS2 61.5 55.0 2878.0 5.76 5.78 3.55
0.79 Ideal F SI1 62.8 56.0 2878.0 5.88 5.9 3.7
0.73 Very Good F SI1 61.4 56.0 2879.0 5.81 5.86 3.58
0.63 Premium E IF 60.3 62.0 2879.0 5.55 5.53 3.34
0.7 Premium F VS1 60.4 60.0 2879.0 5.73 5.7 3.45
0.71 Premium F VS1 62.7 58.0 2879.0 5.71 5.67 3.57
0.84 Ideal G SI2 61.0 56.0 2879.0 6.13 6.1 3.73
0.84 Ideal G SI2 62.3 55.0 2879.0 6.08 6.03 3.77
1.02 Ideal J SI2 60.3 54.0 2879.0 6.53 6.5 3.93
0.72 Fair F VS1 56.9 69.0 2879.0 5.93 5.77 3.33
0.72 Ideal F VS1 62.0 56.0 2879.0 5.76 5.73 3.56
0.92 Very Good J SI2 58.7 61.0 2880.0 6.34 6.43 3.75
0.74 Very Good D SI1 63.9 57.0 2880.0 5.72 5.74 3.66
0.7 Ideal H VVS1 62.0 55.0 2881.0 5.74 5.71 3.55
0.71 Very Good E VS2 60.0 59.0 2881.0 5.84 5.83 3.5
1.05 Premium H I1 62.0 59.0 2881.0 6.5 6.47 4.02
0.7 Very Good H IF 62.8 56.0 2882.0 5.62 5.65 3.54
0.54 Ideal F VVS1 61.8 56.0 2882.0 5.23 5.26 3.24
0.73 Premium F VS2 59.9 58.0 2882.0 5.87 5.84 3.51
0.88 Fair F SI1 56.6 65.0 2882.0 6.39 6.32 3.6
0.73 Premium F VS2 58.7 57.0 2882.0 5.97 5.92 3.49
0.72 Ideal D SI1 61.8 56.0 2883.0 5.75 5.81 3.57
0.9 Good H SI2 62.7 64.0 2883.0 6.09 6.0 3.79
0.9 Fair H SI2 65.0 61.0 2883.0 6.01 5.96 3.89
1.03 Fair I SI2 65.3 55.0 2884.0 6.32 6.27 4.11
0.84 Very Good F SI1 63.8 57.0 2885.0 5.95 6.0 3.81
1.01 Premium I SI1 62.7 60.0 2885.0 6.36 6.27 3.96
0.77 Ideal D SI2 61.5 55.0 2885.0 5.9 5.93 3.64
0.8 Fair E SI1 56.3 63.0 2885.0 6.22 6.14 3.48
0.9 Fair D SI2 66.9 57.0 2885.0 6.02 5.9 3.99
0.73 Ideal E SI1 61.4 56.0 2886.0 5.79 5.81 3.56
0.72 Ideal E SI1 62.7 55.0 2886.0 5.64 5.69 3.55
0.71 Very Good D SI1 62.4 54.0 2887.0 5.71 5.79 3.59
0.7 Premium E VS1 62.6 59.0 2887.0 5.66 5.69 3.55
0.79 Ideal I VS1 61.7 59.0 2888.0 5.93 5.96 3.67
0.72 Very Good G VVS2 62.5 58.0 2889.0 5.68 5.72 3.56
0.7 Very Good E VS2 63.5 54.0 2889.0 5.62 5.66 3.58
0.7 Very Good F VS1 62.2 58.0 2889.0 5.64 5.75 3.54
0.9 Good H SI2 63.5 58.0 2889.0 6.09 6.14 3.88
0.71 Very Good F VS1 62.8 56.0 2889.0 5.69 5.72 3.58
0.5 Ideal E VVS2 62.2 54.0 2889.0 5.08 5.12 3.17
0.5 Ideal E VVS2 62.2 54.0 2889.0 5.09 5.11 3.17
0.74 Ideal F SI1 61.2 56.0 2889.0 5.83 5.87 3.58
0.77 Premium F VS2 61.8 56.0 2889.0 5.94 5.9 3.66
0.77 Premium E SI1 59.8 61.0 2889.0 5.99 5.95 3.57
0.8 Ideal F SI1 61.5 54.0 2890.0 6.07 6.0 3.71
0.8 Ideal F SI1 62.4 57.0 2890.0 5.9 5.87 3.67
0.8 Premium F SI1 61.5 60.0 2890.0 5.97 5.94 3.66
0.8 Good F SI1 63.8 59.0 2890.0 5.87 5.83 3.73
0.66 Ideal G VVS1 61.5 56.0 2890.0 5.61 5.58 3.44
0.71 Very Good E VS2 61.2 58.0 2891.0 5.71 5.79 3.52
0.71 Ideal F VS2 61.2 56.0 2891.0 5.73 5.77 3.52
0.71 Ideal E VS2 61.6 56.0 2891.0 5.74 5.76 3.54
0.71 Ideal E VS2 62.7 56.0 2891.0 5.71 5.75 3.59
0.72 Ideal D SI1 61.1 56.0 2891.0 5.78 5.81 3.54
0.71 Good D VS2 62.3 61.0 2891.0 5.7 5.73 3.56
0.86 Ideal H SI2 61.8 55.0 2892.0 6.12 6.14 3.79
1.19 Fair H I1 65.1 59.0 2892.0 6.62 6.55 4.29
0.71 Very Good F VS1 62.6 55.0 2893.0 5.66 5.71 3.56
0.82 Very Good G SI2 62.5 56.0 2893.0 5.99 6.04 3.76
0.71 Ideal G VVS2 61.5 57.0 2893.0 5.73 5.75 3.53
0.75 Ideal F VS2 62.5 57.0 2893.0 5.78 5.83 3.63
0.7 Very Good H VVS1 59.2 60.0 2893.0 5.87 5.78 3.45
0.8 Ideal G SI2 62.5 55.0 2893.0 5.89 5.92 3.69
0.82 Good G SI2 59.9 62.0 2893.0 6.02 6.04 3.61
0.82 Very Good G SI1 63.4 55.0 2893.0 6.0 5.93 3.78
0.82 Premium G SI1 59.9 59.0 2893.0 6.09 6.06 3.64
0.81 Very Good E SI2 62.4 57.0 2894.0 5.91 5.99 3.71
0.81 Ideal G SI2 62.2 57.0 2894.0 5.96 6.0 3.72
0.76 Ideal F SI1 61.4 56.0 2894.0 5.88 5.92 3.62
0.71 Very Good G VS2 60.9 56.0 2895.0 5.75 5.78 3.51
0.7 Very Good F VS1 61.8 59.0 2895.0 5.66 5.76 3.53
0.7 Ideal G VVS2 62.1 53.0 2895.0 5.71 5.75 3.56
0.74 Very Good G VS1 59.8 58.0 2896.0 5.85 5.89 3.51
0.77 Very Good G VS2 61.3 60.0 2896.0 5.81 5.91 3.59
0.77 Very Good G VS2 58.3 63.0 2896.0 6.0 6.05 3.51
0.53 Ideal F VVS1 61.6 56.0 2896.0 5.18 5.24 3.21
0.79 Ideal D SI1 61.5 56.0 2896.0 5.91 5.96 3.65
0.73 Ideal E SI2 61.5 55.0 2896.0 5.82 5.86 3.59
0.77 Ideal D SI2 62.1 56.0 2896.0 5.83 5.89 3.64
0.77 Premium E SI1 60.9 58.0 2896.0 5.94 5.88 3.6
1.01 Very Good I I1 63.1 57.0 2896.0 6.39 6.35 4.02
1.01 Ideal I I1 61.5 57.0 2896.0 6.46 6.45 3.97
0.6 Very Good D VVS2 60.6 57.0 2897.0 5.48 5.51 3.33
0.76 Premium E SI1 61.1 58.0 2897.0 5.91 5.85 3.59
0.54 Ideal D VVS2 61.4 52.0 2897.0 5.3 5.34 3.26
0.72 Ideal E SI1 62.5 55.0 2897.0 5.69 5.74 3.57
0.72 Good F VS1 59.4 61.0 2897.0 5.82 5.89 3.48
0.74 Premium D VS2 61.8 58.0 2897.0 5.81 5.77 3.58
1.12 Premium J SI2 60.6 59.0 2898.0 6.68 6.61 4.03

Now let's examine one of the continuous features as an example.

//Select: "Plot Options..." --> "Display type" --> "histogram plot" and choose to "Plot over all results" OTHERWISE you get the image from first 1000 rows only
display(diamondsDF.select("carat"))
carat
0.23
0.21
0.23
0.29
0.31
0.24
0.24
0.26
0.22
0.23
0.3
0.23
0.22
0.31
0.2
0.32
0.3
0.3
0.3
0.3
0.3
0.23
0.23
0.31
0.31
0.23
0.24
0.3
0.23
0.23
0.23
0.23
0.23
0.23
0.23
0.23
0.23
0.31
0.26
0.33
0.33
0.33
0.26
0.26
0.32
0.29
0.32
0.32
0.25
0.29
0.24
0.23
0.32
0.22
0.22
0.3
0.3
0.3
0.3
0.3
0.35
0.3
0.3
0.3
0.42
0.28
0.32
0.31
0.31
0.24
0.24
0.3
0.3
0.3
0.3
0.26
0.26
0.26
0.26
0.26
0.26
0.26
0.26
0.38
0.26
0.24
0.24
0.24
0.24
0.32
0.7
0.86
0.7
0.71
0.78
0.7
0.7
0.96
0.73
0.8
0.75
0.75
0.74
0.75
0.8
0.75
0.8
0.74
0.81
0.59
0.8
0.74
0.9
0.74
0.73
0.73
0.8
0.71
0.7
0.8
0.71
0.74
0.7
0.7
0.7
0.7
0.91
0.61
0.91
0.91
0.77
0.71
0.71
0.7
0.77
0.63
0.71
0.71
0.76
0.64
0.71
0.71
0.7
0.7
0.71
0.7
0.71
0.73
0.7
0.7
0.71
0.74
0.71
0.73
0.76
0.76
0.71
0.73
0.73
0.73
0.73
0.72
0.73
0.71
0.79
0.73
0.8
0.58
0.58
0.71
0.75
0.7
1.17
0.6
0.7
0.83
0.74
0.72
0.71
0.71
0.54
0.54
0.72
0.72
0.72
0.71
0.7
0.71
0.71
0.71
0.71
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.72
0.7
0.7
0.7
0.7
0.98
0.78
0.7
0.52
0.73
0.74
0.7
0.77
0.71
0.74
0.7
1.01
0.77
0.78
0.72
0.53
0.76
0.7
0.7
0.75
0.72
0.72
0.7
0.84
0.75
0.52
0.72
0.79
0.72
0.51
0.64
0.7
0.83
0.76
0.71
0.77
0.71
1.01
1.01
0.77
0.76
0.76
0.76
1.05
0.81
0.7
0.55
0.81
0.63
0.63
0.77
1.05
0.64
0.76
0.83
0.71
0.71
0.87
0.73
0.71
0.71
0.71
0.7
0.7
0.76
0.7
0.79
0.7
0.7
0.76
0.73
0.79
0.71
0.81
0.81
0.72
0.72
0.72
0.81
0.72
1.0
0.73
0.81
0.81
0.71
0.71
0.71
0.57
0.51
0.72
0.74
0.74
0.7
0.8
1.01
0.8
0.77
0.83
0.82
0.78
0.6
0.9
0.7
0.9
0.83
0.83
0.83
0.74
0.79
0.61
0.76
0.96
0.73
0.73
0.75
0.71
0.71
0.71
0.71
1.04
1.0
0.87
0.53
0.72
0.72
0.7
0.74
0.71
0.73
0.7
0.71
0.71
0.71
0.77
0.71
0.78
0.71
0.91
0.71
0.71
0.8
0.7
0.72
0.72
0.82
0.7
0.72
0.72
0.9
0.74
0.74
0.73
0.57
0.73
0.72
0.74
0.82
0.81
0.75
0.7
0.71
0.71
0.93
0.8
0.7
1.0
0.75
0.58
0.73
0.81
0.81
0.71
1.2
0.7
0.7
0.74
0.7
0.8
0.75
0.83
1.0
0.99
0.7
0.7
0.7
0.7
0.32
0.32
0.32
0.32
0.32
0.32
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.32
0.33
0.29
0.29
0.31
0.34
0.34
0.34
0.34
0.3
0.29
0.35
0.43
0.32
0.36
0.3
0.26
0.7
0.7
0.71
0.99
0.73
0.51
0.91
0.84
0.91
0.76
0.76
0.75
0.55
0.76
0.74
0.7
0.7
0.7
0.7
0.9
0.95
0.89
0.72
0.96
1.02
0.78
0.61
0.71
0.78
0.87
0.83
0.71
0.71
0.71
0.71
0.63
0.71
0.71
0.71
0.71
0.9
0.71
0.7
0.7
0.7
1.0
0.86
0.8
0.7
0.7
0.7
0.7
1.0
0.72
0.72
0.7
0.86
0.71
0.75
0.73
0.53
0.73
0.73
0.73
0.73
0.73
0.73
0.7
0.72
0.72
0.72
0.7
0.6
0.74
0.73
0.71
0.71
0.7
0.7
0.9
0.71
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.79
0.9
0.71
0.61
0.9
0.71
0.71
0.77
0.74
0.82
0.82
0.71
0.83
0.73
0.83
1.17
0.91
0.73
0.7
0.9
0.7
0.7
0.7
0.9
0.78
0.96
0.7
0.72
0.79
0.7
0.7
0.7
1.01
0.72
0.8
0.59
0.72
0.75
0.8
0.71
0.77
0.97
0.53
0.53
0.8
0.9
0.76
0.72
0.75
0.72
0.79
0.72
0.91
0.71
0.81
0.82
0.71
0.9
0.8
0.56
0.7
0.7
0.61
0.85
0.7
0.8
0.8
0.51
0.53
0.78
0.9
0.9
0.77
0.73
0.63
0.7
0.72
0.72
0.75
0.82
0.71
0.7
0.7
0.71
0.76
0.82
0.72
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.74
0.71
0.7
0.71
0.71
0.71
0.71
0.7
0.73
0.7
0.7
0.71
0.71
0.79
0.71
0.77
0.75
0.7
0.71
0.92
0.83
0.7
0.73
0.71
0.73
0.82
0.82
0.82
0.52
1.0
0.95
0.73
0.73
0.73
0.8
0.7
0.7
0.7
0.71
0.81
0.71
0.73
0.73
0.72
0.81
0.71
0.73
0.7
1.01
1.01
0.79
0.7
0.7
0.8
1.27
0.79
0.72
0.73
1.01
1.01
0.73
0.7
0.7
0.77
0.77
0.77
0.84
0.72
0.76
0.7
0.54
0.75
0.79
0.74
0.7
0.7
0.75
1.2
0.8
0.66
0.87
0.86
0.74
0.58
0.78
0.74
0.73
0.91
0.71
0.71
0.79
0.79
0.71
0.82
0.78
0.7
1.12
0.73
0.91
0.91
0.91
0.91
0.7
0.68
0.73
1.03
0.74
0.98
1.02
1.0
1.02
0.6
0.8
0.97
1.0
0.26
0.26
0.36
0.34
0.34
0.34
0.34
0.34
0.34
0.32
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.33
0.33
0.33
0.33
0.33
0.33
0.33
0.33
0.33
0.33
0.33
1.0
0.77
0.77
0.7
0.9
0.72
0.9
0.72
0.7
0.81
0.81
0.71
0.7
0.71
0.71
0.92
0.76
0.73
0.71
0.7
0.9
0.71
0.7
0.7
0.77
0.71
0.7
0.75
0.83
0.71
0.9
0.6
0.71
0.53
0.71
0.62
0.62
0.9
0.62
0.82
0.66
0.7
0.8
0.8
0.79
0.71
0.7
0.7
0.79
0.7
1.22
1.01
0.73
0.91
0.71
0.83
0.84
0.71
0.71
0.71
0.71
0.71
0.71
0.91
0.9
0.71
0.71
0.72
0.72
0.71
0.81
0.83
0.73
0.56
0.56
0.71
0.7
0.96
0.71
0.7
0.71
0.8
0.95
0.82
0.52
0.82
0.82
0.82
0.8
0.96
0.72
0.62
0.79
0.75
1.08
0.72
0.62
0.73
0.72
0.52
0.83
0.64
0.8
0.74
0.72
0.82
0.73
1.04
0.73
0.73
0.9
0.75
0.79
0.7
0.75
1.02
0.7
0.7
0.7
0.7
0.7
0.7
0.72
0.74
0.84
0.76
0.77
0.76
1.0
1.0
0.9
0.9
0.9
0.9
0.9
0.9
0.78
0.71
0.7
0.7
0.7
0.7
0.7
0.7
0.7
1.0
0.77
1.0
1.0
1.0
0.73
0.79
0.72
0.71
0.74
0.7
0.7
0.79
0.79
0.79
0.71
0.79
0.73
0.63
0.7
0.71
0.84
0.84
1.02
0.72
0.72
0.92
0.74
0.7
0.71
1.05
0.7
0.54
0.73
0.88
0.73
0.72
0.9
0.9
1.03
0.84
1.01
0.77
0.8
0.9
0.73
0.72
0.71
0.7
0.79
0.72
0.7
0.7
0.9
0.71
0.5
0.5
0.74
0.77
0.77
0.8
0.8
0.8
0.8
0.66
0.71
0.71
0.71
0.71
0.72
0.71
0.86
1.19
0.71
0.82
0.71
0.75
0.7
0.8
0.82
0.82
0.82
0.81
0.81
0.76
0.71
0.7
0.7
0.74
0.77
0.77
0.53
0.79
0.73
0.77
0.77
1.01
1.01
0.6
0.76
0.54
0.72
0.72
0.74
1.12

The above histogram of the diamonds' carat ratings shows that carats have a skewed distribution: Many diamonds are small, but there are a number of diamonds in the dataset which are much larger.

  • Extremely skewed distributions can cause problems for some algorithms (e.g., Linear Regression).
  • However, Decision Trees handle skewed distributions very naturally.

Note: When you call display to create a histogram like that above, it will plot using a subsample from the dataset (for efficiency), but you can plot using the full dataset by selecting "Plot over all results". For our dataset, the two plots can actually look very different due to the long-tailed distribution.

We will not examine the label distribution for now. It can be helpful to examine the label distribution, but it is best to do so only on the training set, not on the test set which we will hold out for evaluation. These will be seen in the sequel

You Try! Of course knock youself out visually exploring the dataset more...

display(diamondsDF.select("cut","carat"))
cut carat
Ideal 0.23
Premium 0.21
Good 0.23
Premium 0.29
Good 0.31
Very Good 0.24
Very Good 0.24
Very Good 0.26
Fair 0.22
Very Good 0.23
Good 0.3
Ideal 0.23
Premium 0.22
Ideal 0.31
Premium 0.2
Premium 0.32
Ideal 0.3
Good 0.3
Good 0.3
Very Good 0.3
Good 0.3
Very Good 0.23
Very Good 0.23
Very Good 0.31
Very Good 0.31
Very Good 0.23
Premium 0.24
Very Good 0.3
Very Good 0.23
Very Good 0.23
Very Good 0.23
Very Good 0.23
Very Good 0.23
Very Good 0.23
Very Good 0.23
Good 0.23
Good 0.23
Good 0.31
Very Good 0.26
Ideal 0.33
Ideal 0.33
Ideal 0.33
Good 0.26
Good 0.26
Good 0.32
Premium 0.29
Very Good 0.32
Good 0.32
Very Good 0.25
Very Good 0.29
Very Good 0.24
Ideal 0.23
Ideal 0.32
Premium 0.22
Premium 0.22
Ideal 0.3
Premium 0.3
Very Good 0.3
Very Good 0.3
Good 0.3
Ideal 0.35
Premium 0.3
Ideal 0.3
Ideal 0.3
Premium 0.42
Ideal 0.28
Ideal 0.32
Very Good 0.31
Premium 0.31
Premium 0.24
Very Good 0.24
Very Good 0.3
Premium 0.3
Premium 0.3
Good 0.3
Very Good 0.26
Very Good 0.26
Very Good 0.26
Very Good 0.26
Very Good 0.26
Very Good 0.26
Very Good 0.26
Ideal 0.26
Ideal 0.38
Good 0.26
Premium 0.24
Premium 0.24
Premium 0.24
Premium 0.24
Premium 0.32
Ideal 0.7
Fair 0.86
Ideal 0.7
Very Good 0.71
Very Good 0.78
Good 0.7
Good 0.7
Fair 0.96
Very Good 0.73
Premium 0.8
Very Good 0.75
Premium 0.75
Ideal 0.74
Premium 0.75
Ideal 0.8
Ideal 0.75
Premium 0.8
Ideal 0.74
Ideal 0.81
Ideal 0.59
Ideal 0.8
Ideal 0.74
Premium 0.9
Very Good 0.74
Ideal 0.73
Ideal 0.73
Premium 0.8
Ideal 0.71
Ideal 0.7
Ideal 0.8
Ideal 0.71
Ideal 0.74
Very Good 0.7
Fair 0.7
Fair 0.7
Premium 0.7
Premium 0.91
Very Good 0.61
Fair 0.91
Fair 0.91
Ideal 0.77
Very Good 0.71
Ideal 0.71
Very Good 0.7
Very Good 0.77
Premium 0.63
Very Good 0.71
Premium 0.71
Ideal 0.76
Ideal 0.64
Premium 0.71
Premium 0.71
Very Good 0.7
Very Good 0.7
Ideal 0.71
Good 0.7
Very Good 0.71
Very Good 0.73
Very Good 0.7
Ideal 0.7
Premium 0.71
Ideal 0.74
Premium 0.71
Premium 0.73
Very Good 0.76
Ideal 0.76
Ideal 0.71
Premium 0.73
Premium 0.73
Ideal 0.73
Premium 0.73
Very Good 0.72
Very Good 0.73
Ideal 0.71
Ideal 0.79
Very Good 0.73
Very Good 0.8
Ideal 0.58
Ideal 0.58
Good 0.71
Ideal 0.75
Premium 0.7
Very Good 1.17
Ideal 0.6
Ideal 0.7
Good 0.83
Very Good 0.74
Very Good 0.72
Premium 0.71
Ideal 0.71
Ideal 0.54
Ideal 0.54
Ideal 0.72
Ideal 0.72
Good 0.72
Ideal 0.71
Very Good 0.7
Premium 0.71
Very Good 0.71
Good 0.71
Good 0.71
Ideal 0.7
Premium 0.7
Premium 0.7
Premium 0.7
Premium 0.7
Premium 0.7
Premium 0.7
Ideal 0.7
Premium 0.72
Very Good 0.7
Premium 0.7
Premium 0.7
Good 0.7
Fair 0.98
Premium 0.78
Very Good 0.7
Ideal 0.52
Very Good 0.73
Ideal 0.74
Very Good 0.7
Premium 0.77
Ideal 0.71
Ideal 0.74
Ideal 0.7
Premium 1.01
Ideal 0.77
Ideal 0.78
Very Good 0.72
Very Good 0.53
Ideal 0.76
Good 0.7
Premium 0.7
Very Good 0.75
Ideal 0.72
Premium 0.72
Premium 0.7
Fair 0.84
Premium 0.75
Ideal 0.52
Very Good 0.72
Very Good 0.79
Very Good 0.72
Ideal 0.51
Ideal 0.64
Very Good 0.7
Very Good 0.83
Ideal 0.76
Good 0.71
Good 0.77
Ideal 0.71
Fair 1.01
Premium 1.01
Good 0.77
Good 0.76
Premium 0.76
Premium 0.76
Very Good 1.05
Ideal 0.81
Ideal 0.7
Ideal 0.55
Good 0.81
Premium 0.63
Premium 0.63
Premium 0.77
Fair 1.05
Ideal 0.64
Premium 0.76
Ideal 0.83
Premium 0.71
Premium 0.71
Very Good 0.87
Ideal 0.73
Premium 0.71
Premium 0.71
Ideal 0.71
Premium 0.7
Premium 0.7
Premium 0.76
Ideal 0.7
Very Good 0.79
Very Good 0.7
Good 0.7
Ideal 0.76
Ideal 0.73
Very Good 0.79
Very Good 0.71
Premium 0.81
Ideal 0.81
Good 0.72
Premium 0.72
Premium 0.72
Premium 0.81
Premium 0.72
Premium 1.0
Good 0.73
Very Good 0.81
Very Good 0.81
Premium 0.71
Premium 0.71
Premium 0.71
Ideal 0.57
Ideal 0.51
Ideal 0.72
Ideal 0.74
Ideal 0.74
Fair 0.7
Premium 0.8
Fair 1.01
Very Good 0.8
Ideal 0.77
Very Good 0.83
Ideal 0.82
Ideal 0.78
Very Good 0.6
Good 0.9
Premium 0.7
Very Good 0.9
Ideal 0.83
Ideal 0.83
Very Good 0.83
Premium 0.74
Ideal 0.79
Ideal 0.61
Fair 0.76
Ideal 0.96
Ideal 0.73
Premium 0.73
Ideal 0.75
Premium 0.71
Good 0.71
Good 0.71
Premium 0.71
Premium 1.04
Premium 1.0
Very Good 0.87
Ideal 0.53
Premium 0.72
Premium 0.72
Very Good 0.7
Very Good 0.74
Ideal 0.71
Ideal 0.73
Good 0.7
Premium 0.71
Premium 0.71
Premium 0.71
Premium 0.77
Premium 0.71
Premium 0.78
Very Good 0.71
Ideal 0.91
Very Good 0.71
Very Good 0.71
Very Good 0.8
Very Good 0.7
Ideal 0.72
Very Good 0.72
Ideal 0.82
Ideal 0.7
Ideal 0.72
Ideal 0.72
Fair 0.9
Premium 0.74
Premium 0.74
Ideal 0.73
Fair 0.57
Premium 0.73
Ideal 0.72
Fair 0.74
Good 0.82
Very Good 0.81
Very Good 0.75
Ideal 0.7
Very Good 0.71
Very Good 0.71
Premium 0.93
Very Good 0.8
Very Good 0.7
Fair 1.0
Very Good 0.75
Ideal 0.58
Very Good 0.73
Very Good 0.81
Premium 0.81
Premium 0.71
Fair 1.2
Very Good 0.7
Very Good 0.7
Ideal 0.74
Good 0.7
Good 0.8
Very Good 0.75
Very Good 0.83
Fair 1.0
Fair 0.99
Very Good 0.7
Very Good 0.7
Good 0.7
Very Good 0.7
Premium 0.32
Premium 0.32
Ideal 0.32
Premium 0.32
Very Good 0.32
Ideal 0.32
Premium 0.3
Premium 0.3
Premium 0.3
Very Good 0.3
Good 0.3
Ideal 0.3
Very Good 0.3
Good 0.32
Ideal 0.33
Very Good 0.29
Very Good 0.29
Very Good 0.31
Ideal 0.34
Ideal 0.34
Ideal 0.34
Ideal 0.34
Ideal 0.3
Ideal 0.29
Ideal 0.35
Very Good 0.43
Very Good 0.32
Ideal 0.36
Ideal 0.3
Ideal 0.26
Very Good 0.7
Very Good 0.7
Ideal 0.71
Fair 0.99
Premium 0.73
Ideal 0.51
Premium 0.91
Very Good 0.84
Good 0.91
Premium 0.76
Ideal 0.76
Premium 0.75
Very Good 0.55
Very Good 0.76
Premium 0.74
Ideal 0.7
Ideal 0.7
Ideal 0.7
Ideal 0.7
Fair 0.9
Fair 0.95
Premium 0.89
Premium 0.72
Fair 0.96
Premium 1.02
Very Good 0.78
Ideal 0.61
Good 0.71
Premium 0.78
Ideal 0.87
Ideal 0.83
Premium 0.71
Ideal 0.71
Ideal 0.71
Premium 0.71
Ideal 0.63
Premium 0.71
Premium 0.71
Ideal 0.71
Premium 0.71
Ideal 0.9
Good 0.71
Premium 0.7
Premium 0.7
Premium 0.7
Premium 1.0
Premium 0.86
Ideal 0.8
Ideal 0.7
Premium 0.7
Premium 0.7
Premium 0.7
Fair 1.0
Very Good 0.72
Ideal 0.72
Good 0.7
Good 0.86
Ideal 0.71
Ideal 0.75
Premium 0.73
Ideal 0.53
Premium 0.73
Good 0.73
Premium 0.73
Premium 0.73
Premium 0.73
Very Good 0.73
Premium 0.7
Premium 0.72
Premium 0.72
Premium 0.72
Premium 0.7
Ideal 0.6
Ideal 0.74
Ideal 0.73
Premium 0.71
Premium 0.71
Premium 0.7
Ideal 0.7
Good 0.9
Ideal 0.71
Premium 0.7
Premium 0.7
Ideal 0.7
Good 0.7
Ideal 0.7
Ideal 0.7
Premium 0.7
Premium 0.7
Ideal 0.7
Very Good 0.79
Good 0.9
Premium 0.71
Ideal 0.61
Fair 0.9
Ideal 0.71
Premium 0.71
Ideal 0.77
Good 0.74
Ideal 0.82
Premium 0.82
Premium 0.71
Premium 0.83
Very Good 0.73
Premium 0.83
Premium 1.17
Fair 0.91
Premium 0.73
Good 0.7
Premium 0.9
Premium 0.7
Very Good 0.7
Premium 0.7
Very Good 0.9
Premium 0.78
Ideal 0.96
Very Good 0.7
Good 0.72
Premium 0.79
Ideal 0.7
Ideal 0.7
Ideal 0.7
Premium 1.01
Premium 0.72
Good 0.8
Ideal 0.59
Ideal 0.72
Premium 0.75
Premium 0.8
Very Good 0.71
Very Good 0.77
Ideal 0.97
Ideal 0.53
Ideal 0.53
Ideal 0.8
Premium 0.9
Very Good 0.76
Ideal 0.72
Ideal 0.75
Premium 0.72
Ideal 0.79
Very Good 0.72
Very Good 0.91
Premium 0.71
Premium 0.81
Ideal 0.82
Premium 0.71
Good 0.9
Very Good 0.8
Very Good 0.56
Very Good 0.7
Ideal 0.7
Ideal 0.61
Ideal 0.85
Ideal 0.7
Ideal 0.8
Ideal 0.8
Very Good 0.51
Ideal 0.53
Ideal 0.78
Very Good 0.9
Fair 0.9
Ideal 0.77
Ideal 0.73
Ideal 0.63
Ideal 0.7
Ideal 0.72
Ideal 0.72
Premium 0.75
Very Good 0.82
Good 0.71
Premium 0.7
Ideal 0.7
Ideal 0.71
Very Good 0.76
Fair 0.82
Premium 0.72
Premium 0.7
Premium 0.7
Premium 0.7
Premium 0.7
Very Good 0.7
Premium 0.7
Premium 0.7
Premium 0.7
Very Good 0.74
Ideal 0.71
Ideal 0.7
Ideal 0.71
Premium 0.71
Premium 0.71
Premium 0.71
Ideal 0.7
Ideal 0.73
Ideal 0.7
Ideal 0.7
Premium 0.71
Premium 0.71
Premium 0.79
Premium 0.71
Very Good 0.77
Very Good 0.75
Ideal 0.7
Premium 0.71
Ideal 0.92
Premium 0.83
Premium 0.7
Premium 0.73
Very Good 0.71
Very Good 0.73
Ideal 0.82
Ideal 0.82
Very Good 0.82
Ideal 0.52
Premium 1.0
Fair 0.95
Ideal 0.73
Premium 0.73
Premium 0.73
Ideal 0.8
Premium 0.7
Very Good 0.7
Very Good 0.7
Very Good 0.71
Very Good 0.81
Very Good 0.71
Ideal 0.73
Very Good 0.73
Ideal 0.72
Ideal 0.81
Ideal 0.71
Very Good 0.73
Very Good 0.7
Ideal 1.01
Good 1.01
Ideal 0.79
Very Good 0.7
Very Good 0.7
Good 0.8
Premium 1.27
Ideal 0.79
Very Good 0.72
Ideal 0.73
Fair 1.01
Good 1.01
Ideal 0.73
Ideal 0.7
Good 0.7
Premium 0.77
Premium 0.77
Premium 0.77
Very Good 0.84
Ideal 0.72
Premium 0.76
Very Good 0.7
Ideal 0.54
Fair 0.75
Good 0.79
Very Good 0.74
Very Good 0.7
Ideal 0.7
Ideal 0.75
Very Good 1.2
Very Good 0.8
Ideal 0.66
Very Good 0.87
Premium 0.86
Ideal 0.74
Very Good 0.58
Ideal 0.78
Ideal 0.74
Ideal 0.73
Very Good 0.91
Premium 0.71
Good 0.71
Ideal 0.79
Premium 0.79
Premium 0.71
Premium 0.82
Very Good 0.78
Very Good 0.7
Premium 1.12
Premium 0.73
Fair 0.91
Fair 0.91
Good 0.91
Fair 0.91
Premium 0.7
Premium 0.68
Very Good 0.73
Good 1.03
Premium 0.74
Fair 0.98
Fair 1.02
Fair 1.0
Ideal 1.02
Ideal 0.6
Ideal 0.8
Ideal 0.97
Fair 1.0
Ideal 0.26
Ideal 0.26
Ideal 0.36
Good 0.34
Good 0.34
Good 0.34
Good 0.34
Very Good 0.34
Ideal 0.34
Good 0.32
Ideal 0.31
Ideal 0.31
Ideal 0.31
Ideal 0.31
Ideal 0.31
Ideal 0.31
Ideal 0.31
Ideal 0.31
Ideal 0.31
Premium 0.33
Premium 0.33
Premium 0.33
Ideal 0.33
Premium 0.33
Premium 0.33
Premium 0.33
Ideal 0.33
Ideal 0.33
Good 0.33
Premium 0.33
Fair 1.0
Premium 0.77
Premium 0.77
Good 0.7
Very Good 0.9
Ideal 0.72
Premium 0.9
Premium 0.72
Ideal 0.7
Very Good 0.81
Very Good 0.81
Premium 0.71
Premium 0.7
Premium 0.71
Very Good 0.71
Premium 0.92
Ideal 0.76
Ideal 0.73
Premium 0.71
Good 0.7
Fair 0.9
Fair 0.71
Ideal 0.7
Premium 0.7
Premium 0.77
Ideal 0.71
Premium 0.7
Fair 0.75
Premium 0.83
Very Good 0.71
Very Good 0.9
Ideal 0.6
Premium 0.71
Ideal 0.53
Premium 0.71
Ideal 0.62
Ideal 0.62
Premium 0.9
Fair 0.62
Premium 0.82
Premium 0.66
Very Good 0.7
Very Good 0.8
Very Good 0.8
Premium 0.79
Very Good 0.71
Ideal 0.7
Very Good 0.7
Premium 0.79
Premium 0.7
Premium 1.22
Fair 1.01
Premium 0.73
Good 0.91
Ideal 0.71
Premium 0.83
Premium 0.84
Premium 0.71
Premium 0.71
Premium 0.71
Ideal 0.71
Premium 0.71
Premium 0.71
Premium 0.91
Premium 0.9
Premium 0.71
Premium 0.71
Ideal 0.72
Premium 0.72
Ideal 0.71
Ideal 0.81
Very Good 0.83
Premium 0.73
Very Good 0.56
Very Good 0.56
Ideal 0.71
Ideal 0.7
Premium 0.96
Very Good 0.71
Ideal 0.7
Ideal 0.71
Premium 0.8
Premium 0.95
Ideal 0.82
Ideal 0.52
Ideal 0.82
Ideal 0.82
Premium 0.82
Ideal 0.8
Fair 0.96
Ideal 0.72
Ideal 0.62
Premium 0.79
Very Good 0.75
Premium 1.08
Ideal 0.72
Ideal 0.62
Ideal 0.73
Ideal 0.72
Premium 0.52
Ideal 0.83
Premium 0.64
Ideal 0.8
Ideal 0.74
Ideal 0.72
Ideal 0.82
Premium 0.73
Premium 1.04
Very Good 0.73
Good 0.73
Premium 0.9
Ideal 0.75
Ideal 0.79
Good 0.7
Very Good 0.75
Ideal 1.02
Very Good 0.7
Ideal 0.7
Good 0.7
Ideal 0.7
Ideal 0.7
Very Good 0.7
Very Good 0.72
Ideal 0.74
Good 0.84
Very Good 0.76
Very Good 0.77
Ideal 0.76
Ideal 1.0
Fair 1.0
Fair 0.9
Premium 0.9
Good 0.9
Fair 0.9
Fair 0.9
Premium 0.9
Premium 0.78
Premium 0.71
Premium 0.7
Premium 0.7
Premium 0.7
Ideal 0.7
Ideal 0.7
Ideal 0.7
Premium 0.7
Fair 1.0
Ideal 0.77
Fair 1.0
Fair 1.0
Fair 1.0
Premium 0.73
Premium 0.79
Very Good 0.72
Ideal 0.71
Ideal 0.74
Good 0.7
Good 0.7
Very Good 0.79
Very Good 0.79
Very Good 0.79
Ideal 0.71
Ideal 0.79
Very Good 0.73
Premium 0.63
Premium 0.7
Premium 0.71
Ideal 0.84
Ideal 0.84
Ideal 1.02
Fair 0.72
Ideal 0.72
Very Good 0.92
Very Good 0.74
Ideal 0.7
Very Good 0.71
Premium 1.05
Very Good 0.7
Ideal 0.54
Premium 0.73
Fair 0.88
Premium 0.73
Ideal 0.72
Good 0.9
Fair 0.9
Fair 1.03
Very Good 0.84
Premium 1.01
Ideal 0.77
Fair 0.8
Fair 0.9
Ideal 0.73
Ideal 0.72
Very Good 0.71
Premium 0.7
Ideal 0.79
Very Good 0.72
Very Good 0.7
Very Good 0.7
Good 0.9
Very Good 0.71
Ideal 0.5
Ideal 0.5
Ideal 0.74
Premium 0.77
Premium 0.77
Ideal 0.8
Ideal 0.8
Premium 0.8
Good 0.8
Ideal 0.66
Very Good 0.71
Ideal 0.71
Ideal 0.71
Ideal 0.71
Ideal 0.72
Good 0.71
Ideal 0.86
Fair 1.19
Very Good 0.71
Very Good 0.82
Ideal 0.71
Ideal 0.75
Very Good 0.7
Ideal 0.8
Good 0.82
Very Good 0.82
Premium 0.82
Very Good 0.81
Ideal 0.81
Ideal 0.76
Very Good 0.71
Very Good 0.7
Ideal 0.7
Very Good 0.74
Very Good 0.77
Very Good 0.77
Ideal 0.53
Ideal 0.79
Ideal 0.73
Ideal 0.77
Premium 0.77
Very Good 1.01
Ideal 1.01
Very Good 0.6
Premium 0.76
Ideal 0.54
Ideal 0.72
Good 0.72
Premium 0.74
Premium 1.12

Try scatter plot to see pairwise scatter plots of continuous features.

display(diamondsDF) //Ctrl+Enter
carat cut color clarity depth table price x y z
0.23 Ideal E SI2 61.5 55.0 326.0 3.95 3.98 2.43
0.21 Premium E SI1 59.8 61.0 326.0 3.89 3.84 2.31
0.23 Good E VS1 56.9 65.0 327.0 4.05 4.07 2.31
0.29 Premium I VS2 62.4 58.0 334.0 4.2 4.23 2.63
0.31 Good J SI2 63.3 58.0 335.0 4.34 4.35 2.75
0.24 Very Good J VVS2 62.8 57.0 336.0 3.94 3.96 2.48
0.24 Very Good I VVS1 62.3 57.0 336.0 3.95 3.98 2.47
0.26 Very Good H SI1 61.9 55.0 337.0 4.07 4.11 2.53
0.22 Fair E VS2 65.1 61.0 337.0 3.87 3.78 2.49
0.23 Very Good H VS1 59.4 61.0 338.0 4.0 4.05 2.39
0.3 Good J SI1 64.0 55.0 339.0 4.25 4.28 2.73
0.23 Ideal J VS1 62.8 56.0 340.0 3.93 3.9 2.46
0.22 Premium F SI1 60.4 61.0 342.0 3.88 3.84 2.33
0.31 Ideal J SI2 62.2 54.0 344.0 4.35 4.37 2.71
0.2 Premium E SI2 60.2 62.0 345.0 3.79 3.75 2.27
0.32 Premium E I1 60.9 58.0 345.0 4.38 4.42 2.68
0.3 Ideal I SI2 62.0 54.0 348.0 4.31 4.34 2.68
0.3 Good J SI1 63.4 54.0 351.0 4.23 4.29 2.7
0.3 Good J SI1 63.8 56.0 351.0 4.23 4.26 2.71
0.3 Very Good J SI1 62.7 59.0 351.0 4.21 4.27 2.66
0.3 Good I SI2 63.3 56.0 351.0 4.26 4.3 2.71
0.23 Very Good E VS2 63.8 55.0 352.0 3.85 3.92 2.48
0.23 Very Good H VS1 61.0 57.0 353.0 3.94 3.96 2.41
0.31 Very Good J SI1 59.4 62.0 353.0 4.39 4.43 2.62
0.31 Very Good J SI1 58.1 62.0 353.0 4.44 4.47 2.59
0.23 Very Good G VVS2 60.4 58.0 354.0 3.97 4.01 2.41
0.24 Premium I VS1 62.5 57.0 355.0 3.97 3.94 2.47
0.3 Very Good J VS2 62.2 57.0 357.0 4.28 4.3 2.67
0.23 Very Good D VS2 60.5 61.0 357.0 3.96 3.97 2.4
0.23 Very Good F VS1 60.9 57.0 357.0 3.96 3.99 2.42
0.23 Very Good F VS1 60.0 57.0 402.0 4.0 4.03 2.41
0.23 Very Good F VS1 59.8 57.0 402.0 4.04 4.06 2.42
0.23 Very Good E VS1 60.7 59.0 402.0 3.97 4.01 2.42
0.23 Very Good E VS1 59.5 58.0 402.0 4.01 4.06 2.4
0.23 Very Good D VS1 61.9 58.0 402.0 3.92 3.96 2.44
0.23 Good F VS1 58.2 59.0 402.0 4.06 4.08 2.37
0.23 Good E VS1 64.1 59.0 402.0 3.83 3.85 2.46
0.31 Good H SI1 64.0 54.0 402.0 4.29 4.31 2.75
0.26 Very Good D VS2 60.8 59.0 403.0 4.13 4.16 2.52
0.33 Ideal I SI2 61.8 55.0 403.0 4.49 4.51 2.78
0.33 Ideal I SI2 61.2 56.0 403.0 4.49 4.5 2.75
0.33 Ideal J SI1 61.1 56.0 403.0 4.49 4.55 2.76
0.26 Good D VS2 65.2 56.0 403.0 3.99 4.02 2.61
0.26 Good D VS1 58.4 63.0 403.0 4.19 4.24 2.46
0.32 Good H SI2 63.1 56.0 403.0 4.34 4.37 2.75
0.29 Premium F SI1 62.4 58.0 403.0 4.24 4.26 2.65
0.32 Very Good H SI2 61.8 55.0 403.0 4.35 4.42 2.71
0.32 Good H SI2 63.8 56.0 403.0 4.36 4.38 2.79
0.25 Very Good E VS2 63.3 60.0 404.0 4.0 4.03 2.54
0.29 Very Good H SI2 60.7 60.0 404.0 4.33 4.37 2.64
0.24 Very Good F SI1 60.9 61.0 404.0 4.02 4.03 2.45
0.23 Ideal G VS1 61.9 54.0 404.0 3.93 3.95 2.44
0.32 Ideal I SI1 60.9 55.0 404.0 4.45 4.48 2.72
0.22 Premium E VS2 61.6 58.0 404.0 3.93 3.89 2.41
0.22 Premium D VS2 59.3 62.0 404.0 3.91 3.88 2.31
0.3 Ideal I SI2 61.0 59.0 405.0 4.3 4.33 2.63
0.3 Premium J SI2 59.3 61.0 405.0 4.43 4.38 2.61
0.3 Very Good I SI1 62.6 57.0 405.0 4.25 4.28 2.67
0.3 Very Good I SI1 63.0 57.0 405.0 4.28 4.32 2.71
0.3 Good I SI1 63.2 55.0 405.0 4.25 4.29 2.7
0.35 Ideal I VS1 60.9 57.0 552.0 4.54 4.59 2.78
0.3 Premium D SI1 62.6 59.0 552.0 4.23 4.27 2.66
0.3 Ideal D SI1 62.5 57.0 552.0 4.29 4.32 2.69
0.3 Ideal D SI1 62.1 56.0 552.0 4.3 4.33 2.68
0.42 Premium I SI2 61.5 59.0 552.0 4.78 4.84 2.96
0.28 Ideal G VVS2 61.4 56.0 553.0 4.19 4.22 2.58
0.32 Ideal I VVS1 62.0 55.3 553.0 4.39 4.42 2.73
0.31 Very Good G SI1 63.3 57.0 553.0 4.33 4.3 2.73
0.31 Premium G SI1 61.8 58.0 553.0 4.35 4.32 2.68
0.24 Premium E VVS1 60.7 58.0 553.0 4.01 4.03 2.44
0.24 Very Good D VVS1 61.5 60.0 553.0 3.97 4.0 2.45
0.3 Very Good H SI1 63.1 56.0 554.0 4.29 4.27 2.7
0.3 Premium H SI1 62.9 59.0 554.0 4.28 4.24 2.68
0.3 Premium H SI1 62.5 57.0 554.0 4.29 4.25 2.67
0.3 Good H SI1 63.7 57.0 554.0 4.28 4.26 2.72
0.26 Very Good F VVS2 59.2 60.0 554.0 4.19 4.22 2.49
0.26 Very Good E VVS2 59.9 58.0 554.0 4.15 4.23 2.51
0.26 Very Good D VVS2 62.4 54.0 554.0 4.08 4.13 2.56
0.26 Very Good D VVS2 62.8 60.0 554.0 4.01 4.05 2.53
0.26 Very Good E VVS1 62.6 59.0 554.0 4.06 4.09 2.55
0.26 Very Good E VVS1 63.4 59.0 554.0 4.0 4.04 2.55
0.26 Very Good D VVS1 62.1 60.0 554.0 4.03 4.12 2.53
0.26 Ideal E VVS2 62.9 58.0 554.0 4.02 4.06 2.54
0.38 Ideal I SI2 61.6 56.0 554.0 4.65 4.67 2.87
0.26 Good E VVS1 57.9 60.0 554.0 4.22 4.25 2.45
0.24 Premium G VVS1 62.3 59.0 554.0 3.95 3.92 2.45
0.24 Premium H VVS1 61.2 58.0 554.0 4.01 3.96 2.44
0.24 Premium H VVS1 60.8 59.0 554.0 4.02 4.0 2.44
0.24 Premium H VVS2 60.7 58.0 554.0 4.07 4.04 2.46
0.32 Premium I SI1 62.9 58.0 554.0 4.35 4.33 2.73
0.7 Ideal E SI1 62.5 57.0 2757.0 5.7 5.72 3.57
0.86 Fair E SI2 55.1 69.0 2757.0 6.45 6.33 3.52
0.7 Ideal G VS2 61.6 56.0 2757.0 5.7 5.67 3.5
0.71 Very Good E VS2 62.4 57.0 2759.0 5.68 5.73 3.56
0.78 Very Good G SI2 63.8 56.0 2759.0 5.81 5.85 3.72
0.7 Good E VS2 57.5 58.0 2759.0 5.85 5.9 3.38
0.7 Good F VS1 59.4 62.0 2759.0 5.71 5.76 3.4
0.96 Fair F SI2 66.3 62.0 2759.0 6.27 5.95 4.07
0.73 Very Good E SI1 61.6 59.0 2760.0 5.77 5.78 3.56
0.8 Premium H SI1 61.5 58.0 2760.0 5.97 5.93 3.66
0.75 Very Good D SI1 63.2 56.0 2760.0 5.8 5.75 3.65
0.75 Premium E SI1 59.9 54.0 2760.0 6.0 5.96 3.58
0.74 Ideal G SI1 61.6 55.0 2760.0 5.8 5.85 3.59
0.75 Premium G VS2 61.7 58.0 2760.0 5.85 5.79 3.59
0.8 Ideal I VS1 62.9 56.0 2760.0 5.94 5.87 3.72
0.75 Ideal G SI1 62.2 55.0 2760.0 5.87 5.8 3.63
0.8 Premium G SI1 63.0 59.0 2760.0 5.9 5.81 3.69
0.74 Ideal I VVS2 62.3 55.0 2761.0 5.77 5.81 3.61
0.81 Ideal F SI2 58.8 57.0 2761.0 6.14 6.11 3.6
0.59 Ideal E VVS2 62.0 55.0 2761.0 5.38 5.43 3.35
0.8 Ideal F SI2 61.4 57.0 2761.0 5.96 6.0 3.67
0.74 Ideal E SI2 62.2 56.0 2761.0 5.8 5.84 3.62
0.9 Premium I VS2 63.0 58.0 2761.0 6.16 6.12 3.87
0.74 Very Good G SI1 62.2 59.0 2762.0 5.73 5.82 3.59
0.73 Ideal F VS2 62.6 56.0 2762.0 5.77 5.74 3.6
0.73 Ideal F VS2 62.7 53.0 2762.0 5.8 5.75 3.62
0.8 Premium F SI2 61.7 58.0 2762.0 5.98 5.94 3.68
0.71 Ideal G VS2 62.4 54.0 2762.0 5.72 5.76 3.58
0.7 Ideal E VS2 60.7 58.0 2762.0 5.73 5.76 3.49
0.8 Ideal F SI2 59.9 59.0 2762.0 6.01 6.07 3.62
0.71 Ideal D SI2 62.3 56.0 2762.0 5.73 5.69 3.56
0.74 Ideal E SI1 62.3 54.0 2762.0 5.8 5.83 3.62
0.7 Very Good F VS2 61.7 63.0 2762.0 5.64 5.61 3.47
0.7 Fair F VS2 64.5 57.0 2762.0 5.57 5.53 3.58
0.7 Fair F VS2 65.3 55.0 2762.0 5.63 5.58 3.66
0.7 Premium F VS2 61.6 60.0 2762.0 5.65 5.59 3.46
0.91 Premium H SI1 61.4 56.0 2763.0 6.09 5.97 3.7
0.61 Very Good D VVS2 59.6 57.0 2763.0 5.56 5.58 3.32
0.91 Fair H SI2 64.4 57.0 2763.0 6.11 6.09 3.93
0.91 Fair H SI2 65.7 60.0 2763.0 6.03 5.99 3.95
0.77 Ideal H VS2 62.0 56.0 2763.0 5.89 5.86 3.64
0.71 Very Good D SI1 63.6 58.0 2764.0 5.64 5.68 3.6
0.71 Ideal D SI1 61.9 59.0 2764.0 5.69 5.72 3.53
0.7 Very Good E VS2 62.6 60.0 2765.0 5.62 5.65 3.53
0.77 Very Good H VS1 61.3 60.0 2765.0 5.88 5.9 3.61
0.63 Premium E VVS1 60.9 60.0 2765.0 5.52 5.55 3.37
0.71 Very Good F VS1 60.1 62.0 2765.0 5.74 5.77 3.46
0.71 Premium F VS1 61.8 59.0 2765.0 5.69 5.73 3.53
0.76 Ideal H SI1 61.2 57.0 2765.0 5.88 5.91 3.61
0.64 Ideal G VVS1 61.9 56.0 2766.0 5.53 5.56 3.43
0.71 Premium G VS2 60.9 57.0 2766.0 5.78 5.75 3.51
0.71 Premium G VS2 59.8 56.0 2766.0 5.89 5.81 3.5
0.7 Very Good D VS2 61.8 55.0 2767.0 5.68 5.72 3.52
0.7 Very Good F VS1 60.0 57.0 2767.0 5.8 5.87 3.5
0.71 Ideal D SI2 61.6 55.0 2767.0 5.74 5.76 3.54
0.7 Good H VVS2 62.1 64.0 2767.0 5.62 5.65 3.5
0.71 Very Good G VS1 63.3 59.0 2768.0 5.52 5.61 3.52
0.73 Very Good D SI1 60.2 56.0 2768.0 5.83 5.87 3.52
0.7 Very Good D SI1 61.1 58.0 2768.0 5.66 5.73 3.48
0.7 Ideal E SI1 60.9 57.0 2768.0 5.73 5.76 3.5
0.71 Premium D SI2 61.7 59.0 2768.0 5.71 5.67 3.51
0.74 Ideal I SI1 61.3 56.0 2769.0 5.82 5.86 3.57
0.71 Premium D VS2 62.5 60.0 2770.0 5.65 5.61 3.52
0.73 Premium G VS2 61.4 59.0 2770.0 5.83 5.76 3.56
0.76 Very Good F SI1 62.9 57.0 2770.0 5.79 5.81 3.65
0.76 Ideal D SI2 62.4 57.0 2770.0 5.78 5.83 3.62
0.71 Ideal F SI1 60.7 56.0 2770.0 5.77 5.8 3.51
0.73 Premium G VS2 60.7 58.0 2770.0 5.87 5.82 3.55
0.73 Premium G VS1 61.5 58.0 2770.0 5.79 5.75 3.55
0.73 Ideal D SI2 59.9 57.0 2770.0 5.92 5.89 3.54
0.73 Premium G VS2 59.2 59.0 2770.0 5.92 5.87 3.49
0.72 Very Good H VVS2 60.3 56.0 2771.0 5.81 5.83 3.51
0.73 Very Good F SI1 61.7 60.0 2771.0 5.79 5.82 3.58
0.71 Ideal G VS2 61.9 57.0 2771.0 5.73 5.77 3.56
0.79 Ideal F SI2 61.9 55.0 2771.0 5.97 5.92 3.68
0.73 Very Good H VVS1 60.4 59.0 2772.0 5.83 5.89 3.54
0.8 Very Good F SI2 61.0 57.0 2772.0 6.01 6.03 3.67
0.58 Ideal G VVS1 61.5 55.0 2772.0 5.39 5.44 3.33
0.58 Ideal F VVS1 61.7 56.0 2772.0 5.33 5.37 3.3
0.71 Good E VS2 59.2 61.0 2772.0 5.8 5.88 3.46
0.75 Ideal D SI2 61.3 56.0 2773.0 5.85 5.89 3.6
0.7 Premium D VS2 58.0 62.0 2773.0 5.87 5.78 3.38
1.17 Very Good J I1 60.2 61.0 2774.0 6.83 6.9 4.13
0.6 Ideal E VS1 61.7 55.0 2774.0 5.41 5.44 3.35
0.7 Ideal E SI1 62.7 55.0 2774.0 5.68 5.74 3.58
0.83 Good I VS2 64.6 54.0 2774.0 5.85 5.88 3.79
0.74 Very Good F VS2 61.3 61.0 2775.0 5.8 5.84 3.57
0.72 Very Good G VS2 63.7 56.4 2776.0 5.62 5.69 3.61
0.71 Premium E VS2 62.7 58.0 2776.0 5.74 5.68 3.58
0.71 Ideal E VS2 62.2 57.0 2776.0 5.79 5.62 3.55
0.54 Ideal E VVS2 61.6 56.0 2776.0 5.25 5.27 3.24
0.54 Ideal E VVS2 61.5 57.0 2776.0 5.24 5.26 3.23
0.72 Ideal G SI1 61.8 56.0 2776.0 5.72 5.75 3.55
0.72 Ideal G SI1 60.7 56.0 2776.0 5.79 5.82 3.53
0.72 Good G VS2 59.7 60.5 2776.0 5.8 5.84 3.47
0.71 Ideal G SI1 60.5 56.0 2776.0 5.8 5.76 3.5
0.7 Very Good D VS1 62.7 58.0 2777.0 5.66 5.73 3.57
0.71 Premium F VS2 62.1 58.0 2777.0 5.67 5.7 3.53
0.71 Very Good F VS2 62.8 57.0 2777.0 5.64 5.69 3.56
0.71 Good F VS2 63.8 58.0 2777.0 5.61 5.64 3.59
0.71 Good F VS2 57.8 60.0 2777.0 5.87 5.9 3.4
0.7 Ideal E VS2 62.1 55.0 2777.0 5.7 5.67 3.53
0.7 Premium E VS2 61.1 60.0 2777.0 5.71 5.64 3.47
0.7 Premium E SI1 60.0 59.0 2777.0 5.79 5.75 3.46
0.7 Premium E SI1 61.2 57.0 2777.0 5.73 5.68 3.49
0.7 Premium E SI1 62.7 59.0 2777.0 5.67 5.63 3.54
0.7 Premium E SI1 61.0 57.0 2777.0 5.73 5.68 3.48
0.7 Premium E SI1 61.0 58.0 2777.0 5.78 5.72 3.51
0.7 Ideal E SI1 61.4 57.0 2777.0 5.76 5.7 3.52
0.72 Premium F SI1 61.8 61.0 2777.0 5.82 5.71 3.56
0.7 Very Good E SI1 59.9 63.0 2777.0 5.76 5.7 3.43
0.7 Premium E SI1 61.3 58.0 2777.0 5.71 5.68 3.49
0.7 Premium E SI1 60.5 58.0 2777.0 5.77 5.74 3.48
0.7 Good E VS2 64.1 59.0 2777.0 5.64 5.59 3.6
0.98 Fair H SI2 67.9 60.0 2777.0 6.05 5.97 4.08
0.78 Premium F SI1 62.4 58.0 2777.0 5.83 5.8 3.63
0.7 Very Good E SI1 63.2 60.0 2777.0 5.6 5.51 3.51
0.52 Ideal F VVS1 61.3 55.0 2778.0 5.19 5.22 3.19
0.73 Very Good H VS2 60.8 56.0 2779.0 5.82 5.84 3.55
0.74 Ideal E SI1 61.7 56.0 2779.0 5.84 5.8 3.59
0.7 Very Good F VS2 63.6 57.0 2780.0 5.61 5.65 3.58
0.77 Premium G VS2 61.2 58.0 2780.0 5.9 5.93 3.62
0.71 Ideal F VS2 62.1 54.0 2780.0 5.68 5.72 3.54
0.74 Ideal G VS1 61.5 55.0 2780.0 5.81 5.86 3.59
0.7 Ideal G VS1 61.4 59.0 2780.0 5.64 5.73 3.49
1.01 Premium F I1 61.8 60.0 2781.0 6.39 6.36 3.94
0.77 Ideal H SI1 62.2 56.0 2781.0 5.83 5.88 3.64
0.78 Ideal H SI1 61.2 56.0 2781.0 5.92 5.99 3.64
0.72 Very Good H VS1 60.6 63.0 2782.0 5.83 5.76 3.51
0.53 Very Good D VVS2 57.5 64.0 2782.0 5.34 5.37 3.08
0.76 Ideal G VS2 61.3 56.0 2782.0 5.9 5.94 3.63
0.7 Good E VS1 57.2 62.0 2782.0 5.81 5.77 3.31
0.7 Premium E VS1 62.9 60.0 2782.0 5.62 5.54 3.51
0.75 Very Good D SI2 63.1 58.0 2782.0 5.78 5.73 3.63
0.72 Ideal D SI1 60.8 57.0 2782.0 5.76 5.75 3.5
0.72 Premium D SI1 62.7 59.0 2782.0 5.73 5.69 3.58
0.7 Premium D SI1 62.8 60.0 2782.0 5.68 5.66 3.56
0.84 Fair G SI1 55.1 67.0 2782.0 6.39 6.2 3.47
0.75 Premium F SI1 61.4 59.0 2782.0 5.88 5.85 3.6
0.52 Ideal F IF 62.2 55.0 2783.0 5.14 5.18 3.21
0.72 Very Good F VS2 63.0 54.0 2784.0 5.69 5.73 3.6
0.79 Very Good H VS1 63.7 56.0 2784.0 5.85 5.92 3.75
0.72 Very Good F VS2 63.6 58.0 2787.0 5.66 5.69 3.61
0.51 Ideal F VVS1 62.0 57.0 2787.0 5.11 5.15 3.18
0.64 Ideal D VS1 61.5 56.0 2787.0 5.54 5.55 3.41
0.7 Very Good H VVS1 60.5 60.0 2788.0 5.74 5.77 3.48
0.83 Very Good I VS1 61.1 60.0 2788.0 6.07 6.1 3.72
0.76 Ideal I VVS2 61.8 56.0 2788.0 5.85 5.87 3.62
0.71 Good D VS2 63.3 56.0 2788.0 5.64 5.68 3.58
0.77 Good G VS1 59.4 64.0 2788.0 5.97 5.92 3.53
0.71 Ideal F SI1 62.5 55.0 2788.0 5.71 5.65 3.55
1.01 Fair E I1 64.5 58.0 2788.0 6.29 6.21 4.03
1.01 Premium H SI2 62.7 59.0 2788.0 6.31 6.22 3.93
0.77 Good F SI1 64.2 52.0 2789.0 5.81 5.77 3.72
0.76 Good E SI1 63.7 54.0 2789.0 5.76 5.85 3.7
0.76 Premium E SI1 60.4 58.0 2789.0 5.92 5.94 3.58
0.76 Premium E SI1 61.8 58.0 2789.0 5.82 5.86 3.61
1.05 Very Good J SI2 63.2 56.0 2789.0 6.49 6.45 4.09
0.81 Ideal G SI2 61.6 56.0 2789.0 5.97 6.01 3.69
0.7 Ideal E SI1 61.6 56.0 2789.0 5.72 5.75 3.53
0.55 Ideal G IF 60.9 57.0 2789.0 5.28 5.3 3.22
0.81 Good G SI2 61.0 61.0 2789.0 5.94 5.99 3.64
0.63 Premium E VVS2 62.1 57.0 2789.0 5.48 5.41 3.38
0.63 Premium E VVS1 60.9 60.0 2789.0 5.55 5.52 3.37
0.77 Premium H VS1 61.3 60.0 2789.0 5.9 5.88 3.61
1.05 Fair J SI2 65.8 59.0 2789.0 6.41 6.27 4.18
0.64 Ideal G IF 61.3 56.0 2790.0 5.54 5.58 3.41
0.76 Premium I VVS1 58.8 59.0 2790.0 6.0 5.94 3.51
0.83 Ideal F SI2 62.3 55.0 2790.0 6.02 6.05 3.76
0.71 Premium F VS1 60.1 62.0 2790.0 5.77 5.74 3.46
0.71 Premium F VS1 61.8 59.0 2790.0 5.73 5.69 3.53
0.87 Very Good I SI1 63.6 55.8 2791.0 6.07 6.1 3.87
0.73 Ideal E SI1 62.2 56.0 2791.0 5.74 5.78 3.58
0.71 Premium E SI1 59.2 59.0 2792.0 5.83 5.86 3.46
0.71 Premium E SI1 61.8 59.0 2792.0 5.7 5.75 3.54
0.71 Ideal E SI1 61.3 55.0 2792.0 5.72 5.77 3.52
0.7 Premium F VS1 62.1 60.0 2792.0 5.71 5.65 3.53
0.7 Premium F VS1 60.7 60.0 2792.0 5.78 5.75 3.5
0.76 Premium H VVS2 59.6 57.0 2792.0 5.91 5.86 3.51
0.7 Ideal F VS1 62.2 56.0 2792.0 5.73 5.68 3.55
0.79 Very Good G SI1 60.6 57.0 2793.0 5.98 6.06 3.65
0.7 Very Good E VS2 62.9 57.0 2793.0 5.66 5.69 3.57
0.7 Good E VS2 64.1 55.0 2793.0 5.6 5.66 3.61
0.76 Ideal I VS2 61.3 56.0 2793.0 5.87 5.91 3.61
0.73 Ideal H VS2 62.7 55.0 2793.0 5.72 5.76 3.6
0.79 Very Good E SI1 63.2 56.0 2794.0 5.91 5.86 3.72
0.71 Very Good E VS2 60.7 56.0 2795.0 5.81 5.82 3.53
0.81 Premium I VVS2 61.9 60.0 2795.0 5.91 5.86 3.64
0.81 Ideal F SI2 62.6 55.0 2795.0 5.92 5.96 3.72
0.72 Good F VS1 60.7 60.0 2795.0 5.74 5.72 3.48
0.72 Premium D SI2 62.0 60.0 2795.0 5.73 5.69 3.54
0.72 Premium I IF 63.0 57.0 2795.0 5.72 5.7 3.6
0.81 Premium H VS2 58.0 59.0 2795.0 6.17 6.13 3.57
0.72 Premium G VS2 62.9 57.0 2795.0 5.73 5.65 3.58
1.0 Premium I SI2 58.2 60.0 2795.0 6.61 6.55 3.83
0.73 Good E SI1 63.2 58.0 2796.0 5.7 5.76 3.62
0.81 Very Good H SI2 61.3 59.0 2797.0 5.94 6.01 3.66
0.81 Very Good E SI1 60.3 60.0 2797.0 6.07 6.1 3.67
0.71 Premium D SI1 62.7 60.0 2797.0 5.67 5.71 3.57
0.71 Premium D SI1 61.3 58.0 2797.0 5.73 5.75 3.52
0.71 Premium D SI1 61.6 60.0 2797.0 5.74 5.69 3.52
0.57 Ideal F VVS2 61.9 55.0 2797.0 5.34 5.35 3.31
0.51 Ideal D VVS1 61.7 56.0 2797.0 5.12 5.16 3.17
0.72 Ideal G VS2 61.9 58.0 2797.0 5.72 5.75 3.55
0.74 Ideal H VS1 61.8 58.0 2797.0 5.77 5.81 3.58
0.74 Ideal H VS1 61.6 56.0 2797.0 5.81 5.82 3.58
0.7 Fair G VVS1 58.8 66.0 2797.0 5.81 5.9 3.44
0.8 Premium F SI2 61.0 57.0 2797.0 6.03 6.01 3.67
1.01 Fair E SI2 67.4 60.0 2797.0 6.19 6.05 4.13
0.8 Very Good H VS2 63.4 60.0 2797.0 5.92 5.82 3.72
0.77 Ideal I VS1 61.5 59.0 2798.0 5.87 5.91 3.62
0.83 Very Good E SI2 58.0 62.0 2799.0 6.19 6.25 3.61
0.82 Ideal F SI2 62.4 54.0 2799.0 5.97 6.02 3.74
0.78 Ideal D SI1 61.9 57.0 2799.0 5.91 5.86 3.64
0.6 Very Good G IF 61.6 56.0 2800.0 5.43 5.46 3.35
0.9 Good I SI2 62.2 59.0 2800.0 6.07 6.11 3.79
0.7 Premium E VS1 62.2 58.0 2800.0 5.6 5.66 3.5
0.9 Very Good I SI2 61.3 56.0 2800.0 6.17 6.23 3.8
0.83 Ideal G SI1 62.3 57.0 2800.0 5.99 6.08 3.76
0.83 Ideal G SI1 61.8 57.0 2800.0 6.03 6.07 3.74
0.83 Very Good H SI1 62.5 59.0 2800.0 5.95 6.02 3.74
0.74 Premium G VS1 62.9 60.0 2800.0 5.74 5.68 3.59
0.79 Ideal I VS1 61.8 59.0 2800.0 5.92 5.95 3.67
0.61 Ideal G IF 62.3 56.0 2800.0 5.43 5.45 3.39
0.76 Fair G VS1 59.0 70.0 2800.0 5.89 5.8 3.46
0.96 Ideal F I1 60.7 55.0 2801.0 6.37 6.41 3.88
0.73 Ideal F VS2 62.5 55.0 2801.0 5.8 5.76 3.61
0.73 Premium F VS2 62.7 58.0 2801.0 5.76 5.7 3.59
0.75 Ideal H SI1 60.4 57.0 2801.0 5.93 5.96 3.59
0.71 Premium F VS2 62.1 58.0 2801.0 5.7 5.67 3.53
0.71 Good F VS2 57.8 60.0 2801.0 5.9 5.87 3.4
0.71 Good F VS2 63.8 58.0 2801.0 5.64 5.61 3.59
0.71 Premium F VS2 62.8 57.0 2801.0 5.69 5.64 3.56
1.04 Premium G I1 62.2 58.0 2801.0 6.46 6.41 4.0
1.0 Premium J SI2 62.3 58.0 2801.0 6.45 6.34 3.98
0.87 Very Good G SI2 59.9 58.0 2802.0 6.19 6.23 3.72
0.53 Ideal F IF 61.9 54.0 2802.0 5.22 5.25 3.24
0.72 Premium E VS2 63.0 55.0 2802.0 5.79 5.61 3.59
0.72 Premium F VS1 62.4 58.0 2802.0 5.83 5.7 3.6
0.7 Very Good F VS2 62.9 58.0 2803.0 5.63 5.65 3.55
0.74 Very Good E SI1 63.5 56.0 2803.0 5.74 5.79 3.66
0.71 Ideal G VS2 61.3 56.0 2803.0 5.75 5.71 3.51
0.73 Ideal E SI1 60.6 54.0 2803.0 5.84 5.89 3.55
0.7 Good G VS1 65.1 58.0 2803.0 5.56 5.59 3.63
0.71 Premium F VS2 62.6 58.0 2803.0 5.7 5.67 3.56
0.71 Premium F VS2 58.0 62.0 2803.0 5.85 5.81 3.38
0.71 Premium G VS1 62.4 61.0 2803.0 5.7 5.65 3.54
0.77 Premium G VS2 61.3 57.0 2803.0 5.93 5.88 3.62
0.71 Premium G VS2 59.9 60.0 2803.0 5.81 5.77 3.47
0.78 Premium G VS2 60.8 58.0 2803.0 6.03 5.95 3.64
0.71 Very Good G VS1 63.5 55.0 2803.0 5.66 5.64 3.59
0.91 Ideal D SI2 62.2 57.0 2803.0 6.21 6.15 3.85
0.71 Very Good E VS2 63.8 58.0 2804.0 5.62 5.66 3.6
0.71 Very Good E VS2 64.0 57.0 2804.0 5.66 5.68 3.63
0.8 Very Good E SI2 62.5 56.0 2804.0 5.88 5.96 3.7
0.7 Very Good D SI1 62.3 58.0 2804.0 5.69 5.73 3.56
0.72 Ideal F VS1 61.7 57.0 2804.0 5.74 5.77 3.55
0.72 Very Good F VS1 62.2 58.0 2804.0 5.75 5.7 3.56
0.82 Ideal H VS2 61.5 56.0 2804.0 6.01 6.08 3.72
0.7 Ideal D SI1 61.0 59.0 2804.0 5.68 5.7 3.47
0.72 Ideal D SI1 62.2 56.0 2804.0 5.74 5.77 3.58
0.72 Ideal D SI1 61.5 54.0 2804.0 5.77 5.8 3.56
0.9 Fair I SI1 67.3 59.0 2804.0 5.93 5.84 3.96
0.74 Premium F VS2 61.7 58.0 2805.0 5.85 5.78 3.59
0.74 Premium F VS2 61.9 56.0 2805.0 5.8 5.77 3.58
0.73 Ideal E SI2 61.8 58.0 2805.0 5.77 5.81 3.58
0.57 Fair E VVS1 58.7 66.0 2805.0 5.34 5.43 3.16
0.73 Premium F VS2 62.5 57.0 2805.0 5.75 5.7 3.58
0.72 Ideal G VS2 62.8 56.0 2805.0 5.74 5.7 3.59
0.74 Fair F VS2 61.1 68.0 2805.0 5.82 5.75 3.53
0.82 Good G VS2 64.0 57.0 2805.0 5.92 5.89 3.78
0.81 Very Good G SI1 62.5 60.0 2806.0 5.89 5.94 3.69
0.75 Very Good H VVS1 60.6 58.0 2806.0 5.85 5.9 3.56
0.7 Ideal F SI1 61.6 55.0 2806.0 5.72 5.74 3.53
0.71 Very Good F VS1 62.2 58.0 2807.0 5.66 5.72 3.54
0.71 Very Good F VS1 60.0 57.0 2807.0 5.84 5.9 3.52
0.93 Premium J SI2 61.9 57.0 2807.0 6.21 6.19 3.84
0.8 Very Good H VS2 62.8 57.0 2808.0 5.87 5.91 3.7
0.7 Very Good F VS1 62.0 57.0 2808.0 5.64 5.71 3.52
1.0 Fair G I1 66.4 59.0 2808.0 6.16 6.09 4.07
0.75 Very Good G VS2 63.4 56.0 2808.0 5.78 5.74 3.65
0.58 Ideal E VVS2 60.9 56.0 2808.0 5.41 5.43 3.3
0.73 Very Good D SI1 63.1 57.0 2808.0 5.74 5.7 3.61
0.81 Very Good F SI1 63.1 59.0 2809.0 5.85 5.79 3.67
0.81 Premium D SI2 59.2 57.0 2809.0 6.15 6.05 3.61
0.71 Premium F SI1 60.7 54.0 2809.0 5.84 5.8 3.53
1.2 Fair F I1 64.6 56.0 2809.0 6.73 6.66 4.33
0.7 Very Good F VS1 61.8 56.0 2810.0 5.63 5.7 3.5
0.7 Very Good F VS1 59.9 60.0 2810.0 5.77 5.84 3.48
0.74 Ideal D SI2 61.7 55.0 2810.0 5.81 5.85 3.6
0.7 Good F VS1 62.8 61.0 2810.0 5.57 5.61 3.51
0.8 Good G SI1 62.7 57.0 2810.0 5.84 5.93 3.69
0.75 Very Good F SI1 63.4 58.0 2811.0 5.72 5.76 3.64
0.83 Very Good D SI1 63.5 54.0 2811.0 5.98 5.95 3.79
1.0 Fair J VS2 65.7 59.0 2811.0 6.14 6.07 4.01
0.99 Fair I SI2 68.1 56.0 2811.0 6.21 6.06 4.18
0.7 Very Good G VS1 63.0 60.0 2812.0 5.57 5.64 3.53
0.7 Very Good F VS2 59.5 58.0 2812.0 5.75 5.85 3.45
0.7 Good E SI1 63.5 59.0 2812.0 5.49 5.53 3.5
0.7 Very Good F VS2 61.7 58.0 2812.0 5.63 5.69 3.49
0.32 Premium I SI1 62.7 58.0 554.0 4.37 4.34 2.73
0.32 Premium I SI1 62.8 58.0 554.0 4.39 4.34 2.74
0.32 Ideal I SI1 62.4 57.0 554.0 4.37 4.35 2.72
0.32 Premium I SI1 61.0 59.0 554.0 4.39 4.36 2.67
0.32 Very Good I SI1 63.1 56.0 554.0 4.39 4.36 2.76
0.32 Ideal I SI1 60.7 57.0 554.0 4.47 4.42 2.7
0.3 Premium H SI1 60.9 59.0 554.0 4.31 4.29 2.62
0.3 Premium H SI1 60.1 55.0 554.0 4.41 4.38 2.64
0.3 Premium H SI1 62.9 58.0 554.0 4.28 4.24 2.68
0.3 Very Good H SI1 63.3 56.0 554.0 4.29 4.27 2.71
0.3 Good H SI1 63.8 55.0 554.0 4.26 4.2 2.7
0.3 Ideal H SI1 62.9 57.0 554.0 4.27 4.22 2.67
0.3 Very Good H SI1 63.4 60.0 554.0 4.25 4.23 2.69
0.32 Good I SI1 63.9 55.0 554.0 4.36 4.34 2.78
0.33 Ideal H SI2 61.4 56.0 554.0 4.85 4.79 2.95
0.29 Very Good E VS1 61.9 55.0 555.0 4.28 4.33 2.66
0.29 Very Good E VS1 62.4 55.0 555.0 4.2 4.25 2.63
0.31 Very Good F SI1 61.8 58.0 555.0 4.32 4.35 2.68
0.34 Ideal H VS2 61.5 56.0 555.0 4.47 4.5 2.76
0.34 Ideal H VS2 60.4 57.0 555.0 4.54 4.57 2.75
0.34 Ideal I VS1 61.8 55.0 555.0 4.48 4.52 2.78
0.34 Ideal I VS1 62.0 56.0 555.0 4.5 4.53 2.8
0.3 Ideal G VS1 62.3 56.0 555.0 4.29 4.31 2.68
0.29 Ideal F VS1 61.6 56.0 555.0 4.26 4.31 2.64
0.35 Ideal G SI1 60.6 56.0 555.0 4.56 4.58 2.77
0.43 Very Good E I1 58.4 62.0 555.0 4.94 5.0 2.9
0.32 Very Good F VS2 61.4 58.0 556.0 4.37 4.42 2.7
0.36 Ideal I VS2 61.9 56.0 556.0 4.54 4.57 2.82
0.3 Ideal G VS2 62.0 56.0 556.0 4.28 4.3 2.66
0.26 Ideal E VS1 61.5 57.0 556.0 4.09 4.12 2.52
0.7 Very Good F VS2 62.3 58.0 2812.0 5.64 5.72 3.54
0.7 Very Good F VS2 60.9 61.0 2812.0 5.66 5.71 3.46
0.71 Ideal D SI1 62.4 57.0 2812.0 5.69 5.72 3.56
0.99 Fair J SI1 55.0 61.0 2812.0 6.72 6.67 3.68
0.73 Premium E VS2 58.6 60.0 2812.0 5.92 5.89 3.46
0.51 Ideal F VVS1 62.0 57.0 2812.0 5.15 5.11 3.18
0.91 Premium G SI2 59.8 58.0 2813.0 6.3 6.29 3.77
0.84 Very Good E SI1 63.4 55.0 2813.0 6.0 5.95 3.79
0.91 Good I VS2 64.3 58.0 2813.0 6.09 6.05 3.9
0.76 Premium E SI1 62.2 59.0 2814.0 5.86 5.81 3.63
0.76 Ideal E SI1 61.7 57.0 2814.0 5.88 5.85 3.62
0.75 Premium E SI1 61.1 59.0 2814.0 5.86 5.83 3.57
0.55 Very Good D VVS1 61.5 56.0 2815.0 5.23 5.27 3.23
0.76 Very Good F SI2 58.5 62.0 2815.0 5.93 6.01 3.49
0.74 Premium G VS1 61.7 58.0 2815.0 5.79 5.81 3.58
0.7 Ideal H SI1 60.4 56.0 2815.0 5.75 5.81 3.49
0.7 Ideal H SI1 61.4 56.0 2815.0 5.7 5.76 3.52
0.7 Ideal H SI1 61.5 55.0 2815.0 5.73 5.79 3.54
0.7 Ideal H SI1 61.4 56.0 2815.0 5.72 5.77 3.53
0.9 Fair J VS2 65.0 56.0 2815.0 6.08 6.04 3.94
0.95 Fair F SI2 56.0 60.0 2815.0 6.62 6.53 3.68
0.89 Premium H SI2 60.2 59.0 2815.0 6.26 6.23 3.76
0.72 Premium E VS2 58.3 58.0 2815.0 5.99 5.92 3.47
0.96 Fair E SI2 53.1 63.0 2815.0 6.73 6.65 3.55
1.02 Premium G I1 60.3 58.0 2815.0 6.55 6.5 3.94
0.78 Very Good I VVS2 61.4 56.0 2816.0 5.91 5.95 3.64
0.61 Ideal G VVS2 60.1 57.0 2816.0 5.52 5.54 3.32
0.71 Good D VS1 63.4 55.0 2816.0 5.61 5.69 3.58
0.78 Premium F SI1 61.5 59.0 2816.0 5.96 5.88 3.64
0.87 Ideal H SI2 62.7 56.0 2816.0 6.16 6.13 3.85
0.83 Ideal H SI1 62.5 55.0 2816.0 6.04 6.0 3.76
0.71 Premium E SI1 61.3 56.0 2817.0 5.78 5.73 3.53
0.71 Ideal I VVS2 60.2 56.0 2817.0 5.84 5.89 3.53
0.71 Ideal E VS2 62.7 57.0 2817.0 5.66 5.64 3.54
0.71 Premium E VS2 62.3 58.0 2817.0 5.69 5.65 3.53
0.63 Ideal F VVS2 61.5 56.0 2817.0 5.48 5.52 3.38
0.71 Premium E SI1 59.2 59.0 2817.0 5.86 5.83 3.46
0.71 Premium E SI1 61.8 59.0 2817.0 5.75 5.7 3.54
0.71 Ideal E SI1 61.3 55.0 2817.0 5.77 5.72 3.52
0.71 Premium E SI1 61.4 58.0 2817.0 5.77 5.73 3.53
0.9 Ideal J VS2 62.8 55.0 2817.0 6.2 6.16 3.88
0.71 Good E SI1 62.8 64.0 2817.0 5.6 5.54 3.5
0.7 Premium E VS2 62.4 61.0 2818.0 5.66 5.63 3.52
0.7 Premium E VS2 59.3 60.0 2818.0 5.78 5.73 3.41
0.7 Premium E VS2 63.0 60.0 2818.0 5.64 5.6 3.54
1.0 Premium H I1 61.3 60.0 2818.0 6.43 6.39 3.93
0.86 Premium F SI2 59.3 62.0 2818.0 6.36 6.22 3.73
0.8 Ideal H SI1 61.0 57.0 2818.0 6.07 6.0 3.68
0.7 Ideal E VS1 62.9 57.0 2818.0 5.66 5.61 3.54
0.7 Premium E VS1 59.6 57.0 2818.0 5.91 5.83 3.5
0.7 Premium F VS2 61.8 60.0 2818.0 5.69 5.64 3.5
0.7 Premium E VS1 62.7 57.0 2818.0 5.68 5.64 3.55
1.0 Fair H SI2 65.3 62.0 2818.0 6.34 6.12 4.08
0.72 Very Good G VS1 63.8 58.0 2819.0 5.64 5.68 3.61
0.72 Ideal H VS1 62.3 56.0 2819.0 5.73 5.77 3.58
0.7 Good F VS1 59.7 63.0 2819.0 5.76 5.79 3.45
0.86 Good F SI2 64.3 60.0 2819.0 5.97 5.95 3.83
0.71 Ideal G VS1 62.9 58.0 2820.0 5.66 5.69 3.57
0.75 Ideal E SI1 62.0 57.0 2821.0 5.8 5.78 3.59
0.73 Premium E VS2 61.6 59.0 2821.0 5.77 5.73 3.54
0.53 Ideal E VVS1 61.9 55.0 2821.0 5.2 5.21 3.22
0.73 Premium E SI1 61.3 58.0 2821.0 5.83 5.76 3.55
0.73 Good E SI1 63.6 57.0 2821.0 5.72 5.7 3.63
0.73 Premium E SI1 59.6 61.0 2821.0 5.92 5.85 3.51
0.73 Premium E SI1 62.2 59.0 2821.0 5.77 5.68 3.56
0.73 Premium D SI1 61.7 55.0 2821.0 5.84 5.82 3.6
0.73 Very Good E SI1 63.2 58.0 2821.0 5.76 5.7 3.62
0.7 Premium E VS1 60.8 60.0 2822.0 5.74 5.71 3.48
0.72 Premium E VS2 60.3 59.0 2822.0 5.84 5.8 3.51
0.72 Premium E VS2 60.9 60.0 2822.0 5.8 5.76 3.52
0.72 Premium E VS2 62.4 59.0 2822.0 5.77 5.7 3.58
0.7 Premium E VS2 60.2 60.0 2822.0 5.73 5.7 3.44
0.6 Ideal F VVS2 62.0 55.0 2822.0 5.37 5.4 3.34
0.74 Ideal I VVS1 60.8 57.0 2822.0 5.85 5.89 3.57
0.73 Ideal F SI1 62.1 55.0 2822.0 5.75 5.78 3.58
0.71 Premium D SI1 62.7 60.0 2822.0 5.71 5.67 3.57
0.71 Premium D SI1 61.3 58.0 2822.0 5.75 5.73 3.52
0.7 Premium D SI1 60.2 60.0 2822.0 5.82 5.75 3.48
0.7 Ideal D SI1 60.7 56.0 2822.0 5.75 5.72 3.48
0.9 Good J VS2 64.0 61.0 2822.0 6.04 6.03 3.86
0.71 Ideal D SI1 60.2 56.0 2822.0 5.86 5.83 3.52
0.7 Premium E VS2 61.5 59.0 2822.0 5.73 5.68 3.51
0.7 Premium E VS2 62.6 56.0 2822.0 5.71 5.66 3.56
0.7 Ideal D SI1 59.7 58.0 2822.0 5.82 5.77 3.46
0.7 Good E SI1 61.4 64.0 2822.0 5.71 5.66 3.49
0.7 Ideal D SI1 62.5 57.0 2822.0 5.62 5.59 3.51
0.7 Ideal D SI1 61.8 56.0 2822.0 5.73 5.63 3.51
0.7 Premium E VS2 60.7 62.0 2822.0 5.72 5.68 3.46
0.7 Premium F VS2 60.6 58.0 2822.0 5.8 5.72 3.49
0.7 Ideal D SI1 61.4 54.0 2822.0 5.75 5.71 3.52
0.79 Very Good D SI2 62.8 59.0 2823.0 5.86 5.9 3.69
0.9 Good I SI1 63.8 57.0 2823.0 6.06 6.13 3.89
0.71 Premium E VS2 62.3 58.0 2823.0 5.71 5.66 3.54
0.61 Ideal E VVS2 61.3 54.0 2823.0 5.51 5.59 3.4
0.9 Fair H SI2 65.8 54.0 2823.0 6.05 5.98 3.96
0.71 Ideal E SI1 60.5 56.0 2823.0 5.77 5.73 3.47
0.71 Premium D VS2 61.2 59.0 2824.0 5.74 5.69 3.5
0.77 Ideal I VVS2 62.1 57.0 2824.0 5.84 5.86 3.63
0.74 Good E VS1 63.1 58.0 2824.0 5.73 5.75 3.62
0.82 Ideal F SI2 62.4 54.0 2824.0 6.02 5.97 3.74
0.82 Premium E SI2 60.8 60.0 2824.0 6.05 6.03 3.67
0.71 Premium G VS1 62.2 59.0 2825.0 5.73 5.66 3.54
0.83 Premium H SI1 60.0 59.0 2825.0 6.08 6.05 3.64
0.73 Very Good G VS1 62.0 57.0 2825.0 5.75 5.8 3.58
0.83 Premium H SI1 62.5 59.0 2825.0 6.02 5.95 3.74
1.17 Premium J I1 60.2 61.0 2825.0 6.9 6.83 4.13
0.91 Fair H SI2 61.3 67.0 2825.0 6.24 6.19 3.81
0.73 Premium E VS1 62.6 60.0 2826.0 5.75 5.68 3.58
0.7 Good E VS1 57.2 59.0 2826.0 5.94 5.88 3.38
0.9 Premium I SI2 62.2 59.0 2826.0 6.11 6.07 3.79
0.7 Premium E VS1 62.2 58.0 2826.0 5.66 5.6 3.5
0.7 Very Good D VS2 63.3 56.0 2826.0 5.6 5.58 3.54
0.7 Premium E VS1 59.4 61.0 2826.0 5.78 5.74 3.42
0.9 Very Good I SI2 63.5 56.0 2826.0 6.17 6.07 3.88
0.78 Premium F SI1 60.8 60.0 2826.0 5.97 5.94 3.62
0.96 Ideal F I1 60.7 55.0 2826.0 6.41 6.37 3.88
0.7 Very Good D SI1 62.3 59.0 2827.0 5.67 5.7 3.54
0.72 Good D VS2 64.0 54.0 2827.0 5.68 5.7 3.64
0.79 Premium H VVS2 62.6 58.0 2827.0 5.96 5.9 3.71
0.7 Ideal H VVS1 61.6 57.0 2827.0 5.69 5.74 3.52
0.7 Ideal H VVS1 62.3 55.0 2827.0 5.66 5.7 3.54
0.7 Ideal D SI2 60.6 57.0 2828.0 5.74 5.77 3.49
1.01 Premium H SI2 61.6 61.0 2828.0 6.39 6.31 3.91
0.72 Premium F VS1 62.2 58.0 2829.0 5.75 5.7 3.56
0.8 Good E SI2 63.7 54.0 2829.0 5.91 5.87 3.75
0.59 Ideal E VVS1 62.0 56.0 2829.0 5.36 5.38 3.33
0.72 Ideal F VS1 61.7 57.0 2829.0 5.77 5.74 3.55
0.75 Premium E SI2 61.9 57.0 2829.0 5.88 5.82 3.62
0.8 Premium E SI2 60.2 57.0 2829.0 6.05 6.01 3.63
0.71 Very Good E VS2 62.7 59.0 2830.0 5.65 5.7 3.56
0.77 Very Good H SI1 61.7 56.0 2830.0 5.84 5.89 3.62
0.97 Ideal F I1 60.7 56.0 2830.0 6.41 6.43 3.9
0.53 Ideal F VVS1 60.9 57.0 2830.0 5.23 5.29 3.19
0.53 Ideal F VVS1 61.8 57.0 2830.0 5.16 5.19 3.2
0.8 Ideal I VS2 62.1 54.4 2830.0 5.94 5.99 3.7
0.9 Premium G SI1 60.6 62.0 2830.0 6.21 6.13 3.74
0.76 Very Good E SI2 60.8 60.0 2831.0 5.89 5.98 3.61
0.72 Ideal E SI1 62.3 57.0 2831.0 5.7 5.76 3.57
0.75 Ideal E SI1 61.4 57.0 2831.0 5.82 5.87 3.59
0.72 Premium E SI1 62.1 58.0 2831.0 5.73 5.76 3.57
0.79 Ideal G SI1 61.8 56.0 2831.0 5.93 5.91 3.66
0.72 Very Good F VS2 62.5 58.0 2832.0 5.71 5.75 3.58
0.91 Very Good I SI2 62.8 61.0 2832.0 6.15 6.18 3.87
0.71 Premium G VVS2 62.1 57.0 2832.0 5.75 5.65 3.54
0.81 Premium G SI1 63.0 60.0 2832.0 5.87 5.81 3.68
0.82 Ideal H SI1 62.5 57.0 2832.0 5.91 5.97 3.71
0.71 Premium F VS1 62.2 58.0 2832.0 5.72 5.66 3.54
0.9 Good J SI1 64.3 63.0 2832.0 6.05 6.01 3.88
0.8 Very Good I VS2 62.0 58.0 2833.0 5.86 5.95 3.66
0.56 Very Good E IF 61.0 59.0 2833.0 5.28 5.34 3.24
0.7 Very Good D VS2 59.6 61.0 2833.0 5.77 5.8 3.45
0.7 Ideal D VS2 61.0 57.0 2833.0 5.74 5.76 3.51
0.61 Ideal F VVS2 61.7 55.0 2833.0 5.45 5.48 3.37
0.85 Ideal H SI2 62.5 57.0 2833.0 6.02 6.07 3.78
0.7 Ideal F SI1 60.7 57.0 2833.0 5.73 5.75 3.49
0.8 Ideal G VS2 62.2 56.0 2834.0 5.94 5.87 3.67
0.8 Ideal H VS2 62.8 57.0 2834.0 5.91 5.87 3.7
0.51 Very Good D VVS1 59.9 58.0 2834.0 5.16 5.19 3.1
0.53 Ideal F VVS1 61.4 57.0 2834.0 5.2 5.23 3.2
0.78 Ideal I VS2 61.8 55.0 2834.0 5.92 5.95 3.67
0.9 Very Good J SI1 63.4 54.0 2834.0 6.17 6.14 3.9
0.9 Fair G SI2 65.3 59.0 2834.0 6.07 6.0 3.94
0.77 Ideal E SI2 60.7 55.0 2834.0 6.01 5.95 3.63
0.73 Ideal F VS1 61.2 56.0 2835.0 5.89 5.81 3.58
0.63 Ideal F VVS2 61.9 57.0 2835.0 5.47 5.51 3.4
0.7 Ideal E VS2 61.5 54.0 2835.0 5.7 5.75 3.52
0.72 Ideal E VS2 62.8 57.0 2835.0 5.71 5.73 3.59
0.72 Ideal E SI1 61.0 57.0 2835.0 5.78 5.8 3.53
0.75 Premium F VS2 59.6 59.0 2835.0 6.04 5.94 3.57
0.82 Very Good H SI1 60.7 56.0 2836.0 6.04 6.06 3.67
0.71 Good E VS2 62.8 60.0 2836.0 5.6 5.65 3.53
0.7 Premium E VS1 62.6 59.0 2837.0 5.69 5.66 3.55
0.7 Ideal E VS1 61.8 56.0 2837.0 5.74 5.69 3.53
0.71 Ideal F SI1 59.8 53.0 2838.0 5.86 5.82 3.49
0.76 Very Good H SI1 60.9 55.0 2838.0 5.92 5.94 3.61
0.82 Fair F SI1 64.9 58.0 2838.0 5.83 5.79 3.77
0.72 Premium F VS1 58.8 60.0 2838.0 5.91 5.89 3.47
0.7 Premium F VS2 62.3 58.0 2838.0 5.72 5.64 3.54
0.7 Premium F VS2 61.7 58.0 2838.0 5.69 5.63 3.49
0.7 Premium G VS1 62.6 55.0 2838.0 5.73 5.64 3.56
0.7 Premium F VS2 59.4 61.0 2838.0 5.83 5.79 3.45
0.7 Very Good E SI1 63.5 59.0 2838.0 5.53 5.49 3.5
0.7 Premium F VS2 60.9 61.0 2838.0 5.71 5.66 3.46
0.7 Premium F VS2 59.5 58.0 2838.0 5.85 5.75 3.45
0.7 Premium G VS1 63.0 60.0 2838.0 5.64 5.57 3.53
0.74 Very Good E SI1 60.0 57.0 2839.0 5.85 5.89 3.52
0.71 Ideal F VS1 61.5 57.0 2839.0 5.74 5.71 3.52
0.7 Ideal F VS1 61.6 54.0 2839.0 5.75 5.72 3.53
0.71 Ideal F VS1 62.1 55.0 2839.0 5.82 5.68 3.57
0.71 Premium F VS1 59.1 61.0 2839.0 5.84 5.81 3.44
0.71 Premium F VS1 59.0 60.0 2839.0 5.82 5.8 3.43
0.71 Premium F VS1 60.5 58.0 2839.0 5.75 5.72 3.47
0.7 Ideal F VS1 62.4 53.0 2839.0 5.73 5.71 3.57
0.73 Ideal G VS2 61.8 54.0 2839.0 5.8 5.82 3.59
0.7 Ideal E VS2 62.1 54.0 2839.0 5.69 5.72 3.54
0.7 Ideal G VS1 61.3 57.0 2839.0 5.71 5.74 3.51
0.71 Premium G VVS2 60.3 58.0 2839.0 5.82 5.78 3.5
0.71 Premium F VS1 59.2 58.0 2839.0 5.87 5.82 3.46
0.79 Premium G VS2 59.3 62.0 2839.0 6.09 6.01 3.59
0.71 Premium F VS1 62.7 59.0 2839.0 5.7 5.62 3.55
0.77 Very Good H VS1 61.0 60.0 2840.0 5.9 5.87 3.59
0.75 Very Good F SI2 59.8 56.0 2840.0 5.85 5.92 3.52
0.7 Ideal F SI1 61.0 56.0 2840.0 5.75 5.8 3.52
0.71 Premium F VS2 59.3 56.0 2840.0 5.88 5.82 3.47
0.92 Ideal D SI2 61.9 56.0 2840.0 6.27 6.2 3.86
0.83 Premium F SI2 61.4 59.0 2840.0 6.08 6.04 3.72
0.7 Premium H VVS1 59.2 60.0 2840.0 5.87 5.78 3.45
0.73 Premium F VS2 60.3 59.0 2841.0 5.9 5.87 3.55
0.71 Very Good D VS1 63.4 55.0 2841.0 5.69 5.61 3.58
0.73 Very Good D SI1 63.9 57.0 2841.0 5.66 5.71 3.63
0.82 Ideal F SI2 61.7 53.0 2841.0 6.0 6.12 3.74
0.82 Ideal F SI2 62.3 56.0 2841.0 5.96 6.02 3.73
0.82 Very Good F SI2 59.7 57.0 2841.0 6.12 6.14 3.66
0.52 Ideal F VVS1 61.2 56.0 2841.0 5.19 5.21 3.18
1.0 Premium F I1 58.9 60.0 2841.0 6.6 6.55 3.87
0.95 Fair G SI1 66.7 56.0 2841.0 6.16 6.03 4.06
0.73 Ideal D SI1 61.4 57.0 2841.0 5.76 5.8 3.55
0.73 Premium F VS2 59.9 59.0 2841.0 5.87 5.77 3.5
0.73 Premium G VS1 61.4 58.0 2841.0 5.82 5.77 3.56
0.8 Ideal I VS1 62.6 54.0 2842.0 5.92 5.96 3.72
0.7 Premium F VS2 58.7 61.0 2842.0 5.8 5.72 3.38
0.7 Very Good E VS2 60.2 62.0 2843.0 5.71 5.75 3.45
0.7 Very Good E VS2 62.7 58.0 2843.0 5.65 5.67 3.55
0.71 Very Good E VS2 59.4 58.0 2843.0 5.76 5.82 3.44
0.81 Very Good F SI2 63.2 58.0 2843.0 5.91 5.92 3.74
0.71 Very Good D SI1 61.5 58.0 2843.0 5.73 5.79 3.54
0.73 Ideal G VVS2 61.3 57.0 2843.0 5.81 5.84 3.57
0.73 Very Good F VS1 61.8 59.0 2843.0 5.73 5.79 3.56
0.72 Ideal E VS2 62.0 57.0 2843.0 5.71 5.74 3.55
0.81 Ideal F SI2 62.1 57.0 2843.0 5.91 5.95 3.68
0.71 Ideal G VVS2 60.7 57.0 2843.0 5.81 5.78 3.52
0.73 Very Good E SI1 57.7 61.0 2844.0 5.92 5.96 3.43
0.7 Very Good E VS1 62.0 59.0 2844.0 5.65 5.68 3.51
1.01 Ideal I I1 61.5 57.0 2844.0 6.45 6.46 3.97
1.01 Good I I1 63.1 57.0 2844.0 6.35 6.39 4.02
0.79 Ideal H VS2 62.5 57.0 2844.0 5.91 5.93 3.7
0.7 Very Good E VS2 61.8 59.0 2845.0 5.65 5.68 3.5
0.7 Very Good E VS2 58.9 60.0 2845.0 5.83 5.85 3.44
0.8 Good H VS2 63.4 60.0 2845.0 5.92 5.82 3.72
1.27 Premium H SI2 59.3 61.0 2845.0 7.12 7.05 4.2
0.79 Ideal D SI1 61.5 56.0 2846.0 5.96 5.91 3.65
0.72 Very Good F VS1 60.2 59.0 2846.0 5.79 5.84 3.5
0.73 Ideal H VVS2 61.6 56.0 2846.0 5.79 5.84 3.58
1.01 Fair H SI2 65.4 59.0 2846.0 6.3 6.26 4.11
1.01 Good H I1 64.2 61.0 2846.0 6.25 6.18 3.99
0.73 Ideal E SI1 59.1 59.0 2846.0 5.92 5.95 3.51
0.7 Ideal E SI1 61.6 57.0 2846.0 5.71 5.76 3.53
0.7 Good F VS2 59.1 61.0 2846.0 5.76 5.84 3.43
0.77 Premium E SI1 62.9 59.0 2846.0 5.84 5.79 3.66
0.77 Premium G VS2 61.3 60.0 2846.0 5.91 5.81 3.59
0.77 Premium G VS1 61.4 58.0 2846.0 5.94 5.89 3.63
0.84 Very Good H SI1 61.2 57.0 2847.0 6.1 6.12 3.74
0.72 Ideal E SI1 60.3 57.0 2847.0 5.83 5.85 3.52
0.76 Premium D SI1 61.1 59.0 2847.0 5.93 5.88 3.61
0.7 Very Good G VVS2 62.9 59.0 2848.0 5.61 5.68 3.55
0.54 Ideal D VVS2 61.5 55.0 2848.0 5.25 5.29 3.24
0.75 Fair D SI2 64.6 57.0 2848.0 5.74 5.72 3.7
0.79 Good E SI1 64.1 54.0 2849.0 5.86 5.84 3.75
0.74 Very Good E VS1 63.1 58.0 2849.0 5.75 5.73 3.62
0.7 Very Good E VS2 61.0 60.0 2850.0 5.74 5.77 3.51
0.7 Ideal F VS2 60.8 59.0 2850.0 5.69 5.79 3.49
0.75 Ideal J SI1 61.5 56.0 2850.0 5.83 5.87 3.6
1.2 Very Good H I1 63.1 60.0 2850.0 6.75 6.67 4.23
0.8 Very Good F SI1 63.4 57.0 2851.0 5.89 5.82 3.71
0.66 Ideal D VS1 62.1 56.0 2851.0 5.54 5.57 3.45
0.87 Very Good F SI2 61.0 63.0 2851.0 6.22 6.07 3.75
0.86 Premium H SI1 62.7 59.0 2851.0 6.04 5.98 3.77
0.74 Ideal F SI1 61.0 57.0 2851.0 5.85 5.81 3.56
0.58 Very Good E IF 60.6 59.0 2852.0 5.37 5.43 3.27
0.78 Ideal I VS1 61.5 57.0 2852.0 5.88 5.92 3.63
0.74 Ideal G SI1 61.3 55.0 2852.0 5.85 5.86 3.59
0.73 Ideal E SI1 62.7 55.0 2852.0 5.7 5.79 3.6
0.91 Very Good I SI1 63.5 57.0 2852.0 6.12 6.07 3.87
0.71 Premium F VS2 62.6 58.0 2853.0 5.67 5.7 3.56
0.71 Good G VS1 63.5 55.0 2853.0 5.64 5.66 3.59
0.79 Ideal D SI2 62.8 57.0 2853.0 5.9 5.85 3.69
0.79 Premium D SI2 60.0 60.0 2853.0 6.07 6.03 3.63
0.71 Premium E SI1 62.7 58.0 2853.0 5.73 5.66 3.57
0.82 Premium I VS1 61.9 58.0 2853.0 5.99 5.97 3.7
0.78 Very Good H VS1 61.9 57.1 2854.0 5.87 5.95 3.66
0.7 Very Good E VS1 62.4 56.0 2854.0 5.64 5.7 3.54
1.12 Premium H I1 59.1 61.0 2854.0 6.78 6.75 4.0
0.73 Premium E VS2 62.0 57.0 2854.0 5.86 5.76 3.6
0.91 Fair J VS2 64.4 62.0 2854.0 6.06 6.03 3.89
0.91 Fair J VS2 65.4 60.0 2854.0 6.04 6.0 3.94
0.91 Good J VS2 64.2 58.0 2854.0 6.12 6.09 3.92
0.91 Fair H SI1 65.8 58.0 2854.0 6.04 6.01 3.96
0.7 Premium E VS1 58.4 59.0 2854.0 5.91 5.83 3.43
0.68 Premium F VVS2 61.7 57.0 2854.0 5.67 5.64 3.49
0.73 Very Good F VS2 62.5 57.0 2855.0 5.7 5.75 3.58
1.03 Good J SI1 63.6 57.0 2855.0 6.38 6.29 4.03
0.74 Premium D VS2 62.4 57.0 2855.0 5.8 5.74 3.6
0.98 Fair E SI2 53.3 67.0 2855.0 6.82 6.74 3.61
1.02 Fair I SI1 53.0 63.0 2856.0 6.84 6.77 3.66
1.0 Fair G SI2 67.8 61.0 2856.0 5.96 5.9 4.02
1.02 Ideal H SI2 61.6 55.0 2856.0 6.49 6.43 3.98
0.6 Ideal F VVS2 60.8 57.0 2856.0 5.44 5.49 3.32
0.8 Ideal G SI2 61.6 56.0 2856.0 5.97 6.01 3.69
0.97 Ideal F I1 60.7 56.0 2856.0 6.43 6.41 3.9
1.0 Fair I SI1 67.9 62.0 2856.0 6.19 6.03 4.15
0.26 Ideal E VS1 62.3 57.0 556.0 4.05 4.08 2.53
0.26 Ideal E VS1 62.1 56.0 556.0 4.09 4.12 2.55
0.36 Ideal H SI1 61.9 55.0 556.0 4.57 4.59 2.83
0.34 Good G VS2 57.5 61.0 556.0 4.6 4.66 2.66
0.34 Good E SI1 63.3 57.0 556.0 4.44 4.47 2.82
0.34 Good E SI1 63.5 55.0 556.0 4.44 4.47 2.83
0.34 Good E SI1 63.4 55.0 556.0 4.44 4.46 2.82
0.34 Very Good G VS2 59.6 62.0 556.0 4.54 4.56 2.71
0.34 Ideal E SI1 62.2 54.0 556.0 4.47 4.5 2.79
0.32 Good E VS2 64.1 54.0 556.0 4.34 4.37 2.79
0.31 Ideal I VVS1 61.6 55.0 557.0 4.36 4.41 2.7
0.31 Ideal I VVS1 61.3 56.0 557.0 4.36 4.38 2.68
0.31 Ideal I VVS1 62.3 54.0 557.0 4.37 4.4 2.73
0.31 Ideal I VVS1 62.0 54.0 557.0 4.37 4.4 2.72
0.31 Ideal I VVS1 62.7 53.0 557.0 4.33 4.35 2.72
0.31 Ideal I VVS1 62.2 53.0 557.0 4.36 4.38 2.72
0.31 Ideal G VS2 62.2 53.6 557.0 4.32 4.35 2.7
0.31 Ideal H VS1 61.6 54.8 557.0 4.35 4.37 2.69
0.31 Ideal H VS1 61.8 54.2 557.0 4.33 4.37 2.69
0.33 Premium G SI2 59.4 59.0 557.0 4.52 4.5 2.68
0.33 Premium F SI2 62.3 58.0 557.0 4.43 4.4 2.75
0.33 Premium G SI2 62.6 58.0 557.0 4.42 4.4 2.76
0.33 Ideal G SI2 61.9 56.0 557.0 4.45 4.41 2.74
0.33 Premium F SI2 63.0 58.0 557.0 4.42 4.4 2.78
0.33 Premium J VS1 62.8 58.0 557.0 4.41 4.38 2.76
0.33 Premium J VS1 61.5 61.0 557.0 4.46 4.39 2.72
0.33 Ideal J VS1 62.1 55.0 557.0 4.44 4.41 2.75
0.33 Ideal I SI1 63.0 57.0 557.0 4.39 4.37 2.76
0.33 Good I SI1 63.6 53.0 557.0 4.43 4.4 2.81
0.33 Premium I SI1 60.4 59.0 557.0 4.54 4.5 2.73
1.0 Fair H SI2 66.1 56.0 2856.0 6.21 5.97 4.04
0.77 Premium F SI1 60.8 59.0 2856.0 5.92 5.86 3.58
0.77 Premium F SI1 61.0 58.0 2856.0 5.94 5.9 3.61
0.7 Good E VVS2 60.1 63.0 2857.0 5.68 5.71 3.42
0.9 Very Good G SI2 63.1 58.0 2857.0 6.08 6.02 3.82
0.72 Ideal E SI1 62.3 57.0 2857.0 5.76 5.7 3.57
0.9 Premium I VS2 61.9 59.0 2857.0 6.2 6.14 3.82
0.72 Premium E SI1 62.1 58.0 2857.0 5.76 5.73 3.57
0.7 Ideal G VVS2 62.1 56.0 2858.0 5.63 5.71 3.52
0.81 Very Good F SI1 61.3 57.0 2858.0 6.02 6.05 3.7
0.81 Very Good F SI1 61.7 57.0 2858.0 6.0 6.05 3.72
0.71 Premium E VS2 61.0 60.0 2858.0 5.76 5.69 3.49
0.7 Premium E VS2 61.4 59.0 2858.0 5.73 5.7 3.51
0.71 Premium E VS2 61.5 60.0 2858.0 5.76 5.68 3.52
0.71 Very Good E VS2 63.5 59.0 2858.0 5.68 5.59 3.58
0.92 Premium J SI1 62.9 58.0 2858.0 6.22 6.18 3.9
0.76 Ideal E SI1 62.7 54.0 2858.0 5.88 5.83 3.67
0.73 Ideal D SI1 61.5 56.0 2858.0 5.84 5.8 3.58
0.71 Premium D VS2 60.4 62.0 2858.0 5.74 5.72 3.46
0.7 Good E VVS2 63.6 62.0 2858.0 5.61 5.58 3.56
0.9 Fair G SI2 64.5 56.0 2858.0 6.06 6.0 3.89
0.71 Fair D VS2 56.9 65.0 2858.0 5.89 5.84 3.34
0.7 Ideal D VS2 61.0 57.0 2859.0 5.76 5.74 3.51
0.7 Premium D VS2 62.4 56.0 2859.0 5.72 5.66 3.55
0.77 Premium F VS1 60.9 60.0 2859.0 5.91 5.88 3.59
0.71 Ideal G VS1 61.5 56.0 2859.0 5.74 5.78 3.54
0.7 Premium D VS2 59.6 61.0 2859.0 5.8 5.77 3.45
0.75 Fair F VS1 55.8 70.0 2859.0 6.09 5.98 3.37
0.83 Premium E SI2 59.2 60.0 2859.0 6.17 6.12 3.64
0.71 Very Good F VS2 61.3 61.0 2860.0 5.68 5.73 3.5
0.9 Very Good J SI2 63.6 58.0 2860.0 6.07 6.1 3.87
0.6 Ideal E VVS2 61.9 54.9 2860.0 5.41 5.44 3.35
0.71 Premium D VS1 62.9 57.0 2860.0 5.66 5.6 3.54
0.53 Ideal F VVS1 61.4 57.0 2860.0 5.23 5.2 3.2
0.71 Premium D SI1 60.7 58.0 2861.0 5.95 5.78 3.56
0.62 Ideal G VVS2 61.6 56.0 2861.0 5.45 5.48 3.37
0.62 Ideal G VVS2 61.6 56.0 2861.0 5.48 5.51 3.38
0.9 Premium I SI1 63.0 58.0 2861.0 6.09 6.01 3.81
0.62 Fair F IF 60.1 61.0 2861.0 5.53 5.56 3.33
0.82 Premium E SI2 61.7 59.0 2861.0 6.01 5.98 3.7
0.66 Premium D VS1 61.0 58.0 2861.0 5.67 5.57 3.43
0.7 Very Good D SI1 62.5 55.0 2862.0 5.67 5.72 3.56
0.8 Very Good F SI1 62.6 58.0 2862.0 5.9 5.92 3.7
0.8 Very Good D SI2 62.5 59.0 2862.0 5.88 5.92 3.69
0.79 Premium F SI1 62.3 54.0 2862.0 5.97 5.91 3.7
0.71 Very Good F VVS1 63.2 60.0 2862.0 5.65 5.61 3.56
0.7 Ideal H VS2 61.1 57.0 2862.0 5.71 5.74 3.5
0.7 Very Good E VS2 58.7 63.0 2862.0 5.73 5.69 3.35
0.79 Premium H VS1 60.0 60.0 2862.0 6.07 5.99 3.64
0.7 Premium E VS2 59.5 59.0 2862.0 5.82 5.77 3.45
1.22 Premium E I1 60.9 57.0 2862.0 6.93 6.88 4.21
1.01 Fair E SI2 67.6 57.0 2862.0 6.21 6.11 4.18
0.73 Premium E VS2 62.5 61.0 2862.0 5.78 5.64 3.59
0.91 Good I VS2 64.3 58.0 2863.0 6.05 6.09 3.9
0.71 Ideal D SI1 60.8 56.0 2863.0 5.8 5.77 3.52
0.83 Premium G SI1 62.3 58.0 2863.0 6.01 5.97 3.73
0.84 Premium F SI2 62.3 59.0 2863.0 6.06 6.01 3.76
0.71 Premium D SI1 61.0 61.0 2863.0 5.82 5.75 3.53
0.71 Premium D SI1 59.7 59.0 2863.0 5.82 5.8 3.47
0.71 Premium D SI1 61.7 56.0 2863.0 5.8 5.68 3.54
0.71 Ideal D SI1 61.7 57.0 2863.0 5.75 5.7 3.53
0.71 Premium D SI1 61.4 58.0 2863.0 5.79 5.75 3.54
0.71 Premium D SI1 60.6 58.0 2863.0 5.79 5.77 3.5
0.91 Premium J SI1 59.5 62.0 2863.0 6.4 6.18 3.74
0.9 Premium J VS2 59.8 62.0 2863.0 6.24 6.21 3.72
0.71 Premium H VVS2 61.5 62.0 2863.0 5.74 5.68 3.51
0.71 Premium E SI1 59.1 61.0 2863.0 5.84 5.8 3.44
0.72 Ideal F VS2 59.5 57.0 2863.0 5.91 5.86 3.5
0.72 Premium E SI1 60.9 60.0 2863.0 5.78 5.74 3.51
0.71 Ideal E VS2 61.0 55.0 2863.0 5.79 5.75 3.52
0.81 Ideal E SI2 60.3 57.0 2864.0 6.07 6.04 3.65
0.83 Very Good I VS2 61.6 58.0 2865.0 6.05 6.07 3.73
0.73 Premium D SI1 60.8 55.0 2865.0 5.87 5.81 3.55
0.56 Very Good D VVS1 62.0 56.0 2866.0 5.25 5.3 3.27
0.56 Very Good D VVS1 61.8 55.0 2866.0 5.27 5.31 3.27
0.71 Ideal E VS1 62.2 55.0 2866.0 5.74 5.7 3.56
0.7 Ideal H VVS1 62.3 58.0 2866.0 5.66 5.7 3.54
0.96 Premium I SI1 61.3 58.0 2866.0 6.39 6.3 3.89
0.71 Very Good H VVS1 62.9 57.0 2867.0 5.67 5.69 3.57
0.7 Ideal D VS2 62.4 57.0 2867.0 5.68 5.61 3.52
0.71 Ideal H VVS1 60.4 57.0 2867.0 5.78 5.81 3.5
0.8 Premium H VS2 61.2 53.0 2867.0 6.05 5.98 3.68
0.95 Premium F SI2 58.4 57.0 2867.0 6.49 6.41 3.77
0.82 Ideal F SI2 62.3 56.0 2867.0 5.99 5.95 3.72
0.52 Ideal F VVS1 61.2 56.0 2867.0 5.21 5.19 3.18
0.82 Ideal F SI2 61.7 53.0 2867.0 6.12 6.0 3.74
0.82 Ideal F SI2 62.3 56.0 2867.0 6.02 5.96 3.73
0.82 Premium F SI2 59.7 57.0 2867.0 6.14 6.12 3.66
0.8 Ideal G SI1 61.3 57.0 2867.0 5.96 5.91 3.64
0.96 Fair F SI2 68.2 61.0 2867.0 6.07 5.88 4.1
0.72 Ideal I VS1 62.4 55.0 2868.0 5.72 5.75 3.58
0.62 Ideal G IF 60.5 57.0 2868.0 5.52 5.56 3.35
0.79 Premium E SI2 61.0 58.0 2868.0 5.96 5.9 3.62
0.75 Very Good E SI1 63.1 56.0 2868.0 5.78 5.7 3.62
1.08 Premium D I1 61.9 60.0 2869.0 6.55 6.48 4.03
0.72 Ideal E SI1 60.8 55.0 2869.0 5.77 5.84 3.53
0.62 Ideal G IF 61.8 56.0 2869.0 5.43 5.47 3.37
0.73 Ideal G VVS2 61.3 57.0 2869.0 5.84 5.81 3.57
0.72 Ideal H VVS2 60.9 57.0 2869.0 5.79 5.77 3.52
0.52 Premium F VVS2 61.8 60.0 2870.0 5.16 5.13 3.18
0.83 Ideal E SI2 62.2 57.0 2870.0 6.0 6.05 3.75
0.64 Premium E VVS2 62.1 58.0 2870.0 5.56 5.51 3.44
0.8 Ideal G SI1 62.5 57.0 2870.0 5.94 5.9 3.7
0.74 Ideal H SI1 62.1 56.0 2870.0 5.77 5.83 3.6
0.72 Ideal F SI1 61.5 56.0 2870.0 5.72 5.79 3.54
0.82 Ideal H VS2 59.5 57.0 2870.0 6.12 6.09 3.63
0.73 Premium E VS1 61.3 59.0 2870.0 5.81 5.78 3.55
1.04 Premium I I1 61.6 61.0 2870.0 6.47 6.45 3.98
0.73 Very Good E SI1 61.3 58.0 2871.0 5.76 5.83 3.55
0.73 Good E SI1 63.6 57.0 2871.0 5.7 5.72 3.63
0.9 Premium J SI1 62.8 59.0 2871.0 6.13 6.03 3.82
0.75 Ideal I SI1 61.8 55.0 2871.0 5.83 5.85 3.61
0.79 Ideal G SI1 62.6 55.0 2871.0 5.91 5.95 3.71
0.7 Good D SI1 62.5 56.7 2872.0 5.59 5.62 3.51
0.75 Very Good D SI1 60.7 55.0 2872.0 5.87 5.92 3.58
1.02 Ideal I I1 61.7 56.0 2872.0 6.44 6.49 3.99
0.7 Very Good G SI2 59.0 62.0 2872.0 5.79 5.81 3.42
0.7 Ideal D SI1 61.8 56.0 2872.0 5.63 5.73 3.51
0.7 Good E SI1 61.4 64.0 2872.0 5.66 5.71 3.49
0.7 Ideal D SI1 61.4 54.0 2872.0 5.71 5.75 3.52
0.7 Ideal D SI1 60.7 56.0 2872.0 5.72 5.75 3.48
0.7 Very Good D SI1 60.2 60.0 2872.0 5.75 5.82 3.48
0.72 Very Good E VS2 58.3 57.0 2872.0 5.89 5.94 3.45
0.74 Ideal E SI1 62.3 58.0 2872.0 5.74 5.78 3.59
0.84 Good G SI1 65.1 55.0 2872.0 5.88 5.97 3.86
0.76 Very Good F VS2 62.0 58.0 2873.0 5.8 5.86 3.62
0.77 Very Good E SI1 63.2 58.0 2873.0 5.8 5.84 3.68
0.76 Ideal E SI2 62.8 56.0 2873.0 5.78 5.82 3.64
1.0 Ideal I SI2 61.7 56.0 2873.0 6.45 6.41 3.97
1.0 Fair H SI1 65.5 62.0 2873.0 6.14 6.07 4.0
0.9 Fair I SI1 65.7 58.0 2873.0 6.03 6.0 3.95
0.9 Premium J SI1 61.8 58.0 2873.0 6.16 6.13 3.8
0.9 Good J SI1 64.0 61.0 2873.0 6.0 5.96 3.83
0.9 Fair I SI1 65.3 61.0 2873.0 5.98 5.94 3.89
0.9 Fair I SI1 65.8 56.0 2873.0 6.01 5.96 3.94
0.9 Premium J SI1 60.9 61.0 2873.0 6.26 6.22 3.8
0.78 Premium F VS2 62.6 58.0 2874.0 5.91 5.82 3.67
0.71 Premium D VS2 61.2 59.0 2874.0 5.69 5.74 3.5
0.7 Premium F VS1 59.0 59.0 2874.0 5.79 5.77 3.41
0.7 Premium F VS1 60.8 62.0 2874.0 5.71 5.67 3.46
0.7 Premium G VVS2 61.8 58.0 2874.0 5.67 5.63 3.49
0.7 Ideal F VS1 61.0 55.0 2874.0 5.77 5.73 3.51
0.7 Ideal F VS1 61.6 55.0 2874.0 5.75 5.71 3.53
0.7 Ideal F VS1 62.4 56.0 2874.0 5.69 5.65 3.54
0.7 Premium G VVS2 62.9 59.0 2874.0 5.68 5.61 3.55
1.0 Fair H SI2 67.7 60.0 2875.0 6.11 5.98 4.09
0.77 Ideal H SI1 62.4 56.0 2875.0 5.84 5.9 3.66
1.0 Fair J VS1 65.5 55.0 2875.0 6.3 6.25 4.11
1.0 Fair I SI1 66.3 61.0 2875.0 6.08 6.03 4.01
1.0 Fair H SI2 69.5 55.0 2875.0 6.17 6.1 4.26
0.73 Premium E VS1 62.6 60.0 2876.0 5.68 5.75 3.58
0.79 Premium E VS2 60.6 53.0 2876.0 6.04 5.98 3.64
0.72 Very Good H VS1 62.2 54.0 2877.0 5.74 5.76 3.57
0.71 Ideal E VS1 62.4 56.0 2877.0 5.75 5.7 3.57
0.74 Ideal G VS2 62.3 55.0 2877.0 5.8 5.83 3.62
0.7 Good H VVS1 62.7 56.0 2877.0 5.6 5.66 3.53
0.7 Good F VS1 59.1 62.0 2877.0 5.82 5.86 3.44
0.79 Very Good F SI1 62.8 59.0 2878.0 5.86 5.89 3.69
0.79 Very Good F SI1 62.7 60.0 2878.0 5.82 5.89 3.67
0.79 Very Good D SI2 59.7 58.0 2878.0 6.0 6.07 3.6
0.71 Ideal I VS2 61.5 55.0 2878.0 5.76 5.78 3.55
0.79 Ideal F SI1 62.8 56.0 2878.0 5.88 5.9 3.7
0.73 Very Good F SI1 61.4 56.0 2879.0 5.81 5.86 3.58
0.63 Premium E IF 60.3 62.0 2879.0 5.55 5.53 3.34
0.7 Premium F VS1 60.4 60.0 2879.0 5.73 5.7 3.45
0.71 Premium F VS1 62.7 58.0 2879.0 5.71 5.67 3.57
0.84 Ideal G SI2 61.0 56.0 2879.0 6.13 6.1 3.73
0.84 Ideal G SI2 62.3 55.0 2879.0 6.08 6.03 3.77
1.02 Ideal J SI2 60.3 54.0 2879.0 6.53 6.5 3.93
0.72 Fair F VS1 56.9 69.0 2879.0 5.93 5.77 3.33
0.72 Ideal F VS1 62.0 56.0 2879.0 5.76 5.73 3.56
0.92 Very Good J SI2 58.7 61.0 2880.0 6.34 6.43 3.75
0.74 Very Good D SI1 63.9 57.0 2880.0 5.72 5.74 3.66
0.7 Ideal H VVS1 62.0 55.0 2881.0 5.74 5.71 3.55
0.71 Very Good E VS2 60.0 59.0 2881.0 5.84 5.83 3.5
1.05 Premium H I1 62.0 59.0 2881.0 6.5 6.47 4.02
0.7 Very Good H IF 62.8 56.0 2882.0 5.62 5.65 3.54
0.54 Ideal F VVS1 61.8 56.0 2882.0 5.23 5.26 3.24
0.73 Premium F VS2 59.9 58.0 2882.0 5.87 5.84 3.51
0.88 Fair F SI1 56.6 65.0 2882.0 6.39 6.32 3.6
0.73 Premium F VS2 58.7 57.0 2882.0 5.97 5.92 3.49
0.72 Ideal D SI1 61.8 56.0 2883.0 5.75 5.81 3.57
0.9 Good H SI2 62.7 64.0 2883.0 6.09 6.0 3.79
0.9 Fair H SI2 65.0 61.0 2883.0 6.01 5.96 3.89
1.03 Fair I SI2 65.3 55.0 2884.0 6.32 6.27 4.11
0.84 Very Good F SI1 63.8 57.0 2885.0 5.95 6.0 3.81
1.01 Premium I SI1 62.7 60.0 2885.0 6.36 6.27 3.96
0.77 Ideal D SI2 61.5 55.0 2885.0 5.9 5.93 3.64
0.8 Fair E SI1 56.3 63.0 2885.0 6.22 6.14 3.48
0.9 Fair D SI2 66.9 57.0 2885.0 6.02 5.9 3.99
0.73 Ideal E SI1 61.4 56.0 2886.0 5.79 5.81 3.56
0.72 Ideal E SI1 62.7 55.0 2886.0 5.64 5.69 3.55
0.71 Very Good D SI1 62.4 54.0 2887.0 5.71 5.79 3.59
0.7 Premium E VS1 62.6 59.0 2887.0 5.66 5.69 3.55
0.79 Ideal I VS1 61.7 59.0 2888.0 5.93 5.96 3.67
0.72 Very Good G VVS2 62.5 58.0 2889.0 5.68 5.72 3.56
0.7 Very Good E VS2 63.5 54.0 2889.0 5.62 5.66 3.58
0.7 Very Good F VS1 62.2 58.0 2889.0 5.64 5.75 3.54
0.9 Good H SI2 63.5 58.0 2889.0 6.09 6.14 3.88
0.71 Very Good F VS1 62.8 56.0 2889.0 5.69 5.72 3.58
0.5 Ideal E VVS2 62.2 54.0 2889.0 5.08 5.12 3.17
0.5 Ideal E VVS2 62.2 54.0 2889.0 5.09 5.11 3.17
0.74 Ideal F SI1 61.2 56.0 2889.0 5.83 5.87 3.58
0.77 Premium F VS2 61.8 56.0 2889.0 5.94 5.9 3.66
0.77 Premium E SI1 59.8 61.0 2889.0 5.99 5.95 3.57
0.8 Ideal F SI1 61.5 54.0 2890.0 6.07 6.0 3.71
0.8 Ideal F SI1 62.4 57.0 2890.0 5.9 5.87 3.67
0.8 Premium F SI1 61.5 60.0 2890.0 5.97 5.94 3.66
0.8 Good F SI1 63.8 59.0 2890.0 5.87 5.83 3.73
0.66 Ideal G VVS1 61.5 56.0 2890.0 5.61 5.58 3.44
0.71 Very Good E VS2 61.2 58.0 2891.0 5.71 5.79 3.52
0.71 Ideal F VS2 61.2 56.0 2891.0 5.73 5.77 3.52
0.71 Ideal E VS2 61.6 56.0 2891.0 5.74 5.76 3.54
0.71 Ideal E VS2 62.7 56.0 2891.0 5.71 5.75 3.59
0.72 Ideal D SI1 61.1 56.0 2891.0 5.78 5.81 3.54
0.71 Good D VS2 62.3 61.0 2891.0 5.7 5.73 3.56
0.86 Ideal H SI2 61.8 55.0 2892.0 6.12 6.14 3.79
1.19 Fair H I1 65.1 59.0 2892.0 6.62 6.55 4.29
0.71 Very Good F VS1 62.6 55.0 2893.0 5.66 5.71 3.56
0.82 Very Good G SI2 62.5 56.0 2893.0 5.99 6.04 3.76
0.71 Ideal G VVS2 61.5 57.0 2893.0 5.73 5.75 3.53
0.75 Ideal F VS2 62.5 57.0 2893.0 5.78 5.83 3.63
0.7 Very Good H VVS1 59.2 60.0 2893.0 5.87 5.78 3.45
0.8 Ideal G SI2 62.5 55.0 2893.0 5.89 5.92 3.69
0.82 Good G SI2 59.9 62.0 2893.0 6.02 6.04 3.61
0.82 Very Good G SI1 63.4 55.0 2893.0 6.0 5.93 3.78
0.82 Premium G SI1 59.9 59.0 2893.0 6.09 6.06 3.64
0.81 Very Good E SI2 62.4 57.0 2894.0 5.91 5.99 3.71
0.81 Ideal G SI2 62.2 57.0 2894.0 5.96 6.0 3.72
0.76 Ideal F SI1 61.4 56.0 2894.0 5.88 5.92 3.62
0.71 Very Good G VS2 60.9 56.0 2895.0 5.75 5.78 3.51
0.7 Very Good F VS1 61.8 59.0 2895.0 5.66 5.76 3.53
0.7 Ideal G VVS2 62.1 53.0 2895.0 5.71 5.75 3.56
0.74 Very Good G VS1 59.8 58.0 2896.0 5.85 5.89 3.51
0.77 Very Good G VS2 61.3 60.0 2896.0 5.81 5.91 3.59
0.77 Very Good G VS2 58.3 63.0 2896.0 6.0 6.05 3.51
0.53 Ideal F VVS1 61.6 56.0 2896.0 5.18 5.24 3.21
0.79 Ideal D SI1 61.5 56.0 2896.0 5.91 5.96 3.65
0.73 Ideal E SI2 61.5 55.0 2896.0 5.82 5.86 3.59
0.77 Ideal D SI2 62.1 56.0 2896.0 5.83 5.89 3.64
0.77 Premium E SI1 60.9 58.0 2896.0 5.94 5.88 3.6
1.01 Very Good I I1 63.1 57.0 2896.0 6.39 6.35 4.02
1.01 Ideal I I1 61.5 57.0 2896.0 6.46 6.45 3.97
0.6 Very Good D VVS2 60.6 57.0 2897.0 5.48 5.51 3.33
0.76 Premium E SI1 61.1 58.0 2897.0 5.91 5.85 3.59
0.54 Ideal D VVS2 61.4 52.0 2897.0 5.3 5.34 3.26
0.72 Ideal E SI1 62.5 55.0 2897.0 5.69 5.74 3.57
0.72 Good F VS1 59.4 61.0 2897.0 5.82 5.89 3.48
0.74 Premium D VS2 61.8 58.0 2897.0 5.81 5.77 3.58
1.12 Premium J SI2 60.6 59.0 2898.0 6.68 6.61 4.03

Note that columns of type string are not in the scatter plot!

diamondsDF.printSchema // Ctrl+Enter

root
 |-- carat: double (nullable = true)
 |-- cut: string (nullable = true)
 |-- color: string (nullable = true)
 |-- clarity: string (nullable = true)
 |-- depth: double (nullable = true)
 |-- table: double (nullable = true)
 |-- price: double (nullable = true)
 |-- x: double (nullable = true)
 |-- y: double (nullable = true)
 |-- z: double (nullable = true)

Let us run through some basic inteactive SQL queries next

  • HiveQL supports =, <, >, <=, >= and != operators. It also supports LIKE operator for fuzzy matching of Strings
  • Enclose Strings in single quotes
  • Multiple conditions can be combined using and and or
  • Enclose conditions in () for precedence
  • ...
  • ...

Why do I need to learn interactive SQL queries?

Such queries in the widely known declarative SQL language can help us explore the data and thereby inform the modeling process!!!

Using DataFrame API, we can apply a filter after select to transform the DataFrame diamondsDF to the new DataFrame diamondsDColoredDF.

Below, $ is an alias for column.

Let as select the columns named carat, colour, price where color value is equal to D.

val diamondsDColoredDF = diamondsDF.select("carat", "color", "price").filter($"color" === "D") // Shift+Enter

diamondsDColoredDF: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [carat: double, color: string ... 1 more field]

diamondsDColoredDF.show(10) // Ctrl+Enter

+-----+-----+-----+
|carat|color|price|
+-----+-----+-----+
| 0.23|    D|357.0|
| 0.23|    D|402.0|
| 0.26|    D|403.0|
| 0.26|    D|403.0|
| 0.26|    D|403.0|
| 0.22|    D|404.0|
|  0.3|    D|552.0|
|  0.3|    D|552.0|
|  0.3|    D|552.0|
| 0.24|    D|553.0|
+-----+-----+-----+
only showing top 10 rows

As you can see all the colors are now 'D'. But to really confirm this we can do the following for fun:

diamondsDColoredDF.select("color").distinct().show

+-----+
|color|
+-----+
|    D|
+-----+

Let's try to do the same in SQL for those who know SQL from before.

First we need to see if the table is registerd (not just the DataFrame), and if not we ened to register our DataFrame as a temporary table.

sqlContext.tables.show() // Ctrl+Enter to see available tables

+--------+--------------------+-----------+
|database|           tableName|isTemporary|
+--------+--------------------+-----------+
| default|fxdata_bco_usd_20...|      false|
| default|fxdata_xau_usd_20...|      false|
| default|power_plant_predi...|      false|
| default|    sentimentlex_csv|      false|
| default|        simple_range|      false|
| default|  social_media_usage|      false|
| default|social_media_usag...|      false|
+--------+--------------------+-----------+

Looks like diamonds is already there (if not just execute the following cell).

diamondsDF.createOrReplaceTempView("diamonds")

sqlContext.tables.show() // Ctrl+Enter to see available tables

+--------+--------------------+-----------+
|database|           tableName|isTemporary|
+--------+--------------------+-----------+
| default|fxdata_bco_usd_20...|      false|
| default|fxdata_xau_usd_20...|      false|
| default|power_plant_predi...|      false|
| default|    sentimentlex_csv|      false|
| default|        simple_range|      false|
| default|  social_media_usage|      false|
| default|social_media_usag...|      false|
|        |            diamonds|       true|
+--------+--------------------+-----------+

-- Shift+Enter to do the same in SQL
select carat, color, price from diamonds where color='D'
carat color price
0.23 D 357.0
0.23 D 402.0
0.26 D 403.0
0.26 D 403.0
0.26 D 403.0
0.22 D 404.0
0.3 D 552.0
0.3 D 552.0
0.3 D 552.0
0.24 D 553.0
0.26 D 554.0
0.26 D 554.0
0.26 D 554.0
0.75 D 2760.0
0.71 D 2762.0
0.61 D 2763.0
0.71 D 2764.0
0.71 D 2764.0
0.7 D 2767.0
0.71 D 2767.0
0.73 D 2768.0
0.7 D 2768.0
0.71 D 2768.0
0.71 D 2770.0
0.76 D 2770.0
0.73 D 2770.0
0.75 D 2773.0
0.7 D 2773.0
0.7 D 2777.0
0.53 D 2782.0
0.75 D 2782.0
0.72 D 2782.0
0.72 D 2782.0
0.7 D 2782.0
0.64 D 2787.0
0.71 D 2788.0
0.72 D 2795.0
0.71 D 2797.0
0.71 D 2797.0
0.71 D 2797.0
0.51 D 2797.0
0.78 D 2799.0
0.91 D 2803.0
0.7 D 2804.0
0.7 D 2804.0
0.72 D 2804.0
0.72 D 2804.0
0.73 D 2808.0
0.81 D 2809.0
0.74 D 2810.0
0.83 D 2811.0
0.71 D 2812.0
0.55 D 2815.0
0.71 D 2816.0
0.73 D 2821.0
0.71 D 2822.0
0.71 D 2822.0
0.7 D 2822.0
0.7 D 2822.0
0.71 D 2822.0
0.7 D 2822.0
0.7 D 2822.0
0.7 D 2822.0
0.7 D 2822.0
0.79 D 2823.0
0.71 D 2824.0
0.7 D 2826.0
0.7 D 2827.0
0.72 D 2827.0
0.7 D 2828.0
0.7 D 2833.0
0.7 D 2833.0
0.51 D 2834.0
0.92 D 2840.0
0.71 D 2841.0
0.73 D 2841.0
0.73 D 2841.0
0.71 D 2843.0
0.79 D 2846.0
0.76 D 2847.0
0.54 D 2848.0
0.75 D 2848.0
0.66 D 2851.0
0.79 D 2853.0
0.79 D 2853.0
0.74 D 2855.0
0.73 D 2858.0
0.71 D 2858.0
0.71 D 2858.0
0.7 D 2859.0
0.7 D 2859.0
0.7 D 2859.0
0.71 D 2860.0
0.71 D 2861.0
0.66 D 2861.0
0.7 D 2862.0
0.8 D 2862.0
0.71 D 2863.0
0.71 D 2863.0
0.71 D 2863.0
0.71 D 2863.0
0.71 D 2863.0
0.71 D 2863.0
0.71 D 2863.0
0.73 D 2865.0
0.56 D 2866.0
0.56 D 2866.0
0.7 D 2867.0
1.08 D 2869.0
0.7 D 2872.0
0.75 D 2872.0
0.7 D 2872.0
0.7 D 2872.0
0.7 D 2872.0
0.7 D 2872.0
0.71 D 2874.0
0.79 D 2878.0
0.74 D 2880.0
0.72 D 2883.0
0.77 D 2885.0
0.9 D 2885.0
0.71 D 2887.0
0.72 D 2891.0
0.71 D 2891.0
0.79 D 2896.0
0.77 D 2896.0
0.6 D 2897.0
0.54 D 2897.0
0.74 D 2897.0
0.75 D 2898.0
0.77 D 2898.0
0.72 D 2900.0
0.75 D 2903.0
0.75 D 2903.0
0.72 D 2903.0
0.72 D 2903.0
0.79 D 2904.0
0.53 D 2905.0
0.74 D 2906.0
0.32 D 558.0
0.7 D 2909.0
0.7 D 2909.0
0.71 D 2910.0
0.7 D 2913.0
0.71 D 2913.0
0.71 D 2913.0
0.71 D 2913.0
0.71 D 2913.0
0.71 D 2913.0
0.83 D 2918.0
0.71 D 2921.0
0.77 D 2922.0
0.77 D 2923.0
0.8 D 2925.0
0.81 D 2926.0
0.7 D 2928.0
0.59 D 2933.0
0.75 D 2933.0
0.71 D 2934.0
0.7 D 2936.0
0.77 D 2939.0
0.76 D 2942.0
0.73 D 2943.0
0.57 D 2945.0
0.78 D 2945.0
0.73 D 2947.0
0.73 D 2947.0
0.77 D 2949.0
0.71 D 2950.0
0.72 D 2951.0
0.72 D 2954.0
0.72 D 2954.0
0.75 D 2954.0
0.82 D 2954.0
0.7 D 2956.0
0.56 D 2959.0
0.7 D 2960.0
0.7 D 2960.0
0.7 D 2960.0
0.63 D 2962.0
0.71 D 2964.0
0.71 D 2968.0
0.77 D 2973.0
1.0 D 2974.0
0.76 D 2977.0
0.7 D 2980.0
0.7 D 2985.0
0.74 D 2987.0
0.83 D 2990.0
0.7 D 2991.0
0.72 D 2993.0
0.81 D 2994.0
0.73 D 2995.0
0.77 D 2996.0
0.7 D 2998.0
0.7 D 2999.0
0.72 D 3001.0
0.7 D 3001.0
0.7 D 3001.0
0.7 D 3001.0
0.71 D 3002.0
1.01 D 3003.0
0.65 D 3003.0
0.92 D 3004.0
0.55 D 3006.0
0.76 D 3007.0
0.7 D 3008.0
0.8 D 3011.0
0.77 D 3011.0
0.9 D 3013.0
0.73 D 3014.0
0.72 D 3016.0
0.5 D 3017.0
0.78 D 3019.0
0.71 D 3020.0
0.75 D 3024.0
0.75 D 3024.0
0.65 D 3025.0
0.71 D 3033.0
0.7 D 3033.0
0.7 D 3033.0
0.7 D 3033.0
0.78 D 3035.0
0.71 D 3035.0
0.74 D 3036.0
0.61 D 3036.0
0.77 D 3040.0
0.71 D 3045.0
0.72 D 3045.0
0.75 D 3046.0
0.73 D 3047.0
0.75 D 3048.0
0.72 D 3048.0
0.72 D 3048.0
0.66 D 3049.0
0.62 D 3050.0
0.7 D 3052.0
0.7 D 3053.0
0.7 D 3054.0
0.65 D 3056.0
0.92 D 3057.0
0.79 D 3058.0
0.72 D 3062.0
0.85 D 3066.0
0.7 D 3073.0
0.72 D 3075.0
0.72 D 3075.0
0.7 D 3075.0
0.76 D 3075.0
0.71 D 3077.0
0.71 D 3077.0
0.75 D 3078.0
0.83 D 3078.0
0.91 D 3079.0
0.79 D 3081.0
0.7 D 3082.0
0.8 D 3082.0
0.71 D 3084.0
0.75 D 3085.0
0.7 D 3087.0
0.7 D 3087.0
0.7 D 3087.0
0.74 D 3087.0
0.71 D 3090.0
0.71 D 3090.0
0.7 D 3092.0
0.7 D 3092.0
0.7 D 3092.0
0.7 D 3093.0
0.71 D 3096.0
0.71 D 3096.0
0.53 D 3097.0
0.72 D 3099.0
0.72 D 3102.0
0.66 D 3103.0
0.78 D 3103.0
0.75 D 3105.0
0.7 D 3107.0
0.79 D 3112.0
0.94 D 3125.0
0.57 D 3126.0
0.57 D 3126.0
0.7 D 3129.0
0.7 D 3131.0
0.71 D 3131.0
0.71 D 3135.0
0.71 D 3135.0
0.8 D 3135.0
0.81 D 3135.0
0.71 D 3136.0
0.71 D 3137.0
0.74 D 3138.0
0.72 D 3139.0
0.54 D 3139.0
0.73 D 3140.0
0.71 D 3145.0
0.84 D 3145.0
0.78 D 3145.0
0.75 D 3152.0
0.9 D 3153.0
0.71 D 3153.0
0.58 D 3154.0
0.8 D 3154.0
0.77 D 3158.0
0.82 D 3159.0
0.77 D 3160.0
0.81 D 3160.0
0.71 D 3161.0
0.71 D 3161.0
0.71 D 3161.0
0.77 D 3166.0
0.8 D 3173.0
0.72 D 3176.0
0.74 D 3177.0
0.72 D 3179.0
0.72 D 3179.0
0.72 D 3179.0
0.81 D 3179.0
0.73 D 3182.0
0.73 D 3182.0
0.7 D 3183.0
0.79 D 3185.0
0.73 D 3189.0
0.73 D 3189.0
0.71 D 3192.0
0.7 D 3193.0
0.54 D 3194.0
0.73 D 3195.0
0.8 D 3195.0
0.7 D 3199.0
0.71 D 3203.0
0.9 D 3205.0
0.9 D 3205.0
0.9 D 3205.0
0.9 D 3205.0
0.9 D 3205.0
0.9 D 3205.0
0.9 D 3205.0
0.72 D 3205.0
0.58 D 3206.0
0.83 D 3207.0
0.7 D 3208.0
0.79 D 3209.0
0.8 D 3210.0
0.7 D 3210.0
0.71 D 3212.0
0.78 D 3214.0
0.7 D 3214.0
0.95 D 3214.0
0.71 D 3217.0
0.71 D 3217.0
0.71 D 3217.0
0.52 D 3218.0
0.72 D 3219.0
0.72 D 3219.0
0.71 D 3222.0
0.71 D 3222.0
0.51 D 3223.0
0.8 D 3226.0
0.65 D 3228.0
0.7 D 3229.0
0.7 D 3229.0
0.7 D 3231.0
0.59 D 3234.0
0.71 D 3234.0
0.72 D 3236.0
0.7 D 3239.0
0.7 D 3239.0
0.7 D 3239.0
0.77 D 3241.0
0.79 D 3242.0
0.71 D 3245.0
0.84 D 3246.0
0.25 D 563.0
0.26 D 564.0
0.31 D 565.0
0.31 D 565.0
0.7 D 3247.0
0.52 D 3247.0
0.76 D 3248.0
0.73 D 3250.0
0.77 D 3251.0
0.71 D 3252.0
0.78 D 3253.0
0.73 D 3255.0
0.78 D 3258.0
0.9 D 3262.0
0.71 D 3262.0
0.84 D 3265.0
0.81 D 3266.0
0.7 D 3267.0
0.56 D 3270.0
0.79 D 3270.0
0.72 D 3275.0
0.92 D 3277.0
0.7 D 3278.0
0.52 D 3284.0
0.86 D 3284.0
0.7 D 3287.0
0.7 D 3287.0
0.77 D 3291.0
0.76 D 3293.0
0.74 D 3294.0
0.7 D 3296.0
0.91 D 3298.0
0.78 D 3298.0
0.78 D 3298.0
0.71 D 3299.0
1.0 D 3304.0
1.0 D 3304.0
1.0 D 3304.0
0.76 D 3306.0
0.76 D 3306.0
0.53 D 3307.0
0.73 D 3308.0
0.77 D 3309.0
0.31 D 565.0
0.31 D 565.0
0.8 D 3312.0
0.7 D 3312.0
0.8 D 3312.0
0.9 D 3312.0
0.9 D 3312.0
0.7 D 3312.0
0.9 D 3312.0
0.71 D 3316.0
0.73 D 3319.0
0.52 D 3321.0
0.71 D 3321.0
0.71 D 3321.0
0.72 D 3322.0
0.81 D 3324.0
0.78 D 3326.0
0.79 D 3328.0
0.71 D 3332.0
0.71 D 3333.0
0.92 D 3335.0
0.7 D 3335.0
0.61 D 3336.0
1.01 D 3337.0
0.77 D 3345.0
0.53 D 3346.0
0.73 D 3346.0
0.83 D 3347.0
0.91 D 3349.0
0.77 D 3351.0
0.76 D 3352.0
0.74 D 3353.0
0.76 D 3353.0
0.81 D 3353.0
0.82 D 3357.0
0.91 D 3357.0
0.7 D 3360.0
0.7 D 3361.0
0.7 D 3365.0
0.74 D 3365.0
0.71 D 3366.0
0.69 D 3369.0
0.9 D 3371.0
0.9 D 3371.0
0.71 D 3372.0
0.52 D 3373.0
0.7 D 3375.0
0.72 D 3375.0
0.5 D 3378.0
0.5 D 3378.0
0.6 D 3382.0
0.27 D 567.0
0.31 D 567.0
0.33 D 567.0
0.33 D 567.0
0.33 D 567.0
0.3 D 568.0
0.9 D 3382.0
0.95 D 3384.0
0.76 D 3384.0
0.78 D 3389.0
0.88 D 3390.0
0.61 D 3397.0
0.85 D 3398.0
0.76 D 3401.0
0.91 D 3403.0
0.71 D 3406.0
0.71 D 3406.0
0.91 D 3408.0
0.7 D 3410.0
0.73 D 3411.0
0.73 D 3412.0
0.8 D 3419.0
0.7 D 3419.0
0.96 D 3419.0
0.96 D 3419.0
0.71 D 3420.0
0.9 D 3425.0
0.7 D 3425.0
0.77 D 3428.0
0.77 D 3428.0
0.77 D 3428.0
0.77 D 3428.0
0.79 D 3432.0
0.73 D 3440.0
0.8 D 3441.0
0.53 D 3442.0
0.77 D 3442.0
0.76 D 3443.0
0.76 D 3443.0
0.51 D 3446.0
0.51 D 3446.0
0.7 D 3448.0
0.72 D 3450.0
0.3 D 568.0
0.74 D 3454.0
0.78 D 3454.0
0.7 D 3454.0
0.75 D 3456.0
0.72 D 3459.0
0.74 D 3461.0
0.81 D 3462.0
0.91 D 3463.0
0.7 D 3463.0
0.73 D 3464.0
0.56 D 3465.0
0.71 D 3465.0
0.73 D 3467.0
0.55 D 3468.0
0.55 D 3468.0
0.55 D 3468.0
0.7 D 3471.0
0.7 D 3471.0
0.7 D 3471.0
0.9 D 3473.0
0.9 D 3473.0
0.9 D 3473.0
0.9 D 3473.0
0.9 D 3473.0
0.78 D 3473.0
0.74 D 3476.0
0.7 D 3477.0
0.71 D 3479.0
0.96 D 3480.0
0.74 D 3487.0
0.77 D 3489.0
0.77 D 3489.0
0.72 D 3493.0
0.54 D 3494.0
0.72 D 3495.0
0.56 D 3496.0
0.74 D 3498.0
0.7 D 3501.0
0.8 D 3502.0
0.71 D 3502.0
0.71 D 3502.0
0.71 D 3502.0
0.9 D 3505.0
0.55 D 3509.0
0.73 D 3509.0
0.91 D 3511.0
0.74 D 3517.0
0.53 D 3517.0
0.71 D 3518.0
0.72 D 3522.0
0.71 D 3524.0
0.73 D 3528.0
0.7 D 3529.0
0.32 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.9 D 3534.0
0.9 D 3534.0
0.9 D 3534.0
0.9 D 3534.0
0.78 D 3534.0
0.7 D 3535.0
0.93 D 3540.0
0.71 D 3540.0
0.72 D 3543.0
0.72 D 3550.0
0.92 D 3550.0
0.72 D 3554.0
0.83 D 3556.0
0.83 D 3556.0
0.73 D 3557.0
0.7 D 3561.0
0.75 D 3562.0
0.8 D 3564.0
0.9 D 3567.0
0.7 D 3567.0
0.9 D 3568.0
0.72 D 3568.0
1.0 D 3569.0
0.72 D 3570.0
0.6 D 3570.0
0.91 D 3573.0
0.71 D 3576.0
0.9 D 3578.0
0.9 D 3579.0
0.76 D 3581.0
0.71 D 3582.0
0.97 D 3585.0
1.11 D 3589.0
0.82 D 3593.0
0.78 D 3595.0
0.8 D 3597.0
0.72 D 3601.0
1.01 D 3604.0
0.9 D 3604.0
1.01 D 3605.0
0.79 D 3605.0
1.03 D 3607.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.92 D 3613.0
0.73 D 3615.0
0.7 D 3618.0
0.7 D 3618.0
0.71 D 3618.0
0.72 D 3619.0
0.73 D 3620.0
0.7 D 3622.0
0.7 D 3622.0
0.72 D 3622.0
0.72 D 3622.0
0.75 D 3625.0
0.61 D 3625.0
0.72 D 3629.0
0.9 D 3632.0
0.94 D 3634.0
1.0 D 3634.0
1.0 D 3634.0
1.0 D 3634.0
1.0 D 3634.0
0.9 D 3643.0
0.77 D 3643.0
1.16 D 3644.0
0.77 D 3644.0
1.11 D 3655.0
0.91 D 3660.0
0.87 D 3664.0
0.7 D 3668.0
0.78 D 3668.0
0.74 D 3668.0
0.85 D 3669.0
0.71 D 3670.0
1.01 D 3671.0
1.01 D 3671.0
0.78 D 3672.0
0.73 D 3673.0
0.71 D 3674.0
0.71 D 3674.0
1.03 D 3675.0
0.75 D 3679.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.8 D 3682.0
0.84 D 3685.0
0.9 D 3689.0
0.9 D 3689.0
0.9 D 3689.0
0.9 D 3689.0
0.9 D 3689.0
0.9 D 3689.0
0.71 D 3690.0
0.94 D 3691.0
0.75 D 3696.0
0.9 D 3706.0
0.92 D 3707.0
0.86 D 3709.0
1.16 D 3711.0
0.75 D 3712.0
0.71 D 3716.0
0.71 D 3718.0
0.77 D 3721.0
0.72 D 3722.0
0.91 D 3730.0
0.91 D 3730.0
0.91 D 3730.0
0.58 D 3732.0
0.76 D 3732.0
0.73 D 3735.0
0.78 D 3736.0
0.7 D 3737.0
0.9 D 3740.0
0.9 D 3740.0
0.9 D 3740.0
0.9 D 3740.0
0.58 D 3741.0
0.87 D 3742.0
1.09 D 3742.0
1.03 D 3743.0
1.03 D 3743.0
0.93 D 3744.0
0.74 D 3746.0
0.3 D 574.0
0.9 D 3751.0
0.7 D 3752.0
0.9 D 3755.0
0.9 D 3755.0
0.77 D 3755.0
0.61 D 3758.0
0.78 D 3763.0
0.91 D 3763.0
1.0 D 3767.0
1.02 D 3769.0
1.02 D 3773.0
0.83 D 3774.0
1.04 D 3780.0
1.04 D 3780.0
0.9 D 3780.0
1.04 D 3780.0
1.5 D 3780.0
0.91 D 3781.0
0.91 D 3781.0
0.77 D 3787.0
0.7 D 3788.0
0.9 D 3789.0
0.59 D 3791.0
0.91 D 3796.0
0.79 D 3798.0
0.9 D 3798.0
0.9 D 3798.0
0.9 D 3798.0
0.71 D 3799.0
0.78 D 3800.0
0.71 D 3801.0
0.9 D 3806.0
0.9 D 3806.0
0.9 D 3806.0
0.84 D 3809.0
0.78 D 3811.0
0.74 D 3812.0
0.53 D 3812.0
0.93 D 3812.0
0.9 D 3812.0
0.9 D 3812.0
0.9 D 3812.0
0.93 D 3812.0
0.74 D 3813.0
1.18 D 3816.0
0.84 D 3816.0
1.05 D 3816.0
0.79 D 3818.0
0.9 D 3818.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.85 D 3821.0
0.92 D 3823.0
0.53 D 3827.0
0.91 D 3828.0
0.63 D 3832.0
0.91 D 3837.0
0.77 D 3837.0
0.71 D 3838.0
1.02 D 3838.0
1.02 D 3839.0
0.93 D 3839.0
0.7 D 3840.0
1.02 D 3842.0
0.92 D 3843.0
0.9 D 3847.0
0.91 D 3848.0
0.91 D 3848.0
0.91 D 3848.0
0.6 D 3850.0
0.81 D 3852.0
0.91 D 3855.0
0.73 D 3856.0
0.71 D 3856.0
0.74 D 3858.0
0.94 D 3862.0
0.78 D 3864.0
1.17 D 3866.0
0.9 D 3871.0
1.01 D 3871.0
0.87 D 3873.0
0.92 D 3877.0
0.71 D 3877.0
0.9 D 3880.0
0.9 D 3880.0
0.9 D 3880.0
0.93 D 3880.0
1.13 D 3883.0
1.18 D 3886.0
0.91 D 3889.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.25 D 575.0
0.27 D 575.0
0.25 D 575.0
1.09 D 3890.0
0.92 D 3891.0
1.0 D 3894.0
0.76 D 3894.0
0.72 D 3896.0
1.18 D 3899.0
1.02 D 3909.0
1.02 D 3909.0
0.91 D 3910.0
0.91 D 3911.0
0.66 D 3915.0
0.92 D 3916.0
0.9 D 3918.0
0.7 D 3920.0
0.78 D 3923.0
0.9 D 3931.0
1.01 D 3932.0
0.83 D 3933.0
0.92 D 3936.0
0.73 D 3937.0
0.91 D 3943.0
0.9 D 3945.0
0.91 D 3949.0
1.14 D 3950.0
0.76 D 3950.0
0.71 D 3952.0
0.91 D 3958.0
1.01 D 3959.0
0.75 D 3961.0
1.09 D 3961.0
0.88 D 3962.0
1.0 D 3965.0
1.0 D 3965.0
1.0 D 3965.0
1.0 D 3965.0
1.0 D 3965.0
0.33 D 575.0
1.0 D 3965.0
0.77 D 3966.0
0.62 D 3968.0
1.02 D 3971.0
0.9 D 3975.0
0.9 D 3975.0
1.23 D 3977.0
0.77 D 3980.0
0.73 D 3980.0
0.83 D 3984.0
0.9 D 3989.0
0.96 D 3989.0
0.9 D 3990.0
0.93 D 3990.0
0.83 D 3990.0
0.92 D 3997.0
0.9 D 3997.0
0.9 D 3997.0
0.9 D 3997.0
0.9 D 3997.0
0.9 D 3997.0
0.7 D 4003.0
1.01 D 4004.0
0.75 D 4007.0
0.9 D 4007.0
0.9 D 4007.0
0.87 D 4012.0
0.71 D 4014.0
0.7 D 4022.0
0.65 D 4022.0
1.14 D 4022.0
0.56 D 4025.0
0.71 D 4029.0
0.71 D 4029.0
0.71 D 4029.0
0.71 D 4029.0
0.71 D 4029.0
0.57 D 4032.0
0.77 D 4037.0
0.77 D 4039.0
0.74 D 4040.0
0.91 D 4041.0
0.54 D 4042.0
1.02 D 4044.0
1.02 D 4044.0
1.02 D 4044.0
0.72 D 4047.0
1.23 D 4050.0
0.91 D 4051.0
0.91 D 4051.0
0.91 D 4051.0
0.96 D 4060.0
1.01 D 4064.0
1.0 D 4065.0
0.91 D 4067.0
0.9 D 4068.0
0.9 D 4068.0
0.9 D 4068.0
0.9 D 4068.0
0.9 D 4068.0
0.9 D 4068.0
1.12 D 4071.0
1.01 D 4072.0
0.9 D 4078.0
0.9 D 4078.0
0.9 D 4078.0
0.72 D 4082.0
0.72 D 4082.0
0.64 D 4084.0
0.92 D 4086.0
0.81 D 4087.0
0.7 D 4095.0
0.92 D 4096.0
0.92 D 4096.0
0.25 D 410.0
0.23 D 411.0
0.27 D 413.0
0.3 D 413.0
0.3 D 413.0
0.23 D 577.0
0.91 D 4107.0
0.91 D 4107.0
0.87 D 4108.0
0.91 D 4113.0
0.82 D 4113.0
0.9 D 4114.0
0.73 D 4116.0
0.9 D 4117.0
1.01 D 4118.0
0.9 D 4120.0
0.91 D 4123.0
0.91 D 4123.0
0.91 D 4123.0
1.04 D 4123.0
0.9 D 4128.0
0.9 D 4130.0
0.9 D 4133.0
0.73 D 4134.0
0.73 D 4134.0
0.82 D 4135.0
0.82 D 4135.0
1.12 D 4139.0
0.93 D 4140.0
0.93 D 4140.0
0.92 D 4150.0
0.76 D 4150.0
1.0 D 4155.0
1.06 D 4155.0
0.92 D 4158.0
0.92 D 4158.0
0.83 D 4159.0
0.59 D 4161.0
0.93 D 4165.0
0.91 D 4165.0
0.9 D 4167.0
0.92 D 4168.0
0.92 D 4168.0
1.19 D 4168.0
0.8 D 4170.0
0.6 D 4172.0
1.03 D 4177.0
0.9 D 4178.0

Alternatively, one could just write the SQL statement in scala to create a new DataFrame diamondsDColoredDF_FromTable from the table diamonds and display it, as follows:

val diamondsDColoredDF_FromTable = sqlContext.sql("select carat, color, price from diamonds where color='D'") // Shift+Enter

diamondsDColoredDF_FromTable: org.apache.spark.sql.DataFrame = [carat: double, color: string ... 1 more field]

// or if you like use upper case for SQL then this is equivalent
val diamondsDColoredDF_FromTable = sqlContext.sql("SELECT carat, color, price FROM diamonds WHERE color='D'") // Shift+Enter

diamondsDColoredDF_FromTable: org.apache.spark.sql.DataFrame = [carat: double, color: string ... 1 more field]

// from version 2.x onwards you can call from SparkSession, the pre-made spark in spark-shell or databricks notebook
val diamondsDColoredDF_FromTable = spark.sql("SELECT carat, color, price FROM diamonds WHERE color='D'") // Shift+Enter

diamondsDColoredDF_FromTable: org.apache.spark.sql.DataFrame = [carat: double, color: string ... 1 more field]

display(diamondsDColoredDF_FromTable) // Ctrl+Enter to see the same DF!
carat color price
0.23 D 357.0
0.23 D 402.0
0.26 D 403.0
0.26 D 403.0
0.26 D 403.0
0.22 D 404.0
0.3 D 552.0
0.3 D 552.0
0.3 D 552.0
0.24 D 553.0
0.26 D 554.0
0.26 D 554.0
0.26 D 554.0
0.75 D 2760.0
0.71 D 2762.0
0.61 D 2763.0
0.71 D 2764.0
0.71 D 2764.0
0.7 D 2767.0
0.71 D 2767.0
0.73 D 2768.0
0.7 D 2768.0
0.71 D 2768.0
0.71 D 2770.0
0.76 D 2770.0
0.73 D 2770.0
0.75 D 2773.0
0.7 D 2773.0
0.7 D 2777.0
0.53 D 2782.0
0.75 D 2782.0
0.72 D 2782.0
0.72 D 2782.0
0.7 D 2782.0
0.64 D 2787.0
0.71 D 2788.0
0.72 D 2795.0
0.71 D 2797.0
0.71 D 2797.0
0.71 D 2797.0
0.51 D 2797.0
0.78 D 2799.0
0.91 D 2803.0
0.7 D 2804.0
0.7 D 2804.0
0.72 D 2804.0
0.72 D 2804.0
0.73 D 2808.0
0.81 D 2809.0
0.74 D 2810.0
0.83 D 2811.0
0.71 D 2812.0
0.55 D 2815.0
0.71 D 2816.0
0.73 D 2821.0
0.71 D 2822.0
0.71 D 2822.0
0.7 D 2822.0
0.7 D 2822.0
0.71 D 2822.0
0.7 D 2822.0
0.7 D 2822.0
0.7 D 2822.0
0.7 D 2822.0
0.79 D 2823.0
0.71 D 2824.0
0.7 D 2826.0
0.7 D 2827.0
0.72 D 2827.0
0.7 D 2828.0
0.7 D 2833.0
0.7 D 2833.0
0.51 D 2834.0
0.92 D 2840.0
0.71 D 2841.0
0.73 D 2841.0
0.73 D 2841.0
0.71 D 2843.0
0.79 D 2846.0
0.76 D 2847.0
0.54 D 2848.0
0.75 D 2848.0
0.66 D 2851.0
0.79 D 2853.0
0.79 D 2853.0
0.74 D 2855.0
0.73 D 2858.0
0.71 D 2858.0
0.71 D 2858.0
0.7 D 2859.0
0.7 D 2859.0
0.7 D 2859.0
0.71 D 2860.0
0.71 D 2861.0
0.66 D 2861.0
0.7 D 2862.0
0.8 D 2862.0
0.71 D 2863.0
0.71 D 2863.0
0.71 D 2863.0
0.71 D 2863.0
0.71 D 2863.0
0.71 D 2863.0
0.71 D 2863.0
0.73 D 2865.0
0.56 D 2866.0
0.56 D 2866.0
0.7 D 2867.0
1.08 D 2869.0
0.7 D 2872.0
0.75 D 2872.0
0.7 D 2872.0
0.7 D 2872.0
0.7 D 2872.0
0.7 D 2872.0
0.71 D 2874.0
0.79 D 2878.0
0.74 D 2880.0
0.72 D 2883.0
0.77 D 2885.0
0.9 D 2885.0
0.71 D 2887.0
0.72 D 2891.0
0.71 D 2891.0
0.79 D 2896.0
0.77 D 2896.0
0.6 D 2897.0
0.54 D 2897.0
0.74 D 2897.0
0.75 D 2898.0
0.77 D 2898.0
0.72 D 2900.0
0.75 D 2903.0
0.75 D 2903.0
0.72 D 2903.0
0.72 D 2903.0
0.79 D 2904.0
0.53 D 2905.0
0.74 D 2906.0
0.32 D 558.0
0.7 D 2909.0
0.7 D 2909.0
0.71 D 2910.0
0.7 D 2913.0
0.71 D 2913.0
0.71 D 2913.0
0.71 D 2913.0
0.71 D 2913.0
0.71 D 2913.0
0.83 D 2918.0
0.71 D 2921.0
0.77 D 2922.0
0.77 D 2923.0
0.8 D 2925.0
0.81 D 2926.0
0.7 D 2928.0
0.59 D 2933.0
0.75 D 2933.0
0.71 D 2934.0
0.7 D 2936.0
0.77 D 2939.0
0.76 D 2942.0
0.73 D 2943.0
0.57 D 2945.0
0.78 D 2945.0
0.73 D 2947.0
0.73 D 2947.0
0.77 D 2949.0
0.71 D 2950.0
0.72 D 2951.0
0.72 D 2954.0
0.72 D 2954.0
0.75 D 2954.0
0.82 D 2954.0
0.7 D 2956.0
0.56 D 2959.0
0.7 D 2960.0
0.7 D 2960.0
0.7 D 2960.0
0.63 D 2962.0
0.71 D 2964.0
0.71 D 2968.0
0.77 D 2973.0
1.0 D 2974.0
0.76 D 2977.0
0.7 D 2980.0
0.7 D 2985.0
0.74 D 2987.0
0.83 D 2990.0
0.7 D 2991.0
0.72 D 2993.0
0.81 D 2994.0
0.73 D 2995.0
0.77 D 2996.0
0.7 D 2998.0
0.7 D 2999.0
0.72 D 3001.0
0.7 D 3001.0
0.7 D 3001.0
0.7 D 3001.0
0.71 D 3002.0
1.01 D 3003.0
0.65 D 3003.0
0.92 D 3004.0
0.55 D 3006.0
0.76 D 3007.0
0.7 D 3008.0
0.8 D 3011.0
0.77 D 3011.0
0.9 D 3013.0
0.73 D 3014.0
0.72 D 3016.0
0.5 D 3017.0
0.78 D 3019.0
0.71 D 3020.0
0.75 D 3024.0
0.75 D 3024.0
0.65 D 3025.0
0.71 D 3033.0
0.7 D 3033.0
0.7 D 3033.0
0.7 D 3033.0
0.78 D 3035.0
0.71 D 3035.0
0.74 D 3036.0
0.61 D 3036.0
0.77 D 3040.0
0.71 D 3045.0
0.72 D 3045.0
0.75 D 3046.0
0.73 D 3047.0
0.75 D 3048.0
0.72 D 3048.0
0.72 D 3048.0
0.66 D 3049.0
0.62 D 3050.0
0.7 D 3052.0
0.7 D 3053.0
0.7 D 3054.0
0.65 D 3056.0
0.92 D 3057.0
0.79 D 3058.0
0.72 D 3062.0
0.85 D 3066.0
0.7 D 3073.0
0.72 D 3075.0
0.72 D 3075.0
0.7 D 3075.0
0.76 D 3075.0
0.71 D 3077.0
0.71 D 3077.0
0.75 D 3078.0
0.83 D 3078.0
0.91 D 3079.0
0.79 D 3081.0
0.7 D 3082.0
0.8 D 3082.0
0.71 D 3084.0
0.75 D 3085.0
0.7 D 3087.0
0.7 D 3087.0
0.7 D 3087.0
0.74 D 3087.0
0.71 D 3090.0
0.71 D 3090.0
0.7 D 3092.0
0.7 D 3092.0
0.7 D 3092.0
0.7 D 3093.0
0.71 D 3096.0
0.71 D 3096.0
0.53 D 3097.0
0.72 D 3099.0
0.72 D 3102.0
0.66 D 3103.0
0.78 D 3103.0
0.75 D 3105.0
0.7 D 3107.0
0.79 D 3112.0
0.94 D 3125.0
0.57 D 3126.0
0.57 D 3126.0
0.7 D 3129.0
0.7 D 3131.0
0.71 D 3131.0
0.71 D 3135.0
0.71 D 3135.0
0.8 D 3135.0
0.81 D 3135.0
0.71 D 3136.0
0.71 D 3137.0
0.74 D 3138.0
0.72 D 3139.0
0.54 D 3139.0
0.73 D 3140.0
0.71 D 3145.0
0.84 D 3145.0
0.78 D 3145.0
0.75 D 3152.0
0.9 D 3153.0
0.71 D 3153.0
0.58 D 3154.0
0.8 D 3154.0
0.77 D 3158.0
0.82 D 3159.0
0.77 D 3160.0
0.81 D 3160.0
0.71 D 3161.0
0.71 D 3161.0
0.71 D 3161.0
0.77 D 3166.0
0.8 D 3173.0
0.72 D 3176.0
0.74 D 3177.0
0.72 D 3179.0
0.72 D 3179.0
0.72 D 3179.0
0.81 D 3179.0
0.73 D 3182.0
0.73 D 3182.0
0.7 D 3183.0
0.79 D 3185.0
0.73 D 3189.0
0.73 D 3189.0
0.71 D 3192.0
0.7 D 3193.0
0.54 D 3194.0
0.73 D 3195.0
0.8 D 3195.0
0.7 D 3199.0
0.71 D 3203.0
0.9 D 3205.0
0.9 D 3205.0
0.9 D 3205.0
0.9 D 3205.0
0.9 D 3205.0
0.9 D 3205.0
0.9 D 3205.0
0.72 D 3205.0
0.58 D 3206.0
0.83 D 3207.0
0.7 D 3208.0
0.79 D 3209.0
0.8 D 3210.0
0.7 D 3210.0
0.71 D 3212.0
0.78 D 3214.0
0.7 D 3214.0
0.95 D 3214.0
0.71 D 3217.0
0.71 D 3217.0
0.71 D 3217.0
0.52 D 3218.0
0.72 D 3219.0
0.72 D 3219.0
0.71 D 3222.0
0.71 D 3222.0
0.51 D 3223.0
0.8 D 3226.0
0.65 D 3228.0
0.7 D 3229.0
0.7 D 3229.0
0.7 D 3231.0
0.59 D 3234.0
0.71 D 3234.0
0.72 D 3236.0
0.7 D 3239.0
0.7 D 3239.0
0.7 D 3239.0
0.77 D 3241.0
0.79 D 3242.0
0.71 D 3245.0
0.84 D 3246.0
0.25 D 563.0
0.26 D 564.0
0.31 D 565.0
0.31 D 565.0
0.7 D 3247.0
0.52 D 3247.0
0.76 D 3248.0
0.73 D 3250.0
0.77 D 3251.0
0.71 D 3252.0
0.78 D 3253.0
0.73 D 3255.0
0.78 D 3258.0
0.9 D 3262.0
0.71 D 3262.0
0.84 D 3265.0
0.81 D 3266.0
0.7 D 3267.0
0.56 D 3270.0
0.79 D 3270.0
0.72 D 3275.0
0.92 D 3277.0
0.7 D 3278.0
0.52 D 3284.0
0.86 D 3284.0
0.7 D 3287.0
0.7 D 3287.0
0.77 D 3291.0
0.76 D 3293.0
0.74 D 3294.0
0.7 D 3296.0
0.91 D 3298.0
0.78 D 3298.0
0.78 D 3298.0
0.71 D 3299.0
1.0 D 3304.0
1.0 D 3304.0
1.0 D 3304.0
0.76 D 3306.0
0.76 D 3306.0
0.53 D 3307.0
0.73 D 3308.0
0.77 D 3309.0
0.31 D 565.0
0.31 D 565.0
0.8 D 3312.0
0.7 D 3312.0
0.8 D 3312.0
0.9 D 3312.0
0.9 D 3312.0
0.7 D 3312.0
0.9 D 3312.0
0.71 D 3316.0
0.73 D 3319.0
0.52 D 3321.0
0.71 D 3321.0
0.71 D 3321.0
0.72 D 3322.0
0.81 D 3324.0
0.78 D 3326.0
0.79 D 3328.0
0.71 D 3332.0
0.71 D 3333.0
0.92 D 3335.0
0.7 D 3335.0
0.61 D 3336.0
1.01 D 3337.0
0.77 D 3345.0
0.53 D 3346.0
0.73 D 3346.0
0.83 D 3347.0
0.91 D 3349.0
0.77 D 3351.0
0.76 D 3352.0
0.74 D 3353.0
0.76 D 3353.0
0.81 D 3353.0
0.82 D 3357.0
0.91 D 3357.0
0.7 D 3360.0
0.7 D 3361.0
0.7 D 3365.0
0.74 D 3365.0
0.71 D 3366.0
0.69 D 3369.0
0.9 D 3371.0
0.9 D 3371.0
0.71 D 3372.0
0.52 D 3373.0
0.7 D 3375.0
0.72 D 3375.0
0.5 D 3378.0
0.5 D 3378.0
0.6 D 3382.0
0.27 D 567.0
0.31 D 567.0
0.33 D 567.0
0.33 D 567.0
0.33 D 567.0
0.3 D 568.0
0.9 D 3382.0
0.95 D 3384.0
0.76 D 3384.0
0.78 D 3389.0
0.88 D 3390.0
0.61 D 3397.0
0.85 D 3398.0
0.76 D 3401.0
0.91 D 3403.0
0.71 D 3406.0
0.71 D 3406.0
0.91 D 3408.0
0.7 D 3410.0
0.73 D 3411.0
0.73 D 3412.0
0.8 D 3419.0
0.7 D 3419.0
0.96 D 3419.0
0.96 D 3419.0
0.71 D 3420.0
0.9 D 3425.0
0.7 D 3425.0
0.77 D 3428.0
0.77 D 3428.0
0.77 D 3428.0
0.77 D 3428.0
0.79 D 3432.0
0.73 D 3440.0
0.8 D 3441.0
0.53 D 3442.0
0.77 D 3442.0
0.76 D 3443.0
0.76 D 3443.0
0.51 D 3446.0
0.51 D 3446.0
0.7 D 3448.0
0.72 D 3450.0
0.3 D 568.0
0.74 D 3454.0
0.78 D 3454.0
0.7 D 3454.0
0.75 D 3456.0
0.72 D 3459.0
0.74 D 3461.0
0.81 D 3462.0
0.91 D 3463.0
0.7 D 3463.0
0.73 D 3464.0
0.56 D 3465.0
0.71 D 3465.0
0.73 D 3467.0
0.55 D 3468.0
0.55 D 3468.0
0.55 D 3468.0
0.7 D 3471.0
0.7 D 3471.0
0.7 D 3471.0
0.9 D 3473.0
0.9 D 3473.0
0.9 D 3473.0
0.9 D 3473.0
0.9 D 3473.0
0.78 D 3473.0
0.74 D 3476.0
0.7 D 3477.0
0.71 D 3479.0
0.96 D 3480.0
0.74 D 3487.0
0.77 D 3489.0
0.77 D 3489.0
0.72 D 3493.0
0.54 D 3494.0
0.72 D 3495.0
0.56 D 3496.0
0.74 D 3498.0
0.7 D 3501.0
0.8 D 3502.0
0.71 D 3502.0
0.71 D 3502.0
0.71 D 3502.0
0.9 D 3505.0
0.55 D 3509.0
0.73 D 3509.0
0.91 D 3511.0
0.74 D 3517.0
0.53 D 3517.0
0.71 D 3518.0
0.72 D 3522.0
0.71 D 3524.0
0.73 D 3528.0
0.7 D 3529.0
0.32 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.9 D 3534.0
0.9 D 3534.0
0.9 D 3534.0
0.9 D 3534.0
0.78 D 3534.0
0.7 D 3535.0
0.93 D 3540.0
0.71 D 3540.0
0.72 D 3543.0
0.72 D 3550.0
0.92 D 3550.0
0.72 D 3554.0
0.83 D 3556.0
0.83 D 3556.0
0.73 D 3557.0
0.7 D 3561.0
0.75 D 3562.0
0.8 D 3564.0
0.9 D 3567.0
0.7 D 3567.0
0.9 D 3568.0
0.72 D 3568.0
1.0 D 3569.0
0.72 D 3570.0
0.6 D 3570.0
0.91 D 3573.0
0.71 D 3576.0
0.9 D 3578.0
0.9 D 3579.0
0.76 D 3581.0
0.71 D 3582.0
0.97 D 3585.0
1.11 D 3589.0
0.82 D 3593.0
0.78 D 3595.0
0.8 D 3597.0
0.72 D 3601.0
1.01 D 3604.0
0.9 D 3604.0
1.01 D 3605.0
0.79 D 3605.0
1.03 D 3607.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.92 D 3613.0
0.73 D 3615.0
0.7 D 3618.0
0.7 D 3618.0
0.71 D 3618.0
0.72 D 3619.0
0.73 D 3620.0
0.7 D 3622.0
0.7 D 3622.0
0.72 D 3622.0
0.72 D 3622.0
0.75 D 3625.0
0.61 D 3625.0
0.72 D 3629.0
0.9 D 3632.0
0.94 D 3634.0
1.0 D 3634.0
1.0 D 3634.0
1.0 D 3634.0
1.0 D 3634.0
0.9 D 3643.0
0.77 D 3643.0
1.16 D 3644.0
0.77 D 3644.0
1.11 D 3655.0
0.91 D 3660.0
0.87 D 3664.0
0.7 D 3668.0
0.78 D 3668.0
0.74 D 3668.0
0.85 D 3669.0
0.71 D 3670.0
1.01 D 3671.0
1.01 D 3671.0
0.78 D 3672.0
0.73 D 3673.0
0.71 D 3674.0
0.71 D 3674.0
1.03 D 3675.0
0.75 D 3679.0
0.31 D 571.0
0.31 D 571.0
0.31 D 571.0
0.8 D 3682.0
0.84 D 3685.0
0.9 D 3689.0
0.9 D 3689.0
0.9 D 3689.0
0.9 D 3689.0
0.9 D 3689.0
0.9 D 3689.0
0.71 D 3690.0
0.94 D 3691.0
0.75 D 3696.0
0.9 D 3706.0
0.92 D 3707.0
0.86 D 3709.0
1.16 D 3711.0
0.75 D 3712.0
0.71 D 3716.0
0.71 D 3718.0
0.77 D 3721.0
0.72 D 3722.0
0.91 D 3730.0
0.91 D 3730.0
0.91 D 3730.0
0.58 D 3732.0
0.76 D 3732.0
0.73 D 3735.0
0.78 D 3736.0
0.7 D 3737.0
0.9 D 3740.0
0.9 D 3740.0
0.9 D 3740.0
0.9 D 3740.0
0.58 D 3741.0
0.87 D 3742.0
1.09 D 3742.0
1.03 D 3743.0
1.03 D 3743.0
0.93 D 3744.0
0.74 D 3746.0
0.3 D 574.0
0.9 D 3751.0
0.7 D 3752.0
0.9 D 3755.0
0.9 D 3755.0
0.77 D 3755.0
0.61 D 3758.0
0.78 D 3763.0
0.91 D 3763.0
1.0 D 3767.0
1.02 D 3769.0
1.02 D 3773.0
0.83 D 3774.0
1.04 D 3780.0
1.04 D 3780.0
0.9 D 3780.0
1.04 D 3780.0
1.5 D 3780.0
0.91 D 3781.0
0.91 D 3781.0
0.77 D 3787.0
0.7 D 3788.0
0.9 D 3789.0
0.59 D 3791.0
0.91 D 3796.0
0.79 D 3798.0
0.9 D 3798.0
0.9 D 3798.0
0.9 D 3798.0
0.71 D 3799.0
0.78 D 3800.0
0.71 D 3801.0
0.9 D 3806.0
0.9 D 3806.0
0.9 D 3806.0
0.84 D 3809.0
0.78 D 3811.0
0.74 D 3812.0
0.53 D 3812.0
0.93 D 3812.0
0.9 D 3812.0
0.9 D 3812.0
0.9 D 3812.0
0.93 D 3812.0
0.74 D 3813.0
1.18 D 3816.0
0.84 D 3816.0
1.05 D 3816.0
0.79 D 3818.0
0.9 D 3818.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.85 D 3821.0
0.92 D 3823.0
0.53 D 3827.0
0.91 D 3828.0
0.63 D 3832.0
0.91 D 3837.0
0.77 D 3837.0
0.71 D 3838.0
1.02 D 3838.0
1.02 D 3839.0
0.93 D 3839.0
0.7 D 3840.0
1.02 D 3842.0
0.92 D 3843.0
0.9 D 3847.0
0.91 D 3848.0
0.91 D 3848.0
0.91 D 3848.0
0.6 D 3850.0
0.81 D 3852.0
0.91 D 3855.0
0.73 D 3856.0
0.71 D 3856.0
0.74 D 3858.0
0.94 D 3862.0
0.78 D 3864.0
1.17 D 3866.0
0.9 D 3871.0
1.01 D 3871.0
0.87 D 3873.0
0.92 D 3877.0
0.71 D 3877.0
0.9 D 3880.0
0.9 D 3880.0
0.9 D 3880.0
0.93 D 3880.0
1.13 D 3883.0
1.18 D 3886.0
0.91 D 3889.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.3 D 574.0
0.25 D 575.0
0.27 D 575.0
0.25 D 575.0
1.09 D 3890.0
0.92 D 3891.0
1.0 D 3894.0
0.76 D 3894.0
0.72 D 3896.0
1.18 D 3899.0
1.02 D 3909.0
1.02 D 3909.0
0.91 D 3910.0
0.91 D 3911.0
0.66 D 3915.0
0.92 D 3916.0
0.9 D 3918.0
0.7 D 3920.0
0.78 D 3923.0
0.9 D 3931.0
1.01 D 3932.0
0.83 D 3933.0
0.92 D 3936.0
0.73 D 3937.0
0.91 D 3943.0
0.9 D 3945.0
0.91 D 3949.0
1.14 D 3950.0
0.76 D 3950.0
0.71 D 3952.0
0.91 D 3958.0
1.01 D 3959.0
0.75 D 3961.0
1.09 D 3961.0
0.88 D 3962.0
1.0 D 3965.0
1.0 D 3965.0
1.0 D 3965.0
1.0 D 3965.0
1.0 D 3965.0
0.33 D 575.0
1.0 D 3965.0
0.77 D 3966.0
0.62 D 3968.0
1.02 D 3971.0
0.9 D 3975.0
0.9 D 3975.0
1.23 D 3977.0
0.77 D 3980.0
0.73 D 3980.0
0.83 D 3984.0
0.9 D 3989.0
0.96 D 3989.0
0.9 D 3990.0
0.93 D 3990.0
0.83 D 3990.0
0.92 D 3997.0
0.9 D 3997.0
0.9 D 3997.0
0.9 D 3997.0
0.9 D 3997.0
0.9 D 3997.0
0.7 D 4003.0
1.01 D 4004.0
0.75 D 4007.0
0.9 D 4007.0
0.9 D 4007.0
0.87 D 4012.0
0.71 D 4014.0
0.7 D 4022.0
0.65 D 4022.0
1.14 D 4022.0
0.56 D 4025.0
0.71 D 4029.0
0.71 D 4029.0
0.71 D 4029.0
0.71 D 4029.0
0.71 D 4029.0
0.57 D 4032.0
0.77 D 4037.0
0.77 D 4039.0
0.74 D 4040.0
0.91 D 4041.0
0.54 D 4042.0
1.02 D 4044.0
1.02 D 4044.0
1.02 D 4044.0
0.72 D 4047.0
1.23 D 4050.0
0.91 D 4051.0
0.91 D 4051.0
0.91 D 4051.0
0.96 D 4060.0
1.01 D 4064.0
1.0 D 4065.0
0.91 D 4067.0
0.9 D 4068.0
0.9 D 4068.0
0.9 D 4068.0
0.9 D 4068.0
0.9 D 4068.0
0.9 D 4068.0
1.12 D 4071.0
1.01 D 4072.0
0.9 D 4078.0
0.9 D 4078.0
0.9 D 4078.0
0.72 D 4082.0
0.72 D 4082.0
0.64 D 4084.0
0.92 D 4086.0
0.81 D 4087.0
0.7 D 4095.0
0.92 D 4096.0
0.92 D 4096.0
0.25 D 410.0
0.23 D 411.0
0.27 D 413.0
0.3 D 413.0
0.3 D 413.0
0.23 D 577.0
0.91 D 4107.0
0.91 D 4107.0
0.87 D 4108.0
0.91 D 4113.0
0.82 D 4113.0
0.9 D 4114.0
0.73 D 4116.0
0.9 D 4117.0
1.01 D 4118.0
0.9 D 4120.0
0.91 D 4123.0
0.91 D 4123.0
0.91 D 4123.0
1.04 D 4123.0
0.9 D 4128.0
0.9 D 4130.0
0.9 D 4133.0
0.73 D 4134.0
0.73 D 4134.0
0.82 D 4135.0
0.82 D 4135.0
1.12 D 4139.0
0.93 D 4140.0
0.93 D 4140.0
0.92 D 4150.0
0.76 D 4150.0
1.0 D 4155.0
1.06 D 4155.0
0.92 D 4158.0
0.92 D 4158.0
0.83 D 4159.0
0.59 D 4161.0
0.93 D 4165.0
0.91 D 4165.0
0.9 D 4167.0
0.92 D 4168.0
0.92 D 4168.0
1.19 D 4168.0
0.8 D 4170.0
0.6 D 4172.0
1.03 D 4177.0
0.9 D 4178.0 
// You can also use the familiar wildchard character '%' when matching Strings
display(spark.sql("SELECT * FROM diamonds WHERE clarity LIKE 'V%'"))
carat cut color clarity depth table price x y z
0.23 Good E VS1 56.9 65.0 327.0 4.05 4.07 2.31
0.29 Premium I VS2 62.4 58.0 334.0 4.2 4.23 2.63
0.24 Very Good J VVS2 62.8 57.0 336.0 3.94 3.96 2.48
0.24 Very Good I VVS1 62.3 57.0 336.0 3.95 3.98 2.47
0.22 Fair E VS2 65.1 61.0 337.0 3.87 3.78 2.49
0.23 Very Good H VS1 59.4 61.0 338.0 4.0 4.05 2.39
0.23 Ideal J VS1 62.8 56.0 340.0 3.93 3.9 2.46
0.23 Very Good E VS2 63.8 55.0 352.0 3.85 3.92 2.48
0.23 Very Good H VS1 61.0 57.0 353.0 3.94 3.96 2.41
0.23 Very Good G VVS2 60.4 58.0 354.0 3.97 4.01 2.41
0.24 Premium I VS1 62.5 57.0 355.0 3.97 3.94 2.47
0.3 Very Good J VS2 62.2 57.0 357.0 4.28 4.3 2.67
0.23 Very Good D VS2 60.5 61.0 357.0 3.96 3.97 2.4
0.23 Very Good F VS1 60.9 57.0 357.0 3.96 3.99 2.42
0.23 Very Good F VS1 60.0 57.0 402.0 4.0 4.03 2.41
0.23 Very Good F VS1 59.8 57.0 402.0 4.04 4.06 2.42
0.23 Very Good E VS1 60.7 59.0 402.0 3.97 4.01 2.42
0.23 Very Good E VS1 59.5 58.0 402.0 4.01 4.06 2.4
0.23 Very Good D VS1 61.9 58.0 402.0 3.92 3.96 2.44
0.23 Good F VS1 58.2 59.0 402.0 4.06 4.08 2.37
0.23 Good E VS1 64.1 59.0 402.0 3.83 3.85 2.46
0.26 Very Good D VS2 60.8 59.0 403.0 4.13 4.16 2.52
0.26 Good D VS2 65.2 56.0 403.0 3.99 4.02 2.61
0.26 Good D VS1 58.4 63.0 403.0 4.19 4.24 2.46
0.25 Very Good E VS2 63.3 60.0 404.0 4.0 4.03 2.54
0.23 Ideal G VS1 61.9 54.0 404.0 3.93 3.95 2.44
0.22 Premium E VS2 61.6 58.0 404.0 3.93 3.89 2.41
0.22 Premium D VS2 59.3 62.0 404.0 3.91 3.88 2.31
0.35 Ideal I VS1 60.9 57.0 552.0 4.54 4.59 2.78
0.28 Ideal G VVS2 61.4 56.0 553.0 4.19 4.22 2.58
0.32 Ideal I VVS1 62.0 55.3 553.0 4.39 4.42 2.73
0.24 Premium E VVS1 60.7 58.0 553.0 4.01 4.03 2.44
0.24 Very Good D VVS1 61.5 60.0 553.0 3.97 4.0 2.45
0.26 Very Good F VVS2 59.2 60.0 554.0 4.19 4.22 2.49
0.26 Very Good E VVS2 59.9 58.0 554.0 4.15 4.23 2.51
0.26 Very Good D VVS2 62.4 54.0 554.0 4.08 4.13 2.56
0.26 Very Good D VVS2 62.8 60.0 554.0 4.01 4.05 2.53
0.26 Very Good E VVS1 62.6 59.0 554.0 4.06 4.09 2.55
0.26 Very Good E VVS1 63.4 59.0 554.0 4.0 4.04 2.55
0.26 Very Good D VVS1 62.1 60.0 554.0 4.03 4.12 2.53
0.26 Ideal E VVS2 62.9 58.0 554.0 4.02 4.06 2.54
0.26 Good E VVS1 57.9 60.0 554.0 4.22 4.25 2.45
0.24 Premium G VVS1 62.3 59.0 554.0 3.95 3.92 2.45
0.24 Premium H VVS1 61.2 58.0 554.0 4.01 3.96 2.44
0.24 Premium H VVS1 60.8 59.0 554.0 4.02 4.0 2.44
0.24 Premium H VVS2 60.7 58.0 554.0 4.07 4.04 2.46
0.7 Ideal G VS2 61.6 56.0 2757.0 5.7 5.67 3.5
0.71 Very Good E VS2 62.4 57.0 2759.0 5.68 5.73 3.56
0.7 Good E VS2 57.5 58.0 2759.0 5.85 5.9 3.38
0.7 Good F VS1 59.4 62.0 2759.0 5.71 5.76 3.4
0.75 Premium G VS2 61.7 58.0 2760.0 5.85 5.79 3.59
0.8 Ideal I VS1 62.9 56.0 2760.0 5.94 5.87 3.72
0.74 Ideal I VVS2 62.3 55.0 2761.0 5.77 5.81 3.61
0.59 Ideal E VVS2 62.0 55.0 2761.0 5.38 5.43 3.35
0.9 Premium I VS2 63.0 58.0 2761.0 6.16 6.12 3.87
0.73 Ideal F VS2 62.6 56.0 2762.0 5.77 5.74 3.6
0.73 Ideal F VS2 62.7 53.0 2762.0 5.8 5.75 3.62
0.71 Ideal G VS2 62.4 54.0 2762.0 5.72 5.76 3.58
0.7 Ideal E VS2 60.7 58.0 2762.0 5.73 5.76 3.49
0.7 Very Good F VS2 61.7 63.0 2762.0 5.64 5.61 3.47
0.7 Fair F VS2 64.5 57.0 2762.0 5.57 5.53 3.58
0.7 Fair F VS2 65.3 55.0 2762.0 5.63 5.58 3.66
0.7 Premium F VS2 61.6 60.0 2762.0 5.65 5.59 3.46
0.61 Very Good D VVS2 59.6 57.0 2763.0 5.56 5.58 3.32
0.77 Ideal H VS2 62.0 56.0 2763.0 5.89 5.86 3.64
0.7 Very Good E VS2 62.6 60.0 2765.0 5.62 5.65 3.53
0.77 Very Good H VS1 61.3 60.0 2765.0 5.88 5.9 3.61
0.63 Premium E VVS1 60.9 60.0 2765.0 5.52 5.55 3.37
0.71 Very Good F VS1 60.1 62.0 2765.0 5.74 5.77 3.46
0.71 Premium F VS1 61.8 59.0 2765.0 5.69 5.73 3.53
0.64 Ideal G VVS1 61.9 56.0 2766.0 5.53 5.56 3.43
0.71 Premium G VS2 60.9 57.0 2766.0 5.78 5.75 3.51
0.71 Premium G VS2 59.8 56.0 2766.0 5.89 5.81 3.5
0.7 Very Good D VS2 61.8 55.0 2767.0 5.68 5.72 3.52
0.7 Very Good F VS1 60.0 57.0 2767.0 5.8 5.87 3.5
0.7 Good H VVS2 62.1 64.0 2767.0 5.62 5.65 3.5
0.71 Very Good G VS1 63.3 59.0 2768.0 5.52 5.61 3.52
0.71 Premium D VS2 62.5 60.0 2770.0 5.65 5.61 3.52
0.73 Premium G VS2 61.4 59.0 2770.0 5.83 5.76 3.56
0.73 Premium G VS2 60.7 58.0 2770.0 5.87 5.82 3.55
0.73 Premium G VS1 61.5 58.0 2770.0 5.79 5.75 3.55
0.73 Premium G VS2 59.2 59.0 2770.0 5.92 5.87 3.49
0.72 Very Good H VVS2 60.3 56.0 2771.0 5.81 5.83 3.51
0.71 Ideal G VS2 61.9 57.0 2771.0 5.73 5.77 3.56
0.73 Very Good H VVS1 60.4 59.0 2772.0 5.83 5.89 3.54
0.58 Ideal G VVS1 61.5 55.0 2772.0 5.39 5.44 3.33
0.58 Ideal F VVS1 61.7 56.0 2772.0 5.33 5.37 3.3
0.71 Good E VS2 59.2 61.0 2772.0 5.8 5.88 3.46
0.7 Premium D VS2 58.0 62.0 2773.0 5.87 5.78 3.38
0.6 Ideal E VS1 61.7 55.0 2774.0 5.41 5.44 3.35
0.83 Good I VS2 64.6 54.0 2774.0 5.85 5.88 3.79
0.74 Very Good F VS2 61.3 61.0 2775.0 5.8 5.84 3.57
0.72 Very Good G VS2 63.7 56.4 2776.0 5.62 5.69 3.61
0.71 Premium E VS2 62.7 58.0 2776.0 5.74 5.68 3.58
0.71 Ideal E VS2 62.2 57.0 2776.0 5.79 5.62 3.55
0.54 Ideal E VVS2 61.6 56.0 2776.0 5.25 5.27 3.24
0.54 Ideal E VVS2 61.5 57.0 2776.0 5.24 5.26 3.23
0.72 Good G VS2 59.7 60.5 2776.0 5.8 5.84 3.47
0.7 Very Good D VS1 62.7 58.0 2777.0 5.66 5.73 3.57
0.71 Premium F VS2 62.1 58.0 2777.0 5.67 5.7 3.53
0.71 Very Good F VS2 62.8 57.0 2777.0 5.64 5.69 3.56
0.71 Good F VS2 63.8 58.0 2777.0 5.61 5.64 3.59
0.71 Good F VS2 57.8 60.0 2777.0 5.87 5.9 3.4
0.7 Ideal E VS2 62.1 55.0 2777.0 5.7 5.67 3.53
0.7 Premium E VS2 61.1 60.0 2777.0 5.71 5.64 3.47
0.7 Good E VS2 64.1 59.0 2777.0 5.64 5.59 3.6
0.52 Ideal F VVS1 61.3 55.0 2778.0 5.19 5.22 3.19
0.73 Very Good H VS2 60.8 56.0 2779.0 5.82 5.84 3.55
0.7 Very Good F VS2 63.6 57.0 2780.0 5.61 5.65 3.58
0.77 Premium G VS2 61.2 58.0 2780.0 5.9 5.93 3.62
0.71 Ideal F VS2 62.1 54.0 2780.0 5.68 5.72 3.54
0.74 Ideal G VS1 61.5 55.0 2780.0 5.81 5.86 3.59
0.7 Ideal G VS1 61.4 59.0 2780.0 5.64 5.73 3.49
0.72 Very Good H VS1 60.6 63.0 2782.0 5.83 5.76 3.51
0.53 Very Good D VVS2 57.5 64.0 2782.0 5.34 5.37 3.08
0.76 Ideal G VS2 61.3 56.0 2782.0 5.9 5.94 3.63
0.7 Good E VS1 57.2 62.0 2782.0 5.81 5.77 3.31
0.7 Premium E VS1 62.9 60.0 2782.0 5.62 5.54 3.51
0.72 Very Good F VS2 63.0 54.0 2784.0 5.69 5.73 3.6
0.79 Very Good H VS1 63.7 56.0 2784.0 5.85 5.92 3.75
0.72 Very Good F VS2 63.6 58.0 2787.0 5.66 5.69 3.61
0.51 Ideal F VVS1 62.0 57.0 2787.0 5.11 5.15 3.18
0.64 Ideal D VS1 61.5 56.0 2787.0 5.54 5.55 3.41
0.7 Very Good H VVS1 60.5 60.0 2788.0 5.74 5.77 3.48
0.83 Very Good I VS1 61.1 60.0 2788.0 6.07 6.1 3.72
0.76 Ideal I VVS2 61.8 56.0 2788.0 5.85 5.87 3.62
0.71 Good D VS2 63.3 56.0 2788.0 5.64 5.68 3.58
0.77 Good G VS1 59.4 64.0 2788.0 5.97 5.92 3.53
0.63 Premium E VVS2 62.1 57.0 2789.0 5.48 5.41 3.38
0.63 Premium E VVS1 60.9 60.0 2789.0 5.55 5.52 3.37
0.77 Premium H VS1 61.3 60.0 2789.0 5.9 5.88 3.61
0.76 Premium I VVS1 58.8 59.0 2790.0 6.0 5.94 3.51
0.71 Premium F VS1 60.1 62.0 2790.0 5.77 5.74 3.46
0.71 Premium F VS1 61.8 59.0 2790.0 5.73 5.69 3.53
0.7 Premium F VS1 62.1 60.0 2792.0 5.71 5.65 3.53
0.7 Premium F VS1 60.7 60.0 2792.0 5.78 5.75 3.5
0.76 Premium H VVS2 59.6 57.0 2792.0 5.91 5.86 3.51
0.7 Ideal F VS1 62.2 56.0 2792.0 5.73 5.68 3.55
0.7 Very Good E VS2 62.9 57.0 2793.0 5.66 5.69 3.57
0.7 Good E VS2 64.1 55.0 2793.0 5.6 5.66 3.61
0.76 Ideal I VS2 61.3 56.0 2793.0 5.87 5.91 3.61
0.73 Ideal H VS2 62.7 55.0 2793.0 5.72 5.76 3.6
0.71 Very Good E VS2 60.7 56.0 2795.0 5.81 5.82 3.53
0.81 Premium I VVS2 61.9 60.0 2795.0 5.91 5.86 3.64
0.72 Good F VS1 60.7 60.0 2795.0 5.74 5.72 3.48
0.81 Premium H VS2 58.0 59.0 2795.0 6.17 6.13 3.57
0.72 Premium G VS2 62.9 57.0 2795.0 5.73 5.65 3.58
0.57 Ideal F VVS2 61.9 55.0 2797.0 5.34 5.35 3.31
0.51 Ideal D VVS1 61.7 56.0 2797.0 5.12 5.16 3.17
0.72 Ideal G VS2 61.9 58.0 2797.0 5.72 5.75 3.55
0.74 Ideal H VS1 61.8 58.0 2797.0 5.77 5.81 3.58
0.74 Ideal H VS1 61.6 56.0 2797.0 5.81 5.82 3.58
0.7 Fair G VVS1 58.8 66.0 2797.0 5.81 5.9 3.44
0.8 Very Good H VS2 63.4 60.0 2797.0 5.92 5.82 3.72
0.77 Ideal I VS1 61.5 59.0 2798.0 5.87 5.91 3.62
0.7 Premium E VS1 62.2 58.0 2800.0 5.6 5.66 3.5
0.74 Premium G VS1 62.9 60.0 2800.0 5.74 5.68 3.59
0.79 Ideal I VS1 61.8 59.0 2800.0 5.92 5.95 3.67
0.76 Fair G VS1 59.0 70.0 2800.0 5.89 5.8 3.46
0.73 Ideal F VS2 62.5 55.0 2801.0 5.8 5.76 3.61
0.73 Premium F VS2 62.7 58.0 2801.0 5.76 5.7 3.59
0.71 Premium F VS2 62.1 58.0 2801.0 5.7 5.67 3.53
0.71 Good F VS2 57.8 60.0 2801.0 5.9 5.87 3.4
0.71 Good F VS2 63.8 58.0 2801.0 5.64 5.61 3.59
0.71 Premium F VS2 62.8 57.0 2801.0 5.69 5.64 3.56
0.72 Premium E VS2 63.0 55.0 2802.0 5.79 5.61 3.59
0.72 Premium F VS1 62.4 58.0 2802.0 5.83 5.7 3.6
0.7 Very Good F VS2 62.9 58.0 2803.0 5.63 5.65 3.55
0.71 Ideal G VS2 61.3 56.0 2803.0 5.75 5.71 3.51
0.7 Good G VS1 65.1 58.0 2803.0 5.56 5.59 3.63
0.71 Premium F VS2 62.6 58.0 2803.0 5.7 5.67 3.56
0.71 Premium F VS2 58.0 62.0 2803.0 5.85 5.81 3.38
0.71 Premium G VS1 62.4 61.0 2803.0 5.7 5.65 3.54
0.77 Premium G VS2 61.3 57.0 2803.0 5.93 5.88 3.62
0.71 Premium G VS2 59.9 60.0 2803.0 5.81 5.77 3.47
0.78 Premium G VS2 60.8 58.0 2803.0 6.03 5.95 3.64
0.71 Very Good G VS1 63.5 55.0 2803.0 5.66 5.64 3.59
0.71 Very Good E VS2 63.8 58.0 2804.0 5.62 5.66 3.6
0.71 Very Good E VS2 64.0 57.0 2804.0 5.66 5.68 3.63
0.72 Ideal F VS1 61.7 57.0 2804.0 5.74 5.77 3.55
0.72 Very Good F VS1 62.2 58.0 2804.0 5.75 5.7 3.56
0.82 Ideal H VS2 61.5 56.0 2804.0 6.01 6.08 3.72
0.74 Premium F VS2 61.7 58.0 2805.0 5.85 5.78 3.59
0.74 Premium F VS2 61.9 56.0 2805.0 5.8 5.77 3.58
0.57 Fair E VVS1 58.7 66.0 2805.0 5.34 5.43 3.16
0.73 Premium F VS2 62.5 57.0 2805.0 5.75 5.7 3.58
0.72 Ideal G VS2 62.8 56.0 2805.0 5.74 5.7 3.59
0.74 Fair F VS2 61.1 68.0 2805.0 5.82 5.75 3.53
0.82 Good G VS2 64.0 57.0 2805.0 5.92 5.89 3.78
0.75 Very Good H VVS1 60.6 58.0 2806.0 5.85 5.9 3.56
0.71 Very Good F VS1 62.2 58.0 2807.0 5.66 5.72 3.54
0.71 Very Good F VS1 60.0 57.0 2807.0 5.84 5.9 3.52
0.8 Very Good H VS2 62.8 57.0 2808.0 5.87 5.91 3.7
0.7 Very Good F VS1 62.0 57.0 2808.0 5.64 5.71 3.52
0.75 Very Good G VS2 63.4 56.0 2808.0 5.78 5.74 3.65
0.58 Ideal E VVS2 60.9 56.0 2808.0 5.41 5.43 3.3
0.7 Very Good F VS1 61.8 56.0 2810.0 5.63 5.7 3.5
0.7 Very Good F VS1 59.9 60.0 2810.0 5.77 5.84 3.48
0.7 Good F VS1 62.8 61.0 2810.0 5.57 5.61 3.51
1.0 Fair J VS2 65.7 59.0 2811.0 6.14 6.07 4.01
0.7 Very Good G VS1 63.0 60.0 2812.0 5.57 5.64 3.53
0.7 Very Good F VS2 59.5 58.0 2812.0 5.75 5.85 3.45
0.7 Very Good F VS2 61.7 58.0 2812.0 5.63 5.69 3.49
0.29 Very Good E VS1 61.9 55.0 555.0 4.28 4.33 2.66
0.29 Very Good E VS1 62.4 55.0 555.0 4.2 4.25 2.63
0.34 Ideal H VS2 61.5 56.0 555.0 4.47 4.5 2.76
0.34 Ideal H VS2 60.4 57.0 555.0 4.54 4.57 2.75
0.34 Ideal I VS1 61.8 55.0 555.0 4.48 4.52 2.78
0.34 Ideal I VS1 62.0 56.0 555.0 4.5 4.53 2.8
0.3 Ideal G VS1 62.3 56.0 555.0 4.29 4.31 2.68
0.29 Ideal F VS1 61.6 56.0 555.0 4.26 4.31 2.64
0.32 Very Good F VS2 61.4 58.0 556.0 4.37 4.42 2.7
0.36 Ideal I VS2 61.9 56.0 556.0 4.54 4.57 2.82
0.3 Ideal G VS2 62.0 56.0 556.0 4.28 4.3 2.66
0.26 Ideal E VS1 61.5 57.0 556.0 4.09 4.12 2.52
0.7 Very Good F VS2 62.3 58.0 2812.0 5.64 5.72 3.54
0.7 Very Good F VS2 60.9 61.0 2812.0 5.66 5.71 3.46
0.73 Premium E VS2 58.6 60.0 2812.0 5.92 5.89 3.46
0.51 Ideal F VVS1 62.0 57.0 2812.0 5.15 5.11 3.18
0.91 Good I VS2 64.3 58.0 2813.0 6.09 6.05 3.9
0.55 Very Good D VVS1 61.5 56.0 2815.0 5.23 5.27 3.23
0.74 Premium G VS1 61.7 58.0 2815.0 5.79 5.81 3.58
0.9 Fair J VS2 65.0 56.0 2815.0 6.08 6.04 3.94
0.72 Premium E VS2 58.3 58.0 2815.0 5.99 5.92 3.47
0.78 Very Good I VVS2 61.4 56.0 2816.0 5.91 5.95 3.64
0.61 Ideal G VVS2 60.1 57.0 2816.0 5.52 5.54 3.32
0.71 Good D VS1 63.4 55.0 2816.0 5.61 5.69 3.58
0.71 Ideal I VVS2 60.2 56.0 2817.0 5.84 5.89 3.53
0.71 Ideal E VS2 62.7 57.0 2817.0 5.66 5.64 3.54
0.71 Premium E VS2 62.3 58.0 2817.0 5.69 5.65 3.53
0.63 Ideal F VVS2 61.5 56.0 2817.0 5.48 5.52 3.38
0.9 Ideal J VS2 62.8 55.0 2817.0 6.2 6.16 3.88
0.7 Premium E VS2 62.4 61.0 2818.0 5.66 5.63 3.52
0.7 Premium E VS2 59.3 60.0 2818.0 5.78 5.73 3.41
0.7 Premium E VS2 63.0 60.0 2818.0 5.64 5.6 3.54
0.7 Ideal E VS1 62.9 57.0 2818.0 5.66 5.61 3.54
0.7 Premium E VS1 59.6 57.0 2818.0 5.91 5.83 3.5
0.7 Premium F VS2 61.8 60.0 2818.0 5.69 5.64 3.5
0.7 Premium E VS1 62.7 57.0 2818.0 5.68 5.64 3.55
0.72 Very Good G VS1 63.8 58.0 2819.0 5.64 5.68 3.61
0.72 Ideal H VS1 62.3 56.0 2819.0 5.73 5.77 3.58
0.7 Good F VS1 59.7 63.0 2819.0 5.76 5.79 3.45
0.71 Ideal G VS1 62.9 58.0 2820.0 5.66 5.69 3.57
0.73 Premium E VS2 61.6 59.0 2821.0 5.77 5.73 3.54
0.53 Ideal E VVS1 61.9 55.0 2821.0 5.2 5.21 3.22
0.7 Premium E VS1 60.8 60.0 2822.0 5.74 5.71 3.48
0.72 Premium E VS2 60.3 59.0 2822.0 5.84 5.8 3.51
0.72 Premium E VS2 60.9 60.0 2822.0 5.8 5.76 3.52
0.72 Premium E VS2 62.4 59.0 2822.0 5.77 5.7 3.58
0.7 Premium E VS2 60.2 60.0 2822.0 5.73 5.7 3.44
0.6 Ideal F VVS2 62.0 55.0 2822.0 5.37 5.4 3.34
0.74 Ideal I VVS1 60.8 57.0 2822.0 5.85 5.89 3.57
0.9 Good J VS2 64.0 61.0 2822.0 6.04 6.03 3.86
0.7 Premium E VS2 61.5 59.0 2822.0 5.73 5.68 3.51
0.7 Premium E VS2 62.6 56.0 2822.0 5.71 5.66 3.56
0.7 Premium E VS2 60.7 62.0 2822.0 5.72 5.68 3.46
0.7 Premium F VS2 60.6 58.0 2822.0 5.8 5.72 3.49
0.71 Premium E VS2 62.3 58.0 2823.0 5.71 5.66 3.54
0.61 Ideal E VVS2 61.3 54.0 2823.0 5.51 5.59 3.4
0.71 Premium D VS2 61.2 59.0 2824.0 5.74 5.69 3.5
0.77 Ideal I VVS2 62.1 57.0 2824.0 5.84 5.86 3.63
0.74 Good E VS1 63.1 58.0 2824.0 5.73 5.75 3.62
0.71 Premium G VS1 62.2 59.0 2825.0 5.73 5.66 3.54
0.73 Very Good G VS1 62.0 57.0 2825.0 5.75 5.8 3.58
0.73 Premium E VS1 62.6 60.0 2826.0 5.75 5.68 3.58
0.7 Good E VS1 57.2 59.0 2826.0 5.94 5.88 3.38
0.7 Premium E VS1 62.2 58.0 2826.0 5.66 5.6 3.5
0.7 Very Good D VS2 63.3 56.0 2826.0 5.6 5.58 3.54
0.7 Premium E VS1 59.4 61.0 2826.0 5.78 5.74 3.42
0.72 Good D VS2 64.0 54.0 2827.0 5.68 5.7 3.64
0.79 Premium H VVS2 62.6 58.0 2827.0 5.96 5.9 3.71
0.7 Ideal H VVS1 61.6 57.0 2827.0 5.69 5.74 3.52
0.7 Ideal H VVS1 62.3 55.0 2827.0 5.66 5.7 3.54
0.72 Premium F VS1 62.2 58.0 2829.0 5.75 5.7 3.56
0.59 Ideal E VVS1 62.0 56.0 2829.0 5.36 5.38 3.33
0.72 Ideal F VS1 61.7 57.0 2829.0 5.77 5.74 3.55
0.71 Very Good E VS2 62.7 59.0 2830.0 5.65 5.7 3.56
0.53 Ideal F VVS1 60.9 57.0 2830.0 5.23 5.29 3.19
0.53 Ideal F VVS1 61.8 57.0 2830.0 5.16 5.19 3.2
0.8 Ideal I VS2 62.1 54.4 2830.0 5.94 5.99 3.7
0.72 Very Good F VS2 62.5 58.0 2832.0 5.71 5.75 3.58
0.71 Premium G VVS2 62.1 57.0 2832.0 5.75 5.65 3.54
0.71 Premium F VS1 62.2 58.0 2832.0 5.72 5.66 3.54
0.8 Very Good I VS2 62.0 58.0 2833.0 5.86 5.95 3.66
0.7 Very Good D VS2 59.6 61.0 2833.0 5.77 5.8 3.45
0.7 Ideal D VS2 61.0 57.0 2833.0 5.74 5.76 3.51
0.61 Ideal F VVS2 61.7 55.0 2833.0 5.45 5.48 3.37
0.8 Ideal G VS2 62.2 56.0 2834.0 5.94 5.87 3.67
0.8 Ideal H VS2 62.8 57.0 2834.0 5.91 5.87 3.7
0.51 Very Good D VVS1 59.9 58.0 2834.0 5.16 5.19 3.1
0.53 Ideal F VVS1 61.4 57.0 2834.0 5.2 5.23 3.2
0.78 Ideal I VS2 61.8 55.0 2834.0 5.92 5.95 3.67
0.73 Ideal F VS1 61.2 56.0 2835.0 5.89 5.81 3.58
0.63 Ideal F VVS2 61.9 57.0 2835.0 5.47 5.51 3.4
0.7 Ideal E VS2 61.5 54.0 2835.0 5.7 5.75 3.52
0.72 Ideal E VS2 62.8 57.0 2835.0 5.71 5.73 3.59
0.75 Premium F VS2 59.6 59.0 2835.0 6.04 5.94 3.57
0.71 Good E VS2 62.8 60.0 2836.0 5.6 5.65 3.53
0.7 Premium E VS1 62.6 59.0 2837.0 5.69 5.66 3.55
0.7 Ideal E VS1 61.8 56.0 2837.0 5.74 5.69 3.53
0.72 Premium F VS1 58.8 60.0 2838.0 5.91 5.89 3.47
0.7 Premium F VS2 62.3 58.0 2838.0 5.72 5.64 3.54
0.7 Premium F VS2 61.7 58.0 2838.0 5.69 5.63 3.49
0.7 Premium G VS1 62.6 55.0 2838.0 5.73 5.64 3.56
0.7 Premium F VS2 59.4 61.0 2838.0 5.83 5.79 3.45
0.7 Premium F VS2 60.9 61.0 2838.0 5.71 5.66 3.46
0.7 Premium F VS2 59.5 58.0 2838.0 5.85 5.75 3.45
0.7 Premium G VS1 63.0 60.0 2838.0 5.64 5.57 3.53
0.71 Ideal F VS1 61.5 57.0 2839.0 5.74 5.71 3.52
0.7 Ideal F VS1 61.6 54.0 2839.0 5.75 5.72 3.53
0.71 Ideal F VS1 62.1 55.0 2839.0 5.82 5.68 3.57
0.71 Premium F VS1 59.1 61.0 2839.0 5.84 5.81 3.44
0.71 Premium F VS1 59.0 60.0 2839.0 5.82 5.8 3.43
0.71 Premium F VS1 60.5 58.0 2839.0 5.75 5.72 3.47
0.7 Ideal F VS1 62.4 53.0 2839.0 5.73 5.71 3.57
0.73 Ideal G VS2 61.8 54.0 2839.0 5.8 5.82 3.59
0.7 Ideal E VS2 62.1 54.0 2839.0 5.69 5.72 3.54
0.7 Ideal G VS1 61.3 57.0 2839.0 5.71 5.74 3.51
0.71 Premium G VVS2 60.3 58.0 2839.0 5.82 5.78 3.5
0.71 Premium F VS1 59.2 58.0 2839.0 5.87 5.82 3.46
0.79 Premium G VS2 59.3 62.0 2839.0 6.09 6.01 3.59
0.71 Premium F VS1 62.7 59.0 2839.0 5.7 5.62 3.55
0.77 Very Good H VS1 61.0 60.0 2840.0 5.9 5.87 3.59
0.71 Premium F VS2 59.3 56.0 2840.0 5.88 5.82 3.47
0.7 Premium H VVS1 59.2 60.0 2840.0 5.87 5.78 3.45
0.73 Premium F VS2 60.3 59.0 2841.0 5.9 5.87 3.55
0.71 Very Good D VS1 63.4 55.0 2841.0 5.69 5.61 3.58
0.52 Ideal F VVS1 61.2 56.0 2841.0 5.19 5.21 3.18
0.73 Premium F VS2 59.9 59.0 2841.0 5.87 5.77 3.5
0.73 Premium G VS1 61.4 58.0 2841.0 5.82 5.77 3.56
0.8 Ideal I VS1 62.6 54.0 2842.0 5.92 5.96 3.72
0.7 Premium F VS2 58.7 61.0 2842.0 5.8 5.72 3.38
0.7 Very Good E VS2 60.2 62.0 2843.0 5.71 5.75 3.45
0.7 Very Good E VS2 62.7 58.0 2843.0 5.65 5.67 3.55
0.71 Very Good E VS2 59.4 58.0 2843.0 5.76 5.82 3.44
0.73 Ideal G VVS2 61.3 57.0 2843.0 5.81 5.84 3.57
0.73 Very Good F VS1 61.8 59.0 2843.0 5.73 5.79 3.56
0.72 Ideal E VS2 62.0 57.0 2843.0 5.71 5.74 3.55
0.71 Ideal G VVS2 60.7 57.0 2843.0 5.81 5.78 3.52
0.7 Very Good E VS1 62.0 59.0 2844.0 5.65 5.68 3.51
0.79 Ideal H VS2 62.5 57.0 2844.0 5.91 5.93 3.7
0.7 Very Good E VS2 61.8 59.0 2845.0 5.65 5.68 3.5
0.7 Very Good E VS2 58.9 60.0 2845.0 5.83 5.85 3.44
0.8 Good H VS2 63.4 60.0 2845.0 5.92 5.82 3.72
0.72 Very Good F VS1 60.2 59.0 2846.0 5.79 5.84 3.5
0.73 Ideal H VVS2 61.6 56.0 2846.0 5.79 5.84 3.58
0.7 Good F VS2 59.1 61.0 2846.0 5.76 5.84 3.43
0.77 Premium G VS2 61.3 60.0 2846.0 5.91 5.81 3.59
0.77 Premium G VS1 61.4 58.0 2846.0 5.94 5.89 3.63
0.7 Very Good G VVS2 62.9 59.0 2848.0 5.61 5.68 3.55
0.54 Ideal D VVS2 61.5 55.0 2848.0 5.25 5.29 3.24
0.74 Very Good E VS1 63.1 58.0 2849.0 5.75 5.73 3.62
0.7 Very Good E VS2 61.0 60.0 2850.0 5.74 5.77 3.51
0.7 Ideal F VS2 60.8 59.0 2850.0 5.69 5.79 3.49
0.66 Ideal D VS1 62.1 56.0 2851.0 5.54 5.57 3.45
0.78 Ideal I VS1 61.5 57.0 2852.0 5.88 5.92 3.63
0.71 Premium F VS2 62.6 58.0 2853.0 5.67 5.7 3.56
0.71 Good G VS1 63.5 55.0 2853.0 5.64 5.66 3.59
0.82 Premium I VS1 61.9 58.0 2853.0 5.99 5.97 3.7
0.78 Very Good H VS1 61.9 57.1 2854.0 5.87 5.95 3.66
0.7 Very Good E VS1 62.4 56.0 2854.0 5.64 5.7 3.54
0.73 Premium E VS2 62.0 57.0 2854.0 5.86 5.76 3.6
0.91 Fair J VS2 64.4 62.0 2854.0 6.06 6.03 3.89
0.91 Fair J VS2 65.4 60.0 2854.0 6.04 6.0 3.94
0.91 Good J VS2 64.2 58.0 2854.0 6.12 6.09 3.92
0.7 Premium E VS1 58.4 59.0 2854.0 5.91 5.83 3.43
0.68 Premium F VVS2 61.7 57.0 2854.0 5.67 5.64 3.49
0.73 Very Good F VS2 62.5 57.0 2855.0 5.7 5.75 3.58
0.74 Premium D VS2 62.4 57.0 2855.0 5.8 5.74 3.6
0.6 Ideal F VVS2 60.8 57.0 2856.0 5.44 5.49 3.32
0.26 Ideal E VS1 62.3 57.0 556.0 4.05 4.08 2.53
0.26 Ideal E VS1 62.1 56.0 556.0 4.09 4.12 2.55
0.34 Good G VS2 57.5 61.0 556.0 4.6 4.66 2.66
0.34 Very Good G VS2 59.6 62.0 556.0 4.54 4.56 2.71
0.32 Good E VS2 64.1 54.0 556.0 4.34 4.37 2.79
0.31 Ideal I VVS1 61.6 55.0 557.0 4.36 4.41 2.7
0.31 Ideal I VVS1 61.3 56.0 557.0 4.36 4.38 2.68
0.31 Ideal I VVS1 62.3 54.0 557.0 4.37 4.4 2.73
0.31 Ideal I VVS1 62.0 54.0 557.0 4.37 4.4 2.72
0.31 Ideal I VVS1 62.7 53.0 557.0 4.33 4.35 2.72
0.31 Ideal I VVS1 62.2 53.0 557.0 4.36 4.38 2.72
0.31 Ideal G VS2 62.2 53.6 557.0 4.32 4.35 2.7
0.31 Ideal H VS1 61.6 54.8 557.0 4.35 4.37 2.69
0.31 Ideal H VS1 61.8 54.2 557.0 4.33 4.37 2.69
0.33 Premium J VS1 62.8 58.0 557.0 4.41 4.38 2.76
0.33 Premium J VS1 61.5 61.0 557.0 4.46 4.39 2.72
0.33 Ideal J VS1 62.1 55.0 557.0 4.44 4.41 2.75
0.7 Good E VVS2 60.1 63.0 2857.0 5.68 5.71 3.42
0.9 Premium I VS2 61.9 59.0 2857.0 6.2 6.14 3.82
0.7 Ideal G VVS2 62.1 56.0 2858.0 5.63 5.71 3.52
0.71 Premium E VS2 61.0 60.0 2858.0 5.76 5.69 3.49
0.7 Premium E VS2 61.4 59.0 2858.0 5.73 5.7 3.51
0.71 Premium E VS2 61.5 60.0 2858.0 5.76 5.68 3.52
0.71 Very Good E VS2 63.5 59.0 2858.0 5.68 5.59 3.58
0.71 Premium D VS2 60.4 62.0 2858.0 5.74 5.72 3.46
0.7 Good E VVS2 63.6 62.0 2858.0 5.61 5.58 3.56
0.71 Fair D VS2 56.9 65.0 2858.0 5.89 5.84 3.34
0.7 Ideal D VS2 61.0 57.0 2859.0 5.76 5.74 3.51
0.7 Premium D VS2 62.4 56.0 2859.0 5.72 5.66 3.55
0.77 Premium F VS1 60.9 60.0 2859.0 5.91 5.88 3.59
0.71 Ideal G VS1 61.5 56.0 2859.0 5.74 5.78 3.54
0.7 Premium D VS2 59.6 61.0 2859.0 5.8 5.77 3.45
0.75 Fair F VS1 55.8 70.0 2859.0 6.09 5.98 3.37
0.71 Very Good F VS2 61.3 61.0 2860.0 5.68 5.73 3.5
0.6 Ideal E VVS2 61.9 54.9 2860.0 5.41 5.44 3.35
0.71 Premium D VS1 62.9 57.0 2860.0 5.66 5.6 3.54
0.53 Ideal F VVS1 61.4 57.0 2860.0 5.23 5.2 3.2
0.62 Ideal G VVS2 61.6 56.0 2861.0 5.45 5.48 3.37
0.62 Ideal G VVS2 61.6 56.0 2861.0 5.48 5.51 3.38
0.66 Premium D VS1 61.0 58.0 2861.0 5.67 5.57 3.43
0.71 Very Good F VVS1 63.2 60.0 2862.0 5.65 5.61 3.56
0.7 Ideal H VS2 61.1 57.0 2862.0 5.71 5.74 3.5
0.7 Very Good E VS2 58.7 63.0 2862.0 5.73 5.69 3.35
0.79 Premium H VS1 60.0 60.0 2862.0 6.07 5.99 3.64
0.7 Premium E VS2 59.5 59.0 2862.0 5.82 5.77 3.45
0.73 Premium E VS2 62.5 61.0 2862.0 5.78 5.64 3.59
0.91 Good I VS2 64.3 58.0 2863.0 6.05 6.09 3.9
0.9 Premium J VS2 59.8 62.0 2863.0 6.24 6.21 3.72
0.71 Premium H VVS2 61.5 62.0 2863.0 5.74 5.68 3.51
0.72 Ideal F VS2 59.5 57.0 2863.0 5.91 5.86 3.5
0.71 Ideal E VS2 61.0 55.0 2863.0 5.79 5.75 3.52
0.83 Very Good I VS2 61.6 58.0 2865.0 6.05 6.07 3.73
0.56 Very Good D VVS1 62.0 56.0 2866.0 5.25 5.3 3.27
0.56 Very Good D VVS1 61.8 55.0 2866.0 5.27 5.31 3.27
0.71 Ideal E VS1 62.2 55.0 2866.0 5.74 5.7 3.56
0.7 Ideal H VVS1 62.3 58.0 2866.0 5.66 5.7 3.54
0.71 Very Good H VVS1 62.9 57.0 2867.0 5.67 5.69 3.57
0.7 Ideal D VS2 62.4 57.0 2867.0 5.68 5.61 3.52
0.71 Ideal H VVS1 60.4 57.0 2867.0 5.78 5.81 3.5
0.8 Premium H VS2 61.2 53.0 2867.0 6.05 5.98 3.68
0.52 Ideal F VVS1 61.2 56.0 2867.0 5.21 5.19 3.18
0.72 Ideal I VS1 62.4 55.0 2868.0 5.72 5.75 3.58
0.73 Ideal G VVS2 61.3 57.0 2869.0 5.84 5.81 3.57
0.72 Ideal H VVS2 60.9 57.0 2869.0 5.79 5.77 3.52
0.52 Premium F VVS2 61.8 60.0 2870.0 5.16 5.13 3.18
0.64 Premium E VVS2 62.1 58.0 2870.0 5.56 5.51 3.44
0.82 Ideal H VS2 59.5 57.0 2870.0 6.12 6.09 3.63
0.73 Premium E VS1 61.3 59.0 2870.0 5.81 5.78 3.55
0.72 Very Good E VS2 58.3 57.0 2872.0 5.89 5.94 3.45
0.76 Very Good F VS2 62.0 58.0 2873.0 5.8 5.86 3.62
0.78 Premium F VS2 62.6 58.0 2874.0 5.91 5.82 3.67
0.71 Premium D VS2 61.2 59.0 2874.0 5.69 5.74 3.5
0.7 Premium F VS1 59.0 59.0 2874.0 5.79 5.77 3.41
0.7 Premium F VS1 60.8 62.0 2874.0 5.71 5.67 3.46
0.7 Premium G VVS2 61.8 58.0 2874.0 5.67 5.63 3.49
0.7 Ideal F VS1 61.0 55.0 2874.0 5.77 5.73 3.51
0.7 Ideal F VS1 61.6 55.0 2874.0 5.75 5.71 3.53
0.7 Ideal F VS1 62.4 56.0 2874.0 5.69 5.65 3.54
0.7 Premium G VVS2 62.9 59.0 2874.0 5.68 5.61 3.55
1.0 Fair J VS1 65.5 55.0 2875.0 6.3 6.25 4.11
0.73 Premium E VS1 62.6 60.0 2876.0 5.68 5.75 3.58
0.79 Premium E VS2 60.6 53.0 2876.0 6.04 5.98 3.64
0.72 Very Good H VS1 62.2 54.0 2877.0 5.74 5.76 3.57
0.71 Ideal E VS1 62.4 56.0 2877.0 5.75 5.7 3.57
0.74 Ideal G VS2 62.3 55.0 2877.0 5.8 5.83 3.62
0.7 Good H VVS1 62.7 56.0 2877.0 5.6 5.66 3.53
0.7 Good F VS1 59.1 62.0 2877.0 5.82 5.86 3.44
0.71 Ideal I VS2 61.5 55.0 2878.0 5.76 5.78 3.55
0.7 Premium F VS1 60.4 60.0 2879.0 5.73 5.7 3.45
0.71 Premium F VS1 62.7 58.0 2879.0 5.71 5.67 3.57
0.72 Fair F VS1 56.9 69.0 2879.0 5.93 5.77 3.33
0.72 Ideal F VS1 62.0 56.0 2879.0 5.76 5.73 3.56
0.7 Ideal H VVS1 62.0 55.0 2881.0 5.74 5.71 3.55
0.71 Very Good E VS2 60.0 59.0 2881.0 5.84 5.83 3.5
0.54 Ideal F VVS1 61.8 56.0 2882.0 5.23 5.26 3.24
0.73 Premium F VS2 59.9 58.0 2882.0 5.87 5.84 3.51
0.73 Premium F VS2 58.7 57.0 2882.0 5.97 5.92 3.49
0.7 Premium E VS1 62.6 59.0 2887.0 5.66 5.69 3.55
0.79 Ideal I VS1 61.7 59.0 2888.0 5.93 5.96 3.67
0.72 Very Good G VVS2 62.5 58.0 2889.0 5.68 5.72 3.56
0.7 Very Good E VS2 63.5 54.0 2889.0 5.62 5.66 3.58
0.7 Very Good F VS1 62.2 58.0 2889.0 5.64 5.75 3.54
0.71 Very Good F VS1 62.8 56.0 2889.0 5.69 5.72 3.58
0.5 Ideal E VVS2 62.2 54.0 2889.0 5.08 5.12 3.17
0.5 Ideal E VVS2 62.2 54.0 2889.0 5.09 5.11 3.17
0.77 Premium F VS2 61.8 56.0 2889.0 5.94 5.9 3.66
0.66 Ideal G VVS1 61.5 56.0 2890.0 5.61 5.58 3.44
0.71 Very Good E VS2 61.2 58.0 2891.0 5.71 5.79 3.52
0.71 Ideal F VS2 61.2 56.0 2891.0 5.73 5.77 3.52
0.71 Ideal E VS2 61.6 56.0 2891.0 5.74 5.76 3.54
0.71 Ideal E VS2 62.7 56.0 2891.0 5.71 5.75 3.59
0.71 Good D VS2 62.3 61.0 2891.0 5.7 5.73 3.56
0.71 Very Good F VS1 62.6 55.0 2893.0 5.66 5.71 3.56
0.71 Ideal G VVS2 61.5 57.0 2893.0 5.73 5.75 3.53
0.75 Ideal F VS2 62.5 57.0 2893.0 5.78 5.83 3.63
0.7 Very Good H VVS1 59.2 60.0 2893.0 5.87 5.78 3.45
0.71 Very Good G VS2 60.9 56.0 2895.0 5.75 5.78 3.51
0.7 Very Good F VS1 61.8 59.0 2895.0 5.66 5.76 3.53
0.7 Ideal G VVS2 62.1 53.0 2895.0 5.71 5.75 3.56
0.74 Very Good G VS1 59.8 58.0 2896.0 5.85 5.89 3.51
0.77 Very Good G VS2 61.3 60.0 2896.0 5.81 5.91 3.59
0.77 Very Good G VS2 58.3 63.0 2896.0 6.0 6.05 3.51
0.53 Ideal F VVS1 61.6 56.0 2896.0 5.18 5.24 3.21
0.6 Very Good D VVS2 60.6 57.0 2897.0 5.48 5.51 3.33
0.54 Ideal D VVS2 61.4 52.0 2897.0 5.3 5.34 3.26
0.72 Good F VS1 59.4 61.0 2897.0 5.82 5.89 3.48
0.74 Premium D VS2 61.8 58.0 2897.0 5.81 5.77 3.58
0.7 Good G VVS1 59.9 61.0 2899.0 5.75 5.81 3.46
0.72 Premium D VS1 62.7 58.0 2900.0 5.68 5.65 3.55
0.74 Ideal E VS2 61.9 57.0 2901.0 5.81 5.78 3.59
0.73 Premium E VS2 62.0 60.0 2902.0 5.76 5.73 3.56
0.73 Ideal E VS2 61.4 55.0 2902.0 5.82 5.8 3.57
0.71 Fair E VS2 64.6 59.0 2902.0 5.62 5.59 3.62
0.71 Premium E VS2 59.6 60.0 2902.0 5.85 5.8 3.47
0.72 Premium E VS2 61.1 59.0 2903.0 5.8 5.75 3.53
0.7 Very Good E VS1 58.4 59.0 2904.0 5.83 5.91 3.43
0.62 Ideal E VVS2 62.0 56.0 2904.0 5.48 5.52 3.41
0.7 Very Good G VVS2 59.3 62.0 2905.0 5.78 5.82 3.44
0.7 Very Good G VVS2 63.4 59.0 2905.0 5.62 5.64 3.57
0.7 Very Good G VVS2 63.3 59.0 2905.0 5.59 5.62 3.55
0.71 Very Good G VS2 62.1 58.0 2905.0 5.65 5.71 3.53
0.86 Very Good I VS1 61.2 58.0 2905.0 6.1 6.16 3.75
0.53 Ideal D VVS1 62.5 54.0 2905.0 5.16 5.21 3.24
0.74 Very Good D VS2 62.4 57.0 2906.0 5.74 5.8 3.6
0.8 Ideal I VS1 62.2 58.0 2906.0 5.92 5.95 3.69
0.61 Ideal E VVS2 62.4 53.9 2907.0 5.42 5.43 3.38
0.61 Ideal E VVS2 62.4 53.6 2907.0 5.42 5.45 3.39
0.61 Ideal E VVS2 62.1 54.2 2907.0 5.43 5.45 3.38
0.72 Ideal H VVS1 62.8 57.0 2907.0 5.68 5.72 3.58
0.7 Ideal F VS2 62.3 53.0 2907.0 5.69 5.73 3.56
0.71 Ideal F VS1 61.9 56.0 2907.0 5.7 5.74 3.54
0.25 Premium F VS1 61.2 59.0 558.0 4.05 4.02 2.47
0.25 Good F VS1 63.6 57.0 558.0 4.04 4.01 2.56
0.25 Premium E VS1 60.7 59.0 558.0 4.13 4.11 2.5
0.25 Premium E VS1 61.5 60.0 558.0 4.04 4.02 2.48
0.31 Premium I VS2 60.8 58.0 558.0 4.37 4.34 2.65
0.31 Premium I VS2 59.8 60.0 558.0 4.42 4.38 2.63
0.31 Very Good I VS2 63.2 55.0 558.0 4.4 4.3 2.75
0.31 Premium I VS2 62.3 57.0 558.0 4.35 4.32 2.7
0.31 Premium I VS2 60.8 60.0 558.0 4.42 4.37 2.67
0.31 Ideal I VS2 59.9 57.0 558.0 4.4 4.38 2.63
0.31 Premium I VS2 59.9 60.0 558.0 4.44 4.41 2.65
0.31 Premium I VS2 61.1 58.0 558.0 4.38 4.36 2.67
0.31 Premium I VS2 60.7 61.0 558.0 4.34 4.32 2.63
0.31 Very Good I VS2 63.1 54.0 558.0 4.34 4.31 2.73
0.31 Premium I VS2 62.3 60.0 558.0 4.32 4.31 2.69
0.73 Ideal I VS1 61.5 55.0 2908.0 5.8 5.84 3.58
0.7 Premium D VS2 61.0 60.0 2909.0 5.75 5.7 3.49
0.7 Premium D VS2 60.9 57.0 2909.0 5.71 5.69 3.47
0.71 Ideal H VS1 61.2 56.0 2909.0 5.76 5.81 3.54
0.71 Ideal H VS1 61.9 56.0 2909.0 5.7 5.74 3.54
0.71 Very Good D VS1 62.9 57.0 2910.0 5.6 5.66 3.54
0.59 Ideal E VVS2 61.1 57.0 2911.0 5.39 5.41 3.3
0.71 Ideal G VS2 60.6 56.0 2911.0 5.76 5.8 3.5
0.77 Good F VS2 60.3 61.0 2911.0 5.89 5.96 3.57
0.73 Good E VS2 64.2 54.0 2912.0 5.68 5.72 3.66
0.7 Good E VS2 58.7 63.0 2912.0 5.69 5.73 3.35
0.73 Good E VS2 63.2 56.0 2912.0 5.75 5.76 3.64
0.7 Very Good D VS2 60.7 60.0 2913.0 5.72 5.74 3.48
0.83 Very Good I VS2 62.0 55.0 2915.0 6.03 6.06 3.74
0.71 Ideal F VS2 62.2 56.0 2915.0 5.74 5.71 3.56
0.73 Very Good H VS1 60.8 57.0 2916.0 5.8 5.83 3.54
0.74 Premium F VS1 62.5 60.0 2917.0 5.78 5.74 3.6
0.7 Ideal E VS2 62.5 58.0 2917.0 5.63 5.67 3.53
0.71 Ideal F VS2 61.2 56.0 2917.0 5.77 5.73 3.52
0.71 Very Good F VS2 59.5 58.0 2918.0 5.82 5.87 3.48
0.8 Very Good H VS2 61.2 53.0 2918.0 5.98 6.05 3.68
0.71 Ideal H VVS1 62.1 54.0 2918.0 5.7 5.76 3.56
0.72 Ideal I VS2 61.8 55.0 2918.0 5.75 5.79 3.56
0.72 Very Good G VS1 60.5 57.0 2919.0 5.8 5.83 3.52
0.73 Premium G VVS2 62.2 56.0 2919.0 5.79 5.75 3.59
0.7 Good F VS1 63.8 58.0 2919.0 5.61 5.58 3.57
0.73 Ideal H VS1 61.9 55.0 2919.0 5.79 5.76 3.58
0.73 Ideal G VVS2 61.9 55.0 2919.0 5.83 5.77 3.59
0.71 Premium E VS1 59.7 57.0 2920.0 5.87 5.78 3.48
0.71 Premium F VS1 59.1 59.0 2920.0 5.88 5.83 3.46
0.71 Ideal F VS1 62.6 55.0 2920.0 5.71 5.67 3.56
0.74 Very Good H VVS2 60.5 60.0 2921.0 5.79 5.81 3.51
0.71 Very Good E VS2 59.9 59.0 2921.0 5.77 5.81 3.47
0.71 Very Good E VS2 60.7 60.0 2921.0 5.75 5.78 3.5
0.65 Ideal F VVS2 61.3 56.0 2921.0 5.58 5.61 3.43
0.9 Fair I VS2 64.1 66.0 2921.0 6.04 5.98 3.85
0.71 Very Good E VS2 63.7 58.0 2922.0 5.6 5.64 3.58
0.71 Very Good E VS2 63.3 59.0 2922.0 5.62 5.66 3.57
0.68 Very Good F VS1 59.7 57.0 2922.0 5.79 5.76 3.45
0.53 Ideal F VVS1 61.6 56.0 2922.0 5.24 5.18 3.21
0.72 Very Good E VS2 63.0 57.0 2923.0 5.69 5.73 3.6
0.72 Very Good E VS2 63.2 58.0 2923.0 5.67 5.72 3.6
0.71 Ideal E VS1 62.4 54.0 2923.0 5.71 5.74 3.57
0.9 Premium I VS2 58.7 60.0 2923.0 6.35 6.28 3.7
0.7 Ideal I VS1 61.5 56.0 2924.0 5.71 5.75 3.52
0.7 Very Good F VS1 64.5 58.0 2925.0 5.55 5.59 3.59
0.77 Very Good H VS1 63.3 57.0 2927.0 5.79 5.83 3.68
0.7 Very Good F VS2 61.3 54.0 2928.0 5.72 5.76 3.52
0.7 Very Good D VS2 60.8 59.0 2928.0 5.67 5.71 3.46
0.8 Very Good G VS2 61.1 57.0 2929.0 6.01 6.07 3.69
0.7 Ideal G VS2 61.8 57.0 2929.0 5.68 5.71 3.52
0.71 Very Good E VS2 61.3 60.0 2930.0 5.74 5.71 3.51
0.7 Premium E VS1 60.3 58.0 2930.0 5.7 5.74 3.45
0.7 Ideal E VS1 62.3 54.0 2930.0 5.67 5.72 3.55
0.71 Ideal F VS2 62.3 57.0 2930.0 5.69 5.74 3.56
0.71 Ideal G VS1 62.7 57.0 2930.0 5.69 5.73 3.58
0.71 Ideal G VS1 62.6 57.0 2930.0 5.67 5.7 3.56
0.71 Ideal G VVS1 61.7 57.0 2930.0 5.75 5.7 3.53
0.7 Very Good G VVS2 60.8 57.0 2931.0 5.72 5.76 3.49
0.72 Very Good F VS2 63.3 57.0 2931.0 5.69 5.72 3.61
0.72 Ideal F VS2 61.8 59.0 2931.0 5.71 5.74 3.54
0.7 Premium G VVS1 62.0 61.0 2932.0 5.71 5.62 3.51
0.7 Premium F VVS2 61.0 57.0 2932.0 5.8 5.71 3.51
0.7 Very Good F VVS2 63.2 58.0 2932.0 5.66 5.6 3.56
0.72 Very Good G VVS2 62.2 57.0 2933.0 5.67 5.72 3.54
0.59 Very Good D VVS2 60.6 59.0 2933.0 5.44 5.49 3.31
0.73 Premium F VS2 59.9 58.0 2933.0 5.84 5.87 3.51
0.75 Ideal F VS2 62.3 57.0 2933.0 5.81 5.87 3.64
0.8 Premium H VS1 62.0 60.0 2935.0 5.92 5.86 3.65
0.7 Very Good G VVS2 61.8 60.0 2936.0 5.63 5.69 3.5
0.74 Ideal F VS2 60.5 59.0 2936.0 5.81 5.86 3.53
0.76 Premium G VS1 59.6 57.0 2937.0 6.01 5.91 3.55
0.71 Very Good H VVS1 62.7 57.0 2938.0 5.66 5.72 3.57
0.71 Very Good H VVS1 62.7 59.0 2938.0 5.65 5.67 3.55
0.73 Very Good F VS2 62.7 58.0 2939.0 5.73 5.75 3.6
0.73 Very Good G VS1 60.7 57.0 2939.0 5.76 5.83 3.52
0.73 Ideal F VS2 62.7 58.0 2939.0 5.72 5.77 3.6
0.75 Ideal G VS2 60.6 55.0 2939.0 5.93 5.91 3.59
0.81 Ideal I VS2 61.8 56.0 2939.0 6.02 5.99 3.71
0.82 Premium H VS2 62.6 59.0 2939.0 5.99 5.93 3.73
0.7 Good F VVS2 63.1 57.0 2940.0 5.59 5.66 3.55
0.7 Very Good F VVS2 62.6 59.0 2940.0 5.6 5.64 3.52
0.7 Ideal F VS1 61.2 54.0 2940.0 5.92 5.64 3.54
0.75 Fair E VS2 56.0 67.0 2940.0 6.18 6.08 3.43
0.75 Ideal E VS2 61.6 57.0 2940.0 5.84 5.81 3.59
0.7 Ideal E VS2 61.5 56.0 2940.0 5.73 5.68 3.51
0.71 Premium F VS1 61.1 58.0 2942.0 5.76 5.72 3.51
0.7 Ideal F VS2 60.8 56.0 2942.0 5.78 5.79 3.52
0.72 Ideal F VS2 62.0 56.0 2943.0 5.77 5.75 3.57
0.74 Very Good H VVS2 61.3 58.0 2944.0 5.8 5.85 3.57
0.57 Very Good D VVS1 60.4 57.0 2945.0 5.39 5.44 3.27
0.79 Very Good H VS2 61.5 55.0 2945.0 5.89 5.94 3.64
0.71 Very Good E VS1 63.3 59.0 2946.0 5.64 5.67 3.58
0.71 Very Good E VS1 62.7 57.0 2946.0 5.69 5.73 3.58
0.72 Ideal H VVS1 62.2 56.0 2946.0 5.72 5.75 3.57
0.72 Ideal H VVS1 62.5 57.0 2946.0 5.7 5.73 3.57
0.78 Very Good H VS1 61.7 56.0 2947.0 5.92 5.94 3.66
0.76 Ideal E VS1 62.1 57.0 2947.0 5.82 5.87 3.63
0.73 Premium D VS2 60.9 59.0 2947.0 5.82 5.77 3.53
0.7 Ideal H VVS1 61.2 57.0 2947.0 5.69 5.72 3.49
0.7 Ideal H VVS1 60.5 58.0 2947.0 5.76 5.81 3.5
0.74 Ideal I VS1 62.0 56.0 2947.0 5.79 5.82 3.6
0.74 Ideal I VS1 61.1 57.0 2947.0 5.83 5.86 3.57
0.82 Good H VS2 62.4 54.0 2947.0 5.97 6.04 3.75
0.73 Ideal G VS1 61.7 55.0 2948.0 5.8 5.84 3.59
0.72 Very Good E VS2 63.0 56.0 2949.0 5.66 5.73 3.59
0.72 Ideal H VS1 62.3 55.0 2949.0 5.72 5.74 3.57
0.81 Very Good I VS1 62.7 58.0 2950.0 5.9 5.96 3.72
0.71 Ideal G VS1 62.4 57.0 2950.0 5.68 5.73 3.56
0.71 Premium D VS2 62.1 60.0 2950.0 5.72 5.68 3.54
0.54 Ideal F VVS1 61.6 55.0 2951.0 5.27 5.28 3.25
0.72 Very Good D VS1 62.7 58.0 2951.0 5.65 5.68 3.55
0.7 Very Good E VS2 62.4 58.0 2952.0 5.66 5.68 3.54
0.7 Very Good E VS2 63.4 59.0 2952.0 5.63 5.67 3.58
0.7 Very Good E VS2 61.8 59.0 2952.0 5.63 5.67 3.49
0.7 Very Good E VS1 61.3 60.0 2952.0 5.68 5.7 3.49
0.72 Ideal G VS2 61.5 55.0 2952.0 5.76 5.79 3.55
0.72 Ideal G VS2 61.4 55.0 2952.0 5.76 5.8 3.55
0.7 Ideal E VS2 61.9 58.0 2952.0 5.7 5.73 3.54
0.7 Ideal E VS2 62.6 57.0 2952.0 5.63 5.68 3.54
0.7 Ideal E VS2 62.1 55.0 2952.0 5.71 5.75 3.56
0.7 Good E VS1 61.0 61.0 2952.0 5.69 5.72 3.48
0.8 Very Good H VS2 59.1 59.0 2953.0 6.02 6.07 3.57
0.79 Premium F VS2 63.0 59.0 2953.0 5.84 5.8 3.66
0.75 Good F VS1 64.4 59.0 2953.0 5.67 5.72 3.66
0.71 Very Good E VS2 59.6 60.0 2954.0 5.8 5.85 3.47
0.72 Premium E VS2 61.1 59.0 2954.0 5.75 5.8 3.53
0.76 Ideal G VS2 61.7 54.0 2954.0 5.88 5.92 3.64
0.89 Premium I VS1 62.2 62.0 2955.0 6.14 6.02 3.78
0.7 Very Good F VS2 62.4 57.0 2956.0 5.67 5.71 3.55
0.74 Very Good H VS1 61.4 56.0 2956.0 5.81 5.84 3.57
0.74 Very Good H VS1 62.3 56.0 2956.0 5.75 5.78 3.59
0.7 Ideal F VS2 60.8 57.0 2956.0 5.75 5.77 3.5
0.71 Good F VVS2 58.2 60.0 2956.0 5.89 5.94 3.44
0.7 Premium D VS1 60.4 58.0 2956.0 5.78 5.71 3.47
0.72 Ideal F VS2 62.6 56.0 2956.0 5.75 5.72 3.59
0.72 Ideal F VS2 62.2 56.0 2956.0 5.75 5.73 3.57
0.72 Ideal H VVS1 62.0 55.0 2958.0 5.74 5.77 3.57
0.79 Ideal I VS1 62.2 57.0 2958.0 5.89 5.94 3.68
0.72 Good G VS1 58.0 57.8 2958.0 5.85 5.87 3.4
0.56 Very Good D VVS1 60.1 58.0 2959.0 5.36 5.42 3.24
0.7 Very Good F VS1 60.1 58.0 2959.0 5.73 5.79 3.46
0.79 Premium G VS2 62.3 58.0 2959.0 5.92 5.89 3.68
0.74 Fair G VVS2 65.2 58.0 2959.0 5.7 5.6 3.69
0.71 Very Good H VVS2 61.8 56.0 2960.0 5.7 5.73 3.53
0.7 Very Good D VS2 63.0 56.0 2960.0 5.61 5.69 3.56
0.7 Good D VS2 63.4 57.0 2960.0 5.6 5.67 3.57
0.7 Ideal D VS2 61.3 57.0 2960.0 5.72 5.76 3.52
0.76 Ideal F VS2 62.6 56.0 2960.0 5.82 5.78 3.63
0.72 Ideal G VS2 61.3 56.0 2960.0 5.77 5.81 3.55
0.71 Good F VVS2 58.9 61.0 2960.0 5.8 5.9 3.44
0.74 Ideal G VS1 61.8 55.0 2960.0 5.85 5.8 3.6
0.77 Very Good H VS1 62.8 58.0 2961.0 5.75 5.78 3.62
0.74 Ideal H VVS2 61.2 57.0 2961.0 5.79 5.85 3.56
0.72 Premium E VS1 61.5 60.0 2961.0 5.79 5.75 3.55
0.73 Premium F VS1 61.9 56.0 2961.0 5.81 5.76 3.58
0.73 Premium F VS1 62.7 56.0 2961.0 5.75 5.73 3.6
0.63 Ideal F VVS2 62.3 56.0 2961.0 5.48 5.5 3.42
0.72 Ideal H VS1 61.1 57.0 2961.0 5.8 5.82 3.55
0.71 Premium F VS1 62.1 53.0 2961.0 5.77 5.7 3.56
0.75 Premium H VS1 61.9 61.0 2961.0 5.85 5.82 3.61
0.63 Ideal D VVS2 62.6 56.0 2962.0 5.47 5.49 3.43
0.72 Ideal E VS2 62.0 56.0 2962.0 5.73 5.76 3.56
0.71 Ideal G VS1 62.2 56.0 2962.0 5.69 5.72 3.55
0.71 Ideal E VS1 62.1 53.0 2963.0 5.76 5.73 3.57
0.71 Very Good E VS2 62.9 57.0 2964.0 5.68 5.7 3.58
0.7 Good E VS1 63.6 58.0 2964.0 5.61 5.56 3.55
0.7 Fair E VS1 64.5 57.0 2964.0 5.59 5.55 3.59
0.9 Fair J VS1 65.4 60.0 2964.0 6.02 5.93 3.91
0.9 Premium J VS1 62.1 62.0 2964.0 6.12 6.05 3.78
0.9 Fair J VS1 64.6 58.0 2964.0 6.12 6.06 3.93
0.71 Ideal I VS1 61.8 56.0 2965.0 5.68 5.72 3.52
0.71 Ideal I VS1 61.6 56.0 2965.0 5.71 5.75 3.53
0.71 Ideal I VS1 61.3 57.0 2965.0 5.73 5.76 3.52
0.71 Ideal I VS1 61.5 56.0 2965.0 5.72 5.76 3.52
0.73 Very Good G VS2 62.1 59.0 2966.0 5.68 5.73 3.54
0.7 Ideal I VVS1 61.8 56.0 2966.0 5.69 5.73 3.53
0.7 Very Good E VS1 61.3 56.0 2967.0 5.68 5.71 3.49
0.7 Very Good E VS1 61.5 56.0 2967.0 5.69 5.75 3.52
0.79 Ideal H VS2 62.0 56.0 2967.0 5.91 5.93 3.67
0.3 Very Good H VVS2 62.0 56.0 559.0 4.28 4.3 2.66
0.31 Very Good G VS2 62.6 56.0 559.0 4.33 4.37 2.72
0.31 Very Good G VS2 61.4 55.0 559.0 4.38 4.41 2.69
0.31 Very Good G VS2 60.9 57.0 559.0 4.37 4.39 2.67
0.24 Ideal G VVS1 62.4 56.0 559.0 3.97 3.99 2.48
0.24 Ideal G VVS1 62.1 56.0 559.0 3.97 4.0 2.47
0.24 Ideal G VVS1 62.2 56.0 559.0 4.0 4.04 2.5
0.24 Ideal G VVS1 62.0 55.0 559.0 4.01 4.03 2.49
0.24 Ideal G VVS1 62.0 56.0 559.0 3.97 4.01 2.47
0.32 Ideal G VS1 62.3 55.0 559.0 4.39 4.41 2.74
0.32 Ideal G VS1 61.8 55.0 559.0 4.42 4.45 2.74
0.25 Very Good E VVS2 62.0 56.0 560.0 4.05 4.08 2.52
0.25 Very Good E VVS1 61.5 56.0 560.0 4.06 4.08 2.5
0.32 Ideal G VS2 61.6 54.0 560.0 4.4 4.43 2.72
0.32 Premium H VS1 60.2 58.0 561.0 4.43 4.47 2.68
0.32 Ideal H VS1 61.5 57.0 561.0 4.4 4.42 2.71
0.71 Premium D VS2 58.7 61.0 2968.0 5.88 5.85 3.44
0.8 Ideal G VS2 61.2 57.0 2969.0 6.02 6.07 3.7
0.52 Premium E VVS2 60.1 58.0 2970.0 5.23 5.18 3.13
0.72 Very Good G VS1 60.6 56.0 2970.0 5.84 5.87 3.55
0.7 Good F VS1 63.8 58.0 2970.0 5.58 5.61 3.57
0.78 Premium E VS2 62.6 57.0 2970.0 5.91 5.85 3.68
0.78 Ideal H VS2 61.6 56.0 2970.0 5.94 5.91 3.64
0.76 Ideal G VS1 59.4 57.0 2972.0 5.99 6.03 3.57
0.7 Ideal G VS1 61.7 56.0 2972.0 5.64 5.71 3.5
0.81 Premium H VS1 62.6 58.0 2972.0 5.96 5.9 3.71
0.75 Ideal G VS1 62.3 57.0 2973.0 5.83 5.86 3.64
0.7 Ideal E VS1 60.5 56.0 2973.0 5.74 5.79 3.49
0.7 Good E VS1 59.8 62.0 2973.0 5.74 5.8 3.45
0.71 Ideal G VS2 59.5 57.0 2974.0 5.81 5.8 3.46
0.7 Very Good F VS1 62.1 57.0 2975.0 5.69 5.72 3.54
0.7 Premium F VVS2 62.2 58.0 2975.0 5.72 5.66 3.54
0.83 Ideal H VS2 61.3 54.0 2975.0 6.1 6.06 3.73
0.71 Very Good G VVS2 60.8 58.0 2977.0 5.75 5.77 3.5
0.76 Premium D VS2 60.9 58.0 2977.0 5.9 5.85 3.58
0.54 Ideal F VVS1 61.6 55.0 2977.0 5.28 5.27 3.25
0.71 Ideal G VVS2 62.5 58.0 2978.0 5.7 5.73 3.57
0.7 Ideal E VS1 61.3 54.0 2978.0 5.77 5.83 3.54
0.72 Ideal H VVS1 59.9 59.0 2979.0 5.76 5.82 3.47
0.7 Ideal E VS2 61.7 56.0 2979.0 5.74 5.71 3.53
0.7 Ideal E VS2 61.5 57.0 2980.0 5.67 5.78 3.52
0.7 Ideal E VS2 62.2 55.0 2981.0 5.67 5.71 3.54
0.71 Ideal G VVS1 61.7 57.0 2982.0 5.7 5.75 3.53
0.71 Ideal E VS2 59.5 57.0 2982.0 5.86 5.83 3.48
0.71 Very Good G VS1 60.8 63.0 2982.0 5.76 5.68 3.48
0.71 Premium E VS2 62.6 58.0 2982.0 5.72 5.68 3.57
0.74 Ideal E VS2 62.7 54.0 2984.0 5.8 5.77 3.63
0.9 Very Good J VS2 63.1 57.0 2984.0 6.12 6.06 3.84
0.7 Very Good D VS2 63.1 56.0 2985.0 5.62 5.69 3.57
0.82 Premium H VS1 62.3 60.0 2985.0 5.97 5.94 3.71
0.77 Very Good G VS1 62.8 58.0 2986.0 5.78 5.84 3.65
0.8 Ideal I VS1 61.9 54.1 2986.0 5.92 5.98 3.69
0.82 Ideal I VS1 61.6 57.0 2986.0 6.0 6.05 3.71
0.7 Ideal G VS1 61.3 59.0 2987.0 5.68 5.7 3.49
0.72 Ideal F VS2 62.1 54.0 2989.0 5.76 5.8 3.59
0.76 Very Good G VS2 62.1 54.0 2990.0 5.88 5.94 3.67
0.72 Very Good E VS2 62.9 57.0 2990.0 5.68 5.73 3.59
0.57 Good E VVS1 59.1 65.0 2990.0 5.34 5.43 3.18
0.75 Ideal G VS2 60.6 55.0 2991.0 5.91 5.93 3.59
0.7 Ideal D VS2 60.3 60.0 2991.0 5.71 5.76 3.46
0.7 Very Good E VS2 62.8 56.0 2992.0 5.66 5.68 3.56
0.75 Ideal H VVS2 62.0 55.1 2992.0 5.83 5.85 3.62
0.69 Very Good F VVS2 61.5 60.0 2993.0 5.64 5.67 3.48
0.7 Ideal G VVS2 63.0 55.0 2993.0 5.65 5.69 3.57
0.7 Ideal F VS1 62.4 55.0 2993.0 5.65 5.7 3.54
0.71 Very Good F VS2 59.6 56.0 2994.0 5.84 5.88 3.49
0.71 Very Good G VS1 59.3 55.0 2994.0 5.88 5.95 3.51
0.81 Very Good G VS2 63.1 58.0 2994.0 5.88 5.84 3.7
0.81 Premium G VS2 62.0 58.0 2994.0 5.95 5.92 3.68
0.7 Ideal G VS1 60.9 56.0 2995.0 5.76 5.8 3.52
0.88 Very Good I VS1 63.3 55.0 2996.0 6.11 6.06 3.85
0.74 Ideal I VS2 61.9 55.0 2997.0 5.8 5.83 3.6
0.7 Ideal D VS2 62.8 57.0 2998.0 5.69 5.75 3.59
0.72 Ideal H VS1 61.4 56.0 2998.0 5.79 5.81 3.56
0.7 Ideal F VS1 61.6 57.0 2998.0 5.7 5.73 3.52
1.01 Fair J VVS2 66.0 56.0 2998.0 6.29 6.22 4.13
0.85 Fair G VS1 57.7 67.0 2998.0 6.26 6.19 3.59
0.7 Very Good D VS2 59.7 59.0 2999.0 5.82 5.78 3.46
0.73 Very Good G VS1 62.4 58.1 2999.0 5.71 5.75 3.58
0.7 Premium G VVS2 60.6 60.0 2999.0 5.77 5.69 3.47
0.74 Premium E VS1 62.7 58.0 2999.0 5.83 5.74 3.63
0.74 Premium E VS1 60.9 62.0 2999.0 5.83 5.8 3.54
0.7 Premium G VVS2 60.2 61.0 2999.0 5.74 5.66 3.43
0.93 Good J VS2 63.6 61.0 3000.0 6.16 6.08 3.89
0.7 Premium D VS1 61.6 61.0 3001.0 5.66 5.61 3.47
0.7 Good D VS1 63.6 60.0 3001.0 5.61 5.52 3.54
0.7 Very Good D VS1 63.4 59.0 3001.0 5.58 5.55 3.53
0.6 Ideal G VVS1 62.1 56.0 3001.0 5.42 5.43 3.37
0.75 Very Good H VVS2 60.6 57.0 3002.0 5.86 5.89 3.56
0.71 Premium D VS2 62.1 60.0 3002.0 5.68 5.72 3.54
0.72 Good F VS1 63.8 58.0 3002.0 5.68 5.63 3.61
0.72 Ideal G VVS2 61.6 55.0 3002.0 5.78 5.77 3.56
0.8 Premium G VS2 60.6 59.0 3002.0 6.02 5.97 3.63
0.73 Fair F VS1 58.6 66.0 3002.0 5.92 5.88 3.46
0.65 Premium D VVS2 59.9 58.0 3003.0 5.69 5.63 3.39
0.7 Ideal H VS1 61.7 55.0 3004.0 5.69 5.72 3.52
0.61 Ideal E VS1 61.3 54.0 3004.0 5.53 5.5 3.38
0.55 Ideal F VVS1 61.2 54.0 3005.0 5.3 5.35 3.26
0.72 Ideal I VS1 60.4 56.0 3005.0 5.8 5.86 3.52
0.73 Ideal H VVS1 61.6 57.0 3005.0 5.81 5.78 3.57
0.71 Premium E VS1 61.1 58.0 3006.0 5.8 5.76 3.53
0.71 Very Good E VS1 63.2 60.0 3006.0 5.63 5.6 3.55
0.55 Premium D VVS1 60.3 59.0 3006.0 5.34 5.3 3.21
0.71 Ideal I VVS2 60.7 57.0 3007.0 5.76 5.8 3.51
0.71 Ideal I VVS2 60.4 57.0 3007.0 5.78 5.81 3.5
0.7 Premium E VVS2 62.7 53.0 3007.0 5.65 5.61 3.53
0.7 Very Good D VS1 60.4 58.0 3008.0 5.71 5.78 3.47
0.61 Ideal G VVS1 61.2 56.0 3008.0 5.46 5.48 3.35
0.7 Ideal F VS2 61.3 57.0 3008.0 5.7 5.76 3.51
0.82 Premium H VS1 62.5 59.0 3008.0 5.96 5.94 3.72
0.71 Very Good E VS1 63.7 58.0 3009.0 5.63 5.68 3.6
0.71 Very Good E VS1 62.1 57.0 3009.0 5.67 5.69 3.53
0.71 Very Good E VS1 63.4 58.0 3009.0 5.64 5.68 3.59
0.8 Ideal I VS1 60.7 59.0 3010.0 5.98 6.02 3.64
0.73 Very Good G VS1 60.7 55.0 3011.0 5.87 5.89 3.57
0.61 Ideal E VVS2 62.0 54.0 3011.0 5.43 5.47 3.38
0.7 Ideal F VS2 61.9 55.0 3011.0 5.7 5.74 3.54
0.7 Ideal F VS2 61.8 57.0 3011.0 5.67 5.75 3.53
0.7 Ideal F VS2 62.7 55.0 3011.0 5.66 5.69 3.56
0.7 Ideal F VS2 61.4 58.0 3011.0 5.7 5.73 3.51
0.78 Very Good G VS2 61.3 60.0 3012.0 5.89 5.96 3.63
0.72 Ideal G VS2 61.7 56.0 3012.0 5.74 5.78 3.55
0.75 Premium F VS2 61.6 58.0 3013.0 5.84 5.89 3.61
0.71 Very Good F VS1 62.1 53.0 3013.0 5.7 5.77 3.56
0.71 Ideal F VS1 61.1 57.0 3013.0 5.76 5.82 3.54
0.71 Ideal H VVS1 61.8 56.0 3014.0 5.7 5.75 3.54
0.78 Ideal H VVS2 61.7 55.0 3015.0 5.9 5.94 3.65
0.72 Very Good D VS2 62.1 59.0 3016.0 5.7 5.73 3.55
0.7 Premium E VS1 61.8 58.0 3016.0 5.71 5.75 3.54
0.7 Ideal E VS1 62.7 57.0 3016.0 5.65 5.7 3.56
0.76 Ideal H VS2 61.9 55.0 3016.0 5.85 5.88 3.64
0.7 Very Good G VS1 60.1 60.0 3017.0 5.73 5.76 3.45
0.71 Very Good F VS1 61.8 60.0 3017.0 5.66 5.7 3.51
0.7 Ideal G VS1 61.1 56.0 3017.0 5.72 5.74 3.5
0.5 Good D VVS2 62.4 64.0 3017.0 5.03 5.06 3.14
0.7 Good F VVS1 63.2 58.0 3018.0 5.58 5.62 3.54
0.7 Premium F VVS2 62.5 59.0 3018.0 5.68 5.61 3.53
0.71 Ideal F VVS2 62.6 56.0 3018.0 5.7 5.65 3.55
0.72 Ideal H VS2 61.2 57.0 3018.0 5.79 5.77 3.54
0.7 Good E VS1 60.2 61.0 3018.0 5.71 5.75 3.45
0.77 Premium F VS2 62.4 59.0 3018.0 5.85 5.81 3.64
0.7 Premium F VVS2 62.2 56.0 3018.0 5.72 5.63 3.53
0.71 Ideal D VS2 60.4 53.0 3020.0 5.81 5.85 3.52
0.65 Ideal E VVS2 62.1 57.0 3023.0 5.55 5.6 3.46
0.75 Premium E VS2 62.1 57.0 3024.0 5.9 5.79 3.63
0.9 Very Good J VS2 63.1 59.0 3024.0 6.09 6.05 3.83
0.9 Good J VS2 63.9 58.0 3024.0 6.15 6.08 3.91
0.72 Premium E VS2 60.4 61.0 3024.0 5.79 5.76 3.49
0.72 Premium E VS2 62.5 59.0 3024.0 5.73 5.7 3.57
0.72 Very Good G VS1 60.1 63.0 3024.0 5.86 5.82 3.51
0.65 Very Good D VVS2 57.7 60.0 3025.0 5.69 5.74 3.3
0.7 Very Good G VS2 61.8 55.0 3026.0 5.69 5.74 3.53
0.59 Ideal E VVS2 61.8 57.0 3026.0 5.35 5.4 3.32
0.71 Ideal E VS2 62.3 56.0 3026.0 5.7 5.73 3.56
0.83 Ideal H VS2 61.3 54.0 3027.0 6.06 6.1 3.73
0.77 Good H VVS2 57.9 61.0 3027.0 6.07 6.01 3.5
0.7 Very Good F VVS2 58.5 60.0 3028.0 5.82 5.94 3.44
0.8 Ideal H VS2 62.1 54.0 3030.0 5.96 5.99 3.71
0.74 Ideal H VS1 61.6 55.0 3030.0 5.79 5.83 3.58
0.77 Fair F VS1 66.8 57.0 3031.0 5.66 5.76 3.82
0.72 Premium G VS1 58.9 58.0 3032.0 5.93 5.85 3.47
0.55 Ideal F VVS1 61.2 54.0 3032.0 5.35 5.3 3.26
0.71 Very Good D VS2 63.0 57.0 3033.0 5.67 5.7 3.58
0.73 Ideal G VS1 61.6 57.0 3033.0 5.76 5.79 3.56
0.7 Good D VS2 64.1 59.0 3033.0 5.56 5.49 3.54
0.7 Very Good D VS2 63.2 60.0 3033.0 5.61 5.56 3.53
0.7 Good D VS2 63.9 58.0 3033.0 5.62 5.58 3.58
0.92 Fair I VS2 64.4 58.0 3033.0 6.13 6.1 3.94
0.7 Ideal G VS1 61.4 57.0 3034.0 5.7 5.73 3.51
0.72 Very Good E VS2 63.8 57.0 3035.0 5.66 5.69 3.62
0.71 Ideal E VS2 59.5 57.0 3035.0 5.83 5.86 3.48
0.72 Ideal G VS1 62.4 59.0 3035.0 5.71 5.74 3.57
0.8 Very Good H VVS2 62.9 56.0 3036.0 5.9 5.96 3.73
0.74 Ideal E VS2 62.6 56.0 3036.0 5.73 5.81 3.61
0.61 Ideal D VVS2 62.4 58.0 3036.0 5.38 5.42 3.37
0.7 Very Good G VVS1 63.3 57.0 3037.0 5.59 5.63 3.55
0.32 Premium G VS2 60.5 58.0 561.0 4.41 4.42 2.67
0.32 Premium G VS2 62.5 60.0 561.0 4.32 4.38 2.72
0.32 Ideal G VS2 61.4 56.0 561.0 4.37 4.39 2.69
0.32 Premium G VS2 59.8 59.0 561.0 4.48 4.52 2.69
0.32 Premium I VVS2 60.7 59.0 561.0 4.4 4.43 2.68
0.32 Very Good G VS2 60.2 57.0 561.0 4.42 4.45 2.67
0.32 Good G VS2 63.3 54.0 561.0 4.36 4.39 2.77
0.32 Good H VS1 63.1 57.0 561.0 4.34 4.37 2.75
0.32 Ideal G VS2 61.4 55.0 561.0 4.4 4.46 2.72
0.32 Ideal G VS2 59.8 57.0 561.0 4.43 4.46 2.66
0.32 Ideal G VS2 61.7 57.0 561.0 4.38 4.4 2.71
0.32 Premium H VS1 62.3 58.0 561.0 4.34 4.39 2.72
0.32 Very Good H VS1 63.0 57.0 561.0 4.32 4.35 2.73
0.32 Premium G VS2 61.9 58.0 561.0 4.36 4.43 2.72
0.32 Good G VS2 63.1 57.0 561.0 4.3 4.35 2.73
0.32 Very Good H VS1 63.0 57.0 561.0 4.37 4.39 2.76
0.32 Ideal G VS2 61.8 57.0 561.0 4.37 4.4 2.71
0.32 Very Good H VS1 61.7 58.0 561.0 4.37 4.41 2.71
0.32 Premium H VS1 61.7 58.0 561.0 4.38 4.44 2.72
0.32 Ideal G VS2 61.8 55.0 561.0 4.41 4.42 2.73
0.32 Premium G VS2 61.7 60.0 561.0 4.32 4.4 2.69
0.32 Very Good G VS2 62.6 58.0 561.0 4.37 4.39 2.74
0.32 Premium G VS2 62.3 58.0 561.0 4.36 4.41 2.73
0.32 Ideal G VS2 61.6 57.0 561.0 4.39 4.41 2.71
0.32 Ideal H VS1 61.9 55.0 561.0 4.4 4.42 2.73
0.32 Ideal H VS1 60.2 56.0 561.0 4.44 4.49 2.69
0.76 Ideal H VS2 61.4 57.0 3038.0 5.85 5.88 3.6
0.7 Ideal H VS2 61.5 56.0 3038.0 5.71 5.73 3.52
0.7 Very Good G VVS2 61.0 59.0 3039.0 5.67 5.7 3.47
0.7 Fair F VS1 64.9 59.0 3039.0 5.56 5.59 3.62
0.73 Ideal G VS1 61.8 57.0 3041.0 5.78 5.81 3.58
0.71 Ideal F VS1 62.7 57.0 3041.0 5.66 5.7 3.56
0.71 Ideal F VS1 61.7 55.0 3041.0 5.73 5.77 3.55
0.81 Good I VS1 59.4 56.0 3042.0 5.97 6.11 3.59
0.71 Ideal G VVS2 62.5 57.0 3042.0 5.73 5.7 3.57
0.72 Very Good G VVS2 60.4 58.0 3043.0 5.77 5.82 3.5
0.71 Very Good F VS1 62.2 55.0 3045.0 5.68 5.74 3.56
0.71 Very Good F VS1 61.2 57.0 3045.0 5.73 5.77 3.52
0.71 Very Good D VS2 62.8 56.0 3045.0 5.67 5.7 3.57
0.72 Premium D VS2 60.2 60.0 3045.0 5.76 5.81 3.48
0.7 Good G VVS2 61.1 61.0 3046.0 5.67 5.69 3.47
0.73 Fair D VS1 66.0 54.0 3047.0 5.56 5.66 3.7
0.72 Good E VS1 57.9 60.0 3048.0 5.97 5.91 3.44
0.72 Very Good E VS1 63.1 56.0 3048.0 5.7 5.65 3.58
0.9 Ideal J VS1 62.6 55.0 3048.0 6.13 6.11 3.83
0.66 Ideal D VVS2 61.6 57.0 3049.0 5.64 5.57 3.45
0.62 Very Good D VVS2 58.1 63.0 3050.0 5.59 5.66 3.27
0.7 Very Good D VS2 62.5 55.0 3052.0 5.65 5.71 3.55
0.77 Ideal F VS2 61.2 57.0 3052.0 5.93 5.97 3.64
0.7 Very Good G VVS2 60.2 61.0 3052.0 5.66 5.74 3.43
0.7 Very Good D VS2 62.6 58.0 3053.0 5.67 5.7 3.56
0.71 Very Good E VS2 59.9 59.0 3053.0 5.79 5.83 3.48
0.7 Very Good F VS1 62.8 59.0 3053.0 5.65 5.69 3.56
0.71 Ideal E VS2 60.9 56.0 3053.0 5.77 5.83 3.53
0.79 Premium G VS1 62.3 56.0 3053.0 5.94 5.87 3.68
0.79 Premium G VS1 61.3 59.0 3053.0 5.97 5.91 3.64
0.7 Very Good D VS1 62.9 60.0 3054.0 5.62 5.67 3.55
0.65 Very Good D VVS2 59.9 58.0 3056.0 5.63 5.69 3.39
0.61 Ideal E VVS2 60.8 56.0 3056.0 5.5 5.47 3.34
0.57 Ideal F VVS1 61.1 55.0 3057.0 5.36 5.44 3.3
0.76 Good F VS1 59.9 61.0 3057.0 5.89 5.98 3.56
0.91 Premium J VS2 61.6 58.0 3058.0 6.28 6.23 3.85
0.72 Very Good F VS1 62.1 59.0 3059.0 5.69 5.74 3.55
0.71 Very Good E VS1 61.8 56.0 3059.0 5.74 5.78 3.56
0.74 Very Good H VVS1 62.4 57.0 3061.0 5.76 5.81 3.61
0.7 Very Good E VS1 61.1 55.0 3061.0 5.72 5.77 3.51
0.71 Very Good E VS1 63.3 56.0 3061.0 5.64 5.68 3.58
0.71 Fair G VVS1 62.8 57.0 3062.0 5.67 5.57 3.53
0.7 Premium F VVS2 58.7 60.0 3062.0 5.8 5.75 3.39
0.71 Premium E VS2 62.2 59.0 3062.0 5.71 5.61 3.52
0.71 Premium E VS2 62.0 61.0 3062.0 5.71 5.65 3.52
0.93 Premium J VS1 60.3 58.0 3062.0 6.37 6.31 3.82
0.7 Very Good E VS1 62.2 57.0 3063.0 5.63 5.68 3.52
0.7 Very Good E VS1 62.5 56.0 3063.0 5.64 5.68 3.54
0.7 Good E VS1 59.4 61.0 3063.0 5.79 5.83 3.45
0.71 Very Good E VS1 63.3 59.0 3064.0 5.64 5.68 3.58
0.76 Premium E VS2 61.7 62.0 3064.0 5.85 5.82 3.6
0.7 Ideal F VS2 61.4 56.0 3064.0 5.72 5.75 3.52
0.7 Ideal F VS2 61.6 55.0 3064.0 5.72 5.75 3.53
0.72 Very Good E VS2 63.0 58.0 3065.0 5.69 5.73 3.6
0.7 Ideal G VS1 61.5 56.0 3065.0 5.7 5.75 3.52
0.77 Ideal I VS1 61.4 56.0 3066.0 5.9 5.93 3.63
0.71 Ideal F VS1 62.0 57.0 3066.0 5.7 5.75 3.55
0.71 Ideal F VS1 62.1 57.0 3066.0 5.73 5.76 3.57
0.73 Very Good E VS2 63.1 55.0 3066.0 5.77 5.71 3.62
0.7 Very Good E VS1 63.4 60.0 3068.0 5.63 5.66 3.58
0.7 Ideal E VS2 62.6 56.0 3068.0 5.65 5.69 3.55
0.85 Very Good I VS2 60.0 57.0 3070.0 6.1 6.16 3.68
0.82 Ideal I VS1 61.6 56.0 3071.0 6.05 6.01 3.72
0.71 Good G VVS1 62.7 61.0 3072.0 5.64 5.68 3.55
0.7 Very Good G VVS1 63.1 56.0 3073.0 5.64 5.67 3.57
0.7 Ideal G VVS1 61.6 55.0 3073.0 5.72 5.75 3.53
0.75 Ideal G VS2 61.6 55.0 3073.0 5.86 5.89 3.62
0.71 Ideal E VS2 62.2 57.0 3073.0 5.69 5.73 3.55
0.62 Premium E VVS1 61.9 59.0 3073.0 5.62 5.5 3.44
0.7 Good D VS2 58.0 65.0 3073.0 5.81 5.73 3.39
0.78 Very Good G VS2 61.7 58.0 3074.0 5.87 5.92 3.64
0.9 Fair I VVS2 67.0 56.0 3074.0 5.91 5.83 3.93
0.77 Ideal H VS1 61.4 55.0 3074.0 5.89 5.93 3.63
0.72 Very Good D VS2 61.8 58.0 3075.0 5.73 5.76 3.55
0.72 Very Good D VS2 62.6 59.0 3075.0 5.69 5.72 3.57
0.72 Ideal H VVS1 62.2 57.0 3075.0 5.72 5.75 3.57
0.76 Ideal I VS2 61.7 56.0 3075.0 5.87 5.9 3.63
0.73 Ideal E VS2 62.7 56.0 3077.0 5.75 5.8 3.62
0.71 Fair D VS2 64.7 58.0 3077.0 5.61 5.58 3.62
0.71 Premium D VS2 60.3 62.0 3077.0 5.76 5.69 3.45
0.72 Premium E VS2 62.5 59.0 3078.0 5.7 5.73 3.57
0.76 Ideal E VS2 61.3 56.0 3079.0 5.79 5.83 3.56 
// Combining conditions
display(spark.sql("SELECT * FROM diamonds WHERE clarity LIKE 'V%' AND price > 10000"))
carat cut color clarity depth table price x y z
1.7 Ideal J VS2 60.5 58.0 10002.0 7.73 7.74 4.68
1.03 Ideal E VVS2 60.6 59.0 10003.0 6.5 6.53 3.95
1.23 Very Good G VVS2 60.6 55.0 10004.0 6.93 7.02 4.23
1.25 Ideal F VS2 61.6 55.0 10006.0 6.93 6.96 4.28
1.21 Very Good F VS1 62.3 58.0 10009.0 6.76 6.85 4.24
1.51 Premium I VS2 59.9 60.0 10010.0 7.42 7.36 4.43
1.05 Ideal F VVS2 60.5 55.0 10011.0 6.67 6.58 4.01
1.6 Ideal J VS1 62.0 53.0 10011.0 7.57 7.56 4.69
1.35 Premium G VS1 62.1 59.0 10012.0 7.06 7.02 4.37
1.53 Premium I VS2 62.0 58.0 10013.0 7.36 7.41 4.58
1.13 Ideal F VS1 60.9 57.0 10016.0 6.73 6.76 4.11
1.21 Premium F VS1 62.6 59.0 10018.0 6.81 6.76 4.25
1.01 Very Good F VVS1 62.9 57.0 10019.0 6.35 6.41 4.01
1.04 Ideal E VVS2 62.9 55.0 10019.0 6.47 6.51 4.08
1.26 Very Good G VVS2 60.9 56.0 10020.0 6.95 7.01 4.25
1.5 Very Good H VS2 60.9 59.0 10023.0 7.37 7.43 4.51
1.12 Premium F VVS2 62.4 59.0 10028.0 6.58 6.66 4.13
1.27 Premium F VS1 60.3 58.0 10028.0 7.06 7.04 4.25
1.52 Very Good I VS1 62.9 59.9 10032.0 7.27 7.31 4.59
1.24 Premium F VS1 62.5 58.0 10033.0 6.87 6.83 4.28
1.23 Very Good F VS1 62.0 59.0 10035.0 6.84 6.87 4.25
1.5 Good G VS1 63.6 57.0 10036.0 7.23 7.14 4.57
1.22 Ideal G VVS2 62.3 56.0 10038.0 6.81 6.84 4.25
1.3 Ideal G VS1 62.0 55.0 10038.0 6.98 7.02 4.34
1.59 Premium I VS2 60.2 60.0 10039.0 7.58 7.61 4.57
1.83 Premium I VS2 60.5 60.0 10043.0 7.93 7.86 4.78
1.07 Ideal E VVS2 61.4 56.0 10043.0 6.65 6.55 4.05
1.51 Very Good H VS1 61.5 54.0 10045.0 7.34 7.42 4.54
1.08 Ideal F VVS2 61.6 57.0 10046.0 6.57 6.6 4.06
1.0 Premium D VVS2 61.6 60.0 10046.0 6.41 6.36 3.93
1.03 Ideal F VVS2 61.1 57.0 10049.0 6.51 6.54 3.99
1.52 Very Good I VS2 62.3 58.0 10051.0 7.32 7.28 4.55
1.08 Ideal F VVS2 62.1 55.0 10052.0 6.57 6.6 4.09
1.2 Premium G VVS2 62.8 59.0 10053.0 6.72 6.65 4.2
1.2 Premium E VS1 60.7 57.0 10053.0 6.89 6.81 4.16
1.2 Premium G VVS2 61.2 58.0 10053.0 6.88 6.84 4.2
1.71 Premium I VS1 60.3 62.0 10055.0 7.76 7.7 4.66
1.0 Ideal F VVS1 62.3 53.0 10058.0 6.37 6.43 3.99
1.07 Ideal F VVS2 62.3 57.0 10061.0 6.56 6.58 4.09
1.66 Premium J VVS2 62.6 59.0 10062.0 7.58 7.54 4.73
1.2 Premium F VVS2 60.5 60.0 10064.0 6.98 6.87 4.19
1.11 Very Good F VVS1 62.5 59.0 10069.0 6.59 6.63 4.13
1.34 Ideal G VS1 62.7 57.0 10070.0 7.1 7.04 4.43
1.31 Premium G VS1 61.5 59.0 10071.0 7.06 7.0 4.32
1.31 Ideal G VS1 62.2 56.0 10071.0 7.05 7.01 4.37
1.31 Ideal G VS1 61.5 57.0 10071.0 7.06 7.02 4.33
1.53 Very Good H VS1 59.5 63.0 10076.0 7.51 7.44 4.45
1.26 Premium F VS1 62.7 58.0 10076.0 6.93 6.86 4.32
1.73 Ideal J VS2 63.0 57.0 10076.0 7.64 7.6 4.8
1.19 Ideal D VS1 61.1 57.0 10079.0 6.84 6.87 4.19
1.5 Ideal I VS1 61.3 57.0 10080.0 7.35 7.32 4.5
1.5 Premium I VS1 62.7 59.0 10080.0 7.3 7.25 4.56
1.5 Ideal H VS1 61.3 55.0 10080.0 7.37 7.34 4.51
1.21 Premium D VS1 60.2 59.0 10083.0 6.89 6.86 4.14
1.71 Premium H VS2 59.2 61.0 10084.0 7.83 7.77 4.62
1.82 Very Good J VS1 62.2 56.0 10090.0 7.83 7.96 4.91
1.51 Very Good H VS2 61.9 57.0 10090.0 7.32 7.36 4.54
1.3 Ideal F VS2 62.2 56.0 10090.0 6.98 6.94 4.33
1.3 Premium F VS2 60.4 59.0 10090.0 7.12 7.06 4.28
1.5 Very Good I VVS2 63.3 58.0 10090.0 7.27 7.24 4.59
1.57 Ideal I VS2 61.5 56.0 10093.0 7.56 7.49 4.63
1.07 Ideal F VVS2 60.3 55.0 10093.0 6.65 6.68 4.02
1.31 Very Good E VS2 63.1 56.0 10094.0 6.95 6.9 4.37
1.33 Good G VS1 62.8 60.0 10096.0 6.87 6.92 4.33
1.53 Premium I VS1 61.2 59.0 10098.0 7.39 7.41 4.53
1.61 Ideal I VS2 62.5 57.0 10098.0 7.49 7.43 4.66
1.31 Ideal G VS1 61.9 56.0 10099.0 7.03 7.13 4.38
1.22 Ideal F VS1 62.3 57.0 10100.0 6.83 6.79 4.24
1.07 Ideal E VVS2 61.7 57.0 10104.0 6.55 6.61 4.06
1.59 Very Good I VS2 60.5 63.0 10106.0 7.52 7.45 4.53
1.22 Premium G VVS2 62.0 58.0 10111.0 6.9 6.85 4.26
1.09 Premium E VVS2 59.9 59.0 10111.0 6.73 6.7 4.02
1.58 Very Good I VS1 61.8 57.0 10112.0 7.5 7.56 4.64
1.0 Very Good D VVS2 61.7 58.0 10113.0 6.37 6.41 3.94
1.23 Ideal G VVS1 63.2 56.0 10113.0 6.78 6.83 4.3
1.25 Ideal D VS2 62.6 56.0 10114.0 6.87 6.84 4.29
1.17 Premium D VS1 61.7 59.0 10115.0 6.77 6.72 4.16
1.28 Ideal G VS1 62.1 57.0 10126.0 6.91 6.94 4.3
1.43 Ideal H VVS2 61.6 54.0 10129.0 7.25 7.29 4.48
1.51 Good H VS1 59.9 61.0 10129.0 7.34 7.39 4.41
1.52 Very Good I VS2 61.7 55.0 10130.0 7.39 7.32 4.54
1.04 Very Good D VVS2 60.8 58.0 10130.0 6.49 6.53 3.96
1.07 Ideal E VVS2 62.3 56.0 10133.0 6.51 6.61 4.09
1.5 Good F VS2 64.0 56.0 10134.0 7.18 7.13 4.64
1.0 Premium E VVS1 60.3 54.0 10134.0 6.59 6.47 3.94
1.21 Premium E VS1 60.3 58.0 10137.0 6.95 6.91 4.18
1.24 Ideal F VS1 61.5 54.0 10138.0 6.93 6.89 4.25
1.24 Ideal F VS1 60.9 54.0 10138.0 6.98 6.95 4.24
1.11 Very Good F VVS1 59.7 55.0 10141.0 6.77 6.82 4.06
1.1 Ideal D VS1 61.9 56.0 10144.0 6.58 6.61 4.09
1.01 Premium D VVS2 60.2 58.0 10147.0 6.57 6.51 3.94
1.31 Ideal G VS1 60.5 57.0 10155.0 7.1 7.14 4.31
1.2 Premium D VS2 61.1 58.0 10161.0 6.85 6.83 4.18
1.5 Very Good I VS1 62.2 59.0 10164.0 7.27 7.3 4.53
1.54 Premium I VS1 61.6 58.0 10164.0 7.39 7.42 4.56
1.54 Good I VS1 63.6 60.0 10164.0 7.3 7.33 4.65
1.5 Ideal I VS1 62.0 54.0 10164.0 7.32 7.38 4.56
1.67 Very Good I VS2 60.7 60.0 10165.0 7.61 7.68 4.64
1.7 Very Good J VS1 62.9 58.0 10165.0 7.54 7.67 4.79
1.53 Ideal I VS1 60.2 60.0 10171.0 7.51 7.48 4.51
1.2 Very Good F VVS2 63.8 58.0 10173.0 6.67 6.69 4.26
1.21 Ideal F VS2 61.5 54.0 10177.0 6.88 6.89 4.24
1.01 Good G VS2 63.6 56.0 10181.0 6.31 6.24 3.99
1.24 Very Good E VS1 62.0 58.0 10185.0 6.9 6.96 4.3
1.51 Ideal H VS1 61.2 58.0 10186.0 7.36 7.42 4.52
1.35 Ideal G VS1 61.5 56.0 10193.0 7.12 7.15 4.39
1.53 Premium I VS2 62.0 58.0 10196.0 7.41 7.36 4.58
1.09 Ideal F VVS2 62.0 56.0 10196.0 6.63 6.6 4.1
1.01 Ideal F VVS1 60.5 60.0 10197.0 6.45 6.47 3.91
1.58 Ideal I VS2 61.4 55.0 10197.0 7.49 7.55 4.62
1.24 Premium G VVS2 59.9 60.0 10202.0 6.98 7.0 4.19
1.24 Very Good E VS1 59.9 61.0 10202.0 6.96 6.99 4.18
1.27 Premium G VVS2 61.0 58.0 10203.0 6.96 7.01 4.26
1.08 Very Good F VVS1 61.0 58.0 10204.0 6.64 6.61 4.04
1.5 Very Good H VS2 63.4 57.0 10206.0 7.27 7.2 4.59
1.5 Fair H VS2 65.2 58.0 10206.0 7.12 7.06 4.62
1.57 Ideal I VS1 62.3 57.0 10209.0 7.44 7.48 4.65
1.12 Premium F VVS2 62.4 59.0 10211.0 6.66 6.58 4.13
1.52 Ideal I VS1 62.9 60.0 10214.0 7.31 7.27 4.59
1.2 Ideal E VS2 61.3 56.0 10214.0 6.89 6.84 4.21
1.51 Very Good I VS1 61.1 61.0 10215.0 7.32 7.37 4.49
1.01 Premium D VVS2 62.4 60.0 10221.0 6.31 6.36 3.95
1.3 Ideal G VS1 62.0 55.0 10221.0 7.02 6.98 4.34
1.22 Ideal G VVS2 62.3 56.0 10221.0 6.84 6.81 4.25
1.07 Ideal E VVS2 61.3 56.0 10222.0 6.53 6.6 4.02
1.59 Premium I VS2 60.2 60.0 10222.0 7.61 7.58 4.57
1.53 Premium H VS2 59.3 59.0 10224.0 7.53 7.59 4.48
1.53 Premium H VS2 59.8 58.0 10224.0 7.49 7.52 4.49
1.51 Very Good H VS2 62.8 58.0 10225.0 7.21 7.28 4.55
1.37 Very Good G VS1 58.3 60.0 10226.0 7.3 7.35 4.27
1.21 Ideal G VVS1 60.2 57.0 10232.0 7.02 6.94 4.2
1.12 Ideal F VVS2 60.4 56.0 10236.0 6.82 6.78 4.11
1.6 Very Good I VS1 62.3 59.0 10238.0 7.46 7.51 4.66
1.16 Ideal D VS1 61.2 58.0 10241.0 6.73 6.76 4.13
1.31 Ideal F VS2 59.6 60.0 10243.0 7.06 7.16 4.24
1.35 Ideal G VS1 62.2 57.0 10244.0 7.09 7.05 4.4
1.21 Premium F VS1 61.9 58.0 10245.0 6.82 6.76 4.2
1.09 Ideal F VVS2 62.1 56.0 10246.0 6.55 6.59 4.08
1.34 Ideal H VVS1 62.1 56.0 10255.0 7.05 7.11 4.4
1.5 Good H VS1 63.4 59.0 10256.0 7.2 7.29 4.59
1.21 Ideal G VVS2 60.6 56.0 10256.0 6.9 6.89 4.18
1.14 Premium E VVS2 59.7 58.0 10258.0 6.83 6.91 4.1
1.07 Ideal D VVS2 61.3 58.0 10266.0 6.55 6.64 4.04
1.23 Very Good F VS1 60.8 58.0 10276.0 6.9 6.94 4.21
1.57 Ideal I VS2 62.7 56.0 10278.0 7.36 7.4 4.63
1.53 Premium I VS1 61.2 59.0 10282.0 7.41 7.39 4.53
1.21 Very Good F VS2 60.1 58.0 10283.0 6.85 6.92 4.14
1.01 Ideal E VVS2 61.4 56.0 10283.0 6.49 6.45 3.97
1.26 Very Good F VS1 62.5 58.0 10284.0 6.83 6.94 4.3
1.25 Very Good E VS1 61.5 59.0 10285.0 6.91 6.95 4.26
1.55 Ideal I VS1 61.2 55.0 10286.0 7.49 7.47 4.58
1.07 Ideal E VVS2 61.7 57.0 10288.0 6.61 6.55 4.06
1.5 Good H VS2 63.6 58.0 10291.0 7.22 7.27 4.61
1.5 Good H VS2 61.2 61.0 10291.0 7.25 7.32 4.46
1.5 Premium H VS2 62.2 58.0 10291.0 7.27 7.36 4.55
1.5 Premium H VS2 60.8 59.0 10291.0 7.34 7.36 4.47
1.08 Ideal E VS1 61.7 55.0 10292.0 6.58 6.61 4.07
1.21 Ideal G VVS1 61.0 57.0 10295.0 6.87 6.93 4.21
1.08 Ideal E VVS2 62.5 57.0 10300.0 6.52 6.57 4.09
1.52 Premium I VS1 60.6 57.0 10300.0 7.51 7.44 4.53
1.46 Ideal G VS2 62.3 56.0 10302.0 7.28 7.2 4.51
1.26 Premium G VVS2 62.7 58.0 10302.0 6.95 6.86 4.33
1.23 Ideal G VVS2 60.3 57.0 10304.0 6.98 6.97 4.21
1.23 Ideal G VVS2 61.0 57.0 10304.0 6.93 6.9 4.22
1.12 Ideal G VVS2 61.5 57.0 10305.0 6.65 6.67 4.1
1.18 Ideal G VVS2 61.3 55.0 10308.0 6.86 6.81 4.19
1.59 Very Good I VS2 61.1 58.6 10309.0 7.49 7.53 4.59
1.71 Ideal J VS1 62.4 56.0 10309.0 7.59 7.63 4.75
1.4 Ideal G VS2 61.7 56.0 10311.0 7.2 7.25 4.46
1.86 Ideal J VS2 62.6 56.0 10312.0 7.95 7.87 4.95
1.08 Ideal E VVS2 61.8 56.0 10313.0 6.55 6.59 4.06
1.09 Ideal E VVS2 61.6 56.0 10314.0 6.6 6.64 4.08
1.04 Premium D VVS2 60.8 58.0 10314.0 6.53 6.49 3.96
1.15 Ideal F VS1 61.1 55.0 10316.0 6.76 6.82 4.15
1.23 Ideal G VVS2 62.2 55.0 10317.0 6.86 6.9 4.28
1.23 Ideal G VVS2 62.7 56.0 10317.0 6.81 6.84 4.28
1.51 Very Good H VS2 63.0 57.0 10319.0 7.25 7.3 4.58
1.27 Very Good G VVS2 61.5 58.0 10321.0 6.9 6.96 4.26
1.13 Ideal F VVS2 61.4 56.0 10327.0 6.77 6.72 4.14
1.1 Very Good F VVS1 62.2 59.0 10329.0 6.56 6.69 4.12
1.56 Premium H VS2 62.4 58.0 10331.0 7.44 7.39 4.63
1.09 Ideal E VVS2 60.9 56.0 10333.0 6.66 6.7 4.07
1.56 Ideal I VS2 61.8 56.0 10333.0 7.41 7.45 4.59
1.7 Premium H VS2 60.6 58.0 10333.0 7.72 7.65 4.66
1.7 Ideal H VS2 62.8 55.0 10333.0 7.61 7.54 4.76
1.7 Premium H VS2 59.0 58.0 10333.0 7.77 7.72 4.57
1.21 Ideal D VS1 61.0 57.0 10335.0 6.88 6.85 4.19
1.57 Very Good I VS1 59.7 61.0 10336.0 7.51 7.62 4.52
1.7 Premium H VVS2 61.4 58.0 10337.0 7.66 7.62 4.69
1.58 Ideal I VS2 61.1 55.0 10338.0 7.48 7.53 4.59
1.42 Premium F VS1 58.4 59.0 10338.0 7.36 7.32 4.29
1.29 Premium F VS1 60.9 58.0 10341.0 6.97 7.01 4.26
1.01 Premium F VVS1 60.8 58.0 10341.0 6.55 6.48 3.96
1.27 Ideal G VVS2 62.4 53.3 10342.0 6.94 6.95 4.33
1.17 Ideal G VVS1 61.7 57.0 10342.0 6.84 6.9 4.13
1.23 Ideal F VS2 62.4 54.0 10342.0 6.84 6.87 4.28
1.6 Very Good I VS2 60.0 58.0 10346.0 7.61 7.68 4.59
1.54 Premium I VS1 61.6 58.0 10349.0 7.42 7.39 4.56
1.54 Very Good I VS1 61.1 63.0 10349.0 7.43 7.36 4.52
1.54 Good I VS1 63.6 60.0 10349.0 7.33 7.3 4.65
1.04 Premium E VVS1 62.5 59.0 10350.0 6.41 6.46 4.02
1.1 Ideal D VS2 61.7 56.0 10350.0 6.63 6.67 4.1
1.4 Very Good G VS2 60.1 62.0 10351.0 7.16 7.25 4.33
1.17 Ideal F VVS2 61.9 54.0 10351.0 6.76 6.82 4.2
1.21 Ideal E VS1 62.4 57.0 10351.0 6.75 6.83 4.24
1.21 Ideal D VS2 60.9 60.0 10353.0 6.91 6.86 4.19
1.58 Ideal J VVS1 61.5 56.0 10357.0 7.48 7.5 4.61
1.31 Ideal G VS1 62.0 58.0 10359.0 6.97 7.02 4.34
1.62 Premium I VS1 61.7 59.0 10362.0 7.55 7.47 4.63
1.26 Premium E VS1 60.7 58.0 10367.0 7.02 7.04 4.27
1.26 Ideal G VVS2 60.7 56.0 10367.0 7.03 7.05 4.27
1.2 Ideal F VS1 62.1 58.0 10367.0 6.78 6.84 4.23
1.26 Ideal G VS1 62.2 54.0 10371.0 6.89 6.98 4.31
1.4 Very Good G VS2 62.2 61.0 10378.0 7.09 7.13 4.42
1.21 Ideal G VVS2 62.0 56.0 10378.0 6.8 6.84 4.23
1.35 Ideal G VS1 61.5 56.0 10378.0 7.15 7.12 4.39
1.55 Ideal I VS1 61.5 54.0 10384.0 7.42 7.58 4.61
1.02 Ideal E VVS2 61.5 57.0 10384.0 6.55 6.5 4.01
1.1 Premium E VVS2 60.1 58.0 10387.0 6.69 6.76 4.04
1.24 Premium E VS1 59.9 61.0 10388.0 6.99 6.96 4.18
1.24 Premium G VVS2 59.9 60.0 10388.0 7.0 6.98 4.19
1.27 Premium G VVS2 61.0 58.0 10389.0 7.01 6.96 4.26
1.52 Ideal I VS1 60.7 60.0 10392.0 7.4 7.42 4.5
1.24 Ideal G VVS2 61.4 58.0 10395.0 6.88 6.92 4.24
1.13 Ideal G VVS2 61.6 57.0 10396.0 6.66 6.75 4.13
1.7 Premium I VS2 61.9 56.0 10396.0 7.55 7.5 4.66
1.56 Ideal I VS1 61.2 59.0 10399.0 7.43 7.5 4.57
2.01 Premium J VS2 58.6 61.0 10401.0 8.18 8.14 4.78
1.51 Premium I VS1 61.1 61.0 10401.0 7.37 7.32 4.49
1.5 Ideal I VS1 62.2 54.0 10406.0 7.33 7.4 4.57
1.01 Premium D VVS2 62.4 60.0 10407.0 6.36 6.31 3.95
1.31 Very Good D VS2 59.5 61.0 10409.0 7.16 7.19 4.27
1.1 Ideal D VVS2 61.0 56.0 10410.0 6.67 6.73 4.09
1.51 Premium H VS2 60.6 58.0 10411.0 7.42 7.36 4.48
1.11 Ideal F VVS1 61.9 57.0 10412.0 6.62 6.66 4.11
1.37 Premium G VS1 58.3 60.0 10412.0 7.35 7.3 4.27
1.35 Premium G VS1 61.0 59.0 10415.0 7.15 7.09 4.34
1.55 Premium I VS1 58.2 60.0 10416.0 7.69 7.59 4.45
1.43 Very Good G VS2 62.2 58.0 10419.0 7.15 7.18 4.46
1.32 Premium F VS2 60.9 59.0 10423.0 7.12 7.06 4.32
1.56 Premium H VS2 62.2 58.0 10424.0 7.41 7.44 4.62
1.5 Very Good H VS2 58.9 59.0 10424.0 7.34 7.43 4.35
1.4 Ideal G VS2 62.1 55.0 10427.0 7.12 7.05 4.4
1.58 Premium I VS1 61.1 59.0 10428.0 7.44 7.52 4.57
1.52 Good H VS2 63.3 57.0 10428.0 7.32 7.33 4.64
1.71 Very Good J VS1 61.9 59.0 10428.0 7.6 7.69 4.73
1.75 Premium J VS1 62.2 59.0 10429.0 7.7 7.74 4.8
1.21 Very Good E VS1 60.0 58.0 10430.0 6.89 6.97 4.16
1.34 Premium F VS2 61.1 58.0 10431.0 7.12 7.05 4.33
1.23 Very Good G VVS1 61.3 57.0 10435.0 6.88 6.96 4.24
1.19 Ideal F VVS2 61.7 56.0 10436.0 6.82 6.85 4.22
1.29 Premium F VS1 59.3 60.0 10437.0 7.14 7.1 4.22
1.16 Ideal F VVS2 61.8 56.7 10439.0 6.7 6.78 4.16
1.25 Ideal D VS1 61.7 56.0 10441.0 6.92 7.01 4.3
1.11 Ideal D VS2 61.2 57.0 10443.0 6.69 6.71 4.1
1.23 Ideal G VVS2 61.3 56.0 10445.0 6.89 6.91 4.23
1.14 Premium E VVS2 59.7 58.0 10446.0 6.91 6.83 4.1
1.57 Very Good I VS2 60.3 58.0 10447.0 7.58 7.55 4.56
1.19 Ideal F VS1 60.5 57.0 10449.0 6.82 6.88 4.15
1.01 Very Good D VVS2 62.5 59.0 10453.0 6.34 6.4 3.98
1.07 Ideal E VVS2 61.8 54.0 10453.0 6.56 6.61 4.07
1.2 Ideal G VVS2 61.1 56.0 10454.0 6.88 6.91 4.21
1.36 Ideal G VS1 61.0 56.0 10455.0 7.13 7.11 4.34
1.71 Premium H VS1 62.1 59.0 10457.0 7.63 7.55 4.71
1.57 Ideal I VS2 62.8 57.0 10462.0 7.46 7.37 4.66
1.09 Very Good F VVS1 61.4 58.0 10463.0 6.6 6.65 4.07
1.0 Very Good E VVS1 62.7 54.0 10463.0 6.36 6.39 4.0
1.2 Ideal G VVS2 62.2 53.0 10463.0 6.8 6.84 4.24
1.59 Premium I VS2 62.9 56.0 10471.0 7.48 7.43 4.69
1.35 Ideal G VS1 60.9 54.0 10471.0 7.18 7.15 4.36
1.39 Very Good G VS2 62.6 56.0 10476.0 7.08 7.11 4.44
1.72 Very Good J VS2 60.9 61.0 10477.0 7.77 7.79 4.74
1.5 Good H VS2 63.6 58.0 10478.0 7.27 7.22 4.61
1.5 Premium H VS2 61.2 61.0 10478.0 7.32 7.25 4.46
1.63 Premium I VS2 61.0 60.0 10479.0 7.62 7.59 4.64
1.56 Ideal I VVS2 62.0 56.0 10481.0 7.39 7.42 4.6
1.21 Ideal G VVS1 61.0 57.0 10482.0 6.93 6.87 4.21
1.5 Premium I VVS2 60.2 58.0 10483.0 7.5 7.34 4.47
1.21 Ideal G VVS1 61.4 58.0 10483.0 6.85 6.89 4.22
1.24 Premium G VVS1 60.4 59.0 10485.0 7.02 7.01 4.24
1.1 Ideal F VVS2 61.2 56.0 10487.0 6.66 6.74 4.1
1.22 Premium F VVS2 63.0 56.0 10494.0 6.88 6.76 4.3
1.6 Very Good I VS2 60.0 60.0 10497.0 7.55 7.59 4.54
1.52 Very Good H VS1 59.8 57.0 10497.0 7.47 7.55 4.49
1.01 Very Good E VVS1 63.2 54.0 10498.0 6.41 6.31 4.02
1.01 Very Good D VVS2 59.8 57.0 10499.0 6.49 6.58 3.91
1.55 Premium G VS2 60.5 60.0 10499.0 7.49 7.46 4.52
1.62 Ideal I VS2 62.1 55.0 10501.0 7.53 7.58 4.69
1.21 Ideal F VS1 61.8 56.0 10504.0 6.84 6.86 4.23
1.19 Ideal G VVS1 61.6 59.0 10508.0 6.78 6.79 4.18
1.14 Ideal E VVS2 62.3 55.0 10512.0 6.68 6.71 4.17
1.54 Very Good I VVS2 62.7 57.0 10518.0 7.35 7.43 4.63
1.56 Very Good I VS1 58.2 59.0 10523.0 7.65 7.7 4.47
1.29 Premium F VS1 60.9 58.0 10530.0 7.01 6.97 4.26
1.71 Ideal H VS2 63.0 57.0 10534.0 7.57 7.53 4.76
1.28 Ideal G VS1 62.1 56.0 10537.0 6.97 6.94 4.32
1.22 Ideal D VS2 61.7 56.0 10538.0 6.89 6.86 4.24
1.04 Premium E VVS1 62.5 59.0 10539.0 6.46 6.41 4.02
1.33 Ideal G VS1 62.0 55.0 10539.0 7.12 7.07 4.4
1.15 Ideal D VS2 61.0 57.0 10546.0 6.76 6.78 4.13
1.03 Premium F VVS1 59.7 60.0 10546.0 6.63 6.57 3.94
1.55 Very Good H VS2 63.3 56.0 10546.0 7.38 7.32 4.65
1.51 Premium H VS2 61.5 58.0 10548.0 7.45 7.32 4.45
1.51 Premium H VS2 63.0 58.0 10548.0 7.34 7.27 4.6
1.5 Very Good I VVS2 59.7 60.0 10551.0 7.46 7.62 4.5
1.51 Ideal G VS2 62.8 57.0 10553.0 7.33 7.26 4.58
1.51 Fair G VS2 58.1 67.0 10553.0 7.59 7.49 4.38
1.21 Ideal E VS2 61.8 53.0 10556.0 6.86 6.9 4.25
1.31 Ideal G VS1 62.0 53.0 10556.0 7.06 7.07 4.37
1.26 Ideal G VVS2 60.7 56.0 10556.0 7.05 7.03 4.27
1.5 Very Good H VS2 61.6 55.0 10558.0 7.37 7.43 4.56
1.53 Ideal I VS1 61.5 55.0 10560.0 7.4 7.42 4.56
1.64 Ideal I VS2 62.5 56.0 10562.0 7.58 7.52 4.72
1.0 Fair D VVS2 61.1 57.0 10562.0 6.37 6.3 3.87
1.01 Good E VVS1 63.1 59.0 10567.0 6.31 6.34 3.99
1.21 Ideal G VVS2 61.3 57.0 10568.0 6.83 6.85 4.19
1.23 Ideal G VVS1 61.8 56.0 10572.0 6.8 6.89 4.24
1.32 Very Good F VS1 60.3 57.0 10575.0 7.08 7.12 4.28
1.51 Ideal I VS1 61.6 56.0 10576.0 7.37 7.43 4.56
1.1 Premium E VVS2 60.1 58.0 10577.0 6.76 6.69 4.04
1.0 Ideal F VVS2 61.3 53.0 10577.0 6.44 6.48 3.96
1.02 Premium D VVS2 61.4 58.0 10580.0 6.43 6.46 3.96
1.2 Premium F VVS2 62.8 60.0 10580.0 6.79 6.74 4.25
1.23 Ideal F VVS2 63.0 55.0 10580.0 6.89 6.79 4.31
1.79 Premium J VS2 62.5 60.0 10581.0 7.76 7.69 4.83
1.41 Ideal G VS2 60.8 55.0 10581.0 7.27 7.2 4.4
1.5 Premium H VS1 59.3 61.0 10584.0 7.53 7.47 4.45
1.5 Very Good H VS1 63.4 56.0 10584.0 7.29 7.25 4.61
1.7 Ideal J VS1 60.9 58.0 10589.0 7.73 7.68 4.69
1.69 Premium I VS2 62.4 58.0 10600.0 7.66 7.53 4.74
1.53 Very Good I VS1 62.8 55.0 10602.0 7.35 7.4 4.63
1.35 Ideal D VS2 61.3 57.0 10602.0 7.09 7.13 4.36
1.11 Ideal F VVS1 61.9 57.0 10602.0 6.66 6.62 4.11
1.03 Ideal D VS1 61.7 57.0 10607.0 6.45 6.48 3.99
1.58 Very Good H VS2 61.4 60.0 10608.0 7.49 7.44 4.58
1.43 Premium G VS2 62.2 58.0 10609.0 7.18 7.15 4.46
1.23 Very Good F VS1 59.3 59.0 10609.0 6.98 7.01 4.15
1.29 Ideal G VVS2 62.4 57.0 10614.0 6.94 6.97 4.34
1.52 Very Good H VS2 63.3 57.0 10618.0 7.33 7.32 4.64
1.58 Premium I VS1 61.1 59.0 10618.0 7.52 7.44 4.57
1.03 Ideal F VVS1 62.1 56.3 10619.0 6.43 6.5 4.02
1.75 Premium J VS1 62.2 59.0 10619.0 7.74 7.7 4.8
1.22 Ideal E VS1 62.4 54.0 10622.0 6.77 6.88 4.26
1.52 Fair G VS2 55.2 66.0 10623.0 7.72 7.67 4.26
1.51 Premium I VVS2 61.1 60.0 10623.0 7.33 7.36 4.49
1.4 Premium F VS2 61.5 60.0 10625.0 7.25 7.18 4.44
1.51 Good H VS2 63.5 60.0 10628.0 7.24 7.27 4.61
1.32 Ideal G VS1 62.4 53.0 10631.0 7.03 7.08 4.4
1.25 Ideal G VVS2 61.5 55.0 10636.0 6.92 6.94 4.26
1.25 Ideal G VVS2 62.5 54.0 10636.0 6.88 6.93 4.31
1.35 Ideal H VVS1 61.9 57.0 10639.0 7.06 7.09 4.38
2.0 Good I VS2 64.0 60.0 10640.0 7.9 7.83 5.04
1.25 Premium E VS2 61.5 58.0 10640.0 6.98 6.91 4.27
1.52 Premium G VS1 58.8 61.0 10640.0 7.54 7.45 4.41
1.02 Very Good E VVS1 60.2 60.0 10641.0 6.39 6.54 3.89
1.52 Ideal I VS1 62.4 57.0 10641.0 7.32 7.36 4.58
1.15 Ideal G VVS1 62.7 56.0 10644.0 6.69 6.67 4.19
1.5 Fair G VS2 66.2 53.0 10644.0 7.12 7.08 4.7
1.5 Ideal I VVS2 61.7 55.0 10646.0 7.32 7.39 4.54
1.23 Ideal G VVS2 60.8 57.0 10646.0 6.89 6.92 4.2
1.23 Very Good F VVS2 58.5 59.0 10650.0 6.98 7.07 4.11
1.5 Very Good H VS1 63.4 59.0 10652.0 7.13 7.2 4.54
1.3 Very Good G VS1 62.5 58.0 10654.0 6.9 6.95 4.33
1.51 Premium H VS1 59.6 60.0 10655.0 7.5 7.41 4.44
1.51 Ideal I VS1 63.2 57.0 10655.0 7.4 7.28 4.62
1.35 Ideal G VS1 62.7 57.0 10656.0 7.02 7.07 4.42
1.27 Ideal F VS2 61.0 54.0 10656.0 7.0 7.02 4.28
1.79 Ideal J VS2 61.8 56.0 10658.0 7.74 7.85 4.82
1.7 Ideal I VS2 61.1 57.0 10662.0 7.7 7.66 4.69
1.7 Premium I VS2 62.7 58.0 10662.0 7.57 7.52 4.73
1.5 Premium H VS2 60.6 61.0 10668.0 7.34 7.31 4.44
1.62 Premium I VS2 60.1 59.0 10669.0 7.63 7.6 4.58
1.26 Premium F VS1 62.0 58.0 10669.0 6.95 6.88 4.29
1.5 Good G VS2 63.7 57.0 10669.0 7.29 7.25 4.63
1.02 Very Good E VVS1 62.2 57.0 10672.0 6.4 6.59 4.04
1.5 Very Good H VS1 60.7 58.0 10681.0 7.35 7.42 4.48
1.75 Very Good J VS1 61.5 59.0 10681.0 7.75 7.83 4.79
1.17 Very Good D VS1 60.5 57.0 10681.0 6.79 6.86 4.13
1.68 Ideal J VS1 61.1 57.0 10681.0 7.64 7.7 4.69
1.57 Very Good I VS1 62.7 58.0 10682.0 7.41 7.43 4.65
1.21 Ideal G VS1 61.3 57.0 10685.0 6.82 6.87 4.2
1.21 Ideal G VS1 61.6 57.0 10685.0 6.85 6.87 4.22
1.21 Ideal G VS1 61.8 57.0 10685.0 6.81 6.86 4.23
1.51 Premium H VS2 60.7 58.0 10685.0 7.45 7.4 4.51
1.51 Ideal H VS2 62.6 57.0 10685.0 7.37 7.33 4.6
1.66 Ideal I VS1 61.0 55.0 10691.0 7.67 7.64 4.67
1.5 Good H VS2 63.9 60.0 10692.0 7.17 7.22 4.6
1.5 Ideal H VS2 62.2 57.0 10692.0 7.27 7.33 4.54
1.01 Very Good E VVS1 61.6 58.0 10693.0 6.45 6.57 4.01
1.01 Good E VVS1 63.1 57.0 10696.0 6.36 6.39 4.02
1.1 Very Good F VVS1 59.8 54.0 10701.0 6.74 6.77 4.04
1.56 Premium H VS2 62.5 59.0 10702.0 7.3 7.33 4.57
1.54 Premium H VS2 61.8 59.0 10702.0 7.35 7.4 4.56
1.14 Ideal E VVS2 62.3 55.0 10703.0 6.71 6.68 4.17
1.01 Very Good E VVS1 62.3 56.0 10704.0 6.41 6.47 4.01
1.21 Premium D VS1 62.6 56.0 10706.0 6.83 6.74 4.25
1.26 Ideal F VS2 61.5 56.0 10709.0 6.97 7.01 4.3
1.04 Ideal D VS1 61.6 57.0 10709.0 6.52 6.56 4.03
1.55 Very Good I VS1 59.0 58.0 10711.0 7.56 7.63 4.48
1.31 Ideal E VS1 61.7 55.0 10711.0 7.11 7.05 4.37
1.28 Premium G VVS2 62.1 58.0 10716.0 6.96 6.91 4.31
1.22 Very Good F VVS2 60.7 62.0 10719.0 6.81 6.84 4.14
1.26 Ideal G VVS1 62.1 57.0 10720.0 6.91 7.01 4.32
1.24 Ideal G VVS1 62.3 56.0 10724.0 6.86 6.89 4.28
1.29 Ideal F VS1 62.3 54.4 10727.0 6.93 7.0 4.34
1.53 Premium I VS1 62.4 59.0 10729.0 7.3 7.34 4.57
1.23 Very Good E VS1 61.5 58.0 10730.0 6.88 6.93 4.25
1.01 Premium D VVS2 62.4 58.0 10732.0 6.39 6.44 4.0
1.33 Ideal G VS1 59.9 57.3 10732.0 7.16 7.21 4.3
1.42 Ideal G VS2 62.6 57.0 10735.0 7.19 7.15 4.49
1.67 Premium I VS2 61.9 56.0 10736.0 7.64 7.6 4.72
1.13 Ideal F VVS2 62.1 54.0 10742.0 6.67 6.71 4.16
1.51 Premium H VS1 62.4 60.0 10743.0 7.27 7.34 4.56
1.5 Premium I VVS2 59.7 60.0 10744.0 7.62 7.46 4.5
1.19 Ideal F VVS2 60.8 57.0 10748.0 6.85 6.83 4.16
1.5 Premium H VS2 61.6 55.0 10750.0 7.43 7.37 4.56
1.0 Fair D VVS1 56.7 68.0 10752.0 6.66 6.64 3.77
1.0 Premium D VVS1 62.9 58.0 10752.0 6.34 6.28 3.97
1.59 Ideal I VS2 61.7 57.0 10752.0 7.47 7.52 4.62
1.15 Ideal F VVS2 62.7 57.0 10757.0 6.69 6.65 4.18
1.54 Premium H VS2 61.9 61.0 10758.0 7.41 7.33 4.56
1.01 Very Good E VVS1 63.1 59.0 10760.0 6.34 6.31 3.99
1.01 Ideal D VVS2 61.7 57.0 10761.0 6.43 6.47 3.98
1.51 Ideal H VS2 62.5 55.0 10763.0 7.29 7.34 4.57
1.51 Good H VS2 64.2 58.5 10763.0 7.16 7.22 4.62
1.3 Premium G VVS2 60.2 58.0 10763.0 7.17 7.08 4.29
1.12 Ideal F VVS2 61.3 57.0 10764.0 6.67 6.7 4.1
1.54 Good H VS1 63.1 57.0 10769.0 7.34 7.4 4.65
1.12 Ideal E VVS2 60.6 57.0 10769.0 6.77 6.66 4.07
1.34 Very Good F VS1 59.7 60.0 10771.0 7.17 7.2 4.29
1.02 Premium D VVS2 61.4 58.0 10773.0 6.46 6.43 3.96
1.51 Premium H VS1 62.1 58.0 10775.0 7.32 7.26 4.53
1.71 Ideal J VS1 62.4 56.0 10778.0 7.63 7.59 4.75
1.29 Very Good E VS1 61.8 57.0 10780.0 6.99 6.93 4.3
1.14 Ideal F VVS2 61.9 57.0 10786.0 6.73 6.68 4.15
1.2 Ideal G VVS1 63.1 56.0 10786.0 6.74 6.7 4.24
1.26 Ideal G VVS1 61.1 57.0 10787.0 7.02 6.98 4.28
1.01 Ideal E VVS1 61.8 54.0 10789.0 6.43 6.49 3.99
1.4 Very Good F VS2 60.0 58.0 10790.0 7.24 7.29 4.36
1.53 Ideal I VS1 62.8 55.0 10796.0 7.4 7.35 4.63
1.35 Ideal D VS2 61.3 57.0 10796.0 7.13 7.09 4.36
1.01 Ideal D VVS2 60.6 56.0 10797.0 6.53 6.48 3.94
1.01 Premium D VVS2 61.6 58.0 10797.0 6.44 6.39 3.95
1.86 Very Good J VS2 62.5 55.0 10798.0 7.81 7.9 4.91
1.01 Very Good D VVS2 63.0 56.0 10800.0 6.35 6.41 4.02
1.68 Ideal I VS2 62.1 57.0 10800.0 7.6 7.54 4.7
1.29 Premium F VS1 61.3 61.0 10801.0 7.05 6.95 4.29
1.67 Ideal I VS1 61.2 57.0 10802.0 7.69 7.66 4.7
1.04 Ideal F VVS2 61.6 57.0 10804.0 6.47 6.51 4.0
1.27 Ideal G VVS1 61.7 56.0 10805.0 6.9 7.03 4.3
1.29 Ideal G VVS2 62.4 57.0 10807.0 6.97 6.94 4.34
1.7 Very Good J VVS1 62.9 60.0 10808.0 7.56 7.61 4.77
1.16 Premium F VVS1 60.4 60.0 10809.0 6.9 6.77 4.13
1.2 Ideal G VVS2 62.3 54.0 10812.0 6.82 6.86 4.26
1.32 Premium F VS2 62.2 58.0 10814.0 7.03 6.95 4.35
1.51 Premium I VVS2 61.1 60.0 10817.0 7.36 7.33 4.49
1.51 Good H VS1 58.2 58.0 10819.0 7.49 7.56 4.38
1.51 Ideal I VVS2 61.6 58.0 10821.0 7.35 7.4 4.54
1.71 Ideal J VS1 61.6 57.0 10821.0 7.67 7.62 4.71
1.51 Very Good H VS2 63.5 60.0 10822.0 7.27 7.24 4.61
1.22 Ideal E VS1 61.8 56.0 10823.0 6.84 6.88 4.24
1.6 Very Good I VS1 58.6 58.0 10824.0 7.66 7.72 4.51
1.52 Premium I VVS2 61.6 58.0 10824.0 7.37 7.41 4.55
1.51 Premium I VVS1 61.0 61.0 10824.0 7.47 7.34 4.52
1.04 Ideal F VVS1 61.6 55.0 10825.0 6.5 6.53 4.02
1.55 Premium H VS2 62.7 57.0 10827.0 7.42 7.36 4.63
1.53 Premium H VS2 59.9 60.0 10827.0 7.5 7.46 4.48
1.23 Very Good F VVS2 62.4 58.0 10835.0 6.74 6.89 4.26
1.68 Ideal G VS2 62.1 55.0 10838.0 7.64 7.59 4.73
1.28 Ideal G VVS2 61.2 54.0 10839.0 7.03 7.06 4.31
1.23 Premium F VVS2 58.5 59.0 10844.0 7.07 6.98 4.11
1.2 Ideal G VVS2 61.2 56.0 10846.0 6.92 6.89 4.23
1.35 Ideal G VS1 62.7 57.0 10850.0 7.07 7.02 4.42
1.16 Ideal F VVS2 60.5 58.0 10850.0 6.77 6.85 4.12
1.38 Premium G VS1 62.4 58.0 10850.0 7.14 7.09 4.44
1.52 Ideal I VS1 61.8 55.0 10851.0 7.38 7.41 4.57
1.01 Ideal E VVS2 61.7 55.0 10852.0 6.44 6.49 3.99
1.23 Ideal G VS1 61.6 57.0 10859.0 6.84 6.9 4.23
1.01 Ideal D VVS2 61.1 57.0 10860.0 6.47 6.49 3.96
1.1 Ideal F VVS1 62.7 57.0 10861.0 6.57 6.63 4.14
1.51 Ideal H VS2 61.4 58.0 10861.0 7.36 7.42 4.54
1.02 Premium E VVS1 62.2 57.0 10867.0 6.59 6.4 4.04
1.55 Ideal I VS1 61.9 55.0 10869.0 7.43 7.46 4.61
1.05 Ideal F VVS1 61.6 55.0 10872.0 6.57 6.53 4.04
1.5 Very Good I VVS2 62.0 53.0 10873.0 7.3 7.34 4.54
1.05 Ideal F VVS2 62.1 55.0 10874.0 6.54 6.56 4.07
1.14 Premium F VVS1 60.8 58.0 10878.0 6.79 6.74 4.11
1.57 Premium H VS1 61.7 58.0 10880.0 7.47 7.56 4.64
1.26 Ideal G VVS2 60.9 57.0 10886.0 6.98 7.01 4.26
1.5 Good H VS2 63.9 60.0 10886.0 7.22 7.17 4.6
1.5 Ideal H VS2 62.2 57.0 10886.0 7.33 7.27 4.54
1.01 Ideal E VVS1 62.2 57.0 10887.0 6.38 6.44 3.99
1.22 Ideal G VVS1 61.1 56.0 10888.0 6.91 6.94 4.23
1.01 Premium E VVS1 61.6 58.0 10888.0 6.57 6.45 4.01
1.53 Premium H VS2 60.1 58.0 10889.0 7.57 7.6 4.71
1.2 Very Good F VVS2 62.9 59.0 10891.0 6.72 6.76 4.24
1.01 Very Good E VVS1 63.1 57.0 10891.0 6.39 6.36 4.02
1.54 Premium H VS2 61.8 59.0 10897.0 7.4 7.35 4.56
1.5 Ideal I VVS2 60.3 57.0 10907.0 7.43 7.47 4.49
1.16 Ideal D VS2 61.8 55.0 10907.0 6.72 6.75 4.16
1.7 Premium I VS2 62.4 58.0 10910.0 7.61 7.56 4.73
1.62 Very Good H VS2 62.6 58.0 10912.0 7.57 7.45 4.7
1.05 Very Good E VVS1 59.5 60.0 10915.0 6.56 6.66 3.93
1.26 Ideal G VVS1 62.1 57.0 10916.0 7.01 6.91 4.32
1.32 Ideal E VS2 62.0 56.0 10919.0 7.02 7.07 4.37
1.5 Ideal H VS2 62.3 56.0 10920.0 7.34 7.29 4.56
1.58 Ideal I VS1 62.4 54.0 10920.0 7.43 7.46 4.64
1.26 Very Good G VVS1 60.2 59.0 10922.0 6.98 7.07 4.23
1.29 Ideal F VS1 62.3 54.0 10923.0 7.0 6.93 4.34
1.53 Premium I VS1 62.4 59.0 10924.0 7.34 7.3 4.57
1.37 Ideal G VS1 62.2 55.0 10927.0 7.09 7.12 4.42
1.01 Premium D VVS2 62.4 58.0 10927.0 6.44 6.39 4.0
1.36 Premium F VS2 59.3 60.0 10929.0 7.23 7.2 4.28
1.7 Premium I VS2 62.0 59.0 10929.0 7.6 7.55 4.7
1.3 Premium F VS1 61.0 59.0 10930.0 7.05 7.08 4.31
1.57 Premium H VS1 60.5 61.0 10930.0 7.6 7.51 4.57
1.2 Very Good F VVS2 61.4 60.0 10931.0 6.79 6.83 4.18
1.5 Ideal I VVS2 60.7 60.0 10931.0 7.35 7.4 4.48
1.14 Ideal G VVS1 61.1 58.0 10933.0 6.74 6.77 4.13
1.7 Very Good I VS2 63.0 58.0 10935.0 7.52 7.65 4.78
1.51 Premium H VS1 62.4 60.0 10939.0 7.34 7.27 4.56
1.71 Ideal J VVS2 61.6 59.0 10945.0 7.65 7.68 4.72
1.24 Ideal G VS1 61.8 55.0 10946.0 6.85 6.89 4.25
1.12 Ideal F VVS2 62.2 55.0 10949.0 6.64 6.68 4.14
1.51 Very Good H VS2 63.2 57.0 10950.0 7.18 7.32 4.58
1.51 Premium H VS2 60.2 60.0 10951.0 7.5 7.38 4.48
1.01 Ideal E VVS1 62.0 57.0 10954.0 6.39 6.45 3.98
1.5 Premium I VVS1 62.4 60.0 10956.0 7.29 7.32 4.56
1.31 Ideal F VS1 61.9 54.0 10957.0 7.03 7.06 4.35
1.5 Very Good H VS2 62.8 57.0 10959.0 7.25 7.3 4.57
1.51 Ideal H VS2 64.2 59.0 10959.0 7.22 7.16 4.62
1.51 Ideal H VS2 62.5 55.0 10959.0 7.34 7.29 4.57
1.25 Very Good G VVS1 60.6 60.0 10962.0 6.92 6.95 4.2
1.35 Premium F VS2 61.9 58.0 10962.0 7.06 7.02 4.36
1.57 Ideal I VS2 60.4 58.0 10964.0 7.55 7.59 4.57
1.02 Ideal D VVS2 62.0 56.0 10967.0 6.43 6.48 4.0
1.44 Very Good D VS2 63.1 56.0 10967.0 7.15 7.12 4.5
1.52 Ideal I VVS1 61.9 56.0 10968.0 7.34 7.37 4.55
1.32 Premium F VS1 61.7 59.0 10977.0 6.95 6.99 4.3
1.54 Premium H VS2 61.0 60.0 10977.0 7.42 7.46 4.54
1.2 Good F VVS1 63.6 57.0 10982.0 6.71 6.74 4.28
1.25 Ideal G VVS2 62.2 55.0 10983.0 6.87 6.93 4.29
1.04 Premium D VVS2 61.1 60.0 10984.0 6.54 6.51 3.99
1.01 Very Good E VVS1 63.6 55.0 10993.0 6.35 6.39 4.05
1.66 Premium H VS2 62.3 58.0 10993.0 7.62 7.57 4.73
1.5 Good F VS2 63.6 59.0 10995.0 7.13 7.22 4.56
1.25 Ideal G VVS1 61.3 56.0 10996.0 6.95 6.98 4.28
1.27 Ideal G VVS1 61.7 56.0 11002.0 7.03 6.9 4.3
1.22 Ideal G VVS2 60.0 58.0 11003.0 6.95 6.99 4.18
1.7 Premium J VVS1 62.9 60.0 11005.0 7.61 7.56 4.77
1.5 Premium I VVS2 61.4 58.0 11007.0 7.38 7.32 4.51
1.5 Fair H VS1 61.7 60.0 11007.0 7.37 7.27 4.52
1.5 Premium H VS1 61.1 58.0 11007.0 7.46 7.37 4.53
1.41 Ideal G VS1 60.4 57.0 11009.0 7.22 7.31 4.39
1.37 Ideal G VS1 61.1 55.0 11009.0 7.2 7.16 4.39
1.41 Very Good F VS1 63.4 63.0 11010.0 7.13 6.97 4.47
1.01 Ideal D VVS2 61.5 57.0 11015.0 6.43 6.48 3.97
1.59 Very Good I VS1 61.2 57.8 11018.0 7.5 7.52 4.6
1.63 Ideal I VS2 61.9 54.3 11019.0 7.54 7.58 4.68
1.21 Ideal D VS1 60.1 60.0 11019.0 6.92 6.99 4.18
1.2 Premium F VVS2 62.2 58.0 11021.0 6.83 6.78 4.23
1.52 Premium I VVS2 61.6 58.0 11021.0 7.41 7.37 4.55
1.6 Premium I VS1 58.6 58.0 11021.0 7.72 7.66 4.51
1.33 Good D VS2 63.6 58.0 11023.0 7.02 6.91 4.43
1.5 Very Good H VS2 60.7 61.0 11025.0 7.34 7.41 4.48
1.02 Ideal E VVS1 61.4 57.0 11028.0 6.39 6.47 3.95
1.44 Premium G VS2 59.5 61.0 11032.0 7.38 7.3 4.37
1.71 Premium G VS2 61.3 58.0 11032.0 7.64 7.6 4.67
1.52 Ideal I VVS1 62.3 55.0 11033.0 7.32 7.37 4.58
1.56 Very Good H VS2 63.1 60.0 11039.0 7.43 7.34 4.66
1.23 Ideal F VVS2 61.8 56.0 11040.0 6.84 6.89 4.24
1.61 Ideal H VS2 61.4 57.0 11045.0 7.52 7.57 4.63
1.55 Premium H VS2 60.4 60.0 11048.0 7.39 7.44 4.48
1.74 Premium J VS1 62.5 58.0 11050.0 7.67 7.65 4.79
1.13 Ideal F VVS1 61.7 56.0 11051.0 6.68 6.75 4.14
1.2 Ideal D VS1 61.0 59.0 11053.0 6.79 6.85 4.16
1.01 Ideal D VVS2 61.1 57.0 11057.0 6.49 6.47 3.96
2.02 Premium I VS2 61.2 60.0 11059.0 8.22 8.13 5.0
1.47 Very Good G VS2 62.7 56.0 11060.0 7.15 7.18 4.49
1.3 Premium G VVS1 60.5 60.0 11061.0 7.01 7.05 4.25
1.02 Premium E VVS1 61.5 59.0 11062.0 6.41 6.46 3.96
1.7 Premium I VS2 61.7 59.0 11062.0 7.63 7.57 4.69
1.52 Premium H VS2 61.1 59.0 11066.0 7.45 7.38 4.53
1.55 Ideal I VS1 61.9 55.0 11067.0 7.46 7.43 4.61
1.51 Very Good H VS2 60.9 57.0 11068.0 7.39 7.43 4.51
1.3 Ideal G VVS2 62.0 57.0 11073.0 6.96 7.03 4.34
1.11 Ideal E VVS2 62.9 55.0 11074.0 6.56 6.62 4.14
1.51 Very Good H VS2 60.9 54.0 11077.0 7.38 7.41 4.5
1.32 Ideal G VS1 61.7 56.0 11079.0 7.03 7.11 4.36
1.23 Very Good G VVS1 61.2 55.8 11081.0 6.9 6.94 4.23
1.01 Ideal D VVS2 62.3 53.0 11082.0 6.4 6.47 4.02
1.69 Ideal I VS2 61.7 56.0 11086.0 7.65 7.71 4.74
1.53 Premium H VS2 60.1 58.0 11087.0 7.6 7.57 4.71
1.51 Very Good I VVS1 63.0 59.0 11088.0 7.24 7.3 4.58
1.5 Premium H VS1 62.1 59.0 11088.0 7.27 7.31 4.53
1.5 Premium H VS1 59.9 60.0 11088.0 7.39 7.44 4.44
1.25 Premium E VS1 61.5 59.0 11088.0 6.95 6.91 4.26
1.25 Ideal G VVS2 62.0 59.0 11089.0 6.88 6.96 4.29
1.5 Premium H VS2 62.4 59.0 11092.0 7.29 7.32 4.56
1.52 Very Good G VS2 62.4 56.0 11093.0 7.26 7.36 4.56
1.18 Ideal E VS1 61.4 57.0 11104.0 6.77 6.81 4.17
1.53 Premium H VS2 62.7 56.0 11104.0 7.39 7.31 4.61
1.52 Premium H VS2 59.4 59.0 11105.0 7.45 7.49 4.44
1.31 Very Good G VVS2 62.2 59.0 11108.0 6.91 6.98 4.32
1.41 Ideal G VS1 60.4 57.0 11109.0 7.31 7.22 4.39
1.47 Premium G VS2 62.8 57.0 11113.0 7.27 7.22 4.55
1.62 Ideal H VS2 62.4 57.0 11114.0 7.48 7.53 4.68
1.13 Ideal E VVS2 61.4 57.0 11115.0 6.69 6.74 4.12
1.13 Ideal E VVS2 61.6 56.0 11115.0 6.69 6.71 4.13
2.01 Good I VS2 59.0 64.0 11115.0 8.25 8.19 4.85
1.34 Premium G VVS2 61.3 58.0 11118.0 7.16 7.1 4.37
1.59 Ideal I VS1 61.2 58.0 11119.0 7.52 7.5 4.6
1.16 Ideal F VVS2 60.5 57.0 11120.0 6.8 6.86 4.13
1.53 Premium H VS1 59.4 59.0 11127.0 7.58 7.51 4.48
1.02 Ideal E VVS1 61.3 57.0 11128.0 6.47 6.54 3.99
1.24 Very Good F VVS2 62.0 55.0 11130.0 6.88 6.95 4.29
1.3 Premium F VS1 62.0 58.0 11130.0 6.94 6.9 4.29
1.1 Ideal D VVS2 62.0 57.0 11132.0 6.57 6.62 4.09
1.1 Very Good D VVS2 61.7 56.0 11132.0 6.64 6.65 4.1
1.36 Ideal F VS1 61.4 57.0 11132.0 7.25 7.09 4.4
1.51 Ideal H VS2 62.5 56.0 11133.0 7.37 7.33 4.57
1.7 Very Good H VVS2 63.2 56.0 11133.0 7.59 7.56 4.79
1.31 Ideal F VS1 61.7 56.0 11136.0 7.02 7.04 4.34
1.23 Ideal F VVS2 61.8 56.0 11141.0 6.89 6.84 4.24
1.31 Ideal G VVS1 61.1 57.0 11146.0 7.01 7.06 4.3
1.57 Premium I VVS2 60.7 58.0 11146.0 7.54 7.51 4.57
1.28 Ideal G VVS1 62.1 56.0 11147.0 6.93 6.96 4.31
1.55 Premium H VS2 60.4 60.0 11149.0 7.44 7.39 4.48
1.77 Ideal J VS1 62.2 56.0 11150.0 7.77 7.73 4.82
1.62 Ideal I VS1 60.8 56.0 11152.0 7.56 7.61 4.62
1.52 Ideal H VS1 62.3 55.0 11154.0 7.36 7.32 4.57
1.01 Ideal E VVS1 62.0 57.0 11154.0 6.45 6.39 3.98
1.5 Premium I VVS1 62.4 60.0 11155.0 7.32 7.29 4.56
1.66 Ideal I VS2 62.3 54.0 11156.0 7.58 7.61 4.73
1.5 Ideal H VS2 62.8 57.0 11159.0 7.3 7.25 4.57
1.51 Very Good G VS2 59.3 58.0 11161.0 7.32 7.49 4.39
1.51 Very Good H VS1 61.8 59.0 11161.0 7.27 7.32 4.51
1.51 Premium H VS1 61.0 60.0 11161.0 7.29 7.34 4.46
1.71 Premium H VS1 58.1 62.0 11161.0 8.02 7.84 4.61
1.3 Premium G VVS1 60.5 60.0 11162.0 7.05 7.01 4.25
1.02 Premium E VVS1 61.5 59.0 11163.0 6.46 6.41 3.96
1.51 Very Good H VS2 62.8 60.0 11166.0 7.25 7.28 4.56
1.08 Ideal E VVS2 61.0 56.0 11166.0 6.64 6.67 4.06
1.02 Ideal D VVS2 62.0 56.0 11167.0 6.48 6.43 4.0
1.52 Ideal I VVS1 61.9 56.0 11168.0 7.37 7.34 4.55
1.6 Very Good I VVS1 59.8 56.0 11170.0 7.6 7.68 4.57
1.23 Very Good E VVS2 60.4 62.0 11175.0 6.88 6.93 4.17
1.21 Premium F VVS1 63.0 59.0 11175.0 6.75 6.7 4.24
1.3 Ideal G VVS2 62.0 57.0 11175.0 7.03 6.96 4.34
1.76 Premium J VS1 62.0 58.0 11177.0 7.74 7.7 4.79
1.54 Premium H VS2 61.0 60.0 11177.0 7.46 7.42 4.54
1.32 Premium F VS1 61.7 59.0 11177.0 6.99 6.95 4.3
1.53 Very Good H VS2 62.2 58.0 11178.0 7.3 7.34 4.55
1.2 Good F VVS1 63.6 57.0 11182.0 6.74 6.71 4.28
1.23 Ideal G VVS1 61.2 56.0 11182.0 6.94 6.9 4.23
1.18 Ideal E VVS2 61.6 58.0 11184.0 6.78 6.82 4.19
1.51 Premium H VS2 61.2 58.0 11188.0 7.4 7.36 4.52
1.5 Premium G VS2 59.9 58.0 11189.0 7.38 7.34 4.41
1.5 Premium H VS1 62.1 59.0 11189.0 7.31 7.27 4.53
1.5 Premium G VS2 58.4 58.0 11189.0 7.54 7.5 4.39
1.5 Premium H VS1 59.9 60.0 11189.0 7.44 7.39 4.44
1.7 Very Good I VS2 58.5 59.0 11190.0 7.81 7.89 4.59
1.5 Premium H VS2 62.4 59.0 11194.0 7.32 7.29 4.56
1.74 Ideal J VS1 61.1 56.0 11194.0 7.85 7.79 4.78
1.05 Ideal D VVS2 61.9 54.0 11196.0 6.53 6.55 4.05
1.29 Ideal F VS1 60.7 57.0 11197.0 7.08 7.05 4.29
1.38 Ideal F VS2 61.9 55.0 11205.0 7.16 7.12 4.42
1.52 Premium H VS2 59.4 59.0 11206.0 7.49 7.45 4.44
1.52 Ideal H VS2 60.7 56.0 11206.0 7.49 7.41 4.52
1.14 Ideal E VVS2 61.6 57.0 11206.0 6.68 6.73 4.13
1.27 Ideal G VS1 61.2 57.0 11206.0 6.97 6.98 4.27
2.04 Premium J VS2 60.9 59.0 11209.0 8.25 8.21 5.01
1.06 Ideal D VVS2 61.1 56.0 11209.0 6.58 6.59 4.02
1.28 Very Good G VVS1 60.3 59.0 11214.0 6.99 7.03 4.23
1.5 Very Good G VS1 59.1 62.0 11216.0 7.38 7.42 4.37
1.62 Ideal I VS2 61.8 55.0 11217.0 7.56 7.59 4.68
1.26 Ideal G VVS1 61.7 56.0 11218.0 6.96 6.98 4.3
1.5 Very Good H VS2 60.0 62.0 11220.0 7.38 7.41 4.44
1.72 Ideal I VS2 62.8 57.0 11226.0 7.69 7.63 4.81
1.14 Ideal F VVS1 60.1 60.0 11226.0 6.79 6.83 4.09
1.24 Premium F VVS2 62.0 55.0 11231.0 6.95 6.88 4.29
1.1 Ideal D VVS2 62.0 57.0 11233.0 6.62 6.57 4.09
1.1 Premium D VVS2 61.7 56.0 11233.0 6.65 6.64 4.1
1.24 Very Good F VVS2 59.0 58.0 11234.0 6.98 7.03 4.13
1.52 Good G VS2 63.3 57.0 11235.0 7.27 7.32 4.62
1.31 Ideal F VS1 61.7 56.0 11237.0 7.04 7.02 4.34
1.71 Premium I VS1 60.6 57.0 11246.0 7.82 7.73 4.71
1.31 Ideal G VVS1 61.1 57.0 11247.0 7.06 7.01 4.3
1.28 Ideal G VVS1 62.1 56.0 11248.0 6.96 6.93 4.31
1.39 Premium E VS2 61.7 59.0 11248.0 7.13 7.09 4.39
1.71 Good I VS2 58.0 60.0 11250.0 7.85 7.9 4.57
1.5 Premium F VS2 61.1 59.0 11255.0 7.37 7.35 4.5
1.31 Premium G VVS2 59.6 61.0 11255.0 7.23 7.14 4.28
1.7 Ideal I VS2 61.7 56.0 11257.0 7.64 7.72 4.74
1.7 Premium I VS2 61.7 59.0 11257.0 7.63 7.68 4.72
1.7 Premium I VS2 61.2 59.0 11257.0 7.55 7.62 4.64
1.58 Ideal H VS2 62.7 56.0 11262.0 7.39 7.44 4.65
1.4 Very Good G VS1 62.6 58.0 11262.0 7.03 7.07 4.41
1.51 Premium H VS1 61.8 59.0 11263.0 7.32 7.27 4.51
1.51 Very Good H VS1 63.2 60.0 11263.0 7.23 7.17 4.55
1.51 Premium H VS1 61.0 60.0 11263.0 7.34 7.29 4.46
1.51 Fair H VS1 58.0 67.0 11263.0 7.63 7.57 4.41
1.24 Very Good F VVS2 63.6 56.0 11268.0 6.75 6.8 4.31
1.51 Premium H VS2 62.9 59.0 11268.0 7.31 7.25 4.58
1.51 Premium H VS2 62.8 60.0 11268.0 7.28 7.25 4.56
1.41 Premium E VS2 61.3 58.0 11269.0 7.29 7.25 4.46
1.57 Premium H VS1 59.8 60.0 11272.0 7.63 7.56 4.54
1.52 Ideal H VS2 62.4 58.0 11272.0 7.3 7.37 4.58
1.04 Premium E VVS1 60.9 58.0 11279.0 6.53 6.61 4.0
1.53 Premium H VS2 62.2 58.0 11280.0 7.34 7.3 4.55
1.38 Very Good F VS1 61.4 61.0 11286.0 7.1 7.14 4.37
1.5 Good G VS2 59.0 58.0 11294.0 7.41 7.45 4.38
1.5 Ideal H VS1 62.3 54.7 11296.0 7.29 7.33 4.55
1.2 Premium E VVS2 62.1 58.0 11301.0 6.76 6.7 4.18
1.43 Ideal G VS1 59.9 57.0 11302.0 7.35 7.3 4.39
1.5 Very Good H VVS2 62.7 58.0 11303.0 7.21 7.24 4.53
1.64 Ideal I VS1 60.5 57.0 11305.0 7.68 7.62 4.64
1.37 Ideal E VS2 60.3 54.0 11314.0 7.26 7.2 4.36
2.0 Fair I VS1 58.5 68.0 11322.0 8.26 8.15 4.8
1.5 Premium H VS2 60.0 62.0 11322.0 7.41 7.38 4.44
1.26 Ideal E VS1 61.2 56.0 11323.0 6.97 6.93 4.25
1.8 Premium J VS1 58.2 61.0 11329.0 8.07 7.95 4.66
1.11 Very Good E VVS1 60.2 59.0 11330.0 6.67 6.79 4.05
1.59 Very Good H VS2 60.7 61.1 11333.0 7.5 7.56 4.57
1.59 Premium H VS2 62.1 58.0 11333.0 7.42 7.48 4.63
1.52 Ideal H VS2 61.8 55.1 11333.0 7.38 7.42 4.58
1.03 Premium D VVS2 60.1 58.0 11335.0 6.55 6.6 3.95
1.52 Very Good G VS2 63.3 57.0 11338.0 7.32 7.27 4.62
1.56 Premium H VS1 62.0 57.0 11345.0 7.48 7.43 4.62
1.41 Premium G VS1 62.1 59.0 11347.0 7.1 7.06 4.4
1.5 Premium H VS2 61.8 59.0 11360.0 7.3 7.35 4.53
1.7 Premium I VS2 61.2 59.0 11360.0 7.62 7.55 4.64
1.7 Premium I VS2 61.7 59.0 11360.0 7.68 7.63 4.72
1.7 Ideal I VS2 61.7 56.0 11360.0 7.72 7.64 4.74
1.72 Premium I VS2 58.3 61.0 11360.0 7.91 7.87 4.6
1.55 Good H VS2 61.0 61.0 11364.0 7.42 7.47 4.54
1.58 Ideal H VS2 62.7 56.0 11365.0 7.44 7.39 4.65
1.4 Premium F VS2 60.7 58.0 11368.0 7.26 7.17 4.38
1.7 Premium I VS2 60.5 61.0 11369.0 7.68 7.65 4.64
1.52 Ideal H VS2 61.8 54.0 11379.0 7.42 7.43 4.59
1.52 Ideal H VS2 61.9 55.0 11379.0 7.38 7.43 4.58
1.53 Very Good F VS1 63.2 58.0 11379.0 7.33 7.3 4.62
1.58 Premium G VS1 60.8 58.0 11380.0 7.58 7.52 4.59
1.04 Premium E VVS1 60.9 58.0 11382.0 6.61 6.53 4.0
1.23 Very Good F VVS2 62.2 58.0 11382.0 6.81 6.86 4.25
1.13 Ideal E VVS2 60.1 59.0 11387.0 6.77 6.81 4.08
1.31 Premium G VVS2 62.7 59.0 11389.0 6.96 6.92 4.35
1.71 Very Good I VS2 63.4 59.0 11389.0 7.53 7.45 4.75
1.52 Very Good J VVS2 62.1 60.0 11392.0 7.33 7.36 4.56
1.3 Premium F VS1 62.5 58.0 11392.0 6.97 6.94 4.35
1.21 Premium E VVS2 61.9 58.0 11395.0 6.84 6.79 4.22
1.01 Ideal E VVS2 61.7 57.0 11400.0 6.42 6.44 3.97
1.67 Premium I VS1 61.1 58.0 11400.0 7.69 7.6 4.67
1.12 Ideal F VVS1 61.0 56.0 11403.0 6.71 6.74 4.1
1.52 Ideal F VS2 62.3 55.0 11405.0 7.37 7.33 4.58
1.83 Ideal J VS2 62.0 56.0 11406.0 7.84 7.9 4.88
1.33 Ideal D VS2 62.8 56.0 11409.0 7.08 7.03 4.43
1.53 Premium H VS1 60.8 59.0 11413.0 7.41 7.36 4.49
1.57 Premium H VS2 62.2 58.0 11415.0 7.45 7.4 4.62
1.18 Ideal F VVS2 60.6 55.0 11415.0 6.84 6.88 4.16
1.23 Ideal G VVS1 61.4 55.0 11417.0 6.89 6.93 4.24
1.32 Ideal G VVS2 62.3 57.0 11419.0 6.96 7.04 4.36
1.58 Ideal H VS2 63.0 56.0 11419.0 7.39 7.46 4.68
1.28 Ideal E VS1 61.7 57.0 11419.0 6.93 6.97 4.29
1.25 Ideal E VS2 60.7 56.0 11422.0 6.97 6.99 4.24
1.62 Very Good H VS2 59.6 59.0 11427.0 7.59 7.67 4.55
1.55 Premium H VS2 61.7 59.0 11428.0 7.44 7.4 4.58
1.18 Very Good F VVS2 60.1 58.0 11430.0 6.88 6.92 4.15
1.23 Premium F VVS2 61.6 59.0 11430.0 6.8 6.9 4.22
1.55 Ideal I VVS2 61.3 59.0 11430.0 7.41 7.46 4.56
1.53 Ideal I VS2 61.5 56.0 11434.0 7.39 7.44 4.55
1.42 Premium G VS1 62.1 56.0 11434.0 7.27 7.22 4.5
1.51 Premium H VS2 60.2 60.0 11435.0 7.33 7.35 4.42
1.51 Premium H VS2 62.3 59.0 11435.0 7.28 7.32 4.55
1.51 Ideal H VS2 62.2 57.0 11435.0 7.29 7.33 4.55
1.58 Ideal I VVS1 61.9 55.0 11435.0 7.47 7.51 4.64
1.2 Premium E VVS2 61.4 56.0 11435.0 6.94 6.83 4.23
1.59 Premium H VS2 62.1 58.0 11437.0 7.48 7.42 4.63
1.59 Ideal H VS2 60.7 61.0 11437.0 7.56 7.5 4.57
1.01 Ideal D VVS2 61.9 56.0 11442.0 6.41 6.45 3.98
1.42 Very Good G VS1 62.7 55.0 11452.0 7.11 7.17 4.48
1.53 Very Good H VS2 60.9 63.0 11452.0 7.37 7.41 4.5
1.21 Very Good F VVS2 61.0 58.0 11455.0 6.89 6.92 4.21
1.71 Ideal I VS2 60.5 56.0 11455.0 7.71 7.73 4.67
1.2 Ideal F VVS1 62.0 56.0 11455.0 6.76 6.82 4.21
1.72 Premium H VS2 59.5 60.0 11455.0 7.79 7.75 4.62
1.2 Very Good E VVS2 63.2 56.0 11456.0 6.73 6.78 4.27
1.31 Ideal G VVS2 59.2 59.0 11459.0 7.12 7.18 4.23
1.5 Premium H VS2 61.8 59.0 11464.0 7.35 7.3 4.53
1.23 Ideal G VVS1 59.5 57.0 11469.0 7.0 6.98 4.16
1.57 Premium H VS2 61.0 59.0 11470.0 7.5 7.54 4.59
1.13 Ideal D VS1 61.2 57.0 11477.0 6.7 6.72 4.1
1.28 Very Good G VVS2 59.5 56.0 11478.0 7.12 7.16 4.25
1.01 Premium D VVS1 59.3 59.0 11480.0 6.56 6.53 3.88
1.51 Premium G VS2 60.4 58.0 11480.0 7.38 7.43 4.47
1.7 Premium I VVS2 61.8 61.0 11481.0 7.57 7.5 4.66
1.54 Ideal G VS2 61.5 57.0 11487.0 7.44 7.41 4.57
1.6 Premium G VS2 62.2 59.0 11489.0 7.45 7.48 4.64
1.58 Premium H VS1 61.7 59.0 11491.0 7.48 7.42 4.6
1.43 Ideal H VS1 62.0 55.0 11498.0 7.21 7.28 4.49
2.07 Ideal J VS2 62.2 56.0 11500.0 8.2 8.16 5.09
1.24 Ideal G VVS1 62.1 56.0 11503.0 6.86 6.91 4.28
1.5 Good G VS2 58.8 64.0 11508.0 7.43 7.4 4.36
1.5 Good G VS2 63.8 56.0 11508.0 7.2 7.15 4.58
1.6 Premium H VS2 62.6 58.0 11508.0 7.5 7.45 4.68
1.04 Ideal D VVS2 60.9 57.0 11511.0 6.54 6.6 4.0
1.51 Very Good I VVS1 62.0 58.0 11512.0 7.27 7.31 4.52
1.51 Good H VS1 59.1 58.0 11512.0 7.48 7.52 4.43
1.7 Very Good I VS1 60.6 59.0 11514.0 7.64 7.67 4.64
1.52 Premium H VS1 61.4 58.0 11516.0 7.3 7.44 4.55
1.7 Premium I VS2 61.4 59.0 11519.0 7.6 7.68 4.69
1.7 Premium I VS2 60.7 59.0 11519.0 7.63 7.7 4.65
1.4 Good G VVS2 63.4 59.0 11519.0 7.04 7.12 4.49
1.5 Good G VS1 63.8 59.0 11524.0 7.16 7.22 4.59
2.01 Good J VS1 63.7 59.0 11526.0 7.93 7.86 5.03
1.58 Ideal I VVS1 61.7 53.0 11526.0 7.52 7.53 4.65
1.54 Very Good H VS2 62.1 62.0 11527.0 7.31 7.38 4.56
1.38 Ideal G VS1 62.2 54.0 11527.0 7.18 7.14 4.45
1.2 Ideal G VVS2 60.9 56.0 11530.0 6.86 6.91 4.19
1.2 Ideal G VVS2 61.0 56.0 11530.0 6.86 6.88 4.19
1.56 Ideal H VS2 61.5 56.0 11531.0 7.46 7.5 4.6
1.31 Ideal G VVS2 60.9 56.0 11531.0 7.17 7.07 4.32
1.51 Very Good G VS2 62.1 57.0 11532.0 7.37 7.32 4.5
1.23 Premium F VVS2 61.6 59.0 11534.0 6.9 6.8 4.22
1.63 Premium I VS1 61.1 57.0 11534.0 7.7 7.58 4.67
1.03 Premium E VVS1 58.8 59.0 11538.0 6.63 6.6 3.89
1.51 Ideal H VS2 62.2 57.0 11540.0 7.33 7.29 4.55
1.51 Premium H VS2 60.2 60.0 11540.0 7.35 7.33 4.42
1.51 Premium H VS2 62.3 59.0 11540.0 7.32 7.28 4.55
1.51 Ideal H VS2 60.6 57.0 11540.0 7.46 7.45 4.52
1.14 Premium F VVS1 59.4 59.0 11549.0 6.87 6.8 4.06
1.1 Ideal D VVS2 62.2 57.0 11550.0 6.58 6.54 4.08
1.5 Ideal H VS1 61.0 56.8 11557.0 7.36 7.4 4.5
1.53 Very Good H VS2 60.9 63.0 11557.0 7.41 7.37 4.5
1.71 Ideal I VS2 60.5 56.0 11559.0 7.73 7.71 4.67
1.55 Premium H VS1 62.6 58.0 11562.0 7.4 7.34 4.61
1.55 Premium H VS2 60.7 58.0 11567.0 7.51 7.47 4.55
1.21 Ideal G VVS1 61.5 56.0 11572.0 6.83 6.89 4.22
1.02 Ideal D VVS2 62.2 59.0 11573.0 6.41 6.46 4.0
1.55 Ideal I VS2 61.8 55.0 11574.0 7.4 7.44 4.58
1.57 Premium H VS2 61.0 59.0 11575.0 7.54 7.5 4.59
2.09 Good J VS2 57.2 64.0 11576.0 8.51 8.46 4.85
1.5 Good G VS2 63.3 62.0 11577.0 7.08 7.2 4.52
1.51 Ideal H VS1 62.6 56.0 11580.0 7.28 7.32 4.57
1.28 Ideal F VS1 61.7 55.0 11580.0 7.01 6.98 4.32
1.4 Very Good G VS1 59.9 56.0 11584.0 7.31 7.34 4.39
1.32 Ideal G VS1 61.7 56.0 11584.0 7.04 7.07 4.35
1.62 Ideal I VVS2 62.7 54.0 11587.0 7.47 7.52 4.7
1.44 Premium G VS1 61.8 57.0 11588.0 7.21 7.09 4.42
1.25 Very Good G VVS1 60.2 58.0 11589.0 6.97 7.04 4.22
1.24 Ideal G VVS2 61.1 56.0 11601.0 6.94 6.97 4.25
1.28 Very Good F VVS2 62.0 57.0 11602.0 6.92 7.01 4.32
1.55 Very Good H VS2 61.3 61.0 11602.0 7.39 7.46 4.55
1.57 Very Good H VS1 62.8 60.0 11605.0 7.36 7.44 4.65
1.7 Ideal H VS2 62.3 57.0 11605.0 7.68 7.65 4.78
1.75 Ideal J VVS2 62.0 55.0 11609.0 7.7 7.73 4.78
1.03 Ideal E VVS2 61.7 56.0 11611.0 6.49 6.51 4.01
1.76 Ideal I VS1 62.0 57.0 11616.0 7.71 7.74 4.79
1.52 Premium H VS1 61.4 58.0 11621.0 7.44 7.3 4.55
1.4 Very Good G VVS2 63.4 59.0 11624.0 7.12 7.04 4.49
1.61 Very Good H VS2 59.4 58.0 11627.0 7.64 7.74 4.57
1.54 Premium H VS2 62.1 62.0 11632.0 7.38 7.31 4.56
1.3 Ideal G VVS2 62.4 56.1 11633.0 6.97 7.02 4.36
1.56 Ideal H VS2 61.5 56.0 11636.0 7.5 7.46 4.6
1.51 Very Good G VS2 61.5 59.0 11640.0 7.34 7.38 4.53
1.51 Good G VS2 64.2 54.0 11640.0 7.18 7.27 4.64
1.34 Ideal G VVS1 62.2 56.0 11640.0 7.11 7.04 4.4
1.13 Ideal E VVS1 61.5 56.0 11641.0 6.68 6.71 4.12
1.41 Premium E VS2 62.7 56.0 11644.0 7.18 7.1 4.48
1.37 Ideal F VS1 59.6 57.0 11649.0 7.28 7.22 4.32
1.11 Very Good F VVS2 59.4 58.0 11650.0 6.74 6.79 4.02
1.45 Premium F VS2 61.1 58.0 11650.0 7.31 7.23 4.44
1.54 Ideal I VS1 61.5 56.0 11651.0 7.42 7.47 4.58
1.5 Very Good H VVS1 63.8 57.0 11654.0 7.17 7.21 4.59
1.14 Premium F VVS1 59.4 59.0 11654.0 6.87 6.8 4.06
1.1 Ideal D VVS2 62.2 57.0 11654.0 6.58 6.54 4.08
1.5 Premium I VS2 61.4 58.0 11655.0 7.28 7.24 4.46
1.01 Ideal D VVS1 62.5 55.0 11661.0 6.39 6.44 4.01
1.54 Premium H VS2 61.9 59.0 11663.0 7.31 7.33 4.53
1.45 Very Good F VS2 62.6 58.0 11667.0 7.12 7.2 4.48
1.03 Very Good D VVS2 62.7 58.0 11677.0 6.39 6.43 4.02
1.52 Very Good H VS2 59.7 55.0 11681.0 7.45 7.42 4.44
1.51 Ideal H VS1 62.6 56.0 11686.0 7.32 7.28 4.57
1.5 Very Good H VVS1 60.9 61.0 11688.0 7.36 7.39 4.49
1.51 Ideal H VS2 61.9 59.0 11696.0 7.29 7.34 4.53
1.52 Good F VS2 64.2 59.0 11696.0 7.16 7.2 4.61
1.55 Very Good G VS2 63.1 57.0 11703.0 7.36 7.31 4.63
1.54 Very Good G VS2 63.0 59.0 11708.0 7.3 7.36 4.62
1.55 Premium H VS2 61.3 61.0 11708.0 7.46 7.39 4.55
1.71 Ideal J VS1 62.1 55.0 11711.0 7.73 7.65 4.78
1.57 Premium G VS2 62.7 60.0 11711.0 7.46 7.38 4.65
1.57 Premium H VS1 62.8 60.0 11711.0 7.44 7.36 4.65
1.24 Very Good F VVS1 62.7 61.0 11716.0 6.75 6.84 4.26
1.16 Premium G VVS1 61.6 55.0 11717.0 6.85 6.72 4.18
1.53 Very Good H VS2 62.5 61.0 11722.0 7.28 7.38 4.58
1.76 Ideal I VS1 62.0 57.0 11722.0 7.74 7.71 4.79
1.22 Premium F VVS1 61.9 58.0 11723.0 6.81 6.85 4.23
1.39 Ideal E VS2 60.8 57.0 11726.0 7.24 7.21 4.39
1.22 Ideal F VVS2 61.9 53.0 11730.0 6.9 6.92 4.28
1.14 Very Good F VVS1 62.2 55.9 11737.0 6.67 6.69 4.16
1.55 Premium H VS2 60.7 59.0 11738.0 7.46 7.5 4.54
1.51 Fair G VS1 64.9 55.0 11739.0 7.25 7.14 4.67
1.51 Good G VS2 64.2 54.0 11746.0 7.27 7.18 4.64
1.51 Premium G VS2 58.1 61.0 11746.0 7.57 7.54 4.39
1.5 Very Good H VVS2 62.9 59.0 11748.0 7.26 7.31 4.58
1.02 Ideal D VVS2 61.0 56.0 11765.0 6.52 6.55 3.99
1.01 Ideal D VVS1 62.5 55.0 11767.0 6.44 6.39 4.01
1.54 Premium H VS2 61.9 59.0 11769.0 7.33 7.31 4.53
1.36 Very Good G VVS2 60.8 60.0 11774.0 7.12 7.16 4.34
1.7 Premium I VVS2 62.1 59.0 11775.0 7.6 7.53 4.7
1.7 Premium I VS2 62.2 58.0 11781.0 7.6 7.65 4.74
1.58 Premium G VS2 58.2 58.0 11786.0 7.68 7.64 4.46
2.0 Premium J VS1 62.0 62.0 11793.0 8.02 7.91 4.94
1.36 Ideal D VS2 62.1 55.0 11793.0 7.18 7.13 4.44
1.54 Premium D VS2 59.4 59.0 11795.0 7.61 7.55 4.5
1.6 Premium H VS2 62.1 60.0 11796.0 7.51 7.44 4.64
1.27 Premium F VVS2 61.3 60.0 11797.0 6.9 6.99 4.26
1.56 Very Good H VS1 63.9 57.0 11804.0 7.3 7.37 4.69
1.52 Good H VS1 63.5 60.0 11804.0 7.24 7.28 4.61
1.54 Premium G VS2 63.0 59.0 11815.0 7.36 7.3 4.62
1.06 Very Good D VVS1 61.8 57.0 11815.0 6.49 6.52 4.03
1.41 Ideal G VS1 62.6 56.0 11817.0 7.15 7.2 4.49
1.65 Very Good H VS1 62.0 56.0 11823.0 7.53 7.59 4.68
1.51 Good H VVS2 63.1 59.0 11826.0 7.26 7.28 4.59
1.61 Ideal I VS2 61.7 56.0 11826.0 7.52 7.62 4.67
1.22 Premium F VVS1 61.9 58.0 11830.0 6.85 6.81 4.23
1.06 Ideal D VVS2 62.0 57.0 11837.0 6.52 6.54 4.05
1.2 Ideal E VVS2 62.2 57.0 11839.0 6.81 6.77 4.22
1.27 Ideal E VS1 61.8 57.0 11840.0 6.94 6.98 4.3
1.73 Very Good I VS1 63.4 58.0 11843.0 7.57 7.6 4.81
1.14 Ideal F VVS1 62.2 56.0 11844.0 6.69 6.67 4.16
1.55 Premium H VS2 60.7 59.0 11846.0 7.5 7.46 4.54
1.36 Ideal G VVS2 61.1 57.0 11848.0 7.14 7.2 4.38
1.71 Very Good I VS2 62.8 59.0 11850.0 7.52 7.58 4.74
1.46 Good E VS2 63.9 57.0 11851.0 7.06 7.12 4.53
1.3 Ideal G VVS2 60.9 57.0 11853.0 7.04 7.11 4.31
1.52 Premium H VS1 58.4 59.0 11853.0 7.55 7.52 4.4
1.5 Premium H VVS2 62.9 59.0 11855.0 7.31 7.26 4.58
1.51 Ideal H VS1 60.8 57.0 11856.0 7.43 7.46 4.53
1.42 Premium G VS1 61.7 55.0 11861.0 7.29 7.24 4.48
1.67 Very Good I VS1 61.6 59.1 11867.0 7.61 7.64 4.7
1.73 Premium G VS1 61.6 60.0 11867.0 7.67 7.62 4.71
1.35 Premium G VVS2 60.2 59.0 11868.0 7.2 7.16 4.32
1.55 Ideal I VVS1 62.1 56.0 11869.0 7.36 7.43 4.59
1.22 Ideal F VVS2 62.2 54.0 11870.0 6.83 6.87 4.26
1.5 Very Good I VS1 63.3 54.0 11879.0 7.26 7.33 4.62
1.22 Ideal F VVS2 62.7 54.0 11880.0 6.79 6.84 4.27
1.2 Ideal E VVS2 61.5 57.0 11883.0 6.79 6.89 4.21
1.17 Ideal F VVS1 62.1 57.0 11886.0 6.82 6.73 4.21
1.7 Premium I VS2 62.2 58.0 11888.0 7.65 7.6 4.74
1.52 Very Good H VVS2 63.0 60.0 11904.0 7.25 7.3 4.58
1.27 Premium F VVS2 61.3 60.0 11905.0 6.99 6.9 4.26
1.18 Ideal E VVS2 61.5 57.0 11906.0 6.8 6.75 4.17
1.52 Very Good H VS1 63.5 60.0 11912.0 7.28 7.24 4.61
1.2 Very Good F VVS1 59.8 63.0 11913.0 6.82 6.8 4.07
1.7 Good I VS1 58.0 60.0 11921.0 7.84 7.88 4.56
1.56 Ideal H VS2 61.6 57.0 11922.0 7.51 7.45 4.61
1.01 Very Good D VVS1 63.9 56.0 11923.0 6.32 6.36 4.05
1.51 Very Good G VS2 62.8 57.0 11923.0 7.25 7.3 4.57
1.23 Ideal F VVS2 61.9 55.0 11927.0 6.92 6.89 4.27
1.51 Very Good H VVS2 63.1 59.0 11934.0 7.28 7.26 4.59
1.5 Very Good E VS2 61.9 57.0 11939.0 7.31 7.38 4.55
1.36 Very Good F VS1 62.7 60.0 11946.0 7.05 7.02 4.41
1.52 Ideal H VS1 60.1 60.0 11946.0 7.54 7.51 4.52
2.01 Good I VS2 64.3 60.0 11954.0 7.91 7.86 5.07
1.34 Ideal G VVS2 62.0 55.0 11955.0 7.02 7.08 4.37
1.36 Ideal G VVS2 61.1 57.0 11956.0 7.2 7.14 4.38
1.71 Premium I VS2 62.8 59.0 11958.0 7.58 7.52 4.74
1.63 Ideal I VS2 61.8 56.0 11963.0 7.56 7.59 4.68
2.0 Fair J VS2 65.4 58.0 11966.0 7.96 7.75 5.14
2.0 Premium J VS2 62.9 60.0 11966.0 7.99 7.95 5.01
1.51 Ideal H VS1 62.3 57.0 11967.0 7.34 7.29 4.55
2.24 Premium J VS1 60.9 58.0 11970.0 8.46 8.41 5.14
1.27 Ideal F VS1 61.6 55.0 11973.0 6.97 7.03 4.31
1.31 Ideal G VVS2 61.3 58.0 11975.0 7.03 7.07 4.32
1.67 Ideal I VS1 61.6 59.0 11975.0 7.64 7.61 4.7
1.52 Premium H VVS2 61.2 58.0 11979.0 7.48 7.41 4.56
1.53 Premium H VVS2 60.4 60.0 11982.0 7.41 7.46 4.49
1.52 Very Good G VS2 63.4 58.0 11986.0 7.31 7.24 4.61
1.57 Ideal H VS2 61.8 55.0 12004.0 7.45 7.49 4.62
1.5 Very Good G VS1 63.4 59.0 12005.0 7.25 7.19 4.58
1.31 Ideal G VS1 61.6 57.0 12008.0 6.99 7.04 4.32
1.52 Premium H VVS2 63.0 60.0 12013.0 7.3 7.25 4.58
1.5 Very Good G VS2 60.5 57.0 12014.0 7.39 7.43 4.48
1.11 Ideal D VVS2 63.0 57.0 12016.0 6.58 6.65 4.17
1.7 Ideal I VS1 63.0 55.0 12030.0 7.75 7.54 4.76
1.7 Premium I VS1 58.0 60.0 12030.0 7.88 7.84 4.56
1.07 Ideal E VVS2 61.3 56.0 12031.0 6.57 6.62 4.04
1.02 Ideal E VVS1 62.2 58.0 12035.0 6.42 6.44 4.0
1.22 Ideal E VVS2 63.0 55.0 12036.0 6.83 6.78 4.29
1.52 Very Good G VS2 62.9 60.0 12038.0 7.28 7.31 4.59
1.52 Premium H VS1 60.6 58.0 12047.0 7.46 7.39 4.5
1.59 Ideal H VS1 61.8 57.0 12047.0 7.42 7.49 4.61
1.06 Ideal D VVS2 62.0 56.0 12053.0 6.53 6.57 4.06
1.5 Ideal H VS1 61.2 56.0 12055.0 7.39 7.4 4.52
1.24 Ideal F VVS2 62.0 57.0 12059.0 6.86 6.91 4.27
1.54 Ideal H VVS2 62.6 56.0 12061.0 7.35 7.42 4.62
1.28 Ideal F VS1 61.5 55.0 12061.0 6.98 7.01 4.3
1.26 Ideal G VVS2 61.4 55.0 12066.0 6.96 6.99 4.29
1.51 Premium G VS2 61.4 58.0 12068.0 7.4 7.3 4.51 
// selecting a subset of fields
display(spark.sql("SELECT carat, clarity, price FROM diamonds WHERE color = 'D'"))
carat clarity price
0.23 VS2 357.0
0.23 VS1 402.0
0.26 VS2 403.0
0.26 VS2 403.0
0.26 VS1 403.0
0.22 VS2 404.0
0.3 SI1 552.0
0.3 SI1 552.0
0.3 SI1 552.0
0.24 VVS1 553.0
0.26 VVS2 554.0
0.26 VVS2 554.0
0.26 VVS1 554.0
0.75 SI1 2760.0
0.71 SI2 2762.0
0.61 VVS2 2763.0
0.71 SI1 2764.0
0.71 SI1 2764.0
0.7 VS2 2767.0
0.71 SI2 2767.0
0.73 SI1 2768.0
0.7 SI1 2768.0
0.71 SI2 2768.0
0.71 VS2 2770.0
0.76 SI2 2770.0
0.73 SI2 2770.0
0.75 SI2 2773.0
0.7 VS2 2773.0
0.7 VS1 2777.0
0.53 VVS2 2782.0
0.75 SI2 2782.0
0.72 SI1 2782.0
0.72 SI1 2782.0
0.7 SI1 2782.0
0.64 VS1 2787.0
0.71 VS2 2788.0
0.72 SI2 2795.0
0.71 SI1 2797.0
0.71 SI1 2797.0
0.71 SI1 2797.0
0.51 VVS1 2797.0
0.78 SI1 2799.0
0.91 SI2 2803.0
0.7 SI1 2804.0
0.7 SI1 2804.0
0.72 SI1 2804.0
0.72 SI1 2804.0
0.73 SI1 2808.0
0.81 SI2 2809.0
0.74 SI2 2810.0
0.83 SI1 2811.0
0.71 SI1 2812.0
0.55 VVS1 2815.0
0.71 VS1 2816.0
0.73 SI1 2821.0
0.71 SI1 2822.0
0.71 SI1 2822.0
0.7 SI1 2822.0
0.7 SI1 2822.0
0.71 SI1 2822.0
0.7 SI1 2822.0
0.7 SI1 2822.0
0.7 SI1 2822.0
0.7 SI1 2822.0
0.79 SI2 2823.0
0.71 VS2 2824.0
0.7 VS2 2826.0
0.7 SI1 2827.0
0.72 VS2 2827.0
0.7 SI2 2828.0
0.7 VS2 2833.0
0.7 VS2 2833.0
0.51 VVS1 2834.0
0.92 SI2 2840.0
0.71 VS1 2841.0
0.73 SI1 2841.0
0.73 SI1 2841.0
0.71 SI1 2843.0
0.79 SI1 2846.0
0.76 SI1 2847.0
0.54 VVS2 2848.0
0.75 SI2 2848.0
0.66 VS1 2851.0
0.79 SI2 2853.0
0.79 SI2 2853.0
0.74 VS2 2855.0
0.73 SI1 2858.0
0.71 VS2 2858.0
0.71 VS2 2858.0
0.7 VS2 2859.0
0.7 VS2 2859.0
0.7 VS2 2859.0
0.71 VS1 2860.0
0.71 SI1 2861.0
0.66 VS1 2861.0
0.7 SI1 2862.0
0.8 SI2 2862.0
0.71 SI1 2863.0
0.71 SI1 2863.0
0.71 SI1 2863.0
0.71 SI1 2863.0
0.71 SI1 2863.0
0.71 SI1 2863.0
0.71 SI1 2863.0
0.73 SI1 2865.0
0.56 VVS1 2866.0
0.56 VVS1 2866.0
0.7 VS2 2867.0
1.08 I1 2869.0
0.7 SI1 2872.0
0.75 SI1 2872.0
0.7 SI1 2872.0
0.7 SI1 2872.0
0.7 SI1 2872.0
0.7 SI1 2872.0
0.71 VS2 2874.0
0.79 SI2 2878.0
0.74 SI1 2880.0
0.72 SI1 2883.0
0.77 SI2 2885.0
0.9 SI2 2885.0
0.71 SI1 2887.0
0.72 SI1 2891.0
0.71 VS2 2891.0
0.79 SI1 2896.0
0.77 SI2 2896.0
0.6 VVS2 2897.0
0.54 VVS2 2897.0
0.74 VS2 2897.0
0.75 SI1 2898.0
0.77 SI1 2898.0
0.72 VS1 2900.0
0.75 SI1 2903.0
0.75 SI1 2903.0
0.72 SI1 2903.0
0.72 SI1 2903.0
0.79 SI2 2904.0
0.53 VVS1 2905.0
0.74 VS2 2906.0
0.32 SI1 558.0
0.7 VS2 2909.0
0.7 VS2 2909.0
0.71 VS1 2910.0
0.7 VS2 2913.0
0.71 SI1 2913.0
0.71 SI1 2913.0
0.71 SI1 2913.0
0.71 SI1 2913.0
0.71 SI1 2913.0
0.83 SI2 2918.0
0.71 SI1 2921.0
0.77 SI2 2922.0
0.77 SI2 2923.0
0.8 SI1 2925.0
0.81 SI2 2926.0
0.7 VS2 2928.0
0.59 VVS2 2933.0
0.75 SI2 2933.0
0.71 SI2 2934.0
0.7 SI2 2936.0
0.77 SI1 2939.0
0.76 SI1 2942.0
0.73 SI1 2943.0
0.57 VVS1 2945.0
0.78 SI1 2945.0
0.73 VS2 2947.0
0.73 SI1 2947.0
0.77 SI1 2949.0
0.71 VS2 2950.0
0.72 VS1 2951.0
0.72 SI1 2954.0
0.72 SI1 2954.0
0.75 SI1 2954.0
0.82 SI1 2954.0
0.7 VS1 2956.0
0.56 VVS1 2959.0
0.7 VS2 2960.0
0.7 VS2 2960.0
0.7 VS2 2960.0
0.63 VVS2 2962.0
0.71 SI1 2964.0
0.71 VS2 2968.0
0.77 SI2 2973.0
1.0 SI2 2974.0
0.76 VS2 2977.0
0.7 SI1 2980.0
0.7 VS2 2985.0
0.74 SI1 2987.0
0.83 SI1 2990.0
0.7 VS2 2991.0
0.72 SI1 2993.0
0.81 SI2 2994.0
0.73 SI1 2995.0
0.77 SI1 2996.0
0.7 VS2 2998.0
0.7 VS2 2999.0
0.72 SI1 3001.0
0.7 VS1 3001.0
0.7 VS1 3001.0
0.7 VS1 3001.0
0.71 VS2 3002.0
1.01 SI2 3003.0
0.65 VVS2 3003.0
0.92 SI2 3004.0
0.55 VVS1 3006.0
0.76 SI1 3007.0
0.7 VS1 3008.0
0.8 SI1 3011.0
0.77 SI2 3011.0
0.9 SI1 3013.0
0.73 SI1 3014.0
0.72 VS2 3016.0
0.5 VVS2 3017.0
0.78 SI1 3019.0
0.71 VS2 3020.0
0.75 SI1 3024.0
0.75 SI1 3024.0
0.65 VVS2 3025.0
0.71 VS2 3033.0
0.7 VS2 3033.0
0.7 VS2 3033.0
0.7 VS2 3033.0
0.78 SI1 3035.0
0.71 SI1 3035.0
0.74 SI1 3036.0
0.61 VVS2 3036.0
0.77 SI1 3040.0
0.71 VS2 3045.0
0.72 VS2 3045.0
0.75 SI1 3046.0
0.73 VS1 3047.0
0.75 SI1 3048.0
0.72 SI1 3048.0
0.72 SI1 3048.0
0.66 VVS2 3049.0
0.62 VVS2 3050.0
0.7 VS2 3052.0
0.7 VS2 3053.0
0.7 VS1 3054.0
0.65 VVS2 3056.0
0.92 SI2 3057.0
0.79 SI1 3058.0
0.72 SI1 3062.0
0.85 SI2 3066.0
0.7 VS2 3073.0
0.72 VS2 3075.0
0.72 VS2 3075.0
0.7 SI1 3075.0
0.76 SI1 3075.0
0.71 VS2 3077.0
0.71 VS2 3077.0
0.75 SI1 3078.0
0.83 SI2 3078.0
0.91 SI2 3079.0
0.79 SI2 3081.0
0.7 VS2 3082.0
0.8 SI2 3082.0
0.71 VS2 3084.0
0.75 SI1 3085.0
0.7 VS2 3087.0
0.7 VS2 3087.0
0.7 VS2 3087.0
0.74 VS2 3087.0
0.71 VS1 3090.0
0.71 VS1 3090.0
0.7 VS2 3092.0
0.7 VS2 3092.0
0.7 VS2 3092.0
0.7 VS1 3093.0
0.71 VS2 3096.0
0.71 VS2 3096.0
0.53 VVS1 3097.0
0.72 VS2 3099.0
0.72 SI1 3102.0
0.66 VVS2 3103.0
0.78 SI1 3103.0
0.75 SI1 3105.0
0.7 VS1 3107.0
0.79 SI1 3112.0
0.94 SI2 3125.0
0.57 VVS1 3126.0
0.57 VVS1 3126.0
0.7 VS2 3129.0
0.7 VS2 3131.0
0.71 VS2 3131.0
0.71 VS2 3135.0
0.71 VS2 3135.0
0.8 VS2 3135.0
0.81 SI1 3135.0
0.71 VS1 3136.0
0.71 VS2 3137.0
0.74 SI1 3138.0
0.72 VS2 3139.0
0.54 VVS1 3139.0
0.73 SI1 3140.0
0.71 VS1 3145.0
0.84 SI2 3145.0
0.78 SI1 3145.0
0.75 SI1 3152.0
0.9 SI2 3153.0
0.71 VS2 3153.0
0.58 VVS1 3154.0
0.8 SI2 3154.0
0.77 SI1 3158.0
0.82 SI2 3159.0
0.77 SI1 3160.0
0.81 SI2 3160.0
0.71 VS2 3161.0
0.71 VS2 3161.0
0.71 VS2 3161.0
0.77 SI1 3166.0
0.8 SI2 3173.0
0.72 SI2 3176.0
0.74 VS2 3177.0
0.72 VS2 3179.0
0.72 VS2 3179.0
0.72 VS2 3179.0
0.81 SI1 3179.0
0.73 VS2 3182.0
0.73 VS2 3182.0
0.7 VS1 3183.0
0.79 SI1 3185.0
0.73 SI1 3189.0
0.73 SI1 3189.0
0.71 VS1 3192.0
0.7 VS1 3193.0
0.54 VVS1 3194.0
0.73 SI1 3195.0
0.8 SI1 3195.0
0.7 SI1 3199.0
0.71 VS2 3203.0
0.9 SI2 3205.0
0.9 SI2 3205.0
0.9 SI2 3205.0
0.9 SI2 3205.0
0.9 SI2 3205.0
0.9 SI2 3205.0
0.9 SI2 3205.0
0.72 VS2 3205.0
0.58 VVS1 3206.0
0.83 SI2 3207.0
0.7 VS1 3208.0
0.79 SI1 3209.0
0.8 SI2 3210.0
0.7 VVS2 3210.0
0.71 VS2 3212.0
0.78 SI1 3214.0
0.7 VS1 3214.0
0.95 SI2 3214.0
0.71 VS2 3217.0
0.71 VS2 3217.0
0.71 VS2 3217.0
0.52 VVS1 3218.0
0.72 VS2 3219.0
0.72 VS2 3219.0
0.71 VS2 3222.0
0.71 VS2 3222.0
0.51 VVS2 3223.0
0.8 SI1 3226.0
0.65 VVS2 3228.0
0.7 VS1 3229.0
0.7 VS1 3229.0
0.7 VS1 3231.0
0.59 VVS1 3234.0
0.71 VS2 3234.0
0.72 VS2 3236.0
0.7 VS1 3239.0
0.7 VS1 3239.0
0.7 VS1 3239.0
0.77 SI1 3241.0
0.79 SI1 3242.0
0.71 VS2 3245.0
0.84 SI2 3246.0
0.25 VS1 563.0
0.26 VVS2 564.0
0.31 SI1 565.0
0.31 SI1 565.0
0.7 VS1 3247.0
0.52 VVS1 3247.0
0.76 VS2 3248.0
0.73 VS2 3250.0
0.77 SI1 3251.0
0.71 SI1 3252.0
0.78 SI1 3253.0
0.73 VS2 3255.0
0.78 SI1 3258.0
0.9 SI2 3262.0
0.71 SI1 3262.0
0.84 SI1 3265.0
0.81 SI1 3266.0
0.7 VVS2 3267.0
0.56 VVS1 3270.0
0.79 SI1 3270.0
0.72 VS2 3275.0
0.92 SI2 3277.0
0.7 VS1 3278.0
0.52 VVS2 3284.0
0.86 SI2 3284.0
0.7 VS1 3287.0
0.7 VS1 3287.0
0.77 VS2 3291.0
0.76 VS2 3293.0
0.74 VS2 3294.0
0.7 VVS2 3296.0
0.91 SI2 3298.0
0.78 VS2 3298.0
0.78 VS2 3298.0
0.71 VS2 3299.0
1.0 SI2 3304.0
1.0 SI2 3304.0
1.0 SI2 3304.0
0.76 VS2 3306.0
0.76 SI1 3306.0
0.53 VVS1 3307.0
0.73 VS2 3308.0
0.77 SI1 3309.0
0.31 SI1 565.0
0.31 SI1 565.0
0.8 SI1 3312.0
0.7 VVS2 3312.0
0.8 SI1 3312.0
0.9 SI2 3312.0
0.9 SI2 3312.0
0.7 VVS2 3312.0
0.9 SI2 3312.0
0.71 SI1 3316.0
0.73 VS2 3319.0
0.52 VVS1 3321.0
0.71 VS2 3321.0
0.71 VS2 3321.0
0.72 SI1 3322.0
0.81 SI1 3324.0
0.78 SI1 3326.0
0.79 SI1 3328.0
0.71 VS1 3332.0
0.71 VS1 3333.0
0.92 SI2 3335.0
0.7 VS1 3335.0
0.61 VVS2 3336.0
1.01 SI2 3337.0
0.77 SI1 3345.0
0.53 VVS2 3346.0
0.73 VS2 3346.0
0.83 SI1 3347.0
0.91 SI2 3349.0
0.77 VS2 3351.0
0.76 VS2 3352.0
0.74 VS2 3353.0
0.76 VS1 3353.0
0.81 SI1 3353.0
0.82 SI2 3357.0
0.91 SI1 3357.0
0.7 VS2 3360.0
0.7 VS1 3361.0
0.7 VS1 3365.0
0.74 VS1 3365.0
0.71 VS2 3366.0
0.69 VVS2 3369.0
0.9 SI2 3371.0
0.9 SI2 3371.0
0.71 VS2 3372.0
0.52 VVS1 3373.0
0.7 VS1 3375.0
0.72 VS1 3375.0
0.5 IF 3378.0
0.5 IF 3378.0
0.6 VVS2 3382.0
0.27 VS2 567.0
0.31 VS2 567.0
0.33 SI1 567.0
0.33 SI1 567.0
0.33 SI1 567.0
0.3 VS2 568.0
0.9 SI1 3382.0
0.95 SI2 3384.0
0.76 VS2 3384.0
0.78 SI1 3389.0
0.88 SI2 3390.0
0.61 VVS2 3397.0
0.85 SI2 3398.0
0.76 VS2 3401.0
0.91 SI2 3403.0
0.71 VS1 3406.0
0.71 VS1 3406.0
0.91 SI2 3408.0
0.7 VS1 3410.0
0.73 VS2 3411.0
0.73 VS2 3412.0
0.8 VS2 3419.0
0.7 VS1 3419.0
0.96 SI2 3419.0
0.96 SI2 3419.0
0.71 VS1 3420.0
0.9 SI2 3425.0
0.7 VS1 3425.0
0.77 VS2 3428.0
0.77 VS2 3428.0
0.77 VS2 3428.0
0.77 VS2 3428.0
0.79 SI1 3432.0
0.73 VS2 3440.0
0.8 SI1 3441.0
0.53 VVS1 3442.0
0.77 VS2 3442.0
0.76 VS2 3443.0
0.76 VS2 3443.0
0.51 IF 3446.0
0.51 IF 3446.0
0.7 VS2 3448.0
0.72 VS2 3450.0
0.3 VS2 568.0
0.74 VS2 3454.0
0.78 SI2 3454.0
0.7 SI1 3454.0
0.75 VS2 3456.0
0.72 VVS2 3459.0
0.74 VS1 3461.0
0.81 SI1 3462.0
0.91 SI2 3463.0
0.7 VS1 3463.0
0.73 VS2 3464.0
0.56 VVS1 3465.0
0.71 VS1 3465.0
0.73 VS2 3467.0
0.55 VVS2 3468.0
0.55 VVS2 3468.0
0.55 VVS2 3468.0
0.7 VS1 3471.0
0.7 SI1 3471.0
0.7 SI1 3471.0
0.9 SI2 3473.0
0.9 SI2 3473.0
0.9 SI2 3473.0
0.9 SI2 3473.0
0.9 SI2 3473.0
0.78 VS2 3473.0
0.74 VS2 3476.0
0.7 VS1 3477.0
0.71 VS1 3479.0
0.96 SI2 3480.0
0.74 VS2 3487.0
0.77 VS2 3489.0
0.77 VS2 3489.0
0.72 VS2 3493.0
0.54 VVS1 3494.0
0.72 VS2 3495.0
0.56 VVS1 3496.0
0.74 VS2 3498.0
0.7 VS1 3501.0
0.8 SI1 3502.0
0.71 SI1 3502.0
0.71 SI1 3502.0
0.71 SI1 3502.0
0.9 SI1 3505.0
0.55 IF 3509.0
0.73 VS1 3509.0
0.91 SI2 3511.0
0.74 SI1 3517.0
0.53 IF 3517.0
0.71 VS1 3518.0
0.72 VS1 3522.0
0.71 VS1 3524.0
0.73 VS2 3528.0
0.7 VS1 3529.0
0.32 SI2 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.9 SI2 3534.0
0.9 SI2 3534.0
0.9 SI2 3534.0
0.9 SI2 3534.0
0.78 VS2 3534.0
0.7 VS1 3535.0
0.93 SI2 3540.0
0.71 VS2 3540.0
0.72 VS2 3543.0
0.72 SI1 3550.0
0.92 SI2 3550.0
0.72 VS1 3554.0
0.83 SI1 3556.0
0.83 SI1 3556.0
0.73 VS1 3557.0
0.7 VS2 3561.0
0.75 VS2 3562.0
0.8 SI1 3564.0
0.9 SI1 3567.0
0.7 VS1 3567.0
0.9 SI1 3568.0
0.72 SI1 3568.0
1.0 SI2 3569.0
0.72 VS1 3570.0
0.6 VVS1 3570.0
0.91 SI2 3573.0
0.71 VS1 3576.0
0.9 SI2 3578.0
0.9 SI2 3579.0
0.76 VS2 3581.0
0.71 VS1 3582.0
0.97 SI2 3585.0
1.11 I1 3589.0
0.82 SI1 3593.0
0.78 VS2 3595.0
0.8 SI1 3597.0
0.72 VS1 3601.0
1.01 SI2 3604.0
0.9 VS2 3604.0
1.01 SI2 3605.0
0.79 SI1 3605.0
1.03 SI2 3607.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.92 SI2 3613.0
0.73 SI1 3615.0
0.7 VS1 3618.0
0.7 VS1 3618.0
0.71 VVS2 3618.0
0.72 VS1 3619.0
0.73 VS1 3620.0
0.7 VVS2 3622.0
0.7 VVS2 3622.0
0.72 VS1 3622.0
0.72 VS1 3622.0
0.75 VS2 3625.0
0.61 VVS1 3625.0
0.72 VS1 3629.0
0.9 SI2 3632.0
0.94 SI2 3634.0
1.0 SI2 3634.0
1.0 SI2 3634.0
1.0 SI2 3634.0
1.0 SI2 3634.0
0.9 SI2 3643.0
0.77 VS1 3643.0
1.16 I1 3644.0
0.77 VS1 3644.0
1.11 I1 3655.0
0.91 SI2 3660.0
0.87 SI1 3664.0
0.7 VS2 3668.0
0.78 VS2 3668.0
0.74 VS2 3668.0
0.85 SI1 3669.0
0.71 VVS2 3670.0
1.01 SI2 3671.0
1.01 SI2 3671.0
0.78 VS2 3672.0
0.73 VS2 3673.0
0.71 SI1 3674.0
0.71 SI1 3674.0
1.03 SI2 3675.0
0.75 VS2 3679.0
0.31 SI1 571.0
0.31 SI1 571.0
0.31 SI1 571.0
0.8 SI2 3682.0
0.84 SI1 3685.0
0.9 SI1 3689.0
0.9 SI1 3689.0
0.9 SI1 3689.0
0.9 SI1 3689.0
0.9 SI1 3689.0
0.9 SI1 3689.0
0.71 VS1 3690.0
0.94 SI2 3691.0
0.75 VS1 3696.0
0.9 SI2 3706.0
0.92 SI2 3707.0
0.86 SI1 3709.0
1.16 I1 3711.0
0.75 SI1 3712.0
0.71 VS1 3716.0
0.71 VS1 3718.0
0.77 VS2 3721.0
0.72 SI1 3722.0
0.91 SI1 3730.0
0.91 SI1 3730.0
0.91 SI1 3730.0
0.58 VVS1 3732.0
0.76 SI1 3732.0
0.73 VS2 3735.0
0.78 VS2 3736.0
0.7 VVS2 3737.0
0.9 SI2 3740.0
0.9 SI2 3740.0
0.9 SI2 3740.0
0.9 SI2 3740.0
0.58 VVS1 3741.0
0.87 SI1 3742.0
1.09 SI2 3742.0
1.03 SI2 3743.0
1.03 SI2 3743.0
0.93 SI2 3744.0
0.74 VS1 3746.0
0.3 SI2 574.0
0.9 SI1 3751.0
0.7 VS1 3752.0
0.9 SI1 3755.0
0.9 SI1 3755.0
0.77 VS2 3755.0
0.61 VVS2 3758.0
0.78 VS2 3763.0
0.91 SI2 3763.0
1.0 SI2 3767.0
1.02 I1 3769.0
1.02 SI2 3773.0
0.83 SI2 3774.0
1.04 SI2 3780.0
1.04 SI2 3780.0
0.9 SI2 3780.0
1.04 SI2 3780.0
1.5 I1 3780.0
0.91 SI2 3781.0
0.91 SI2 3781.0
0.77 VS2 3787.0
0.7 VS2 3788.0
0.9 SI2 3789.0
0.59 VVS1 3791.0
0.91 SI1 3796.0
0.79 VS1 3798.0
0.9 SI2 3798.0
0.9 SI2 3798.0
0.9 SI2 3798.0
0.71 VVS2 3799.0
0.78 VS1 3800.0
0.71 VS1 3801.0
0.9 SI2 3806.0
0.9 SI2 3806.0
0.9 SI2 3806.0
0.84 SI1 3809.0
0.78 VS2 3811.0
0.74 VS1 3812.0
0.53 IF 3812.0
0.93 SI1 3812.0
0.9 SI1 3812.0
0.9 SI1 3812.0
0.9 SI1 3812.0
0.93 SI1 3812.0
0.74 VS1 3813.0
1.18 I1 3816.0
0.84 SI1 3816.0
1.05 SI2 3816.0
0.79 VS2 3818.0
0.9 SI2 3818.0
0.3 SI2 574.0
0.3 SI2 574.0
0.3 SI2 574.0
0.3 SI2 574.0
0.3 SI2 574.0
0.3 SI2 574.0
0.3 SI2 574.0
0.3 SI2 574.0
0.3 SI2 574.0
0.3 SI2 574.0
0.3 SI2 574.0
0.3 SI2 574.0
0.85 VS2 3821.0
0.92 SI2 3823.0
0.53 IF 3827.0
0.91 SI2 3828.0
0.63 IF 3832.0
0.91 SI2 3837.0
0.77 VS2 3837.0
0.71 VS2 3838.0
1.02 I1 3838.0
1.02 SI2 3839.0
0.93 SI2 3839.0
0.7 VS1 3840.0
1.02 SI2 3842.0
0.92 SI2 3843.0
0.9 SI2 3847.0
0.91 SI2 3848.0
0.91 SI2 3848.0
0.91 SI2 3848.0
0.6 VVS1 3850.0
0.81 SI1 3852.0
0.91 SI1 3855.0
0.73 VS1 3856.0
0.71 VVS2 3856.0
0.74 VS2 3858.0
0.94 SI2 3862.0
0.78 VS2 3864.0
1.17 SI2 3866.0
0.9 SI2 3871.0
1.01 SI2 3871.0
0.87 VS2 3873.0
0.92 SI2 3877.0
0.71 VVS2 3877.0
0.9 SI1 3880.0
0.9 SI1 3880.0
0.9 SI1 3880.0
0.93 SI1 3880.0
1.13 SI2 3883.0
1.18 I1 3886.0
0.91 SI2 3889.0
0.3 SI2 574.0
0.3 SI2 574.0
0.3 SI2 574.0
0.3 SI2 574.0
0.3 SI2 574.0
0.3 SI2 574.0
0.25 VVS2 575.0
0.27 VVS2 575.0
0.25 VVS2 575.0
1.09 SI2 3890.0
0.92 SI2 3891.0
1.0 SI2 3894.0
0.76 VS1 3894.0
0.72 VS1 3896.0
1.18 SI2 3899.0
1.02 SI2 3909.0
1.02 SI2 3909.0
0.91 SI2 3910.0
0.91 SI2 3911.0
0.66 VVS2 3915.0
0.92 SI2 3916.0
0.9 SI2 3918.0
0.7 VVS1 3920.0
0.78 VS1 3923.0
0.9 VS2 3931.0
1.01 SI2 3932.0
0.83 SI1 3933.0
0.92 SI2 3936.0
0.73 VS1 3937.0
0.91 SI2 3943.0
0.9 SI1 3945.0
0.91 SI2 3949.0
1.14 I1 3950.0
0.76 VS1 3950.0
0.71 VVS1 3952.0
0.91 SI2 3958.0
1.01 SI2 3959.0
0.75 VS1 3961.0
1.09 SI2 3961.0
0.88 SI2 3962.0
1.0 SI2 3965.0
1.0 SI2 3965.0
1.0 SI2 3965.0
1.0 SI2 3965.0
1.0 SI2 3965.0
0.33 SI1 575.0
1.0 SI2 3965.0
0.77 VS1 3966.0
0.62 VVS1 3968.0
1.02 SI2 3971.0
0.9 SI2 3975.0
0.9 SI2 3975.0
1.23 I1 3977.0
0.77 VS2 3980.0
0.73 VS2 3980.0
0.83 VS1 3984.0
0.9 SI2 3989.0
0.96 SI2 3989.0
0.9 SI2 3990.0
0.93 SI2 3990.0
0.83 SI1 3990.0
0.92 SI2 3997.0
0.9 SI1 3997.0
0.9 SI1 3997.0
0.9 SI1 3997.0
0.9 SI1 3997.0
0.9 SI1 3997.0
0.7 VS1 4003.0
1.01 SI2 4004.0
0.75 VS1 4007.0
0.9 SI2 4007.0
0.9 SI2 4007.0
0.87 SI2 4012.0
0.71 VVS2 4014.0
0.7 VVS2 4022.0
0.65 VVS1 4022.0
1.14 I1 4022.0
0.56 IF 4025.0
0.71 VS2 4029.0
0.71 VS2 4029.0
0.71 VS2 4029.0
0.71 VS2 4029.0
0.71 VS2 4029.0
0.57 IF 4032.0
0.77 VS1 4037.0
0.77 VS1 4039.0
0.74 VVS2 4040.0
0.91 SI1 4041.0
0.54 VVS1 4042.0
1.02 SI2 4044.0
1.02 SI2 4044.0
1.02 SI2 4044.0
0.72 VS1 4047.0
1.23 I1 4050.0
0.91 SI2 4051.0
0.91 SI2 4051.0
0.91 SI2 4051.0
0.96 SI2 4060.0
1.01 SI2 4064.0
1.0 SI2 4065.0
0.91 SI2 4067.0
0.9 SI1 4068.0
0.9 SI1 4068.0
0.9 SI1 4068.0
0.9 SI1 4068.0
0.9 SI1 4068.0
0.9 SI1 4068.0
1.12 SI2 4071.0
1.01 SI2 4072.0
0.9 SI2 4078.0
0.9 SI2 4078.0
0.9 SI2 4078.0
0.72 VS2 4082.0
0.72 VS2 4082.0
0.64 VVS1 4084.0
0.92 SI1 4086.0
0.81 VS2 4087.0
0.7 VS1 4095.0
0.92 SI2 4096.0
0.92 SI2 4096.0
0.25 VS1 410.0
0.23 VS2 411.0
0.27 VS1 413.0
0.3 SI2 413.0
0.3 SI2 413.0
0.23 VS2 577.0
0.91 VS2 4107.0
0.91 VS2 4107.0
0.87 SI1 4108.0
0.91 SI1 4113.0
0.82 SI1 4113.0
0.9 SI2 4114.0
0.73 VS1 4116.0
0.9 SI1 4117.0
1.01 SI1 4118.0
0.9 SI1 4120.0
0.91 SI2 4123.0
0.91 SI2 4123.0
0.91 SI2 4123.0
1.04 SI2 4123.0
0.9 VS2 4128.0
0.9 SI1 4130.0
0.9 SI2 4133.0
0.73 VS2 4134.0
0.73 VS2 4134.0
0.82 SI1 4135.0
0.82 SI1 4135.0
1.12 I1 4139.0
0.93 SI2 4140.0
0.93 SI2 4140.0
0.92 SI2 4150.0
0.76 VVS2 4150.0
1.0 SI1 4155.0
1.06 SI2 4155.0
0.92 SI1 4158.0
0.92 SI1 4158.0
0.83 SI1 4159.0
0.59 IF 4161.0
0.93 SI2 4165.0
0.91 SI1 4165.0
0.9 SI2 4167.0
0.92 SI2 4168.0
0.92 SI2 4168.0
1.19 SI2 4168.0
0.8 VS2 4170.0
0.6 VVS1 4172.0
1.03 SI2 4177.0
0.9 SI1 4178.0 
//renaming a field using as
display(spark.sql("SELECT carat AS carrot, clarity, price FROM diamonds"))
carrot clarity price
0.23 SI2 326.0
0.21 SI1 326.0
0.23 VS1 327.0
0.29 VS2 334.0
0.31 SI2 335.0
0.24 VVS2 336.0
0.24 VVS1 336.0
0.26 SI1 337.0
0.22 VS2 337.0
0.23 VS1 338.0
0.3 SI1 339.0
0.23 VS1 340.0
0.22 SI1 342.0
0.31 SI2 344.0
0.2 SI2 345.0
0.32 I1 345.0
0.3 SI2 348.0
0.3 SI1 351.0
0.3 SI1 351.0
0.3 SI1 351.0
0.3 SI2 351.0
0.23 VS2 352.0
0.23 VS1 353.0
0.31 SI1 353.0
0.31 SI1 353.0
0.23 VVS2 354.0
0.24 VS1 355.0
0.3 VS2 357.0
0.23 VS2 357.0
0.23 VS1 357.0
0.23 VS1 402.0
0.23 VS1 402.0
0.23 VS1 402.0
0.23 VS1 402.0
0.23 VS1 402.0
0.23 VS1 402.0
0.23 VS1 402.0
0.31 SI1 402.0
0.26 VS2 403.0
0.33 SI2 403.0
0.33 SI2 403.0
0.33 SI1 403.0
0.26 VS2 403.0
0.26 VS1 403.0
0.32 SI2 403.0
0.29 SI1 403.0
0.32 SI2 403.0
0.32 SI2 403.0
0.25 VS2 404.0
0.29 SI2 404.0
0.24 SI1 404.0
0.23 VS1 404.0
0.32 SI1 404.0
0.22 VS2 404.0
0.22 VS2 404.0
0.3 SI2 405.0
0.3 SI2 405.0
0.3 SI1 405.0
0.3 SI1 405.0
0.3 SI1 405.0
0.35 VS1 552.0
0.3 SI1 552.0
0.3 SI1 552.0
0.3 SI1 552.0
0.42 SI2 552.0
0.28 VVS2 553.0
0.32 VVS1 553.0
0.31 SI1 553.0
0.31 SI1 553.0
0.24 VVS1 553.0
0.24 VVS1 553.0
0.3 SI1 554.0
0.3 SI1 554.0
0.3 SI1 554.0
0.3 SI1 554.0
0.26 VVS2 554.0
0.26 VVS2 554.0
0.26 VVS2 554.0
0.26 VVS2 554.0
0.26 VVS1 554.0
0.26 VVS1 554.0
0.26 VVS1 554.0
0.26 VVS2 554.0
0.38 SI2 554.0
0.26 VVS1 554.0
0.24 VVS1 554.0
0.24 VVS1 554.0
0.24 VVS1 554.0
0.24 VVS2 554.0
0.32 SI1 554.0
0.7 SI1 2757.0
0.86 SI2 2757.0
0.7 VS2 2757.0
0.71 VS2 2759.0
0.78 SI2 2759.0
0.7 VS2 2759.0
0.7 VS1 2759.0
0.96 SI2 2759.0
0.73 SI1 2760.0
0.8 SI1 2760.0
0.75 SI1 2760.0
0.75 SI1 2760.0
0.74 SI1 2760.0
0.75 VS2 2760.0
0.8 VS1 2760.0
0.75 SI1 2760.0
0.8 SI1 2760.0
0.74 VVS2 2761.0
0.81 SI2 2761.0
0.59 VVS2 2761.0
0.8 SI2 2761.0
0.74 SI2 2761.0
0.9 VS2 2761.0
0.74 SI1 2762.0
0.73 VS2 2762.0
0.73 VS2 2762.0
0.8 SI2 2762.0
0.71 VS2 2762.0
0.7 VS2 2762.0
0.8 SI2 2762.0
0.71 SI2 2762.0
0.74 SI1 2762.0
0.7 VS2 2762.0
0.7 VS2 2762.0
0.7 VS2 2762.0
0.7 VS2 2762.0
0.91 SI1 2763.0
0.61 VVS2 2763.0
0.91 SI2 2763.0
0.91 SI2 2763.0
0.77 VS2 2763.0
0.71 SI1 2764.0
0.71 SI1 2764.0
0.7 VS2 2765.0
0.77 VS1 2765.0
0.63 VVS1 2765.0
0.71 VS1 2765.0
0.71 VS1 2765.0
0.76 SI1 2765.0
0.64 VVS1 2766.0
0.71 VS2 2766.0
0.71 VS2 2766.0
0.7 VS2 2767.0
0.7 VS1 2767.0
0.71 SI2 2767.0
0.7 VVS2 2767.0
0.71 VS1 2768.0
0.73 SI1 2768.0
0.7 SI1 2768.0
0.7 SI1 2768.0
0.71 SI2 2768.0
0.74 SI1 2769.0
0.71 VS2 2770.0
0.73 VS2 2770.0
0.76 SI1 2770.0
0.76 SI2 2770.0
0.71 SI1 2770.0
0.73 VS2 2770.0
0.73 VS1 2770.0
0.73 SI2 2770.0
0.73 VS2 2770.0
0.72 VVS2 2771.0
0.73 SI1 2771.0
0.71 VS2 2771.0
0.79 SI2 2771.0
0.73 VVS1 2772.0
0.8 SI2 2772.0
0.58 VVS1 2772.0
0.58 VVS1 2772.0
0.71 VS2 2772.0
0.75 SI2 2773.0
0.7 VS2 2773.0
1.17 I1 2774.0
0.6 VS1 2774.0
0.7 SI1 2774.0
0.83 VS2 2774.0
0.74 VS2 2775.0
0.72 VS2 2776.0
0.71 VS2 2776.0
0.71 VS2 2776.0
0.54 VVS2 2776.0
0.54 VVS2 2776.0
0.72 SI1 2776.0
0.72 SI1 2776.0
0.72 VS2 2776.0
0.71 SI1 2776.0
0.7 VS1 2777.0
0.71 VS2 2777.0
0.71 VS2 2777.0
0.71 VS2 2777.0
0.71 VS2 2777.0
0.7 VS2 2777.0
0.7 VS2 2777.0
0.7 SI1 2777.0
0.7 SI1 2777.0
0.7 SI1 2777.0
0.7 SI1 2777.0
0.7 SI1 2777.0
0.7 SI1 2777.0
0.72 SI1 2777.0
0.7 SI1 2777.0
0.7 SI1 2777.0
0.7 SI1 2777.0
0.7 VS2 2777.0
0.98 SI2 2777.0
0.78 SI1 2777.0
0.7 SI1 2777.0
0.52 VVS1 2778.0
0.73 VS2 2779.0
0.74 SI1 2779.0
0.7 VS2 2780.0
0.77 VS2 2780.0
0.71 VS2 2780.0
0.74 VS1 2780.0
0.7 VS1 2780.0
1.01 I1 2781.0
0.77 SI1 2781.0
0.78 SI1 2781.0
0.72 VS1 2782.0
0.53 VVS2 2782.0
0.76 VS2 2782.0
0.7 VS1 2782.0
0.7 VS1 2782.0
0.75 SI2 2782.0
0.72 SI1 2782.0
0.72 SI1 2782.0
0.7 SI1 2782.0
0.84 SI1 2782.0
0.75 SI1 2782.0
0.52 IF 2783.0
0.72 VS2 2784.0
0.79 VS1 2784.0
0.72 VS2 2787.0
0.51 VVS1 2787.0
0.64 VS1 2787.0
0.7 VVS1 2788.0
0.83 VS1 2788.0
0.76 VVS2 2788.0
0.71 VS2 2788.0
0.77 VS1 2788.0
0.71 SI1 2788.0
1.01 I1 2788.0
1.01 SI2 2788.0
0.77 SI1 2789.0
0.76 SI1 2789.0
0.76 SI1 2789.0
0.76 SI1 2789.0
1.05 SI2 2789.0
0.81 SI2 2789.0
0.7 SI1 2789.0
0.55 IF 2789.0
0.81 SI2 2789.0
0.63 VVS2 2789.0
0.63 VVS1 2789.0
0.77 VS1 2789.0
1.05 SI2 2789.0
0.64 IF 2790.0
0.76 VVS1 2790.0
0.83 SI2 2790.0
0.71 VS1 2790.0
0.71 VS1 2790.0
0.87 SI1 2791.0
0.73 SI1 2791.0
0.71 SI1 2792.0
0.71 SI1 2792.0
0.71 SI1 2792.0
0.7 VS1 2792.0
0.7 VS1 2792.0
0.76 VVS2 2792.0
0.7 VS1 2792.0
0.79 SI1 2793.0
0.7 VS2 2793.0
0.7 VS2 2793.0
0.76 VS2 2793.0
0.73 VS2 2793.0
0.79 SI1 2794.0
0.71 VS2 2795.0
0.81 VVS2 2795.0
0.81 SI2 2795.0
0.72 VS1 2795.0
0.72 SI2 2795.0
0.72 IF 2795.0
0.81 VS2 2795.0
0.72 VS2 2795.0
1.0 SI2 2795.0
0.73 SI1 2796.0
0.81 SI2 2797.0
0.81 SI1 2797.0
0.71 SI1 2797.0
0.71 SI1 2797.0
0.71 SI1 2797.0
0.57 VVS2 2797.0
0.51 VVS1 2797.0
0.72 VS2 2797.0
0.74 VS1 2797.0
0.74 VS1 2797.0
0.7 VVS1 2797.0
0.8 SI2 2797.0
1.01 SI2 2797.0
0.8 VS2 2797.0
0.77 VS1 2798.0
0.83 SI2 2799.0
0.82 SI2 2799.0
0.78 SI1 2799.0
0.6 IF 2800.0
0.9 SI2 2800.0
0.7 VS1 2800.0
0.9 SI2 2800.0
0.83 SI1 2800.0
0.83 SI1 2800.0
0.83 SI1 2800.0
0.74 VS1 2800.0
0.79 VS1 2800.0
0.61 IF 2800.0
0.76 VS1 2800.0
0.96 I1 2801.0
0.73 VS2 2801.0
0.73 VS2 2801.0
0.75 SI1 2801.0
0.71 VS2 2801.0
0.71 VS2 2801.0
0.71 VS2 2801.0
0.71 VS2 2801.0
1.04 I1 2801.0
1.0 SI2 2801.0
0.87 SI2 2802.0
0.53 IF 2802.0
0.72 VS2 2802.0
0.72 VS1 2802.0
0.7 VS2 2803.0
0.74 SI1 2803.0
0.71 VS2 2803.0
0.73 SI1 2803.0
0.7 VS1 2803.0
0.71 VS2 2803.0
0.71 VS2 2803.0
0.71 VS1 2803.0
0.77 VS2 2803.0
0.71 VS2 2803.0
0.78 VS2 2803.0
0.71 VS1 2803.0
0.91 SI2 2803.0
0.71 VS2 2804.0
0.71 VS2 2804.0
0.8 SI2 2804.0
0.7 SI1 2804.0
0.72 VS1 2804.0
0.72 VS1 2804.0
0.82 VS2 2804.0
0.7 SI1 2804.0
0.72 SI1 2804.0
0.72 SI1 2804.0
0.9 SI1 2804.0
0.74 VS2 2805.0
0.74 VS2 2805.0
0.73 SI2 2805.0
0.57 VVS1 2805.0
0.73 VS2 2805.0
0.72 VS2 2805.0
0.74 VS2 2805.0
0.82 VS2 2805.0
0.81 SI1 2806.0
0.75 VVS1 2806.0
0.7 SI1 2806.0
0.71 VS1 2807.0
0.71 VS1 2807.0
0.93 SI2 2807.0
0.8 VS2 2808.0
0.7 VS1 2808.0
1.0 I1 2808.0
0.75 VS2 2808.0
0.58 VVS2 2808.0
0.73 SI1 2808.0
0.81 SI1 2809.0
0.81 SI2 2809.0
0.71 SI1 2809.0
1.2 I1 2809.0
0.7 VS1 2810.0
0.7 VS1 2810.0
0.74 SI2 2810.0
0.7 VS1 2810.0
0.8 SI1 2810.0
0.75 SI1 2811.0
0.83 SI1 2811.0
1.0 VS2 2811.0
0.99 SI2 2811.0
0.7 VS1 2812.0
0.7 VS2 2812.0
0.7 SI1 2812.0
0.7 VS2 2812.0
0.32 SI1 554.0
0.32 SI1 554.0
0.32 SI1 554.0
0.32 SI1 554.0
0.32 SI1 554.0
0.32 SI1 554.0
0.3 SI1 554.0
0.3 SI1 554.0
0.3 SI1 554.0
0.3 SI1 554.0
0.3 SI1 554.0
0.3 SI1 554.0
0.3 SI1 554.0
0.32 SI1 554.0
0.33 SI2 554.0
0.29 VS1 555.0
0.29 VS1 555.0
0.31 SI1 555.0
0.34 VS2 555.0
0.34 VS2 555.0
0.34 VS1 555.0
0.34 VS1 555.0
0.3 VS1 555.0
0.29 VS1 555.0
0.35 SI1 555.0
0.43 I1 555.0
0.32 VS2 556.0
0.36 VS2 556.0
0.3 VS2 556.0
0.26 VS1 556.0
0.7 VS2 2812.0
0.7 VS2 2812.0
0.71 SI1 2812.0
0.99 SI1 2812.0
0.73 VS2 2812.0
0.51 VVS1 2812.0
0.91 SI2 2813.0
0.84 SI1 2813.0
0.91 VS2 2813.0
0.76 SI1 2814.0
0.76 SI1 2814.0
0.75 SI1 2814.0
0.55 VVS1 2815.0
0.76 SI2 2815.0
0.74 VS1 2815.0
0.7 SI1 2815.0
0.7 SI1 2815.0
0.7 SI1 2815.0
0.7 SI1 2815.0
0.9 VS2 2815.0
0.95 SI2 2815.0
0.89 SI2 2815.0
0.72 VS2 2815.0
0.96 SI2 2815.0
1.02 I1 2815.0
0.78 VVS2 2816.0
0.61 VVS2 2816.0
0.71 VS1 2816.0
0.78 SI1 2816.0
0.87 SI2 2816.0
0.83 SI1 2816.0
0.71 SI1 2817.0
0.71 VVS2 2817.0
0.71 VS2 2817.0
0.71 VS2 2817.0
0.63 VVS2 2817.0
0.71 SI1 2817.0
0.71 SI1 2817.0
0.71 SI1 2817.0
0.71 SI1 2817.0
0.9 VS2 2817.0
0.71 SI1 2817.0
0.7 VS2 2818.0
0.7 VS2 2818.0
0.7 VS2 2818.0
1.0 I1 2818.0
0.86 SI2 2818.0
0.8 SI1 2818.0
0.7 VS1 2818.0
0.7 VS1 2818.0
0.7 VS2 2818.0
0.7 VS1 2818.0
1.0 SI2 2818.0
0.72 VS1 2819.0
0.72 VS1 2819.0
0.7 VS1 2819.0
0.86 SI2 2819.0
0.71 VS1 2820.0
0.75 SI1 2821.0
0.73 VS2 2821.0
0.53 VVS1 2821.0
0.73 SI1 2821.0
0.73 SI1 2821.0
0.73 SI1 2821.0
0.73 SI1 2821.0
0.73 SI1 2821.0
0.73 SI1 2821.0
0.7 VS1 2822.0
0.72 VS2 2822.0
0.72 VS2 2822.0
0.72 VS2 2822.0
0.7 VS2 2822.0
0.6 VVS2 2822.0
0.74 VVS1 2822.0
0.73 SI1 2822.0
0.71 SI1 2822.0
0.71 SI1 2822.0
0.7 SI1 2822.0
0.7 SI1 2822.0
0.9 VS2 2822.0
0.71 SI1 2822.0
0.7 VS2 2822.0
0.7 VS2 2822.0
0.7 SI1 2822.0
0.7 SI1 2822.0
0.7 SI1 2822.0
0.7 SI1 2822.0
0.7 VS2 2822.0
0.7 VS2 2822.0
0.7 SI1 2822.0
0.79 SI2 2823.0
0.9 SI1 2823.0
0.71 VS2 2823.0
0.61 VVS2 2823.0
0.9 SI2 2823.0
0.71 SI1 2823.0
0.71 VS2 2824.0
0.77 VVS2 2824.0
0.74 VS1 2824.0
0.82 SI2 2824.0
0.82 SI2 2824.0
0.71 VS1 2825.0
0.83 SI1 2825.0
0.73 VS1 2825.0
0.83 SI1 2825.0
1.17 I1 2825.0
0.91 SI2 2825.0
0.73 VS1 2826.0
0.7 VS1 2826.0
0.9 SI2 2826.0
0.7 VS1 2826.0
0.7 VS2 2826.0
0.7 VS1 2826.0
0.9 SI2 2826.0
0.78 SI1 2826.0
0.96 I1 2826.0
0.7 SI1 2827.0
0.72 VS2 2827.0
0.79 VVS2 2827.0
0.7 VVS1 2827.0
0.7 VVS1 2827.0
0.7 SI2 2828.0
1.01 SI2 2828.0
0.72 VS1 2829.0
0.8 SI2 2829.0
0.59 VVS1 2829.0
0.72 VS1 2829.0
0.75 SI2 2829.0
0.8 SI2 2829.0
0.71 VS2 2830.0
0.77 SI1 2830.0
0.97 I1 2830.0
0.53 VVS1 2830.0
0.53 VVS1 2830.0
0.8 VS2 2830.0
0.9 SI1 2830.0
0.76 SI2 2831.0
0.72 SI1 2831.0
0.75 SI1 2831.0
0.72 SI1 2831.0
0.79 SI1 2831.0
0.72 VS2 2832.0
0.91 SI2 2832.0
0.71 VVS2 2832.0
0.81 SI1 2832.0
0.82 SI1 2832.0
0.71 VS1 2832.0
0.9 SI1 2832.0
0.8 VS2 2833.0
0.56 IF 2833.0
0.7 VS2 2833.0
0.7 VS2 2833.0
0.61 VVS2 2833.0
0.85 SI2 2833.0
0.7 SI1 2833.0
0.8 VS2 2834.0
0.8 VS2 2834.0
0.51 VVS1 2834.0
0.53 VVS1 2834.0
0.78 VS2 2834.0
0.9 SI1 2834.0
0.9 SI2 2834.0
0.77 SI2 2834.0
0.73 VS1 2835.0
0.63 VVS2 2835.0
0.7 VS2 2835.0
0.72 VS2 2835.0
0.72 SI1 2835.0
0.75 VS2 2835.0
0.82 SI1 2836.0
0.71 VS2 2836.0
0.7 VS1 2837.0
0.7 VS1 2837.0
0.71 SI1 2838.0
0.76 SI1 2838.0
0.82 SI1 2838.0
0.72 VS1 2838.0
0.7 VS2 2838.0
0.7 VS2 2838.0
0.7 VS1 2838.0
0.7 VS2 2838.0
0.7 SI1 2838.0
0.7 VS2 2838.0
0.7 VS2 2838.0
0.7 VS1 2838.0
0.74 SI1 2839.0
0.71 VS1 2839.0
0.7 VS1 2839.0
0.71 VS1 2839.0
0.71 VS1 2839.0
0.71 VS1 2839.0
0.71 VS1 2839.0
0.7 VS1 2839.0
0.73 VS2 2839.0
0.7 VS2 2839.0
0.7 VS1 2839.0
0.71 VVS2 2839.0
0.71 VS1 2839.0
0.79 VS2 2839.0
0.71 VS1 2839.0
0.77 VS1 2840.0
0.75 SI2 2840.0
0.7 SI1 2840.0
0.71 VS2 2840.0
0.92 SI2 2840.0
0.83 SI2 2840.0
0.7 VVS1 2840.0
0.73 VS2 2841.0
0.71 VS1 2841.0
0.73 SI1 2841.0
0.82 SI2 2841.0
0.82 SI2 2841.0
0.82 SI2 2841.0
0.52 VVS1 2841.0
1.0 I1 2841.0
0.95 SI1 2841.0
0.73 SI1 2841.0
0.73 VS2 2841.0
0.73 VS1 2841.0
0.8 VS1 2842.0
0.7 VS2 2842.0
0.7 VS2 2843.0
0.7 VS2 2843.0
0.71 VS2 2843.0
0.81 SI2 2843.0
0.71 SI1 2843.0
0.73 VVS2 2843.0
0.73 VS1 2843.0
0.72 VS2 2843.0
0.81 SI2 2843.0
0.71 VVS2 2843.0
0.73 SI1 2844.0
0.7 VS1 2844.0
1.01 I1 2844.0
1.01 I1 2844.0
0.79 VS2 2844.0
0.7 VS2 2845.0
0.7 VS2 2845.0
0.8 VS2 2845.0
1.27 SI2 2845.0
0.79 SI1 2846.0
0.72 VS1 2846.0
0.73 VVS2 2846.0
1.01 SI2 2846.0
1.01 I1 2846.0
0.73 SI1 2846.0
0.7 SI1 2846.0
0.7 VS2 2846.0
0.77 SI1 2846.0
0.77 VS2 2846.0
0.77 VS1 2846.0
0.84 SI1 2847.0
0.72 SI1 2847.0
0.76 SI1 2847.0
0.7 VVS2 2848.0
0.54 VVS2 2848.0
0.75 SI2 2848.0
0.79 SI1 2849.0
0.74 VS1 2849.0
0.7 VS2 2850.0
0.7 VS2 2850.0
0.75 SI1 2850.0
1.2 I1 2850.0
0.8 SI1 2851.0
0.66 VS1 2851.0
0.87 SI2 2851.0
0.86 SI1 2851.0
0.74 SI1 2851.0
0.58 IF 2852.0
0.78 VS1 2852.0
0.74 SI1 2852.0
0.73 SI1 2852.0
0.91 SI1 2852.0
0.71 VS2 2853.0
0.71 VS1 2853.0
0.79 SI2 2853.0
0.79 SI2 2853.0
0.71 SI1 2853.0
0.82 VS1 2853.0
0.78 VS1 2854.0
0.7 VS1 2854.0
1.12 I1 2854.0
0.73 VS2 2854.0
0.91 VS2 2854.0
0.91 VS2 2854.0
0.91 VS2 2854.0
0.91 SI1 2854.0
0.7 VS1 2854.0
0.68 VVS2 2854.0
0.73 VS2 2855.0
1.03 SI1 2855.0
0.74 VS2 2855.0
0.98 SI2 2855.0
1.02 SI1 2856.0
1.0 SI2 2856.0
1.02 SI2 2856.0
0.6 VVS2 2856.0
0.8 SI2 2856.0
0.97 I1 2856.0
1.0 SI1 2856.0
0.26 VS1 556.0
0.26 VS1 556.0
0.36 SI1 556.0
0.34 VS2 556.0
0.34 SI1 556.0
0.34 SI1 556.0
0.34 SI1 556.0
0.34 VS2 556.0
0.34 SI1 556.0
0.32 VS2 556.0
0.31 VVS1 557.0
0.31 VVS1 557.0
0.31 VVS1 557.0
0.31 VVS1 557.0
0.31 VVS1 557.0
0.31 VVS1 557.0
0.31 VS2 557.0
0.31 VS1 557.0
0.31 VS1 557.0
0.33 SI2 557.0
0.33 SI2 557.0
0.33 SI2 557.0
0.33 SI2 557.0
0.33 SI2 557.0
0.33 VS1 557.0
0.33 VS1 557.0
0.33 VS1 557.0
0.33 SI1 557.0
0.33 SI1 557.0
0.33 SI1 557.0
1.0 SI2 2856.0
0.77 SI1 2856.0
0.77 SI1 2856.0
0.7 VVS2 2857.0
0.9 SI2 2857.0
0.72 SI1 2857.0
0.9 VS2 2857.0
0.72 SI1 2857.0
0.7 VVS2 2858.0
0.81 SI1 2858.0
0.81 SI1 2858.0
0.71 VS2 2858.0
0.7 VS2 2858.0
0.71 VS2 2858.0
0.71 VS2 2858.0
0.92 SI1 2858.0
0.76 SI1 2858.0
0.73 SI1 2858.0
0.71 VS2 2858.0
0.7 VVS2 2858.0
0.9 SI2 2858.0
0.71 VS2 2858.0
0.7 VS2 2859.0
0.7 VS2 2859.0
0.77 VS1 2859.0
0.71 VS1 2859.0
0.7 VS2 2859.0
0.75 VS1 2859.0
0.83 SI2 2859.0
0.71 VS2 2860.0
0.9 SI2 2860.0
0.6 VVS2 2860.0
0.71 VS1 2860.0
0.53 VVS1 2860.0
0.71 SI1 2861.0
0.62 VVS2 2861.0
0.62 VVS2 2861.0
0.9 SI1 2861.0
0.62 IF 2861.0
0.82 SI2 2861.0
0.66 VS1 2861.0
0.7 SI1 2862.0
0.8 SI1 2862.0
0.8 SI2 2862.0
0.79 SI1 2862.0
0.71 VVS1 2862.0
0.7 VS2 2862.0
0.7 VS2 2862.0
0.79 VS1 2862.0
0.7 VS2 2862.0
1.22 I1 2862.0
1.01 SI2 2862.0
0.73 VS2 2862.0
0.91 VS2 2863.0
0.71 SI1 2863.0
0.83 SI1 2863.0
0.84 SI2 2863.0
0.71 SI1 2863.0
0.71 SI1 2863.0
0.71 SI1 2863.0
0.71 SI1 2863.0
0.71 SI1 2863.0
0.71 SI1 2863.0
0.91 SI1 2863.0
0.9 VS2 2863.0
0.71 VVS2 2863.0
0.71 SI1 2863.0
0.72 VS2 2863.0
0.72 SI1 2863.0
0.71 VS2 2863.0
0.81 SI2 2864.0
0.83 VS2 2865.0
0.73 SI1 2865.0
0.56 VVS1 2866.0
0.56 VVS1 2866.0
0.71 VS1 2866.0
0.7 VVS1 2866.0
0.96 SI1 2866.0
0.71 VVS1 2867.0
0.7 VS2 2867.0
0.71 VVS1 2867.0
0.8 VS2 2867.0
0.95 SI2 2867.0
0.82 SI2 2867.0
0.52 VVS1 2867.0
0.82 SI2 2867.0
0.82 SI2 2867.0
0.82 SI2 2867.0
0.8 SI1 2867.0
0.96 SI2 2867.0
0.72 VS1 2868.0
0.62 IF 2868.0
0.79 SI2 2868.0
0.75 SI1 2868.0
1.08 I1 2869.0
0.72 SI1 2869.0
0.62 IF 2869.0
0.73 VVS2 2869.0
0.72 VVS2 2869.0
0.52 VVS2 2870.0
0.83 SI2 2870.0
0.64 VVS2 2870.0
0.8 SI1 2870.0
0.74 SI1 2870.0
0.72 SI1 2870.0
0.82 VS2 2870.0
0.73 VS1 2870.0
1.04 I1 2870.0
0.73 SI1 2871.0
0.73 SI1 2871.0
0.9 SI1 2871.0
0.75 SI1 2871.0
0.79 SI1 2871.0
0.7 SI1 2872.0
0.75 SI1 2872.0
1.02 I1 2872.0
0.7 SI2 2872.0
0.7 SI1 2872.0
0.7 SI1 2872.0
0.7 SI1 2872.0
0.7 SI1 2872.0
0.7 SI1 2872.0
0.72 VS2 2872.0
0.74 SI1 2872.0
0.84 SI1 2872.0
0.76 VS2 2873.0
0.77 SI1 2873.0
0.76 SI2 2873.0
1.0 SI2 2873.0
1.0 SI1 2873.0
0.9 SI1 2873.0
0.9 SI1 2873.0
0.9 SI1 2873.0
0.9 SI1 2873.0
0.9 SI1 2873.0
0.9 SI1 2873.0
0.78 VS2 2874.0
0.71 VS2 2874.0
0.7 VS1 2874.0
0.7 VS1 2874.0
0.7 VVS2 2874.0
0.7 VS1 2874.0
0.7 VS1 2874.0
0.7 VS1 2874.0
0.7 VVS2 2874.0
1.0 SI2 2875.0
0.77 SI1 2875.0
1.0 VS1 2875.0
1.0 SI1 2875.0
1.0 SI2 2875.0
0.73 VS1 2876.0
0.79 VS2 2876.0
0.72 VS1 2877.0
0.71 VS1 2877.0
0.74 VS2 2877.0
0.7 VVS1 2877.0
0.7 VS1 2877.0
0.79 SI1 2878.0
0.79 SI1 2878.0
0.79 SI2 2878.0
0.71 VS2 2878.0
0.79 SI1 2878.0
0.73 SI1 2879.0
0.63 IF 2879.0
0.7 VS1 2879.0
0.71 VS1 2879.0
0.84 SI2 2879.0
0.84 SI2 2879.0
1.02 SI2 2879.0
0.72 VS1 2879.0
0.72 VS1 2879.0
0.92 SI2 2880.0
0.74 SI1 2880.0
0.7 VVS1 2881.0
0.71 VS2 2881.0
1.05 I1 2881.0
0.7 IF 2882.0
0.54 VVS1 2882.0
0.73 VS2 2882.0
0.88 SI1 2882.0
0.73 VS2 2882.0
0.72 SI1 2883.0
0.9 SI2 2883.0
0.9 SI2 2883.0
1.03 SI2 2884.0
0.84 SI1 2885.0
1.01 SI1 2885.0
0.77 SI2 2885.0
0.8 SI1 2885.0
0.9 SI2 2885.0
0.73 SI1 2886.0
0.72 SI1 2886.0
0.71 SI1 2887.0
0.7 VS1 2887.0
0.79 VS1 2888.0
0.72 VVS2 2889.0
0.7 VS2 2889.0
0.7 VS1 2889.0
0.9 SI2 2889.0
0.71 VS1 2889.0
0.5 VVS2 2889.0
0.5 VVS2 2889.0
0.74 SI1 2889.0
0.77 VS2 2889.0
0.77 SI1 2889.0
0.8 SI1 2890.0
0.8 SI1 2890.0
0.8 SI1 2890.0
0.8 SI1 2890.0
0.66 VVS1 2890.0
0.71 VS2 2891.0
0.71 VS2 2891.0
0.71 VS2 2891.0
0.71 VS2 2891.0
0.72 SI1 2891.0
0.71 VS2 2891.0
0.86 SI2 2892.0
1.19 I1 2892.0
0.71 VS1 2893.0
0.82 SI2 2893.0
0.71 VVS2 2893.0
0.75 VS2 2893.0
0.7 VVS1 2893.0
0.8 SI2 2893.0
0.82 SI2 2893.0
0.82 SI1 2893.0
0.82 SI1 2893.0
0.81 SI2 2894.0
0.81 SI2 2894.0
0.76 SI1 2894.0
0.71 VS2 2895.0
0.7 VS1 2895.0
0.7 VVS2 2895.0
0.74 VS1 2896.0
0.77 VS2 2896.0
0.77 VS2 2896.0
0.53 VVS1 2896.0
0.79 SI1 2896.0
0.73 SI2 2896.0
0.77 SI2 2896.0
0.77 SI1 2896.0
1.01 I1 2896.0
1.01 I1 2896.0
0.6 VVS2 2897.0
0.76 SI1 2897.0
0.54 VVS2 2897.0
0.72 SI1 2897.0
0.72 VS1 2897.0
0.74 VS2 2897.0
1.12 SI2 2898.0 
//sorting
display(spark.sql("SELECT carat, clarity, price FROM diamonds ORDER BY price DESC"))
carat clarity price
2.29 VS2 18823.0
2.0 SI1 18818.0
1.51 IF 18806.0
2.07 SI2 18804.0
2.0 SI1 18803.0
2.29 SI1 18797.0
2.0 VS1 18795.0
2.04 SI1 18795.0
1.71 VS2 18791.0
2.15 SI2 18791.0
2.8 SI2 18788.0
2.05 SI1 18787.0
2.05 SI2 18784.0
2.03 SI1 18781.0
1.6 VS1 18780.0
2.06 VS2 18779.0
1.51 VVS1 18777.0
1.71 VVS2 18768.0
2.55 VS1 18766.0
2.08 SI1 18760.0
2.0 SI1 18759.0
2.03 SI1 18757.0
2.61 SI2 18756.0
2.36 SI2 18745.0
2.01 SI1 18741.0
2.01 SI1 18741.0
2.01 SI1 18741.0
2.01 SI1 18736.0
1.94 SI1 18735.0
2.02 SI1 18731.0
1.72 VVS2 18730.0
1.51 VS1 18729.0
1.7 VVS2 18718.0
2.18 SI1 18717.0
3.01 SI2 18710.0
3.01 SI2 18710.0
2.0 SI1 18709.0
2.07 VS2 18707.0
2.22 VS1 18706.0
2.01 SI2 18705.0
3.51 VS2 18701.0
1.28 IF 18700.0
2.02 VS2 18700.0
2.19 SI2 18693.0
2.43 VS2 18692.0
2.48 SI2 18692.0
1.5 VS2 18691.0
2.67 SI2 18686.0
1.42 VVS1 18682.0
2.03 VS2 18680.0
2.02 SI2 18678.0
2.16 SI2 18678.0
2.01 SI2 18674.0
2.04 SI1 18663.0
2.05 VS2 18659.0
2.12 SI1 18656.0
2.29 VS2 18653.0
2.1 SI1 18648.0
2.01 VS2 18640.0
2.09 SI2 18640.0
2.03 SI1 18630.0
2.01 SI1 18625.0
2.42 VS2 18615.0
1.49 VVS2 18614.0
2.07 SI2 18611.0
2.01 VS2 18607.0
2.0 SI1 18604.0
1.71 VVS2 18599.0
1.7 VS1 18598.0
2.29 IF 18594.0
3.01 SI2 18593.0
2.03 SI2 18578.0
2.11 SI2 18575.0
2.01 SI1 18574.0
2.01 SI1 18572.0
1.6 VS1 18571.0
2.02 VS2 18565.0
2.01 VS2 18561.0
2.01 VS2 18561.0
2.09 SI1 18559.0
3.04 SI2 18559.0
2.38 VS2 18559.0
1.72 VS2 18557.0
1.5 IF 18552.0
1.04 IF 18542.0
2.4 SI1 18541.0
2.4 SI2 18541.0
2.03 SI2 18535.0
2.32 SI2 18532.0
2.22 VS2 18531.0
4.5 I1 18531.0
2.14 SI1 18528.0
2.14 SI2 18526.0
1.83 VS2 18525.0
2.0 SI1 18524.0
2.38 VS1 18522.0
2.0 VS2 18515.0
2.09 SI2 18509.0
2.32 SI2 18508.0
2.37 SI1 18508.0
2.01 VS2 18507.0
2.03 SI1 18507.0
2.01 VS1 18500.0
2.66 SI2 18495.0
2.0 SI1 18493.0
2.07 SI1 18489.0
2.02 SI2 18487.0
2.57 SI1 18485.0
2.21 SI2 18483.0
2.16 VS2 18481.0
2.1 SI1 18480.0
2.03 SI2 18477.0
2.19 SI1 18475.0
2.01 VS2 18474.0
2.09 SI2 18472.0
2.15 SI1 18470.0
2.04 SI1 18468.0
2.1 SI2 18462.0
2.16 VS1 18462.0
2.03 SI2 18458.0
2.5 SI2 18447.0
2.08 VS2 18447.0
1.7 VVS1 18445.0
2.09 VS2 18443.0
2.13 SI1 18442.0
2.0 SI1 18440.0
2.0 SI1 18440.0
2.06 SI1 18439.0
1.33 IF 18435.0
2.22 SI1 18432.0
1.72 VVS2 18431.0
2.44 VS2 18430.0
1.74 VS2 18430.0
1.7 VS1 18430.0
1.79 VS2 18429.0
2.26 SI1 18426.0
2.29 IF 18426.0
2.0 SI2 18426.0
2.03 SI1 18423.0
1.6 VS2 18421.0
1.79 VS1 18419.0
1.54 VS1 18416.0
2.11 SI2 18407.0
2.08 SI2 18405.0
2.01 SI1 18398.0
2.02 VS2 18398.0
1.7 VS1 18398.0
2.01 SI2 18395.0
2.01 SI2 18394.0
2.09 SI1 18392.0
1.73 VVS2 18377.0
2.0 SI1 18376.0
2.4 SI2 18374.0
2.01 SI1 18374.0
2.32 SI1 18371.0
2.0 SI1 18371.0
2.0 SI1 18371.0
2.06 SI2 18371.0
2.6 SI2 18369.0
2.2 VS2 18364.0
2.22 VS2 18363.0
2.07 VS2 18359.0
1.83 VS2 18358.0
2.07 SI2 18344.0
2.27 SI1 18343.0
1.7 VS2 18342.0
2.16 VS1 18342.0
2.01 VS1 18340.0
2.5 VS2 18325.0
2.49 SI2 18325.0
2.02 VS1 18324.0
2.02 SI2 18320.0
2.05 VS2 18318.0
1.61 VS2 18318.0
2.1 SI2 18312.0
2.03 SI2 18310.0
2.51 SI2 18308.0
1.93 SI1 18306.0
2.3 SI2 18304.0
2.24 SI1 18299.0
2.02 SI2 18296.0
2.01 SI1 18295.0
2.01 SI1 18295.0
1.54 VVS1 18294.0
2.06 SI2 18293.0
2.14 SI2 18291.0
2.03 SI2 18286.0
2.08 VS2 18281.0
1.62 VS2 18281.0
1.07 IF 18279.0
1.7 VVS1 18279.0
2.21 SI2 18276.0
2.13 SI1 18275.0
2.02 SI2 18274.0
2.01 SI2 18259.0
2.03 SI1 18257.0
2.51 SI2 18255.0
2.53 SI1 18254.0
2.52 SI1 18252.0
2.01 SI1 18252.0
1.7 VS2 18251.0
3.01 SI2 18242.0
3.01 SI2 18242.0
3.01 SI2 18242.0
3.01 SI2 18242.0
3.01 SI2 18242.0
2.3 VS2 18239.0
2.02 SI1 18236.0
2.02 SI1 18236.0
2.02 SI1 18236.0
2.02 SI2 18236.0
2.19 SI2 18232.0
2.04 SI2 18231.0
1.09 IF 18231.0
2.09 VS1 18215.0
1.73 VVS2 18211.0
2.02 VS2 18207.0
2.02 VS2 18207.0
2.01 VS1 18206.0
2.07 SI2 18198.0
2.2 SI1 18193.0
2.05 SI1 18193.0
2.07 VS2 18193.0
2.3 VS2 18190.0
2.01 SI1 18188.0
1.55 VVS2 18188.0
2.01 SI1 18186.0
2.0 SI1 18186.0
2.01 SI2 18183.0
2.05 SI2 18181.0
2.52 SI2 18179.0
1.63 VS1 18179.0
1.76 VS1 18178.0
1.7 VS2 18176.0
2.0 SI2 18172.0
2.01 VS2 18172.0
2.2 SI2 18168.0
2.03 VS2 18166.0
2.12 SI2 18164.0
1.5 VVS2 18159.0
2.04 SI1 18153.0
2.05 VS2 18152.0
2.23 VS2 18149.0
2.01 SI1 18149.0
2.03 VS2 18139.0
2.03 VS2 18139.0
2.08 SI2 18128.0
2.08 SI2 18128.0
1.78 VS2 18128.0
2.21 SI2 18128.0
2.04 VS1 18127.0
2.14 SI2 18125.0
2.08 SI2 18124.0
2.1 SI2 18124.0
2.03 SI2 18120.0
2.12 SI2 18120.0
2.33 SI1 18119.0
2.12 SI1 18118.0
2.02 SI2 18117.0
2.04 SI1 18115.0
2.04 SI1 18115.0
2.03 SI2 18115.0
1.07 IF 18114.0
2.45 VS2 18113.0
1.14 IF 18112.0
2.3 SI2 18108.0
1.7 VS2 18107.0
1.7 VS2 18107.0
2.04 VS1 18104.0
1.51 IF 18102.0
2.51 SI2 18090.0
2.32 SI1 18080.0
2.02 VS2 18077.0
2.01 SI1 18077.0
2.01 SI1 18077.0
2.01 SI1 18077.0
2.11 SI2 18071.0
2.0 SI1 18069.0
2.29 SI1 18068.0
2.19 SI2 18067.0
2.04 SI2 18066.0
2.21 SI1 18062.0
2.0 SI1 18062.0
2.2 SI1 18059.0
1.58 VS1 18057.0
1.7 VS2 18055.0
2.28 SI2 18055.0
2.02 VS2 18050.0
2.01 VS1 18041.0
2.51 SI2 18037.0
2.11 SI2 18034.0
2.25 SI1 18034.0
2.25 SI2 18034.0
2.16 SI1 18029.0
2.51 SI2 18029.0
2.26 SI2 18028.0
2.01 SI1 18027.0
2.01 SI1 18027.0
2.32 SI1 18026.0
2.03 SI1 18026.0
2.32 SI1 18026.0
2.04 SI1 18026.0
2.0 VS2 18023.0
2.51 VS2 18020.0
5.01 I1 18018.0
2.05 SI2 18017.0
1.76 VS1 18014.0
2.25 SI2 18007.0
2.06 SI2 18005.0
2.18 SI2 18003.0
2.09 SI2 18002.0
2.16 SI2 18001.0
2.35 SI1 17999.0
2.09 VS2 17999.0
2.54 SI2 17996.0
1.93 VS1 17995.0
2.24 SI2 17989.0
2.05 SI2 17988.0
2.01 VS2 17987.0
2.08 IF 17986.0
2.01 VS1 17983.0
2.03 VS2 17975.0
2.05 SI2 17957.0
2.4 SI2 17955.0
2.39 VS2 17955.0
2.0 SI2 17953.0
2.04 SI1 17952.0
2.04 SI1 17952.0
1.54 VS1 17949.0
2.02 SI2 17938.0
1.51 VS1 17936.0
2.02 VS1 17936.0
2.16 SI1 17934.0
1.29 VVS1 17932.0
2.0 SI1 17930.0
2.57 SI2 17924.0
2.41 SI2 17923.0
2.39 VS1 17920.0
2.01 VS2 17917.0
2.08 VS2 17916.0
1.07 IF 17909.0
2.2 VS2 17905.0
1.74 VS1 17904.0
1.74 VS1 17904.0
2.0 SI1 17902.0
2.0 VS2 17898.0
2.2 SI1 17895.0
1.58 VS1 17894.0
2.07 SI1 17893.0
2.48 SI1 17893.0
2.02 VS2 17893.0
1.7 VS2 17892.0
2.28 SI2 17892.0
2.01 SI1 17892.0
2.32 VS1 17891.0
2.16 VVS1 17891.0
2.0 VS1 17889.0
1.76 VVS2 17888.0
2.02 VS2 17887.0
2.02 SI1 17882.0
2.01 VS1 17877.0
2.11 SI2 17871.0
2.0 SI2 17871.0
2.0 SI1 17869.0
2.03 SI1 17864.0
2.43 SI2 17856.0
2.01 SI2 17849.0
2.01 SI1 17849.0
2.01 SI1 17849.0
2.01 SI1 17849.0
2.18 SI2 17841.0
2.09 SI2 17840.0
2.01 VS1 17838.0
2.1 VS2 17837.0
2.0 SI2 17835.0
2.36 VS1 17829.0
2.01 VS2 17826.0
1.63 VS2 17825.0
2.02 SI2 17825.0
2.16 SI1 17820.0
2.11 SI1 17816.0
2.05 SI1 17811.0
2.09 SI2 17805.0
2.17 SI2 17805.0
2.01 SI1 17804.0
2.03 SI1 17803.0
1.69 VS2 17803.0
2.72 SI2 17801.0
2.01 SI2 17798.0
2.21 SI1 17784.0
2.08 SI1 17778.0
2.05 SI1 17776.0
1.55 VS1 17773.0
2.16 SI1 17772.0
1.71 VS2 17766.0
1.72 VS1 17765.0
1.87 VS1 17761.0
2.0 SI2 17760.0
2.0 SI1 17760.0
2.0 SI1 17760.0
2.0 VS2 17760.0
2.0 VS2 17760.0
2.0 SI2 17760.0
2.01 SI2 17759.0
2.01 SI1 17759.0
2.56 SI1 17753.0
2.03 SI2 17752.0
2.01 VS1 17751.0
2.17 SI1 17747.0
2.01 SI2 17746.0
2.14 SI2 17742.0
2.0 SI1 17740.0
2.12 SI2 17730.0
1.65 IF 17729.0
2.0 SI1 17724.0
1.52 VS1 17723.0
2.0 VS2 17716.0
2.31 SI1 17715.0
2.21 SI1 17714.0
1.99 VS2 17713.0
2.11 VS2 17712.0
2.0 SI2 17710.0
2.12 SI2 17694.0
2.02 SI1 17692.0
2.52 SI2 17689.0
1.51 VVS2 17689.0
2.01 SI1 17688.0
2.01 SI2 17688.0
1.71 VS1 17685.0
2.32 SI2 17676.0
2.01 SI1 17676.0
2.0 SI2 17674.0
2.14 SI2 17673.0
2.28 SI1 17673.0
2.02 SI1 17672.0
1.5 VVS2 17667.0
2.29 SI2 17666.0
1.34 IF 17663.0
1.7 VS2 17662.0
1.52 VS1 17659.0
2.02 SI1 17658.0
2.06 SI2 17650.0
1.51 VS1 17649.0
2.39 VS1 17642.0
2.05 VS1 17640.0
2.2 SI2 17634.0
2.08 SI1 17617.0
1.74 VS2 17614.0
2.07 SI2 17614.0
2.01 SI2 17609.0
2.52 SI2 17608.0
2.0 VS1 17607.0
2.48 SI2 17607.0
2.1 VVS1 17606.0
1.72 VS1 17605.0
1.64 VVS2 17604.0
2.0 SI1 17600.0
1.38 IF 17598.0
1.7 VS2 17597.0
1.7 VS2 17597.0
1.71 VS1 17595.0
2.01 VS2 17592.0
2.53 SI2 17591.0
1.03 IF 17590.0
2.14 SI2 17582.0
2.02 VS2 17581.0
2.02 SI1 17579.0
2.0 VS2 17574.0
2.01 VS2 17570.0
2.36 SI2 17569.0
1.65 IF 17569.0
2.01 SI2 17555.0
2.19 SI2 17554.0
1.89 VS1 17553.0
1.59 VS1 17552.0
1.57 VS2 17548.0
1.45 VVS2 17545.0
2.29 VS2 17539.0
1.97 VS2 17535.0
2.36 VS2 17534.0
2.0 SI2 17534.0
2.02 VS1 17533.0
2.02 SI1 17530.0
2.21 SI2 17525.0
2.01 VS1 17523.0
2.52 SI2 17522.0
2.05 SI1 17521.0
2.32 SI2 17516.0
1.51 VS1 17515.0
2.01 SI2 17514.0
2.14 SI2 17513.0
1.91 SI1 17509.0
2.33 VS2 17504.0
1.14 IF 17499.0
2.01 VS2 17497.0
1.31 VVS1 17496.0
2.01 SI1 17492.0
1.7 VS1 17492.0
2.02 SI1 17489.0
1.7 VS2 17485.0
2.01 SI1 17476.0
2.18 SI2 17475.0
2.01 SI1 17474.0
2.18 SI2 17473.0
2.44 SI1 17472.0
2.08 SI1 17469.0
2.08 SI1 17469.0
2.2 VS2 17460.0
2.01 SI1 17458.0
1.56 VS1 17455.0
2.51 SI1 17452.0
2.31 VS1 17451.0
1.5 VVS2 17449.0
2.48 SI2 17448.0
2.0 SI1 17447.0
1.76 VS2 17442.0
1.55 VS2 17441.0
2.0 VS1 17436.0
2.01 SI1 17434.0
2.01 VS2 17433.0
2.19 SI2 17433.0
1.65 VS1 17425.0
2.01 VS2 17422.0
2.14 VS2 17416.0
1.61 VS1 17414.0
2.05 VS1 17408.0
2.64 SI2 17407.0
2.5 SI2 17405.0
2.0 SI1 17405.0
2.01 SI2 17403.0
2.01 SI1 17403.0
2.01 SI1 17403.0
2.01 SI1 17403.0
1.69 VS1 17400.0
1.59 VS1 17393.0
2.03 SI2 17393.0
2.02 SI1 17392.0
2.01 SI1 17383.0
2.17 SI1 17381.0
2.04 SI2 17379.0
1.97 VS2 17377.0
1.95 VS2 17374.0
1.59 VS1 17366.0
2.01 VS1 17365.0
2.39 VS1 17365.0
1.6 VVS1 17360.0
1.7 VS2 17360.0
1.79 VS2 17358.0
2.02 SI1 17357.0
1.21 IF 17353.0
1.93 VS2 17353.0
1.67 VS2 17351.0
2.01 SI1 17347.0
1.75 VS2 17343.0
2.54 SI2 17339.0
1.69 VS1 17338.0
2.01 SI1 17334.0
1.7 VS1 17330.0
4.13 I1 17329.0
1.58 IF 17329.0
2.04 IF 17327.0
2.04 SI1 17323.0
1.7 VS2 17323.0
1.51 VVS2 17317.0
2.04 VS2 17315.0
2.16 SI1 17313.0
2.01 SI2 17313.0
2.26 SI2 17312.0
2.03 VS2 17297.0
2.35 SI2 17294.0
2.35 SI2 17294.0
2.05 SI1 17294.0
2.05 SI2 17294.0
1.5 VVS1 17279.0
2.0 VS1 17278.0
1.8 SI1 17273.0
1.86 VVS2 17267.0
2.02 SI2 17265.0
2.02 SI1 17265.0
2.2 SI1 17265.0
2.16 SI2 17263.0
2.02 SI2 17263.0
2.42 VS1 17262.0
2.08 VS2 17258.0
1.61 VS1 17256.0
2.12 VS2 17254.0
2.1 SI1 17250.0
2.0 SI1 17247.0
2.02 SI1 17245.0
2.01 SI2 17244.0
1.54 VS2 17240.0
2.05 SI2 17237.0
2.01 SI1 17235.0
2.01 SI2 17235.0
2.25 SI1 17233.0
2.52 SI1 17231.0
1.7 VVS2 17228.0
2.01 VS1 17227.0
2.17 SI1 17224.0
1.53 VS2 17223.0
2.15 SI2 17221.0
2.01 SI2 17220.0
2.15 SI2 17219.0
1.7 VS1 17219.0
2.31 SI2 17218.0
1.41 VVS2 17216.0
2.05 VVS2 17214.0
2.14 SI1 17213.0
2.61 SI2 17209.0
2.6 SI2 17209.0
1.71 VS1 17206.0
1.71 VS1 17204.0
1.6 VVS1 17204.0
1.51 VVS2 17203.0
1.5 VVS2 17203.0
2.01 VS2 17197.0
1.71 VS1 17197.0
1.67 VS2 17194.0
2.3 SI1 17193.0
2.14 VS2 17193.0
1.21 VVS1 17192.0
2.01 VS2 17191.0
1.75 VS1 17191.0
2.56 SI1 17186.0
2.74 SI2 17184.0
2.03 SI2 17182.0
2.01 SI2 17179.0
2.01 SI2 17179.0
1.5 VVS2 17176.0
1.57 VS1 17175.0
1.75 VS2 17172.0
2.0 SI1 17172.0
2.42 VS2 17168.0
2.42 VS2 17168.0
2.02 SI2 17166.0
2.74 SI2 17164.0
2.51 SI2 17162.0
2.22 SI1 17160.0
2.09 SI2 17156.0
1.5 VS2 17153.0
2.02 VS1 17153.0
2.22 SI1 17151.0
2.27 VS1 17149.0
2.71 SI2 17146.0
1.5 VS1 17143.0
2.25 SI2 17143.0
2.02 SI1 17141.0
2.05 SI2 17138.0
2.0 SI2 17136.0
2.14 SI2 17127.0
2.01 SI1 17126.0
1.72 VS2 17125.0
2.22 SI2 17123.0
2.01 SI2 17118.0
2.57 SI2 17116.0
2.01 SI2 17115.0
2.09 SI1 17114.0
1.7 VS2 17114.0
1.51 VS1 17111.0
1.56 VS2 17108.0
2.53 SI2 17103.0
1.02 IF 17100.0
2.02 SI2 17099.0
1.93 VS1 17096.0
2.07 SI2 17095.0
2.01 SI1 17095.0
2.0 SI2 17094.0
2.0 VS2 17084.0
2.05 SI2 17081.0
2.01 SI2 17079.0
2.01 SI2 17078.0
1.61 VS1 17076.0
2.13 SI2 17073.0
1.7 VVS2 17073.0
2.01 VS1 17068.0
2.01 VS2 17068.0
2.01 VS1 17068.0
2.12 VS2 17068.0
2.01 VS1 17068.0
2.05 SI2 17066.0
2.15 SI2 17065.0
2.15 SI2 17063.0
2.31 SI2 17062.0
2.01 VS2 17057.0
1.53 VVS2 17057.0
2.32 VS2 17053.0
1.71 VS2 17052.0
2.27 VS1 17051.0
2.09 VS2 17051.0
1.6 VS1 17050.0
1.71 VS1 17049.0
2.04 SI2 17049.0
2.07 VS2 17045.0
2.13 SI2 17045.0
1.07 IF 17042.0
1.71 VS1 17041.0
2.4 SI2 17039.0
2.14 VS2 17038.0
1.75 VS1 17036.0
1.54 VS2 17029.0
2.04 VS2 17028.0
1.67 VVS2 17028.0
2.5 SI1 17028.0
2.6 SI2 17027.0
2.01 SI2 17024.0
2.01 SI2 17024.0
2.07 SI1 17019.0
1.75 VS2 17017.0
2.19 SI1 17016.0
2.01 SI2 17014.0
2.06 SI2 17012.0
2.26 VS2 17010.0
1.71 VS1 17009.0
2.05 SI1 17006.0
2.01 SI2 17005.0
2.01 SI1 17003.0
2.09 SI2 17001.0
2.31 SI2 17000.0
2.2 SI2 16996.0
2.27 VS1 16994.0
2.12 SI2 16992.0
1.5 VS1 16988.0
2.41 SI2 16987.0
1.68 VS1 16985.0
2.02 SI1 16985.0
1.73 VS2 16975.0
3.0 SI2 16970.0
3.0 SI2 16970.0
2.28 SI2 16969.0
1.73 VS2 16960.0
2.06 SI1 16960.0
1.73 VS2 16960.0
2.07 SI1 16957.0
2.01 SI1 16956.0
2.01 SI1 16956.0
1.7 VS1 16955.0
2.5 VS2 16955.0
2.28 SI2 16954.0
1.5 VVS2 16948.0
2.03 VS2 16945.0
2.02 SI2 16944.0
2.04 SI1 16942.0
1.93 VS1 16941.0
2.37 SI2 16937.0
2.53 SI2 16934.0
2.13 SI2 16931.0
2.02 SI2 16929.0
2.01 SI2 16922.0
1.54 VS2 16921.0
1.52 VS1 16916.0
2.49 SI1 16915.0
2.01 VS1 16914.0
2.63 SI2 16914.0
2.14 VS2 16914.0
2.01 VS1 16914.0
2.26 SI1 16904.0
2.01 SI1 16901.0
2.03 SI1 16900.0
2.03 SI1 16900.0
2.03 SI1 16900.0
2.05 VS2 16896.0
1.54 VS2 16889.0
2.01 SI2 16881.0
2.01 SI2 16881.0
1.76 VS2 16879.0
2.18 SI2 16878.0
2.04 VS2 16874.0
2.04 SI2 16872.0
2.0 SI1 16872.0
2.02 VS2 16861.0
2.06 SI2 16857.0
2.08 VS2 16854.0
2.18 SI2 16842.0
2.51 SI1 16842.0
2.42 SI2 16826.0
2.09 SI2 16824.0
1.25 IF 16823.0
2.48 VS2 16820.0
2.05 SI2 16819.0
1.71 VS2 16817.0
1.71 VS1 16813.0
2.01 VS1 16811.0
1.4 VVS1 16808.0
1.73 VS2 16807.0
2.23 SI2 16805.0
2.21 SI1 16804.0
1.75 VS2 16803.0
2.31 SI1 16801.0
2.04 SI2 16800.0
2.4 SI2 16797.0
1.5 VS1 16793.0
2.05 VS1 16793.0
2.03 VS2 16792.0
2.39 VS2 16791.0
1.62 VS1 16790.0
2.37 VS2 16789.0
2.02 VS2 16789.0
1.69 VS2 16789.0
2.03 SI1 16787.0
1.52 VVS2 16786.0
1.75 VS2 16783.0
2.1 SI2 16783.0
1.75 VS2 16783.0
1.5 VS1 16783.0
1.71 VS2 16779.0
1.52 VS1 16779.0
1.52 VS1 16779.0
2.01 VS2 16778.0
2.01 VS2 16778.0
2.01 VS2 16778.0
2.01 VS2 16778.0
2.01 VS2 16778.0
2.01 VS2 16778.0
2.13 SI2 16778.0
2.01 SI2 16776.0
2.01 SI2 16776.0
2.07 VVS2 16769.0
2.04 VS2 16768.0
1.51 VVS2 16754.0
1.54 VS1 16750.0
2.03 SI1 16747.0
2.01 SI1 16742.0
2.01 SI1 16737.0
1.54 VS2 16736.0
2.01 SI1 16733.0
2.01 SI1 16733.0
2.01 SI2 16733.0
2.01 SI2 16733.0
2.01 SI1 16733.0
2.01 SI1 16733.0
2.01 SI1 16731.0
2.01 SI2 16728.0
2.11 VS1 16723.0
2.14 SI2 16723.0
2.04 SI1 16718.0
2.04 SI1 16718.0
2.51 SI2 16717.0
1.51 VVS2 16716.0
1.51 VVS2 16716.0
2.17 SI1 16716.0
2.48 SI1 16715.0
2.53 SI1 16709.0
2.02 VS2 16709.0
2.24 SI2 16709.0
2.36 SI2 16707.0
2.09 SI1 16704.0
2.02 SI1 16704.0
2.09 SI2 16703.0
2.01 VS2 16700.0
2.0 VS2 16694.0
2.0 VS2 16694.0
2.03 SI1 16693.0
2.18 SI2 16690.0
2.22 VS1 16689.0
1.51 VS1 16688.0
2.39 SI1 16687.0
2.4 SI1 16687.0
1.8 VS1 16683.0
2.01 VS2 16677.0
1.52 VS1 16670.0
1.51 VS1 16669.0
2.02 SI1 16665.0
2.21 SI2 16657.0
2.24 SI2 16656.0
2.0 SI2 16650.0
2.0 SI1 16650.0
2.0 SI1 16650.0
2.38 SI1 16643.0
1.67 VS1 16643.0
2.03 SI2 16642.0
2.05 VS1 16641.0
1.52 VS1 16636.0
1.75 VS2 16632.0
2.03 SI1 16629.0
2.1 SI2 16629.0
2.1 SI2 16629.0
1.52 VS1 16628.0
1.52 VS1 16628.0
2.01 VS2 16626.0
2.01 VS2 16626.0
2.06 SI1 16626.0
2.01 VS2 16626.0
2.01 SI2 16624.0
1.71 VS1 16618.0
2.07 VVS2 16617.0
2.04 VS2 16616.0
1.51 VVS2 16613.0
2.05 SI1 16611.0
2.06 SI2 16603.0
1.53 VVS1 16601.0
1.59 VVS1 16599.0
2.02 VS2 16593.0
2.28 VS1 16592.0
2.45 SI2 16589.0
2.06 SI2 16587.0
1.69 VS2 16583.0
2.01 SI2 16582.0
2.01 SI1 16582.0
2.0 SI1 16580.0
1.71 SI1 16575.0
2.25 SI2 16575.0
1.57 VS2 16570.0
2.02 SI2 16565.0
2.28 VS2 16564.0
2.01 SI1 16562.0
2.02 SI1 16560.0
2.21 SI2 16558.0
1.5 VS1 16558.0
2.24 SI2 16558.0
1.51 VS1 16551.0
1.6 IF 16547.0
2.22 SI2 16547.0
1.41 VVS1 16545.0
2.0 VS2 16544.0
2.0 VS2 16544.0
3.01 I1 16538.0
2.44 VS2 16533.0
2.47 SI2 16532.0
2.2 SI2 16530.0
1.7 VS1 16521.0
1.51 VS1 16520.0
1.52 VS1 16519.0
1.51 VS1 16518.0
1.8 SI1 16513.0
2.11 VS2 16512.0
2.06 SI2 16512.0
1.62 VS2 16507.0
2.05 SI2 16506.0
2.1 SI2 16506.0
2.03 SI2 16505.0
2.01 SI1 16499.0
2.01 SI2 16499.0
1.52 VVS1 16492.0
1.52 VS1 16485.0
2.0 SI2 16484.0
2.03 VS2 16483.0
2.1 SI2 16479.0
2.1 SI2 16479.0
2.17 SI2 16472.0
1.0 IF 16469.0
2.46 SI2 16466.0
2.12 SI2 16466.0
2.59 VS1 16465.0
2.13 SI2 16462.0
2.0 SI2 16462.0
2.0 VS2 16459.0
1.53 VVS1 16451.0
2.28 VS2 16450.0
2.05 SI2 16446.0
2.03 SI2 16442.0
2.0 VS2 16439.0
2.06 SI2 16437.0
2.05 SI2 16431.0
2.51 SI2 16427.0
2.18 VS2 16427.0
2.18 VS2 16427.0
2.04 SI2 16426.0
2.0 SI2 16425.0
2.03 VS2 16422.0
2.04 SI1 16420.0
2.03 SI2 16412.0
2.01 SI2 16410.0
1.5 VS1 16409.0
2.0 SI1 16407.0
1.5 VS1 16407.0
1.09 IF 16406.0
2.11 SI2 16404.0
1.51 VS1 16402.0
2.16 SI2 16400.0
2.22 SI2 16398.0
2.19 SI1 16397.0
2.02 VS1 16397.0
2.11 VS2 16395.0
2.03 SI1 16392.0
2.07 SI1 16392.0
2.14 VS2 16390.0
2.04 SI2 16389.0
2.02 SI1 16386.0
1.71 VS2 16384.0
2.01 VS2 16383.0
2.0 SI2 16380.0
2.07 VS2 16378.0
1.51 VS1 16370.0
2.28 VS2 16369.0
2.02 SI1 16368.0
1.5 VVS2 16364.0
1.8 SI1 16364.0
2.11 VS2 16363.0
1.62 VS2 16358.0
2.1 SI2 16357.0
2.05 SI2 16357.0
2.54 SI2 16353.0
2.54 SI2 16353.0
1.52 VVS1 16343.0
2.35 SI2 16340.0
1.8 SI1 16340.0
1.6 VVS1 16339.0
2.07 SI2 16337.0
2.11 SI2 16336.0
2.3 SI2 16329.0 
diamondsDF.printSchema // since price is double in the DF that was turned into table we can rely on the descenting sort on doubles

root
 |-- carat: double (nullable = true)
 |-- cut: string (nullable = true)
 |-- color: string (nullable = true)
 |-- clarity: string (nullable = true)
 |-- depth: double (nullable = true)
 |-- table: double (nullable = true)
 |-- price: double (nullable = true)
 |-- x: double (nullable = true)
 |-- y: double (nullable = true)
 |-- z: double (nullable = true)

// sort by multiple fields
display(spark.sql("SELECT carat, clarity, price FROM diamonds ORDER BY carat ASC, price DESC"))
carat clarity price
0.2 VS2 367.0
0.2 VS2 367.0
0.2 VS2 367.0
0.2 VS2 367.0
0.2 VS2 367.0
0.2 VS2 367.0
0.2 VS2 367.0
0.2 VS2 367.0
0.2 VS2 367.0
0.2 VS2 367.0
0.2 VS2 367.0
0.2 SI2 345.0
0.21 SI2 394.0
0.21 VS2 386.0
0.21 VS2 386.0
0.21 VS2 386.0
0.21 VS2 386.0
0.21 VS2 386.0
0.21 VS2 386.0
0.21 VS2 386.0
0.21 SI1 326.0
0.22 SI1 470.0
0.22 VS2 404.0
0.22 VS2 404.0
0.22 SI1 342.0
0.22 VS2 337.0
0.23 VVS2 688.0
0.23 VVS1 682.0
0.23 VVS1 680.0
0.23 VVS1 680.0
0.23 VVS1 680.0
0.23 VVS2 680.0
0.23 VVS2 680.0
0.23 VVS2 680.0
0.23 VVS2 680.0
0.23 VVS2 650.0
0.23 VVS2 640.0
0.23 VVS1 640.0
0.23 VS1 611.0
0.23 VVS2 600.0
0.23 VS1 586.0
0.23 VS1 586.0
0.23 VVS2 583.0
0.23 VVS2 583.0
0.23 VVS1 583.0
0.23 VVS1 583.0
0.23 VVS1 583.0
0.23 VVS1 583.0
0.23 VVS1 583.0
0.23 VVS2 583.0
0.23 VS2 577.0
0.23 VVS2 571.0
0.23 VVS2 550.0
0.23 VVS2 549.0
0.23 VS2 548.0
0.23 VS1 548.0
0.23 VS1 548.0
0.23 VS2 548.0
0.23 VS2 543.0
0.23 VVS2 538.0
0.23 VVS2 537.0
0.23 IF 536.0
0.23 VVS1 536.0
0.23 IF 536.0
0.23 VVS1 536.0
0.23 IF 536.0
0.23 VVS1 536.0
0.23 VVS1 531.0
0.23 VVS1 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS1 530.0
0.23 VVS1 530.0
0.23 VVS1 530.0
0.23 VVS1 530.0
0.23 VVS1 530.0
0.23 VVS1 530.0
0.23 VVS1 530.0
0.23 VVS1 530.0
0.23 VVS1 530.0
0.23 VVS1 530.0
0.23 VVS1 530.0
0.23 VVS1 530.0
0.23 VVS1 530.0
0.23 IF 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS1 530.0
0.23 VVS2 530.0
0.23 VVS1 530.0
0.23 VVS1 530.0
0.23 VVS1 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS2 530.0
0.23 VVS1 525.0
0.23 VVS1 518.0
0.23 VS2 513.0
0.23 VS1 513.0
0.23 VS2 512.0
0.23 VVS2 511.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS2 505.0
0.23 VVS1 505.0
0.23 VVS1 505.0
0.23 VVS2 500.0
0.23 VVS1 499.0
0.23 VVS2 499.0
0.23 VVS2 498.0
0.23 VVS2 498.0
0.23 VS2 498.0
0.23 VVS1 498.0
0.23 VS2 498.0
0.23 VS2 498.0
0.23 VVS2 498.0
0.23 VS1 493.0
0.23 VS2 493.0
0.23 VVS2 492.0
0.23 VVS1 492.0
0.23 VVS1 492.0
0.23 IF 492.0
0.23 VVS2 485.0
0.23 VVS2 485.0
0.23 VVS2 485.0
0.23 VVS2 485.0
0.23 VVS2 485.0
0.23 VVS2 485.0
0.23 VVS2 485.0
0.23 VVS1 485.0
0.23 VVS1 485.0
0.23 VVS1 485.0
0.23 VVS1 485.0
0.23 VVS2 485.0
0.23 VVS2 485.0
0.23 VVS1 485.0
0.23 IF 485.0
0.23 VVS1 484.0
0.23 VVS1 484.0
0.23 VVS1 484.0
0.23 VS1 483.0
0.23 VVS1 478.0
0.23 VVS1 478.0
0.23 VVS1 478.0
0.23 VVS1 478.0
0.23 VVS1 478.0
0.23 VVS1 478.0
0.23 VVS2 478.0
0.23 VVS1 478.0
0.23 VVS1 472.0
0.23 VVS1 472.0
0.23 VVS1 472.0
0.23 VVS1 472.0
0.23 VVS1 472.0
0.23 VVS2 472.0
0.23 VVS1 472.0
0.23 VVS1 472.0
0.23 VVS2 472.0
0.23 VVS1 472.0
0.23 VVS2 472.0
0.23 VVS2 472.0
0.23 VS1 468.0
0.23 VVS2 468.0
0.23 VVS2 465.0
0.23 VVS2 465.0
0.23 VVS2 465.0
0.23 VVS2 465.0
0.23 VVS1 465.0
0.23 VVS1 465.0
0.23 VVS2 465.0
0.23 VVS2 465.0
0.23 VVS2 465.0
0.23 VVS2 465.0
0.23 VVS2 465.0
0.23 VVS2 462.0
0.23 VVS1 462.0
0.23 VVS1 458.0
0.23 VVS1 458.0
0.23 VVS1 458.0
0.23 VVS2 458.0
0.23 VVS2 458.0
0.23 VVS2 452.0
0.23 SI2 449.0
0.23 VS2 447.0
0.23 VVS2 445.0
0.23 VS1 442.0
0.23 VS2 442.0
0.23 VVS1 439.0
0.23 VVS2 438.0
0.23 VS1 434.0
0.23 VVS1 434.0
0.23 VVS2 431.0
0.23 VVS2 431.0
0.23 VVS2 431.0
0.23 VVS2 431.0
0.23 VVS2 431.0
0.23 VVS2 431.0
0.23 VVS2 428.0
0.23 VVS2 425.0
0.23 VVS2 425.0
0.23 VVS1 425.0
0.23 VS1 423.0
0.23 VS2 423.0
0.23 VVS1 415.0
0.23 VVS1 414.0
0.23 VVS1 414.0
0.23 VVS1 414.0
0.23 VS2 411.0
0.23 VS1 404.0
0.23 VS1 402.0
0.23 VS1 402.0
0.23 VS2 402.0
0.23 VS1 402.0
0.23 VS2 402.0
0.23 VS2 402.0
0.23 VS1 402.0
0.23 VS1 402.0
0.23 VS1 402.0
0.23 VS2 402.0
0.23 VS2 402.0
0.23 VS2 402.0
0.23 VS2 402.0
0.23 VS1 402.0
0.23 VS2 402.0
0.23 VS2 402.0
0.23 VS1 402.0
0.23 VS2 402.0
0.23 VS2 402.0
0.23 VS1 400.0
0.23 VS2 400.0
0.23 VVS1 395.0
0.23 VS1 391.0
0.23 VS1 391.0
0.23 VVS2 389.0
0.23 VS1 384.0
0.23 VS1 378.0
0.23 VS1 378.0
0.23 VVS2 378.0
0.23 VS1 376.0
0.23 SI1 375.0
0.23 VS1 373.0
0.23 VS2 373.0
0.23 VS1 373.0
0.23 VS1 373.0
0.23 VVS2 369.0
0.23 VS2 369.0
0.23 IF 369.0
0.23 SI1 364.0
0.23 SI1 364.0
0.23 VS2 362.0
0.23 VS2 357.0
0.23 VS1 357.0
0.23 VS2 357.0
0.23 VS1 357.0
0.23 VS2 357.0
0.23 VVS2 354.0
0.23 VS1 353.0
0.23 VS2 352.0
0.23 VS1 340.0
0.23 VS1 338.0
0.23 VS1 327.0
0.23 SI2 326.0
0.24 VVS1 963.0
0.24 VVS1 752.0
0.24 VVS1 710.0
0.24 VVS1 710.0
0.24 VS1 687.0
0.24 VVS2 678.0
0.24 VVS1 678.0
0.24 VVS1 678.0
0.24 IF 678.0
0.24 VVS2 678.0
0.24 VVS2 678.0
0.24 VVS1 678.0
0.24 VVS1 678.0
0.24 VVS1 678.0
0.24 VVS2 678.0
0.24 IF 678.0
0.24 IF 678.0
0.24 VVS2 678.0
0.24 IF 678.0
0.24 VVS2 678.0
0.24 VVS2 668.0
0.24 VVS2 668.0
0.24 VVS1 668.0
0.24 VVS1 668.0
0.24 VVS2 608.0
0.24 VVS2 608.0
0.24 VVS1 608.0
0.24 VVS1 608.0
0.24 VVS1 608.0
0.24 VVS1 608.0
0.24 VVS2 608.0
0.24 VVS2 608.0
0.24 VVS2 608.0
0.24 VS1 572.0
0.24 VS1 572.0
0.24 SI1 571.0
0.24 VVS1 559.0
0.24 IF 559.0
0.24 VVS1 559.0
0.24 VVS1 559.0
0.24 VVS1 559.0
0.24 IF 559.0
0.24 IF 559.0
0.24 IF 559.0
0.24 IF 559.0
0.24 VVS1 559.0
0.24 VVS1 554.0
0.24 VVS2 554.0
0.24 VVS1 554.0
0.24 VVS1 554.0
0.24 VVS1 553.0
0.24 VVS1 553.0
0.24 VVS2 552.0
0.24 VVS2 552.0
0.24 VVS2 552.0
0.24 VVS2 552.0
0.24 VVS2 552.0
0.24 VVS2 552.0
0.24 VVS1 552.0
0.24 VVS1 552.0
0.24 VVS1 552.0
0.24 VVS2 552.0
0.24 VVS2 552.0
0.24 VVS2 552.0
0.24 VVS2 552.0
0.24 VVS2 552.0
0.24 VVS2 552.0
0.24 VVS2 552.0
0.24 VVS2 552.0
0.24 VVS2 552.0
0.24 VVS2 552.0
0.24 VVS1 552.0
0.24 VVS1 552.0
0.24 VVS1 552.0
0.24 VVS1 552.0
0.24 VVS1 552.0
0.24 VVS1 552.0
0.24 VVS1 552.0
0.24 IF 552.0
0.24 IF 552.0
0.24 VVS2 552.0
0.24 VVS2 552.0
0.24 VVS2 552.0
0.24 VVS1 552.0
0.24 VVS2 552.0
0.24 VVS2 552.0
0.24 VVS2 552.0
0.24 VVS2 547.0
0.24 VVS2 540.0
0.24 VVS2 540.0
0.24 VVS2 538.0
0.24 VVS2 538.0
0.24 VS2 536.0
0.24 VS2 536.0
0.24 VS1 536.0
0.24 VVS2 533.0
0.24 VVS1 526.0
0.24 VVS2 526.0
0.24 VVS2 526.0
0.24 VVS1 526.0
0.24 VVS2 526.0
0.24 VVS2 526.0
0.24 VVS2 526.0
0.24 VVS2 523.0
0.24 VVS2 521.0
0.24 VVS2 521.0
0.24 VVS1 521.0
0.24 VVS1 521.0
0.24 IF 504.0
0.24 VVS2 504.0
0.24 VVS1 504.0
0.24 IF 504.0
0.24 IF 504.0
0.24 VVS1 504.0
0.24 VVS1 504.0
0.24 VVS2 499.0
0.24 VVS1 499.0
0.24 VVS2 498.0
0.24 VVS1 492.0
0.24 VVS2 492.0
0.24 VVS1 492.0
0.24 VVS2 492.0
0.24 VVS2 492.0
0.24 VVS1 492.0
0.24 VVS1 492.0
0.24 IF 492.0
0.24 VVS2 492.0
0.24 VS1 490.0
0.24 SI1 486.0
0.24 VVS2 485.0
0.24 VVS2 485.0
0.24 VVS2 485.0
0.24 VVS2 485.0
0.24 VVS1 485.0
0.24 VVS2 485.0
0.24 VVS1 485.0
0.24 VVS1 485.0
0.24 VVS2 485.0
0.24 VVS2 485.0
0.24 VVS2 485.0
0.24 VVS2 485.0
0.24 VVS2 485.0
0.24 VVS2 485.0
0.24 VVS2 485.0
0.24 VVS1 485.0
0.24 VVS1 485.0
0.24 VVS1 485.0
0.24 VVS1 485.0
0.24 VVS1 485.0
0.24 VVS1 485.0
0.24 VVS2 485.0
0.24 VVS1 485.0
0.24 VS2 485.0
0.24 VVS1 485.0
0.24 VVS2 485.0
0.24 VVS1 485.0
0.24 VVS2 478.0
0.24 VVS2 478.0
0.24 VVS1 478.0
0.24 VVS2 478.0
0.24 VVS2 478.0
0.24 VVS2 478.0
0.24 VVS1 478.0
0.24 VVS1 478.0
0.24 VVS1 478.0
0.24 VVS1 478.0
0.24 VVS1 478.0
0.24 VVS2 478.0
0.24 VVS2 478.0
0.24 VVS2 478.0
0.24 SI1 475.0
0.24 VVS2 471.0
0.24 VVS2 471.0
0.24 VS2 461.0
0.24 VS1 461.0
0.24 VS2 461.0
0.24 VS1 461.0
0.24 VVS2 449.0
0.24 VVS1 449.0
0.24 VVS2 449.0
0.24 IF 449.0
0.24 VVS1 449.0
0.24 VVS2 449.0
0.24 VVS2 449.0
0.24 VVS2 449.0
0.24 VVS1 449.0
0.24 VVS2 449.0
0.24 VVS2 449.0
0.24 VS1 442.0
0.24 VVS2 442.0
0.24 VS1 436.0
0.24 VVS2 432.0
0.24 VVS1 432.0
0.24 VVS1 432.0
0.24 VVS1 432.0
0.24 VVS2 432.0
0.24 VS1 432.0
0.24 VVS1 432.0
0.24 VS1 430.0
0.24 VS2 419.0
0.24 VS2 419.0
0.24 VS2 419.0
0.24 VS2 419.0
0.24 VS2 419.0
0.24 VS1 419.0
0.24 VS2 419.0
0.24 VS2 419.0
0.24 VS1 419.0
0.24 VS1 419.0
0.24 VS1 419.0
0.24 VS2 419.0
0.24 VS2 419.0
0.24 VS2 419.0
0.24 VS1 419.0
0.24 VS1 419.0
0.24 VS1 419.0
0.24 VS2 419.0
0.24 VS2 419.0
0.24 VS2 419.0
0.24 VS2 419.0
0.24 VS1 419.0
0.24 VS1 419.0
0.24 VS1 419.0
0.24 VS2 419.0
0.24 VS2 417.0
0.24 VS2 417.0
0.24 VS1 417.0
0.24 VS1 412.0
0.24 VS2 408.0
0.24 SI1 404.0
0.24 VS1 399.0
0.24 VS1 397.0
0.24 VS1 393.0
0.24 VS1 393.0
0.24 VS2 391.0
0.24 VS1 391.0
0.24 VS1 391.0
0.24 VS1 383.0
0.24 VS1 378.0
0.24 VS2 378.0
0.24 VS1 373.0
0.24 VS1 373.0
0.24 VS2 373.0
0.24 VS1 373.0
0.24 SI1 370.0
0.24 VS1 367.0
0.24 VS2 367.0
0.24 SI1 364.0
0.24 VS2 362.0
0.24 VS2 362.0
0.24 VS1 357.0
0.24 VS1 355.0
0.24 VVS1 336.0
0.24 VVS2 336.0
0.25 SI2 1186.0
0.25 SI2 1186.0
0.25 SI2 1013.0
0.25 VVS1 817.0
0.25 VVS1 783.0
0.25 VVS2 740.0
0.25 VVS2 740.0
0.25 VVS2 740.0
0.25 VVS1 740.0
0.25 VVS2 740.0
0.25 VVS2 740.0
0.25 VVS2 740.0
0.25 VVS2 740.0
0.25 VVS1 740.0
0.25 VVS1 740.0
0.25 VVS2 740.0
0.25 VVS1 740.0
0.25 VVS1 740.0
0.25 IF 740.0
0.25 IF 739.0
0.25 VVS2 705.0
0.25 VVS1 705.0
0.25 VVS2 705.0
0.25 VVS1 705.0
0.25 VVS2 696.0
0.25 VVS2 633.0
0.25 VVS2 633.0
0.25 VVS1 633.0
0.25 VVS1 633.0
0.25 VVS1 633.0
0.25 IF 633.0
0.25 VVS1 633.0
0.25 VVS2 633.0
0.25 VVS1 633.0
0.25 VVS2 633.0
0.25 VVS1 633.0
0.25 VVS2 633.0
0.25 VVS2 633.0
0.25 IF 633.0
0.25 VVS2 633.0
0.25 IF 624.0
0.25 VS1 595.0
0.25 VS1 595.0
0.25 VS1 595.0
0.25 VVS2 583.0
0.25 VVS1 582.0
0.25 IF 582.0
0.25 VVS1 582.0
0.25 VVS1 582.0
0.25 VVS1 577.0
0.25 VVS1 577.0
0.25 VVS1 577.0
0.25 VVS1 577.0
0.25 VVS1 576.0
0.25 VVS1 576.0
0.25 VVS2 576.0
0.25 VVS2 576.0
0.25 VVS2 576.0
0.25 VVS2 576.0
0.25 VVS1 576.0
0.25 VVS2 576.0
0.25 IF 576.0
0.25 VVS2 576.0
0.25 VVS2 576.0
0.25 VVS1 576.0
0.25 VVS2 576.0
0.25 VVS1 576.0
0.25 VVS2 575.0
0.25 VVS2 575.0
0.25 VVS2 575.0
0.25 VVS2 575.0
0.25 VVS2 575.0
0.25 VVS2 575.0
0.25 VVS2 575.0
0.25 VVS1 575.0
0.25 VVS2 575.0
0.25 VVS2 575.0
0.25 VVS2 575.0
0.25 VVS2 575.0
0.25 VVS2 575.0
0.25 VVS1 575.0
0.25 VVS1 575.0
0.25 VVS1 575.0
0.25 VVS2 575.0
0.25 VVS2 575.0
0.25 VVS2 575.0
0.25 VVS2 575.0
0.25 VVS2 575.0
0.25 VVS1 575.0
0.25 VVS1 575.0
0.25 VVS2 575.0
0.25 VS1 563.0
0.25 VVS1 560.0
0.25 IF 560.0
0.25 VVS2 560.0
0.25 VS1 558.0
0.25 VS1 558.0
0.25 VS1 558.0
0.25 VS1 558.0
0.25 VVS2 548.0
0.25 VVS2 548.0
0.25 VVS2 548.0
0.25 VVS2 548.0
0.25 VVS2 548.0
0.25 VVS2 548.0
0.25 IF 548.0
0.25 VVS2 548.0
0.25 VVS2 548.0
0.25 VVS1 548.0
0.25 VVS1 548.0
0.25 VVS2 548.0
0.25 VVS2 548.0
0.25 VVS2 548.0
0.25 VVS2 548.0
0.25 VVS1 548.0
0.25 VVS2 548.0
0.25 VS2 535.0
0.25 VS2 535.0
0.25 VS2 535.0
0.25 VS2 535.0
0.25 VS2 535.0
0.25 VS2 535.0
0.25 VVS2 533.0
0.25 VVS1 533.0
0.25 VVS2 526.0
0.25 VVS1 526.0
0.25 IF 526.0
0.25 VVS1 525.0
0.25 VVS2 525.0
0.25 VVS1 525.0
0.25 VVS2 525.0
0.25 VVS1 525.0
0.25 VVS1 525.0
0.25 VVS2 525.0
0.25 VVS2 525.0
0.25 IF 525.0
0.25 VVS1 525.0
0.25 VVS1 525.0
0.25 VVS1 525.0
0.25 VVS2 525.0
0.25 VS2 525.0
0.25 VVS1 525.0
0.25 IF 512.0
0.25 VVS2 512.0
0.25 VVS2 512.0
0.25 VVS2 505.0
0.25 VVS2 500.0
0.25 VVS1 498.0
0.25 VVS1 490.0
0.25 VS2 480.0
0.25 VS1 480.0
0.25 VVS2 476.0
0.25 VVS2 476.0
0.25 VS2 472.0
0.25 VVS2 467.0
0.25 VVS1 467.0
0.25 VVS2 462.0
0.25 VS1 460.0
0.25 VS2 460.0
0.25 VS2 459.0
0.25 VS2 459.0
0.25 VVS1 457.0
0.25 VVS2 455.0
0.25 VS1 454.0
0.25 VS1 454.0
0.25 SI1 452.0
0.25 VVS1 451.0
0.25 VS2 450.0
0.25 VVS1 450.0
0.25 VVS1 450.0
0.25 VVS1 450.0
0.25 VS1 445.0
0.25 VS1 445.0
0.25 VS1 438.0
0.25 VS1 438.0
0.25 VS1 438.0
0.25 VS1 438.0
0.25 VS1 436.0
0.25 VS2 436.0
0.25 VS1 436.0
0.25 VS1 436.0
0.25 VS1 436.0
0.25 VS1 436.0
0.25 VS1 436.0
0.25 VS2 436.0
0.25 VS1 435.0
0.25 VS1 435.0
0.25 VS1 435.0
0.25 VS1 435.0
0.25 VS1 431.0
0.25 SI1 430.0
0.25 SI1 430.0
0.25 VS1 426.0
0.25 VVS1 421.0
0.25 VS1 410.0
0.25 VS2 409.0
0.25 VS2 407.0
0.25 VS2 407.0
0.25 SI1 407.0
0.25 VS2 404.0
0.25 VVS1 401.0
0.25 VS2 399.0
0.25 VS1 399.0
0.25 SI1 395.0
0.25 VS1 388.0
0.25 VS1 388.0
0.25 VS1 388.0
0.25 VS2 367.0
0.25 SI1 363.0
0.25 VS1 361.0
0.25 VS1 361.0
0.25 SI1 357.0
0.26 VVS1 814.0
0.26 VVS1 814.0
0.26 VVS1 814.0
0.26 VVS2 777.0
0.26 VVS1 769.0
0.26 VVS1 769.0
0.26 VVS1 769.0
0.26 VVS2 769.0
0.26 VVS1 769.0
0.26 VVS1 769.0
0.26 VVS1 769.0
0.26 VVS2 769.0
0.26 VVS2 769.0
0.26 VVS2 769.0
0.26 VVS1 769.0
0.26 VVS2 769.0
0.26 VVS1 769.0
0.26 VVS2 769.0
0.26 VVS2 769.0
0.26 VVS2 769.0
0.26 VVS2 769.0
0.26 IF 768.0
0.26 VVS2 733.0
0.26 VVS1 733.0
0.26 IF 733.0
0.26 VVS2 724.0
0.26 VVS1 724.0
0.26 VVS2 724.0
0.26 VVS2 724.0
0.26 VVS2 724.0
0.26 VVS2 724.0
0.26 VVS1 679.0
0.26 VVS1 679.0
0.26 SI1 658.0
0.26 VVS2 657.0
0.26 VVS2 657.0
0.26 VVS1 657.0
0.26 IF 657.0
0.26 VVS1 657.0
0.26 VVS1 657.0
0.26 VVS1 657.0
0.26 VVS1 657.0
0.26 IF 648.0
0.26 IF 648.0
0.26 VVS1 635.0
0.26 VVS1 635.0
0.26 VVS1 635.0
0.26 VS1 618.0
0.26 VS1 618.0
0.26 VVS2 614.0
0.26 IF 605.0
0.26 IF 605.0
0.26 VVS1 605.0
0.26 VVS1 605.0
0.26 VS2 601.0
0.26 IF 600.0
0.26 VVS1 600.0
0.26 VVS1 600.0
0.26 VVS2 600.0
0.26 VVS1 600.0
0.26 VVS2 600.0
0.26 VVS1 599.0
0.26 VVS1 599.0
0.26 VVS1 599.0
0.26 VVS1 599.0
0.26 VVS1 599.0
0.26 VVS1 599.0
0.26 VVS2 599.0
0.26 VVS2 599.0
0.26 VVS2 599.0
0.26 VVS1 599.0
0.26 VVS1 599.0
0.26 VVS2 599.0
0.26 VVS2 599.0
0.26 VVS2 599.0
0.26 VVS2 599.0
0.26 VVS2 599.0
0.26 VVS2 599.0
0.26 VVS2 599.0
0.26 VVS2 597.0
0.26 VVS2 597.0
0.26 VVS2 597.0
0.26 VVS2 597.0
0.26 VVS2 597.0
0.26 VVS1 597.0
0.26 VVS1 597.0
0.26 VVS2 597.0
0.26 VVS1 597.0
0.26 VVS1 597.0
0.26 VVS2 597.0
0.26 IF 597.0
0.26 VVS2 597.0
0.26 VVS2 597.0
0.26 VVS1 597.0
0.26 VVS2 597.0
0.26 VVS2 597.0
0.26 VVS1 591.0
0.26 SI1 590.0
0.26 VVS2 584.0
0.26 VS1 580.0
0.26 VS1 580.0
0.26 VS1 580.0
0.26 VS1 580.0
0.26 VS2 580.0
0.26 VS2 580.0
0.26 VS1 580.0
0.26 VS1 580.0
0.26 VS1 580.0
0.26 VS1 578.0
0.26 VVS2 569.0
0.26 VVS2 565.0
0.26 VVS2 565.0
0.26 VVS2 564.0
0.26 VVS2 564.0
0.26 VVS2 564.0
0.26 VVS2 564.0
0.26 VVS1 564.0
0.26 VVS2 564.0
0.26 VVS1 562.0
0.26 VS1 556.0
0.26 VS1 556.0
0.26 VS1 556.0
0.26 VVS2 554.0
0.26 VVS2 554.0
0.26 VVS1 554.0
0.26 VVS2 554.0
0.26 VVS1 554.0
0.26 VVS1 554.0
0.26 VVS2 554.0
0.26 VVS1 554.0
0.26 VVS2 554.0
0.26 VVS2 547.0
0.26 IF 547.0
0.26 VVS1 547.0
0.26 VVS1 547.0
0.26 VS1 546.0
0.26 VVS2 545.0
0.26 VVS2 545.0
0.26 VVS1 545.0
0.26 IF 545.0
0.26 VVS1 545.0
0.26 IF 545.0
0.26 VVS2 545.0
0.26 VVS1 545.0
0.26 VVS1 545.0
0.26 VVS1 545.0
0.26 VVS1 539.0
0.26 VVS1 532.0
0.26 VVS1 532.0
0.26 VVS1 524.0
0.26 VVS2 517.0
0.26 VVS2 514.0
0.26 VS1 508.0
0.26 VVS2 508.0
0.26 VVS2 506.0
0.26 VS2 499.0
0.26 VS1 499.0
0.26 VS1 499.0
0.26 VS1 499.0
0.26 VS1 491.0
0.26 VS1 491.0
0.26 VVS2 486.0
0.26 VVS2 486.0
0.26 VVS2 486.0
0.26 VVS2 486.0
0.26 VVS2 486.0
0.26 VVS2 486.0
0.26 VS1 482.0
0.26 VS1 482.0
0.26 VS1 482.0
0.26 VS1 482.0
0.26 VS1 482.0
0.26 VVS2 478.0
0.26 VS1 478.0
0.26 SI1 474.0
0.26 VVS2 468.0
0.26 VVS2 468.0
0.26 VVS1 468.0
0.26 VVS1 468.0
0.26 VVS1 468.0
0.26 IF 468.0
0.26 VS1 462.0
0.26 VS2 456.0
0.26 VS1 456.0
0.26 VS1 456.0
0.26 VS2 456.0
0.26 VS1 453.0
0.26 VS1 453.0
0.26 VS1 453.0
0.26 VS2 453.0
0.26 VS2 453.0
0.26 VS1 453.0
0.26 VS1 453.0
0.26 VS2 453.0
0.26 VS2 453.0
0.26 VS2 453.0
0.26 VS2 453.0
0.26 VS1 453.0
0.26 VS2 453.0
0.26 VS1 452.0
0.26 VS1 452.0
0.26 VS1 452.0
0.26 VS2 452.0
0.26 VS1 452.0
0.26 VS1 452.0
0.26 VS1 452.0
0.26 VS2 452.0
0.26 VS1 452.0
0.26 VS1 448.0
0.26 VS1 448.0
0.26 VS1 448.0
0.26 VS1 448.0
0.26 SI1 447.0
0.26 SI1 445.0
0.26 VVS2 440.0 
// use this to type cast strings into Int when the table is loaded with string-valued columns
//display(spark.sql("select cast(carat as Int) as carat, clarity, cast(price as Int) as price from diamond order by carat asc, price desc"))

// sort by multiple fields and limit to first 5
// I prefer lowercase for SQL - and you can use either in this course - but in the field do what your Boss or your colleagues prefer :)
display(spark.sql("select carat, clarity, price from diamonds order by carat desc, price desc limit 5"))
carat clarity price
5.01 I1 18018.0
4.5 I1 18531.0
4.13 I1 17329.0
4.01 I1 15223.0
4.01 I1 15223.0 
//aggregate functions
display(spark.sql("select avg(price) as avgprice from diamonds"))
avgprice
3932.799721913237 
//average operator is doing an auto-type conversion from int to double
display(spark.sql("select avg(cast(price as Integer)) as avgprice from diamonds"))
avgprice
3932.799721913237 
//aggregate function and grouping
display(spark.sql("select color, avg(price) as avgprice from diamonds group by color"))
color avgprice
F 3724.886396981765
E 3076.7524752475247
D 3169.9540959409596
J 5323.81801994302
G 3999.135671271697
I 5091.874953891553
H 4486.669195568401

Why do we need to know these interactive SQL queries?

Such queries can help us explore the data and thereby inform the modeling process!!!

Of course, if you don't know SQL then don't worry, we will be doing these things in scala using DataFrames.

Finally, those who are planning to take the Spark Developer Exams online, then you can't escape from SQL questions there...

We will continue later with ML pipelines to do prediction with a fitted model from this dataset

ScaDaMaLe Course site and book

Power Plant ML Pipeline Application - DataFrame Part

This is the Spark SQL parts of an end-to-end example of using a number of different machine learning algorithms to solve a supervised regression problem.

This is a break-down of Power Plant ML Pipeline Application from databricks.

This will be a recurring example in the sequel

Table of Contents
  • Step 1: Business Understanding
  • Step 2: Load Your Data
  • Step 3: Explore Your Data
  • Step 4: Visualize Your Data
  • Step 5: Data Preparation
  • Step 6: Data Modeling
  • Step 7: Tuning and Evaluation
  • Step 8: Deployment

We are trying to predict power output given a set of readings from various sensors in a gas-fired power generation plant. Power generation is a complex process, and understanding and predicting power output is an important element in managing a plant and its connection to the power grid.

  • Given this business problem, we need to translate it to a Machine Learning task (actually a Statistical Machine Learning task).
  • The ML task here is regression since the label (or target) we will be trying to predict takes a continuous numeric value
    • Note: if the labels took values from a finite discrete set, such as, Spam/Not-Spam or Good/Bad/Ugly, then the ML task would be classification.

Today, we will only cover Steps 1, 2, 3 and 4 above. You need introductions to linear algebra, stochastic gradient descent and decision trees before we can accomplish the applied ML task with some intuitive understanding. If you can't wait for ML then check out Spark MLLib Programming Guide for comming attractions!

The example data is provided by UCI at UCI Machine Learning Repository Combined Cycle Power Plant Data Set

You can read the background on the UCI page, but in summary:

  • we have collected a number of readings from sensors at a Gas Fired Power Plant (also called a Peaker Plant) and
  • want to use those sensor readings to predict how much power the plant will generate in a couple weeks from now.
  • Again, today we will just focus on Steps 1-4 above that pertain to DataFrames.

More information about Peaker or Peaking Power Plants can be found on Wikipedia https://en.wikipedia.org/wiki/Peakingpowerplant.

displayHTML(frameIt("https://archive.ics.uci.edu/ml/datasets/Combined+Cycle+Power+Plant",500))

sc.version.replace(".", "").toInt

res2: Int = 301

// a good habit to ensure the code is being run on the appropriate version of Spark - we are using Spark 2.2 actually if we use SparkSession object spark down the road...
require(sc.version.replace(".", "").toInt >= 140, "Spark 1.4.0+ is required to run this notebook. Please attach it to a Spark 1.4.0+ cluster.")

Step 1: Business Understanding

The first step in any machine learning task is to understand the business need.

As described in the overview we are trying to predict power output given a set of readings from various sensors in a gas-fired power generation plant.

The problem is a regression problem since the label (or target) we are trying to predict is numeric

Step 2: Load Your Data

Now that we understand what we are trying to do, we need to load our data and describe it, explore it and verify it.

Data was downloaded already as these five Tab-separated-variable or tsv files.

display(dbutils.fs.ls("/databricks-datasets/power-plant/data")) // Ctrl+Enter
path name size
dbfs:/databricks-datasets/power-plant/data/Sheet1.tsv Sheet1.tsv 308693.0
dbfs:/databricks-datasets/power-plant/data/Sheet2.tsv Sheet2.tsv 308693.0
dbfs:/databricks-datasets/power-plant/data/Sheet3.tsv Sheet3.tsv 308693.0
dbfs:/databricks-datasets/power-plant/data/Sheet4.tsv Sheet4.tsv 308693.0
dbfs:/databricks-datasets/power-plant/data/Sheet5.tsv Sheet5.tsv 308693.0

Now let us load the data from the Tab-separated-variable or tsv text file into an RDD[String] using the familiar textFile method.

val powerPlantRDD = sc.textFile("/databricks-datasets/power-plant/data/Sheet1.tsv") // Ctrl+Enter

powerPlantRDD: org.apache.spark.rdd.RDD[String] = /databricks-datasets/power-plant/data/Sheet1.tsv MapPartitionsRDD[187] at textFile at command-685894176422961:1

powerPlantRDD.take(5).foreach(println) // Ctrl+Enter to print first 5 lines

AT	V	AP	RH	PE
14.96	41.76	1024.07	73.17	463.26
25.18	62.96	1020.04	59.08	444.37
5.11	39.4	1012.16	92.14	488.56
20.86	57.32	1010.24	76.64	446.48

// let us make sure we are using Spark version greater than 2.2 - we need a version closer to 2.0 if we want to use SparkSession and SQLContext 
require(sc.version.replace(".", "").toInt >= 220, "Spark 2.2.0+ is required to run this notebook. Please attach it to a Spark 2.2.0+ cluster.")

// this reads the tsv file and turns it into a dataframe
val powerPlantDF = spark.read // use 'sqlContext.read' instead if you want to use older Spark version > 1.3  see 008_ notebook
    .format("csv") // use spark.csv package
    .option("header", "true") // Use first line of all files as header
    .option("inferSchema", "true") // Automatically infer data types
    .option("delimiter", "\t") // Specify the delimiter as Tab or '\t'
    .load("/databricks-datasets/power-plant/data/Sheet1.tsv")

powerPlantDF: org.apache.spark.sql.DataFrame = [AT: double, V: double ... 3 more fields]

powerPlantDF.printSchema // print the schema of the DataFrame that was inferred

root
 |-- AT: double (nullable = true)
 |-- V: double (nullable = true)
 |-- AP: double (nullable = true)
 |-- RH: double (nullable = true)
 |-- PE: double (nullable = true)

powerPlantDF.count

res9: Long = 9568

2.1. Alternatively, load data via the upload GUI feature in databricks

USE THIS FOR OTHER SMALLish DataSets you want to import to your CE

Since the dataset is relatively small, we will use the upload feature in Databricks to upload the data as a table.

First download the Data Folder from UCI Machine Learning Repository Combined Cycle Power Plant Data Set

The file is a multi-tab Excel document so you will need to save each tab as a Text file export.

I prefer exporting as a Tab-Separated-Values (TSV) since it is more consistent than CSV.

Call each file Folds5x2_pp<Sheet 1..5>.tsv and save to your machine.

Go to the Databricks Menu > Tables > Create Table

Select Datasource as "File"

Upload ALL 5 files at once.

See screenshots below (but refer https://docs.databricks.com/user-guide/importing-data.html for latest methods to import data):

2.1.1. Create Table _________________

When you import your data, name your table power_plant, specify all of the columns with the datatype Double and make sure you check the First row is header box.

alt text

2.1.2. Review Schema __________________

Your table schema and preview should look like this after you click Create Table:

alt text

Now that your data is loaded let's explore it.

Step 3: Explore Your Data

Now that we understand what we are trying to do, we need to load our data and describe it, explore it and verify it.

Viewing the table as text

By uisng .show method we can see some of the contents of the table in plain text.

This works in pure Apache Spark, say in Spark-Shell without any notebook layer on top of Spark like databricks, zeppelin or jupyter.

It is a good idea to use this method when possible.

powerPlantDF.show(10) // try putting 1000 here instead of 10

+-----+-----+-------+-----+------+
|   AT|    V|     AP|   RH|    PE|
+-----+-----+-------+-----+------+
|14.96|41.76|1024.07|73.17|463.26|
|25.18|62.96|1020.04|59.08|444.37|
| 5.11| 39.4|1012.16|92.14|488.56|
|20.86|57.32|1010.24|76.64|446.48|
|10.82| 37.5|1009.23|96.62| 473.9|
|26.27|59.44|1012.23|58.77|443.67|
|15.89|43.96|1014.02|75.24|467.35|
| 9.48|44.71|1019.12|66.43|478.42|
|14.64| 45.0|1021.78|41.25|475.98|
|11.74|43.56|1015.14|70.72| 477.5|
+-----+-----+-------+-----+------+
only showing top 10 rows

Viewing as DataFrame

This is almost necessary for a data scientist to gain visual insights into all pair-wise relationships between the several (3 to 6 or so) variables in question.

display(powerPlantDF)
AT V AP RH PE
14.96 41.76 1024.07 73.17 463.26
25.18 62.96 1020.04 59.08 444.37
5.11 39.4 1012.16 92.14 488.56
20.86 57.32 1010.24 76.64 446.48
10.82 37.5 1009.23 96.62 473.9
26.27 59.44 1012.23 58.77 443.67
15.89 43.96 1014.02 75.24 467.35
9.48 44.71 1019.12 66.43 478.42
14.64 45.0 1021.78 41.25 475.98
11.74 43.56 1015.14 70.72 477.5
17.99 43.72 1008.64 75.04 453.02
20.14 46.93 1014.66 64.22 453.99
24.34 73.5 1011.31 84.15 440.29
25.71 58.59 1012.77 61.83 451.28
26.19 69.34 1009.48 87.59 433.99
21.42 43.79 1015.76 43.08 462.19
18.21 45.0 1022.86 48.84 467.54
11.04 41.74 1022.6 77.51 477.2
14.45 52.75 1023.97 63.59 459.85
13.97 38.47 1015.15 55.28 464.3
17.76 42.42 1009.09 66.26 468.27
5.41 40.07 1019.16 64.77 495.24
7.76 42.28 1008.52 83.31 483.8
27.23 63.9 1014.3 47.19 443.61
27.36 48.6 1003.18 54.93 436.06
27.47 70.72 1009.97 74.62 443.25
14.6 39.31 1011.11 72.52 464.16
7.91 39.96 1023.57 88.44 475.52
5.81 35.79 1012.14 92.28 484.41
30.53 65.18 1012.69 41.85 437.89
23.87 63.94 1019.02 44.28 445.11
26.09 58.41 1013.64 64.58 438.86
29.27 66.85 1011.11 63.25 440.98
27.38 74.16 1010.08 78.61 436.65
24.81 63.94 1018.76 44.51 444.26
12.75 44.03 1007.29 89.46 465.86
24.66 63.73 1011.4 74.52 444.37
16.38 47.45 1010.08 88.86 450.69
13.91 39.35 1014.69 75.51 469.02
23.18 51.3 1012.04 78.64 448.86
22.47 47.45 1007.62 76.65 447.14
13.39 44.85 1017.24 80.44 469.18
9.28 41.54 1018.33 79.89 482.8
11.82 42.86 1014.12 88.28 476.7
10.27 40.64 1020.63 84.6 474.99
22.92 63.94 1019.28 42.69 444.22
16.0 37.87 1020.24 78.41 461.33
21.22 43.43 1010.96 61.07 448.06
13.46 44.71 1014.51 50.0 474.6
9.39 40.11 1029.14 77.29 473.05
31.07 73.5 1010.58 43.66 432.06
12.82 38.62 1018.71 83.8 467.41
32.57 78.92 1011.6 66.47 430.12
8.11 42.18 1014.82 93.09 473.62
13.92 39.39 1012.94 80.52 471.81
23.04 59.43 1010.23 68.99 442.99
27.31 64.44 1014.65 57.27 442.77
5.91 39.33 1010.18 95.53 491.49
25.26 61.08 1013.68 71.72 447.46
27.97 58.84 1002.25 57.88 446.11
26.08 52.3 1007.03 63.34 442.44
29.01 65.71 1013.61 48.07 446.22
12.18 40.1 1016.67 91.87 471.49
13.76 45.87 1008.89 87.27 463.5
25.5 58.79 1016.02 64.4 440.01
28.26 65.34 1014.56 43.4 441.03
21.39 62.96 1019.49 72.24 452.68
7.26 40.69 1020.43 90.22 474.91
10.54 34.03 1018.71 74.0 478.77
27.71 74.34 998.14 71.85 434.2
23.11 68.3 1017.83 86.62 437.91
7.51 41.01 1024.61 97.41 477.61
26.46 74.67 1016.65 84.44 431.65
29.34 74.34 998.58 81.55 430.57
10.32 42.28 1008.82 75.66 481.09
22.74 61.02 1009.56 79.41 445.56
13.48 39.85 1012.71 58.91 475.74
25.52 69.75 1010.36 90.06 435.12
21.58 67.25 1017.39 79.0 446.15
27.66 76.86 1001.31 69.47 436.64
26.96 69.45 1013.89 51.47 436.69
12.29 42.18 1016.53 83.13 468.75
15.86 43.02 1012.18 40.33 466.6
13.87 45.08 1024.42 81.69 465.48
24.09 73.68 1014.93 94.55 441.34
20.45 69.45 1012.53 91.81 441.83
15.07 39.3 1019.0 63.62 464.7
32.72 69.75 1009.6 49.35 437.99
18.23 58.96 1015.55 69.61 459.12
35.56 68.94 1006.56 38.75 429.69
18.36 51.43 1010.57 90.17 459.8
26.35 64.05 1009.81 81.24 433.63
25.92 60.95 1014.62 48.46 442.84
8.01 41.66 1014.49 76.72 485.13
19.63 52.72 1025.09 51.16 459.12
20.02 67.32 1012.05 76.34 445.31
10.08 40.72 1022.7 67.3 480.8
27.23 66.48 1005.23 52.38 432.55
23.37 63.77 1013.42 76.44 443.86
18.74 59.21 1018.3 91.55 449.77
14.81 43.69 1017.19 71.9 470.71
23.1 51.3 1011.93 80.05 452.17
10.72 41.38 1021.6 63.77 478.29
29.46 71.94 1006.96 62.26 428.54
8.1 40.64 1020.66 89.04 478.27
27.29 62.66 1007.63 58.02 439.58
17.1 49.69 1005.53 81.82 457.32
11.49 44.2 1018.79 91.14 475.51
23.69 65.59 1010.85 88.92 439.66
13.51 40.89 1011.03 84.83 471.99
9.64 39.35 1015.1 91.76 479.81
25.65 78.92 1010.83 86.56 434.78
21.59 61.87 1011.18 57.21 446.58
27.98 58.33 1013.92 54.25 437.76
18.8 39.72 1001.24 63.8 459.36
18.28 44.71 1016.99 33.71 462.28
13.55 43.48 1016.08 67.25 464.33
22.99 46.21 1010.71 60.11 444.36
23.94 59.39 1014.32 74.55 438.64
13.74 34.03 1018.69 67.34 470.49
21.3 41.1 1001.86 42.75 455.13
27.54 66.93 1017.06 55.2 450.22
24.81 63.73 1009.34 83.61 440.43
4.97 42.85 1014.02 88.78 482.98
15.22 50.88 1014.19 100.12 460.44
23.88 54.2 1012.81 64.52 444.97
33.01 68.67 1005.2 51.41 433.94
25.98 73.18 1012.28 85.78 439.73
28.18 73.88 1005.89 75.41 434.48
21.67 60.84 1017.93 81.63 442.33
17.67 45.09 1014.26 51.92 457.67
21.37 57.76 1018.8 70.12 454.66
28.69 67.25 1017.71 53.83 432.21
16.61 43.77 1012.25 77.23 457.66
27.91 63.76 1010.27 65.67 435.21
20.97 47.43 1007.64 71.18 448.22
10.8 41.66 1013.79 81.96 475.51
20.61 62.91 1013.24 79.54 446.53
25.45 57.32 1011.7 47.09 441.3
30.16 69.34 1007.67 57.69 433.54
4.99 39.04 1020.45 78.89 472.52
10.51 44.78 1012.59 85.29 474.77
33.79 69.05 1001.62 40.13 435.1
21.34 59.8 1016.92 77.06 450.74
23.4 65.06 1014.32 67.38 442.7
32.21 68.14 1003.34 62.44 426.56
14.26 42.32 1016.0 77.43 463.71
27.71 66.93 1016.85 58.77 447.06
21.95 57.76 1018.02 67.72 452.27
25.76 63.94 1018.49 42.14 445.78
23.68 68.3 1017.93 84.16 438.65
8.28 40.77 1011.55 89.79 480.15
23.44 62.52 1016.46 67.21 447.19
25.32 48.41 1008.47 72.14 443.04
3.94 39.9 1008.06 97.49 488.81
17.3 57.76 1016.26 87.74 455.75
18.2 49.39 1018.83 96.3 455.86
21.43 46.97 1013.94 61.25 457.68
11.16 40.05 1014.95 88.38 479.11
30.38 74.16 1007.44 74.77 432.84
23.36 62.52 1016.18 68.18 448.37
21.69 47.45 1007.56 77.2 447.06
23.62 49.21 1014.1 49.54 443.53
21.87 61.45 1011.13 92.22 445.21
29.25 66.51 1015.53 33.65 441.7
20.03 66.86 1013.05 64.59 450.93
18.14 49.78 1002.95 100.09 451.44
24.23 56.89 1012.32 68.04 441.29
18.11 44.85 1014.48 48.94 458.85
6.57 43.65 1018.24 74.47 481.46
12.56 43.41 1016.93 81.02 467.19
13.4 41.58 1020.5 71.17 461.54
27.1 52.84 1006.28 53.85 439.08
14.28 42.74 1028.79 70.67 467.22
16.29 44.34 1019.49 59.36 468.8
31.24 71.98 1004.66 57.17 426.93
10.57 37.73 1024.36 70.29 474.65
13.8 44.21 1022.93 83.37 468.97
25.3 71.58 1010.18 87.36 433.97
18.06 50.16 1009.52 100.09 450.53
25.42 59.04 1011.98 68.78 444.51
15.07 40.69 1015.29 70.98 469.03
11.75 71.14 1019.36 75.68 466.56
20.23 52.05 1012.15 47.49 457.57
27.31 59.54 1006.24 71.99 440.13
28.57 69.84 1003.57 66.55 433.24
17.9 43.72 1008.64 74.73 452.55
23.83 71.37 1002.04 64.78 443.29
27.92 74.99 1005.47 75.13 431.76
17.34 44.78 1007.81 56.38 454.97
17.94 63.07 1012.42 94.35 456.7
6.4 39.9 1007.75 86.55 486.03
11.78 39.96 1011.37 82.95 472.79
20.28 57.25 1010.12 88.42 452.03
21.04 54.2 1012.26 85.61 443.41
25.11 67.32 1014.49 58.39 441.93
30.28 70.98 1007.51 74.28 432.64
8.14 36.24 1013.15 87.85 480.25
16.86 39.63 1004.47 83.5 466.68
6.25 40.07 1020.19 65.24 494.39
22.35 54.42 1012.46 75.01 454.72
17.98 56.85 1012.28 84.52 448.71
21.19 42.48 1013.43 80.52 469.76
20.94 44.89 1009.64 75.14 450.71
24.23 58.79 1009.8 75.75 444.01
19.18 58.2 1017.46 76.72 453.2
20.88 57.85 1012.39 85.47 450.87
23.67 63.86 1019.67 57.95 441.73
14.12 39.52 1018.41 78.32 465.09
25.23 64.63 1020.59 52.2 447.28
6.54 39.33 1011.54 93.69 491.16
20.08 62.52 1017.99 75.74 450.98
24.67 63.56 1013.75 67.56 446.3
27.82 79.74 1008.37 69.46 436.48
15.55 42.03 1017.41 74.58 460.84
24.26 69.51 1013.43 53.23 442.56
13.45 41.49 1020.19 88.72 467.3
11.06 40.64 1021.47 96.16 479.13
24.91 52.3 1008.72 68.26 441.15
22.39 59.04 1011.78 86.39 445.52
11.95 40.69 1015.62 85.34 475.4
14.85 40.69 1014.91 72.64 469.3
10.11 41.62 1017.17 97.82 463.57
23.67 68.67 1006.71 77.22 445.32
16.14 44.21 1020.36 80.59 461.03
15.11 43.13 1014.99 46.91 466.74
24.14 59.87 1018.47 57.76 444.04
30.08 67.25 1017.6 53.09 434.01
14.77 44.9 1020.5 84.31 465.23
27.6 69.34 1009.63 71.58 440.6
13.89 44.84 1023.66 92.97 466.74
26.85 75.6 1017.43 74.55 433.48
12.41 40.96 1023.36 78.96 473.59
13.08 41.74 1020.75 64.44 474.81
18.93 44.06 1017.58 68.23 454.75
20.5 49.69 1009.6 70.81 452.94
30.72 69.13 1009.94 61.66 435.83
7.55 39.22 1014.53 77.76 482.19
13.49 44.47 1030.46 69.49 466.66
15.62 40.12 1013.03 96.26 462.59
24.8 64.63 1020.69 55.74 447.82
10.03 41.62 1014.55 95.61 462.73
22.43 63.21 1012.06 84.75 447.98
14.95 39.31 1009.15 75.3 462.72
24.78 58.46 1016.82 67.5 442.42
23.2 48.41 1008.64 80.92 444.69
14.01 39.0 1016.73 79.23 466.7
19.4 64.63 1020.38 81.1 453.84
30.15 67.32 1013.83 32.8 436.92
6.91 36.08 1021.82 84.31 486.37
29.04 60.07 1015.42 46.15 440.43
26.02 63.07 1010.94 53.96 446.82
5.89 39.48 1005.11 59.83 484.91
26.52 71.64 1008.27 75.3 437.76
28.53 68.08 1013.27 42.53 438.91
16.59 39.54 1007.97 70.58 464.19
22.95 67.79 1009.89 91.69 442.19
23.96 47.43 1008.38 63.55 446.86
17.48 44.2 1018.89 61.51 457.15
6.69 43.65 1020.14 69.55 482.57
10.25 41.26 1007.44 98.08 476.03
28.87 72.58 1008.69 79.34 428.89
12.04 40.23 1018.07 81.28 472.7
22.58 52.3 1009.04 78.99 445.6
15.12 52.05 1014.63 80.38 464.78
25.48 58.95 1017.02 51.16 440.42
27.87 70.79 1003.96 72.17 428.41
23.72 70.47 1010.65 75.39 438.5
25.0 59.43 1007.84 68.91 438.28
8.42 40.64 1022.35 96.38 476.29
22.46 58.49 1011.5 70.54 448.46
29.92 57.19 1008.62 45.8 438.99
11.68 39.22 1017.9 57.95 471.8
14.04 42.44 1012.74 81.89 471.81
19.86 59.14 1016.12 69.32 449.82
25.99 68.08 1013.13 59.14 442.14
23.42 58.79 1009.74 81.54 441.46
10.6 40.22 1011.37 85.81 477.62
20.97 61.87 1011.45 65.41 446.76
14.14 39.82 1012.46 81.15 472.52
8.56 40.71 1021.27 95.87 471.58
24.86 72.39 1001.15 90.24 440.85
29.0 77.54 1011.33 75.13 431.37
27.59 71.97 1008.64 88.22 437.33
10.45 40.71 1015.68 81.48 469.22
8.51 40.78 1023.51 89.84 471.11
29.82 66.51 1010.98 43.57 439.17
22.56 62.26 1012.11 63.16 445.33
11.38 39.22 1018.62 57.14 473.71
20.25 57.76 1016.28 77.76 452.66
22.42 59.43 1007.12 90.56 440.99
14.85 38.91 1014.48 60.98 467.42
25.62 58.82 1010.02 70.31 444.14
19.85 56.53 1020.57 74.05 457.17
13.67 54.3 1015.92 75.42 467.87
24.39 70.72 1009.78 82.25 442.04
16.07 44.58 1019.52 67.95 471.36
11.6 39.1 1009.81 100.09 460.7
31.38 70.83 1010.35 47.28 431.33
29.91 76.86 998.59 72.41 432.6
19.67 59.39 1014.07 77.67 447.61
27.18 64.79 1016.27 63.7 443.87
21.39 52.3 1009.2 79.77 446.87
10.45 41.01 1020.57 93.84 465.74
19.46 56.89 1014.02 84.95 447.86
23.55 62.96 1020.16 70.16 447.65
23.35 63.47 1011.78 84.24 437.87
9.26 41.66 1016.87 73.32 483.51
10.3 41.46 1018.21 86.17 479.65
20.94 58.16 1016.88 65.43 455.16
23.13 71.25 1002.49 94.59 431.91
12.77 41.5 1014.13 86.8 470.68
28.29 69.13 1009.29 58.18 429.28
19.13 59.21 1018.32 89.66 450.81
24.44 73.5 1011.49 87.39 437.73
20.32 44.6 1015.16 36.35 460.21
20.54 69.05 1001.6 79.62 442.86
12.16 45.0 1021.51 50.52 482.99
28.09 65.27 1013.27 51.96 440.0
9.25 41.82 1033.25 74.73 478.48
21.75 49.82 1015.01 78.33 455.28
23.7 66.56 1002.07 85.19 436.94
16.22 37.87 1022.36 83.13 461.06
24.75 69.45 1013.97 53.49 438.28
10.48 39.58 1011.81 88.86 472.61
29.53 70.79 1003.7 60.89 426.85
12.59 39.72 1017.76 61.14 470.18
23.5 54.42 1012.31 68.29 455.38
29.01 66.56 1006.44 57.62 428.32
9.75 42.49 1010.57 83.63 480.35
19.55 56.53 1020.2 78.1 455.56
21.05 58.33 1013.14 66.34 447.66
24.72 68.67 1006.74 79.02 443.06
21.19 58.86 1014.19 68.96 452.43
10.77 41.54 1019.94 71.13 477.81
28.68 73.77 1004.72 87.01 431.66
29.87 73.91 1004.53 74.3 431.8
22.99 68.67 1006.65 77.62 446.67
24.66 60.29 1018.0 59.56 445.26
32.63 69.89 1013.85 41.66 425.72
31.38 72.29 1008.73 73.27 430.58
23.87 60.27 1018.94 77.16 439.86
25.6 59.15 1013.31 67.02 441.11
27.62 71.14 1011.6 52.8 434.72
30.1 67.45 1014.23 39.04 434.01
12.19 41.17 1019.43 65.47 475.64
13.11 41.58 1020.43 74.32 460.44
28.29 68.67 1005.46 69.22 436.4
13.45 40.73 1018.7 93.88 461.03
10.98 41.54 1019.94 69.83 479.08
26.48 69.14 1009.31 84.11 435.76
13.07 45.51 1015.22 78.65 460.14
25.56 75.6 1017.37 69.31 442.2
22.68 50.78 1008.83 70.3 447.69
28.86 73.67 1006.65 68.23 431.15
22.7 63.56 1014.32 71.76 445.0
27.89 73.21 1001.32 85.88 431.59
13.78 44.47 1027.94 71.09 467.22
28.14 51.43 1012.16 52.67 445.33
11.8 45.09 1013.21 89.68 470.57
10.71 39.61 1018.72 73.66 473.77
24.54 60.29 1017.42 58.94 447.67
11.54 40.05 1014.78 87.05 474.29
29.47 71.32 1008.07 67.0 437.14
29.24 69.05 1003.12 43.18 432.56
14.51 41.79 1009.72 80.62 459.14
22.91 60.07 1016.03 59.72 446.19
27.02 71.77 1006.38 72.1 428.1
13.49 44.47 1030.18 69.15 468.46
30.24 66.75 1017.95 55.66 435.02
23.19 48.6 1002.38 61.19 445.52
17.73 40.55 1003.36 74.62 462.69
18.62 61.27 1019.26 73.35 455.75
12.85 40.0 1015.89 68.85 463.74
32.33 69.68 1011.95 39.89 439.79
25.09 58.95 1016.99 53.16 443.26
29.45 69.13 1009.3 52.97 432.04
16.91 43.96 1013.32 79.87 465.86
14.09 45.87 1009.05 84.09 465.6
10.73 25.36 1009.35 100.15 469.43
23.2 49.3 1003.4 79.77 440.75
8.21 38.91 1015.82 88.99 481.32
9.3 40.56 1022.64 76.14 479.87
16.97 39.16 1005.7 69.13 458.59
23.69 71.97 1009.62 93.03 438.62
25.13 59.44 1012.38 77.92 445.59
9.86 43.56 1015.13 74.89 481.87
11.33 41.5 1013.58 88.7 475.01
26.95 48.41 1008.53 62.94 436.54
15.0 40.66 1016.28 89.62 456.63
20.76 62.52 1015.63 81.04 451.69
14.29 39.59 1010.93 94.53 463.04
19.74 67.71 1007.68 64.02 446.1
26.68 59.92 1009.94 70.57 438.67
14.24 41.4 1019.7 70.32 466.88
21.98 48.41 1008.42 84.86 444.6
22.75 59.39 1015.4 81.41 440.26
8.34 40.96 1023.28 89.45 483.92
11.8 41.2 1017.18 82.71 475.19
8.81 44.68 1023.06 93.93 479.24
30.05 73.68 1014.95 70.6 434.92
16.01 65.46 1014.0 87.68 454.16
21.75 58.79 1012.42 87.58 447.58
13.94 41.26 1021.67 74.4 467.9
29.25 69.13 1010.27 67.35 426.29
22.33 45.87 1007.8 63.61 447.02
16.43 41.79 1005.47 76.89 455.85
11.5 40.22 1010.31 78.08 476.46
23.53 68.94 1007.53 69.17 437.48
21.86 49.21 1014.61 53.31 452.77
6.17 39.33 1012.57 93.32 491.54
30.19 64.79 1017.22 42.47 438.41
11.67 41.93 1019.81 82.58 476.1
15.34 36.99 1007.87 94.59 464.58
11.5 40.78 1023.91 86.31 467.74
25.53 57.17 1010.0 72.57 442.12
21.27 57.5 1014.53 80.76 453.34
28.37 69.13 1010.44 71.93 425.29
28.39 51.43 1011.74 47.54 449.63
13.78 45.78 1025.27 95.72 462.88
14.6 42.32 1015.71 77.03 464.67
5.1 35.57 1027.17 80.49 489.96
7.0 38.08 1020.27 77.67 482.38
26.3 77.95 1009.45 78.72 437.95
30.56 71.98 1004.74 58.77 429.2
21.09 46.63 1013.03 74.8 453.34
28.21 70.02 1010.58 51.34 442.47
15.84 49.69 1015.14 90.41 462.6
10.03 40.96 1024.57 91.1 478.79
20.37 52.05 1012.34 62.57 456.11
21.19 50.16 1005.81 84.27 450.33
33.73 69.88 1007.21 42.93 434.83
29.87 73.68 1015.1 40.96 433.43
19.62 62.96 1020.76 76.53 456.02
9.93 40.67 1018.08 69.74 485.23
9.43 37.14 1013.03 74.99 473.57
14.24 39.58 1011.17 70.45 469.94
12.97 49.83 1008.69 91.49 452.07
7.6 41.04 1021.82 88.97 475.32
8.39 36.24 1013.39 89.13 480.69
25.41 48.06 1013.12 46.52 444.01
18.43 56.03 1020.41 60.55 465.17
10.31 39.82 1012.87 88.71 480.61
11.29 41.5 1013.39 89.15 476.04
22.61 49.3 1003.51 83.02 441.76
29.34 71.98 1005.19 75.19 428.24
18.87 67.71 1004.0 87.35 444.77
13.21 45.87 1008.58 85.66 463.1
11.3 44.6 1018.19 91.66 470.5
29.23 72.99 1007.04 63.47 431.0
27.76 69.4 1004.27 72.25 430.68
29.26 67.17 1006.6 70.58 436.42
25.72 49.82 1016.19 60.1 452.33
23.43 63.94 1010.64 89.29 440.16
25.6 63.76 1010.18 67.43 435.75
22.3 44.57 1008.48 67.58 449.74
27.91 72.24 1010.74 70.8 430.73
30.35 77.17 1009.55 63.62 432.75
21.78 47.43 1007.88 66.68 446.79
7.19 41.39 1018.12 90.76 486.35
20.88 59.8 1015.66 75.34 453.18
24.19 50.23 1015.73 59.77 458.31
9.98 41.54 1019.7 80.79 480.26
23.47 51.3 1011.89 74.1 448.65
26.35 49.5 1012.67 41.34 458.41
29.89 64.69 1006.37 58.78 435.39
19.29 50.16 1010.49 97.78 450.21
17.48 43.14 1018.68 74.85 459.59
25.21 75.6 1017.19 69.84 445.84
23.3 48.78 1018.17 75.36 441.08
15.42 37.85 1009.89 85.8 467.33
21.44 63.09 1016.56 90.11 444.19
29.45 68.27 1007.96 61.63 432.96
29.69 47.93 1002.85 44.76 438.09
15.52 36.99 1006.86 89.7 467.9
11.47 43.67 1012.68 72.51 475.72
9.77 34.69 1027.72 74.98 477.51
22.6 69.84 1006.37 79.59 435.13
8.24 39.61 1017.99 78.42 477.9
17.01 44.2 1019.18 61.23 457.26
19.64 44.6 1015.88 47.56 467.53
10.61 41.58 1021.08 93.06 465.15
12.04 40.1 1014.42 89.65 474.28
29.19 65.71 1013.85 50.5 444.49
21.75 45.09 1014.15 44.84 452.84
23.66 77.54 1008.5 85.32 435.38
27.05 75.33 1003.88 82.94 433.57
29.63 69.71 1009.04 67.26 435.27
18.2 39.63 1005.35 79.05 468.49
32.22 70.8 1009.9 62.03 433.07
26.88 73.56 1004.85 94.36 430.63
29.05 65.74 1013.29 60.02 440.74
8.9 39.96 1026.31 95.46 474.49
18.93 48.6 1005.72 84.92 449.74
27.49 63.76 1010.09 62.8 436.73
23.1 70.79 1006.53 90.81 434.58
11.22 43.13 1017.24 80.9 473.93
31.97 79.74 1007.03 55.84 435.99
13.32 43.22 1009.45 75.3 466.83
31.68 68.24 1005.29 37.34 427.22
23.69 63.77 1013.39 79.5 444.07
13.83 41.49 1020.11 87.29 469.57
18.32 66.51 1015.18 81.5 459.89
11.05 40.71 1024.91 76.42 479.59
22.03 64.69 1007.21 75.75 440.92
10.23 41.46 1020.45 84.95 480.87
23.92 66.54 1009.93 62.37 441.9
29.38 69.68 1011.35 49.25 430.2
17.35 42.86 1014.62 74.16 465.16
9.81 44.45 1021.19 90.55 471.32
4.97 40.64 1020.91 94.28 485.43
5.15 40.07 1012.27 63.31 495.35
21.54 58.49 1010.85 78.9 449.12
7.94 42.02 1006.22 90.97 480.53
18.77 50.66 1014.89 87.34 457.07
21.69 69.94 1010.7 80.8 443.67
10.07 44.68 1023.44 90.95 477.52
13.83 39.64 1012.52 69.97 472.95
10.45 39.69 1003.92 89.45 472.54
11.56 40.71 1015.85 76.08 469.17
23.64 70.04 1011.09 83.35 435.21
10.48 40.22 1004.81 92.16 477.78
13.09 39.85 1012.86 58.42 475.89
10.67 40.23 1017.75 85.06 483.9
12.57 39.16 1016.53 88.91 476.2
14.45 43.34 1015.47 83.33 462.16
14.22 37.85 1011.24 88.49 471.05
6.97 41.26 1010.6 96.88 484.71
20.61 63.86 1015.43 73.86 446.34
14.67 42.28 1007.21 65.17 469.02
29.06 72.86 1004.23 69.41 432.12
14.38 40.1 1015.51 81.23 467.28
32.51 69.98 1013.29 54.07 429.66
11.79 45.09 1013.16 89.17 469.49
8.65 40.56 1023.23 78.85 485.87
9.75 40.81 1026.0 84.44 481.95
9.11 40.02 1031.1 83.02 479.03
23.39 69.13 1010.99 90.66 434.5
14.3 54.3 1015.16 75.29 464.9
17.49 63.94 1020.02 82.6 452.71
31.1 69.51 1010.84 45.4 429.74
19.77 56.65 1020.67 66.33 457.09
28.61 72.29 1011.61 45.33 446.77
13.52 41.48 1014.46 67.12 460.76
13.52 40.83 1008.31 84.14 471.95
17.57 46.21 1014.09 80.81 453.29
28.18 60.07 1016.34 49.13 441.61
14.29 46.18 1017.01 87.29 464.73
18.12 43.69 1016.91 52.95 464.68
31.27 73.91 1003.72 68.92 430.59
26.24 77.95 1014.19 85.21 438.01
7.44 41.04 1021.84 88.56 479.08
29.78 74.78 1009.28 55.09 436.39
23.37 65.46 1016.25 48.64 447.07
10.62 39.58 1011.9 87.85 479.91
5.84 43.02 1013.88 87.42 489.05
14.51 53.82 1016.46 62.75 463.17
11.31 42.02 1001.18 94.86 471.26
11.25 40.67 1011.64 63.54 480.49
9.18 39.42 1025.41 69.46 473.78
19.82 58.16 1016.76 74.66 455.5
24.77 58.41 1013.78 80.57 446.27
9.66 41.06 1021.21 84.7 482.2
21.96 59.8 1016.72 72.6 452.48
18.59 43.14 1011.92 52.63 464.48
24.75 69.89 1015.29 82.01 438.1
24.37 63.47 1012.77 75.22 445.6
29.6 67.79 1010.37 51.05 442.43
25.32 61.25 1011.56 80.1 436.67
16.15 41.85 1016.54 81.58 466.56
15.74 71.14 1019.65 65.94 457.29
5.97 36.25 1029.65 86.74 487.03
15.84 52.72 1026.45 62.57 464.93
14.84 44.63 1019.28 57.37 466.0
12.25 48.79 1017.44 88.91 469.52
27.38 70.04 1011.18 72.26 428.88
8.76 41.48 1018.49 74.98 474.3
15.54 39.31 1009.69 71.19 461.06
18.71 39.39 1014.09 62.82 465.57
13.06 41.78 1012.3 55.31 467.67
12.72 40.71 1016.02 71.57 466.99
19.83 39.39 1013.73 59.16 463.72
27.23 49.16 1004.03 40.8 443.78
24.27 68.28 1005.43 67.63 445.23
11.8 40.66 1017.13 97.2 464.43
6.76 36.25 1028.31 91.16 484.36
25.99 63.07 1012.5 64.81 442.16
16.3 39.63 1004.64 85.61 464.11
16.5 49.39 1018.35 93.42 462.48
10.59 42.49 1009.59 77.36 477.49
26.05 65.59 1012.78 67.03 437.04
19.5 40.79 1003.8 89.45 457.09
22.21 45.01 1012.22 54.84 450.6
17.86 45.0 1023.25 53.48 465.78
29.96 70.04 1010.15 54.47 427.1
19.08 44.63 1020.14 43.36 459.81
23.59 47.43 1006.64 48.92 447.36
3.38 39.64 1011.0 81.22 488.92
26.39 66.49 1012.96 60.35 433.36
8.99 39.04 1021.99 75.98 483.35
10.91 41.04 1026.57 74.24 469.53
13.08 39.82 1012.27 85.21 476.96
23.95 58.46 1017.5 68.46 440.75
15.64 43.71 1024.51 78.31 462.55
18.78 54.2 1012.05 89.25 448.04
20.65 50.59 1016.22 68.57 455.24
4.96 40.07 1011.8 67.38 494.75
23.51 57.32 1012.55 53.6 444.58
5.99 35.79 1011.56 91.69 484.82
23.65 66.05 1019.6 78.21 442.9
5.17 39.33 1009.68 94.19 485.46
26.38 49.5 1012.82 37.19 457.81
6.02 43.65 1013.85 83.53 481.92
23.2 61.02 1009.63 79.45 443.23
8.57 39.69 1000.91 99.9 474.29
30.72 71.58 1009.98 50.39 430.46
21.52 50.66 1013.56 74.33 455.71
22.93 62.26 1011.25 83.66 438.34
5.71 41.31 1003.24 89.48 485.83
18.62 44.06 1017.76 64.59 452.82
27.88 68.94 1007.68 75.68 435.04
22.32 59.8 1016.82 64.18 451.21
14.55 42.74 1028.41 70.09 465.81
17.83 44.92 1025.04 70.58 458.42
9.68 39.96 1026.09 99.28 470.22
19.41 49.39 1020.84 81.89 449.24
13.22 44.92 1023.84 87.99 471.43
12.24 44.92 1023.74 88.21 473.26
19.21 58.49 1011.7 91.29 452.82
29.74 70.32 1008.1 52.72 432.69
23.28 60.84 1017.91 67.5 444.13
8.02 41.92 1029.8 92.05 467.21
22.47 48.6 1002.33 63.23 445.98
27.51 73.77 1002.42 90.88 436.91
17.51 44.9 1009.05 74.91 455.01
23.22 66.56 1002.47 85.39 437.11
11.73 40.64 1020.68 96.98 477.06
21.19 67.71 1006.65 56.28 441.71
5.48 40.07 1019.63 65.62 495.76
24.26 66.44 1011.33 55.32 445.63
12.32 41.62 1012.88 88.88 464.72
31.26 68.94 1005.94 39.49 438.03
32.09 72.86 1003.47 54.59 434.78
24.98 60.32 1015.63 57.19 444.67
27.48 61.41 1012.2 45.06 452.24
21.04 45.09 1014.19 40.62 450.92
27.75 70.4 1006.65 90.21 436.53
22.79 71.77 1005.75 90.91 435.53
24.22 68.51 1013.23 74.96 440.01
27.06 64.45 1008.72 54.21 443.1
29.25 71.94 1007.18 63.62 427.49
26.86 68.08 1012.99 50.04 436.25
29.64 67.79 1009.99 51.23 440.74
19.92 63.31 1015.02 82.71 443.54
18.5 51.43 1010.82 92.04 459.42
23.71 60.23 1009.76 90.67 439.66
14.39 44.84 1023.55 91.14 464.15
19.3 56.65 1020.55 70.43 459.1
24.65 52.36 1014.76 66.63 455.68
13.5 45.51 1015.33 86.95 469.08
9.82 41.26 1007.71 96.69 478.02
18.4 44.06 1017.36 70.88 456.8
28.12 44.89 1009.18 47.14 441.13
17.15 43.69 1017.05 63.36 463.88
30.69 73.67 1006.14 60.58 430.45
28.82 65.71 1014.24 54.3 449.18
21.3 48.92 1010.92 65.09 447.89
30.58 70.04 1010.4 48.16 431.59
21.17 52.3 1009.36 81.51 447.5
9.87 41.82 1033.04 68.57 475.58
22.18 59.8 1016.77 73.16 453.24
24.39 63.21 1012.59 80.88 446.4
10.73 44.92 1025.1 85.4 476.81
9.38 40.46 1019.29 75.77 474.1
20.27 57.76 1016.66 75.76 450.71
24.82 66.48 1006.4 70.21 433.62
16.55 41.66 1011.45 55.53 465.14
20.73 59.87 1019.08 80.48 445.18
9.51 39.22 1015.3 72.41 474.12
8.63 43.79 1016.08 83.25 483.91
6.48 40.27 1010.55 82.12 486.68
14.95 43.52 1022.43 94.75 464.98
5.76 45.87 1010.83 95.79 481.4
10.94 39.04 1021.81 86.02 479.2
15.87 41.16 1005.85 78.29 463.86
12.42 38.25 1012.76 82.23 472.3
29.12 58.84 1001.31 52.86 446.51
29.12 51.43 1005.93 60.66 437.71
19.08 41.1 1001.96 62.77 458.94
31.06 67.17 1007.62 65.54 437.91
5.72 39.33 1009.96 95.4 490.76
26.52 65.06 1013.4 51.78 439.66
13.84 44.9 1007.58 63.62 463.27
13.03 39.52 1016.68 83.09 473.99
25.94 66.49 1012.83 61.81 433.38
16.64 53.82 1015.13 68.24 459.01
14.13 40.75 1016.05 72.41 471.44
13.65 39.28 1012.97 79.64 471.91
14.5 44.47 1028.2 66.95 465.15
19.8 51.19 1008.25 91.98 446.66
25.2 63.76 1009.78 64.96 438.15
20.66 51.19 1008.81 88.93 447.14
12.07 43.71 1025.53 85.62 472.32
25.64 70.72 1010.16 84.0 441.68
23.33 72.99 1009.33 89.41 440.04
29.41 64.05 1009.82 67.4 444.82
16.6 53.16 1014.5 76.75 457.26
27.53 72.58 1009.13 89.06 428.83
20.62 43.43 1009.93 64.02 449.07
26.02 71.94 1009.38 64.12 435.21
12.75 44.2 1017.59 81.22 471.03
12.87 48.04 1012.47 100.13 465.56
25.77 62.96 1019.86 58.07 442.83
14.84 41.48 1017.26 63.42 460.3
7.41 40.71 1023.07 83.32 474.25
8.87 41.82 1033.3 74.28 477.97
9.69 40.46 1019.1 71.91 472.16
16.17 46.97 1014.22 85.8 456.08
26.24 49.82 1014.9 55.58 452.41
13.78 43.22 1011.31 69.7 463.71
26.3 67.07 1006.26 63.79 433.72
17.37 57.76 1016.0 86.59 456.4
23.6 48.98 1015.41 48.28 448.43
8.3 36.08 1020.63 80.42 481.6
18.86 42.18 1001.16 98.58 457.07
22.12 49.39 1019.8 72.83 451.0
28.41 75.6 1018.48 56.07 440.28
29.42 71.32 1002.26 67.13 437.47
18.61 67.71 1004.07 84.49 443.57
27.57 69.84 1004.91 68.37 426.6
12.83 41.5 1013.12 86.07 470.87
9.64 39.85 1012.9 83.82 478.37
19.13 58.66 1013.32 74.86 453.92
15.92 40.56 1020.79 53.52 470.22
24.64 72.24 1011.37 80.61 434.54
27.62 63.9 1013.11 43.56 442.89
8.9 36.24 1013.29 89.35 479.03
9.55 43.99 1020.5 97.28 476.06
10.57 36.71 1022.62 80.49 473.88
19.8 57.25 1010.84 88.9 451.75
25.63 56.85 1012.68 49.7 439.2
24.7 58.46 1015.58 68.64 439.7
15.26 46.18 1013.68 98.58 463.6
20.06 52.84 1004.21 82.12 447.47
19.84 56.89 1013.23 78.32 447.92
11.49 44.63 1020.44 86.04 471.08
23.74 72.43 1007.99 91.36 437.55
22.62 51.3 1012.36 81.02 448.27
29.53 72.39 998.47 76.05 431.69
21.32 48.14 1016.57 71.81 449.09
20.3 58.46 1015.93 82.13 448.79
16.97 44.92 1025.21 74.27 460.21
12.07 41.17 1013.54 71.32 479.28
7.46 41.82 1032.67 74.59 483.11
19.2 54.2 1011.46 84.44 450.75
28.64 66.54 1010.43 43.39 437.97
13.56 41.48 1008.53 87.2 459.76
17.4 44.9 1020.5 77.11 457.75
14.08 40.1 1015.48 82.81 469.33
27.11 69.75 1009.74 85.67 433.28
20.92 70.02 1010.23 95.58 444.64
16.18 44.9 1021.3 74.46 463.1
15.57 44.68 1022.01 90.02 460.91
10.37 39.04 1023.95 81.93 479.35
19.6 59.21 1017.65 86.29 449.23
9.22 40.92 1021.83 85.43 474.51
27.76 72.99 1007.81 71.66 435.02
28.68 70.72 1009.43 71.33 435.45
20.95 48.14 1013.3 67.72 452.38
9.06 39.3 1019.73 84.23 480.41
9.21 39.72 1019.54 74.44 478.96
13.65 42.74 1026.58 71.48 468.87
31.79 76.2 1007.89 56.3 434.01
14.32 44.6 1013.85 68.13 466.36
26.28 75.23 1011.44 68.35 435.28
7.69 43.02 1014.51 85.23 486.46
14.44 40.1 1015.51 79.78 468.19
9.19 41.01 1022.14 98.98 468.37
13.35 41.39 1019.17 72.87 474.19
23.04 74.22 1009.52 90.93 440.32
4.83 38.44 1015.35 72.94 485.32
17.29 42.86 1014.38 72.3 464.27
8.73 36.18 1013.66 77.74 479.25
26.21 70.32 1007.0 78.29 430.4
23.72 58.62 1016.65 69.1 447.49
29.27 64.69 1006.85 55.79 438.23
10.4 40.43 1025.46 75.09 492.09
12.19 40.75 1015.13 88.98 475.36
20.4 54.9 1016.68 64.26 452.56
34.3 74.67 1015.98 25.89 427.84
27.56 68.08 1010.8 59.18 433.95
30.9 70.8 1008.48 67.48 435.27
14.85 58.59 1014.04 89.85 454.62
16.42 40.56 1020.36 50.62 472.17
16.45 63.31 1015.96 83.97 452.42
10.14 42.02 1003.19 96.51 472.17
9.53 41.44 1018.01 80.09 481.83
17.01 49.15 1021.83 84.02 458.78
23.94 62.08 1022.47 61.97 447.5
15.95 49.25 1019.04 88.51 463.4
11.15 41.26 1022.67 81.83 473.57
25.56 70.32 1009.07 90.63 433.72
27.16 66.44 1011.2 73.37 431.85
26.71 77.95 1012.13 77.5 433.47
29.56 74.22 1007.45 57.46 432.84
31.19 70.94 1007.29 51.91 436.6
6.86 41.17 1020.12 79.14 490.23
12.36 41.74 1020.58 69.24 477.16
32.82 68.31 1010.44 41.85 441.06
25.3 70.98 1007.22 95.1 440.86
8.71 41.82 1033.08 74.53 477.94
13.34 40.8 1026.56 64.85 474.47
14.2 43.02 1012.18 57.07 470.67
23.74 65.34 1013.7 62.9 447.31
16.9 44.88 1018.14 72.21 466.8
28.54 71.94 1007.4 65.99 430.91
30.15 69.88 1007.2 73.67 434.75
14.33 42.86 1010.82 88.59 469.52
25.57 59.43 1008.88 61.19 438.9
30.55 70.04 1010.51 49.37 429.56
28.04 74.33 1013.53 48.65 432.92
26.39 49.16 1005.68 56.18 442.87
15.3 41.76 1022.57 71.56 466.59
6.03 41.14 1028.04 87.46 479.61
13.49 44.63 1019.12 70.02 471.08
27.67 59.14 1016.51 61.2 433.37
24.19 65.48 1018.8 60.54 443.92
24.44 59.14 1016.74 71.82 443.5
29.86 64.79 1017.37 44.8 439.89
30.2 69.59 1008.9 67.32 434.66
7.99 41.38 1021.95 78.77 487.57
9.93 41.62 1013.76 96.02 464.64
11.03 42.32 1017.26 90.56 470.92
22.34 63.73 1014.37 83.19 444.39
25.33 48.6 1002.54 68.45 442.48
18.87 52.08 1005.25 99.19 449.61
25.97 69.34 1009.43 88.11 435.02
16.58 43.99 1021.81 79.29 458.67
14.35 46.18 1016.63 87.76 461.74
25.06 62.39 1008.09 82.56 438.31
13.85 48.92 1011.68 79.24 462.38
16.09 44.2 1019.39 67.24 460.56
26.34 59.21 1013.37 58.98 439.22
23.01 58.79 1009.71 84.22 444.64
26.39 71.25 999.8 89.12 430.34
31.32 71.29 1008.37 50.07 430.46
16.64 45.87 1009.02 98.86 456.79
13.42 41.23 994.17 95.79 468.82
20.06 44.9 1008.79 70.06 448.51
14.8 44.71 1014.67 41.71 470.77
12.59 41.14 1025.79 86.55 465.74
26.7 66.56 1005.31 71.97 430.21
19.78 50.32 1008.62 96.4 449.23
15.17 49.15 1021.91 91.73 461.89
21.71 61.45 1010.97 91.62 445.72
19.09 39.39 1013.36 59.14 466.13
19.76 51.19 1008.38 92.56 448.71
14.68 41.23 998.43 83.71 469.25
21.3 66.86 1013.04 55.43 450.56
16.73 39.64 1008.94 74.91 464.46
12.26 41.5 1014.87 89.41 471.13
14.77 48.06 1010.92 69.81 461.52
18.26 59.15 1012.04 86.01 451.09
27.1 79.74 1005.43 86.05 431.51
14.72 40.83 1009.65 80.98 469.8
26.3 51.43 1012.05 63.62 442.28
16.48 48.92 1011.84 64.16 458.67
17.99 43.79 1016.13 75.63 462.4
20.34 59.8 1015.18 80.21 453.54
25.53 62.96 1019.81 59.7 444.38
31.59 58.9 1003.39 47.6 440.52
30.8 69.14 1007.68 63.78 433.62
10.75 45.0 1023.68 89.37 481.96
19.3 44.9 1008.89 70.55 452.75
4.71 39.42 1026.4 84.42 481.28
23.1 66.05 1020.28 80.62 439.03
32.63 73.88 1005.64 52.56 435.75
26.63 74.16 1009.72 83.26 436.03
24.35 58.49 1011.03 70.64 445.6
15.11 56.03 1020.27 89.95 462.65
29.1 50.05 1005.87 51.53 438.66
21.24 50.32 1008.54 84.83 447.32
6.16 39.48 1004.85 59.68 484.55
7.36 41.01 1024.9 97.88 476.8
10.44 39.04 1023.99 85.03 480.34
26.76 48.41 1010.53 47.38 440.63
16.79 44.6 1014.27 48.08 459.48
10.76 40.43 1025.98 79.65 490.78
6.07 38.91 1019.25 83.39 483.56
27.33 73.18 1012.26 82.18 429.38
27.15 59.21 1013.49 51.71 440.27
22.35 51.43 1011.34 77.33 445.34
21.82 65.27 1013.86 72.81 447.43
21.11 69.94 1004.37 84.26 439.91
19.95 50.59 1016.11 73.23 459.27
7.45 39.61 1017.88 79.73 478.89
15.36 41.66 1012.41 62.32 466.7
15.65 43.5 1021.39 78.58 463.5
25.31 74.33 1015.04 79.88 436.21
25.88 63.47 1011.95 65.87 443.94
24.6 63.94 1012.87 80.28 439.63
22.58 41.54 1013.21 71.33 460.95
19.69 59.14 1015.99 70.33 448.69
25.85 75.08 1006.24 57.73 444.63
10.06 37.83 1005.49 99.46 473.51
18.59 39.54 1008.56 68.61 462.56
18.27 50.16 1011.07 95.91 451.76
8.85 40.43 1025.68 80.42 491.81
30.04 68.08 1011.04 51.01 429.52
26.06 49.02 1007.59 74.08 437.9
14.8 38.73 1003.18 80.73 467.54
23.93 64.45 1015.35 54.71 449.97
23.72 66.48 1003.61 73.75 436.62
11.44 40.55 1023.37 88.43 477.68
20.28 63.86 1016.04 74.66 447.26
27.9 63.13 1011.8 70.04 439.76
24.74 59.39 1015.23 74.64 437.49
14.8 58.2 1018.29 85.11 455.14
8.22 41.03 1021.76 82.97 485.5
27.56 66.93 1016.81 55.59 444.1
32.07 70.94 1006.91 49.9 432.33
9.53 44.03 1008.87 89.99 471.23
13.61 42.34 1017.93 91.61 463.89
22.2 51.19 1009.2 82.95 445.54
21.36 59.54 1007.99 92.62 446.09
23.25 63.86 1017.82 59.64 445.12
23.5 59.21 1018.29 63.0 443.31
8.46 39.66 1015.14 85.38 484.16
8.19 40.69 1019.86 85.23 477.76
30.67 71.29 1008.36 52.08 430.28
32.48 62.04 1010.39 38.05 446.48
8.99 36.66 1028.11 71.98 481.03
13.77 47.83 1007.41 90.66 466.07
19.05 67.32 1013.2 83.14 447.47
21.19 55.5 1019.83 65.22 455.93
10.12 40.0 1021.15 91.67 479.62
24.93 47.01 1014.28 66.04 455.06
8.47 40.46 1019.87 78.19 475.06
24.52 56.85 1012.59 54.47 438.89
28.55 69.84 1003.38 67.26 432.7
20.58 50.9 1011.89 72.56 452.6
18.31 46.21 1010.46 82.15 451.75
27.18 71.06 1008.16 86.32 430.66
4.43 38.91 1019.04 88.17 491.9
26.02 74.78 1010.04 72.78 439.82
15.75 39.0 1015.91 69.58 460.73
22.99 60.95 1015.14 69.86 449.7
25.52 59.15 1013.88 65.37 439.42
27.04 65.06 1013.33 52.37 439.84
6.42 35.57 1025.58 79.63 485.86
17.04 40.12 1011.81 83.14 458.1
10.79 39.82 1012.89 88.25 479.92
20.41 56.03 1019.94 55.85 458.29
7.36 40.07 1017.29 52.55 489.45
28.08 73.42 1012.17 62.74 434.0
24.74 69.13 1010.69 90.08 431.24
28.32 47.93 1003.26 54.5 439.5
16.71 40.56 1019.48 49.88 467.46
30.7 71.58 1010.0 48.96 429.27
18.42 58.95 1016.95 86.77 452.1
10.62 42.02 999.83 96.66 472.41
22.18 69.05 1002.75 70.84 442.14
22.38 49.3 1003.56 83.83 441.0
13.94 41.58 1020.76 68.22 463.07
21.24 60.84 1017.99 82.22 445.71
6.76 39.81 1017.11 87.9 483.16
26.73 68.84 1010.75 66.83 440.45
7.24 38.06 1020.6 85.36 481.83
10.84 40.62 1015.53 60.9 467.6
19.32 52.84 1004.29 83.51 450.88
29.0 69.13 1001.22 52.96 425.5
23.38 54.42 1013.95 73.02 451.87
31.17 69.51 1010.51 43.11 428.94
26.17 48.6 1002.59 61.41 439.86
30.9 73.42 1011.21 65.32 433.44
24.92 73.68 1015.12 93.68 438.23
32.77 71.32 1007.68 42.39 436.95
14.37 40.56 1021.67 68.18 470.19
8.36 40.22 1011.6 89.18 484.66
31.45 68.27 1007.56 64.79 430.81
31.6 73.17 1010.05 43.48 433.37
17.9 48.98 1014.17 80.4 453.02
20.35 50.9 1012.6 72.43 453.5
16.21 41.23 995.88 80.0 463.09
19.36 44.6 1016.25 45.65 464.56
21.04 65.46 1017.22 63.02 452.12
14.05 40.69 1015.66 74.39 470.9
23.48 64.15 1021.08 57.77 450.89
21.91 63.76 1009.85 76.8 445.04
24.42 63.07 1011.49 67.39 444.72
14.26 40.92 1022.07 73.96 460.38
21.38 58.33 1013.05 72.75 446.8
15.71 44.06 1018.34 71.69 465.05
5.78 40.62 1016.55 84.98 484.13
6.77 39.81 1017.01 87.68 488.27
23.84 49.21 1013.85 50.36 447.09
21.17 58.16 1017.16 68.11 452.02
19.94 58.96 1014.16 66.27 455.55
8.73 41.92 1029.41 89.72 480.99
16.39 41.67 1012.96 61.07 467.68 
powerPlantDF.count() // count the number of rows in DF

res12: Long = 9568

Viewing as Table via SQL

Let us look at what tables are already available, as follows:

sqlContext.tables.show() // Ctrl+Enter to see available tables

+--------+--------------------+-----------+
|database|           tableName|isTemporary|
+--------+--------------------+-----------+
| default|fxdata_bco_usd_20...|      false|
| default|fxdata_xau_usd_20...|      false|
| default|power_plant_predi...|      false|
| default|    sentimentlex_csv|      false|
| default|        simple_range|      false|
| default|  social_media_usage|      false|
| default|social_media_usag...|      false|
+--------+--------------------+-----------+

We can also access the list of tables and databases using spark.catalog methods as explained here:

spark.catalog.listTables.show(false)

+------------------------------------------------------------+--------+-----------+---------+-----------+
|name                                                        |database|description|tableType|isTemporary|
+------------------------------------------------------------+--------+-----------+---------+-----------+
|fxdata_bco_usd_2010_2020                                    |default |null       |EXTERNAL |false      |
|fxdata_xau_usd_2009_2020                                    |default |null       |EXTERNAL |false      |
|power_plant_predictions                                     |default |null       |MANAGED  |false      |
|sentimentlex_csv                                            |default |null       |EXTERNAL |false      |
|simple_range                                                |default |null       |MANAGED  |false      |
|social_media_usage                                          |default |null       |MANAGED  |false      |
|social_media_usage_table_partitionedbyplatformbucketedbydate|default |null       |MANAGED  |false      |
+------------------------------------------------------------+--------+-----------+---------+-----------+

spark.catalog.listDatabases.show(false)

+--------+---------------------+-------------------------------------+
|name    |description          |locationUri                          |
+--------+---------------------+-------------------------------------+
|default |Default Hive database|dbfs:/user/hive/warehouse            |
|nih_xray|                     |dbfs:/user/hive/warehouse/nih_xray.db|
+--------+---------------------+-------------------------------------+

We need to create a temporary view of the DataFrame as a table before being able to access it via SQL.

powerPlantDF.createOrReplaceTempView("power_plant_table") // Shift+Enter

sqlContext.tables.show() 

+--------+--------------------+-----------+
|database|           tableName|isTemporary|
+--------+--------------------+-----------+
| default|fxdata_bco_usd_20...|      false|
| default|fxdata_xau_usd_20...|      false|
| default|power_plant_predi...|      false|
| default|    sentimentlex_csv|      false|
| default|        simple_range|      false|
| default|  social_media_usage|      false|
| default|social_media_usag...|      false|
|        |   power_plant_table|       true|
+--------+--------------------+-----------+

Note that table names are in lower-case only!

You Try!

//sqlContext // uncomment and put . after sqlContext and hit Tab to see what methods are available

//sqlContext.dropTempTable("power_plant_table") // uncomment and Ctrl+Enter if you want to remove the table!

The following SQL statement simply selects all the columns (due to *) from powerPlantTable.

-- Ctrl+Enter to query the rows via SQL
SELECT * FROM power_plant_table
AT V AP RH PE
14.96 41.76 1024.07 73.17 463.26
25.18 62.96 1020.04 59.08 444.37
5.11 39.4 1012.16 92.14 488.56
20.86 57.32 1010.24 76.64 446.48
10.82 37.5 1009.23 96.62 473.9
26.27 59.44 1012.23 58.77 443.67
15.89 43.96 1014.02 75.24 467.35
9.48 44.71 1019.12 66.43 478.42
14.64 45.0 1021.78 41.25 475.98
11.74 43.56 1015.14 70.72 477.5
17.99 43.72 1008.64 75.04 453.02
20.14 46.93 1014.66 64.22 453.99
24.34 73.5 1011.31 84.15 440.29
25.71 58.59 1012.77 61.83 451.28
26.19 69.34 1009.48 87.59 433.99
21.42 43.79 1015.76 43.08 462.19
18.21 45.0 1022.86 48.84 467.54
11.04 41.74 1022.6 77.51 477.2
14.45 52.75 1023.97 63.59 459.85
13.97 38.47 1015.15 55.28 464.3
17.76 42.42 1009.09 66.26 468.27
5.41 40.07 1019.16 64.77 495.24
7.76 42.28 1008.52 83.31 483.8
27.23 63.9 1014.3 47.19 443.61
27.36 48.6 1003.18 54.93 436.06
27.47 70.72 1009.97 74.62 443.25
14.6 39.31 1011.11 72.52 464.16
7.91 39.96 1023.57 88.44 475.52
5.81 35.79 1012.14 92.28 484.41
30.53 65.18 1012.69 41.85 437.89
23.87 63.94 1019.02 44.28 445.11
26.09 58.41 1013.64 64.58 438.86
29.27 66.85 1011.11 63.25 440.98
27.38 74.16 1010.08 78.61 436.65
24.81 63.94 1018.76 44.51 444.26
12.75 44.03 1007.29 89.46 465.86
24.66 63.73 1011.4 74.52 444.37
16.38 47.45 1010.08 88.86 450.69
13.91 39.35 1014.69 75.51 469.02
23.18 51.3 1012.04 78.64 448.86
22.47 47.45 1007.62 76.65 447.14
13.39 44.85 1017.24 80.44 469.18
9.28 41.54 1018.33 79.89 482.8
11.82 42.86 1014.12 88.28 476.7
10.27 40.64 1020.63 84.6 474.99
22.92 63.94 1019.28 42.69 444.22
16.0 37.87 1020.24 78.41 461.33
21.22 43.43 1010.96 61.07 448.06
13.46 44.71 1014.51 50.0 474.6
9.39 40.11 1029.14 77.29 473.05
31.07 73.5 1010.58 43.66 432.06
12.82 38.62 1018.71 83.8 467.41
32.57 78.92 1011.6 66.47 430.12
8.11 42.18 1014.82 93.09 473.62
13.92 39.39 1012.94 80.52 471.81
23.04 59.43 1010.23 68.99 442.99
27.31 64.44 1014.65 57.27 442.77
5.91 39.33 1010.18 95.53 491.49
25.26 61.08 1013.68 71.72 447.46
27.97 58.84 1002.25 57.88 446.11
26.08 52.3 1007.03 63.34 442.44
29.01 65.71 1013.61 48.07 446.22
12.18 40.1 1016.67 91.87 471.49
13.76 45.87 1008.89 87.27 463.5
25.5 58.79 1016.02 64.4 440.01
28.26 65.34 1014.56 43.4 441.03
21.39 62.96 1019.49 72.24 452.68
7.26 40.69 1020.43 90.22 474.91
10.54 34.03 1018.71 74.0 478.77
27.71 74.34 998.14 71.85 434.2
23.11 68.3 1017.83 86.62 437.91
7.51 41.01 1024.61 97.41 477.61
26.46 74.67 1016.65 84.44 431.65
29.34 74.34 998.58 81.55 430.57
10.32 42.28 1008.82 75.66 481.09
22.74 61.02 1009.56 79.41 445.56
13.48 39.85 1012.71 58.91 475.74
25.52 69.75 1010.36 90.06 435.12
21.58 67.25 1017.39 79.0 446.15
27.66 76.86 1001.31 69.47 436.64
26.96 69.45 1013.89 51.47 436.69
12.29 42.18 1016.53 83.13 468.75
15.86 43.02 1012.18 40.33 466.6
13.87 45.08 1024.42 81.69 465.48
24.09 73.68 1014.93 94.55 441.34
20.45 69.45 1012.53 91.81 441.83
15.07 39.3 1019.0 63.62 464.7
32.72 69.75 1009.6 49.35 437.99
18.23 58.96 1015.55 69.61 459.12
35.56 68.94 1006.56 38.75 429.69
18.36 51.43 1010.57 90.17 459.8
26.35 64.05 1009.81 81.24 433.63
25.92 60.95 1014.62 48.46 442.84
8.01 41.66 1014.49 76.72 485.13
19.63 52.72 1025.09 51.16 459.12
20.02 67.32 1012.05 76.34 445.31
10.08 40.72 1022.7 67.3 480.8
27.23 66.48 1005.23 52.38 432.55
23.37 63.77 1013.42 76.44 443.86
18.74 59.21 1018.3 91.55 449.77
14.81 43.69 1017.19 71.9 470.71
23.1 51.3 1011.93 80.05 452.17
10.72 41.38 1021.6 63.77 478.29
29.46 71.94 1006.96 62.26 428.54
8.1 40.64 1020.66 89.04 478.27
27.29 62.66 1007.63 58.02 439.58
17.1 49.69 1005.53 81.82 457.32
11.49 44.2 1018.79 91.14 475.51
23.69 65.59 1010.85 88.92 439.66
13.51 40.89 1011.03 84.83 471.99
9.64 39.35 1015.1 91.76 479.81
25.65 78.92 1010.83 86.56 434.78
21.59 61.87 1011.18 57.21 446.58
27.98 58.33 1013.92 54.25 437.76
18.8 39.72 1001.24 63.8 459.36
18.28 44.71 1016.99 33.71 462.28
13.55 43.48 1016.08 67.25 464.33
22.99 46.21 1010.71 60.11 444.36
23.94 59.39 1014.32 74.55 438.64
13.74 34.03 1018.69 67.34 470.49
21.3 41.1 1001.86 42.75 455.13
27.54 66.93 1017.06 55.2 450.22
24.81 63.73 1009.34 83.61 440.43
4.97 42.85 1014.02 88.78 482.98
15.22 50.88 1014.19 100.12 460.44
23.88 54.2 1012.81 64.52 444.97
33.01 68.67 1005.2 51.41 433.94
25.98 73.18 1012.28 85.78 439.73
28.18 73.88 1005.89 75.41 434.48
21.67 60.84 1017.93 81.63 442.33
17.67 45.09 1014.26 51.92 457.67
21.37 57.76 1018.8 70.12 454.66
28.69 67.25 1017.71 53.83 432.21
16.61 43.77 1012.25 77.23 457.66
27.91 63.76 1010.27 65.67 435.21
20.97 47.43 1007.64 71.18 448.22
10.8 41.66 1013.79 81.96 475.51
20.61 62.91 1013.24 79.54 446.53
25.45 57.32 1011.7 47.09 441.3
30.16 69.34 1007.67 57.69 433.54
4.99 39.04 1020.45 78.89 472.52
10.51 44.78 1012.59 85.29 474.77
33.79 69.05 1001.62 40.13 435.1
21.34 59.8 1016.92 77.06 450.74
23.4 65.06 1014.32 67.38 442.7
32.21 68.14 1003.34 62.44 426.56
14.26 42.32 1016.0 77.43 463.71
27.71 66.93 1016.85 58.77 447.06
21.95 57.76 1018.02 67.72 452.27
25.76 63.94 1018.49 42.14 445.78
23.68 68.3 1017.93 84.16 438.65
8.28 40.77 1011.55 89.79 480.15
23.44 62.52 1016.46 67.21 447.19
25.32 48.41 1008.47 72.14 443.04
3.94 39.9 1008.06 97.49 488.81
17.3 57.76 1016.26 87.74 455.75
18.2 49.39 1018.83 96.3 455.86
21.43 46.97 1013.94 61.25 457.68
11.16 40.05 1014.95 88.38 479.11
30.38 74.16 1007.44 74.77 432.84
23.36 62.52 1016.18 68.18 448.37
21.69 47.45 1007.56 77.2 447.06
23.62 49.21 1014.1 49.54 443.53
21.87 61.45 1011.13 92.22 445.21
29.25 66.51 1015.53 33.65 441.7
20.03 66.86 1013.05 64.59 450.93
18.14 49.78 1002.95 100.09 451.44
24.23 56.89 1012.32 68.04 441.29
18.11 44.85 1014.48 48.94 458.85
6.57 43.65 1018.24 74.47 481.46
12.56 43.41 1016.93 81.02 467.19
13.4 41.58 1020.5 71.17 461.54
27.1 52.84 1006.28 53.85 439.08
14.28 42.74 1028.79 70.67 467.22
16.29 44.34 1019.49 59.36 468.8
31.24 71.98 1004.66 57.17 426.93
10.57 37.73 1024.36 70.29 474.65
13.8 44.21 1022.93 83.37 468.97
25.3 71.58 1010.18 87.36 433.97
18.06 50.16 1009.52 100.09 450.53
25.42 59.04 1011.98 68.78 444.51
15.07 40.69 1015.29 70.98 469.03
11.75 71.14 1019.36 75.68 466.56
20.23 52.05 1012.15 47.49 457.57
27.31 59.54 1006.24 71.99 440.13
28.57 69.84 1003.57 66.55 433.24
17.9 43.72 1008.64 74.73 452.55
23.83 71.37 1002.04 64.78 443.29
27.92 74.99 1005.47 75.13 431.76
17.34 44.78 1007.81 56.38 454.97
17.94 63.07 1012.42 94.35 456.7
6.4 39.9 1007.75 86.55 486.03
11.78 39.96 1011.37 82.95 472.79
20.28 57.25 1010.12 88.42 452.03
21.04 54.2 1012.26 85.61 443.41
25.11 67.32 1014.49 58.39 441.93
30.28 70.98 1007.51 74.28 432.64
8.14 36.24 1013.15 87.85 480.25
16.86 39.63 1004.47 83.5 466.68
6.25 40.07 1020.19 65.24 494.39
22.35 54.42 1012.46 75.01 454.72
17.98 56.85 1012.28 84.52 448.71
21.19 42.48 1013.43 80.52 469.76
20.94 44.89 1009.64 75.14 450.71
24.23 58.79 1009.8 75.75 444.01
19.18 58.2 1017.46 76.72 453.2
20.88 57.85 1012.39 85.47 450.87
23.67 63.86 1019.67 57.95 441.73
14.12 39.52 1018.41 78.32 465.09
25.23 64.63 1020.59 52.2 447.28
6.54 39.33 1011.54 93.69 491.16
20.08 62.52 1017.99 75.74 450.98
24.67 63.56 1013.75 67.56 446.3
27.82 79.74 1008.37 69.46 436.48
15.55 42.03 1017.41 74.58 460.84
24.26 69.51 1013.43 53.23 442.56
13.45 41.49 1020.19 88.72 467.3
11.06 40.64 1021.47 96.16 479.13
24.91 52.3 1008.72 68.26 441.15
22.39 59.04 1011.78 86.39 445.52
11.95 40.69 1015.62 85.34 475.4
14.85 40.69 1014.91 72.64 469.3
10.11 41.62 1017.17 97.82 463.57
23.67 68.67 1006.71 77.22 445.32
16.14 44.21 1020.36 80.59 461.03
15.11 43.13 1014.99 46.91 466.74
24.14 59.87 1018.47 57.76 444.04
30.08 67.25 1017.6 53.09 434.01
14.77 44.9 1020.5 84.31 465.23
27.6 69.34 1009.63 71.58 440.6
13.89 44.84 1023.66 92.97 466.74
26.85 75.6 1017.43 74.55 433.48
12.41 40.96 1023.36 78.96 473.59
13.08 41.74 1020.75 64.44 474.81
18.93 44.06 1017.58 68.23 454.75
20.5 49.69 1009.6 70.81 452.94
30.72 69.13 1009.94 61.66 435.83
7.55 39.22 1014.53 77.76 482.19
13.49 44.47 1030.46 69.49 466.66
15.62 40.12 1013.03 96.26 462.59
24.8 64.63 1020.69 55.74 447.82
10.03 41.62 1014.55 95.61 462.73
22.43 63.21 1012.06 84.75 447.98
14.95 39.31 1009.15 75.3 462.72
24.78 58.46 1016.82 67.5 442.42
23.2 48.41 1008.64 80.92 444.69
14.01 39.0 1016.73 79.23 466.7
19.4 64.63 1020.38 81.1 453.84
30.15 67.32 1013.83 32.8 436.92
6.91 36.08 1021.82 84.31 486.37
29.04 60.07 1015.42 46.15 440.43
26.02 63.07 1010.94 53.96 446.82
5.89 39.48 1005.11 59.83 484.91
26.52 71.64 1008.27 75.3 437.76
28.53 68.08 1013.27 42.53 438.91
16.59 39.54 1007.97 70.58 464.19
22.95 67.79 1009.89 91.69 442.19
23.96 47.43 1008.38 63.55 446.86
17.48 44.2 1018.89 61.51 457.15
6.69 43.65 1020.14 69.55 482.57
10.25 41.26 1007.44 98.08 476.03
28.87 72.58 1008.69 79.34 428.89
12.04 40.23 1018.07 81.28 472.7
22.58 52.3 1009.04 78.99 445.6
15.12 52.05 1014.63 80.38 464.78
25.48 58.95 1017.02 51.16 440.42
27.87 70.79 1003.96 72.17 428.41
23.72 70.47 1010.65 75.39 438.5
25.0 59.43 1007.84 68.91 438.28
8.42 40.64 1022.35 96.38 476.29
22.46 58.49 1011.5 70.54 448.46
29.92 57.19 1008.62 45.8 438.99
11.68 39.22 1017.9 57.95 471.8
14.04 42.44 1012.74 81.89 471.81
19.86 59.14 1016.12 69.32 449.82
25.99 68.08 1013.13 59.14 442.14
23.42 58.79 1009.74 81.54 441.46
10.6 40.22 1011.37 85.81 477.62
20.97 61.87 1011.45 65.41 446.76
14.14 39.82 1012.46 81.15 472.52
8.56 40.71 1021.27 95.87 471.58
24.86 72.39 1001.15 90.24 440.85
29.0 77.54 1011.33 75.13 431.37
27.59 71.97 1008.64 88.22 437.33
10.45 40.71 1015.68 81.48 469.22
8.51 40.78 1023.51 89.84 471.11
29.82 66.51 1010.98 43.57 439.17
22.56 62.26 1012.11 63.16 445.33
11.38 39.22 1018.62 57.14 473.71
20.25 57.76 1016.28 77.76 452.66
22.42 59.43 1007.12 90.56 440.99
14.85 38.91 1014.48 60.98 467.42
25.62 58.82 1010.02 70.31 444.14
19.85 56.53 1020.57 74.05 457.17
13.67 54.3 1015.92 75.42 467.87
24.39 70.72 1009.78 82.25 442.04
16.07 44.58 1019.52 67.95 471.36
11.6 39.1 1009.81 100.09 460.7
31.38 70.83 1010.35 47.28 431.33
29.91 76.86 998.59 72.41 432.6
19.67 59.39 1014.07 77.67 447.61
27.18 64.79 1016.27 63.7 443.87
21.39 52.3 1009.2 79.77 446.87
10.45 41.01 1020.57 93.84 465.74
19.46 56.89 1014.02 84.95 447.86
23.55 62.96 1020.16 70.16 447.65
23.35 63.47 1011.78 84.24 437.87
9.26 41.66 1016.87 73.32 483.51
10.3 41.46 1018.21 86.17 479.65
20.94 58.16 1016.88 65.43 455.16
23.13 71.25 1002.49 94.59 431.91
12.77 41.5 1014.13 86.8 470.68
28.29 69.13 1009.29 58.18 429.28
19.13 59.21 1018.32 89.66 450.81
24.44 73.5 1011.49 87.39 437.73
20.32 44.6 1015.16 36.35 460.21
20.54 69.05 1001.6 79.62 442.86
12.16 45.0 1021.51 50.52 482.99
28.09 65.27 1013.27 51.96 440.0
9.25 41.82 1033.25 74.73 478.48
21.75 49.82 1015.01 78.33 455.28
23.7 66.56 1002.07 85.19 436.94
16.22 37.87 1022.36 83.13 461.06
24.75 69.45 1013.97 53.49 438.28
10.48 39.58 1011.81 88.86 472.61
29.53 70.79 1003.7 60.89 426.85
12.59 39.72 1017.76 61.14 470.18
23.5 54.42 1012.31 68.29 455.38
29.01 66.56 1006.44 57.62 428.32
9.75 42.49 1010.57 83.63 480.35
19.55 56.53 1020.2 78.1 455.56
21.05 58.33 1013.14 66.34 447.66
24.72 68.67 1006.74 79.02 443.06
21.19 58.86 1014.19 68.96 452.43
10.77 41.54 1019.94 71.13 477.81
28.68 73.77 1004.72 87.01 431.66
29.87 73.91 1004.53 74.3 431.8
22.99 68.67 1006.65 77.62 446.67
24.66 60.29 1018.0 59.56 445.26
32.63 69.89 1013.85 41.66 425.72
31.38 72.29 1008.73 73.27 430.58
23.87 60.27 1018.94 77.16 439.86
25.6 59.15 1013.31 67.02 441.11
27.62 71.14 1011.6 52.8 434.72
30.1 67.45 1014.23 39.04 434.01
12.19 41.17 1019.43 65.47 475.64
13.11 41.58 1020.43 74.32 460.44
28.29 68.67 1005.46 69.22 436.4
13.45 40.73 1018.7 93.88 461.03
10.98 41.54 1019.94 69.83 479.08
26.48 69.14 1009.31 84.11 435.76
13.07 45.51 1015.22 78.65 460.14
25.56 75.6 1017.37 69.31 442.2
22.68 50.78 1008.83 70.3 447.69
28.86 73.67 1006.65 68.23 431.15
22.7 63.56 1014.32 71.76 445.0
27.89 73.21 1001.32 85.88 431.59
13.78 44.47 1027.94 71.09 467.22
28.14 51.43 1012.16 52.67 445.33
11.8 45.09 1013.21 89.68 470.57
10.71 39.61 1018.72 73.66 473.77
24.54 60.29 1017.42 58.94 447.67
11.54 40.05 1014.78 87.05 474.29
29.47 71.32 1008.07 67.0 437.14
29.24 69.05 1003.12 43.18 432.56
14.51 41.79 1009.72 80.62 459.14
22.91 60.07 1016.03 59.72 446.19
27.02 71.77 1006.38 72.1 428.1
13.49 44.47 1030.18 69.15 468.46
30.24 66.75 1017.95 55.66 435.02
23.19 48.6 1002.38 61.19 445.52
17.73 40.55 1003.36 74.62 462.69
18.62 61.27 1019.26 73.35 455.75
12.85 40.0 1015.89 68.85 463.74
32.33 69.68 1011.95 39.89 439.79
25.09 58.95 1016.99 53.16 443.26
29.45 69.13 1009.3 52.97 432.04
16.91 43.96 1013.32 79.87 465.86
14.09 45.87 1009.05 84.09 465.6
10.73 25.36 1009.35 100.15 469.43
23.2 49.3 1003.4 79.77 440.75
8.21 38.91 1015.82 88.99 481.32
9.3 40.56 1022.64 76.14 479.87
16.97 39.16 1005.7 69.13 458.59
23.69 71.97 1009.62 93.03 438.62
25.13 59.44 1012.38 77.92 445.59
9.86 43.56 1015.13 74.89 481.87
11.33 41.5 1013.58 88.7 475.01
26.95 48.41 1008.53 62.94 436.54
15.0 40.66 1016.28 89.62 456.63
20.76 62.52 1015.63 81.04 451.69
14.29 39.59 1010.93 94.53 463.04
19.74 67.71 1007.68 64.02 446.1
26.68 59.92 1009.94 70.57 438.67
14.24 41.4 1019.7 70.32 466.88
21.98 48.41 1008.42 84.86 444.6
22.75 59.39 1015.4 81.41 440.26
8.34 40.96 1023.28 89.45 483.92
11.8 41.2 1017.18 82.71 475.19
8.81 44.68 1023.06 93.93 479.24
30.05 73.68 1014.95 70.6 434.92
16.01 65.46 1014.0 87.68 454.16
21.75 58.79 1012.42 87.58 447.58
13.94 41.26 1021.67 74.4 467.9
29.25 69.13 1010.27 67.35 426.29
22.33 45.87 1007.8 63.61 447.02
16.43 41.79 1005.47 76.89 455.85
11.5 40.22 1010.31 78.08 476.46
23.53 68.94 1007.53 69.17 437.48
21.86 49.21 1014.61 53.31 452.77
6.17 39.33 1012.57 93.32 491.54
30.19 64.79 1017.22 42.47 438.41
11.67 41.93 1019.81 82.58 476.1
15.34 36.99 1007.87 94.59 464.58
11.5 40.78 1023.91 86.31 467.74
25.53 57.17 1010.0 72.57 442.12
21.27 57.5 1014.53 80.76 453.34
28.37 69.13 1010.44 71.93 425.29
28.39 51.43 1011.74 47.54 449.63
13.78 45.78 1025.27 95.72 462.88
14.6 42.32 1015.71 77.03 464.67
5.1 35.57 1027.17 80.49 489.96
7.0 38.08 1020.27 77.67 482.38
26.3 77.95 1009.45 78.72 437.95
30.56 71.98 1004.74 58.77 429.2
21.09 46.63 1013.03 74.8 453.34
28.21 70.02 1010.58 51.34 442.47
15.84 49.69 1015.14 90.41 462.6
10.03 40.96 1024.57 91.1 478.79
20.37 52.05 1012.34 62.57 456.11
21.19 50.16 1005.81 84.27 450.33
33.73 69.88 1007.21 42.93 434.83
29.87 73.68 1015.1 40.96 433.43
19.62 62.96 1020.76 76.53 456.02
9.93 40.67 1018.08 69.74 485.23
9.43 37.14 1013.03 74.99 473.57
14.24 39.58 1011.17 70.45 469.94
12.97 49.83 1008.69 91.49 452.07
7.6 41.04 1021.82 88.97 475.32
8.39 36.24 1013.39 89.13 480.69
25.41 48.06 1013.12 46.52 444.01
18.43 56.03 1020.41 60.55 465.17
10.31 39.82 1012.87 88.71 480.61
11.29 41.5 1013.39 89.15 476.04
22.61 49.3 1003.51 83.02 441.76
29.34 71.98 1005.19 75.19 428.24
18.87 67.71 1004.0 87.35 444.77
13.21 45.87 1008.58 85.66 463.1
11.3 44.6 1018.19 91.66 470.5
29.23 72.99 1007.04 63.47 431.0
27.76 69.4 1004.27 72.25 430.68
29.26 67.17 1006.6 70.58 436.42
25.72 49.82 1016.19 60.1 452.33
23.43 63.94 1010.64 89.29 440.16
25.6 63.76 1010.18 67.43 435.75
22.3 44.57 1008.48 67.58 449.74
27.91 72.24 1010.74 70.8 430.73
30.35 77.17 1009.55 63.62 432.75
21.78 47.43 1007.88 66.68 446.79
7.19 41.39 1018.12 90.76 486.35
20.88 59.8 1015.66 75.34 453.18
24.19 50.23 1015.73 59.77 458.31
9.98 41.54 1019.7 80.79 480.26
23.47 51.3 1011.89 74.1 448.65
26.35 49.5 1012.67 41.34 458.41
29.89 64.69 1006.37 58.78 435.39
19.29 50.16 1010.49 97.78 450.21
17.48 43.14 1018.68 74.85 459.59
25.21 75.6 1017.19 69.84 445.84
23.3 48.78 1018.17 75.36 441.08
15.42 37.85 1009.89 85.8 467.33
21.44 63.09 1016.56 90.11 444.19
29.45 68.27 1007.96 61.63 432.96
29.69 47.93 1002.85 44.76 438.09
15.52 36.99 1006.86 89.7 467.9
11.47 43.67 1012.68 72.51 475.72
9.77 34.69 1027.72 74.98 477.51
22.6 69.84 1006.37 79.59 435.13
8.24 39.61 1017.99 78.42 477.9
17.01 44.2 1019.18 61.23 457.26
19.64 44.6 1015.88 47.56 467.53
10.61 41.58 1021.08 93.06 465.15
12.04 40.1 1014.42 89.65 474.28
29.19 65.71 1013.85 50.5 444.49
21.75 45.09 1014.15 44.84 452.84
23.66 77.54 1008.5 85.32 435.38
27.05 75.33 1003.88 82.94 433.57
29.63 69.71 1009.04 67.26 435.27
18.2 39.63 1005.35 79.05 468.49
32.22 70.8 1009.9 62.03 433.07
26.88 73.56 1004.85 94.36 430.63
29.05 65.74 1013.29 60.02 440.74
8.9 39.96 1026.31 95.46 474.49
18.93 48.6 1005.72 84.92 449.74
27.49 63.76 1010.09 62.8 436.73
23.1 70.79 1006.53 90.81 434.58
11.22 43.13 1017.24 80.9 473.93
31.97 79.74 1007.03 55.84 435.99
13.32 43.22 1009.45 75.3 466.83
31.68 68.24 1005.29 37.34 427.22
23.69 63.77 1013.39 79.5 444.07
13.83 41.49 1020.11 87.29 469.57
18.32 66.51 1015.18 81.5 459.89
11.05 40.71 1024.91 76.42 479.59
22.03 64.69 1007.21 75.75 440.92
10.23 41.46 1020.45 84.95 480.87
23.92 66.54 1009.93 62.37 441.9
29.38 69.68 1011.35 49.25 430.2
17.35 42.86 1014.62 74.16 465.16
9.81 44.45 1021.19 90.55 471.32
4.97 40.64 1020.91 94.28 485.43
5.15 40.07 1012.27 63.31 495.35
21.54 58.49 1010.85 78.9 449.12
7.94 42.02 1006.22 90.97 480.53
18.77 50.66 1014.89 87.34 457.07
21.69 69.94 1010.7 80.8 443.67
10.07 44.68 1023.44 90.95 477.52
13.83 39.64 1012.52 69.97 472.95
10.45 39.69 1003.92 89.45 472.54
11.56 40.71 1015.85 76.08 469.17
23.64 70.04 1011.09 83.35 435.21
10.48 40.22 1004.81 92.16 477.78
13.09 39.85 1012.86 58.42 475.89
10.67 40.23 1017.75 85.06 483.9
12.57 39.16 1016.53 88.91 476.2
14.45 43.34 1015.47 83.33 462.16
14.22 37.85 1011.24 88.49 471.05
6.97 41.26 1010.6 96.88 484.71
20.61 63.86 1015.43 73.86 446.34
14.67 42.28 1007.21 65.17 469.02
29.06 72.86 1004.23 69.41 432.12
14.38 40.1 1015.51 81.23 467.28
32.51 69.98 1013.29 54.07 429.66
11.79 45.09 1013.16 89.17 469.49
8.65 40.56 1023.23 78.85 485.87
9.75 40.81 1026.0 84.44 481.95
9.11 40.02 1031.1 83.02 479.03
23.39 69.13 1010.99 90.66 434.5
14.3 54.3 1015.16 75.29 464.9
17.49 63.94 1020.02 82.6 452.71
31.1 69.51 1010.84 45.4 429.74
19.77 56.65 1020.67 66.33 457.09
28.61 72.29 1011.61 45.33 446.77
13.52 41.48 1014.46 67.12 460.76
13.52 40.83 1008.31 84.14 471.95
17.57 46.21 1014.09 80.81 453.29
28.18 60.07 1016.34 49.13 441.61
14.29 46.18 1017.01 87.29 464.73
18.12 43.69 1016.91 52.95 464.68
31.27 73.91 1003.72 68.92 430.59
26.24 77.95 1014.19 85.21 438.01
7.44 41.04 1021.84 88.56 479.08
29.78 74.78 1009.28 55.09 436.39
23.37 65.46 1016.25 48.64 447.07
10.62 39.58 1011.9 87.85 479.91
5.84 43.02 1013.88 87.42 489.05
14.51 53.82 1016.46 62.75 463.17
11.31 42.02 1001.18 94.86 471.26
11.25 40.67 1011.64 63.54 480.49
9.18 39.42 1025.41 69.46 473.78
19.82 58.16 1016.76 74.66 455.5
24.77 58.41 1013.78 80.57 446.27
9.66 41.06 1021.21 84.7 482.2
21.96 59.8 1016.72 72.6 452.48
18.59 43.14 1011.92 52.63 464.48
24.75 69.89 1015.29 82.01 438.1
24.37 63.47 1012.77 75.22 445.6
29.6 67.79 1010.37 51.05 442.43
25.32 61.25 1011.56 80.1 436.67
16.15 41.85 1016.54 81.58 466.56
15.74 71.14 1019.65 65.94 457.29
5.97 36.25 1029.65 86.74 487.03
15.84 52.72 1026.45 62.57 464.93
14.84 44.63 1019.28 57.37 466.0
12.25 48.79 1017.44 88.91 469.52
27.38 70.04 1011.18 72.26 428.88
8.76 41.48 1018.49 74.98 474.3
15.54 39.31 1009.69 71.19 461.06
18.71 39.39 1014.09 62.82 465.57
13.06 41.78 1012.3 55.31 467.67
12.72 40.71 1016.02 71.57 466.99
19.83 39.39 1013.73 59.16 463.72
27.23 49.16 1004.03 40.8 443.78
24.27 68.28 1005.43 67.63 445.23
11.8 40.66 1017.13 97.2 464.43
6.76 36.25 1028.31 91.16 484.36
25.99 63.07 1012.5 64.81 442.16
16.3 39.63 1004.64 85.61 464.11
16.5 49.39 1018.35 93.42 462.48
10.59 42.49 1009.59 77.36 477.49
26.05 65.59 1012.78 67.03 437.04
19.5 40.79 1003.8 89.45 457.09
22.21 45.01 1012.22 54.84 450.6
17.86 45.0 1023.25 53.48 465.78
29.96 70.04 1010.15 54.47 427.1
19.08 44.63 1020.14 43.36 459.81
23.59 47.43 1006.64 48.92 447.36
3.38 39.64 1011.0 81.22 488.92
26.39 66.49 1012.96 60.35 433.36
8.99 39.04 1021.99 75.98 483.35
10.91 41.04 1026.57 74.24 469.53
13.08 39.82 1012.27 85.21 476.96
23.95 58.46 1017.5 68.46 440.75
15.64 43.71 1024.51 78.31 462.55
18.78 54.2 1012.05 89.25 448.04
20.65 50.59 1016.22 68.57 455.24
4.96 40.07 1011.8 67.38 494.75
23.51 57.32 1012.55 53.6 444.58
5.99 35.79 1011.56 91.69 484.82
23.65 66.05 1019.6 78.21 442.9
5.17 39.33 1009.68 94.19 485.46
26.38 49.5 1012.82 37.19 457.81
6.02 43.65 1013.85 83.53 481.92
23.2 61.02 1009.63 79.45 443.23
8.57 39.69 1000.91 99.9 474.29
30.72 71.58 1009.98 50.39 430.46
21.52 50.66 1013.56 74.33 455.71
22.93 62.26 1011.25 83.66 438.34
5.71 41.31 1003.24 89.48 485.83
18.62 44.06 1017.76 64.59 452.82
27.88 68.94 1007.68 75.68 435.04
22.32 59.8 1016.82 64.18 451.21
14.55 42.74 1028.41 70.09 465.81
17.83 44.92 1025.04 70.58 458.42
9.68 39.96 1026.09 99.28 470.22
19.41 49.39 1020.84 81.89 449.24
13.22 44.92 1023.84 87.99 471.43
12.24 44.92 1023.74 88.21 473.26
19.21 58.49 1011.7 91.29 452.82
29.74 70.32 1008.1 52.72 432.69
23.28 60.84 1017.91 67.5 444.13
8.02 41.92 1029.8 92.05 467.21
22.47 48.6 1002.33 63.23 445.98
27.51 73.77 1002.42 90.88 436.91
17.51 44.9 1009.05 74.91 455.01
23.22 66.56 1002.47 85.39 437.11
11.73 40.64 1020.68 96.98 477.06
21.19 67.71 1006.65 56.28 441.71
5.48 40.07 1019.63 65.62 495.76
24.26 66.44 1011.33 55.32 445.63
12.32 41.62 1012.88 88.88 464.72
31.26 68.94 1005.94 39.49 438.03
32.09 72.86 1003.47 54.59 434.78
24.98 60.32 1015.63 57.19 444.67
27.48 61.41 1012.2 45.06 452.24
21.04 45.09 1014.19 40.62 450.92
27.75 70.4 1006.65 90.21 436.53
22.79 71.77 1005.75 90.91 435.53
24.22 68.51 1013.23 74.96 440.01
27.06 64.45 1008.72 54.21 443.1
29.25 71.94 1007.18 63.62 427.49
26.86 68.08 1012.99 50.04 436.25
29.64 67.79 1009.99 51.23 440.74
19.92 63.31 1015.02 82.71 443.54
18.5 51.43 1010.82 92.04 459.42
23.71 60.23 1009.76 90.67 439.66
14.39 44.84 1023.55 91.14 464.15
19.3 56.65 1020.55 70.43 459.1
24.65 52.36 1014.76 66.63 455.68
13.5 45.51 1015.33 86.95 469.08
9.82 41.26 1007.71 96.69 478.02
18.4 44.06 1017.36 70.88 456.8
28.12 44.89 1009.18 47.14 441.13
17.15 43.69 1017.05 63.36 463.88
30.69 73.67 1006.14 60.58 430.45
28.82 65.71 1014.24 54.3 449.18
21.3 48.92 1010.92 65.09 447.89
30.58 70.04 1010.4 48.16 431.59
21.17 52.3 1009.36 81.51 447.5
9.87 41.82 1033.04 68.57 475.58
22.18 59.8 1016.77 73.16 453.24
24.39 63.21 1012.59 80.88 446.4
10.73 44.92 1025.1 85.4 476.81
9.38 40.46 1019.29 75.77 474.1
20.27 57.76 1016.66 75.76 450.71
24.82 66.48 1006.4 70.21 433.62
16.55 41.66 1011.45 55.53 465.14
20.73 59.87 1019.08 80.48 445.18
9.51 39.22 1015.3 72.41 474.12
8.63 43.79 1016.08 83.25 483.91
6.48 40.27 1010.55 82.12 486.68
14.95 43.52 1022.43 94.75 464.98
5.76 45.87 1010.83 95.79 481.4
10.94 39.04 1021.81 86.02 479.2
15.87 41.16 1005.85 78.29 463.86
12.42 38.25 1012.76 82.23 472.3
29.12 58.84 1001.31 52.86 446.51
29.12 51.43 1005.93 60.66 437.71
19.08 41.1 1001.96 62.77 458.94
31.06 67.17 1007.62 65.54 437.91
5.72 39.33 1009.96 95.4 490.76
26.52 65.06 1013.4 51.78 439.66
13.84 44.9 1007.58 63.62 463.27
13.03 39.52 1016.68 83.09 473.99
25.94 66.49 1012.83 61.81 433.38
16.64 53.82 1015.13 68.24 459.01
14.13 40.75 1016.05 72.41 471.44
13.65 39.28 1012.97 79.64 471.91
14.5 44.47 1028.2 66.95 465.15
19.8 51.19 1008.25 91.98 446.66
25.2 63.76 1009.78 64.96 438.15
20.66 51.19 1008.81 88.93 447.14
12.07 43.71 1025.53 85.62 472.32
25.64 70.72 1010.16 84.0 441.68
23.33 72.99 1009.33 89.41 440.04
29.41 64.05 1009.82 67.4 444.82
16.6 53.16 1014.5 76.75 457.26
27.53 72.58 1009.13 89.06 428.83
20.62 43.43 1009.93 64.02 449.07
26.02 71.94 1009.38 64.12 435.21
12.75 44.2 1017.59 81.22 471.03
12.87 48.04 1012.47 100.13 465.56
25.77 62.96 1019.86 58.07 442.83
14.84 41.48 1017.26 63.42 460.3
7.41 40.71 1023.07 83.32 474.25
8.87 41.82 1033.3 74.28 477.97
9.69 40.46 1019.1 71.91 472.16
16.17 46.97 1014.22 85.8 456.08
26.24 49.82 1014.9 55.58 452.41
13.78 43.22 1011.31 69.7 463.71
26.3 67.07 1006.26 63.79 433.72
17.37 57.76 1016.0 86.59 456.4
23.6 48.98 1015.41 48.28 448.43
8.3 36.08 1020.63 80.42 481.6
18.86 42.18 1001.16 98.58 457.07
22.12 49.39 1019.8 72.83 451.0
28.41 75.6 1018.48 56.07 440.28
29.42 71.32 1002.26 67.13 437.47
18.61 67.71 1004.07 84.49 443.57
27.57 69.84 1004.91 68.37 426.6
12.83 41.5 1013.12 86.07 470.87
9.64 39.85 1012.9 83.82 478.37
19.13 58.66 1013.32 74.86 453.92
15.92 40.56 1020.79 53.52 470.22
24.64 72.24 1011.37 80.61 434.54
27.62 63.9 1013.11 43.56 442.89
8.9 36.24 1013.29 89.35 479.03
9.55 43.99 1020.5 97.28 476.06
10.57 36.71 1022.62 80.49 473.88
19.8 57.25 1010.84 88.9 451.75
25.63 56.85 1012.68 49.7 439.2
24.7 58.46 1015.58 68.64 439.7
15.26 46.18 1013.68 98.58 463.6
20.06 52.84 1004.21 82.12 447.47
19.84 56.89 1013.23 78.32 447.92
11.49 44.63 1020.44 86.04 471.08
23.74 72.43 1007.99 91.36 437.55
22.62 51.3 1012.36 81.02 448.27
29.53 72.39 998.47 76.05 431.69
21.32 48.14 1016.57 71.81 449.09
20.3 58.46 1015.93 82.13 448.79
16.97 44.92 1025.21 74.27 460.21
12.07 41.17 1013.54 71.32 479.28
7.46 41.82 1032.67 74.59 483.11
19.2 54.2 1011.46 84.44 450.75
28.64 66.54 1010.43 43.39 437.97
13.56 41.48 1008.53 87.2 459.76
17.4 44.9 1020.5 77.11 457.75
14.08 40.1 1015.48 82.81 469.33
27.11 69.75 1009.74 85.67 433.28
20.92 70.02 1010.23 95.58 444.64
16.18 44.9 1021.3 74.46 463.1
15.57 44.68 1022.01 90.02 460.91
10.37 39.04 1023.95 81.93 479.35
19.6 59.21 1017.65 86.29 449.23
9.22 40.92 1021.83 85.43 474.51
27.76 72.99 1007.81 71.66 435.02
28.68 70.72 1009.43 71.33 435.45
20.95 48.14 1013.3 67.72 452.38
9.06 39.3 1019.73 84.23 480.41
9.21 39.72 1019.54 74.44 478.96
13.65 42.74 1026.58 71.48 468.87
31.79 76.2 1007.89 56.3 434.01
14.32 44.6 1013.85 68.13 466.36
26.28 75.23 1011.44 68.35 435.28
7.69 43.02 1014.51 85.23 486.46
14.44 40.1 1015.51 79.78 468.19
9.19 41.01 1022.14 98.98 468.37
13.35 41.39 1019.17 72.87 474.19
23.04 74.22 1009.52 90.93 440.32
4.83 38.44 1015.35 72.94 485.32
17.29 42.86 1014.38 72.3 464.27
8.73 36.18 1013.66 77.74 479.25
26.21 70.32 1007.0 78.29 430.4
23.72 58.62 1016.65 69.1 447.49
29.27 64.69 1006.85 55.79 438.23
10.4 40.43 1025.46 75.09 492.09
12.19 40.75 1015.13 88.98 475.36
20.4 54.9 1016.68 64.26 452.56
34.3 74.67 1015.98 25.89 427.84
27.56 68.08 1010.8 59.18 433.95
30.9 70.8 1008.48 67.48 435.27
14.85 58.59 1014.04 89.85 454.62
16.42 40.56 1020.36 50.62 472.17
16.45 63.31 1015.96 83.97 452.42
10.14 42.02 1003.19 96.51 472.17
9.53 41.44 1018.01 80.09 481.83
17.01 49.15 1021.83 84.02 458.78
23.94 62.08 1022.47 61.97 447.5
15.95 49.25 1019.04 88.51 463.4
11.15 41.26 1022.67 81.83 473.57
25.56 70.32 1009.07 90.63 433.72
27.16 66.44 1011.2 73.37 431.85
26.71 77.95 1012.13 77.5 433.47
29.56 74.22 1007.45 57.46 432.84
31.19 70.94 1007.29 51.91 436.6
6.86 41.17 1020.12 79.14 490.23
12.36 41.74 1020.58 69.24 477.16
32.82 68.31 1010.44 41.85 441.06
25.3 70.98 1007.22 95.1 440.86
8.71 41.82 1033.08 74.53 477.94
13.34 40.8 1026.56 64.85 474.47
14.2 43.02 1012.18 57.07 470.67
23.74 65.34 1013.7 62.9 447.31
16.9 44.88 1018.14 72.21 466.8
28.54 71.94 1007.4 65.99 430.91
30.15 69.88 1007.2 73.67 434.75
14.33 42.86 1010.82 88.59 469.52
25.57 59.43 1008.88 61.19 438.9
30.55 70.04 1010.51 49.37 429.56
28.04 74.33 1013.53 48.65 432.92
26.39 49.16 1005.68 56.18 442.87
15.3 41.76 1022.57 71.56 466.59
6.03 41.14 1028.04 87.46 479.61
13.49 44.63 1019.12 70.02 471.08
27.67 59.14 1016.51 61.2 433.37
24.19 65.48 1018.8 60.54 443.92
24.44 59.14 1016.74 71.82 443.5
29.86 64.79 1017.37 44.8 439.89
30.2 69.59 1008.9 67.32 434.66
7.99 41.38 1021.95 78.77 487.57
9.93 41.62 1013.76 96.02 464.64
11.03 42.32 1017.26 90.56 470.92
22.34 63.73 1014.37 83.19 444.39
25.33 48.6 1002.54 68.45 442.48
18.87 52.08 1005.25 99.19 449.61
25.97 69.34 1009.43 88.11 435.02
16.58 43.99 1021.81 79.29 458.67
14.35 46.18 1016.63 87.76 461.74
25.06 62.39 1008.09 82.56 438.31
13.85 48.92 1011.68 79.24 462.38
16.09 44.2 1019.39 67.24 460.56
26.34 59.21 1013.37 58.98 439.22
23.01 58.79 1009.71 84.22 444.64
26.39 71.25 999.8 89.12 430.34
31.32 71.29 1008.37 50.07 430.46
16.64 45.87 1009.02 98.86 456.79
13.42 41.23 994.17 95.79 468.82
20.06 44.9 1008.79 70.06 448.51
14.8 44.71 1014.67 41.71 470.77
12.59 41.14 1025.79 86.55 465.74
26.7 66.56 1005.31 71.97 430.21
19.78 50.32 1008.62 96.4 449.23
15.17 49.15 1021.91 91.73 461.89
21.71 61.45 1010.97 91.62 445.72
19.09 39.39 1013.36 59.14 466.13
19.76 51.19 1008.38 92.56 448.71
14.68 41.23 998.43 83.71 469.25
21.3 66.86 1013.04 55.43 450.56
16.73 39.64 1008.94 74.91 464.46
12.26 41.5 1014.87 89.41 471.13
14.77 48.06 1010.92 69.81 461.52
18.26 59.15 1012.04 86.01 451.09
27.1 79.74 1005.43 86.05 431.51
14.72 40.83 1009.65 80.98 469.8
26.3 51.43 1012.05 63.62 442.28
16.48 48.92 1011.84 64.16 458.67
17.99 43.79 1016.13 75.63 462.4
20.34 59.8 1015.18 80.21 453.54
25.53 62.96 1019.81 59.7 444.38
31.59 58.9 1003.39 47.6 440.52
30.8 69.14 1007.68 63.78 433.62
10.75 45.0 1023.68 89.37 481.96
19.3 44.9 1008.89 70.55 452.75
4.71 39.42 1026.4 84.42 481.28
23.1 66.05 1020.28 80.62 439.03
32.63 73.88 1005.64 52.56 435.75
26.63 74.16 1009.72 83.26 436.03
24.35 58.49 1011.03 70.64 445.6
15.11 56.03 1020.27 89.95 462.65
29.1 50.05 1005.87 51.53 438.66
21.24 50.32 1008.54 84.83 447.32
6.16 39.48 1004.85 59.68 484.55
7.36 41.01 1024.9 97.88 476.8
10.44 39.04 1023.99 85.03 480.34
26.76 48.41 1010.53 47.38 440.63
16.79 44.6 1014.27 48.08 459.48
10.76 40.43 1025.98 79.65 490.78
6.07 38.91 1019.25 83.39 483.56
27.33 73.18 1012.26 82.18 429.38
27.15 59.21 1013.49 51.71 440.27
22.35 51.43 1011.34 77.33 445.34
21.82 65.27 1013.86 72.81 447.43
21.11 69.94 1004.37 84.26 439.91
19.95 50.59 1016.11 73.23 459.27
7.45 39.61 1017.88 79.73 478.89
15.36 41.66 1012.41 62.32 466.7
15.65 43.5 1021.39 78.58 463.5
25.31 74.33 1015.04 79.88 436.21
25.88 63.47 1011.95 65.87 443.94
24.6 63.94 1012.87 80.28 439.63
22.58 41.54 1013.21 71.33 460.95
19.69 59.14 1015.99 70.33 448.69
25.85 75.08 1006.24 57.73 444.63
10.06 37.83 1005.49 99.46 473.51
18.59 39.54 1008.56 68.61 462.56
18.27 50.16 1011.07 95.91 451.76
8.85 40.43 1025.68 80.42 491.81
30.04 68.08 1011.04 51.01 429.52
26.06 49.02 1007.59 74.08 437.9
14.8 38.73 1003.18 80.73 467.54
23.93 64.45 1015.35 54.71 449.97
23.72 66.48 1003.61 73.75 436.62
11.44 40.55 1023.37 88.43 477.68
20.28 63.86 1016.04 74.66 447.26
27.9 63.13 1011.8 70.04 439.76
24.74 59.39 1015.23 74.64 437.49
14.8 58.2 1018.29 85.11 455.14
8.22 41.03 1021.76 82.97 485.5
27.56 66.93 1016.81 55.59 444.1
32.07 70.94 1006.91 49.9 432.33
9.53 44.03 1008.87 89.99 471.23
13.61 42.34 1017.93 91.61 463.89
22.2 51.19 1009.2 82.95 445.54
21.36 59.54 1007.99 92.62 446.09
23.25 63.86 1017.82 59.64 445.12
23.5 59.21 1018.29 63.0 443.31
8.46 39.66 1015.14 85.38 484.16
8.19 40.69 1019.86 85.23 477.76
30.67 71.29 1008.36 52.08 430.28
32.48 62.04 1010.39 38.05 446.48
8.99 36.66 1028.11 71.98 481.03
13.77 47.83 1007.41 90.66 466.07
19.05 67.32 1013.2 83.14 447.47
21.19 55.5 1019.83 65.22 455.93
10.12 40.0 1021.15 91.67 479.62
24.93 47.01 1014.28 66.04 455.06
8.47 40.46 1019.87 78.19 475.06
24.52 56.85 1012.59 54.47 438.89
28.55 69.84 1003.38 67.26 432.7
20.58 50.9 1011.89 72.56 452.6
18.31 46.21 1010.46 82.15 451.75
27.18 71.06 1008.16 86.32 430.66
4.43 38.91 1019.04 88.17 491.9
26.02 74.78 1010.04 72.78 439.82
15.75 39.0 1015.91 69.58 460.73
22.99 60.95 1015.14 69.86 449.7
25.52 59.15 1013.88 65.37 439.42
27.04 65.06 1013.33 52.37 439.84
6.42 35.57 1025.58 79.63 485.86
17.04 40.12 1011.81 83.14 458.1
10.79 39.82 1012.89 88.25 479.92
20.41 56.03 1019.94 55.85 458.29
7.36 40.07 1017.29 52.55 489.45
28.08 73.42 1012.17 62.74 434.0
24.74 69.13 1010.69 90.08 431.24
28.32 47.93 1003.26 54.5 439.5
16.71 40.56 1019.48 49.88 467.46
30.7 71.58 1010.0 48.96 429.27
18.42 58.95 1016.95 86.77 452.1
10.62 42.02 999.83 96.66 472.41
22.18 69.05 1002.75 70.84 442.14
22.38 49.3 1003.56 83.83 441.0
13.94 41.58 1020.76 68.22 463.07
21.24 60.84 1017.99 82.22 445.71
6.76 39.81 1017.11 87.9 483.16
26.73 68.84 1010.75 66.83 440.45
7.24 38.06 1020.6 85.36 481.83
10.84 40.62 1015.53 60.9 467.6
19.32 52.84 1004.29 83.51 450.88
29.0 69.13 1001.22 52.96 425.5
23.38 54.42 1013.95 73.02 451.87
31.17 69.51 1010.51 43.11 428.94
26.17 48.6 1002.59 61.41 439.86
30.9 73.42 1011.21 65.32 433.44
24.92 73.68 1015.12 93.68 438.23
32.77 71.32 1007.68 42.39 436.95
14.37 40.56 1021.67 68.18 470.19
8.36 40.22 1011.6 89.18 484.66
31.45 68.27 1007.56 64.79 430.81
31.6 73.17 1010.05 43.48 433.37
17.9 48.98 1014.17 80.4 453.02
20.35 50.9 1012.6 72.43 453.5
16.21 41.23 995.88 80.0 463.09
19.36 44.6 1016.25 45.65 464.56
21.04 65.46 1017.22 63.02 452.12
14.05 40.69 1015.66 74.39 470.9
23.48 64.15 1021.08 57.77 450.89
21.91 63.76 1009.85 76.8 445.04
24.42 63.07 1011.49 67.39 444.72
14.26 40.92 1022.07 73.96 460.38
21.38 58.33 1013.05 72.75 446.8
15.71 44.06 1018.34 71.69 465.05
5.78 40.62 1016.55 84.98 484.13
6.77 39.81 1017.01 87.68 488.27
23.84 49.21 1013.85 50.36 447.09
21.17 58.16 1017.16 68.11 452.02
19.94 58.96 1014.16 66.27 455.55
8.73 41.92 1029.41 89.72 480.99
16.39 41.67 1012.96 61.07 467.68

Note that the output of the above command is the same as display(powerPlantDF) we did earlier.

We can use the SQL desc command to describe the schema. This is the SQL equivalent of powerPlantDF.printSchema we saw earlier.

desc power_plant_table
col_name data_type comment
AT double null
V double null
AP double null
RH double null
PE double null

Schema Definition

Our schema definition from UCI appears below:

  • AT = Atmospheric Temperature in C
  • V = Exhaust Vaccum Speed
  • AP = Atmospheric Pressure
  • RH = Relative Humidity
  • PE = Power Output

PE is our label or target. This is the value we are trying to predict given the measurements.

Reference UCI Machine Learning Repository Combined Cycle Power Plant Data Set

Let's do some basic statistical analysis of all the columns.

We can use the describe function with no parameters to get some basic stats for each column like count, mean, max, min and standard deviation. More information can be found in the Spark API docs

display(powerPlantDF.describe())
summary AT V AP RH PE
count 9568 9568 9568 9568 9568
mean 19.65123118729102 54.30580372073601 1013.2590781772603 73.30897784280926 454.3650094063554
stddev 7.4524732296110825 12.707892998326784 5.938783705811581 14.600268756728964 17.066994999803402
min 1.81 25.36 992.89 25.56 420.26
max 37.11 81.56 1033.3 100.16 495.76

Step 4: Visualize Your Data

To understand our data, we will look for correlations between features and the label. This can be important when choosing a model. E.g., if features and a label are linearly correlated, a linear model like Linear Regression can do well; if the relationship is very non-linear, more complex models such as Decision Trees or neural networks can be better. We use the Databricks built in visualization to view each of our predictors in relation to the label column as a scatter plot to see the correlation between the predictors and the label.

select AT as Temperature, PE as Power from power_plant_table
Temperature Power
14.96 463.26
25.18 444.37
5.11 488.56
20.86 446.48
10.82 473.9
26.27 443.67
15.89 467.35
9.48 478.42
14.64 475.98
11.74 477.5
17.99 453.02
20.14 453.99
24.34 440.29
25.71 451.28
26.19 433.99
21.42 462.19
18.21 467.54
11.04 477.2
14.45 459.85
13.97 464.3
17.76 468.27
5.41 495.24
7.76 483.8
27.23 443.61
27.36 436.06
27.47 443.25
14.6 464.16
7.91 475.52
5.81 484.41
30.53 437.89
23.87 445.11
26.09 438.86
29.27 440.98
27.38 436.65
24.81 444.26
12.75 465.86
24.66 444.37
16.38 450.69
13.91 469.02
23.18 448.86
22.47 447.14
13.39 469.18
9.28 482.8
11.82 476.7
10.27 474.99
22.92 444.22
16.0 461.33
21.22 448.06
13.46 474.6
9.39 473.05
31.07 432.06
12.82 467.41
32.57 430.12
8.11 473.62
13.92 471.81
23.04 442.99
27.31 442.77
5.91 491.49
25.26 447.46
27.97 446.11
26.08 442.44
29.01 446.22
12.18 471.49
13.76 463.5
25.5 440.01
28.26 441.03
21.39 452.68
7.26 474.91
10.54 478.77
27.71 434.2
23.11 437.91
7.51 477.61
26.46 431.65
29.34 430.57
10.32 481.09
22.74 445.56
13.48 475.74
25.52 435.12
21.58 446.15
27.66 436.64
26.96 436.69
12.29 468.75
15.86 466.6
13.87 465.48
24.09 441.34
20.45 441.83
15.07 464.7
32.72 437.99
18.23 459.12
35.56 429.69
18.36 459.8
26.35 433.63
25.92 442.84
8.01 485.13
19.63 459.12
20.02 445.31
10.08 480.8
27.23 432.55
23.37 443.86
18.74 449.77
14.81 470.71
23.1 452.17
10.72 478.29
29.46 428.54
8.1 478.27
27.29 439.58
17.1 457.32
11.49 475.51
23.69 439.66
13.51 471.99
9.64 479.81
25.65 434.78
21.59 446.58
27.98 437.76
18.8 459.36
18.28 462.28
13.55 464.33
22.99 444.36
23.94 438.64
13.74 470.49
21.3 455.13
27.54 450.22
24.81 440.43
4.97 482.98
15.22 460.44
23.88 444.97
33.01 433.94
25.98 439.73
28.18 434.48
21.67 442.33
17.67 457.67
21.37 454.66
28.69 432.21
16.61 457.66
27.91 435.21
20.97 448.22
10.8 475.51
20.61 446.53
25.45 441.3
30.16 433.54
4.99 472.52
10.51 474.77
33.79 435.1
21.34 450.74
23.4 442.7
32.21 426.56
14.26 463.71
27.71 447.06
21.95 452.27
25.76 445.78
23.68 438.65
8.28 480.15
23.44 447.19
25.32 443.04
3.94 488.81
17.3 455.75
18.2 455.86
21.43 457.68
11.16 479.11
30.38 432.84
23.36 448.37
21.69 447.06
23.62 443.53
21.87 445.21
29.25 441.7
20.03 450.93
18.14 451.44
24.23 441.29
18.11 458.85
6.57 481.46
12.56 467.19
13.4 461.54
27.1 439.08
14.28 467.22
16.29 468.8
31.24 426.93
10.57 474.65
13.8 468.97
25.3 433.97
18.06 450.53
25.42 444.51
15.07 469.03
11.75 466.56
20.23 457.57
27.31 440.13
28.57 433.24
17.9 452.55
23.83 443.29
27.92 431.76
17.34 454.97
17.94 456.7
6.4 486.03
11.78 472.79
20.28 452.03
21.04 443.41
25.11 441.93
30.28 432.64
8.14 480.25
16.86 466.68
6.25 494.39
22.35 454.72
17.98 448.71
21.19 469.76
20.94 450.71
24.23 444.01
19.18 453.2
20.88 450.87
23.67 441.73
14.12 465.09
25.23 447.28
6.54 491.16
20.08 450.98
24.67 446.3
27.82 436.48
15.55 460.84
24.26 442.56
13.45 467.3
11.06 479.13
24.91 441.15
22.39 445.52
11.95 475.4
14.85 469.3
10.11 463.57
23.67 445.32
16.14 461.03
15.11 466.74
24.14 444.04
30.08 434.01
14.77 465.23
27.6 440.6
13.89 466.74
26.85 433.48
12.41 473.59
13.08 474.81
18.93 454.75
20.5 452.94
30.72 435.83
7.55 482.19
13.49 466.66
15.62 462.59
24.8 447.82
10.03 462.73
22.43 447.98
14.95 462.72
24.78 442.42
23.2 444.69
14.01 466.7
19.4 453.84
30.15 436.92
6.91 486.37
29.04 440.43
26.02 446.82
5.89 484.91
26.52 437.76
28.53 438.91
16.59 464.19
22.95 442.19
23.96 446.86
17.48 457.15
6.69 482.57
10.25 476.03
28.87 428.89
12.04 472.7
22.58 445.6
15.12 464.78
25.48 440.42
27.87 428.41
23.72 438.5
25.0 438.28
8.42 476.29
22.46 448.46
29.92 438.99
11.68 471.8
14.04 471.81
19.86 449.82
25.99 442.14
23.42 441.46
10.6 477.62
20.97 446.76
14.14 472.52
8.56 471.58
24.86 440.85
29.0 431.37
27.59 437.33
10.45 469.22
8.51 471.11
29.82 439.17
22.56 445.33
11.38 473.71
20.25 452.66
22.42 440.99
14.85 467.42
25.62 444.14
19.85 457.17
13.67 467.87
24.39 442.04
16.07 471.36
11.6 460.7
31.38 431.33
29.91 432.6
19.67 447.61
27.18 443.87
21.39 446.87
10.45 465.74
19.46 447.86
23.55 447.65
23.35 437.87
9.26 483.51
10.3 479.65
20.94 455.16
23.13 431.91
12.77 470.68
28.29 429.28
19.13 450.81
24.44 437.73
20.32 460.21
20.54 442.86
12.16 482.99
28.09 440.0
9.25 478.48
21.75 455.28
23.7 436.94
16.22 461.06
24.75 438.28
10.48 472.61
29.53 426.85
12.59 470.18
23.5 455.38
29.01 428.32
9.75 480.35
19.55 455.56
21.05 447.66
24.72 443.06
21.19 452.43
10.77 477.81
28.68 431.66
29.87 431.8
22.99 446.67
24.66 445.26
32.63 425.72
31.38 430.58
23.87 439.86
25.6 441.11
27.62 434.72
30.1 434.01
12.19 475.64
13.11 460.44
28.29 436.4
13.45 461.03
10.98 479.08
26.48 435.76
13.07 460.14
25.56 442.2
22.68 447.69
28.86 431.15
22.7 445.0
27.89 431.59
13.78 467.22
28.14 445.33
11.8 470.57
10.71 473.77
24.54 447.67
11.54 474.29
29.47 437.14
29.24 432.56
14.51 459.14
22.91 446.19
27.02 428.1
13.49 468.46
30.24 435.02
23.19 445.52
17.73 462.69
18.62 455.75
12.85 463.74
32.33 439.79
25.09 443.26
29.45 432.04
16.91 465.86
14.09 465.6
10.73 469.43
23.2 440.75
8.21 481.32
9.3 479.87
16.97 458.59
23.69 438.62
25.13 445.59
9.86 481.87
11.33 475.01
26.95 436.54
15.0 456.63
20.76 451.69
14.29 463.04
19.74 446.1
26.68 438.67
14.24 466.88
21.98 444.6
22.75 440.26
8.34 483.92
11.8 475.19
8.81 479.24
30.05 434.92
16.01 454.16
21.75 447.58
13.94 467.9
29.25 426.29
22.33 447.02
16.43 455.85
11.5 476.46
23.53 437.48
21.86 452.77
6.17 491.54
30.19 438.41
11.67 476.1
15.34 464.58
11.5 467.74
25.53 442.12
21.27 453.34
28.37 425.29
28.39 449.63
13.78 462.88
14.6 464.67
5.1 489.96
7.0 482.38
26.3 437.95
30.56 429.2
21.09 453.34
28.21 442.47
15.84 462.6
10.03 478.79
20.37 456.11
21.19 450.33
33.73 434.83
29.87 433.43
19.62 456.02
9.93 485.23
9.43 473.57
14.24 469.94
12.97 452.07
7.6 475.32
8.39 480.69
25.41 444.01
18.43 465.17
10.31 480.61
11.29 476.04
22.61 441.76
29.34 428.24
18.87 444.77
13.21 463.1
11.3 470.5
29.23 431.0
27.76 430.68
29.26 436.42
25.72 452.33
23.43 440.16
25.6 435.75
22.3 449.74
27.91 430.73
30.35 432.75
21.78 446.79
7.19 486.35
20.88 453.18
24.19 458.31
9.98 480.26
23.47 448.65
26.35 458.41
29.89 435.39
19.29 450.21
17.48 459.59
25.21 445.84
23.3 441.08
15.42 467.33
21.44 444.19
29.45 432.96
29.69 438.09
15.52 467.9
11.47 475.72
9.77 477.51
22.6 435.13
8.24 477.9
17.01 457.26
19.64 467.53
10.61 465.15
12.04 474.28
29.19 444.49
21.75 452.84
23.66 435.38
27.05 433.57
29.63 435.27
18.2 468.49
32.22 433.07
26.88 430.63
29.05 440.74
8.9 474.49
18.93 449.74
27.49 436.73
23.1 434.58
11.22 473.93
31.97 435.99
13.32 466.83
31.68 427.22
23.69 444.07
13.83 469.57
18.32 459.89
11.05 479.59
22.03 440.92
10.23 480.87
23.92 441.9
29.38 430.2
17.35 465.16
9.81 471.32
4.97 485.43
5.15 495.35
21.54 449.12
7.94 480.53
18.77 457.07
21.69 443.67
10.07 477.52
13.83 472.95
10.45 472.54
11.56 469.17
23.64 435.21
10.48 477.78
13.09 475.89
10.67 483.9
12.57 476.2
14.45 462.16
14.22 471.05
6.97 484.71
20.61 446.34
14.67 469.02
29.06 432.12
14.38 467.28
32.51 429.66
11.79 469.49
8.65 485.87
9.75 481.95
9.11 479.03
23.39 434.5
14.3 464.9
17.49 452.71
31.1 429.74
19.77 457.09
28.61 446.77
13.52 460.76
13.52 471.95
17.57 453.29
28.18 441.61
14.29 464.73
18.12 464.68
31.27 430.59
26.24 438.01
7.44 479.08
29.78 436.39
23.37 447.07
10.62 479.91
5.84 489.05
14.51 463.17
11.31 471.26
11.25 480.49
9.18 473.78
19.82 455.5
24.77 446.27
9.66 482.2
21.96 452.48
18.59 464.48
24.75 438.1
24.37 445.6
29.6 442.43
25.32 436.67
16.15 466.56
15.74 457.29
5.97 487.03
15.84 464.93
14.84 466.0
12.25 469.52
27.38 428.88
8.76 474.3
15.54 461.06
18.71 465.57
13.06 467.67
12.72 466.99
19.83 463.72
27.23 443.78
24.27 445.23
11.8 464.43
6.76 484.36
25.99 442.16
16.3 464.11
16.5 462.48
10.59 477.49
26.05 437.04
19.5 457.09
22.21 450.6
17.86 465.78
29.96 427.1
19.08 459.81
23.59 447.36
3.38 488.92
26.39 433.36
8.99 483.35
10.91 469.53
13.08 476.96
23.95 440.75
15.64 462.55
18.78 448.04
20.65 455.24
4.96 494.75
23.51 444.58
5.99 484.82
23.65 442.9
5.17 485.46
26.38 457.81
6.02 481.92
23.2 443.23
8.57 474.29
30.72 430.46
21.52 455.71
22.93 438.34
5.71 485.83
18.62 452.82
27.88 435.04
22.32 451.21
14.55 465.81
17.83 458.42
9.68 470.22
19.41 449.24
13.22 471.43
12.24 473.26
19.21 452.82
29.74 432.69
23.28 444.13
8.02 467.21
22.47 445.98
27.51 436.91
17.51 455.01
23.22 437.11
11.73 477.06
21.19 441.71
5.48 495.76
24.26 445.63
12.32 464.72
31.26 438.03
32.09 434.78
24.98 444.67
27.48 452.24
21.04 450.92
27.75 436.53
22.79 435.53
24.22 440.01
27.06 443.1
29.25 427.49
26.86 436.25
29.64 440.74
19.92 443.54
18.5 459.42
23.71 439.66
14.39 464.15
19.3 459.1
24.65 455.68
13.5 469.08
9.82 478.02
18.4 456.8
28.12 441.13
17.15 463.88
30.69 430.45
28.82 449.18
21.3 447.89
30.58 431.59
21.17 447.5
9.87 475.58
22.18 453.24
24.39 446.4
10.73 476.81
9.38 474.1
20.27 450.71
24.82 433.62
16.55 465.14
20.73 445.18
9.51 474.12
8.63 483.91
6.48 486.68
14.95 464.98
5.76 481.4
10.94 479.2
15.87 463.86
12.42 472.3
29.12 446.51
29.12 437.71
19.08 458.94
31.06 437.91
5.72 490.76
26.52 439.66
13.84 463.27
13.03 473.99
25.94 433.38
16.64 459.01
14.13 471.44
13.65 471.91
14.5 465.15
19.8 446.66
25.2 438.15
20.66 447.14
12.07 472.32
25.64 441.68
23.33 440.04
29.41 444.82
16.6 457.26
27.53 428.83
20.62 449.07
26.02 435.21
12.75 471.03
12.87 465.56
25.77 442.83
14.84 460.3
7.41 474.25
8.87 477.97
9.69 472.16
16.17 456.08
26.24 452.41
13.78 463.71
26.3 433.72
17.37 456.4
23.6 448.43
8.3 481.6
18.86 457.07
22.12 451.0
28.41 440.28
29.42 437.47
18.61 443.57
27.57 426.6
12.83 470.87
9.64 478.37
19.13 453.92
15.92 470.22
24.64 434.54
27.62 442.89
8.9 479.03
9.55 476.06
10.57 473.88
19.8 451.75
25.63 439.2
24.7 439.7
15.26 463.6
20.06 447.47
19.84 447.92
11.49 471.08
23.74 437.55
22.62 448.27
29.53 431.69
21.32 449.09
20.3 448.79
16.97 460.21
12.07 479.28
7.46 483.11
19.2 450.75
28.64 437.97
13.56 459.76
17.4 457.75
14.08 469.33
27.11 433.28
20.92 444.64
16.18 463.1
15.57 460.91
10.37 479.35
19.6 449.23
9.22 474.51
27.76 435.02
28.68 435.45
20.95 452.38
9.06 480.41
9.21 478.96
13.65 468.87
31.79 434.01
14.32 466.36
26.28 435.28
7.69 486.46
14.44 468.19
9.19 468.37
13.35 474.19
23.04 440.32
4.83 485.32
17.29 464.27
8.73 479.25
26.21 430.4
23.72 447.49
29.27 438.23
10.4 492.09
12.19 475.36
20.4 452.56
34.3 427.84
27.56 433.95
30.9 435.27
14.85 454.62
16.42 472.17
16.45 452.42
10.14 472.17
9.53 481.83
17.01 458.78
23.94 447.5
15.95 463.4
11.15 473.57
25.56 433.72
27.16 431.85
26.71 433.47
29.56 432.84
31.19 436.6
6.86 490.23
12.36 477.16
32.82 441.06
25.3 440.86
8.71 477.94
13.34 474.47
14.2 470.67
23.74 447.31
16.9 466.8
28.54 430.91
30.15 434.75
14.33 469.52
25.57 438.9
30.55 429.56
28.04 432.92
26.39 442.87
15.3 466.59
6.03 479.61
13.49 471.08
27.67 433.37
24.19 443.92
24.44 443.5
29.86 439.89
30.2 434.66
7.99 487.57
9.93 464.64
11.03 470.92
22.34 444.39
25.33 442.48
18.87 449.61
25.97 435.02
16.58 458.67
14.35 461.74
25.06 438.31
13.85 462.38
16.09 460.56
26.34 439.22
23.01 444.64
26.39 430.34
31.32 430.46
16.64 456.79
13.42 468.82
20.06 448.51
14.8 470.77
12.59 465.74
26.7 430.21
19.78 449.23
15.17 461.89
21.71 445.72
19.09 466.13
19.76 448.71
14.68 469.25
21.3 450.56
16.73 464.46
12.26 471.13
14.77 461.52
18.26 451.09
27.1 431.51
14.72 469.8
26.3 442.28
16.48 458.67
17.99 462.4
20.34 453.54
25.53 444.38
31.59 440.52
30.8 433.62
10.75 481.96
19.3 452.75
4.71 481.28
23.1 439.03
32.63 435.75
26.63 436.03
24.35 445.6
15.11 462.65
29.1 438.66
21.24 447.32
6.16 484.55
7.36 476.8
10.44 480.34
26.76 440.63
16.79 459.48
10.76 490.78
6.07 483.56
27.33 429.38
27.15 440.27
22.35 445.34
21.82 447.43
21.11 439.91
19.95 459.27
7.45 478.89
15.36 466.7
15.65 463.5
25.31 436.21
25.88 443.94
24.6 439.63
22.58 460.95
19.69 448.69
25.85 444.63
10.06 473.51
18.59 462.56
18.27 451.76
8.85 491.81
30.04 429.52
26.06 437.9
14.8 467.54
23.93 449.97
23.72 436.62
11.44 477.68
20.28 447.26
27.9 439.76
24.74 437.49
14.8 455.14
8.22 485.5
27.56 444.1
32.07 432.33
9.53 471.23
13.61 463.89
22.2 445.54
21.36 446.09
23.25 445.12
23.5 443.31
8.46 484.16
8.19 477.76
30.67 430.28
32.48 446.48
8.99 481.03
13.77 466.07
19.05 447.47
21.19 455.93
10.12 479.62
24.93 455.06
8.47 475.06
24.52 438.89
28.55 432.7
20.58 452.6
18.31 451.75
27.18 430.66
4.43 491.9
26.02 439.82
15.75 460.73
22.99 449.7
25.52 439.42
27.04 439.84
6.42 485.86
17.04 458.1
10.79 479.92
20.41 458.29
7.36 489.45
28.08 434.0
24.74 431.24
28.32 439.5
16.71 467.46
30.7 429.27
18.42 452.1
10.62 472.41
22.18 442.14
22.38 441.0
13.94 463.07
21.24 445.71
6.76 483.16
26.73 440.45
7.24 481.83
10.84 467.6
19.32 450.88
29.0 425.5
23.38 451.87
31.17 428.94
26.17 439.86
30.9 433.44
24.92 438.23
32.77 436.95
14.37 470.19
8.36 484.66
31.45 430.81
31.6 433.37
17.9 453.02
20.35 453.5
16.21 463.09
19.36 464.56
21.04 452.12
14.05 470.9
23.48 450.89
21.91 445.04
24.42 444.72
14.26 460.38
21.38 446.8
15.71 465.05
5.78 484.13
6.77 488.27
23.84 447.09
21.17 452.02
19.94 455.55
8.73 480.99
16.39 467.68

From the above plot, it looks like there is strong linear correlation between temperature and Power Output!

select V as ExhaustVaccum, PE as Power from power_plant_table;
ExhaustVaccum Power
41.76 463.26
62.96 444.37
39.4 488.56
57.32 446.48
37.5 473.9
59.44 443.67
43.96 467.35
44.71 478.42
45.0 475.98
43.56 477.5
43.72 453.02
46.93 453.99
73.5 440.29
58.59 451.28
69.34 433.99
43.79 462.19
45.0 467.54
41.74 477.2
52.75 459.85
38.47 464.3
42.42 468.27
40.07 495.24
42.28 483.8
63.9 443.61
48.6 436.06
70.72 443.25
39.31 464.16
39.96 475.52
35.79 484.41
65.18 437.89
63.94 445.11
58.41 438.86
66.85 440.98
74.16 436.65
63.94 444.26
44.03 465.86
63.73 444.37
47.45 450.69
39.35 469.02
51.3 448.86
47.45 447.14
44.85 469.18
41.54 482.8
42.86 476.7
40.64 474.99
63.94 444.22
37.87 461.33
43.43 448.06
44.71 474.6
40.11 473.05
73.5 432.06
38.62 467.41
78.92 430.12
42.18 473.62
39.39 471.81
59.43 442.99
64.44 442.77
39.33 491.49
61.08 447.46
58.84 446.11
52.3 442.44
65.71 446.22
40.1 471.49
45.87 463.5
58.79 440.01
65.34 441.03
62.96 452.68
40.69 474.91
34.03 478.77
74.34 434.2
68.3 437.91
41.01 477.61
74.67 431.65
74.34 430.57
42.28 481.09
61.02 445.56
39.85 475.74
69.75 435.12
67.25 446.15
76.86 436.64
69.45 436.69
42.18 468.75
43.02 466.6
45.08 465.48
73.68 441.34
69.45 441.83
39.3 464.7
69.75 437.99
58.96 459.12
68.94 429.69
51.43 459.8
64.05 433.63
60.95 442.84
41.66 485.13
52.72 459.12
67.32 445.31
40.72 480.8
66.48 432.55
63.77 443.86
59.21 449.77
43.69 470.71
51.3 452.17
41.38 478.29
71.94 428.54
40.64 478.27
62.66 439.58
49.69 457.32
44.2 475.51
65.59 439.66
40.89 471.99
39.35 479.81
78.92 434.78
61.87 446.58
58.33 437.76
39.72 459.36
44.71 462.28
43.48 464.33
46.21 444.36
59.39 438.64
34.03 470.49
41.1 455.13
66.93 450.22
63.73 440.43
42.85 482.98
50.88 460.44
54.2 444.97
68.67 433.94
73.18 439.73
73.88 434.48
60.84 442.33
45.09 457.67
57.76 454.66
67.25 432.21
43.77 457.66
63.76 435.21
47.43 448.22
41.66 475.51
62.91 446.53
57.32 441.3
69.34 433.54
39.04 472.52
44.78 474.77
69.05 435.1
59.8 450.74
65.06 442.7
68.14 426.56
42.32 463.71
66.93 447.06
57.76 452.27
63.94 445.78
68.3 438.65
40.77 480.15
62.52 447.19
48.41 443.04
39.9 488.81
57.76 455.75
49.39 455.86
46.97 457.68
40.05 479.11
74.16 432.84
62.52 448.37
47.45 447.06
49.21 443.53
61.45 445.21
66.51 441.7
66.86 450.93
49.78 451.44
56.89 441.29
44.85 458.85
43.65 481.46
43.41 467.19
41.58 461.54
52.84 439.08
42.74 467.22
44.34 468.8
71.98 426.93
37.73 474.65
44.21 468.97
71.58 433.97
50.16 450.53
59.04 444.51
40.69 469.03
71.14 466.56
52.05 457.57
59.54 440.13
69.84 433.24
43.72 452.55
71.37 443.29
74.99 431.76
44.78 454.97
63.07 456.7
39.9 486.03
39.96 472.79
57.25 452.03
54.2 443.41
67.32 441.93
70.98 432.64
36.24 480.25
39.63 466.68
40.07 494.39
54.42 454.72
56.85 448.71
42.48 469.76
44.89 450.71
58.79 444.01
58.2 453.2
57.85 450.87
63.86 441.73
39.52 465.09
64.63 447.28
39.33 491.16
62.52 450.98
63.56 446.3
79.74 436.48
42.03 460.84
69.51 442.56
41.49 467.3
40.64 479.13
52.3 441.15
59.04 445.52
40.69 475.4
40.69 469.3
41.62 463.57
68.67 445.32
44.21 461.03
43.13 466.74
59.87 444.04
67.25 434.01
44.9 465.23
69.34 440.6
44.84 466.74
75.6 433.48
40.96 473.59
41.74 474.81
44.06 454.75
49.69 452.94
69.13 435.83
39.22 482.19
44.47 466.66
40.12 462.59
64.63 447.82
41.62 462.73
63.21 447.98
39.31 462.72
58.46 442.42
48.41 444.69
39.0 466.7
64.63 453.84
67.32 436.92
36.08 486.37
60.07 440.43
63.07 446.82
39.48 484.91
71.64 437.76
68.08 438.91
39.54 464.19
67.79 442.19
47.43 446.86
44.2 457.15
43.65 482.57
41.26 476.03
72.58 428.89
40.23 472.7
52.3 445.6
52.05 464.78
58.95 440.42
70.79 428.41
70.47 438.5
59.43 438.28
40.64 476.29
58.49 448.46
57.19 438.99
39.22 471.8
42.44 471.81
59.14 449.82
68.08 442.14
58.79 441.46
40.22 477.62
61.87 446.76
39.82 472.52
40.71 471.58
72.39 440.85
77.54 431.37
71.97 437.33
40.71 469.22
40.78 471.11
66.51 439.17
62.26 445.33
39.22 473.71
57.76 452.66
59.43 440.99
38.91 467.42
58.82 444.14
56.53 457.17
54.3 467.87
70.72 442.04
44.58 471.36
39.1 460.7
70.83 431.33
76.86 432.6
59.39 447.61
64.79 443.87
52.3 446.87
41.01 465.74
56.89 447.86
62.96 447.65
63.47 437.87
41.66 483.51
41.46 479.65
58.16 455.16
71.25 431.91
41.5 470.68
69.13 429.28
59.21 450.81
73.5 437.73
44.6 460.21
69.05 442.86
45.0 482.99
65.27 440.0
41.82 478.48
49.82 455.28
66.56 436.94
37.87 461.06
69.45 438.28
39.58 472.61
70.79 426.85
39.72 470.18
54.42 455.38
66.56 428.32
42.49 480.35
56.53 455.56
58.33 447.66
68.67 443.06
58.86 452.43
41.54 477.81
73.77 431.66
73.91 431.8
68.67 446.67
60.29 445.26
69.89 425.72
72.29 430.58
60.27 439.86
59.15 441.11
71.14 434.72
67.45 434.01
41.17 475.64
41.58 460.44
68.67 436.4
40.73 461.03
41.54 479.08
69.14 435.76
45.51 460.14
75.6 442.2
50.78 447.69
73.67 431.15
63.56 445.0
73.21 431.59
44.47 467.22
51.43 445.33
45.09 470.57
39.61 473.77
60.29 447.67
40.05 474.29
71.32 437.14
69.05 432.56
41.79 459.14
60.07 446.19
71.77 428.1
44.47 468.46
66.75 435.02
48.6 445.52
40.55 462.69
61.27 455.75
40.0 463.74
69.68 439.79
58.95 443.26
69.13 432.04
43.96 465.86
45.87 465.6
25.36 469.43
49.3 440.75
38.91 481.32
40.56 479.87
39.16 458.59
71.97 438.62
59.44 445.59
43.56 481.87
41.5 475.01
48.41 436.54
40.66 456.63
62.52 451.69
39.59 463.04
67.71 446.1
59.92 438.67
41.4 466.88
48.41 444.6
59.39 440.26
40.96 483.92
41.2 475.19
44.68 479.24
73.68 434.92
65.46 454.16
58.79 447.58
41.26 467.9
69.13 426.29
45.87 447.02
41.79 455.85
40.22 476.46
68.94 437.48
49.21 452.77
39.33 491.54
64.79 438.41
41.93 476.1
36.99 464.58
40.78 467.74
57.17 442.12
57.5 453.34
69.13 425.29
51.43 449.63
45.78 462.88
42.32 464.67
35.57 489.96
38.08 482.38
77.95 437.95
71.98 429.2
46.63 453.34
70.02 442.47
49.69 462.6
40.96 478.79
52.05 456.11
50.16 450.33
69.88 434.83
73.68 433.43
62.96 456.02
40.67 485.23
37.14 473.57
39.58 469.94
49.83 452.07
41.04 475.32
36.24 480.69
48.06 444.01
56.03 465.17
39.82 480.61
41.5 476.04
49.3 441.76
71.98 428.24
67.71 444.77
45.87 463.1
44.6 470.5
72.99 431.0
69.4 430.68
67.17 436.42
49.82 452.33
63.94 440.16
63.76 435.75
44.57 449.74
72.24 430.73
77.17 432.75
47.43 446.79
41.39 486.35
59.8 453.18
50.23 458.31
41.54 480.26
51.3 448.65
49.5 458.41
64.69 435.39
50.16 450.21
43.14 459.59
75.6 445.84
48.78 441.08
37.85 467.33
63.09 444.19
68.27 432.96
47.93 438.09
36.99 467.9
43.67 475.72
34.69 477.51
69.84 435.13
39.61 477.9
44.2 457.26
44.6 467.53
41.58 465.15
40.1 474.28
65.71 444.49
45.09 452.84
77.54 435.38
75.33 433.57
69.71 435.27
39.63 468.49
70.8 433.07
73.56 430.63
65.74 440.74
39.96 474.49
48.6 449.74
63.76 436.73
70.79 434.58
43.13 473.93
79.74 435.99
43.22 466.83
68.24 427.22
63.77 444.07
41.49 469.57
66.51 459.89
40.71 479.59
64.69 440.92
41.46 480.87
66.54 441.9
69.68 430.2
42.86 465.16
44.45 471.32
40.64 485.43
40.07 495.35
58.49 449.12
42.02 480.53
50.66 457.07
69.94 443.67
44.68 477.52
39.64 472.95
39.69 472.54
40.71 469.17
70.04 435.21
40.22 477.78
39.85 475.89
40.23 483.9
39.16 476.2
43.34 462.16
37.85 471.05
41.26 484.71
63.86 446.34
42.28 469.02
72.86 432.12
40.1 467.28
69.98 429.66
45.09 469.49
40.56 485.87
40.81 481.95
40.02 479.03
69.13 434.5
54.3 464.9
63.94 452.71
69.51 429.74
56.65 457.09
72.29 446.77
41.48 460.76
40.83 471.95
46.21 453.29
60.07 441.61
46.18 464.73
43.69 464.68
73.91 430.59
77.95 438.01
41.04 479.08
74.78 436.39
65.46 447.07
39.58 479.91
43.02 489.05
53.82 463.17
42.02 471.26
40.67 480.49
39.42 473.78
58.16 455.5
58.41 446.27
41.06 482.2
59.8 452.48
43.14 464.48
69.89 438.1
63.47 445.6
67.79 442.43
61.25 436.67
41.85 466.56
71.14 457.29
36.25 487.03
52.72 464.93
44.63 466.0
48.79 469.52
70.04 428.88
41.48 474.3
39.31 461.06
39.39 465.57
41.78 467.67
40.71 466.99
39.39 463.72
49.16 443.78
68.28 445.23
40.66 464.43
36.25 484.36
63.07 442.16
39.63 464.11
49.39 462.48
42.49 477.49
65.59 437.04
40.79 457.09
45.01 450.6
45.0 465.78
70.04 427.1
44.63 459.81
47.43 447.36
39.64 488.92
66.49 433.36
39.04 483.35
41.04 469.53
39.82 476.96
58.46 440.75
43.71 462.55
54.2 448.04
50.59 455.24
40.07 494.75
57.32 444.58
35.79 484.82
66.05 442.9
39.33 485.46
49.5 457.81
43.65 481.92
61.02 443.23
39.69 474.29
71.58 430.46
50.66 455.71
62.26 438.34
41.31 485.83
44.06 452.82
68.94 435.04
59.8 451.21
42.74 465.81
44.92 458.42
39.96 470.22
49.39 449.24
44.92 471.43
44.92 473.26
58.49 452.82
70.32 432.69
60.84 444.13
41.92 467.21
48.6 445.98
73.77 436.91
44.9 455.01
66.56 437.11
40.64 477.06
67.71 441.71
40.07 495.76
66.44 445.63
41.62 464.72
68.94 438.03
72.86 434.78
60.32 444.67
61.41 452.24
45.09 450.92
70.4 436.53
71.77 435.53
68.51 440.01
64.45 443.1
71.94 427.49
68.08 436.25
67.79 440.74
63.31 443.54
51.43 459.42
60.23 439.66
44.84 464.15
56.65 459.1
52.36 455.68
45.51 469.08
41.26 478.02
44.06 456.8
44.89 441.13
43.69 463.88
73.67 430.45
65.71 449.18
48.92 447.89
70.04 431.59
52.3 447.5
41.82 475.58
59.8 453.24
63.21 446.4
44.92 476.81
40.46 474.1
57.76 450.71
66.48 433.62
41.66 465.14
59.87 445.18
39.22 474.12
43.79 483.91
40.27 486.68
43.52 464.98
45.87 481.4
39.04 479.2
41.16 463.86
38.25 472.3
58.84 446.51
51.43 437.71
41.1 458.94
67.17 437.91
39.33 490.76
65.06 439.66
44.9 463.27
39.52 473.99
66.49 433.38
53.82 459.01
40.75 471.44
39.28 471.91
44.47 465.15
51.19 446.66
63.76 438.15
51.19 447.14
43.71 472.32
70.72 441.68
72.99 440.04
64.05 444.82
53.16 457.26
72.58 428.83
43.43 449.07
71.94 435.21
44.2 471.03
48.04 465.56
62.96 442.83
41.48 460.3
40.71 474.25
41.82 477.97
40.46 472.16
46.97 456.08
49.82 452.41
43.22 463.71
67.07 433.72
57.76 456.4
48.98 448.43
36.08 481.6
42.18 457.07
49.39 451.0
75.6 440.28
71.32 437.47
67.71 443.57
69.84 426.6
41.5 470.87
39.85 478.37
58.66 453.92
40.56 470.22
72.24 434.54
63.9 442.89
36.24 479.03
43.99 476.06
36.71 473.88
57.25 451.75
56.85 439.2
58.46 439.7
46.18 463.6
52.84 447.47
56.89 447.92
44.63 471.08
72.43 437.55
51.3 448.27
72.39 431.69
48.14 449.09
58.46 448.79
44.92 460.21
41.17 479.28
41.82 483.11
54.2 450.75
66.54 437.97
41.48 459.76
44.9 457.75
40.1 469.33
69.75 433.28
70.02 444.64
44.9 463.1
44.68 460.91
39.04 479.35
59.21 449.23
40.92 474.51
72.99 435.02
70.72 435.45
48.14 452.38
39.3 480.41
39.72 478.96
42.74 468.87
76.2 434.01
44.6 466.36
75.23 435.28
43.02 486.46
40.1 468.19
41.01 468.37
41.39 474.19
74.22 440.32
38.44 485.32
42.86 464.27
36.18 479.25
70.32 430.4
58.62 447.49
64.69 438.23
40.43 492.09
40.75 475.36
54.9 452.56
74.67 427.84
68.08 433.95
70.8 435.27
58.59 454.62
40.56 472.17
63.31 452.42
42.02 472.17
41.44 481.83
49.15 458.78
62.08 447.5
49.25 463.4
41.26 473.57
70.32 433.72
66.44 431.85
77.95 433.47
74.22 432.84
70.94 436.6
41.17 490.23
41.74 477.16
68.31 441.06
70.98 440.86
41.82 477.94
40.8 474.47
43.02 470.67
65.34 447.31
44.88 466.8
71.94 430.91
69.88 434.75
42.86 469.52
59.43 438.9
70.04 429.56
74.33 432.92
49.16 442.87
41.76 466.59
41.14 479.61
44.63 471.08
59.14 433.37
65.48 443.92
59.14 443.5
64.79 439.89
69.59 434.66
41.38 487.57
41.62 464.64
42.32 470.92
63.73 444.39
48.6 442.48
52.08 449.61
69.34 435.02
43.99 458.67
46.18 461.74
62.39 438.31
48.92 462.38
44.2 460.56
59.21 439.22
58.79 444.64
71.25 430.34
71.29 430.46
45.87 456.79
41.23 468.82
44.9 448.51
44.71 470.77
41.14 465.74
66.56 430.21
50.32 449.23
49.15 461.89
61.45 445.72
39.39 466.13
51.19 448.71
41.23 469.25
66.86 450.56
39.64 464.46
41.5 471.13
48.06 461.52
59.15 451.09
79.74 431.51
40.83 469.8
51.43 442.28
48.92 458.67
43.79 462.4
59.8 453.54
62.96 444.38
58.9 440.52
69.14 433.62
45.0 481.96
44.9 452.75
39.42 481.28
66.05 439.03
73.88 435.75
74.16 436.03
58.49 445.6
56.03 462.65
50.05 438.66
50.32 447.32
39.48 484.55
41.01 476.8
39.04 480.34
48.41 440.63
44.6 459.48
40.43 490.78
38.91 483.56
73.18 429.38
59.21 440.27
51.43 445.34
65.27 447.43
69.94 439.91
50.59 459.27
39.61 478.89
41.66 466.7
43.5 463.5
74.33 436.21
63.47 443.94
63.94 439.63
41.54 460.95
59.14 448.69
75.08 444.63
37.83 473.51
39.54 462.56
50.16 451.76
40.43 491.81
68.08 429.52
49.02 437.9
38.73 467.54
64.45 449.97
66.48 436.62
40.55 477.68
63.86 447.26
63.13 439.76
59.39 437.49
58.2 455.14
41.03 485.5
66.93 444.1
70.94 432.33
44.03 471.23
42.34 463.89
51.19 445.54
59.54 446.09
63.86 445.12
59.21 443.31
39.66 484.16
40.69 477.76
71.29 430.28
62.04 446.48
36.66 481.03
47.83 466.07
67.32 447.47
55.5 455.93
40.0 479.62
47.01 455.06
40.46 475.06
56.85 438.89
69.84 432.7
50.9 452.6
46.21 451.75
71.06 430.66
38.91 491.9
74.78 439.82
39.0 460.73
60.95 449.7
59.15 439.42
65.06 439.84
35.57 485.86
40.12 458.1
39.82 479.92
56.03 458.29
40.07 489.45
73.42 434.0
69.13 431.24
47.93 439.5
40.56 467.46
71.58 429.27
58.95 452.1
42.02 472.41
69.05 442.14
49.3 441.0
41.58 463.07
60.84 445.71
39.81 483.16
68.84 440.45
38.06 481.83
40.62 467.6
52.84 450.88
69.13 425.5
54.42 451.87
69.51 428.94
48.6 439.86
73.42 433.44
73.68 438.23
71.32 436.95
40.56 470.19
40.22 484.66
68.27 430.81
73.17 433.37
48.98 453.02
50.9 453.5
41.23 463.09
44.6 464.56
65.46 452.12
40.69 470.9
64.15 450.89
63.76 445.04
63.07 444.72
40.92 460.38
58.33 446.8
44.06 465.05
40.62 484.13
39.81 488.27
49.21 447.09
58.16 452.02
58.96 455.55
41.92 480.99
41.67 467.68

The linear correlation is not as strong between Exhaust Vacuum Speed and Power Output but there is some semblance of a pattern.

select AP as Pressure, PE as Power from power_plant_table;
Pressure Power
1024.07 463.26
1020.04 444.37
1012.16 488.56
1010.24 446.48
1009.23 473.9
1012.23 443.67
1014.02 467.35
1019.12 478.42
1021.78 475.98
1015.14 477.5
1008.64 453.02
1014.66 453.99
1011.31 440.29
1012.77 451.28
1009.48 433.99
1015.76 462.19
1022.86 467.54
1022.6 477.2
1023.97 459.85
1015.15 464.3
1009.09 468.27
1019.16 495.24
1008.52 483.8
1014.3 443.61
1003.18 436.06
1009.97 443.25
1011.11 464.16
1023.57 475.52
1012.14 484.41
1012.69 437.89
1019.02 445.11
1013.64 438.86
1011.11 440.98
1010.08 436.65
1018.76 444.26
1007.29 465.86
1011.4 444.37
1010.08 450.69
1014.69 469.02
1012.04 448.86
1007.62 447.14
1017.24 469.18
1018.33 482.8
1014.12 476.7
1020.63 474.99
1019.28 444.22
1020.24 461.33
1010.96 448.06
1014.51 474.6
1029.14 473.05
1010.58 432.06
1018.71 467.41
1011.6 430.12
1014.82 473.62
1012.94 471.81
1010.23 442.99
1014.65 442.77
1010.18 491.49
1013.68 447.46
1002.25 446.11
1007.03 442.44
1013.61 446.22
1016.67 471.49
1008.89 463.5
1016.02 440.01
1014.56 441.03
1019.49 452.68
1020.43 474.91
1018.71 478.77
998.14 434.2
1017.83 437.91
1024.61 477.61
1016.65 431.65
998.58 430.57
1008.82 481.09
1009.56 445.56
1012.71 475.74
1010.36 435.12
1017.39 446.15
1001.31 436.64
1013.89 436.69
1016.53 468.75
1012.18 466.6
1024.42 465.48
1014.93 441.34
1012.53 441.83
1019.0 464.7
1009.6 437.99
1015.55 459.12
1006.56 429.69
1010.57 459.8
1009.81 433.63
1014.62 442.84
1014.49 485.13
1025.09 459.12
1012.05 445.31
1022.7 480.8
1005.23 432.55
1013.42 443.86
1018.3 449.77
1017.19 470.71
1011.93 452.17
1021.6 478.29
1006.96 428.54
1020.66 478.27
1007.63 439.58
1005.53 457.32
1018.79 475.51
1010.85 439.66
1011.03 471.99
1015.1 479.81
1010.83 434.78
1011.18 446.58
1013.92 437.76
1001.24 459.36
1016.99 462.28
1016.08 464.33
1010.71 444.36
1014.32 438.64
1018.69 470.49
1001.86 455.13
1017.06 450.22
1009.34 440.43
1014.02 482.98
1014.19 460.44
1012.81 444.97
1005.2 433.94
1012.28 439.73
1005.89 434.48
1017.93 442.33
1014.26 457.67
1018.8 454.66
1017.71 432.21
1012.25 457.66
1010.27 435.21
1007.64 448.22
1013.79 475.51
1013.24 446.53
1011.7 441.3
1007.67 433.54
1020.45 472.52
1012.59 474.77
1001.62 435.1
1016.92 450.74
1014.32 442.7
1003.34 426.56
1016.0 463.71
1016.85 447.06
1018.02 452.27
1018.49 445.78
1017.93 438.65
1011.55 480.15
1016.46 447.19
1008.47 443.04
1008.06 488.81
1016.26 455.75
1018.83 455.86
1013.94 457.68
1014.95 479.11
1007.44 432.84
1016.18 448.37
1007.56 447.06
1014.1 443.53
1011.13 445.21
1015.53 441.7
1013.05 450.93
1002.95 451.44
1012.32 441.29
1014.48 458.85
1018.24 481.46
1016.93 467.19
1020.5 461.54
1006.28 439.08
1028.79 467.22
1019.49 468.8
1004.66 426.93
1024.36 474.65
1022.93 468.97
1010.18 433.97
1009.52 450.53
1011.98 444.51
1015.29 469.03
1019.36 466.56
1012.15 457.57
1006.24 440.13
1003.57 433.24
1008.64 452.55
1002.04 443.29
1005.47 431.76
1007.81 454.97
1012.42 456.7
1007.75 486.03
1011.37 472.79
1010.12 452.03
1012.26 443.41
1014.49 441.93
1007.51 432.64
1013.15 480.25
1004.47 466.68
1020.19 494.39
1012.46 454.72
1012.28 448.71
1013.43 469.76
1009.64 450.71
1009.8 444.01
1017.46 453.2
1012.39 450.87
1019.67 441.73
1018.41 465.09
1020.59 447.28
1011.54 491.16
1017.99 450.98
1013.75 446.3
1008.37 436.48
1017.41 460.84
1013.43 442.56
1020.19 467.3
1021.47 479.13
1008.72 441.15
1011.78 445.52
1015.62 475.4
1014.91 469.3
1017.17 463.57
1006.71 445.32
1020.36 461.03
1014.99 466.74
1018.47 444.04
1017.6 434.01
1020.5 465.23
1009.63 440.6
1023.66 466.74
1017.43 433.48
1023.36 473.59
1020.75 474.81
1017.58 454.75
1009.6 452.94
1009.94 435.83
1014.53 482.19
1030.46 466.66
1013.03 462.59
1020.69 447.82
1014.55 462.73
1012.06 447.98
1009.15 462.72
1016.82 442.42
1008.64 444.69
1016.73 466.7
1020.38 453.84
1013.83 436.92
1021.82 486.37
1015.42 440.43
1010.94 446.82
1005.11 484.91
1008.27 437.76
1013.27 438.91
1007.97 464.19
1009.89 442.19
1008.38 446.86
1018.89 457.15
1020.14 482.57
1007.44 476.03
1008.69 428.89
1018.07 472.7
1009.04 445.6
1014.63 464.78
1017.02 440.42
1003.96 428.41
1010.65 438.5
1007.84 438.28
1022.35 476.29
1011.5 448.46
1008.62 438.99
1017.9 471.8
1012.74 471.81
1016.12 449.82
1013.13 442.14
1009.74 441.46
1011.37 477.62
1011.45 446.76
1012.46 472.52
1021.27 471.58
1001.15 440.85
1011.33 431.37
1008.64 437.33
1015.68 469.22
1023.51 471.11
1010.98 439.17
1012.11 445.33
1018.62 473.71
1016.28 452.66
1007.12 440.99
1014.48 467.42
1010.02 444.14
1020.57 457.17
1015.92 467.87
1009.78 442.04
1019.52 471.36
1009.81 460.7
1010.35 431.33
998.59 432.6
1014.07 447.61
1016.27 443.87
1009.2 446.87
1020.57 465.74
1014.02 447.86
1020.16 447.65
1011.78 437.87
1016.87 483.51
1018.21 479.65
1016.88 455.16
1002.49 431.91
1014.13 470.68
1009.29 429.28
1018.32 450.81
1011.49 437.73
1015.16 460.21
1001.6 442.86
1021.51 482.99
1013.27 440.0
1033.25 478.48
1015.01 455.28
1002.07 436.94
1022.36 461.06
1013.97 438.28
1011.81 472.61
1003.7 426.85
1017.76 470.18
1012.31 455.38
1006.44 428.32
1010.57 480.35
1020.2 455.56
1013.14 447.66
1006.74 443.06
1014.19 452.43
1019.94 477.81
1004.72 431.66
1004.53 431.8
1006.65 446.67
1018.0 445.26
1013.85 425.72
1008.73 430.58
1018.94 439.86
1013.31 441.11
1011.6 434.72
1014.23 434.01
1019.43 475.64
1020.43 460.44
1005.46 436.4
1018.7 461.03
1019.94 479.08
1009.31 435.76
1015.22 460.14
1017.37 442.2
1008.83 447.69
1006.65 431.15
1014.32 445.0
1001.32 431.59
1027.94 467.22
1012.16 445.33
1013.21 470.57
1018.72 473.77
1017.42 447.67
1014.78 474.29
1008.07 437.14
1003.12 432.56
1009.72 459.14
1016.03 446.19
1006.38 428.1
1030.18 468.46
1017.95 435.02
1002.38 445.52
1003.36 462.69
1019.26 455.75
1015.89 463.74
1011.95 439.79
1016.99 443.26
1009.3 432.04
1013.32 465.86
1009.05 465.6
1009.35 469.43
1003.4 440.75
1015.82 481.32
1022.64 479.87
1005.7 458.59
1009.62 438.62
1012.38 445.59
1015.13 481.87
1013.58 475.01
1008.53 436.54
1016.28 456.63
1015.63 451.69
1010.93 463.04
1007.68 446.1
1009.94 438.67
1019.7 466.88
1008.42 444.6
1015.4 440.26
1023.28 483.92
1017.18 475.19
1023.06 479.24
1014.95 434.92
1014.0 454.16
1012.42 447.58
1021.67 467.9
1010.27 426.29
1007.8 447.02
1005.47 455.85
1010.31 476.46
1007.53 437.48
1014.61 452.77
1012.57 491.54
1017.22 438.41
1019.81 476.1
1007.87 464.58
1023.91 467.74
1010.0 442.12
1014.53 453.34
1010.44 425.29
1011.74 449.63
1025.27 462.88
1015.71 464.67
1027.17 489.96
1020.27 482.38
1009.45 437.95
1004.74 429.2
1013.03 453.34
1010.58 442.47
1015.14 462.6
1024.57 478.79
1012.34 456.11
1005.81 450.33
1007.21 434.83
1015.1 433.43
1020.76 456.02
1018.08 485.23
1013.03 473.57
1011.17 469.94
1008.69 452.07
1021.82 475.32
1013.39 480.69
1013.12 444.01
1020.41 465.17
1012.87 480.61
1013.39 476.04
1003.51 441.76
1005.19 428.24
1004.0 444.77
1008.58 463.1
1018.19 470.5
1007.04 431.0
1004.27 430.68
1006.6 436.42
1016.19 452.33
1010.64 440.16
1010.18 435.75
1008.48 449.74
1010.74 430.73
1009.55 432.75
1007.88 446.79
1018.12 486.35
1015.66 453.18
1015.73 458.31
1019.7 480.26
1011.89 448.65
1012.67 458.41
1006.37 435.39
1010.49 450.21
1018.68 459.59
1017.19 445.84
1018.17 441.08
1009.89 467.33
1016.56 444.19
1007.96 432.96
1002.85 438.09
1006.86 467.9
1012.68 475.72
1027.72 477.51
1006.37 435.13
1017.99 477.9
1019.18 457.26
1015.88 467.53
1021.08 465.15
1014.42 474.28
1013.85 444.49
1014.15 452.84
1008.5 435.38
1003.88 433.57
1009.04 435.27
1005.35 468.49
1009.9 433.07
1004.85 430.63
1013.29 440.74
1026.31 474.49
1005.72 449.74
1010.09 436.73
1006.53 434.58
1017.24 473.93
1007.03 435.99
1009.45 466.83
1005.29 427.22
1013.39 444.07
1020.11 469.57
1015.18 459.89
1024.91 479.59
1007.21 440.92
1020.45 480.87
1009.93 441.9
1011.35 430.2
1014.62 465.16
1021.19 471.32
1020.91 485.43
1012.27 495.35
1010.85 449.12
1006.22 480.53
1014.89 457.07
1010.7 443.67
1023.44 477.52
1012.52 472.95
1003.92 472.54
1015.85 469.17
1011.09 435.21
1004.81 477.78
1012.86 475.89
1017.75 483.9
1016.53 476.2
1015.47 462.16
1011.24 471.05
1010.6 484.71
1015.43 446.34
1007.21 469.02
1004.23 432.12
1015.51 467.28
1013.29 429.66
1013.16 469.49
1023.23 485.87
1026.0 481.95
1031.1 479.03
1010.99 434.5
1015.16 464.9
1020.02 452.71
1010.84 429.74
1020.67 457.09
1011.61 446.77
1014.46 460.76
1008.31 471.95
1014.09 453.29
1016.34 441.61
1017.01 464.73
1016.91 464.68
1003.72 430.59
1014.19 438.01
1021.84 479.08
1009.28 436.39
1016.25 447.07
1011.9 479.91
1013.88 489.05
1016.46 463.17
1001.18 471.26
1011.64 480.49
1025.41 473.78
1016.76 455.5
1013.78 446.27
1021.21 482.2
1016.72 452.48
1011.92 464.48
1015.29 438.1
1012.77 445.6
1010.37 442.43
1011.56 436.67
1016.54 466.56
1019.65 457.29
1029.65 487.03
1026.45 464.93
1019.28 466.0
1017.44 469.52
1011.18 428.88
1018.49 474.3
1009.69 461.06
1014.09 465.57
1012.3 467.67
1016.02 466.99
1013.73 463.72
1004.03 443.78
1005.43 445.23
1017.13 464.43
1028.31 484.36
1012.5 442.16
1004.64 464.11
1018.35 462.48
1009.59 477.49
1012.78 437.04
1003.8 457.09
1012.22 450.6
1023.25 465.78
1010.15 427.1
1020.14 459.81
1006.64 447.36
1011.0 488.92
1012.96 433.36
1021.99 483.35
1026.57 469.53
1012.27 476.96
1017.5 440.75
1024.51 462.55
1012.05 448.04
1016.22 455.24
1011.8 494.75
1012.55 444.58
1011.56 484.82
1019.6 442.9
1009.68 485.46
1012.82 457.81
1013.85 481.92
1009.63 443.23
1000.91 474.29
1009.98 430.46
1013.56 455.71
1011.25 438.34
1003.24 485.83
1017.76 452.82
1007.68 435.04
1016.82 451.21
1028.41 465.81
1025.04 458.42
1026.09 470.22
1020.84 449.24
1023.84 471.43
1023.74 473.26
1011.7 452.82
1008.1 432.69
1017.91 444.13
1029.8 467.21
1002.33 445.98
1002.42 436.91
1009.05 455.01
1002.47 437.11
1020.68 477.06
1006.65 441.71
1019.63 495.76
1011.33 445.63
1012.88 464.72
1005.94 438.03
1003.47 434.78
1015.63 444.67
1012.2 452.24
1014.19 450.92
1006.65 436.53
1005.75 435.53
1013.23 440.01
1008.72 443.1
1007.18 427.49
1012.99 436.25
1009.99 440.74
1015.02 443.54
1010.82 459.42
1009.76 439.66
1023.55 464.15
1020.55 459.1
1014.76 455.68
1015.33 469.08
1007.71 478.02
1017.36 456.8
1009.18 441.13
1017.05 463.88
1006.14 430.45
1014.24 449.18
1010.92 447.89
1010.4 431.59
1009.36 447.5
1033.04 475.58
1016.77 453.24
1012.59 446.4
1025.1 476.81
1019.29 474.1
1016.66 450.71
1006.4 433.62
1011.45 465.14
1019.08 445.18
1015.3 474.12
1016.08 483.91
1010.55 486.68
1022.43 464.98
1010.83 481.4
1021.81 479.2
1005.85 463.86
1012.76 472.3
1001.31 446.51
1005.93 437.71
1001.96 458.94
1007.62 437.91
1009.96 490.76
1013.4 439.66
1007.58 463.27
1016.68 473.99
1012.83 433.38
1015.13 459.01
1016.05 471.44
1012.97 471.91
1028.2 465.15
1008.25 446.66
1009.78 438.15
1008.81 447.14
1025.53 472.32
1010.16 441.68
1009.33 440.04
1009.82 444.82
1014.5 457.26
1009.13 428.83
1009.93 449.07
1009.38 435.21
1017.59 471.03
1012.47 465.56
1019.86 442.83
1017.26 460.3
1023.07 474.25
1033.3 477.97
1019.1 472.16
1014.22 456.08
1014.9 452.41
1011.31 463.71
1006.26 433.72
1016.0 456.4
1015.41 448.43
1020.63 481.6
1001.16 457.07
1019.8 451.0
1018.48 440.28
1002.26 437.47
1004.07 443.57
1004.91 426.6
1013.12 470.87
1012.9 478.37
1013.32 453.92
1020.79 470.22
1011.37 434.54
1013.11 442.89
1013.29 479.03
1020.5 476.06
1022.62 473.88
1010.84 451.75
1012.68 439.2
1015.58 439.7
1013.68 463.6
1004.21 447.47
1013.23 447.92
1020.44 471.08
1007.99 437.55
1012.36 448.27
998.47 431.69
1016.57 449.09
1015.93 448.79
1025.21 460.21
1013.54 479.28
1032.67 483.11
1011.46 450.75
1010.43 437.97
1008.53 459.76
1020.5 457.75
1015.48 469.33
1009.74 433.28
1010.23 444.64
1021.3 463.1
1022.01 460.91
1023.95 479.35
1017.65 449.23
1021.83 474.51
1007.81 435.02
1009.43 435.45
1013.3 452.38
1019.73 480.41
1019.54 478.96
1026.58 468.87
1007.89 434.01
1013.85 466.36
1011.44 435.28
1014.51 486.46
1015.51 468.19
1022.14 468.37
1019.17 474.19
1009.52 440.32
1015.35 485.32
1014.38 464.27
1013.66 479.25
1007.0 430.4
1016.65 447.49
1006.85 438.23
1025.46 492.09
1015.13 475.36
1016.68 452.56
1015.98 427.84
1010.8 433.95
1008.48 435.27
1014.04 454.62
1020.36 472.17
1015.96 452.42
1003.19 472.17
1018.01 481.83
1021.83 458.78
1022.47 447.5
1019.04 463.4
1022.67 473.57
1009.07 433.72
1011.2 431.85
1012.13 433.47
1007.45 432.84
1007.29 436.6
1020.12 490.23
1020.58 477.16
1010.44 441.06
1007.22 440.86
1033.08 477.94
1026.56 474.47
1012.18 470.67
1013.7 447.31
1018.14 466.8
1007.4 430.91
1007.2 434.75
1010.82 469.52
1008.88 438.9
1010.51 429.56
1013.53 432.92
1005.68 442.87
1022.57 466.59
1028.04 479.61
1019.12 471.08
1016.51 433.37
1018.8 443.92
1016.74 443.5
1017.37 439.89
1008.9 434.66
1021.95 487.57
1013.76 464.64
1017.26 470.92
1014.37 444.39
1002.54 442.48
1005.25 449.61
1009.43 435.02
1021.81 458.67
1016.63 461.74
1008.09 438.31
1011.68 462.38
1019.39 460.56
1013.37 439.22
1009.71 444.64
999.8 430.34
1008.37 430.46
1009.02 456.79
994.17 468.82
1008.79 448.51
1014.67 470.77
1025.79 465.74
1005.31 430.21
1008.62 449.23
1021.91 461.89
1010.97 445.72
1013.36 466.13
1008.38 448.71
998.43 469.25
1013.04 450.56
1008.94 464.46
1014.87 471.13
1010.92 461.52
1012.04 451.09
1005.43 431.51
1009.65 469.8
1012.05 442.28
1011.84 458.67
1016.13 462.4
1015.18 453.54
1019.81 444.38
1003.39 440.52
1007.68 433.62
1023.68 481.96
1008.89 452.75
1026.4 481.28
1020.28 439.03
1005.64 435.75
1009.72 436.03
1011.03 445.6
1020.27 462.65
1005.87 438.66
1008.54 447.32
1004.85 484.55
1024.9 476.8
1023.99 480.34
1010.53 440.63
1014.27 459.48
1025.98 490.78
1019.25 483.56
1012.26 429.38
1013.49 440.27
1011.34 445.34
1013.86 447.43
1004.37 439.91
1016.11 459.27
1017.88 478.89
1012.41 466.7
1021.39 463.5
1015.04 436.21
1011.95 443.94
1012.87 439.63
1013.21 460.95
1015.99 448.69
1006.24 444.63
1005.49 473.51
1008.56 462.56
1011.07 451.76
1025.68 491.81
1011.04 429.52
1007.59 437.9
1003.18 467.54
1015.35 449.97
1003.61 436.62
1023.37 477.68
1016.04 447.26
1011.8 439.76
1015.23 437.49
1018.29 455.14
1021.76 485.5
1016.81 444.1
1006.91 432.33
1008.87 471.23
1017.93 463.89
1009.2 445.54
1007.99 446.09
1017.82 445.12
1018.29 443.31
1015.14 484.16
1019.86 477.76
1008.36 430.28
1010.39 446.48
1028.11 481.03
1007.41 466.07
1013.2 447.47
1019.83 455.93
1021.15 479.62
1014.28 455.06
1019.87 475.06
1012.59 438.89
1003.38 432.7
1011.89 452.6
1010.46 451.75
1008.16 430.66
1019.04 491.9
1010.04 439.82
1015.91 460.73
1015.14 449.7
1013.88 439.42
1013.33 439.84
1025.58 485.86
1011.81 458.1
1012.89 479.92
1019.94 458.29
1017.29 489.45
1012.17 434.0
1010.69 431.24
1003.26 439.5
1019.48 467.46
1010.0 429.27
1016.95 452.1
999.83 472.41
1002.75 442.14
1003.56 441.0
1020.76 463.07
1017.99 445.71
1017.11 483.16
1010.75 440.45
1020.6 481.83
1015.53 467.6
1004.29 450.88
1001.22 425.5
1013.95 451.87
1010.51 428.94
1002.59 439.86
1011.21 433.44
1015.12 438.23
1007.68 436.95
1021.67 470.19
1011.6 484.66
1007.56 430.81
1010.05 433.37
1014.17 453.02
1012.6 453.5
995.88 463.09
1016.25 464.56
1017.22 452.12
1015.66 470.9
1021.08 450.89
1009.85 445.04
1011.49 444.72
1022.07 460.38
1013.05 446.8
1018.34 465.05
1016.55 484.13
1017.01 488.27
1013.85 447.09
1017.16 452.02
1014.16 455.55
1029.41 480.99
1012.96 467.68 
select RH as Humidity, PE as Power from power_plant_table;
Humidity Power
73.17 463.26
59.08 444.37
92.14 488.56
76.64 446.48
96.62 473.9
58.77 443.67
75.24 467.35
66.43 478.42
41.25 475.98
70.72 477.5
75.04 453.02
64.22 453.99
84.15 440.29
61.83 451.28
87.59 433.99
43.08 462.19
48.84 467.54
77.51 477.2
63.59 459.85
55.28 464.3
66.26 468.27
64.77 495.24
83.31 483.8
47.19 443.61
54.93 436.06
74.62 443.25
72.52 464.16
88.44 475.52
92.28 484.41
41.85 437.89
44.28 445.11
64.58 438.86
63.25 440.98
78.61 436.65
44.51 444.26
89.46 465.86
74.52 444.37
88.86 450.69
75.51 469.02
78.64 448.86
76.65 447.14
80.44 469.18
79.89 482.8
88.28 476.7
84.6 474.99
42.69 444.22
78.41 461.33
61.07 448.06
50.0 474.6
77.29 473.05
43.66 432.06
83.8 467.41
66.47 430.12
93.09 473.62
80.52 471.81
68.99 442.99
57.27 442.77
95.53 491.49
71.72 447.46
57.88 446.11
63.34 442.44
48.07 446.22
91.87 471.49
87.27 463.5
64.4 440.01
43.4 441.03
72.24 452.68
90.22 474.91
74.0 478.77
71.85 434.2
86.62 437.91
97.41 477.61
84.44 431.65
81.55 430.57
75.66 481.09
79.41 445.56
58.91 475.74
90.06 435.12
79.0 446.15
69.47 436.64
51.47 436.69
83.13 468.75
40.33 466.6
81.69 465.48
94.55 441.34
91.81 441.83
63.62 464.7
49.35 437.99
69.61 459.12
38.75 429.69
90.17 459.8
81.24 433.63
48.46 442.84
76.72 485.13
51.16 459.12
76.34 445.31
67.3 480.8
52.38 432.55
76.44 443.86
91.55 449.77
71.9 470.71
80.05 452.17
63.77 478.29
62.26 428.54
89.04 478.27
58.02 439.58
81.82 457.32
91.14 475.51
88.92 439.66
84.83 471.99
91.76 479.81
86.56 434.78
57.21 446.58
54.25 437.76
63.8 459.36
33.71 462.28
67.25 464.33
60.11 444.36
74.55 438.64
67.34 470.49
42.75 455.13
55.2 450.22
83.61 440.43
88.78 482.98
100.12 460.44
64.52 444.97
51.41 433.94
85.78 439.73
75.41 434.48
81.63 442.33
51.92 457.67
70.12 454.66
53.83 432.21
77.23 457.66
65.67 435.21
71.18 448.22
81.96 475.51
79.54 446.53
47.09 441.3
57.69 433.54
78.89 472.52
85.29 474.77
40.13 435.1
77.06 450.74
67.38 442.7
62.44 426.56
77.43 463.71
58.77 447.06
67.72 452.27
42.14 445.78
84.16 438.65
89.79 480.15
67.21 447.19
72.14 443.04
97.49 488.81
87.74 455.75
96.3 455.86
61.25 457.68
88.38 479.11
74.77 432.84
68.18 448.37
77.2 447.06
49.54 443.53
92.22 445.21
33.65 441.7
64.59 450.93
100.09 451.44
68.04 441.29
48.94 458.85
74.47 481.46
81.02 467.19
71.17 461.54
53.85 439.08
70.67 467.22
59.36 468.8
57.17 426.93
70.29 474.65
83.37 468.97
87.36 433.97
100.09 450.53
68.78 444.51
70.98 469.03
75.68 466.56
47.49 457.57
71.99 440.13
66.55 433.24
74.73 452.55
64.78 443.29
75.13 431.76
56.38 454.97
94.35 456.7
86.55 486.03
82.95 472.79
88.42 452.03
85.61 443.41
58.39 441.93
74.28 432.64
87.85 480.25
83.5 466.68
65.24 494.39
75.01 454.72
84.52 448.71
80.52 469.76
75.14 450.71
75.75 444.01
76.72 453.2
85.47 450.87
57.95 441.73
78.32 465.09
52.2 447.28
93.69 491.16
75.74 450.98
67.56 446.3
69.46 436.48
74.58 460.84
53.23 442.56
88.72 467.3
96.16 479.13
68.26 441.15
86.39 445.52
85.34 475.4
72.64 469.3
97.82 463.57
77.22 445.32
80.59 461.03
46.91 466.74
57.76 444.04
53.09 434.01
84.31 465.23
71.58 440.6
92.97 466.74
74.55 433.48
78.96 473.59
64.44 474.81
68.23 454.75
70.81 452.94
61.66 435.83
77.76 482.19
69.49 466.66
96.26 462.59
55.74 447.82
95.61 462.73
84.75 447.98
75.3 462.72
67.5 442.42
80.92 444.69
79.23 466.7
81.1 453.84
32.8 436.92
84.31 486.37
46.15 440.43
53.96 446.82
59.83 484.91
75.3 437.76
42.53 438.91
70.58 464.19
91.69 442.19
63.55 446.86
61.51 457.15
69.55 482.57
98.08 476.03
79.34 428.89
81.28 472.7
78.99 445.6
80.38 464.78
51.16 440.42
72.17 428.41
75.39 438.5
68.91 438.28
96.38 476.29
70.54 448.46
45.8 438.99
57.95 471.8
81.89 471.81
69.32 449.82
59.14 442.14
81.54 441.46
85.81 477.62
65.41 446.76
81.15 472.52
95.87 471.58
90.24 440.85
75.13 431.37
88.22 437.33
81.48 469.22
89.84 471.11
43.57 439.17
63.16 445.33
57.14 473.71
77.76 452.66
90.56 440.99
60.98 467.42
70.31 444.14
74.05 457.17
75.42 467.87
82.25 442.04
67.95 471.36
100.09 460.7
47.28 431.33
72.41 432.6
77.67 447.61
63.7 443.87
79.77 446.87
93.84 465.74
84.95 447.86
70.16 447.65
84.24 437.87
73.32 483.51
86.17 479.65
65.43 455.16
94.59 431.91
86.8 470.68
58.18 429.28
89.66 450.81
87.39 437.73
36.35 460.21
79.62 442.86
50.52 482.99
51.96 440.0
74.73 478.48
78.33 455.28
85.19 436.94
83.13 461.06
53.49 438.28
88.86 472.61
60.89 426.85
61.14 470.18
68.29 455.38
57.62 428.32
83.63 480.35
78.1 455.56
66.34 447.66
79.02 443.06
68.96 452.43
71.13 477.81
87.01 431.66
74.3 431.8
77.62 446.67
59.56 445.26
41.66 425.72
73.27 430.58
77.16 439.86
67.02 441.11
52.8 434.72
39.04 434.01
65.47 475.64
74.32 460.44
69.22 436.4
93.88 461.03
69.83 479.08
84.11 435.76
78.65 460.14
69.31 442.2
70.3 447.69
68.23 431.15
71.76 445.0
85.88 431.59
71.09 467.22
52.67 445.33
89.68 470.57
73.66 473.77
58.94 447.67
87.05 474.29
67.0 437.14
43.18 432.56
80.62 459.14
59.72 446.19
72.1 428.1
69.15 468.46
55.66 435.02
61.19 445.52
74.62 462.69
73.35 455.75
68.85 463.74
39.89 439.79
53.16 443.26
52.97 432.04
79.87 465.86
84.09 465.6
100.15 469.43
79.77 440.75
88.99 481.32
76.14 479.87
69.13 458.59
93.03 438.62
77.92 445.59
74.89 481.87
88.7 475.01
62.94 436.54
89.62 456.63
81.04 451.69
94.53 463.04
64.02 446.1
70.57 438.67
70.32 466.88
84.86 444.6
81.41 440.26
89.45 483.92
82.71 475.19
93.93 479.24
70.6 434.92
87.68 454.16
87.58 447.58
74.4 467.9
67.35 426.29
63.61 447.02
76.89 455.85
78.08 476.46
69.17 437.48
53.31 452.77
93.32 491.54
42.47 438.41
82.58 476.1
94.59 464.58
86.31 467.74
72.57 442.12
80.76 453.34
71.93 425.29
47.54 449.63
95.72 462.88
77.03 464.67
80.49 489.96
77.67 482.38
78.72 437.95
58.77 429.2
74.8 453.34
51.34 442.47
90.41 462.6
91.1 478.79
62.57 456.11
84.27 450.33
42.93 434.83
40.96 433.43
76.53 456.02
69.74 485.23
74.99 473.57
70.45 469.94
91.49 452.07
88.97 475.32
89.13 480.69
46.52 444.01
60.55 465.17
88.71 480.61
89.15 476.04
83.02 441.76
75.19 428.24
87.35 444.77
85.66 463.1
91.66 470.5
63.47 431.0
72.25 430.68
70.58 436.42
60.1 452.33
89.29 440.16
67.43 435.75
67.58 449.74
70.8 430.73
63.62 432.75
66.68 446.79
90.76 486.35
75.34 453.18
59.77 458.31
80.79 480.26
74.1 448.65
41.34 458.41
58.78 435.39
97.78 450.21
74.85 459.59
69.84 445.84
75.36 441.08
85.8 467.33
90.11 444.19
61.63 432.96
44.76 438.09
89.7 467.9
72.51 475.72
74.98 477.51
79.59 435.13
78.42 477.9
61.23 457.26
47.56 467.53
93.06 465.15
89.65 474.28
50.5 444.49
44.84 452.84
85.32 435.38
82.94 433.57
67.26 435.27
79.05 468.49
62.03 433.07
94.36 430.63
60.02 440.74
95.46 474.49
84.92 449.74
62.8 436.73
90.81 434.58
80.9 473.93
55.84 435.99
75.3 466.83
37.34 427.22
79.5 444.07
87.29 469.57
81.5 459.89
76.42 479.59
75.75 440.92
84.95 480.87
62.37 441.9
49.25 430.2
74.16 465.16
90.55 471.32
94.28 485.43
63.31 495.35
78.9 449.12
90.97 480.53
87.34 457.07
80.8 443.67
90.95 477.52
69.97 472.95
89.45 472.54
76.08 469.17
83.35 435.21
92.16 477.78
58.42 475.89
85.06 483.9
88.91 476.2
83.33 462.16
88.49 471.05
96.88 484.71
73.86 446.34
65.17 469.02
69.41 432.12
81.23 467.28
54.07 429.66
89.17 469.49
78.85 485.87
84.44 481.95
83.02 479.03
90.66 434.5
75.29 464.9
82.6 452.71
45.4 429.74
66.33 457.09
45.33 446.77
67.12 460.76
84.14 471.95
80.81 453.29
49.13 441.61
87.29 464.73
52.95 464.68
68.92 430.59
85.21 438.01
88.56 479.08
55.09 436.39
48.64 447.07
87.85 479.91
87.42 489.05
62.75 463.17
94.86 471.26
63.54 480.49
69.46 473.78
74.66 455.5
80.57 446.27
84.7 482.2
72.6 452.48
52.63 464.48
82.01 438.1
75.22 445.6
51.05 442.43
80.1 436.67
81.58 466.56
65.94 457.29
86.74 487.03
62.57 464.93
57.37 466.0
88.91 469.52
72.26 428.88
74.98 474.3
71.19 461.06
62.82 465.57
55.31 467.67
71.57 466.99
59.16 463.72
40.8 443.78
67.63 445.23
97.2 464.43
91.16 484.36
64.81 442.16
85.61 464.11
93.42 462.48
77.36 477.49
67.03 437.04
89.45 457.09
54.84 450.6
53.48 465.78
54.47 427.1
43.36 459.81
48.92 447.36
81.22 488.92
60.35 433.36
75.98 483.35
74.24 469.53
85.21 476.96
68.46 440.75
78.31 462.55
89.25 448.04
68.57 455.24
67.38 494.75
53.6 444.58
91.69 484.82
78.21 442.9
94.19 485.46
37.19 457.81
83.53 481.92
79.45 443.23
99.9 474.29
50.39 430.46
74.33 455.71
83.66 438.34
89.48 485.83
64.59 452.82
75.68 435.04
64.18 451.21
70.09 465.81
70.58 458.42
99.28 470.22
81.89 449.24
87.99 471.43
88.21 473.26
91.29 452.82
52.72 432.69
67.5 444.13
92.05 467.21
63.23 445.98
90.88 436.91
74.91 455.01
85.39 437.11
96.98 477.06
56.28 441.71
65.62 495.76
55.32 445.63
88.88 464.72
39.49 438.03
54.59 434.78
57.19 444.67
45.06 452.24
40.62 450.92
90.21 436.53
90.91 435.53
74.96 440.01
54.21 443.1
63.62 427.49
50.04 436.25
51.23 440.74
82.71 443.54
92.04 459.42
90.67 439.66
91.14 464.15
70.43 459.1
66.63 455.68
86.95 469.08
96.69 478.02
70.88 456.8
47.14 441.13
63.36 463.88
60.58 430.45
54.3 449.18
65.09 447.89
48.16 431.59
81.51 447.5
68.57 475.58
73.16 453.24
80.88 446.4
85.4 476.81
75.77 474.1
75.76 450.71
70.21 433.62
55.53 465.14
80.48 445.18
72.41 474.12
83.25 483.91
82.12 486.68
94.75 464.98
95.79 481.4
86.02 479.2
78.29 463.86
82.23 472.3
52.86 446.51
60.66 437.71
62.77 458.94
65.54 437.91
95.4 490.76
51.78 439.66
63.62 463.27
83.09 473.99
61.81 433.38
68.24 459.01
72.41 471.44
79.64 471.91
66.95 465.15
91.98 446.66
64.96 438.15
88.93 447.14
85.62 472.32
84.0 441.68
89.41 440.04
67.4 444.82
76.75 457.26
89.06 428.83
64.02 449.07
64.12 435.21
81.22 471.03
100.13 465.56
58.07 442.83
63.42 460.3
83.32 474.25
74.28 477.97
71.91 472.16
85.8 456.08
55.58 452.41
69.7 463.71
63.79 433.72
86.59 456.4
48.28 448.43
80.42 481.6
98.58 457.07
72.83 451.0
56.07 440.28
67.13 437.47
84.49 443.57
68.37 426.6
86.07 470.87
83.82 478.37
74.86 453.92
53.52 470.22
80.61 434.54
43.56 442.89
89.35 479.03
97.28 476.06
80.49 473.88
88.9 451.75
49.7 439.2
68.64 439.7
98.58 463.6
82.12 447.47
78.32 447.92
86.04 471.08
91.36 437.55
81.02 448.27
76.05 431.69
71.81 449.09
82.13 448.79
74.27 460.21
71.32 479.28
74.59 483.11
84.44 450.75
43.39 437.97
87.2 459.76
77.11 457.75
82.81 469.33
85.67 433.28
95.58 444.64
74.46 463.1
90.02 460.91
81.93 479.35
86.29 449.23
85.43 474.51
71.66 435.02
71.33 435.45
67.72 452.38
84.23 480.41
74.44 478.96
71.48 468.87
56.3 434.01
68.13 466.36
68.35 435.28
85.23 486.46
79.78 468.19
98.98 468.37
72.87 474.19
90.93 440.32
72.94 485.32
72.3 464.27
77.74 479.25
78.29 430.4
69.1 447.49
55.79 438.23
75.09 492.09
88.98 475.36
64.26 452.56
25.89 427.84
59.18 433.95
67.48 435.27
89.85 454.62
50.62 472.17
83.97 452.42
96.51 472.17
80.09 481.83
84.02 458.78
61.97 447.5
88.51 463.4
81.83 473.57
90.63 433.72
73.37 431.85
77.5 433.47
57.46 432.84
51.91 436.6
79.14 490.23
69.24 477.16
41.85 441.06
95.1 440.86
74.53 477.94
64.85 474.47
57.07 470.67
62.9 447.31
72.21 466.8
65.99 430.91
73.67 434.75
88.59 469.52
61.19 438.9
49.37 429.56
48.65 432.92
56.18 442.87
71.56 466.59
87.46 479.61
70.02 471.08
61.2 433.37
60.54 443.92
71.82 443.5
44.8 439.89
67.32 434.66
78.77 487.57
96.02 464.64
90.56 470.92
83.19 444.39
68.45 442.48
99.19 449.61
88.11 435.02
79.29 458.67
87.76 461.74
82.56 438.31
79.24 462.38
67.24 460.56
58.98 439.22
84.22 444.64
89.12 430.34
50.07 430.46
98.86 456.79
95.79 468.82
70.06 448.51
41.71 470.77
86.55 465.74
71.97 430.21
96.4 449.23
91.73 461.89
91.62 445.72
59.14 466.13
92.56 448.71
83.71 469.25
55.43 450.56
74.91 464.46
89.41 471.13
69.81 461.52
86.01 451.09
86.05 431.51
80.98 469.8
63.62 442.28
64.16 458.67
75.63 462.4
80.21 453.54
59.7 444.38
47.6 440.52
63.78 433.62
89.37 481.96
70.55 452.75
84.42 481.28
80.62 439.03
52.56 435.75
83.26 436.03
70.64 445.6
89.95 462.65
51.53 438.66
84.83 447.32
59.68 484.55
97.88 476.8
85.03 480.34
47.38 440.63
48.08 459.48
79.65 490.78
83.39 483.56
82.18 429.38
51.71 440.27
77.33 445.34
72.81 447.43
84.26 439.91
73.23 459.27
79.73 478.89
62.32 466.7
78.58 463.5
79.88 436.21
65.87 443.94
80.28 439.63
71.33 460.95
70.33 448.69
57.73 444.63
99.46 473.51
68.61 462.56
95.91 451.76
80.42 491.81
51.01 429.52
74.08 437.9
80.73 467.54
54.71 449.97
73.75 436.62
88.43 477.68
74.66 447.26
70.04 439.76
74.64 437.49
85.11 455.14
82.97 485.5
55.59 444.1
49.9 432.33
89.99 471.23
91.61 463.89
82.95 445.54
92.62 446.09
59.64 445.12
63.0 443.31
85.38 484.16
85.23 477.76
52.08 430.28
38.05 446.48
71.98 481.03
90.66 466.07
83.14 447.47
65.22 455.93
91.67 479.62
66.04 455.06
78.19 475.06
54.47 438.89
67.26 432.7
72.56 452.6
82.15 451.75
86.32 430.66
88.17 491.9
72.78 439.82
69.58 460.73
69.86 449.7
65.37 439.42
52.37 439.84
79.63 485.86
83.14 458.1
88.25 479.92
55.85 458.29
52.55 489.45
62.74 434.0
90.08 431.24
54.5 439.5
49.88 467.46
48.96 429.27
86.77 452.1
96.66 472.41
70.84 442.14
83.83 441.0
68.22 463.07
82.22 445.71
87.9 483.16
66.83 440.45
85.36 481.83
60.9 467.6
83.51 450.88
52.96 425.5
73.02 451.87
43.11 428.94
61.41 439.86
65.32 433.44
93.68 438.23
42.39 436.95
68.18 470.19
89.18 484.66
64.79 430.81
43.48 433.37
80.4 453.02
72.43 453.5
80.0 463.09
45.65 464.56
63.02 452.12
74.39 470.9
57.77 450.89
76.8 445.04
67.39 444.72
73.96 460.38
72.75 446.8
71.69 465.05
84.98 484.13
87.68 488.27
50.36 447.09
68.11 452.02
66.27 455.55
89.72 480.99
61.07 467.68

...and atmospheric pressure and relative humidity seem to have little to no linear correlation.

These pairwise plots can also be done directly using display on selected columns of the DataFrame powerPlantDF.

In general we will shy from SQL as much as possible to focus on ML pipelines written with DataFrames and DataSets with occassional in-and-out of RDDs.

The illustations in %sql above are to mainly reassure those with a RDBMS background and SQL that their SQL expressibility can be directly used in Apache Spark and in databricks notebooks.

display(powerPlantDF.select($"RH", $"PE"))
RH PE
73.17 463.26
59.08 444.37
92.14 488.56
76.64 446.48
96.62 473.9
58.77 443.67
75.24 467.35
66.43 478.42
41.25 475.98
70.72 477.5
75.04 453.02
64.22 453.99
84.15 440.29
61.83 451.28
87.59 433.99
43.08 462.19
48.84 467.54
77.51 477.2
63.59 459.85
55.28 464.3
66.26 468.27
64.77 495.24
83.31 483.8
47.19 443.61
54.93 436.06
74.62 443.25
72.52 464.16
88.44 475.52
92.28 484.41
41.85 437.89
44.28 445.11
64.58 438.86
63.25 440.98
78.61 436.65
44.51 444.26
89.46 465.86
74.52 444.37
88.86 450.69
75.51 469.02
78.64 448.86
76.65 447.14
80.44 469.18
79.89 482.8
88.28 476.7
84.6 474.99
42.69 444.22
78.41 461.33
61.07 448.06
50.0 474.6
77.29 473.05
43.66 432.06
83.8 467.41
66.47 430.12
93.09 473.62
80.52 471.81
68.99 442.99
57.27 442.77
95.53 491.49
71.72 447.46
57.88 446.11
63.34 442.44
48.07 446.22
91.87 471.49
87.27 463.5
64.4 440.01
43.4 441.03
72.24 452.68
90.22 474.91
74.0 478.77
71.85 434.2
86.62 437.91
97.41 477.61
84.44 431.65
81.55 430.57
75.66 481.09
79.41 445.56
58.91 475.74
90.06 435.12
79.0 446.15
69.47 436.64
51.47 436.69
83.13 468.75
40.33 466.6
81.69 465.48
94.55 441.34
91.81 441.83
63.62 464.7
49.35 437.99
69.61 459.12
38.75 429.69
90.17 459.8
81.24 433.63
48.46 442.84
76.72 485.13
51.16 459.12
76.34 445.31
67.3 480.8
52.38 432.55
76.44 443.86
91.55 449.77
71.9 470.71
80.05 452.17
63.77 478.29
62.26 428.54
89.04 478.27
58.02 439.58
81.82 457.32
91.14 475.51
88.92 439.66
84.83 471.99
91.76 479.81
86.56 434.78
57.21 446.58
54.25 437.76
63.8 459.36
33.71 462.28
67.25 464.33
60.11 444.36
74.55 438.64
67.34 470.49
42.75 455.13
55.2 450.22
83.61 440.43
88.78 482.98
100.12 460.44
64.52 444.97
51.41 433.94
85.78 439.73
75.41 434.48
81.63 442.33
51.92 457.67
70.12 454.66
53.83 432.21
77.23 457.66
65.67 435.21
71.18 448.22
81.96 475.51
79.54 446.53
47.09 441.3
57.69 433.54
78.89 472.52
85.29 474.77
40.13 435.1
77.06 450.74
67.38 442.7
62.44 426.56
77.43 463.71
58.77 447.06
67.72 452.27
42.14 445.78
84.16 438.65
89.79 480.15
67.21 447.19
72.14 443.04
97.49 488.81
87.74 455.75
96.3 455.86
61.25 457.68
88.38 479.11
74.77 432.84
68.18 448.37
77.2 447.06
49.54 443.53
92.22 445.21
33.65 441.7
64.59 450.93
100.09 451.44
68.04 441.29
48.94 458.85
74.47 481.46
81.02 467.19
71.17 461.54
53.85 439.08
70.67 467.22
59.36 468.8
57.17 426.93
70.29 474.65
83.37 468.97
87.36 433.97
100.09 450.53
68.78 444.51
70.98 469.03
75.68 466.56
47.49 457.57
71.99 440.13
66.55 433.24
74.73 452.55
64.78 443.29
75.13 431.76
56.38 454.97
94.35 456.7
86.55 486.03
82.95 472.79
88.42 452.03
85.61 443.41
58.39 441.93
74.28 432.64
87.85 480.25
83.5 466.68
65.24 494.39
75.01 454.72
84.52 448.71
80.52 469.76
75.14 450.71
75.75 444.01
76.72 453.2
85.47 450.87
57.95 441.73
78.32 465.09
52.2 447.28
93.69 491.16
75.74 450.98
67.56 446.3
69.46 436.48
74.58 460.84
53.23 442.56
88.72 467.3
96.16 479.13
68.26 441.15
86.39 445.52
85.34 475.4
72.64 469.3
97.82 463.57
77.22 445.32
80.59 461.03
46.91 466.74
57.76 444.04
53.09 434.01
84.31 465.23
71.58 440.6
92.97 466.74
74.55 433.48
78.96 473.59
64.44 474.81
68.23 454.75
70.81 452.94
61.66 435.83
77.76 482.19
69.49 466.66
96.26 462.59
55.74 447.82
95.61 462.73
84.75 447.98
75.3 462.72
67.5 442.42
80.92 444.69
79.23 466.7
81.1 453.84
32.8 436.92
84.31 486.37
46.15 440.43
53.96 446.82
59.83 484.91
75.3 437.76
42.53 438.91
70.58 464.19
91.69 442.19
63.55 446.86
61.51 457.15
69.55 482.57
98.08 476.03
79.34 428.89
81.28 472.7
78.99 445.6
80.38 464.78
51.16 440.42
72.17 428.41
75.39 438.5
68.91 438.28
96.38 476.29
70.54 448.46
45.8 438.99
57.95 471.8
81.89 471.81
69.32 449.82
59.14 442.14
81.54 441.46
85.81 477.62
65.41 446.76
81.15 472.52
95.87 471.58
90.24 440.85
75.13 431.37
88.22 437.33
81.48 469.22
89.84 471.11
43.57 439.17
63.16 445.33
57.14 473.71
77.76 452.66
90.56 440.99
60.98 467.42
70.31 444.14
74.05 457.17
75.42 467.87
82.25 442.04
67.95 471.36
100.09 460.7
47.28 431.33
72.41 432.6
77.67 447.61
63.7 443.87
79.77 446.87
93.84 465.74
84.95 447.86
70.16 447.65
84.24 437.87
73.32 483.51
86.17 479.65
65.43 455.16
94.59 431.91
86.8 470.68
58.18 429.28
89.66 450.81
87.39 437.73
36.35 460.21
79.62 442.86
50.52 482.99
51.96 440.0
74.73 478.48
78.33 455.28
85.19 436.94
83.13 461.06
53.49 438.28
88.86 472.61
60.89 426.85
61.14 470.18
68.29 455.38
57.62 428.32
83.63 480.35
78.1 455.56
66.34 447.66
79.02 443.06
68.96 452.43
71.13 477.81
87.01 431.66
74.3 431.8
77.62 446.67
59.56 445.26
41.66 425.72
73.27 430.58
77.16 439.86
67.02 441.11
52.8 434.72
39.04 434.01
65.47 475.64
74.32 460.44
69.22 436.4
93.88 461.03
69.83 479.08
84.11 435.76
78.65 460.14
69.31 442.2
70.3 447.69
68.23 431.15
71.76 445.0
85.88 431.59
71.09 467.22
52.67 445.33
89.68 470.57
73.66 473.77
58.94 447.67
87.05 474.29
67.0 437.14
43.18 432.56
80.62 459.14
59.72 446.19
72.1 428.1
69.15 468.46
55.66 435.02
61.19 445.52
74.62 462.69
73.35 455.75
68.85 463.74
39.89 439.79
53.16 443.26
52.97 432.04
79.87 465.86
84.09 465.6
100.15 469.43
79.77 440.75
88.99 481.32
76.14 479.87
69.13 458.59
93.03 438.62
77.92 445.59
74.89 481.87
88.7 475.01
62.94 436.54
89.62 456.63
81.04 451.69
94.53 463.04
64.02 446.1
70.57 438.67
70.32 466.88
84.86 444.6
81.41 440.26
89.45 483.92
82.71 475.19
93.93 479.24
70.6 434.92
87.68 454.16
87.58 447.58
74.4 467.9
67.35 426.29
63.61 447.02
76.89 455.85
78.08 476.46
69.17 437.48
53.31 452.77
93.32 491.54
42.47 438.41
82.58 476.1
94.59 464.58
86.31 467.74
72.57 442.12
80.76 453.34
71.93 425.29
47.54 449.63
95.72 462.88
77.03 464.67
80.49 489.96
77.67 482.38
78.72 437.95
58.77 429.2
74.8 453.34
51.34 442.47
90.41 462.6
91.1 478.79
62.57 456.11
84.27 450.33
42.93 434.83
40.96 433.43
76.53 456.02
69.74 485.23
74.99 473.57
70.45 469.94
91.49 452.07
88.97 475.32
89.13 480.69
46.52 444.01
60.55 465.17
88.71 480.61
89.15 476.04
83.02 441.76
75.19 428.24
87.35 444.77
85.66 463.1
91.66 470.5
63.47 431.0
72.25 430.68
70.58 436.42
60.1 452.33
89.29 440.16
67.43 435.75
67.58 449.74
70.8 430.73
63.62 432.75
66.68 446.79
90.76 486.35
75.34 453.18
59.77 458.31
80.79 480.26
74.1 448.65
41.34 458.41
58.78 435.39
97.78 450.21
74.85 459.59
69.84 445.84
75.36 441.08
85.8 467.33
90.11 444.19
61.63 432.96
44.76 438.09
89.7 467.9
72.51 475.72
74.98 477.51
79.59 435.13
78.42 477.9
61.23 457.26
47.56 467.53
93.06 465.15
89.65 474.28
50.5 444.49
44.84 452.84
85.32 435.38
82.94 433.57
67.26 435.27
79.05 468.49
62.03 433.07
94.36 430.63
60.02 440.74
95.46 474.49
84.92 449.74
62.8 436.73
90.81 434.58
80.9 473.93
55.84 435.99
75.3 466.83
37.34 427.22
79.5 444.07
87.29 469.57
81.5 459.89
76.42 479.59
75.75 440.92
84.95 480.87
62.37 441.9
49.25 430.2
74.16 465.16
90.55 471.32
94.28 485.43
63.31 495.35
78.9 449.12
90.97 480.53
87.34 457.07
80.8 443.67
90.95 477.52
69.97 472.95
89.45 472.54
76.08 469.17
83.35 435.21
92.16 477.78
58.42 475.89
85.06 483.9
88.91 476.2
83.33 462.16
88.49 471.05
96.88 484.71
73.86 446.34
65.17 469.02
69.41 432.12
81.23 467.28
54.07 429.66
89.17 469.49
78.85 485.87
84.44 481.95
83.02 479.03
90.66 434.5
75.29 464.9
82.6 452.71
45.4 429.74
66.33 457.09
45.33 446.77
67.12 460.76
84.14 471.95
80.81 453.29
49.13 441.61
87.29 464.73
52.95 464.68
68.92 430.59
85.21 438.01
88.56 479.08
55.09 436.39
48.64 447.07
87.85 479.91
87.42 489.05
62.75 463.17
94.86 471.26
63.54 480.49
69.46 473.78
74.66 455.5
80.57 446.27
84.7 482.2
72.6 452.48
52.63 464.48
82.01 438.1
75.22 445.6
51.05 442.43
80.1 436.67
81.58 466.56
65.94 457.29
86.74 487.03
62.57 464.93
57.37 466.0
88.91 469.52
72.26 428.88
74.98 474.3
71.19 461.06
62.82 465.57
55.31 467.67
71.57 466.99
59.16 463.72
40.8 443.78
67.63 445.23
97.2 464.43
91.16 484.36
64.81 442.16
85.61 464.11
93.42 462.48
77.36 477.49
67.03 437.04
89.45 457.09
54.84 450.6
53.48 465.78
54.47 427.1
43.36 459.81
48.92 447.36
81.22 488.92
60.35 433.36
75.98 483.35
74.24 469.53
85.21 476.96
68.46 440.75
78.31 462.55
89.25 448.04
68.57 455.24
67.38 494.75
53.6 444.58
91.69 484.82
78.21 442.9
94.19 485.46
37.19 457.81
83.53 481.92
79.45 443.23
99.9 474.29
50.39 430.46
74.33 455.71
83.66 438.34
89.48 485.83
64.59 452.82
75.68 435.04
64.18 451.21
70.09 465.81
70.58 458.42
99.28 470.22
81.89 449.24
87.99 471.43
88.21 473.26
91.29 452.82
52.72 432.69
67.5 444.13
92.05 467.21
63.23 445.98
90.88 436.91
74.91 455.01
85.39 437.11
96.98 477.06
56.28 441.71
65.62 495.76
55.32 445.63
88.88 464.72
39.49 438.03
54.59 434.78
57.19 444.67
45.06 452.24
40.62 450.92
90.21 436.53
90.91 435.53
74.96 440.01
54.21 443.1
63.62 427.49
50.04 436.25
51.23 440.74
82.71 443.54
92.04 459.42
90.67 439.66
91.14 464.15
70.43 459.1
66.63 455.68
86.95 469.08
96.69 478.02
70.88 456.8
47.14 441.13
63.36 463.88
60.58 430.45
54.3 449.18
65.09 447.89
48.16 431.59
81.51 447.5
68.57 475.58
73.16 453.24
80.88 446.4
85.4 476.81
75.77 474.1
75.76 450.71
70.21 433.62
55.53 465.14
80.48 445.18
72.41 474.12
83.25 483.91
82.12 486.68
94.75 464.98
95.79 481.4
86.02 479.2
78.29 463.86
82.23 472.3
52.86 446.51
60.66 437.71
62.77 458.94
65.54 437.91
95.4 490.76
51.78 439.66
63.62 463.27
83.09 473.99
61.81 433.38
68.24 459.01
72.41 471.44
79.64 471.91
66.95 465.15
91.98 446.66
64.96 438.15
88.93 447.14
85.62 472.32
84.0 441.68
89.41 440.04
67.4 444.82
76.75 457.26
89.06 428.83
64.02 449.07
64.12 435.21
81.22 471.03
100.13 465.56
58.07 442.83
63.42 460.3
83.32 474.25
74.28 477.97
71.91 472.16
85.8 456.08
55.58 452.41
69.7 463.71
63.79 433.72
86.59 456.4
48.28 448.43
80.42 481.6
98.58 457.07
72.83 451.0
56.07 440.28
67.13 437.47
84.49 443.57
68.37 426.6
86.07 470.87
83.82 478.37
74.86 453.92
53.52 470.22
80.61 434.54
43.56 442.89
89.35 479.03
97.28 476.06
80.49 473.88
88.9 451.75
49.7 439.2
68.64 439.7
98.58 463.6
82.12 447.47
78.32 447.92
86.04 471.08
91.36 437.55
81.02 448.27
76.05 431.69
71.81 449.09
82.13 448.79
74.27 460.21
71.32 479.28
74.59 483.11
84.44 450.75
43.39 437.97
87.2 459.76
77.11 457.75
82.81 469.33
85.67 433.28
95.58 444.64
74.46 463.1
90.02 460.91
81.93 479.35
86.29 449.23
85.43 474.51
71.66 435.02
71.33 435.45
67.72 452.38
84.23 480.41
74.44 478.96
71.48 468.87
56.3 434.01
68.13 466.36
68.35 435.28
85.23 486.46
79.78 468.19
98.98 468.37
72.87 474.19
90.93 440.32
72.94 485.32
72.3 464.27
77.74 479.25
78.29 430.4
69.1 447.49
55.79 438.23
75.09 492.09
88.98 475.36
64.26 452.56
25.89 427.84
59.18 433.95
67.48 435.27
89.85 454.62
50.62 472.17
83.97 452.42
96.51 472.17
80.09 481.83
84.02 458.78
61.97 447.5
88.51 463.4
81.83 473.57
90.63 433.72
73.37 431.85
77.5 433.47
57.46 432.84
51.91 436.6
79.14 490.23
69.24 477.16
41.85 441.06
95.1 440.86
74.53 477.94
64.85 474.47
57.07 470.67
62.9 447.31
72.21 466.8
65.99 430.91
73.67 434.75
88.59 469.52
61.19 438.9
49.37 429.56
48.65 432.92
56.18 442.87
71.56 466.59
87.46 479.61
70.02 471.08
61.2 433.37
60.54 443.92
71.82 443.5
44.8 439.89
67.32 434.66
78.77 487.57
96.02 464.64
90.56 470.92
83.19 444.39
68.45 442.48
99.19 449.61
88.11 435.02
79.29 458.67
87.76 461.74
82.56 438.31
79.24 462.38
67.24 460.56
58.98 439.22
84.22 444.64
89.12 430.34
50.07 430.46
98.86 456.79
95.79 468.82
70.06 448.51
41.71 470.77
86.55 465.74
71.97 430.21
96.4 449.23
91.73 461.89
91.62 445.72
59.14 466.13
92.56 448.71
83.71 469.25
55.43 450.56
74.91 464.46
89.41 471.13
69.81 461.52
86.01 451.09
86.05 431.51
80.98 469.8
63.62 442.28
64.16 458.67
75.63 462.4
80.21 453.54
59.7 444.38
47.6 440.52
63.78 433.62
89.37 481.96
70.55 452.75
84.42 481.28
80.62 439.03
52.56 435.75
83.26 436.03
70.64 445.6
89.95 462.65
51.53 438.66
84.83 447.32
59.68 484.55
97.88 476.8
85.03 480.34
47.38 440.63
48.08 459.48
79.65 490.78
83.39 483.56
82.18 429.38
51.71 440.27
77.33 445.34
72.81 447.43
84.26 439.91
73.23 459.27
79.73 478.89
62.32 466.7
78.58 463.5
79.88 436.21
65.87 443.94
80.28 439.63
71.33 460.95
70.33 448.69
57.73 444.63
99.46 473.51
68.61 462.56
95.91 451.76
80.42 491.81
51.01 429.52
74.08 437.9
80.73 467.54
54.71 449.97
73.75 436.62
88.43 477.68
74.66 447.26
70.04 439.76
74.64 437.49
85.11 455.14
82.97 485.5
55.59 444.1
49.9 432.33
89.99 471.23
91.61 463.89
82.95 445.54
92.62 446.09
59.64 445.12
63.0 443.31
85.38 484.16
85.23 477.76
52.08 430.28
38.05 446.48
71.98 481.03
90.66 466.07
83.14 447.47
65.22 455.93
91.67 479.62
66.04 455.06
78.19 475.06
54.47 438.89
67.26 432.7
72.56 452.6
82.15 451.75
86.32 430.66
88.17 491.9
72.78 439.82
69.58 460.73
69.86 449.7
65.37 439.42
52.37 439.84
79.63 485.86
83.14 458.1
88.25 479.92
55.85 458.29
52.55 489.45
62.74 434.0
90.08 431.24
54.5 439.5
49.88 467.46
48.96 429.27
86.77 452.1
96.66 472.41
70.84 442.14
83.83 441.0
68.22 463.07
82.22 445.71
87.9 483.16
66.83 440.45
85.36 481.83
60.9 467.6
83.51 450.88
52.96 425.5
73.02 451.87
43.11 428.94
61.41 439.86
65.32 433.44
93.68 438.23
42.39 436.95
68.18 470.19
89.18 484.66
64.79 430.81
43.48 433.37
80.4 453.02
72.43 453.5
80.0 463.09
45.65 464.56
63.02 452.12
74.39 470.9
57.77 450.89
76.8 445.04
67.39 444.72
73.96 460.38
72.75 446.8
71.69 465.05
84.98 484.13
87.68 488.27
50.36 447.09
68.11 452.02
66.27 455.55
89.72 480.99
61.07 467.68

Furthermore, you can interactively start playing with display on the full DataFrame!

display(powerPlantDF) // just as we did for the diamonds dataset
AT V AP RH PE
14.96 41.76 1024.07 73.17 463.26
25.18 62.96 1020.04 59.08 444.37
5.11 39.4 1012.16 92.14 488.56
20.86 57.32 1010.24 76.64 446.48
10.82 37.5 1009.23 96.62 473.9
26.27 59.44 1012.23 58.77 443.67
15.89 43.96 1014.02 75.24 467.35
9.48 44.71 1019.12 66.43 478.42
14.64 45.0 1021.78 41.25 475.98
11.74 43.56 1015.14 70.72 477.5
17.99 43.72 1008.64 75.04 453.02
20.14 46.93 1014.66 64.22 453.99
24.34 73.5 1011.31 84.15 440.29
25.71 58.59 1012.77 61.83 451.28
26.19 69.34 1009.48 87.59 433.99
21.42 43.79 1015.76 43.08 462.19
18.21 45.0 1022.86 48.84 467.54
11.04 41.74 1022.6 77.51 477.2
14.45 52.75 1023.97 63.59 459.85
13.97 38.47 1015.15 55.28 464.3
17.76 42.42 1009.09 66.26 468.27
5.41 40.07 1019.16 64.77 495.24
7.76 42.28 1008.52 83.31 483.8
27.23 63.9 1014.3 47.19 443.61
27.36 48.6 1003.18 54.93 436.06
27.47 70.72 1009.97 74.62 443.25
14.6 39.31 1011.11 72.52 464.16
7.91 39.96 1023.57 88.44 475.52
5.81 35.79 1012.14 92.28 484.41
30.53 65.18 1012.69 41.85 437.89
23.87 63.94 1019.02 44.28 445.11
26.09 58.41 1013.64 64.58 438.86
29.27 66.85 1011.11 63.25 440.98
27.38 74.16 1010.08 78.61 436.65
24.81 63.94 1018.76 44.51 444.26
12.75 44.03 1007.29 89.46 465.86
24.66 63.73 1011.4 74.52 444.37
16.38 47.45 1010.08 88.86 450.69
13.91 39.35 1014.69 75.51 469.02
23.18 51.3 1012.04 78.64 448.86
22.47 47.45 1007.62 76.65 447.14
13.39 44.85 1017.24 80.44 469.18
9.28 41.54 1018.33 79.89 482.8
11.82 42.86 1014.12 88.28 476.7
10.27 40.64 1020.63 84.6 474.99
22.92 63.94 1019.28 42.69 444.22
16.0 37.87 1020.24 78.41 461.33
21.22 43.43 1010.96 61.07 448.06
13.46 44.71 1014.51 50.0 474.6
9.39 40.11 1029.14 77.29 473.05
31.07 73.5 1010.58 43.66 432.06
12.82 38.62 1018.71 83.8 467.41
32.57 78.92 1011.6 66.47 430.12
8.11 42.18 1014.82 93.09 473.62
13.92 39.39 1012.94 80.52 471.81
23.04 59.43 1010.23 68.99 442.99
27.31 64.44 1014.65 57.27 442.77
5.91 39.33 1010.18 95.53 491.49
25.26 61.08 1013.68 71.72 447.46
27.97 58.84 1002.25 57.88 446.11
26.08 52.3 1007.03 63.34 442.44
29.01 65.71 1013.61 48.07 446.22
12.18 40.1 1016.67 91.87 471.49
13.76 45.87 1008.89 87.27 463.5
25.5 58.79 1016.02 64.4 440.01
28.26 65.34 1014.56 43.4 441.03
21.39 62.96 1019.49 72.24 452.68
7.26 40.69 1020.43 90.22 474.91
10.54 34.03 1018.71 74.0 478.77
27.71 74.34 998.14 71.85 434.2
23.11 68.3 1017.83 86.62 437.91
7.51 41.01 1024.61 97.41 477.61
26.46 74.67 1016.65 84.44 431.65
29.34 74.34 998.58 81.55 430.57
10.32 42.28 1008.82 75.66 481.09
22.74 61.02 1009.56 79.41 445.56
13.48 39.85 1012.71 58.91 475.74
25.52 69.75 1010.36 90.06 435.12
21.58 67.25 1017.39 79.0 446.15
27.66 76.86 1001.31 69.47 436.64
26.96 69.45 1013.89 51.47 436.69
12.29 42.18 1016.53 83.13 468.75
15.86 43.02 1012.18 40.33 466.6
13.87 45.08 1024.42 81.69 465.48
24.09 73.68 1014.93 94.55 441.34
20.45 69.45 1012.53 91.81 441.83
15.07 39.3 1019.0 63.62 464.7
32.72 69.75 1009.6 49.35 437.99
18.23 58.96 1015.55 69.61 459.12
35.56 68.94 1006.56 38.75 429.69
18.36 51.43 1010.57 90.17 459.8
26.35 64.05 1009.81 81.24 433.63
25.92 60.95 1014.62 48.46 442.84
8.01 41.66 1014.49 76.72 485.13
19.63 52.72 1025.09 51.16 459.12
20.02 67.32 1012.05 76.34 445.31
10.08 40.72 1022.7 67.3 480.8
27.23 66.48 1005.23 52.38 432.55
23.37 63.77 1013.42 76.44 443.86
18.74 59.21 1018.3 91.55 449.77
14.81 43.69 1017.19 71.9 470.71
23.1 51.3 1011.93 80.05 452.17
10.72 41.38 1021.6 63.77 478.29
29.46 71.94 1006.96 62.26 428.54
8.1 40.64 1020.66 89.04 478.27
27.29 62.66 1007.63 58.02 439.58
17.1 49.69 1005.53 81.82 457.32
11.49 44.2 1018.79 91.14 475.51
23.69 65.59 1010.85 88.92 439.66
13.51 40.89 1011.03 84.83 471.99
9.64 39.35 1015.1 91.76 479.81
25.65 78.92 1010.83 86.56 434.78
21.59 61.87 1011.18 57.21 446.58
27.98 58.33 1013.92 54.25 437.76
18.8 39.72 1001.24 63.8 459.36
18.28 44.71 1016.99 33.71 462.28
13.55 43.48 1016.08 67.25 464.33
22.99 46.21 1010.71 60.11 444.36
23.94 59.39 1014.32 74.55 438.64
13.74 34.03 1018.69 67.34 470.49
21.3 41.1 1001.86 42.75 455.13
27.54 66.93 1017.06 55.2 450.22
24.81 63.73 1009.34 83.61 440.43
4.97 42.85 1014.02 88.78 482.98
15.22 50.88 1014.19 100.12 460.44
23.88 54.2 1012.81 64.52 444.97
33.01 68.67 1005.2 51.41 433.94
25.98 73.18 1012.28 85.78 439.73
28.18 73.88 1005.89 75.41 434.48
21.67 60.84 1017.93 81.63 442.33
17.67 45.09 1014.26 51.92 457.67
21.37 57.76 1018.8 70.12 454.66
28.69 67.25 1017.71 53.83 432.21
16.61 43.77 1012.25 77.23 457.66
27.91 63.76 1010.27 65.67 435.21
20.97 47.43 1007.64 71.18 448.22
10.8 41.66 1013.79 81.96 475.51
20.61 62.91 1013.24 79.54 446.53
25.45 57.32 1011.7 47.09 441.3
30.16 69.34 1007.67 57.69 433.54
4.99 39.04 1020.45 78.89 472.52
10.51 44.78 1012.59 85.29 474.77
33.79 69.05 1001.62 40.13 435.1
21.34 59.8 1016.92 77.06 450.74
23.4 65.06 1014.32 67.38 442.7
32.21 68.14 1003.34 62.44 426.56
14.26 42.32 1016.0 77.43 463.71
27.71 66.93 1016.85 58.77 447.06
21.95 57.76 1018.02 67.72 452.27
25.76 63.94 1018.49 42.14 445.78
23.68 68.3 1017.93 84.16 438.65
8.28 40.77 1011.55 89.79 480.15
23.44 62.52 1016.46 67.21 447.19
25.32 48.41 1008.47 72.14 443.04
3.94 39.9 1008.06 97.49 488.81
17.3 57.76 1016.26 87.74 455.75
18.2 49.39 1018.83 96.3 455.86
21.43 46.97 1013.94 61.25 457.68
11.16 40.05 1014.95 88.38 479.11
30.38 74.16 1007.44 74.77 432.84
23.36 62.52 1016.18 68.18 448.37
21.69 47.45 1007.56 77.2 447.06
23.62 49.21 1014.1 49.54 443.53
21.87 61.45 1011.13 92.22 445.21
29.25 66.51 1015.53 33.65 441.7
20.03 66.86 1013.05 64.59 450.93
18.14 49.78 1002.95 100.09 451.44
24.23 56.89 1012.32 68.04 441.29
18.11 44.85 1014.48 48.94 458.85
6.57 43.65 1018.24 74.47 481.46
12.56 43.41 1016.93 81.02 467.19
13.4 41.58 1020.5 71.17 461.54
27.1 52.84 1006.28 53.85 439.08
14.28 42.74 1028.79 70.67 467.22
16.29 44.34 1019.49 59.36 468.8
31.24 71.98 1004.66 57.17 426.93
10.57 37.73 1024.36 70.29 474.65
13.8 44.21 1022.93 83.37 468.97
25.3 71.58 1010.18 87.36 433.97
18.06 50.16 1009.52 100.09 450.53
25.42 59.04 1011.98 68.78 444.51
15.07 40.69 1015.29 70.98 469.03
11.75 71.14 1019.36 75.68 466.56
20.23 52.05 1012.15 47.49 457.57
27.31 59.54 1006.24 71.99 440.13
28.57 69.84 1003.57 66.55 433.24
17.9 43.72 1008.64 74.73 452.55
23.83 71.37 1002.04 64.78 443.29
27.92 74.99 1005.47 75.13 431.76
17.34 44.78 1007.81 56.38 454.97
17.94 63.07 1012.42 94.35 456.7
6.4 39.9 1007.75 86.55 486.03
11.78 39.96 1011.37 82.95 472.79
20.28 57.25 1010.12 88.42 452.03
21.04 54.2 1012.26 85.61 443.41
25.11 67.32 1014.49 58.39 441.93
30.28 70.98 1007.51 74.28 432.64
8.14 36.24 1013.15 87.85 480.25
16.86 39.63 1004.47 83.5 466.68
6.25 40.07 1020.19 65.24 494.39
22.35 54.42 1012.46 75.01 454.72
17.98 56.85 1012.28 84.52 448.71
21.19 42.48 1013.43 80.52 469.76
20.94 44.89 1009.64 75.14 450.71
24.23 58.79 1009.8 75.75 444.01
19.18 58.2 1017.46 76.72 453.2
20.88 57.85 1012.39 85.47 450.87
23.67 63.86 1019.67 57.95 441.73
14.12 39.52 1018.41 78.32 465.09
25.23 64.63 1020.59 52.2 447.28
6.54 39.33 1011.54 93.69 491.16
20.08 62.52 1017.99 75.74 450.98
24.67 63.56 1013.75 67.56 446.3
27.82 79.74 1008.37 69.46 436.48
15.55 42.03 1017.41 74.58 460.84
24.26 69.51 1013.43 53.23 442.56
13.45 41.49 1020.19 88.72 467.3
11.06 40.64 1021.47 96.16 479.13
24.91 52.3 1008.72 68.26 441.15
22.39 59.04 1011.78 86.39 445.52
11.95 40.69 1015.62 85.34 475.4
14.85 40.69 1014.91 72.64 469.3
10.11 41.62 1017.17 97.82 463.57
23.67 68.67 1006.71 77.22 445.32
16.14 44.21 1020.36 80.59 461.03
15.11 43.13 1014.99 46.91 466.74
24.14 59.87 1018.47 57.76 444.04
30.08 67.25 1017.6 53.09 434.01
14.77 44.9 1020.5 84.31 465.23
27.6 69.34 1009.63 71.58 440.6
13.89 44.84 1023.66 92.97 466.74
26.85 75.6 1017.43 74.55 433.48
12.41 40.96 1023.36 78.96 473.59
13.08 41.74 1020.75 64.44 474.81
18.93 44.06 1017.58 68.23 454.75
20.5 49.69 1009.6 70.81 452.94
30.72 69.13 1009.94 61.66 435.83
7.55 39.22 1014.53 77.76 482.19
13.49 44.47 1030.46 69.49 466.66
15.62 40.12 1013.03 96.26 462.59
24.8 64.63 1020.69 55.74 447.82
10.03 41.62 1014.55 95.61 462.73
22.43 63.21 1012.06 84.75 447.98
14.95 39.31 1009.15 75.3 462.72
24.78 58.46 1016.82 67.5 442.42
23.2 48.41 1008.64 80.92 444.69
14.01 39.0 1016.73 79.23 466.7
19.4 64.63 1020.38 81.1 453.84
30.15 67.32 1013.83 32.8 436.92
6.91 36.08 1021.82 84.31 486.37
29.04 60.07 1015.42 46.15 440.43
26.02 63.07 1010.94 53.96 446.82
5.89 39.48 1005.11 59.83 484.91
26.52 71.64 1008.27 75.3 437.76
28.53 68.08 1013.27 42.53 438.91
16.59 39.54 1007.97 70.58 464.19
22.95 67.79 1009.89 91.69 442.19
23.96 47.43 1008.38 63.55 446.86
17.48 44.2 1018.89 61.51 457.15
6.69 43.65 1020.14 69.55 482.57
10.25 41.26 1007.44 98.08 476.03
28.87 72.58 1008.69 79.34 428.89
12.04 40.23 1018.07 81.28 472.7
22.58 52.3 1009.04 78.99 445.6
15.12 52.05 1014.63 80.38 464.78
25.48 58.95 1017.02 51.16 440.42
27.87 70.79 1003.96 72.17 428.41
23.72 70.47 1010.65 75.39 438.5
25.0 59.43 1007.84 68.91 438.28
8.42 40.64 1022.35 96.38 476.29
22.46 58.49 1011.5 70.54 448.46
29.92 57.19 1008.62 45.8 438.99
11.68 39.22 1017.9 57.95 471.8
14.04 42.44 1012.74 81.89 471.81
19.86 59.14 1016.12 69.32 449.82
25.99 68.08 1013.13 59.14 442.14
23.42 58.79 1009.74 81.54 441.46
10.6 40.22 1011.37 85.81 477.62
20.97 61.87 1011.45 65.41 446.76
14.14 39.82 1012.46 81.15 472.52
8.56 40.71 1021.27 95.87 471.58
24.86 72.39 1001.15 90.24 440.85
29.0 77.54 1011.33 75.13 431.37
27.59 71.97 1008.64 88.22 437.33
10.45 40.71 1015.68 81.48 469.22
8.51 40.78 1023.51 89.84 471.11
29.82 66.51 1010.98 43.57 439.17
22.56 62.26 1012.11 63.16 445.33
11.38 39.22 1018.62 57.14 473.71
20.25 57.76 1016.28 77.76 452.66
22.42 59.43 1007.12 90.56 440.99
14.85 38.91 1014.48 60.98 467.42
25.62 58.82 1010.02 70.31 444.14
19.85 56.53 1020.57 74.05 457.17
13.67 54.3 1015.92 75.42 467.87
24.39 70.72 1009.78 82.25 442.04
16.07 44.58 1019.52 67.95 471.36
11.6 39.1 1009.81 100.09 460.7
31.38 70.83 1010.35 47.28 431.33
29.91 76.86 998.59 72.41 432.6
19.67 59.39 1014.07 77.67 447.61
27.18 64.79 1016.27 63.7 443.87
21.39 52.3 1009.2 79.77 446.87
10.45 41.01 1020.57 93.84 465.74
19.46 56.89 1014.02 84.95 447.86
23.55 62.96 1020.16 70.16 447.65
23.35 63.47 1011.78 84.24 437.87
9.26 41.66 1016.87 73.32 483.51
10.3 41.46 1018.21 86.17 479.65
20.94 58.16 1016.88 65.43 455.16
23.13 71.25 1002.49 94.59 431.91
12.77 41.5 1014.13 86.8 470.68
28.29 69.13 1009.29 58.18 429.28
19.13 59.21 1018.32 89.66 450.81
24.44 73.5 1011.49 87.39 437.73
20.32 44.6 1015.16 36.35 460.21
20.54 69.05 1001.6 79.62 442.86
12.16 45.0 1021.51 50.52 482.99
28.09 65.27 1013.27 51.96 440.0
9.25 41.82 1033.25 74.73 478.48
21.75 49.82 1015.01 78.33 455.28
23.7 66.56 1002.07 85.19 436.94
16.22 37.87 1022.36 83.13 461.06
24.75 69.45 1013.97 53.49 438.28
10.48 39.58 1011.81 88.86 472.61
29.53 70.79 1003.7 60.89 426.85
12.59 39.72 1017.76 61.14 470.18
23.5 54.42 1012.31 68.29 455.38
29.01 66.56 1006.44 57.62 428.32
9.75 42.49 1010.57 83.63 480.35
19.55 56.53 1020.2 78.1 455.56
21.05 58.33 1013.14 66.34 447.66
24.72 68.67 1006.74 79.02 443.06
21.19 58.86 1014.19 68.96 452.43
10.77 41.54 1019.94 71.13 477.81
28.68 73.77 1004.72 87.01 431.66
29.87 73.91 1004.53 74.3 431.8
22.99 68.67 1006.65 77.62 446.67
24.66 60.29 1018.0 59.56 445.26
32.63 69.89 1013.85 41.66 425.72
31.38 72.29 1008.73 73.27 430.58
23.87 60.27 1018.94 77.16 439.86
25.6 59.15 1013.31 67.02 441.11
27.62 71.14 1011.6 52.8 434.72
30.1 67.45 1014.23 39.04 434.01
12.19 41.17 1019.43 65.47 475.64
13.11 41.58 1020.43 74.32 460.44
28.29 68.67 1005.46 69.22 436.4
13.45 40.73 1018.7 93.88 461.03
10.98 41.54 1019.94 69.83 479.08
26.48 69.14 1009.31 84.11 435.76
13.07 45.51 1015.22 78.65 460.14
25.56 75.6 1017.37 69.31 442.2
22.68 50.78 1008.83 70.3 447.69
28.86 73.67 1006.65 68.23 431.15
22.7 63.56 1014.32 71.76 445.0
27.89 73.21 1001.32 85.88 431.59
13.78 44.47 1027.94 71.09 467.22
28.14 51.43 1012.16 52.67 445.33
11.8 45.09 1013.21 89.68 470.57
10.71 39.61 1018.72 73.66 473.77
24.54 60.29 1017.42 58.94 447.67
11.54 40.05 1014.78 87.05 474.29
29.47 71.32 1008.07 67.0 437.14
29.24 69.05 1003.12 43.18 432.56
14.51 41.79 1009.72 80.62 459.14
22.91 60.07 1016.03 59.72 446.19
27.02 71.77 1006.38 72.1 428.1
13.49 44.47 1030.18 69.15 468.46
30.24 66.75 1017.95 55.66 435.02
23.19 48.6 1002.38 61.19 445.52
17.73 40.55 1003.36 74.62 462.69
18.62 61.27 1019.26 73.35 455.75
12.85 40.0 1015.89 68.85 463.74
32.33 69.68 1011.95 39.89 439.79
25.09 58.95 1016.99 53.16 443.26
29.45 69.13 1009.3 52.97 432.04
16.91 43.96 1013.32 79.87 465.86
14.09 45.87 1009.05 84.09 465.6
10.73 25.36 1009.35 100.15 469.43
23.2 49.3 1003.4 79.77 440.75
8.21 38.91 1015.82 88.99 481.32
9.3 40.56 1022.64 76.14 479.87
16.97 39.16 1005.7 69.13 458.59
23.69 71.97 1009.62 93.03 438.62
25.13 59.44 1012.38 77.92 445.59
9.86 43.56 1015.13 74.89 481.87
11.33 41.5 1013.58 88.7 475.01
26.95 48.41 1008.53 62.94 436.54
15.0 40.66 1016.28 89.62 456.63
20.76 62.52 1015.63 81.04 451.69
14.29 39.59 1010.93 94.53 463.04
19.74 67.71 1007.68 64.02 446.1
26.68 59.92 1009.94 70.57 438.67
14.24 41.4 1019.7 70.32 466.88
21.98 48.41 1008.42 84.86 444.6
22.75 59.39 1015.4 81.41 440.26
8.34 40.96 1023.28 89.45 483.92
11.8 41.2 1017.18 82.71 475.19
8.81 44.68 1023.06 93.93 479.24
30.05 73.68 1014.95 70.6 434.92
16.01 65.46 1014.0 87.68 454.16
21.75 58.79 1012.42 87.58 447.58
13.94 41.26 1021.67 74.4 467.9
29.25 69.13 1010.27 67.35 426.29
22.33 45.87 1007.8 63.61 447.02
16.43 41.79 1005.47 76.89 455.85
11.5 40.22 1010.31 78.08 476.46
23.53 68.94 1007.53 69.17 437.48
21.86 49.21 1014.61 53.31 452.77
6.17 39.33 1012.57 93.32 491.54
30.19 64.79 1017.22 42.47 438.41
11.67 41.93 1019.81 82.58 476.1
15.34 36.99 1007.87 94.59 464.58
11.5 40.78 1023.91 86.31 467.74
25.53 57.17 1010.0 72.57 442.12
21.27 57.5 1014.53 80.76 453.34
28.37 69.13 1010.44 71.93 425.29
28.39 51.43 1011.74 47.54 449.63
13.78 45.78 1025.27 95.72 462.88
14.6 42.32 1015.71 77.03 464.67
5.1 35.57 1027.17 80.49 489.96
7.0 38.08 1020.27 77.67 482.38
26.3 77.95 1009.45 78.72 437.95
30.56 71.98 1004.74 58.77 429.2
21.09 46.63 1013.03 74.8 453.34
28.21 70.02 1010.58 51.34 442.47
15.84 49.69 1015.14 90.41 462.6
10.03 40.96 1024.57 91.1 478.79
20.37 52.05 1012.34 62.57 456.11
21.19 50.16 1005.81 84.27 450.33
33.73 69.88 1007.21 42.93 434.83
29.87 73.68 1015.1 40.96 433.43
19.62 62.96 1020.76 76.53 456.02
9.93 40.67 1018.08 69.74 485.23
9.43 37.14 1013.03 74.99 473.57
14.24 39.58 1011.17 70.45 469.94
12.97 49.83 1008.69 91.49 452.07
7.6 41.04 1021.82 88.97 475.32
8.39 36.24 1013.39 89.13 480.69
25.41 48.06 1013.12 46.52 444.01
18.43 56.03 1020.41 60.55 465.17
10.31 39.82 1012.87 88.71 480.61
11.29 41.5 1013.39 89.15 476.04
22.61 49.3 1003.51 83.02 441.76
29.34 71.98 1005.19 75.19 428.24
18.87 67.71 1004.0 87.35 444.77
13.21 45.87 1008.58 85.66 463.1
11.3 44.6 1018.19 91.66 470.5
29.23 72.99 1007.04 63.47 431.0
27.76 69.4 1004.27 72.25 430.68
29.26 67.17 1006.6 70.58 436.42
25.72 49.82 1016.19 60.1 452.33
23.43 63.94 1010.64 89.29 440.16
25.6 63.76 1010.18 67.43 435.75
22.3 44.57 1008.48 67.58 449.74
27.91 72.24 1010.74 70.8 430.73
30.35 77.17 1009.55 63.62 432.75
21.78 47.43 1007.88 66.68 446.79
7.19 41.39 1018.12 90.76 486.35
20.88 59.8 1015.66 75.34 453.18
24.19 50.23 1015.73 59.77 458.31
9.98 41.54 1019.7 80.79 480.26
23.47 51.3 1011.89 74.1 448.65
26.35 49.5 1012.67 41.34 458.41
29.89 64.69 1006.37 58.78 435.39
19.29 50.16 1010.49 97.78 450.21
17.48 43.14 1018.68 74.85 459.59
25.21 75.6 1017.19 69.84 445.84
23.3 48.78 1018.17 75.36 441.08
15.42 37.85 1009.89 85.8 467.33
21.44 63.09 1016.56 90.11 444.19
29.45 68.27 1007.96 61.63 432.96
29.69 47.93 1002.85 44.76 438.09
15.52 36.99 1006.86 89.7 467.9
11.47 43.67 1012.68 72.51 475.72
9.77 34.69 1027.72 74.98 477.51
22.6 69.84 1006.37 79.59 435.13
8.24 39.61 1017.99 78.42 477.9
17.01 44.2 1019.18 61.23 457.26
19.64 44.6 1015.88 47.56 467.53
10.61 41.58 1021.08 93.06 465.15
12.04 40.1 1014.42 89.65 474.28
29.19 65.71 1013.85 50.5 444.49
21.75 45.09 1014.15 44.84 452.84
23.66 77.54 1008.5 85.32 435.38
27.05 75.33 1003.88 82.94 433.57
29.63 69.71 1009.04 67.26 435.27
18.2 39.63 1005.35 79.05 468.49
32.22 70.8 1009.9 62.03 433.07
26.88 73.56 1004.85 94.36 430.63
29.05 65.74 1013.29 60.02 440.74
8.9 39.96 1026.31 95.46 474.49
18.93 48.6 1005.72 84.92 449.74
27.49 63.76 1010.09 62.8 436.73
23.1 70.79 1006.53 90.81 434.58
11.22 43.13 1017.24 80.9 473.93
31.97 79.74 1007.03 55.84 435.99
13.32 43.22 1009.45 75.3 466.83
31.68 68.24 1005.29 37.34 427.22
23.69 63.77 1013.39 79.5 444.07
13.83 41.49 1020.11 87.29 469.57
18.32 66.51 1015.18 81.5 459.89
11.05 40.71 1024.91 76.42 479.59
22.03 64.69 1007.21 75.75 440.92
10.23 41.46 1020.45 84.95 480.87
23.92 66.54 1009.93 62.37 441.9
29.38 69.68 1011.35 49.25 430.2
17.35 42.86 1014.62 74.16 465.16
9.81 44.45 1021.19 90.55 471.32
4.97 40.64 1020.91 94.28 485.43
5.15 40.07 1012.27 63.31 495.35
21.54 58.49 1010.85 78.9 449.12
7.94 42.02 1006.22 90.97 480.53
18.77 50.66 1014.89 87.34 457.07
21.69 69.94 1010.7 80.8 443.67
10.07 44.68 1023.44 90.95 477.52
13.83 39.64 1012.52 69.97 472.95
10.45 39.69 1003.92 89.45 472.54
11.56 40.71 1015.85 76.08 469.17
23.64 70.04 1011.09 83.35 435.21
10.48 40.22 1004.81 92.16 477.78
13.09 39.85 1012.86 58.42 475.89
10.67 40.23 1017.75 85.06 483.9
12.57 39.16 1016.53 88.91 476.2
14.45 43.34 1015.47 83.33 462.16
14.22 37.85 1011.24 88.49 471.05
6.97 41.26 1010.6 96.88 484.71
20.61 63.86 1015.43 73.86 446.34
14.67 42.28 1007.21 65.17 469.02
29.06 72.86 1004.23 69.41 432.12
14.38 40.1 1015.51 81.23 467.28
32.51 69.98 1013.29 54.07 429.66
11.79 45.09 1013.16 89.17 469.49
8.65 40.56 1023.23 78.85 485.87
9.75 40.81 1026.0 84.44 481.95
9.11 40.02 1031.1 83.02 479.03
23.39 69.13 1010.99 90.66 434.5
14.3 54.3 1015.16 75.29 464.9
17.49 63.94 1020.02 82.6 452.71
31.1 69.51 1010.84 45.4 429.74
19.77 56.65 1020.67 66.33 457.09
28.61 72.29 1011.61 45.33 446.77
13.52 41.48 1014.46 67.12 460.76
13.52 40.83 1008.31 84.14 471.95
17.57 46.21 1014.09 80.81 453.29
28.18 60.07 1016.34 49.13 441.61
14.29 46.18 1017.01 87.29 464.73
18.12 43.69 1016.91 52.95 464.68
31.27 73.91 1003.72 68.92 430.59
26.24 77.95 1014.19 85.21 438.01
7.44 41.04 1021.84 88.56 479.08
29.78 74.78 1009.28 55.09 436.39
23.37 65.46 1016.25 48.64 447.07
10.62 39.58 1011.9 87.85 479.91
5.84 43.02 1013.88 87.42 489.05
14.51 53.82 1016.46 62.75 463.17
11.31 42.02 1001.18 94.86 471.26
11.25 40.67 1011.64 63.54 480.49
9.18 39.42 1025.41 69.46 473.78
19.82 58.16 1016.76 74.66 455.5
24.77 58.41 1013.78 80.57 446.27
9.66 41.06 1021.21 84.7 482.2
21.96 59.8 1016.72 72.6 452.48
18.59 43.14 1011.92 52.63 464.48
24.75 69.89 1015.29 82.01 438.1
24.37 63.47 1012.77 75.22 445.6
29.6 67.79 1010.37 51.05 442.43
25.32 61.25 1011.56 80.1 436.67
16.15 41.85 1016.54 81.58 466.56
15.74 71.14 1019.65 65.94 457.29
5.97 36.25 1029.65 86.74 487.03
15.84 52.72 1026.45 62.57 464.93
14.84 44.63 1019.28 57.37 466.0
12.25 48.79 1017.44 88.91 469.52
27.38 70.04 1011.18 72.26 428.88
8.76 41.48 1018.49 74.98 474.3
15.54 39.31 1009.69 71.19 461.06
18.71 39.39 1014.09 62.82 465.57
13.06 41.78 1012.3 55.31 467.67
12.72 40.71 1016.02 71.57 466.99
19.83 39.39 1013.73 59.16 463.72
27.23 49.16 1004.03 40.8 443.78
24.27 68.28 1005.43 67.63 445.23
11.8 40.66 1017.13 97.2 464.43
6.76 36.25 1028.31 91.16 484.36
25.99 63.07 1012.5 64.81 442.16
16.3 39.63 1004.64 85.61 464.11
16.5 49.39 1018.35 93.42 462.48
10.59 42.49 1009.59 77.36 477.49
26.05 65.59 1012.78 67.03 437.04
19.5 40.79 1003.8 89.45 457.09
22.21 45.01 1012.22 54.84 450.6
17.86 45.0 1023.25 53.48 465.78
29.96 70.04 1010.15 54.47 427.1
19.08 44.63 1020.14 43.36 459.81
23.59 47.43 1006.64 48.92 447.36
3.38 39.64 1011.0 81.22 488.92
26.39 66.49 1012.96 60.35 433.36
8.99 39.04 1021.99 75.98 483.35
10.91 41.04 1026.57 74.24 469.53
13.08 39.82 1012.27 85.21 476.96
23.95 58.46 1017.5 68.46 440.75
15.64 43.71 1024.51 78.31 462.55
18.78 54.2 1012.05 89.25 448.04
20.65 50.59 1016.22 68.57 455.24
4.96 40.07 1011.8 67.38 494.75
23.51 57.32 1012.55 53.6 444.58
5.99 35.79 1011.56 91.69 484.82
23.65 66.05 1019.6 78.21 442.9
5.17 39.33 1009.68 94.19 485.46
26.38 49.5 1012.82 37.19 457.81
6.02 43.65 1013.85 83.53 481.92
23.2 61.02 1009.63 79.45 443.23
8.57 39.69 1000.91 99.9 474.29
30.72 71.58 1009.98 50.39 430.46
21.52 50.66 1013.56 74.33 455.71
22.93 62.26 1011.25 83.66 438.34
5.71 41.31 1003.24 89.48 485.83
18.62 44.06 1017.76 64.59 452.82
27.88 68.94 1007.68 75.68 435.04
22.32 59.8 1016.82 64.18 451.21
14.55 42.74 1028.41 70.09 465.81
17.83 44.92 1025.04 70.58 458.42
9.68 39.96 1026.09 99.28 470.22
19.41 49.39 1020.84 81.89 449.24
13.22 44.92 1023.84 87.99 471.43
12.24 44.92 1023.74 88.21 473.26
19.21 58.49 1011.7 91.29 452.82
29.74 70.32 1008.1 52.72 432.69
23.28 60.84 1017.91 67.5 444.13
8.02 41.92 1029.8 92.05 467.21
22.47 48.6 1002.33 63.23 445.98
27.51 73.77 1002.42 90.88 436.91
17.51 44.9 1009.05 74.91 455.01
23.22 66.56 1002.47 85.39 437.11
11.73 40.64 1020.68 96.98 477.06
21.19 67.71 1006.65 56.28 441.71
5.48 40.07 1019.63 65.62 495.76
24.26 66.44 1011.33 55.32 445.63
12.32 41.62 1012.88 88.88 464.72
31.26 68.94 1005.94 39.49 438.03
32.09 72.86 1003.47 54.59 434.78
24.98 60.32 1015.63 57.19 444.67
27.48 61.41 1012.2 45.06 452.24
21.04 45.09 1014.19 40.62 450.92
27.75 70.4 1006.65 90.21 436.53
22.79 71.77 1005.75 90.91 435.53
24.22 68.51 1013.23 74.96 440.01
27.06 64.45 1008.72 54.21 443.1
29.25 71.94 1007.18 63.62 427.49
26.86 68.08 1012.99 50.04 436.25
29.64 67.79 1009.99 51.23 440.74
19.92 63.31 1015.02 82.71 443.54
18.5 51.43 1010.82 92.04 459.42
23.71 60.23 1009.76 90.67 439.66
14.39 44.84 1023.55 91.14 464.15
19.3 56.65 1020.55 70.43 459.1
24.65 52.36 1014.76 66.63 455.68
13.5 45.51 1015.33 86.95 469.08
9.82 41.26 1007.71 96.69 478.02
18.4 44.06 1017.36 70.88 456.8
28.12 44.89 1009.18 47.14 441.13
17.15 43.69 1017.05 63.36 463.88
30.69 73.67 1006.14 60.58 430.45
28.82 65.71 1014.24 54.3 449.18
21.3 48.92 1010.92 65.09 447.89
30.58 70.04 1010.4 48.16 431.59
21.17 52.3 1009.36 81.51 447.5
9.87 41.82 1033.04 68.57 475.58
22.18 59.8 1016.77 73.16 453.24
24.39 63.21 1012.59 80.88 446.4
10.73 44.92 1025.1 85.4 476.81
9.38 40.46 1019.29 75.77 474.1
20.27 57.76 1016.66 75.76 450.71
24.82 66.48 1006.4 70.21 433.62
16.55 41.66 1011.45 55.53 465.14
20.73 59.87 1019.08 80.48 445.18
9.51 39.22 1015.3 72.41 474.12
8.63 43.79 1016.08 83.25 483.91
6.48 40.27 1010.55 82.12 486.68
14.95 43.52 1022.43 94.75 464.98
5.76 45.87 1010.83 95.79 481.4
10.94 39.04 1021.81 86.02 479.2
15.87 41.16 1005.85 78.29 463.86
12.42 38.25 1012.76 82.23 472.3
29.12 58.84 1001.31 52.86 446.51
29.12 51.43 1005.93 60.66 437.71
19.08 41.1 1001.96 62.77 458.94
31.06 67.17 1007.62 65.54 437.91
5.72 39.33 1009.96 95.4 490.76
26.52 65.06 1013.4 51.78 439.66
13.84 44.9 1007.58 63.62 463.27
13.03 39.52 1016.68 83.09 473.99
25.94 66.49 1012.83 61.81 433.38
16.64 53.82 1015.13 68.24 459.01
14.13 40.75 1016.05 72.41 471.44
13.65 39.28 1012.97 79.64 471.91
14.5 44.47 1028.2 66.95 465.15
19.8 51.19 1008.25 91.98 446.66
25.2 63.76 1009.78 64.96 438.15
20.66 51.19 1008.81 88.93 447.14
12.07 43.71 1025.53 85.62 472.32
25.64 70.72 1010.16 84.0 441.68
23.33 72.99 1009.33 89.41 440.04
29.41 64.05 1009.82 67.4 444.82
16.6 53.16 1014.5 76.75 457.26
27.53 72.58 1009.13 89.06 428.83
20.62 43.43 1009.93 64.02 449.07
26.02 71.94 1009.38 64.12 435.21
12.75 44.2 1017.59 81.22 471.03
12.87 48.04 1012.47 100.13 465.56
25.77 62.96 1019.86 58.07 442.83
14.84 41.48 1017.26 63.42 460.3
7.41 40.71 1023.07 83.32 474.25
8.87 41.82 1033.3 74.28 477.97
9.69 40.46 1019.1 71.91 472.16
16.17 46.97 1014.22 85.8 456.08
26.24 49.82 1014.9 55.58 452.41
13.78 43.22 1011.31 69.7 463.71
26.3 67.07 1006.26 63.79 433.72
17.37 57.76 1016.0 86.59 456.4
23.6 48.98 1015.41 48.28 448.43
8.3 36.08 1020.63 80.42 481.6
18.86 42.18 1001.16 98.58 457.07
22.12 49.39 1019.8 72.83 451.0
28.41 75.6 1018.48 56.07 440.28
29.42 71.32 1002.26 67.13 437.47
18.61 67.71 1004.07 84.49 443.57
27.57 69.84 1004.91 68.37 426.6
12.83 41.5 1013.12 86.07 470.87
9.64 39.85 1012.9 83.82 478.37
19.13 58.66 1013.32 74.86 453.92
15.92 40.56 1020.79 53.52 470.22
24.64 72.24 1011.37 80.61 434.54
27.62 63.9 1013.11 43.56 442.89
8.9 36.24 1013.29 89.35 479.03
9.55 43.99 1020.5 97.28 476.06
10.57 36.71 1022.62 80.49 473.88
19.8 57.25 1010.84 88.9 451.75
25.63 56.85 1012.68 49.7 439.2
24.7 58.46 1015.58 68.64 439.7
15.26 46.18 1013.68 98.58 463.6
20.06 52.84 1004.21 82.12 447.47
19.84 56.89 1013.23 78.32 447.92
11.49 44.63 1020.44 86.04 471.08
23.74 72.43 1007.99 91.36 437.55
22.62 51.3 1012.36 81.02 448.27
29.53 72.39 998.47 76.05 431.69
21.32 48.14 1016.57 71.81 449.09
20.3 58.46 1015.93 82.13 448.79
16.97 44.92 1025.21 74.27 460.21
12.07 41.17 1013.54 71.32 479.28
7.46 41.82 1032.67 74.59 483.11
19.2 54.2 1011.46 84.44 450.75
28.64 66.54 1010.43 43.39 437.97
13.56 41.48 1008.53 87.2 459.76
17.4 44.9 1020.5 77.11 457.75
14.08 40.1 1015.48 82.81 469.33
27.11 69.75 1009.74 85.67 433.28
20.92 70.02 1010.23 95.58 444.64
16.18 44.9 1021.3 74.46 463.1
15.57 44.68 1022.01 90.02 460.91
10.37 39.04 1023.95 81.93 479.35
19.6 59.21 1017.65 86.29 449.23
9.22 40.92 1021.83 85.43 474.51
27.76 72.99 1007.81 71.66 435.02
28.68 70.72 1009.43 71.33 435.45
20.95 48.14 1013.3 67.72 452.38
9.06 39.3 1019.73 84.23 480.41
9.21 39.72 1019.54 74.44 478.96
13.65 42.74 1026.58 71.48 468.87
31.79 76.2 1007.89 56.3 434.01
14.32 44.6 1013.85 68.13 466.36
26.28 75.23 1011.44 68.35 435.28
7.69 43.02 1014.51 85.23 486.46
14.44 40.1 1015.51 79.78 468.19
9.19 41.01 1022.14 98.98 468.37
13.35 41.39 1019.17 72.87 474.19
23.04 74.22 1009.52 90.93 440.32
4.83 38.44 1015.35 72.94 485.32
17.29 42.86 1014.38 72.3 464.27
8.73 36.18 1013.66 77.74 479.25
26.21 70.32 1007.0 78.29 430.4
23.72 58.62 1016.65 69.1 447.49
29.27 64.69 1006.85 55.79 438.23
10.4 40.43 1025.46 75.09 492.09
12.19 40.75 1015.13 88.98 475.36
20.4 54.9 1016.68 64.26 452.56
34.3 74.67 1015.98 25.89 427.84
27.56 68.08 1010.8 59.18 433.95
30.9 70.8 1008.48 67.48 435.27
14.85 58.59 1014.04 89.85 454.62
16.42 40.56 1020.36 50.62 472.17
16.45 63.31 1015.96 83.97 452.42
10.14 42.02 1003.19 96.51 472.17
9.53 41.44 1018.01 80.09 481.83
17.01 49.15 1021.83 84.02 458.78
23.94 62.08 1022.47 61.97 447.5
15.95 49.25 1019.04 88.51 463.4
11.15 41.26 1022.67 81.83 473.57
25.56 70.32 1009.07 90.63 433.72
27.16 66.44 1011.2 73.37 431.85
26.71 77.95 1012.13 77.5 433.47
29.56 74.22 1007.45 57.46 432.84
31.19 70.94 1007.29 51.91 436.6
6.86 41.17 1020.12 79.14 490.23
12.36 41.74 1020.58 69.24 477.16
32.82 68.31 1010.44 41.85 441.06
25.3 70.98 1007.22 95.1 440.86
8.71 41.82 1033.08 74.53 477.94
13.34 40.8 1026.56 64.85 474.47
14.2 43.02 1012.18 57.07 470.67
23.74 65.34 1013.7 62.9 447.31
16.9 44.88 1018.14 72.21 466.8
28.54 71.94 1007.4 65.99 430.91
30.15 69.88 1007.2 73.67 434.75
14.33 42.86 1010.82 88.59 469.52
25.57 59.43 1008.88 61.19 438.9
30.55 70.04 1010.51 49.37 429.56
28.04 74.33 1013.53 48.65 432.92
26.39 49.16 1005.68 56.18 442.87
15.3 41.76 1022.57 71.56 466.59
6.03 41.14 1028.04 87.46 479.61
13.49 44.63 1019.12 70.02 471.08
27.67 59.14 1016.51 61.2 433.37
24.19 65.48 1018.8 60.54 443.92
24.44 59.14 1016.74 71.82 443.5
29.86 64.79 1017.37 44.8 439.89
30.2 69.59 1008.9 67.32 434.66
7.99 41.38 1021.95 78.77 487.57
9.93 41.62 1013.76 96.02 464.64
11.03 42.32 1017.26 90.56 470.92
22.34 63.73 1014.37 83.19 444.39
25.33 48.6 1002.54 68.45 442.48
18.87 52.08 1005.25 99.19 449.61
25.97 69.34 1009.43 88.11 435.02
16.58 43.99 1021.81 79.29 458.67
14.35 46.18 1016.63 87.76 461.74
25.06 62.39 1008.09 82.56 438.31
13.85 48.92 1011.68 79.24 462.38
16.09 44.2 1019.39 67.24 460.56
26.34 59.21 1013.37 58.98 439.22
23.01 58.79 1009.71 84.22 444.64
26.39 71.25 999.8 89.12 430.34
31.32 71.29 1008.37 50.07 430.46
16.64 45.87 1009.02 98.86 456.79
13.42 41.23 994.17 95.79 468.82
20.06 44.9 1008.79 70.06 448.51
14.8 44.71 1014.67 41.71 470.77
12.59 41.14 1025.79 86.55 465.74
26.7 66.56 1005.31 71.97 430.21
19.78 50.32 1008.62 96.4 449.23
15.17 49.15 1021.91 91.73 461.89
21.71 61.45 1010.97 91.62 445.72
19.09 39.39 1013.36 59.14 466.13
19.76 51.19 1008.38 92.56 448.71
14.68 41.23 998.43 83.71 469.25
21.3 66.86 1013.04 55.43 450.56
16.73 39.64 1008.94 74.91 464.46
12.26 41.5 1014.87 89.41 471.13
14.77 48.06 1010.92 69.81 461.52
18.26 59.15 1012.04 86.01 451.09
27.1 79.74 1005.43 86.05 431.51
14.72 40.83 1009.65 80.98 469.8
26.3 51.43 1012.05 63.62 442.28
16.48 48.92 1011.84 64.16 458.67
17.99 43.79 1016.13 75.63 462.4
20.34 59.8 1015.18 80.21 453.54
25.53 62.96 1019.81 59.7 444.38
31.59 58.9 1003.39 47.6 440.52
30.8 69.14 1007.68 63.78 433.62
10.75 45.0 1023.68 89.37 481.96
19.3 44.9 1008.89 70.55 452.75
4.71 39.42 1026.4 84.42 481.28
23.1 66.05 1020.28 80.62 439.03
32.63 73.88 1005.64 52.56 435.75
26.63 74.16 1009.72 83.26 436.03
24.35 58.49 1011.03 70.64 445.6
15.11 56.03 1020.27 89.95 462.65
29.1 50.05 1005.87 51.53 438.66
21.24 50.32 1008.54 84.83 447.32
6.16 39.48 1004.85 59.68 484.55
7.36 41.01 1024.9 97.88 476.8
10.44 39.04 1023.99 85.03 480.34
26.76 48.41 1010.53 47.38 440.63
16.79 44.6 1014.27 48.08 459.48
10.76 40.43 1025.98 79.65 490.78
6.07 38.91 1019.25 83.39 483.56
27.33 73.18 1012.26 82.18 429.38
27.15 59.21 1013.49 51.71 440.27
22.35 51.43 1011.34 77.33 445.34
21.82 65.27 1013.86 72.81 447.43
21.11 69.94 1004.37 84.26 439.91
19.95 50.59 1016.11 73.23 459.27
7.45 39.61 1017.88 79.73 478.89
15.36 41.66 1012.41 62.32 466.7
15.65 43.5 1021.39 78.58 463.5
25.31 74.33 1015.04 79.88 436.21
25.88 63.47 1011.95 65.87 443.94
24.6 63.94 1012.87 80.28 439.63
22.58 41.54 1013.21 71.33 460.95
19.69 59.14 1015.99 70.33 448.69
25.85 75.08 1006.24 57.73 444.63
10.06 37.83 1005.49 99.46 473.51
18.59 39.54 1008.56 68.61 462.56
18.27 50.16 1011.07 95.91 451.76
8.85 40.43 1025.68 80.42 491.81
30.04 68.08 1011.04 51.01 429.52
26.06 49.02 1007.59 74.08 437.9
14.8 38.73 1003.18 80.73 467.54
23.93 64.45 1015.35 54.71 449.97
23.72 66.48 1003.61 73.75 436.62
11.44 40.55 1023.37 88.43 477.68
20.28 63.86 1016.04 74.66 447.26
27.9 63.13 1011.8 70.04 439.76
24.74 59.39 1015.23 74.64 437.49
14.8 58.2 1018.29 85.11 455.14
8.22 41.03 1021.76 82.97 485.5
27.56 66.93 1016.81 55.59 444.1
32.07 70.94 1006.91 49.9 432.33
9.53 44.03 1008.87 89.99 471.23
13.61 42.34 1017.93 91.61 463.89
22.2 51.19 1009.2 82.95 445.54
21.36 59.54 1007.99 92.62 446.09
23.25 63.86 1017.82 59.64 445.12
23.5 59.21 1018.29 63.0 443.31
8.46 39.66 1015.14 85.38 484.16
8.19 40.69 1019.86 85.23 477.76
30.67 71.29 1008.36 52.08 430.28
32.48 62.04 1010.39 38.05 446.48
8.99 36.66 1028.11 71.98 481.03
13.77 47.83 1007.41 90.66 466.07
19.05 67.32 1013.2 83.14 447.47
21.19 55.5 1019.83 65.22 455.93
10.12 40.0 1021.15 91.67 479.62
24.93 47.01 1014.28 66.04 455.06
8.47 40.46 1019.87 78.19 475.06
24.52 56.85 1012.59 54.47 438.89
28.55 69.84 1003.38 67.26 432.7
20.58 50.9 1011.89 72.56 452.6
18.31 46.21 1010.46 82.15 451.75
27.18 71.06 1008.16 86.32 430.66
4.43 38.91 1019.04 88.17 491.9
26.02 74.78 1010.04 72.78 439.82
15.75 39.0 1015.91 69.58 460.73
22.99 60.95 1015.14 69.86 449.7
25.52 59.15 1013.88 65.37 439.42
27.04 65.06 1013.33 52.37 439.84
6.42 35.57 1025.58 79.63 485.86
17.04 40.12 1011.81 83.14 458.1
10.79 39.82 1012.89 88.25 479.92
20.41 56.03 1019.94 55.85 458.29
7.36 40.07 1017.29 52.55 489.45
28.08 73.42 1012.17 62.74 434.0
24.74 69.13 1010.69 90.08 431.24
28.32 47.93 1003.26 54.5 439.5
16.71 40.56 1019.48 49.88 467.46
30.7 71.58 1010.0 48.96 429.27
18.42 58.95 1016.95 86.77 452.1
10.62 42.02 999.83 96.66 472.41
22.18 69.05 1002.75 70.84 442.14
22.38 49.3 1003.56 83.83 441.0
13.94 41.58 1020.76 68.22 463.07
21.24 60.84 1017.99 82.22 445.71
6.76 39.81 1017.11 87.9 483.16
26.73 68.84 1010.75 66.83 440.45
7.24 38.06 1020.6 85.36 481.83
10.84 40.62 1015.53 60.9 467.6
19.32 52.84 1004.29 83.51 450.88
29.0 69.13 1001.22 52.96 425.5
23.38 54.42 1013.95 73.02 451.87
31.17 69.51 1010.51 43.11 428.94
26.17 48.6 1002.59 61.41 439.86
30.9 73.42 1011.21 65.32 433.44
24.92 73.68 1015.12 93.68 438.23
32.77 71.32 1007.68 42.39 436.95
14.37 40.56 1021.67 68.18 470.19
8.36 40.22 1011.6 89.18 484.66
31.45 68.27 1007.56 64.79 430.81
31.6 73.17 1010.05 43.48 433.37
17.9 48.98 1014.17 80.4 453.02
20.35 50.9 1012.6 72.43 453.5
16.21 41.23 995.88 80.0 463.09
19.36 44.6 1016.25 45.65 464.56
21.04 65.46 1017.22 63.02 452.12
14.05 40.69 1015.66 74.39 470.9
23.48 64.15 1021.08 57.77 450.89
21.91 63.76 1009.85 76.8 445.04
24.42 63.07 1011.49 67.39 444.72
14.26 40.92 1022.07 73.96 460.38
21.38 58.33 1013.05 72.75 446.8
15.71 44.06 1018.34 71.69 465.05
5.78 40.62 1016.55 84.98 484.13
6.77 39.81 1017.01 87.68 488.27
23.84 49.21 1013.85 50.36 447.09
21.17 58.16 1017.16 68.11 452.02
19.94 58.96 1014.16 66.27 455.55
8.73 41.92 1029.41 89.72 480.99
16.39 41.67 1012.96 61.07 467.68

We will do the following steps in the sequel.

  • Step 5: Data Preparation
  • Step 6: Data Modeling
  • Step 7: Tuning and Evaluation
  • Step 8: Deployment

Datasource References:

  • Pinar Tüfekci, Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods, International Journal of Electrical Power & Energy Systems, Volume 60, September 2014, Pages 126-140, ISSN 0142-0615, Web Link
  • Heysem Kaya, Pinar Tüfekci , Sadik Fikret Gürgen: Local and Global Learning Methods for Predicting Power of a Combined Gas & Steam Turbine, Proceedings of the International Conference on Emerging Trends in Computer and Electronics Engineering ICETCEE 2012, pp. 13-18 (Mar. 2012, Dubai) Web Link

We will continue later with ML pipelines to do prediction with a fitted model from this dataset

ScaDaMaLe Course site and book

Wiki Clickstream Analysis

** Dataset: 3.2 billion requests collected during the month of February 2015 grouped by (src, dest) **

** Source: https://datahub.io/dataset/wikipedia-clickstream/ **

NY clickstream image

This notebook requires Spark 1.6+.

This notebook was originally a data analysis workflow developed with Databricks Community Edition, a free version of Databricks designed for learning Apache Spark.

Here we elucidate the original python notebook (also linked here) used in the talk by Michael Armbrust at Spark Summit East February 2016 shared from https://twitter.com/michaelarmbrust/status/699969850475737088 (watch later)

Michael Armbrust Spark Summit East

Data set

Wikipedia Logo

The data we are exploring in this lab is the February 2015 English Wikipedia Clickstream data, and it is available here: http://datahub.io/dataset/wikipedia-clickstream/resource/be85cc68-d1e6-4134-804a-fd36b94dbb82.

According to Wikimedia:

"The data contains counts of (referer, resource) pairs extracted from the request logs of English Wikipedia. When a client requests a resource by following a link or performing a search, the URI of the webpage that linked to the resource is included with the request in an HTTP header called the "referer". This data captures 22 million (referer, resource) pairs from a total of 3.2 billion requests collected during the month of February 2015."

The data is approximately 1.2GB and it is hosted in the following Databricks file: /databricks-datasets/wikipedia-datasets/data-001/clickstream/raw-uncompressed

display(dbutils.fs.ls("/databricks-datasets/wikipedia-datasets/"))
path name size
dbfs:/databricks-datasets/wikipedia-datasets/data-001/ data-001/ 0.0

Let us first understand this Wikimedia data set a bit more

Let's read the datahub-hosted link https://datahub.io/dataset/wikipedia-clickstream in the embedding below. Also click the blog by Ellery Wulczyn, Data Scientist at The Wikimedia Foundation, to better understand how the data was generated (remember to Right-Click and use -> and <- if navigating within the embedded html frame below).

Run the next two cells for some housekeeping.

if (org.apache.spark.BuildInfo.sparkBranch < "1.6") sys.error("Attach this notebook to a cluster running Spark 1.6+")

Loading and Exploring the data

val data = sc.textFile("dbfs:///databricks-datasets/wikipedia-datasets/data-001/clickstream/raw-uncompressed")

data: org.apache.spark.rdd.RDD[String] = dbfs:///databricks-datasets/wikipedia-datasets/data-001/clickstream/raw-uncompressed MapPartitionsRDD[240] at textFile at command-685894176423189:1
Looking at the first few lines of the data
data.take(5).foreach(println) 

prev_id	curr_id	n	prev_title	curr_title	type
	3632887	121	other-google	!!	other
	3632887	93	other-wikipedia	!!	other
	3632887	46	other-empty	!!	other
	3632887	10	other-other	!!	other

data.take(2)

res4: Array[String] = Array(prev_id	curr_id	n	prev_title	curr_title	type, "	3632887	121	other-google	!!	other")
  • The first line looks like a header
  • The second line (separated from the first by ",") contains data organized according to the header, i.e., prev_id = 3632887, curr_id = 121", and so on.

Actually, here is the meaning of each column:

  • prev_id: if the referer does not correspond to an article in the main namespace of English Wikipedia, this value will be empty. Otherwise, it contains the unique MediaWiki page ID of the article corresponding to the referer i.e. the previous article the client was on

  • curr_id: the MediaWiki unique page ID of the article the client requested

  • prev_title: the result of mapping the referer URL to the fixed set of values described below

  • curr_title: the title of the article the client requested

  • n: the number of occurrences of the (referer, resource) pair

  • type

    • "link" if the referer and request are both articles and the referer links to the request
    • "redlink" if the referer is an article and links to the request, but the request is not in the production enwiki.page table
    • "other" if the referer and request are both articles but the referer does not link to the request. This can happen when clients search or spoof their refer

Referers were mapped to a fixed set of values corresponding to internal traffic or external traffic from one of the top 5 global traffic sources to English Wikipedia, based on this scheme:

  • an article in the main namespace of English Wikipedia -> the article title
  • any Wikipedia page that is not in the main namespace of English Wikipedia -> other-wikipedia
  • an empty referer -> other-empty
  • a page from any other Wikimedia project -> other-internal
  • Google -> other-google
  • Yahoo -> other-yahoo
  • Bing -> other-bing
  • Facebook -> other-facebook
  • Twitter -> other-twitter
  • anything else -> other-other

In the second line of the file above, we can see there were 121 clicks from Google to the Wikipedia page on "!!" (double exclamation marks). People search for everything!

  • prev_id = (nothing)
  • curr_id = 3632887 --> (Wikipedia page ID)
  • n = 121 (People clicked from Google to this page 121 times in this month.)
  • prev_title = other-google (This data record is for referals from Google.)
  • curr_title = !! (This Wikipedia page is about a double exclamation mark.)
  • type = other

Create a DataFrame from this CSV

  • From the next Spark release - 2.0, CSV as a datasource will be part of Spark's standard release. But, we are using Spark 1.6
// Load the raw dataset stored as a CSV file
val clickstream = sqlContext
    .read
    .format("com.databricks.spark.csv")
    .options(Map("header" -> "true", "delimiter" -> "\t", "mode" -> "PERMISSIVE", "inferSchema" -> "true"))
    .load("dbfs:///databricks-datasets/wikipedia-datasets/data-001/clickstream/raw-uncompressed")
  

clickstream: org.apache.spark.sql.DataFrame = [prev_id: int, curr_id: int ... 4 more fields]

clickstream.printSchema

root
 |-- prev_id: integer (nullable = true)
 |-- curr_id: integer (nullable = true)
 |-- n: integer (nullable = true)
 |-- prev_title: string (nullable = true)
 |-- curr_title: string (nullable = true)
 |-- type: string (nullable = true)

Display some sample data

display(clickstream)
prev_id curr_id n prev_title curr_title type
null 3632887.0 121.0 other-google !! other
null 3632887.0 93.0 other-wikipedia !! other
null 3632887.0 46.0 other-empty !! other
null 3632887.0 10.0 other-other !! other
64486.0 3632887.0 11.0 !_(disambiguation) !! other
2061699.0 2556962.0 19.0 Louden_Up_Now !!!_(album) link
null 2556962.0 25.0 other-empty !!!_(album) other
null 2556962.0 16.0 other-google !!!_(album) other
null 2556962.0 44.0 other-wikipedia !!!_(album) other
64486.0 2556962.0 15.0 !_(disambiguation) !!!_(album) link
600744.0 2556962.0 297.0 !!! !!!_(album) link
null 6893310.0 11.0 other-empty !Hero_(album) other
1921683.0 6893310.0 26.0 !Hero !Hero_(album) link
null 6893310.0 16.0 other-wikipedia !Hero_(album) other
null 6893310.0 23.0 other-google !Hero_(album) other
8127304.0 2.2602473e7 16.0 Jericho_Rosales !Oka_Tokat link
3.5978874e7 2.2602473e7 20.0 List_of_telenovelas_of_ABS-CBN !Oka_Tokat link
null 2.2602473e7 57.0 other-google !Oka_Tokat other
null 2.2602473e7 12.0 other-wikipedia !Oka_Tokat other
null 2.2602473e7 23.0 other-empty !Oka_Tokat other
7360687.0 2.2602473e7 10.0 Rica_Peralejo !Oka_Tokat link
3.7104582e7 2.2602473e7 11.0 Jeepney_TV !Oka_Tokat link
3.437659e7 2.2602473e7 22.0 Oka_Tokat_(2012_TV_series) !Oka_Tokat link
null 6810768.0 20.0 other-wikipedia !T.O.O.H.! other
null 6810768.0 81.0 other-google !T.O.O.H.! other
3.1976181e7 6810768.0 51.0 List_of_death_metal_bands,_!–K !T.O.O.H.! link
null 6810768.0 35.0 other-empty !T.O.O.H.! other
null 3243047.0 21.0 other-empty !_(album) other
1337475.0 3243047.0 208.0 The_Dismemberment_Plan !_(album) link
3284285.0 3243047.0 78.0 The_Dismemberment_Plan_Is_Terrified !_(album) link
null 3243047.0 28.0 other-wikipedia !_(album) other
2098292.0 899480.0 58.0 United_States_military_award_devices "A"_Device link
194844.0 899480.0 15.0 USS_Yorktown_(CV-5) "A"_Device link
null 899480.0 17.0 other-google "A"_Device other
null 899480.0 13.0 other-empty "A"_Device other
null 899480.0 29.0 other-wikipedia "A"_Device other
878246.0 899480.0 11.0 American_Defense_Service_Medal "A"_Device link
855901.0 899480.0 24.0 Overseas_Service_Ribbon "A"_Device other
206427.0 899480.0 33.0 USS_Ranger_(CV-4) "A"_Device link
773691.0 899480.0 47.0 Antarctica_Service_Medal "A"_Device link
2301720.0 1282996.0 43.0 Kinsey_Millhone "A"_Is_for_Alibi link
null 1282996.0 45.0 other-empty "A"_Is_for_Alibi other
null 1282996.0 10.0 other-yahoo "A"_Is_for_Alibi other
470006.0 1282996.0 207.0 Sue_Grafton "A"_Is_for_Alibi link
null 1282996.0 18.0 other-other "A"_Is_for_Alibi other
null 1282996.0 31.0 other-wikipedia "A"_Is_for_Alibi other
null 1282996.0 272.0 other-google "A"_Is_for_Alibi other
3.9606873e7 1282996.0 10.0 "W"_Is_for_Wasted "A"_Is_for_Alibi link
2.6181056e7 9003666.0 17.0 And "And"_theory_of_conservatism link
null 9003666.0 109.0 other-wikipedia "And"_theory_of_conservatism other
null 9003666.0 18.0 other-google "And"_theory_of_conservatism other
null 3.9072529e7 49.0 other-google "Bassy"_Bob_Brockmann other
null 3.9072529e7 10.0 other-other "Bassy"_Bob_Brockmann other
1.1273993e7 null 15.0 Colt_1851_Navy_Revolver "Bigfoot"_Wallace redlink
1.2571133e7 2.5033979e7 12.0 "V"_Is_for_Vagina "C"_is_for_(Please_Insert_Sophomoric_Genitalia_Reference_HERE) link
113468.0 2.5033979e7 24.0 The_Mission "C"_is_for_(Please_Insert_Sophomoric_Genitalia_Reference_HERE) link
1.4096078e7 2.5033979e7 15.0 Trent_Reznor_discography "C"_is_for_(Please_Insert_Sophomoric_Genitalia_Reference_HERE) other
null 2.5033979e7 42.0 other-empty "C"_is_for_(Please_Insert_Sophomoric_Genitalia_Reference_HERE) other
1375614.0 2.5033979e7 15.0 Tapeworm_(band) "C"_is_for_(Please_Insert_Sophomoric_Genitalia_Reference_HERE) other
159547.0 2.5033979e7 25.0 Milla_Jovovich "C"_is_for_(Please_Insert_Sophomoric_Genitalia_Reference_HERE) other
2.8639397e7 2.5033979e7 73.0 Sound_into_Blood_into_Wine "C"_is_for_(Please_Insert_Sophomoric_Genitalia_Reference_HERE) link
1893465.0 2.5033979e7 30.0 Carina_Round "C"_is_for_(Please_Insert_Sophomoric_Genitalia_Reference_HERE) other
3.3622887e7 2.5033979e7 10.0 Conditions_of_My_Parole "C"_is_for_(Please_Insert_Sophomoric_Genitalia_Reference_HERE) link
147692.0 2.5033979e7 25.0 Tim_Alexander "C"_is_for_(Please_Insert_Sophomoric_Genitalia_Reference_HERE) other
4619790.0 2.5033979e7 593.0 Puscifer "C"_is_for_(Please_Insert_Sophomoric_Genitalia_Reference_HERE) link
null 2.5033979e7 36.0 other-wikipedia "C"_is_for_(Please_Insert_Sophomoric_Genitalia_Reference_HERE) other
null 2.5033979e7 93.0 other-google "C"_is_for_(Please_Insert_Sophomoric_Genitalia_Reference_HERE) other
69161.0 null 51.0 Tết "Chúc_Mừng_Năm_Mới"_or_best_wishes_for_the_new_year. redlink
1438509.0 null 14.0 List_of_Old_West_gunfighters "Cool_Hand_Conor"_O'Neill redlink
null 331586.0 6820.0 other-google "Crocodile"_Dundee other
null 331586.0 20.0 other-twitter "Crocodile"_Dundee other
null 331586.0 781.0 other-wikipedia "Crocodile"_Dundee other
489033.0 331586.0 59.0 List_of_Academy_Awards_ceremonies "Crocodile"_Dundee link
1.0040606e7 331586.0 38.0 List_of_Australian_films "Crocodile"_Dundee other
2564144.0 331586.0 154.0 Crocodile_Dundee_in_Los_Angeles "Crocodile"_Dundee link
6127928.0 331586.0 14.0 Bobby_Alto "Crocodile"_Dundee other
152171.0 331586.0 13.0 Baz_Luhrmann "Crocodile"_Dundee link
8078282.0 331586.0 348.0 Australia_(2008_film) "Crocodile"_Dundee link
3.7386608e7 331586.0 66.0 2015_in_film "Crocodile"_Dundee link
34557.0 331586.0 12.0 1980s "Crocodile"_Dundee other
1118809.0 331586.0 297.0 "Crocodile"_Dundee_II "Crocodile"_Dundee link
7033.0 331586.0 52.0 Caitlin_Clarke "Crocodile"_Dundee other
72766.0 331586.0 31.0 Dundee_(disambiguation) "Crocodile"_Dundee other
171612.0 331586.0 221.0 1986_in_film "Crocodile"_Dundee link
2376452.0 331586.0 34.0 Australian_New_Wave "Crocodile"_Dundee other
1248074.0 331586.0 60.0 David_Gulpilil "Crocodile"_Dundee link
865241.0 331586.0 10.0 Crocodile_Hunter "Crocodile"_Dundee other
196020.0 331586.0 12.0 Crocodilia "Crocodile"_Dundee link
643649.0 331586.0 85.0 List_of_most_watched_television_broadcasts "Crocodile"_Dundee link
8306521.0 331586.0 13.0 Anne_Carlisle "Crocodile"_Dundee other
1448969.0 331586.0 18.0 Bart_vs._Australia "Crocodile"_Dundee other
70209.0 331586.0 153.0 Cinema_of_Australia "Crocodile"_Dundee link
4008173.0 331586.0 18.0 59th_Academy_Awards "Crocodile"_Dundee link
331460.0 331586.0 17.0 Bowie_knife "Crocodile"_Dundee link
37882.0 331586.0 21.0 Crocodile "Crocodile"_Dundee other
4.4789934e7 331586.0 1283.0 Deaths_in_2015 "Crocodile"_Dundee link
2.2344579e7 331586.0 30.0 Academy_Award_for_Best_Original_Screenplay "Crocodile"_Dundee link
1872502.0 331586.0 10.0 Boy-Scoutz_'n_the_Hood "Crocodile"_Dundee other
5644.0 331586.0 13.0 Comedy_film "Crocodile"_Dundee link
458340.0 331586.0 10.0 List_of_films_set_in_New_York_City "Crocodile"_Dundee other
905528.0 331586.0 20.0 List_of_films_set_in_Australia "Crocodile"_Dundee other
1.9924718e7 331586.0 12.0 Hello_Kitty's_Furry_Tale_Theater "Crocodile"_Dundee other
1422400.0 331586.0 147.0 Linda_Kozlowski "Crocodile"_Dundee other
1.0449888e7 331586.0 28.0 List_of_Paramount_Pictures_films "Crocodile"_Dundee link
1.1730578e7 331586.0 20.0 List_of_American_films_of_1986 "Crocodile"_Dundee other
2321513.0 331586.0 45.0 John_Meillon "Crocodile"_Dundee link
null 331586.0 910.0 other-empty "Crocodile"_Dundee other
238004.0 331586.0 15.0 The_Rescuers_Down_Under "Crocodile"_Dundee other
483895.0 331586.0 13.0 Young_Einstein "Crocodile"_Dundee link
6534317.0 331586.0 21.0 The_Man_from_Snowy_River_(1982_film) "Crocodile"_Dundee other
61066.0 331586.0 11.0 Skippy_the_Bush_Kangaroo "Crocodile"_Dundee link
1.0670306e7 331586.0 83.0 Michael_"Crocodile"_Dundee "Crocodile"_Dundee link
693780.0 331586.0 1222.0 Paul_Hogan "Crocodile"_Dundee link
566405.0 331586.0 26.0 Saltwater_crocodile "Crocodile"_Dundee other
null 331586.0 274.0 other-yahoo "Crocodile"_Dundee other
2.081381e7 331586.0 13.0 Peter_Best_(composer) "Crocodile"_Dundee other
1260945.0 331586.0 28.0 Reginald_VelJohnson "Crocodile"_Dundee other
1065264.0 331586.0 12.0 Plaza_Hotel "Crocodile"_Dundee link
4.0156059e7 331586.0 30.0 Live_It_Up_(Mental_As_Anything_song) "Crocodile"_Dundee link
1.5580374e7 331586.0 289.0 Main_Page "Crocodile"_Dundee other
1264623.0 331586.0 14.0 Mental_As_Anything "Crocodile"_Dundee link
2.246655e7 331586.0 16.0 Paul_Greco "Crocodile"_Dundee other
22918.0 331586.0 140.0 Paramount_Pictures "Crocodile"_Dundee link
1352817.0 331586.0 31.0 Rodney_Ansell "Crocodile"_Dundee link
null 331586.0 308.0 other-other "Crocodile"_Dundee other
6873934.0 331586.0 11.0 Steve_Irwin "Crocodile"_Dundee other
null 331586.0 417.0 other-bing "Crocodile"_Dundee other
88609.0 331586.0 38.0 Star_Trek_IV:_The_Voyage_Home "Crocodile"_Dundee link
4464034.0 331586.0 1690.0 Terry_Gill "Crocodile"_Dundee link
4148842.0 331586.0 17.0 The_Cowboy_Way_(film) "Crocodile"_Dundee other
2747201.0 331586.0 33.0 Shrimp_on_the_barbie "Crocodile"_Dundee other
885480.0 331586.0 20.0 The_Adventures_of_Bayou_Billy "Crocodile"_Dundee other
470006.0 1.6250593e7 24.0 Sue_Grafton "D"_Is_for_Deadbeat link
1.6250549e7 1.6250593e7 31.0 "C"_Is_for_Corpse "D"_Is_for_Deadbeat link
null 1.6250593e7 21.0 other-google "D"_Is_for_Deadbeat other
2301720.0 1.6250593e7 10.0 Kinsey_Millhone "D"_Is_for_Deadbeat link
null 1.6250593e7 15.0 other-empty "D"_Is_for_Deadbeat other
4619790.0 null 47.0 Puscifer "D"_Is_for_Dubby_–_The_Lustmord_Dub_Mixes redlink
1.6079543e7 null 43.0 "V"_Is_for_Viagra._The_Remixes "D"_Is_for_Dubby_–_The_Lustmord_Dub_Mixes redlink
2.5033979e7 null 18.0 "C"_is_for_(Please_Insert_Sophomoric_Genitalia_Reference_HERE) "D"_Is_for_Dubby_–_The_Lustmord_Dub_Mixes redlink
238341.0 3.9304968e7 44.0 David_Hockney "David_Hockney:_A_Bigger_Picture"_in_Bilbao link
1728819.0 3.9304968e7 17.0 Brian_Sewell "David_Hockney:_A_Bigger_Picture"_in_Bilbao link
null 3.9304968e7 108.0 other-google "David_Hockney:_A_Bigger_Picture"_in_Bilbao other
2548364.0 1896643.0 25.0 Dutch_Mantel "Dr._Death"_Steve_Williams link
1910311.0 1896643.0 34.0 Clash_of_the_Champions "Dr._Death"_Steve_Williams link
1451758.0 1896643.0 33.0 Alexey_Ignashov "Dr._Death"_Steve_Williams link
1983348.0 1896643.0 15.0 Dan_Spivey "Dr._Death"_Steve_Williams link
1691016.0 1896643.0 31.0 Kevin_Sullivan_(wrestler) "Dr._Death"_Steve_Williams link
1.6409926e7 1896643.0 19.0 Learning_the_Ropes "Dr._Death"_Steve_Williams link
1002441.0 1896643.0 238.0 Jim_Ross "Dr._Death"_Steve_Williams link
2004376.0 1896643.0 12.0 List_of_NWA_World_Tag_Team_Champions "Dr._Death"_Steve_Williams link
306115.0 1896643.0 27.0 List_of_Extreme_Championship_Wrestling_alumni "Dr._Death"_Steve_Williams link
2.4445746e7 1896643.0 20.0 List_of_Legends_of_Wrestling_characters "Dr._Death"_Steve_Williams link
1441354.0 1896643.0 19.0 Jushin_Thunder_Liger "Dr._Death"_Steve_Williams link
1902117.0 1896643.0 24.0 List_of_World_Championship_Wrestling_alumni "Dr._Death"_Steve_Williams link
3.9275628e7 1896643.0 38.0 List_of_professional_wrestlers_by_MMA_record "Dr._Death"_Steve_Williams link
3.3244435e7 1896643.0 72.0 List_of_Wrestling_Observer_Newsletter_awards "Dr._Death"_Steve_Williams link
null 1896643.0 1227.0 other-google "Dr._Death"_Steve_Williams other
null 1896643.0 226.0 other-wikipedia "Dr._Death"_Steve_Williams other
307526.0 1896643.0 24.0 Raven_(wrestler) "Dr._Death"_Steve_Williams link
1871528.0 1896643.0 75.0 Mike_Polchlopek "Dr._Death"_Steve_Williams link
1235858.0 1896643.0 31.0 Ron_Simmons "Dr._Death"_Steve_Williams link
3730230.0 1896643.0 46.0 Powerslam "Dr._Death"_Steve_Williams link
1779766.0 1896643.0 21.0 Nikita_Koloff "Dr._Death"_Steve_Williams link
9044129.0 1896643.0 10.0 Salman_Hashimikov "Dr._Death"_Steve_Williams link
1854348.0 1896643.0 36.0 Michael_Hayes_(wrestler) "Dr._Death"_Steve_Williams link
690045.0 1896643.0 21.0 Sid_Eudy "Dr._Death"_Steve_Williams link
1984531.0 1896643.0 30.0 Skandor_Akbar "Dr._Death"_Steve_Williams link
844984.0 1896643.0 23.0 Ring_name "Dr._Death"_Steve_Williams link
3.086496e7 1896643.0 46.0 Mike_Rotunda "Dr._Death"_Steve_Williams link
1887139.0 1896643.0 15.0 The_Blade_Runners "Dr._Death"_Steve_Williams link
1.7599211e7 1896643.0 17.0 Starrcade_(1987) "Dr._Death"_Steve_Williams link
2143297.0 1896643.0 42.0 Triple_Crown_Heavyweight_Championship "Dr._Death"_Steve_Williams link
1873599.0 1896643.0 104.0 Terry_Gordy "Dr._Death"_Steve_Williams link
472883.0 1896643.0 47.0 Universal_Wrestling_Federation_(Bill_Watts) "Dr._Death"_Steve_Williams link
null 1896643.0 190.0 other-empty "Dr._Death"_Steve_Williams other
1848273.0 1896643.0 39.0 Vampiro "Dr._Death"_Steve_Williams link
579700.0 1896643.0 245.0 Steve_Williams "Dr._Death"_Steve_Williams link
1.326827e7 1896643.0 17.0 The_Miracle_Violence_Connection "Dr._Death"_Steve_Williams link
2100406.0 1896643.0 38.0 The_Bushwhackers "Dr._Death"_Steve_Williams link
2267673.0 1896643.0 45.0 Wrestling_Observer_Newsletter_Hall_of_Fame "Dr._Death"_Steve_Williams link
2001780.0 1896643.0 10.0 Carl_Ouellet "Dr._Death"_Steve_Williams link
1889491.0 1896643.0 128.0 Dr._Death "Dr._Death"_Steve_Williams link
2748007.0 1896643.0 28.0 Bill_Watts "Dr._Death"_Steve_Williams link
591757.0 1896643.0 33.0 Big_Boss_Man_(wrestler) "Dr._Death"_Steve_Williams link
1912776.0 1896643.0 12.0 Chi-Town_Rumble "Dr._Death"_Steve_Williams link
1027406.0 1896643.0 18.0 Big_Van_Vader "Dr._Death"_Steve_Williams link
1690819.0 1896643.0 11.0 Barry_Windham "Dr._Death"_Steve_Williams link
2024278.0 1896643.0 17.0 Kenta_Kobashi "Dr._Death"_Steve_Williams link
1931006.0 1896643.0 44.0 John_Laurinaitis "Dr._Death"_Steve_Williams link
272874.0 1896643.0 24.0 Lex_Luger "Dr._Death"_Steve_Williams link
3864676.0 1896643.0 10.0 Gary_Albright "Dr._Death"_Steve_Williams link
3.0654736e7 1896643.0 17.0 List_of_Pro_Wrestling_Illustrated_awards "Dr._Death"_Steve_Williams link
1911262.0 1896643.0 11.0 Halloween_Havoc "Dr._Death"_Steve_Williams link
795587.0 1896643.0 105.0 Jerry_Only "Dr._Death"_Steve_Williams link
1785504.0 1896643.0 16.0 Ed_Ferrara "Dr._Death"_Steve_Williams link
2.1771811e7 1896643.0 51.0 List_of_WWE_alumni_(S–Z) "Dr._Death"_Steve_Williams link
1985456.0 1896643.0 12.0 List_of_WCW_World_Tag_Team_Champions "Dr._Death"_Steve_Williams link
null 1896643.0 54.0 other-other "Dr._Death"_Steve_Williams other
null 1896643.0 70.0 other-yahoo "Dr._Death"_Steve_Williams other
1683451.0 1896643.0 10.0 Paul_Orndorff "Dr._Death"_Steve_Williams link
1766555.0 1896643.0 34.0 One_Man_Gang "Dr._Death"_Steve_Williams link
1940350.0 1896643.0 12.0 Powerbomb "Dr._Death"_Steve_Williams other
993933.0 1896643.0 17.0 Rikishi_(wrestler) "Dr._Death"_Steve_Williams link
1286898.0 1896643.0 19.0 Mitsuharu_Misawa "Dr._Death"_Steve_Williams link
1840022.0 1896643.0 36.0 Pro_Wrestling_Illustrated "Dr._Death"_Steve_Williams link
4167276.0 1896643.0 27.0 Serena_Deeb "Dr._Death"_Steve_Williams link
674611.0 1896643.0 18.0 Tazz "Dr._Death"_Steve_Williams link
471752.0 1896643.0 19.0 World_Class_Championship_Wrestling "Dr._Death"_Steve_Williams link
null 1896643.0 75.0 other-bing "Dr._Death"_Steve_Williams other
2652496.0 1896643.0 12.0 Virtual_Pro_Wrestling_2:_Ōdō_Keishō "Dr._Death"_Steve_Williams link
882433.0 1896643.0 14.0 Theodore_Long "Dr._Death"_Steve_Williams link
1.7935523e7 1896643.0 17.0 Starrcade_(1999) "Dr._Death"_Steve_Williams link
1896801.0 1896643.0 35.0 The_Varsity_Club "Dr._Death"_Steve_Williams link
3202675.0 1896643.0 255.0 WWF_Brawl_for_All "Dr._Death"_Steve_Williams link
1513139.0 1896643.0 12.0 Starrcade "Dr._Death"_Steve_Williams link
5978726.0 1896643.0 13.0 The_Great_American_Bash "Dr._Death"_Steve_Williams link
3209327.0 1896643.0 33.0 Universal_Wrestling_Federation_(Herb_Abrams) "Dr._Death"_Steve_Williams link
470006.0 1.6251903e7 26.0 Sue_Grafton "E"_Is_for_Evidence link
null 1.6251903e7 10.0 other-wikipedia "E"_Is_for_Evidence other
null 1.6251903e7 26.0 other-google "E"_Is_for_Evidence other
1.6250593e7 1.6251903e7 27.0 "D"_Is_for_Deadbeat "E"_Is_for_Evidence link
2301720.0 1.6251903e7 10.0 Kinsey_Millhone "E"_Is_for_Evidence link
3.9737124e7 null 23.0 Yoon_So-hee EXO_Music_Video_Drama redlink
2.9668256e7 null 24.0 Separation_(statistics) "Firth"_logistic_regression redlink
null 2.7653497e7 12.0 other-empty "Five_stars_rise_in_the_East"_arm_protector other
null 2.7653497e7 32.0 other-wikipedia "Five_stars_rise_in_the_East"_arm_protector other
3.3109636e7 2.7653497e7 17.0 List_of_Chinese_cultural_relics_forbidden_to_be_exhibited_abroad "Five_stars_rise_in_the_East"_arm_protector other
3855385.0 4.4783572e7 10.0 Albanian_Subversion "Free_Albania"_National_Committee link
1733948.0 4.4783572e7 12.0 Midhat_Frashëri "Free_Albania"_National_Committee link
null 4.4783572e7 13.0 other-google "Free_Albania"_National_Committee other
1696824.0 null 20.0 Kirkwood,_Atlanta "Future_(rapper)"_on_@Wikipedia:_https://en.wikipedia.org/wiki/Future_(rapper) redlink
null 3.7732991e7 14.0 other-google "Good_Day,_Fellow!"_"Axe_Handle!" other
null 3.7732991e7 16.0 other-wikipedia "Good_Day,_Fellow!"_"Axe_Handle!" other
null 8617839.0 15.0 other-wikipedia Good_Luck,_Father_Ted other
null 8617839.0 64.0 other-google Good_Luck,_Father_Ted other
2.6941571e7 8617839.0 16.0 List_of_Father_Ted_characters Good_Luck,_Father_Ted link
11313.0 8617839.0 10.0 Father_Ted Good_Luck,_Father_Ted link
6734598.0 8617839.0 94.0 List_of_Father_Ted_episodes Good_Luck,_Father_Ted link
8618299.0 8617839.0 13.0 Entertaining_Father_Stone Good_Luck,_Father_Ted link
null 8617839.0 18.0 other-empty Good_Luck,_Father_Ted other
1.6203597e7 1.6204072e7 23.0 "G"_Is_for_Gumshoe "H"_Is_for_Homicide link
null 1.6204072e7 24.0 other-google "H"_Is_for_Homicide other
2301720.0 1.6204072e7 11.0 Kinsey_Millhone "H"_Is_for_Homicide link
470006.0 1.6204072e7 26.0 Sue_Grafton "H"_Is_for_Homicide link
3.8164596e7 1261557.0 13.0 Where_Are_We_Now? Heroes link
null 1261557.0 38.0 other-bing Heroes other
1261548.0 1261557.0 59.0 Station_to_Station Heroes link
809108.0 1261557.0 61.0 The_Thin_White_Duke Heroes link
3041018.0 1261557.0 395.0 Stage_(David_Bowie_album) Heroes link
77767.0 1261557.0 23.0 The_Rise_and_Fall_of_Ziggy_Stardust_and_the_Spiders_from_Mars Heroes link
4231979.0 1261557.0 29.0 V-2_Schneider Heroes link
1261545.0 1261557.0 17.0 Young_Americans_(album) Heroes link
1448584.0 1261557.0 14.0 Symphony_No._1_(Glass) Heroes link
null 1261557.0 30.0 other-other Heroes other
954203.0 1261557.0 11.0 Neu!_'75 Heroes link
24540.0 1261557.0 10.0 Philip_Glass Heroes link
1.5580374e7 1261557.0 40.0 Main_Page Heroes other
4232032.0 1261557.0 16.0 Sense_of_Doubt Heroes link
286992.0 1261557.0 12.0 Oblique_Strategies Heroes link
1448572.0 1261557.0 2625.0 Low_(David_Bowie_album) Heroes link
2.1222062e7 1261557.0 17.0 Brian_Eno Heroes link
8786.0 1261557.0 2302.0 David_Bowie Heroes link
331174.0 1261557.0 1076.0 David_Bowie_discography Heroes link
195834.0 1261557.0 18.0 Aladdin_Sane Heroes link
3371542.0 1261557.0 30.0 Beauty_and_the_Beast_(David_Bowie_song) Heroes link
2371832.0 1261557.0 16.0 Carlos_Alomar Heroes link
2242811.0 1261557.0 10.0 Best_of_Bowie Heroes link
3239642.0 1261557.0 748.0 "Heroes"_(David_Bowie_song) Heroes link
2717533.0 1261557.0 17.0 Joe_the_Lion Heroes link
1.0631715e7 1261557.0 12.0 Fripp_&_Eno Heroes link
1.2439517e7 1261557.0 13.0 Hansa_Tonstudio Heroes link
735563.0 1261557.0 11.0 Florian_Schneider Heroes link
null 1261557.0 1904.0 other-google Heroes other
null 1261557.0 423.0 other-wikipedia Heroes other
null 1261557.0 13.0 other-twitter Heroes other
525018.0 1261557.0 15.0 Hunky_Dory Heroes link
1261524.0 1261557.0 10.0 Let's_Dance_(David_Bowie_album) Heroes link
1.1327948e7 1261557.0 482.0 Heroes Heroes other
1261561.0 1261557.0 88.0 Lodger_(album) Heroes link
4231891.0 1261557.0 17.0 Blackout_(David_Bowie_song) Heroes link
160783.0 1261557.0 25.0 1977_in_music Heroes link
1055167.0 1261557.0 10.0 Diamond_Dogs Heroes link
700842.0 1261557.0 11.0 David_Bowie_(1969_album) Heroes link
null 1261557.0 33.0 other-yahoo Heroes other
null 1261557.0 410.0 other-empty Heroes other
3.8164057e7 1261557.0 150.0 The_Next_Day Heroes link
751761.0 1261557.0 26.0 Tony_Visconti Heroes link
1448522.0 1261557.0 35.0 Symphony_No._4_(Glass) Heroes link
1260011.0 1261557.0 27.0 The_Idiot_(album) Heroes link
4232134.0 1261557.0 21.0 The_Secret_Life_of_Arabia Heroes link
207103.0 1261557.0 10.0 Vince_Clarke Heroes link
1261535.0 1261557.0 11.0 The_Man_Who_Sold_the_World_(album) Heroes link
3.5060284e7 1261557.0 31.0 Robert_Fripp_discography Heroes link
25893.0 1261557.0 143.0 Robert_Fripp Heroes link
4232070.0 1261557.0 12.0 Neuköln Heroes link
923669.0 1261557.0 26.0 Lust_for_Life_(album) Heroes link
4232056.0 1261557.0 14.0 Moss_Garden Heroes link
1261565.0 1261557.0 51.0 Scary_Monsters_(And_Super_Creeps) Heroes link
3.0871303e7 3564374.0 65.0 I_Am_that_I_Am "I_AM"_Activity link
704528.0 3564374.0 27.0 Elizabeth_Clare_Prophet "I_AM"_Activity other
2.3085408e7 3564374.0 76.0 Legends_of_Mount_Shasta "I_AM"_Activity link
1172131.0 3564374.0 47.0 List_of_founders_of_religious_traditions "I_AM"_Activity link
1.3251936e7 3564374.0 37.0 Ascended_Master_Teachings "I_AM"_Activity other
425823.0 3564374.0 16.0 Alice_Bailey "I_AM"_Activity other
1130388.0 3564374.0 18.0 Robert_LeFevre "I_AM"_Activity link
3.1597516e7 3564374.0 21.0 St._Germain_(Theosophy) "I_AM"_Activity link
21742.0 3564374.0 17.0 New_Age "I_AM"_Activity link
null 3564374.0 369.0 other-empty "I_AM"_Activity other
2399670.0 3564374.0 10.0 The_Aquarian_Gospel_of_Jesus_the_Christ "I_AM"_Activity other
2880213.0 3564374.0 18.0 United_States_v._Ballard "I_AM"_Activity link
200264.0 3564374.0 85.0 Theosophical_Society "I_AM"_Activity link
5501897.0 3564374.0 11.0 Seven_rays "I_AM"_Activity other
833097.0 3564374.0 18.0 William_Dudley_Pelley "I_AM"_Activity link
31480.0 3564374.0 41.0 Theosophy "I_AM"_Activity link
null 3564374.0 60.0 other-bing "I_AM"_Activity other
null 3564374.0 118.0 other-other "I_AM"_Activity other
null 3564374.0 33.0 other-facebook "I_AM"_Activity other
null 3564374.0 43.0 other-yahoo "I_AM"_Activity other
null 3564374.0 51.0 other-wikipedia "I_AM"_Activity other
null 3564374.0 640.0 other-google "I_AM"_Activity other
null 3564374.0 24.0 other-twitter "I_AM"_Activity other
609606.0 3564374.0 42.0 Church_Universal_and_Triumphant "I_AM"_Activity link
3207665.0 3564374.0 14.0 A_Dweller_on_Two_Planets "I_AM"_Activity link
804126.0 3564374.0 52.0 Ascended_master "I_AM"_Activity link
914523.0 3564374.0 100.0 Guy_Ballard "I_AM"_Activity link
1426548.0 3564374.0 20.0 I_Am "I_AM"_Activity link
1.4703837e7 3564374.0 38.0 Great_White_Brotherhood "I_AM"_Activity link
2399768.0 3564374.0 42.0 List_of_new_religious_movements "I_AM"_Activity other
2.4146787e7 3564374.0 40.0 List_of_UFO_religions "I_AM"_Activity link
4.2247669e7 1.7495549e7 30.0 Just_Around_the_Riverbend "I_Want"_song other
4.1895775e7 1.7495549e7 23.0 For_the_First_Time_in_Forever "I_Want"_song link
6131602.0 1.7495549e7 38.0 Go_the_Distance "I_Want"_song other
null 1.7495549e7 36.0 other-empty "I_Want"_song other
4.2850941e7 1.7495549e7 38.0 When_Will_My_Life_Begin? "I_Want"_song link
1.0090698e7 1.7495549e7 30.0 Part_of_Your_World "I_Want"_song link
null 1.7495549e7 135.0 other-google "I_Want"_song other
null 1.7495549e7 23.0 other-other "I_Want"_song other
2.2825519e7 2.315869e7 11.0 Anca_Petrescu "Ion_Mincu"_University_of_Architecture_and_Urbanism other
1249478.0 2.315869e7 24.0 List_of_universities_in_Romania "Ion_Mincu"_University_of_Architecture_and_Urbanism other
null 2.315869e7 60.0 other-google "Ion_Mincu"_University_of_Architecture_and_Urbanism other
null 2.315869e7 22.0 other-empty "Ion_Mincu"_University_of_Architecture_and_Urbanism other
null 1.6252208e7 11.0 other-empty "J"_Is_for_Judgment other
470006.0 1.6252208e7 17.0 Sue_Grafton "J"_Is_for_Judgment link
null 1.6252208e7 20.0 other-google "J"_Is_for_Judgment other
1.6252078e7 1.6252208e7 25.0 "I"_Is_for_Innocent "J"_Is_for_Judgment link
1960430.0 4.4810634e7 12.0 Fear_of_Pop:_Volume_1 "Just_Your_Average_Second_On_This_Planet"_1997-1998 link
null 4.4810634e7 10.0 other-empty "Just_Your_Average_Second_On_This_Planet"_1997-1998 other
null 1.6252523e7 16.0 other-empty "K"_Is_for_Killer other
1.6252208e7 1.6252523e7 24.0 "J"_Is_for_Judgment "K"_Is_for_Killer link
null 1.6252523e7 35.0 other-google "K"_Is_for_Killer other
null 1.6252523e7 16.0 other-wikipedia "K"_Is_for_Killer other
470006.0 1.6252523e7 28.0 Sue_Grafton "K"_Is_for_Killer link
null 487976.0 12.0 other-empty "King"_Bennie_Nawahi other
null 487976.0 10.0 other-other "King"_Bennie_Nawahi other
null 487976.0 11.0 other-wikipedia "King"_Bennie_Nawahi other
null 487976.0 36.0 other-google "King"_Bennie_Nawahi other
2110406.0 null 10.0 Pretty_Ricky Knockin'_Boots redlink null
null 3.8499517e7 68.0 other-empty "Left-Wing"_Communism:_An_Infantile_Disorder other
null 3.8499517e7 215.0 other-google "Left-Wing"_Communism:_An_Infantile_Disorder other
null 3.8499517e7 70.0 other-wikipedia "Left-Wing"_Communism:_An_Infantile_Disorder other
242476.0 3.8499517e7 50.0 Jack_Straw "Left-Wing"_Communism:_An_Infantile_Disorder link
null 3.8499517e7 15.0 other-other "Left-Wing"_Communism:_An_Infantile_Disorder other
1.1015252e7 3.8499517e7 11.0 Vladimir_Lenin "Left-Wing"_Communism:_An_Infantile_Disorder link
420150.0 3.8499517e7 12.0 The_State_and_Revolution "Left-Wing"_Communism:_An_Infantile_Disorder link
2075551.0 3.8499517e7 14.0 Ultra-leftism "Left-Wing"_Communism:_An_Infantile_Disorder other
302252.0 3.8499517e7 20.0 Council_communism "Left-Wing"_Communism:_An_Infantile_Disorder link
4.036248e7 3.8499517e7 13.0 Communist_Party_of_Great_Britain "Left-Wing"_Communism:_An_Infantile_Disorder other
3.9722572e7 3.8499517e7 19.0 Left_communism "Left-Wing"_Communism:_An_Infantile_Disorder link
null 2.8480474e7 18.0 other-google "M"_Circle other
null 1.6252603e7 11.0 other-empty "M"_Is_for_Malice other
null 1.6252603e7 53.0 other-google "M"_Is_for_Malice other
2301720.0 1.6252603e7 11.0 Kinsey_Millhone "M"_Is_for_Malice link
470006.0 1.6252603e7 32.0 Sue_Grafton "M"_Is_for_Malice link
null 1.6252603e7 12.0 other-other "M"_Is_for_Malice other
1.6252578e7 1.6252603e7 23.0 "L"_Is_for_Lawless "M"_Is_for_Malice link
null 3.6758937e7 15.0 other-empty "Major"_John_Buchanan other
null 3.6758937e7 17.0 other-wikipedia "Major"_John_Buchanan other
null 3.6758937e7 53.0 other-google "Major"_John_Buchanan other
2.1163352e7 563697.0 10.0 Master_Harold...and_the_Boys_(2010_film) "Master_Harold"...and_the_Boys link
7028414.0 563697.0 101.0 Eddie_Redmayne "Master_Harold"...and_the_Boys link
null 563697.0 36.0 other-wikipedia "Master_Harold"...and_the_Boys other
null 563697.0 170.0 other-empty "Master_Harold"...and_the_Boys other
null 563697.0 1967.0 other-google "Master_Harold"...and_the_Boys other
null 563697.0 38.0 other-other "Master_Harold"...and_the_Boys other
3747865.0 563697.0 32.0 Master_Harold_and_the_Boys "Master_Harold"...and_the_Boys link
null 563697.0 92.0 other-bing "Master_Harold"...and_the_Boys other
692334.0 563697.0 165.0 Athol_Fugard "Master_Harold"...and_the_Boys link
null 563697.0 78.0 other-yahoo "Master_Harold"...and_the_Boys other
3419979.0 null 20.0 Woodlouse_spider "Mothercare"_spider redlink
null 1.6252712e7 14.0 other-empty "N"_Is_for_Noose other
470006.0 1.6252712e7 22.0 Sue_Grafton "N"_Is_for_Noose link
null 1.6252712e7 27.0 other-google "N"_Is_for_Noose other
1.6252603e7 1.6252712e7 28.0 "M"_Is_for_Malice "N"_Is_for_Noose link
2301720.0 1.6252712e7 11.0 Kinsey_Millhone "N"_Is_for_Noose link
null 5400322.0 31.0 other-empty "No_Flashlight":_Songs_of_the_Fulfilled_Night other
null 5400322.0 29.0 other-google "No_Flashlight":_Songs_of_the_Fulfilled_Night other
1367146.0 5400322.0 475.0 Mount_Eerie "No_Flashlight":_Songs_of_the_Fulfilled_Night link
1.3792594e7 5400322.0 64.0 Mount_Eerie_pts._6_&_7 "No_Flashlight":_Songs_of_the_Fulfilled_Night link
5412817.0 5400322.0 12.0 Mount_Eerie_(album) "No_Flashlight":_Songs_of_the_Fulfilled_Night link
1990114.0 5400322.0 14.0 Phil_Elvrum "No_Flashlight":_Songs_of_the_Fulfilled_Night link
6626648.0 1.1898394e7 58.0 Masque_(The_Mission_album) "No_Snow,_No_Show"_for_the_Eskimo link
2.1221111e7 1.1898394e7 17.0 The_Mission_discography "No_Snow,_No_Show"_for_the_Eskimo other
null 1.1898394e7 14.0 other-google "No_Snow,_No_Show"_for_the_Eskimo other
null 1.2565141e7 38.0 other-empty "O"-Jung.Ban.Hap. other
7194112.0 1.2565141e7 79.0 TVXQ_albums_discography "O"-Jung.Ban.Hap. link
3.6922845e7 1.2565141e7 25.0 TVXQ_filmography "O"-Jung.Ban.Hap. link
2103628.0 1.2565141e7 333.0 TVXQ "O"-Jung.Ban.Hap. link
4.4402525e7 1.2565141e7 10.0 TVXQ_singles_discography "O"-Jung.Ban.Hap. link
1.0138105e7 1.2565141e7 26.0 Five_in_the_Black "O"-Jung.Ban.Hap. link
4.4314654e7 1.2565141e7 38.0 Jung_Chanwoo "O"-Jung.Ban.Hap. link
1.013822e7 1.2565141e7 53.0 Heart,_Mind_and_Soul_(TVXQ_album) "O"-Jung.Ban.Hap. link
3.3482901e7 1.2565141e7 13.0 List_of_songs_recorded_by_TVXQ "O"-Jung.Ban.Hap. link
null 1.2565141e7 168.0 other-google "O"-Jung.Ban.Hap. other
null 1.2565141e7 11.0 other-wikipedia "O"-Jung.Ban.Hap. other
null 2.8907601e7 12.0 other-other "Ode-to-Napoleon"_hexachord other
378674.0 2.8907601e7 11.0 Luigi_Nono "Ode-to-Napoleon"_hexachord link
null 2.8907601e7 82.0 other-google "Ode-to-Napoleon"_hexachord other
2.5133882e7 null 13.0 Goodnight–Loving_Trail "One_Arm_Bill"_Wilson redlink
null 1.6253383e7 39.0 other-google "P"_Is_for_Peril other
1.6252757e7 1.6253383e7 30.0 "O"_Is_for_Outlaw "P"_Is_for_Peril link
1.6253409e7 1.6253383e7 11.0 "Q"_Is_for_Quarry "P"_Is_for_Peril link
470006.0 1.6253383e7 27.0 Sue_Grafton "P"_Is_for_Peril link
null 1.6253383e7 72.0 other-empty "P"_Is_for_Peril other
null 1362990.0 31.0 other-yahoo "Pimpernel"_Smith other
83545.0 1362990.0 44.0 The_Scarlet_Pimpernel "Pimpernel"_Smith link
null 1362990.0 48.0 other-bing "Pimpernel"_Smith other
1.5580374e7 1362990.0 41.0 Main_Page "Pimpernel"_Smith other
null 1362990.0 48.0 other-other "Pimpernel"_Smith other
83538.0 1362990.0 205.0 Leslie_Howard_(actor) "Pimpernel"_Smith link
4331044.0 1362990.0 13.0 List_of_World_War_II_films "Pimpernel"_Smith other
null 1362990.0 73.0 other-wikipedia "Pimpernel"_Smith other
null 1362990.0 625.0 other-google "Pimpernel"_Smith other
null 1362990.0 101.0 other-empty "Pimpernel"_Smith other
2295388.0 1362990.0 26.0 Mary_Morris "Pimpernel"_Smith link
2722166.0 1362990.0 17.0 Francis_L._Sullivan "Pimpernel"_Smith link
609020.0 1362990.0 18.0 David_Tomlinson "Pimpernel"_Smith link
8245402.0 1362990.0 11.0 BOAC_Flight_777 "Pimpernel"_Smith other
null 4.3720216e7 32.0 other-wikipedia "Pro_knigi"_("About_books") other
8401830.0 4.5258416e7 59.0 Red_Terror_(Ethiopia) "Red_Terror"_Martyrs'_Memorial_Museum link
1.5580374e7 1478845.0 11.0 Main_Page "Repent,_Harlequin!"_Said_the_Ticktockman other
419269.0 1478845.0 10.0 Nebula_Award_for_Best_Short_Story "Repent,_Harlequin!"_Said_the_Ticktockman other
2838416.0 1478845.0 86.0 Run-on_sentence "Repent,_Harlequin!"_Said_the_Ticktockman link
null 1478845.0 27.0 other-bing "Repent,_Harlequin!"_Said_the_Ticktockman other
null 1478845.0 40.0 other-other "Repent,_Harlequin!"_Said_the_Ticktockman other
null 1478845.0 73.0 other-wikipedia "Repent,_Harlequin!"_Said_the_Ticktockman other
null 1478845.0 974.0 other-google "Repent,_Harlequin!"_Said_the_Ticktockman other
593138.0 1478845.0 10.0 I_Have_No_Mouth,_and_I_Must_Scream "Repent,_Harlequin!"_Said_the_Ticktockman link
2000840.0 1478845.0 24.0 List_of_joint_winners_of_the_Hugo_and_Nebula_awards "Repent,_Harlequin!"_Said_the_Ticktockman link
13462.0 1478845.0 320.0 Harlan_Ellison "Repent,_Harlequin!"_Said_the_Ticktockman link
38341.0 1478845.0 228.0 Harlequin "Repent,_Harlequin!"_Said_the_Ticktockman link
2.9446866e7 1478845.0 271.0 In_Time "Repent,_Harlequin!"_Said_the_Ticktockman link
151501.0 1478845.0 21.0 Jelly_bean "Repent,_Harlequin!"_Said_the_Ticktockman link
418599.0 1478845.0 21.0 Hugo_Award_for_Best_Short_Story "Repent,_Harlequin!"_Said_the_Ticktockman link
null 1478845.0 28.0 other-yahoo "Repent,_Harlequin!"_Said_the_Ticktockman other
1.0472351e7 1478845.0 14.0 Paingod_and_Other_Delusions "Repent,_Harlequin!"_Said_the_Ticktockman link
73257.0 1478845.0 51.0 V_for_Vendetta "Repent,_Harlequin!"_Said_the_Ticktockman link
null 1478845.0 136.0 other-empty "Repent,_Harlequin!"_Said_the_Ticktockman other
1.6253457e7 1.6253763e7 33.0 "R"_Is_for_Ricochet "S"_Is_for_Silence link
null 1.6253763e7 27.0 other-google "S"_Is_for_Silence other
470006.0 1.6253763e7 21.0 Sue_Grafton "S"_Is_for_Silence link
1187218.0 2398711.0 30.0 Bergen_Airport,_Flesland "Solidarity"_Szczecin–Goleniów_Airport other
636093.0 2398711.0 39.0 Warsaw_Chopin_Airport "Solidarity"_Szczecin–Goleniów_Airport link
28456.0 2398711.0 51.0 Szczecin "Solidarity"_Szczecin–Goleniów_Airport link
6135578.0 2398711.0 66.0 Szczecin_Airport "Solidarity"_Szczecin–Goleniów_Airport other
164508.0 2398711.0 21.0 Liverpool_John_Lennon_Airport "Solidarity"_Szczecin–Goleniów_Airport other
9688173.0 2398711.0 27.0 Moss_Airport,_Rygge "Solidarity"_Szczecin–Goleniów_Airport other
null 2398711.0 16.0 other-other "Solidarity"_Szczecin–Goleniów_Airport other
null 2398711.0 59.0 other-wikipedia "Solidarity"_Szczecin–Goleniów_Airport other
193169.0 2398711.0 11.0 Świnoujście "Solidarity"_Szczecin–Goleniów_Airport other
null 2398711.0 174.0 other-google "Solidarity"_Szczecin–Goleniów_Airport other
1.099953e7 2398711.0 14.0 Eurolot "Solidarity"_Szczecin–Goleniów_Airport other
293794.0 2398711.0 12.0 Dublin_Airport "Solidarity"_Szczecin–Goleniów_Airport other
400401.0 2398711.0 74.0 List_of_airports_in_Poland "Solidarity"_Szczecin–Goleniów_Airport link
null 2398711.0 55.0 other-empty "Solidarity"_Szczecin–Goleniów_Airport other
2161284.0 2398711.0 10.0 Wrocław–Copernicus_Airport "Solidarity"_Szczecin–Goleniów_Airport link
2716198.0 2398711.0 24.0 Rzeszów–Jasionka_Airport "Solidarity"_Szczecin–Goleniów_Airport link
1272752.0 2398711.0 10.0 Ryanair_destinations "Solidarity"_Szczecin–Goleniów_Airport link
206934.0 2398711.0 16.0 Luton_Airport "Solidarity"_Szczecin–Goleniów_Airport link
null 2641242.0 237.0 other-google "Still_Life"_(American_Concert_1981) other
null 2641242.0 60.0 other-wikipedia "Still_Life"_(American_Concert_1981) other
null 2641242.0 53.0 other-empty "Still_Life"_(American_Concert_1981) other
31056.0 2641242.0 19.0 The_Rolling_Stones "Still_Life"_(American_Concert_1981) link
8545280.0 2641242.0 13.0 The_Rolling_Stones_American_Tour_1981 "Still_Life"_(American_Concert_1981) other
1348046.0 2641242.0 402.0 The_Rolling_Stones_discography "Still_Life"_(American_Concert_1981) link
1458754.0 2641242.0 11.0 Take_the_"A"_Train "Still_Life"_(American_Concert_1981) link
null 2641242.0 10.0 other-other "Still_Life"_(American_Concert_1981) other
null 2641242.0 16.0 other-bing "Still_Life"_(American_Concert_1981) other
2290650.0 2641242.0 18.0 Still_Life_(disambiguation) "Still_Life"_(American_Concert_1981) link
5254045.0 2641242.0 11.0 Time_Is_on_My_Side "Still_Life"_(American_Concert_1981) link
2630454.0 2641242.0 152.0 Love_You_Live "Still_Life"_(American_Concert_1981) link
8799796.0 2641242.0 26.0 Going_to_a_Go-Go_(song) "Still_Life"_(American_Concert_1981) link
2644231.0 2641242.0 54.0 Flashpoint_(album) "Still_Life"_(American_Concert_1981) link
1.4416996e7 2641242.0 22.0 Let's_Spend_the_Night_Together_(film) "Still_Life"_(American_Concert_1981) link
2.4961418e7 null 14.0 Caro_Emerald That_Man redlink
4677458.0 2.249931e7 112.0 Jake_Holmes "The_Above_Ground_Sound"_of_Jake_Holmes link
null 2.249931e7 11.0 other-empty "The_Above_Ground_Sound"_of_Jake_Holmes other
null 2.249931e7 32.0 other-google "The_Above_Ground_Sound"_of_Jake_Holmes other
286821.0 2.249931e7 58.0 Dazed_and_Confused_(song) "The_Above_Ground_Sound"_of_Jake_Holmes link
1442421.0 null 12.0 Chadds_Ford_Township,_Delaware_County,_Pennsylvania "The_Mills",_Andrew_Wyeth_home_and_studio redlink
2804228.0 null 24.0 Champ_Ferguson "Tinker_Dave"_Beaty redlink
3.1466206e7 3.1466526e7 73.0 Nightmare:_The_Acoustic_M.S.G. "Unplugged"_Live link
207953.0 3.1466526e7 41.0 Michael_Schenker "Unplugged"_Live link
null 3.1466526e7 15.0 other-empty "Unplugged"_Live other
5399286.0 3.1466526e7 69.0 McAuley_Schenker_Group "Unplugged"_Live link
null 3.1466526e7 39.0 other-google "Unplugged"_Live other
null 1.2571133e7 223.0 other-google "V"_Is_for_Vagina other
null 1.2571133e7 51.0 other-wikipedia "V"_Is_for_Vagina other
9469360.0 1.2571133e7 14.0 Queen_B. "V"_Is_for_Vagina link
4619790.0 1.2571133e7 1031.0 Puscifer "V"_Is_for_Vagina link
null 1.2571133e7 22.0 other-other "V"_Is_for_Vagina other
222217.0 1.2571133e7 48.0 Tim_Commerford "V"_Is_for_Vagina link
147692.0 1.2571133e7 32.0 Tim_Alexander "V"_Is_for_Vagina link
3145813.0 1.2571133e7 12.0 Alessandro_Cortini "V"_Is_for_Vagina other
3.3622887e7 1.2571133e7 19.0 Conditions_of_My_Parole "V"_Is_for_Vagina link
1.186153e7 1.2571133e7 15.0 Jarboe "V"_Is_for_Vagina link
1.3087996e7 1.2571133e7 103.0 Eddie_McClintock "V"_Is_for_Vagina other
1.1981948e7 1.2571133e7 12.0 Gil_Sharone "V"_Is_for_Vagina other
1.6079543e7 1.2571133e7 19.0 "V"_Is_for_Viagra._The_Remixes "V"_Is_for_Vagina link
2.5033979e7 1.2571133e7 26.0 "C"_is_for_(Please_Insert_Sophomoric_Genitalia_Reference_HERE) "V"_Is_for_Vagina link
269859.0 1.2571133e7 11.0 A_Perfect_Circle "V"_Is_for_Vagina other
1.4098392e7 1.2571133e7 76.0 Don't_Shoot_the_Messenger "V"_Is_for_Vagina link
782360.0 1.2571133e7 11.0 Lustmord "V"_Is_for_Vagina other
1.5331615e7 1.2571133e7 23.0 Maynard_James_Keenan_discography "V"_Is_for_Vagina link
920501.0 1.2571133e7 320.0 Maynard_James_Keenan "V"_Is_for_Vagina link
null 1.2571133e7 94.0 other-empty "V"_Is_for_Vagina other
null 3.1438273e7 16.0 other-empty "V"_Is_for_Vengeance other
3.9606873e7 3.1438273e7 12.0 "W"_Is_for_Wasted "V"_Is_for_Vengeance link
2.5577776e7 3.1438273e7 30.0 "U"_Is_for_Undertow "V"_Is_for_Vengeance link
470006.0 3.1438273e7 59.0 Sue_Grafton "V"_Is_for_Vengeance link
null 3.1438273e7 76.0 other-google "V"_Is_for_Vengeance other
2301720.0 3.1438273e7 20.0 Kinsey_Millhone "V"_Is_for_Vengeance link
4619790.0 1.6079543e7 234.0 Puscifer "V"_Is_for_Viagra._The_Remixes link
null 1.6079543e7 23.0 other-empty "V"_Is_for_Viagra._The_Remixes other
1.2571133e7 1.6079543e7 175.0 "V"_Is_for_Vagina "V"_Is_for_Viagra._The_Remixes link
null 1.6079543e7 49.0 other-google "V"_Is_for_Viagra._The_Remixes other
null 1.6079543e7 12.0 other-wikipedia "V"_Is_for_Viagra._The_Remixes other
null 1.8938265e7 3683.0 other-wikipedia "Weird_Al"_Yankovic other
null 1.8938265e7 38430.0 other-google "Weird_Al"_Yankovic other
null 1.8938265e7 406.0 other-twitter "Weird_Al"_Yankovic other
3.3525518e7 1.8938265e7 22.0 Noretta "Weird_Al"_Yankovic link
26179.0 1.8938265e7 10.0 Ron_Popeil "Weird_Al"_Yankovic link
2560896.0 1.8938265e7 16.0 Lump_(song) "Weird_Al"_Yankovic link
4.1681071e7 1.8938265e7 26.0 My_Little_Pony:_Friendship_Is_Magic_(season_5) "Weird_Al"_Yankovic link
1093963.0 1.8938265e7 28.0 Polka_Party! "Weird_Al"_Yankovic link
1743090.0 1.8938265e7 52.0 Parody_music "Weird_Al"_Yankovic link
897080.0 1.8938265e7 12.0 Nash_Metropolitan "Weird_Al"_Yankovic link
21494.0 1.8938265e7 16.0 Nerd "Weird_Al"_Yankovic link
1124279.0 1.8938265e7 16.0 Mmm_Mmm_Mmm_Mmm "Weird_Al"_Yankovic link
2.6095723e7 1.8938265e7 16.0 Nothin'_on_You "Weird_Al"_Yankovic link
3.4826435e7 1.8938265e7 17.0 Parody_in_popular_music "Weird_Al"_Yankovic link
43397.0 1.8938265e7 52.0 Polka "Weird_Al"_Yankovic link
3.9863336e7 1.8938265e7 18.0 Royals_(song) "Weird_Al"_Yankovic link
1706988.0 1.8938265e7 15.0 Mickey_(song) "Weird_Al"_Yankovic link
1237220.0 1.8938265e7 25.0 Patton_Oswalt "Weird_Al"_Yankovic link
2.1985451e7 1.8938265e7 76.0 Lynwood,_California "Weird_Al"_Yankovic link
4711303.0 1.8938265e7 35.0 Ridin' "Weird_Al"_Yankovic link
4.180637e7 1.8938265e7 59.0 Pinkie_Pride "Weird_Al"_Yankovic link
1.5580374e7 1.8938265e7 1791.0 Main_Page "Weird_Al"_Yankovic other
1.8960192e7 1.8938265e7 66.0 Parody "Weird_Al"_Yankovic link
3070450.0 1.8938265e7 14.0 My_Sharona "Weird_Al"_Yankovic link
2.6330755e7 1.8938265e7 13.0 Matt_L._Jones "Weird_Al"_Yankovic other
1680148.0 1.8938265e7 37.0 Piano_Man_(song) "Weird_Al"_Yankovic link
667089.0 1.8938265e7 140.0 Poodle_Hat "Weird_Al"_Yankovic link
1346791.0 1.8938265e7 16.0 Richard_Cheese "Weird_Al"_Yankovic link
2366938.0 1.8938265e7 44.0 Oy_vey "Weird_Al"_Yankovic link
1614455.0 1.8938265e7 38.0 Money_for_Nothing_(song) "Weird_Al"_Yankovic link
3.3054146e7 1.8938265e7 13.0 Sexy_and_I_Know_It "Weird_Al"_Yankovic link
4072442.0 1.8938265e7 10.0 One_More_Minute "Weird_Al"_Yankovic link
3307306.0 1.8938265e7 17.0 More_Than_Words "Weird_Al"_Yankovic link
2271575.0 1.8938265e7 21.0 Lose_Yourself "Weird_Al"_Yankovic link
569820.0 1.8938265e7 28.0 Losing_My_Religion "Weird_Al"_Yankovic link
183389.0 1.8938265e7 11.0 Scott_Raynor "Weird_Al"_Yankovic link
183860.0 1.8938265e7 13.0 National_Speech_and_Debate_Association "Weird_Al"_Yankovic link
4.30701e7 1.8938265e7 1180.0 Mandatory_Fun "Weird_Al"_Yankovic link
1.1423454e7 1.8938265e7 56.0 Pretty_Fly_for_a_Rabbi "Weird_Al"_Yankovic link
4070747.0 1.8938265e7 17.0 Ricky_(song) "Weird_Al"_Yankovic link
2102697.0 1.8938265e7 25.0 My_Bologna "Weird_Al"_Yankovic link
3.3145323e7 1.8938265e7 11.0 Peter_Shukoff "Weird_Al"_Yankovic other
288469.0 1.8938265e7 23.0 Novelty_song "Weird_Al"_Yankovic link
1.0090978e7 1.8938265e7 21.0 Peter_and_the_Wolf_("Weird_Al"_Yankovic_&_Wendy_Carlos_album) "Weird_Al"_Yankovic link
2051328.0 1.8938265e7 16.0 Pretty_Fly_(for_a_White_Guy) "Weird_Al"_Yankovic link
1982071.0 1.8938265e7 54.0 Lola_(song) "Weird_Al"_Yankovic link
177256.0 1.8938265e7 112.0 RCA_Records "Weird_Al"_Yankovic link
30951.0 1.8938265e7 15.0 Tom_Lehrer "Weird_Al"_Yankovic link
48110.0 1.8938265e7 10.0 Tonya_Harding "Weird_Al"_Yankovic link
4.1599803e7 1.8938265e7 10.0 The_Hotwives_of_Orlando "Weird_Al"_Yankovic link
4.3303551e7 1.8938265e7 91.0 Tacky_(song) "Weird_Al"_Yankovic link
3588232.0 1.8938265e7 10.0 We're_Not_Gonna_Take_It_(Twisted_Sister_song) "Weird_Al"_Yankovic link
2.4204138e7 1.8938265e7 36.0 The_Essential_"Weird_Al"_Yankovic "Weird_Al"_Yankovic link
2.6423707e7 1.8938265e7 38.0 Yugoslav_American "Weird_Al"_Yankovic link
847078.0 1.8938265e7 13.0 Taxman "Weird_Al"_Yankovic link
8567624.0 1.8938265e7 18.0 Tim_and_Eric_Awesome_Show,_Great_Job! "Weird_Al"_Yankovic link
1.4295471e7 1.8938265e7 11.0 Sussudio "Weird_Al"_Yankovic other
3.7064351e7 1.8938265e7 10.0 The_Wrong_Ferarri "Weird_Al"_Yankovic link
924771.0 1.8938265e7 40.0 The_Naked_Gun_2½:_The_Smell_of_Fear "Weird_Al"_Yankovic link
1.0091063e7 1.8938265e7 34.0 Trapped_in_the_Drive-Thru "Weird_Al"_Yankovic link
1412241.0 1.8938265e7 10.0 The_Late_Late_Show_with_Craig_Ferguson "Weird_Al"_Yankovic other
6088238.0 1.8938265e7 18.0 The_Grim_Adventures_of_Billy_&_Mandy_(video_game) "Weird_Al"_Yankovic link
4110029.0 1.8938265e7 10.0 The_Night_Santa_Went_Crazy "Weird_Al"_Yankovic link
113092.0 1.8938265e7 57.0 Twinkie "Weird_Al"_Yankovic link
7051503.0 1.8938265e7 10.0 The_Drew_Carey_Show "Weird_Al"_Yankovic link
1.9156218e7 1.8938265e7 11.0 Star_Wars_Kid "Weird_Al"_Yankovic link
null 1.8938265e7 1580.0 other-bing "Weird_Al"_Yankovic other
28367.0 1.8938265e7 33.0 Spam_(food) "Weird_Al"_Yankovic other
5578884.0 1.8938265e7 34.0 You're_Pitiful "Weird_Al"_Yankovic link
1996695.0 1.8938265e7 20.0 You're_Beautiful "Weird_Al"_Yankovic link
390867.0 1.8938265e7 438.0 UHF_(film) "Weird_Al"_Yankovic link
7894054.0 1.8938265e7 10.0 Wayne_Bergeron "Weird_Al"_Yankovic link
59993.0 1.8938265e7 21.0 Smells_Like_Teen_Spirit "Weird_Al"_Yankovic link
1708927.0 1.8938265e7 25.0 Weird "Weird_Al"_Yankovic link
627511.0 1.8938265e7 12.0 Victoria_Jackson "Weird_Al"_Yankovic link
2446828.0 1.8938265e7 12.0 Trinidad_Silva "Weird_Al"_Yankovic link
3.8575277e7 1.8938265e7 15.0 Toby_Turner "Weird_Al"_Yankovic other
194627.0 1.8938265e7 19.0 Straight_Outta_Compton "Weird_Al"_Yankovic link
1331938.0 1.8938265e7 32.0 Three_Gays_of_the_Condo "Weird_Al"_Yankovic link
189288.0 1.8938265e7 11.0 The_Adventures_of_Captain_Underpants "Weird_Al"_Yankovic other
2315700.0 1.8938265e7 85.0 The_Saga_Begins "Weird_Al"_Yankovic link
5043734.0 1.8938265e7 67.0 Wikipedia "Weird_Al"_Yankovic link
1.3932969e7 1.8938265e7 46.0 That_'90s_Show "Weird_Al"_Yankovic link
8109388.0 1.8938265e7 16.0 Tomorrow_(TV_series) "Weird_Al"_Yankovic link
5254612.0 1.8938265e7 11.0 Yo_Gabba_Gabba! "Weird_Al"_Yankovic link
983849.0 1.8938265e7 51.0 Top_Secret! "Weird_Al"_Yankovic link
6075882.0 1.8938265e7 257.0 White_&_Nerdy "Weird_Al"_Yankovic link
6064795.0 1.8938265e7 232.0 Straight_Outta_Lynwood "Weird_Al"_Yankovic link
1050549.0 1.8938265e7 17.0 Volcano_Entertainment "Weird_Al"_Yankovic link
1314319.0 1.8938265e7 142.0 The_Weird_Al_Show "Weird_Al"_Yankovic link
4109934.0 1.8938265e7 11.0 Spy_Hard_(song) "Weird_Al"_Yankovic link
4.4076048e7 1.8938265e7 241.0 Super_Bowl_XLIX_halftime_show "Weird_Al"_Yankovic link
null 1.8938265e7 1369.0 other-other "Weird_Al"_Yankovic other
null 1.8938265e7 33.0 other-facebook "Weird_Al"_Yankovic other
681786.0 1.8938265e7 94.0 Bad_Hair_Day "Weird_Al"_Yankovic link
693809.0 1.8938265e7 15.0 Chop_Suey!_(song) "Weird_Al"_Yankovic link
1779981.0 1.8938265e7 16.0 Chamillionaire "Weird_Al"_Yankovic link
2.6534206e7 1.8938265e7 11.0 Comedy_Bang!_Bang! "Weird_Al"_Yankovic link
3380816.0 1.8938265e7 11.0 Down_with_the_Sickness "Weird_Al"_Yankovic link
3.6682042e7 1.8938265e7 213.0 Dollmaker_(comics) "Weird_Al"_Yankovic link
143435.0 1.8938265e7 20.0 Band_Aid_(band) "Weird_Al"_Yankovic other
157548.0 1.8938265e7 31.0 Allan_Sherman "Weird_Al"_Yankovic link
107622.0 1.8938265e7 27.0 Downey,_California "Weird_Al"_Yankovic link
2320524.0 1.8938265e7 37.0 Black_or_White "Weird_Al"_Yankovic link
1.9375754e7 1.8938265e7 10.0 Craigslist "Weird_Al"_Yankovic link
1.6046327e7 1.8938265e7 242.0 DC_Universe_Animated_Original_Movies "Weird_Al"_Yankovic link
1.1560543e7 1.8938265e7 37.0 Comedy_music "Weird_Al"_Yankovic link
495591.0 1.8938265e7 25.0 Cola_Wars "Weird_Al"_Yankovic other
149681.0 1.8938265e7 13.0 Beck "Weird_Al"_Yankovic other
2102759.0 1.8938265e7 29.0 Another_One_Rides_the_Bus "Weird_Al"_Yankovic link
1856564.0 1.8938265e7 40.0 Comedy_rock "Weird_Al"_Yankovic link
2258507.0 1.8938265e7 39.0 Albuquerque_(song) "Weird_Al"_Yankovic link
539377.0 1.8938265e7 10.0 Animal_Man "Weird_Al"_Yankovic link
8133962.0 1.8938265e7 10.0 Back_at_the_Barnyard "Weird_Al"_Yankovic link
3.1561319e7 1.8938265e7 130.0 Alpocalypse "Weird_Al"_Yankovic link
59610.0 1.8938265e7 105.0 Atlantic_Records "Weird_Al"_Yankovic link
3.9384064e7 1.8938265e7 965.0 57th_Annual_Grammy_Awards "Weird_Al"_Yankovic link
1.1700181e7 1.8938265e7 13.0 Close_but_No_Cigar "Weird_Al"_Yankovic link
3456183.0 1.8938265e7 12.0 Christmas_at_Ground_Zero "Weird_Al"_Yankovic link
3.9949337e7 1.8938265e7 24.0 Drunk_History "Weird_Al"_Yankovic link
1.0294516e7 1.8938265e7 17.0 "Weird_Al"_Yankovic_videography "Weird_Al"_Yankovic link
5828575.0 1.8938265e7 21.0 Couch_Potato_(song) "Weird_Al"_Yankovic link
680375.0 1.8938265e7 10.0 Bill_Plympton "Weird_Al"_Yankovic link
681837.0 1.8938265e7 160.0 "Weird_Al"_Yankovic_in_3-D "Weird_Al"_Yankovic link
3.3246249e7 1.8938265e7 13.0 "Weird_Al"_Yankovic_Live!:_The_Alpocalypse_Tour "Weird_Al"_Yankovic link
681846.0 1.8938265e7 284.0 "Weird_Al"_Yankovic_(album) "Weird_Al"_Yankovic link
3.1218217e7 1.8938265e7 25.0 Comedy_hip_hop "Weird_Al"_Yankovic link
3913016.0 1.8938265e7 26.0 "Weird_Al"_Yankovic's_Greatest_Hits "Weird_Al"_Yankovic link
231982.0 1.8938265e7 242.0 Dr._Demento "Weird_Al"_Yankovic link
51278.0 1.8938265e7 21.0 Albuquerque,_New_Mexico "Weird_Al"_Yankovic other
440017.0 1.8938265e7 48.0 Aisha_Tyler "Weird_Al"_Yankovic link
168263.0 1.8938265e7 26.0 Coolio "Weird_Al"_Yankovic link
1745307.0 1.8938265e7 26.0 Cledus_T._Judd "Weird_Al"_Yankovic link
4.1156988e7 1.8938265e7 52.0 Doge_(meme) "Weird_Al"_Yankovic link
7260565.0 1.8938265e7 26.0 Canadian_Idiot "Weird_Al"_Yankovic link
4082868.0 1.8938265e7 33.0 Dare_to_Be_Stupid_(song) "Weird_Al"_Yankovic link
286323.0 1.8938265e7 11.0 Crash_Test_Dummies "Weird_Al"_Yankovic link
1.1871605e7 1.8938265e7 10.0 Cherry_Pie_(Warrant_song) "Weird_Al"_Yankovic other
239034.0 1.8938265e7 20.0 Dweezil_Zappa "Weird_Al"_Yankovic link
726651.0 1.8938265e7 18.0 Donny_Osmond "Weird_Al"_Yankovic link
3.3629854e7 1.8938265e7 13.0 DC_Nation_Shorts "Weird_Al"_Yankovic link
5779338.0 1.8938265e7 13.0 Closing_Time_(Semisonic_song) "Weird_Al"_Yankovic link
4.3318058e7 1.8938265e7 11.0 Claudia_O'Doherty "Weird_Al"_Yankovic link
10672.0 1.8938265e7 56.0 Frank_Zappa "Weird_Al"_Yankovic link
1439800.0 1.8938265e7 82.0 Eye_of_the_Tiger "Weird_Al"_Yankovic link
428865.0 1.8938265e7 10.0 Ironic_(song) "Weird_Al"_Yankovic link
2.391975e7 1.8938265e7 11.0 Fireflies_(Owl_City_song) "Weird_Al"_Yankovic link
2125672.0 1.8938265e7 11.0 Ghetto_Supastar_(That_Is_What_You_Are) "Weird_Al"_Yankovic link
9181653.0 1.8938265e7 13.0 List_of_How_I_Met_Your_Mother_characters "Weird_Al"_Yankovic link
197889.0 1.8938265e7 17.0 Felix_the_Cat "Weird_Al"_Yankovic link
1.1901898e7 1.8938265e7 12.0 Here's_Johnny "Weird_Al"_Yankovic link
1.015197e7 1.8938265e7 12.0 Hip_to_Be_Square "Weird_Al"_Yankovic other
343560.0 1.8938265e7 19.0 Jack_Black "Weird_Al"_Yankovic link
2683218.0 1.8938265e7 15.0 List_of_Lilo_&_Stitch:_The_Series_episodes "Weird_Al"_Yankovic link
1.1311126e7 1.8938265e7 35.0 It's_All_About_the_Pentiums "Weird_Al"_Yankovic link
7297505.0 1.8938265e7 25.0 Kristen_Schaal "Weird_Al"_Yankovic other
4072138.0 1.8938265e7 52.0 Like_a_Surgeon_("Weird_Al"_Yankovic_song) "Weird_Al"_Yankovic link
7419707.0 1.8938265e7 24.0 John_Morrison_(wrestler) "Weird_Al"_Yankovic link
1178120.0 1.8938265e7 24.0 Headline_News_(song) "Weird_Al"_Yankovic link
4.1170737e7 1.8938265e7 37.0 Happy_(Pharrell_Williams_song) "Weird_Al"_Yankovic link
639642.0 1.8938265e7 13.0 I'll_Be_There_for_You_(The_Rembrandts_song) "Weird_Al"_Yankovic link
2.0593859e7 1.8938265e7 15.0 Jizz_in_My_Pants "Weird_Al"_Yankovic link
1053554.0 1.8938265e7 94.0 Gangsta's_Paradise "Weird_Al"_Yankovic link
1.2798266e7 1.8938265e7 182.0 List_of_"Weird_Al"_Yankovic_polka_medleys "Weird_Al"_Yankovic link
4012959.0 1.8938265e7 12.0 I_Can't_Dance "Weird_Al"_Yankovic link
9903893.0 1.8938265e7 12.0 List_of_Italian-American_entertainers "Weird_Al"_Yankovic link
4.3368354e7 1.8938265e7 33.0 Handy_(song) "Weird_Al"_Yankovic link
5888557.0 1.8938265e7 10.0 Footloose_(song) "Weird_Al"_Yankovic link
4429395.0 1.8938265e7 57.0 Eminem "Weird_Al"_Yankovic link
43727.0 1.8938265e7 144.0 Frankie_Yankovic "Weird_Al"_Yankovic link
681823.0 1.8938265e7 101.0 Even_Worse "Weird_Al"_Yankovic link
2706860.0 1.8938265e7 11.0 Got_My_Mind_Set_on_You "Weird_Al"_Yankovic link
3955585.0 1.8938265e7 12.0 Janie's_Got_a_Gun "Weird_Al"_Yankovic link
2.7748226e7 1.8938265e7 11.0 Jeopardy! "Weird_Al"_Yankovic link
238443.0 1.8938265e7 11.0 Liam_Lynch_(musician) "Weird_Al"_Yankovic link
4.009594e7 1.8938265e7 16.0 List_of_2014_albums "Weird_Al"_Yankovic link
8473867.0 1.8938265e7 10.0 List_of_30_Rock_characters "Weird_Al"_Yankovic other
3815781.0 1.8938265e7 42.0 Keegan-Michael_Key "Weird_Al"_Yankovic link
158879.0 1.8938265e7 60.0 George_Carlin "Weird_Al"_Yankovic other
912417.0 1.8938265e7 10.0 Grammy_Award_for_Best_Concept_Music_Video "Weird_Al"_Yankovic link
487070.0 1.8938265e7 14.0 Emo_Philips "Weird_Al"_Yankovic other
738015.0 1.8938265e7 27.0 Johnny_Bravo "Weird_Al"_Yankovic link
1.1224587e7 1.8938265e7 31.0 I_Lost_on_Jeopardy "Weird_Al"_Yankovic link
3671687.0 1.8938265e7 10.0 Leibniz–Newton_calculus_controversy "Weird_Al"_Yankovic link
1.0340709e7 1.8938265e7 13.0 List_of_RCA_Records_artists "Weird_Al"_Yankovic link
2.1207345e7 1.8938265e7 17.0 Jerry_Springer "Weird_Al"_Yankovic link
4.162918e7 1.8938265e7 87.0 Game_Grumps "Weird_Al"_Yankovic link
8376816.0 1.8938265e7 12.0 Electric_Avenue_(song) "Weird_Al"_Yankovic link
155452.0 1.8938265e7 16.0 Hey_Jude "Weird_Al"_Yankovic link
4.33773e7 1.8938265e7 31.0 Foil_(song) "Weird_Al"_Yankovic link
9030340.0 1.8938265e7 38.0 List_of_Cal_Poly_at_San_Luis_Obispo_alumni "Weird_Al"_Yankovic link
3795017.0 1.8938265e7 84.0 Liberian_Girl "Weird_Al"_Yankovic link
9410052.0 1.8938265e7 17.0 List_of_polka_artists "Weird_Al"_Yankovic link
2.0706022e7 1.8938265e7 10.0 List_of_Volcano_Entertainment_artists "Weird_Al"_Yankovic link
2018105.0 1.8938265e7 140.0 List_of_songs_recorded_by_"Weird_Al"_Yankovic "Weird_Al"_Yankovic link
703188.0 1.8938265e7 70.0 List_of_vegans "Weird_Al"_Yankovic link
640911.0 1.8938265e7 29.0 List_of_satirists_and_satires "Weird_Al"_Yankovic link
873835.0 1.8938265e7 19.0 Al_TV "Weird_Al"_Yankovic link
681811.0 1.8938265e7 53.0 Alapalooza "Weird_Al"_Yankovic link
156683.0 1.8938265e7 23.0 Don_Pardo "Weird_Al"_Yankovic link
3.4290593e7 1.8938265e7 31.0 Call_Me_Maybe "Weird_Al"_Yankovic link
4161738.0 1.8938265e7 11.0 "Weird_Al"_Yankovic_Live! "Weird_Al"_Yankovic link
1.8938265e7 1.8938265e7 38.0 "Weird_Al"_Yankovic "Weird_Al"_Yankovic other
2661948.0 1.8938265e7 54.0 Auto-Tune "Weird_Al"_Yankovic other
998258.0 1.8938265e7 180.0 Amish_Paradise "Weird_Al"_Yankovic link
7630017.0 1.8938265e7 140.0 "Weird_Al"_Yankovic_discography "Weird_Al"_Yankovic link
4.5137822e7 1.8938265e7 469.0 Batman_vs._Robin "Weird_Al"_Yankovic link
1.1424214e7 1.8938265e7 43.0 EBay_(song) "Weird_Al"_Yankovic link
1.5957378e7 1.8938265e7 844.0 Aaron_Paul "Weird_Al"_Yankovic link
739408.0 1.8938265e7 102.0 Eat_It "Weird_Al"_Yankovic link
41953.0 1.8938265e7 75.0 Bohemian_Rhapsody "Weird_Al"_Yankovic link
432147.0 1.8938265e7 10.0 Charles_Nelson_Reilly "Weird_Al"_Yankovic link
1.852763e7 1.8938265e7 16.0 Eat_It_("Weird_Al"_Yankovic_album) "Weird_Al"_Yankovic link
3716049.0 1.8938265e7 29.0 Addicted_to_Love_(song) "Weird_Al"_Yankovic link
154247.0 1.8938265e7 11.0 Eddie_Vedder "Weird_Al"_Yankovic other
1.6757649e7 1.8938265e7 22.0 Batman:_The_Brave_and_the_Bold "Weird_Al"_Yankovic link
681830.0 1.8938265e7 87.0 Dare_to_Be_Stupid "Weird_Al"_Yankovic link
6600777.0 1.8938265e7 29.0 Don't_Download_This_Song "Weird_Al"_Yankovic link
51744.0 1.8938265e7 86.0 American_Pie_(song) "Weird_Al"_Yankovic link
663903.0 1.8938265e7 16.0 Behind_the_Music "Weird_Al"_Yankovic link
209416.0 1.8938265e7 44.0 Ben_Folds "Weird_Al"_Yankovic link
1.1748645e7 1.8938265e7 17.0 Another_One_Rides_the_Bus_(EP) "Weird_Al"_Yankovic link
3281387.0 1.8938265e7 11.0 Bedrock_Anthem "Weird_Al"_Yankovic link
1.7080248e7 1.8938265e7 51.0 Beat_It "Weird_Al"_Yankovic link
3.9026706e7 1.8938265e7 11.0 Blurred_Lines "Weird_Al"_Yankovic link
2.3138118e7 1.8938265e7 23.0 Craigslist_(song) "Weird_Al"_Yankovic link
1782176.0 1.8938265e7 14.0 Bad_(Michael_Jackson_song) "Weird_Al"_Yankovic link
433230.0 1.8938265e7 29.0 California_Polytechnic_State_University "Weird_Al"_Yankovic other
1263456.0 1.8938265e7 40.0 ApologetiX "Weird_Al"_Yankovic link
1162.0 1.8938265e7 33.0 Accordion "Weird_Al"_Yankovic link
1.6173682e7 1.8938265e7 14.0 List_of_keytarists "Weird_Al"_Yankovic link
4807288.0 1.8938265e7 16.0 List_of_backmasked_messages "Weird_Al"_Yankovic link
5747670.0 1.8938265e7 58.0 Live_and_Let_Die_(song) "Weird_Al"_Yankovic link
6041.0 1.8938265e7 10.0 List_of_comedians "Weird_Al"_Yankovic link
2.5011929e7 1.8938265e7 10.0 John_and_Lorena_Bobbitt "Weird_Al"_Yankovic link
3.1526507e7 1.8938265e7 31.0 How_I_Met_Your_Mother_(season_7) "Weird_Al"_Yankovic link
4.274688e7 1.8938265e7 431.0 Galavant "Weird_Al"_Yankovic link
1503921.0 1.8938265e7 19.0 Hidden_track "Weird_Al"_Yankovic link
1.2089889e7 1.8938265e7 31.0 Eric_Stonestreet "Weird_Al"_Yankovic link
4094664.0 1.8938265e7 11.0 Lasagna_(song) "Weird_Al"_Yankovic link
4169218.0 1.8938265e7 11.0 Haunted_Lighthouse "Weird_Al"_Yankovic link
4109863.0 1.8938265e7 13.0 Gump_(song) "Weird_Al"_Yankovic link
4070828.0 1.8938265e7 17.0 I_Love_Rocky_Road "Weird_Al"_Yankovic link
2739735.0 1.8938265e7 10.0 I_Touch_Myself "Weird_Al"_Yankovic link
1847150.0 1.8938265e7 18.0 Ice_Ice_Baby "Weird_Al"_Yankovic link
4.4786839e7 1.8938265e7 31.0 List_of_The_Late_Late_Show_episodes_(2015_guest_hosts) "Weird_Al"_Yankovic other
3615165.0 1.8938265e7 18.0 I_Want_It_That_Way "Weird_Al"_Yankovic link
3785221.0 1.8938265e7 26.0 Jim_West_(guitarist) "Weird_Al"_Yankovic link
1928807.0 1.8938265e7 12.0 Fanny_pack "Weird_Al"_Yankovic link
3.3592458e7 1.8938265e7 213.0 Epic_Rap_Battles_of_History "Weird_Al"_Yankovic link
185416.0 1.8938265e7 220.0 Grammy_Award_for_Best_Comedy_Album "Weird_Al"_Yankovic link
1.5022585e7 1.8938265e7 12.0 James_Blunt "Weird_Al"_Yankovic other
4.3552374e7 1.8938265e7 26.0 Ice_Bucket_Challenge "Weird_Al"_Yankovic link
1.0417152e7 1.8938265e7 13.0 I_Want_a_New_Drug "Weird_Al"_Yankovic link
3.0821628e7 1.8938265e7 16.0 List_of_My_Little_Pony:_Friendship_Is_Magic_episodes "Weird_Al"_Yankovic link
4.3350433e7 1.8938265e7 25.0 Lame_Claim_to_Fame "Weird_Al"_Yankovic link
3.5191643e7 1.8938265e7 27.0 Kidnapped_by_Danger "Weird_Al"_Yankovic other
3.2291112e7 1.8938265e7 45.0 List_of_My_Little_Pony:_Friendship_Is_Magic_characters "Weird_Al"_Yankovic link
5458198.0 1.8938265e7 14.0 Judy_Tenuta "Weird_Al"_Yankovic link
85307.0 1.8938265e7 25.0 Huey_Lewis_and_the_News "Weird_Al"_Yankovic link
2157741.0 1.8938265e7 35.0 Jon_Schwartz_(drummer) "Weird_Al"_Yankovic link
210836.0 1.8938265e7 13.0 Falco_(musician) "Weird_Al"_Yankovic link
1.3101139e7 1.8938265e7 11.0 Here's_Johnny_(song) "Weird_Al"_Yankovic link
4196662.0 1.8938265e7 27.0 Jordan_Peele "Weird_Al"_Yankovic link
625478.0 1.8938265e7 22.0 Gilbert_Gottfried "Weird_Al"_Yankovic other
2.1604923e7 1.8938265e7 15.0 Isaac_Newton_in_popular_culture "Weird_Al"_Yankovic link
359520.0 1.8938265e7 43.0 Joan_Jett "Weird_Al"_Yankovic link
4109668.0 1.8938265e7 21.0 Jurassic_Park_(song) "Weird_Al"_Yankovic link
2.3138134e7 1.8938265e7 15.0 Internet_Leaks "Weird_Al"_Yankovic link
4.4955052e7 1.8938265e7 14.0 List_of_Epic_Rap_Battles_of_History_episodes "Weird_Al"_Yankovic link
1601775.0 1.8938265e7 10.0 Let's_Get_It_Started "Weird_Al"_Yankovic link
462389.0 1.8938265e7 19.0 List_of_The_Simpsons_guest_stars "Weird_Al"_Yankovic link
2.8271869e7 1.8938265e7 10.0 Hurley_(album) "Weird_Al"_Yankovic other
1742323.0 1.8938265e7 114.0 Fat_(song) "Weird_Al"_Yankovic link
null 1.8938265e7 5129.0 other-empty "Weird_Al"_Yankovic other
32851.0 1.8938265e7 23.0 Wiki "Weird_Al"_Yankovic other
228355.0 1.8938265e7 95.0 The_Naked_Gun:_From_the_Files_of_Police_Squad! "Weird_Al"_Yankovic link
6932227.0 1.8938265e7 11.0 The_Saga_Begins_(album) "Weird_Al"_Yankovic link
1.0128701e7 1.8938265e7 14.0 The_Biggest_Ball_of_Twine_in_Minnesota "Weird_Al"_Yankovic link
7198320.0 1.8938265e7 24.0 Weasel_Stomping_Day "Weird_Al"_Yankovic link
2026444.0 1.8938265e7 44.0 The_Safety_Dance "Weird_Al"_Yankovic link
1.9653364e7 1.8938265e7 11.0 Whatever_You_Like_("Weird_Al"_Yankovic_song) "Weird_Al"_Yankovic link
4.1684727e7 1.8938265e7 10.0 Whiplash_(2014_film) "Weird_Al"_Yankovic other
5419140.0 1.8938265e7 91.0 The_Naked_Gun "Weird_Al"_Yankovic link
4.0688399e7 1.8938265e7 10.0 Timber_(song) "Weird_Al"_Yankovic link
1584368.0 1.8938265e7 65.0 Yankovic "Weird_Al"_Yankovic link
3732122.0 1.8938265e7 15.0 Steve_Jay "Weird_Al"_Yankovic link
890630.0 1.8938265e7 80.0 Vanna_White "Weird_Al"_Yankovic link
4.3630148e7 1.8938265e7 21.0 Wallykazam! "Weird_Al"_Yankovic link
681822.0 1.8938265e7 27.0 UHF_–_Original_Motion_Picture_Soundtrack_and_Other_Stuff "Weird_Al"_Yankovic link
3070376.0 1.8938265e7 16.0 The_Food_Album "Weird_Al"_Yankovic link
4584644.0 1.8938265e7 34.0 Yoda_(song) "Weird_Al"_Yankovic link
8567806.0 1.8938265e7 23.0 WordGirl "Weird_Al"_Yankovic link
4.3309524e7 1.8938265e7 92.0 Word_Crimes "Weird_Al"_Yankovic link
87474.0 1.8938265e7 13.0 Tony_Hawk "Weird_Al"_Yankovic link
1969519.0 1.8938265e7 24.0 U_Can't_Touch_This "Weird_Al"_Yankovic link
3.7656763e7 1.8938265e7 32.0 Thrift_Shop "Weird_Al"_Yankovic link
1.878465e7 1.8938265e7 832.0 Super_Bowl_XLIX "Weird_Al"_Yankovic other
4109439.0 1.8938265e7 21.0 You_Don't_Love_Me_Anymore_("Weird_Al"_Yankovic_song) "Weird_Al"_Yankovic link
1867173.0 1.8938265e7 57.0 Naked_Gun_33⅓:_The_Final_Insult "Weird_Al"_Yankovic link
22572.0 1.8938265e7 10.0 October_23 "Weird_Al"_Yankovic link
382199.0 1.8938265e7 14.0 Ray_Manzarek "Weird_Al"_Yankovic link
6967012.0 1.8938265e7 11.0 Seth_Green "Weird_Al"_Yankovic link
2.3811071e7 1.8938265e7 24.0 Party_in_the_U.S.A. "Weird_Al"_Yankovic link
1273502.0 1.8938265e7 118.0 Loser_(Beck_song) "Weird_Al"_Yankovic link
262457.0 1.8938265e7 16.0 R._Lee_Ermey "Weird_Al"_Yankovic link
26678.0 1.8938265e7 11.0 Star_Wars "Weird_Al"_Yankovic link
611396.0 1.8938265e7 39.0 Rick_Derringer "Weird_Al"_Yankovic link
37287.0 1.8938265e7 12.0 Scooby-Doo "Weird_Al"_Yankovic link
194036.0 1.8938265e7 11.0 Spike_Jones "Weird_Al"_Yankovic link
57317.0 1.8938265e7 11.0 Prince_(musician) "Weird_Al"_Yankovic other
2043990.0 1.8938265e7 102.0 Spy_Hard "Weird_Al"_Yankovic link
1.2694177e7 1.8938265e7 84.0 Lynwood_High_School "Weird_Al"_Yankovic link
681814.0 1.8938265e7 54.0 Off_the_Deep_End "Weird_Al"_Yankovic link
21073.0 1.8938265e7 14.0 Mad_(magazine) "Weird_Al"_Yankovic link
3434143.0 1.8938265e7 18.0 Richard_Stallman "Weird_Al"_Yankovic link
89662.0 1.8938265e7 20.0 Margaret_Cho "Weird_Al"_Yankovic link
1417851.0 1.8938265e7 11.0 One_Week_(song) "Weird_Al"_Yankovic link
3.9762022e7 1.8938265e7 18.0 My_Little_Pony:_Friendship_Is_Magic_(season_4) "Weird_Al"_Yankovic link
573460.0 1.8938265e7 12.0 Space_Ghost_Coast_to_Coast "Weird_Al"_Yankovic link
2246914.0 1.8938265e7 14.0 Ray_of_Light_(song) "Weird_Al"_Yankovic link
2.5140047e7 1.8938265e7 29.0 NOH8_Campaign "Weird_Al"_Yankovic other
1955961.0 1.8938265e7 85.0 Smells_Like_Nirvana "Weird_Al"_Yankovic link
371187.0 1.8938265e7 13.0 National_Enquirer "Weird_Al"_Yankovic link
3.680974e7 1.8938265e7 14.0 Radioactive_(Imagine_Dragons_song) "Weird_Al"_Yankovic link
681810.0 1.8938265e7 99.0 Running_with_Scissors_("Weird_Al"_Yankovic_album) "Weird_Al"_Yankovic link
1490639.0 1.8938265e7 20.0 Pokémon:_The_Movie_2000 "Weird_Al"_Yankovic link
4109077.0 1.8938265e7 14.0 Money_for_Nothing/Beverly_Hillbillies* "Weird_Al"_Yankovic link
2.8214451e7 1.8938265e7 15.0 Mad_(TV_series) "Weird_Al"_Yankovic link
1700806.0 1.8938265e7 14.0 One_of_Us_(Joan_Osborne_song) "Weird_Al"_Yankovic other
3.1552317e7 1.8938265e7 39.0 Perform_This_Way "Weird_Al"_Yankovic link
104998.0 1.8938265e7 27.0 Michael_Richards "Weird_Al"_Yankovic link
1527386.0 1.8938265e7 31.0 Robot_Chicken "Weird_Al"_Yankovic link
2300912.0 1.8938265e7 19.0 Party_All_the_Time "Weird_Al"_Yankovic link
null 1.8938265e7 1553.0 other-yahoo "Weird_Al"_Yankovic other
7630017.0 4161738.0 12.0 "Weird_Al"_Yankovic_discography "Weird_Al"_Yankovic_Live! link
null 4161738.0 10.0 other-wikipedia "Weird_Al"_Yankovic_Live! other
null 4161738.0 26.0 other-google "Weird_Al"_Yankovic_Live! other
3.3246249e7 4161738.0 10.0 "Weird_Al"_Yankovic_Live!:_The_Alpocalypse_Tour "Weird_Al"_Yankovic_Live! link
null 4161738.0 11.0 other-empty "Weird_Al"_Yankovic_Live! other
null 7630017.0 532.0 other-empty "Weird_Al"_Yankovic_discography other
1955961.0 7630017.0 27.0 Smells_Like_Nirvana "Weird_Al"_Yankovic_discography link
3.1552317e7 7630017.0 15.0 Perform_This_Way "Weird_Al"_Yankovic_discography link
739408.0 7630017.0 25.0 Eat_It "Weird_Al"_Yankovic_discography link
4649999.0 7630017.0 21.0 911_(Wyclef_Jean_song) "Weird_Al"_Yankovic_discography other
1.8938265e7 7630017.0 1871.0 "Weird_Al"_Yankovic "Weird_Al"_Yankovic_discography link
998258.0 7630017.0 30.0 Amish_Paradise "Weird_Al"_Yankovic_discography link
6600777.0 7630017.0 11.0 Don't_Download_This_Song "Weird_Al"_Yankovic_discography link
1742323.0 7630017.0 17.0 Fat_(song) "Weird_Al"_Yankovic_discography link
4.3350433e7 7630017.0 10.0 Lame_Claim_to_Fame "Weird_Al"_Yankovic_discography link
4.303942e7 7630017.0 11.0 Everything_Will_Be_Alright_in_the_End "Weird_Al"_Yankovic_discography other
null 7630017.0 167.0 other-yahoo "Weird_Al"_Yankovic_discography other
null 7630017.0 21.0 other-other "Weird_Al"_Yankovic_discography other
4070747.0 7630017.0 13.0 Ricky_(song) "Weird_Al"_Yankovic_discography link
4.30701e7 7630017.0 33.0 Mandatory_Fun "Weird_Al"_Yankovic_discography link
1.5580374e7 7630017.0 189.0 Main_Page "Weird_Al"_Yankovic_discography other
6075882.0 7630017.0 36.0 White_&_Nerdy "Weird_Al"_Yankovic_discography link
null 7630017.0 84.0 other-bing "Weird_Al"_Yankovic_discography other
2018105.0 7630017.0 52.0 List_of_songs_recorded_by_"Weird_Al"_Yankovic "Weird_Al"_Yankovic_discography link
1.1311126e7 7630017.0 10.0 It's_All_About_the_Pentiums "Weird_Al"_Yankovic_discography link
null 7630017.0 67.0 other-twitter "Weird_Al"_Yankovic_discography other
null 7630017.0 1478.0 other-google "Weird_Al"_Yankovic_discography other
null 7630017.0 137.0 other-wikipedia "Weird_Al"_Yankovic_discography other
null 4.3882355e7 21.0 other-wikipedia What_Is_This_Heart? other
null 4.3882355e7 132.0 other-google What_Is_This_Heart? other
null 4.3882355e7 56.0 other-empty What_Is_This_Heart? other
2.9054561e7 4.3882355e7 1025.0 How_to_Dress_Well What_Is_This_Heart? link
3.3658579e7 4.3882355e7 12.0 List_of_albums_awarded_Pitchfork_Best_New_Album What_Is_This_Heart? other
3.7202658e7 4.3882355e7 128.0 Total_Loss_(album) What_Is_This_Heart? other
null 3.781321e7 12.0 other-other Where_Are_Your_Keys? other
null 3.781321e7 37.0 other-google Where_Are_Your_Keys? other
null 3.781321e7 11.0 other-empty Where_Are_Your_Keys? other
null 3.781321e7 17.0 other-wikipedia Where_Are_Your_Keys? other
595282.0 3.781321e7 71.0 Hand_game Where_Are_Your_Keys? other
1.0077425e7 3.781321e7 29.0 Alutiiq_language Where_Are_Your_Keys? link
261070.0 3.781321e7 58.0 Total_physical_response Where_Are_Your_Keys? other
9058458.0 3.781321e7 71.0 Squamish_language Where_Are_Your_Keys? other
null 4.136318e7 15.0 other-google "Yes,_And"_rule other
null 2.7111155e7 13.0 other-empty "You've_Got"_the_Touch other
null 2.7111155e7 187.0 other-google "You've_Got"_the_Touch other
null 3.5881722e7 12.0 other-google "Yume"_~Mugen_no_Kanata~ other
2.9264213e7 3.5881722e7 10.0 Vivid_(band) "Yume"_~Mugen_no_Kanata~ link
null 3.6439291e7 10.0 other-wikipedia _(disambiguation) other null
7468179.0 3.6439291e7 231.0 Quotation_mark _(disambiguation) link null
null 3.6439291e7 17.0 other-empty _(disambiguation) other null
202033.0 null 115.0 ECT et_cetera redlink
202033.0 null 244.0 ECT etc. redlink
null 1.1489847e7 11.0 other-wikipedia $1.99_Romances other
5885018.0 1.1489847e7 18.0 God_Street_Wine $1.99_Romances link
null 1.1489847e7 16.0 other-empty $1.99_Romances other
null 3.9007767e7 10.0 other-wikipedia $100_Guitar_Project other
null 3.9007767e7 33.0 other-google $100_Guitar_Project other
null 3.9007767e7 14.0 other-empty $100_Guitar_Project other
1588040.0 3.9007767e7 10.0 Nick_Didkovsky $100_Guitar_Project link
1.5608128e7 3.9007767e7 25.0 Ibanez_Jet_King $100_Guitar_Project link
null 4042422.0 86.0 other-google $2 other
null 4042422.0 45.0 other-wikipedia $2 other
1.5580374e7 4042422.0 21.0 Main_Page $2 other
null 4042422.0 42.0 other-empty $2 other
null 1419311.0 38.0 other-empty $20 other
null 1419311.0 20.0 other-wikipedia $20 other
null 1419311.0 21.0 other-google $20 other
477331.0 1419311.0 10.0 United_States_ten-dollar_bill $20 other
null 2.4648172e7 19.0 other-google $200 other
null 1245168.0 16.0 other-wikipedia $25_Million_Dollar_Hoax other
null 1245168.0 14.0 other-google $25_Million_Dollar_Hoax other
1023249.0 1245168.0 11.0 George_Gray_(television_personality) $25_Million_Dollar_Hoax link
300567.0 1245168.0 21.0 Hidden_camera $25_Million_Dollar_Hoax link
429120.0 1245168.0 19.0 List_of_reality_television_programs $25_Million_Dollar_Hoax link
3.6094463e7 2.8612415e7 11.0 Diplo_production_discography $O$_(Die_Antwoord_album) other
2.8588118e7 2.8612415e7 624.0 5_(Die_Antwoord_EP) $O$_(Die_Antwoord_album) other
2.928693e7 2.8612415e7 385.0 Yolandi_Visser $O$_(Die_Antwoord_album) other
2.6182691e7 2.8612415e7 294.0 Watkin_Tudor_Jones $O$_(Die_Antwoord_album) other
3.4571991e7 2.8612415e7 446.0 Tension_(Die_Antwoord_album) $O$_(Die_Antwoord_album) other
2.8196738e7 2.8612415e7 32.0 MaxNormal.TV $O$_(Die_Antwoord_album) other
null 2.8612415e7 241.0 other-empty $O$_(Die_Antwoord_album) other
null 2.8612415e7 541.0 other-google $O$_(Die_Antwoord_album) other
null 2.8612415e7 30.0 other-wikipedia $O$_(Die_Antwoord_album) other
3.5421075e7 2.8612415e7 49.0 Evil_Boy $O$_(Die_Antwoord_album) other
2.6050642e7 2.8612415e7 2527.0 Die_Antwoord $O$_(Die_Antwoord_album) other
2.2155074e7 1.0231968e7 11.0 C$_(disambiguation) $_(disambiguation) link
2308563.0 1.0231968e7 140.0 Dollar_sign $_(disambiguation) link
null 1.0231968e7 14.0 other-google $_(disambiguation) other
null 1.0231968e7 88.0 other-wikipedia $_(disambiguation) other
null 1.0231968e7 116.0 other-empty $_(disambiguation) other
3361673.0 4213160.0 25.0 Town_&_Country_(film) $_(film) link
801116.0 4213160.0 31.0 Richard_Brooks $_(film) link
null 4213160.0 75.0 other-empty $_(film) other
171680.0 4213160.0 10.0 1971_in_film $_(film) link
313518.0 4213160.0 37.0 Gert_Fröbe $_(film) link
48071.0 4213160.0 123.0 Goldie_Hawn $_(film) link
1022198.0 4213160.0 12.0 Heist $_(film) link
null 4213160.0 10.0 other-other $_(film) other
62809.0 4213160.0 140.0 Warren_Beatty $_(film) link
null 4213160.0 40.0 other-wikipedia $_(film) other
null 4213160.0 86.0 other-google $_(film) other
1.2379987e7 4213160.0 14.0 List_of_crime_films_of_the_1970s $_(film) link
1.198473e7 4597115.0 11.0 Index_of_human_sexuality_articles $pread link
null 4597115.0 26.0 other-empty $pread other
2.2814756e7 4597115.0 12.0 Madison_Young $pread link
null 4597115.0 99.0 other-google $pread other
null 4597115.0 32.0 other-wikipedia $pread other
3.3545134e7 4597115.0 27.0 Nica_Noelle $pread link
326309.0 1831030.0 15.0 Robert_Goulet $pringfield_(or,_How_I_Learned_to_Stop_Worrying_and_Love_Legalized_Gambling) other
1681057.0 1831030.0 14.0 Treehouse_of_Horror_XIII $pringfield_(or,_How_I_Learned_to_Stop_Worrying_and_Love_Legalized_Gambling) other
null 1831030.0 17.0 other-bing $pringfield_(or,_How_I_Learned_to_Stop_Worrying_and_Love_Legalized_Gambling) other
3620042.0 1831030.0 11.0 Sweet_Seymour_Skinner's_Baadasssss_Song $pringfield_(or,_How_I_Learned_to_Stop_Worrying_and_Love_Legalized_Gambling) link
57850.0 1831030.0 13.0 Springfield $pringfield_(or,_How_I_Learned_to_Stop_Worrying_and_Love_Legalized_Gambling) other
4939444.0 1831030.0 84.0 The_Simpsons_(season_5) $pringfield_(or,_How_I_Learned_to_Stop_Worrying_and_Love_Legalized_Gambling) link
292279.0 1831030.0 32.0 List_of_recurring_The_Simpsons_characters $pringfield_(or,_How_I_Learned_to_Stop_Worrying_and_Love_Legalized_Gambling) other
140332.0 1831030.0 149.0 List_of_The_Simpsons_episodes $pringfield_(or,_How_I_Learned_to_Stop_Worrying_and_Love_Legalized_Gambling) link
14059.0 1831030.0 77.0 Howard_Hughes $pringfield_(or,_How_I_Learned_to_Stop_Worrying_and_Love_Legalized_Gambling) other
null 1831030.0 48.0 other-wikipedia $pringfield_(or,_How_I_Learned_to_Stop_Worrying_and_Love_Legalized_Gambling) other
null 1831030.0 741.0 other-google $pringfield_(or,_How_I_Learned_to_Stop_Worrying_and_Love_Legalized_Gambling) other
598368.0 1831030.0 12.0 Josh_Weinstein $pringfield_(or,_How_I_Learned_to_Stop_Worrying_and_Love_Legalized_Gambling) other
2011385.0 1831030.0 29.0 Homer_the_Vigilante $pringfield_(or,_How_I_Learned_to_Stop_Worrying_and_Love_Legalized_Gambling) link
1889929.0 1831030.0 12.0 Saddlesore_Galactica $pringfield_(or,_How_I_Learned_to_Stop_Worrying_and_Love_Legalized_Gambling) other
null 1831030.0 270.0 other-empty $pringfield_(or,_How_I_Learned_to_Stop_Worrying_and_Love_Legalized_Gambling) other
3605255.0 1831030.0 44.0 The_Last_Temptation_of_Homer $pringfield_(or,_How_I_Learned_to_Stop_Worrying_and_Love_Legalized_Gambling) link
null 1578140.0 1820.0 other-google %s other
null 1578140.0 13.0 other-twitter %s other
null 1578140.0 233.0 other-wikipedia %s other
null 1578140.0 29756.0 other-empty %s other

Display is a utility provided by Databricks. If you are programming directly in Spark, use the show(numRows: Int) function of DataFrame

clickstream.show(5)

+-------+-------+---+------------------+----------+-----+
|prev_id|curr_id|  n|        prev_title|curr_title| type|
+-------+-------+---+------------------+----------+-----+
|   null|3632887|121|      other-google|        !!|other|
|   null|3632887| 93|   other-wikipedia|        !!|other|
|   null|3632887| 46|       other-empty|        !!|other|
|   null|3632887| 10|       other-other|        !!|other|
|  64486|3632887| 11|!_(disambiguation)|        !!|other|
+-------+-------+---+------------------+----------+-----+
only showing top 5 rows

Reading from disk vs memory

The 1.2 GB Clickstream file is currently on S3, which means each time you scan through it, your Spark cluster has to read the 1.2 GB of data remotely over the network.

Call the count() action to check how many rows are in the DataFrame and to see how long it takes to read the DataFrame from S3.

clickstream.cache().count()

res9: Long = 22509897
  • It took about several minutes to read the 1.2 GB file into your Spark cluster. The file has 22.5 million rows/lines.
  • Although we have called cache, remember that it is evaluated (cached) only when an action(count) is called

Now call count again to see how much faster it is to read from memory

clickstream.count()

res10: Long = 22509897
  • Orders of magnitude faster!
  • If you are going to be using the same data source multiple times, it is better to cache it in memory

What are the top 10 articles requested?

To do this we also need to order by the sum of column n, in descending order.

//Type in your answer here...
display(clickstream
  .select(clickstream("curr_title"), clickstream("n"))
  .groupBy("curr_title")
  .sum()
  .orderBy($"sum(n)".desc)
  .limit(10))
curr_title sum(n)
Main_Page 1.2750062e8
87th_Academy_Awards 2559794.0
Fifty_Shades_of_Grey 2326175.0
Alive 2244781.0
Chris_Kyle 1709341.0
Fifty_Shades_of_Grey_(film) 1683892.0
Deaths_in_2015 1614577.0
Birdman_(film) 1545842.0
Islamic_State_of_Iraq_and_the_Levant 1406530.0
Stephen_Hawking 1384193.0

Who sent the most traffic to Wikipedia in Feb 2015?

In other words, who were the top referers to Wikipedia?

display(clickstream
  .select(clickstream("prev_title"), clickstream("n"))
  .groupBy("prev_title")
  .sum()
  .orderBy($"sum(n)".desc)
  .limit(10))
prev_title sum(n)
other-google 1.496209976e9
other-empty 3.47693595e8
other-wikipedia 1.29772279e8
other-other 7.7569671e7
other-bing 6.5962792e7
other-yahoo 4.8501171e7
Main_Page 2.9923502e7
other-twitter 1.9241298e7
other-facebook 2314026.0
87th_Academy_Awards 1680675.0

As expected, the top referer by a large margin is Google. Next comes refererless traffic (usually clients using HTTPS). The third largest sender of traffic to English Wikipedia are Wikipedia pages that are not in the main namespace (ns = 0) of English Wikipedia. Learn about the Wikipedia namespaces here: https://en.wikipedia.org/wiki/Wikipedia:Project_namespace

Also, note that Twitter sends 10x more requests to Wikipedia than Facebook.

//Type in your answer here...
display(clickstream
  .select(clickstream("curr_title"), clickstream("prev_title"), clickstream("n"))
  .filter("prev_title = 'other-twitter'")
  .groupBy("curr_title")
  .sum()
  .orderBy($"sum(n)".desc)
  .limit(5))
curr_title sum(n)
Johnny_Knoxville 198908.0
Peter_Woodcock 126259.0
2002_Tampa_plane_crash 119906.0
Sơn_Đoòng_Cave 116012.0
The_boy_Jones 114401.0

What percentage of page visits in Wikipedia are from other pages in Wikipedia itself?

val allClicks = clickstream.selectExpr("sum(n)").first.getLong(0)
val referals = clickstream.
                filter(clickstream("prev_id").isNotNull).
                selectExpr("sum(n)").first.getLong(0)
(referals * 100.0) / allClicks

allClicks: Long = 3283067885
referals: Long = 1095462001
res14: Double = 33.36702253416853

Register the DataFrame to perform more complex queries

clickstream.createOrReplaceTempView("clicks")

Which Wikipedia pages have the most referrals to the Donald Trump page?

SELECT *
FROM clicks
WHERE 
  curr_title = 'Donald_Trump' AND
  prev_id IS NOT NULL AND prev_title != 'Main_Page'
ORDER BY n DESC
LIMIT 20
prev_id curr_id n prev_title curr_title type
1861441.0 4848272.0 4658.0 Ivanka_Trump Donald_Trump link
4848272.0 4848272.0 2212.0 Donald_Trump Donald_Trump link
1209075.0 4848272.0 1855.0 Melania_Trump Donald_Trump link
1057887.0 4848272.0 1760.0 Ivana_Trump Donald_Trump link
5679119.0 4848272.0 1074.0 Donald_Trump_Jr. Donald_Trump link
2.1377251e7 4848272.0 918.0 United_States_presidential_election,_2016 Donald_Trump link
8095589.0 4848272.0 728.0 Eric_Trump Donald_Trump link
473806.0 4848272.0 652.0 Marla_Maples Donald_Trump link
2565136.0 4848272.0 651.0 The_Trump_Organization Donald_Trump link
9917693.0 4848272.0 599.0 The_Celebrity_Apprentice Donald_Trump link
9289480.0 4848272.0 597.0 The_Apprentice_(U.S._TV_series) Donald_Trump link
290327.0 4848272.0 596.0 German_American Donald_Trump link
1.2643497e7 4848272.0 585.0 Comedy_Central_Roast Donald_Trump link
3.7643999e7 4848272.0 549.0 Republican_Party_presidential_candidates,_2016 Donald_Trump link
417559.0 4848272.0 543.0 Alan_Sugar Donald_Trump link
1203316.0 4848272.0 489.0 Fred_Trump Donald_Trump link
303951.0 4848272.0 426.0 Vince_McMahon Donald_Trump link
6191053.0 4848272.0 413.0 Jared_Kushner Donald_Trump link
1295216.0 4848272.0 412.0 Trump_Tower_(New_York_City) Donald_Trump link
6509278.0 4848272.0 402.0 Trump Donald_Trump link

YouTry: Top referrers to other 2016 US presidential candidate pages

'Donald_Trump', 'Bernie_Sanders', 'Hillary_Rodham_Clinton', 'Ted_Cruz'

-- YouTry 
---
-- fill in the right sql query here

Load a visualization library

This code is copied after doing a live google search (by Michael Armbrust at Spark Summit East February 2016 shared from https://twitter.com/michaelarmbrust/status/699969850475737088). The d3ivan package is an updated version of the original package used by Michael Armbrust as it needed some TLC for Spark 2.2 on newer databricks notebook. These changes were kindly made by Ivan Sadikov from Middle Earth.

You need to hit the Play Button in next cell and 'Run Cell' exactly once.

package d3ivan
// We use a package object so that we can define top level classes like Edge that need to be used in other cells

import org.apache.spark.sql._
import com.databricks.backend.daemon.driver.EnhancedRDDFunctions.displayHTML

case class Edge(src: String, dest: String, count: Long)

case class Node(name: String)
case class Link(source: Int, target: Int, value: Long)
case class Graph(nodes: Seq[Node], links: Seq[Link])

object graphs {
// val sqlContext = SQLContext.getOrCreate(org.apache.spark.SparkContext.getOrCreate())  /// fix
val sqlContext = SparkSession.builder().getOrCreate().sqlContext
import sqlContext.implicits._
  
def force(clicks: Dataset[Edge], height: Int = 100, width: Int = 960): Unit = {
  val data = clicks.collect()
  val nodes = (data.map(_.src) ++ data.map(_.dest)).map(_.replaceAll("_", " ")).toSet.toSeq.map(Node)
  val links = data.map { t =>
    Link(nodes.indexWhere(_.name == t.src.replaceAll("_", " ")), nodes.indexWhere(_.name == t.dest.replaceAll("_", " ")), t.count / 20 + 1)
  }
  showGraph(height, width, Seq(Graph(nodes, links)).toDF().toJSON.first())
}

/**
 * Displays a force directed graph using d3
 * input: {"nodes": [{"name": "..."}], "links": [{"source": 1, "target": 2, "value": 0}]}
 */
def showGraph(height: Int, width: Int, graph: String): Unit = {

displayHTML(s"""
<style>

.node_circle {
  stroke: #777;
  stroke-width: 1.3px;
}

.node_label {
  pointer-events: none;
}

.link {
  stroke: #777;
  stroke-opacity: .2;
}

.node_count {
  stroke: #777;
  stroke-width: 1.0px;
  fill: #999;
}

text.legend {
  font-family: Verdana;
  font-size: 13px;
  fill: #000;
}

.node text {
  font-family: "Helvetica Neue","Helvetica","Arial",sans-serif;
  font-size: 17px;
  font-weight: 200;
}

</style>

<div id="clicks-graph">
<script src="//d3js.org/d3.v3.min.js"></script>
<script>

var graph = $graph;

var width = $width,
    height = $height;

var color = d3.scale.category20();

var force = d3.layout.force()
    .charge(-700)
    .linkDistance(180)
    .size([width, height]);

var svg = d3.select("#clicks-graph").append("svg")
    .attr("width", width)
    .attr("height", height);
    
force
    .nodes(graph.nodes)
    .links(graph.links)
    .start();

var link = svg.selectAll(".link")
    .data(graph.links)
    .enter().append("line")
    .attr("class", "link")
    .style("stroke-width", function(d) { return Math.sqrt(d.value); });

var node = svg.selectAll(".node")
    .data(graph.nodes)
    .enter().append("g")
    .attr("class", "node")
    .call(force.drag);

node.append("circle")
    .attr("r", 10)
    .style("fill", function (d) {
    if (d.name.startsWith("other")) { return color(1); } else { return color(2); };
})

node.append("text")
      .attr("dx", 10)
      .attr("dy", ".35em")
      .text(function(d) { return d.name });
      
//Now we are giving the SVGs co-ordinates - the force layout is generating the co-ordinates which this code is using to update the attributes of the SVG elements
force.on("tick", function () {
    link.attr("x1", function (d) {
        return d.source.x;
    })
        .attr("y1", function (d) {
        return d.source.y;
    })
        .attr("x2", function (d) {
        return d.target.x;
    })
        .attr("y2", function (d) {
        return d.target.y;
    });
    d3.selectAll("circle").attr("cx", function (d) {
        return d.x;
    })
        .attr("cy", function (d) {
        return d.y;
    });
    d3.selectAll("text").attr("x", function (d) {
        return d.x;
    })
        .attr("y", function (d) {
        return d.y;
    });
});
</script>
</div>
""")
}
  
  def help() = {
displayHTML("""
<p>
Produces a force-directed graph given a collection of edges of the following form:</br>
<tt><font color="#a71d5d">case class</font> <font color="#795da3">Edge</font>(<font color="#ed6a43">src</font>: <font color="#a71d5d">String</font>, <font color="#ed6a43">dest</font>: <font color="#a71d5d">String</font>, <font color="#ed6a43">count</font>: <font color="#a71d5d">Long</font>)</tt>
</p>
<p>Usage:<br/>
<tt><font color="#a71d5d">import</font> <font color="#ed6a43">d3._</font></tt><br/>
<tt><font color="#795da3">graphs.force</font>(</br>
&nbsp;&nbsp;<font color="#ed6a43">height</font> = <font color="#795da3">500</font>,<br/>
&nbsp;&nbsp;<font color="#ed6a43">width</font> = <font color="#795da3">500</font>,<br/>
&nbsp;&nbsp;<font color="#ed6a43">clicks</font>: <font color="#795da3">Dataset</font>[<font color="#795da3">Edge</font>])</tt>
</p>""")
  }
}

Warning: classes defined within packages cannot be redefined without a cluster restart.
Compilation successful.

d3ivan.graphs.help()

Produces a force-directed graph given a collection of edges of the following form:
case class Edge(src: String, dest: String, count: Long)

Usage:
import d3._
graphs.force(
  height = 500,
  width = 500,
  clicks: Dataset[Edge]) 

d3ivan.graphs.force(
  height = 800,
  width = 800,
  clicks = sql("""
    SELECT 
      prev_title AS src,
      curr_title AS dest,
      n AS count FROM clicks
    WHERE 
      curr_title IN ('Donald_Trump', 'Bernie_Sanders', 'Hillary_Rodham_Clinton', 'Ted_Cruz') AND
      prev_id IS NOT NULL AND prev_title != 'Main_Page'
    ORDER BY n DESC
    LIMIT 20""").as[d3ivan.Edge])

What we have done above is essentially pass the output of an SQL query into a D3 visualizer via javascript. Don't worry about all the details. The main idea here is that SQL and interactive visualizations usually come together in a proper data exploratory tool and the above steps are minimal excursions into how to do it in a simple way from within a notebook environment like databricks. Python and R have many plotting libraries and we can always write the dataframe to parquet and load it into pySpark or SparkR to leverage those languages. But D3 is a nice solutions also especially if you want somethinf customized for your queries.

Convert raw data to parquet

Recall:

Apache Parquet is a columnar storage format available to any project in the Hadoop ecosystem, regardless of the choice of data processing framework, data model or programming language. It is a more efficient way to store data frames.

displayHTML(frameIt("https://parquet.apache.org/documentation/latest/",350))

// Convert the DatFrame to a more efficent format to speed up our analysis
clickstream.
  write.
  mode(SaveMode.Overwrite).
  parquet("/datasets/wiki-clickstream")

Load parquet file efficiently and quickly into a DataFrame

Now we can simply load from this parquet file next time instead of creating the RDD from the text file (much slower).

Also using parquet files to store DataFrames allows us to go between languages quickly in a a scalable manner.

val clicks = sqlContext.read.parquet("/datasets/wiki-clickstream")

clicks: org.apache.spark.sql.DataFrame = [prev_id: int, curr_id: int ... 4 more fields]

clicks.printSchema

root
 |-- prev_id: integer (nullable = true)
 |-- curr_id: integer (nullable = true)
 |-- n: integer (nullable = true)
 |-- prev_title: string (nullable = true)
 |-- curr_title: string (nullable = true)
 |-- type: string (nullable = true)

display(clicks)  // let's display this DataFrame
prev_id curr_id n prev_title curr_title type
1.3710401e7 1.2653094e7 12.0 Punk_rock_subgenres Music_genre link
25423.0 1.2653094e7 16.0 Rock_music Music_genre other
178244.0 1.2653094e7 10.0 Muse_(band) Music_genre link
156547.0 1.2653094e7 10.0 Remix Music_genre link
1564758.0 1.2653094e7 73.0 Pop_rock Music_genre link
18839.0 1.2653094e7 203.0 Music Music_genre link
5079506.0 1.2653094e7 10.0 Pink_Floyd Music_genre link
24624.0 1.2653094e7 167.0 Pop_music Music_genre link
379560.0 1.2653094e7 15.0 Musical_form Music_genre link
1.5580374e7 1.2653094e7 197.0 Main_Page Music_genre other
2.4297671e7 1.2653094e7 862.0 Popular_music Music_genre link
1.2653094e7 1.2653094e7 23.0 Music_genre Music_genre other
25520.0 1.2653094e7 90.0 Reggae Music_genre link
54783.0 1.2653094e7 18.0 Music_theory Music_genre link
147311.0 1.2653094e7 14.0 Ray_Charles Music_genre link
8886086.0 1.2653094e7 11.0 Oi! Music_genre link
19499.0 1.2653094e7 10.0 Mariah_Carey Music_genre link
3.8954428e7 1.2653094e7 23.0 Sam_Smith_(singer) Music_genre link
2110323.0 1.2653094e7 12.0 Rihanna Music_genre link
null 1.2653094e7 632.0 other-other Music_genre other
null 1.2653094e7 20.0 other-facebook Music_genre other
62808.0 1.2653094e7 56.0 Soul_music Music_genre link
null 1.2653094e7 514.0 other-bing Music_genre other
162707.0 1.2653094e7 95.0 Singing Music_genre link
5422144.0 1.2653094e7 35.0 Taylor_Swift Music_genre link
27176.0 1.2653094e7 16.0 Ska Music_genre link
28830.0 1.2653094e7 21.0 Song Music_genre link
4.1884523e7 1.2653094e7 22.0 Vaporwave Music_genre other
295560.0 1.2653094e7 13.0 Style Music_genre link
424093.0 1.2653094e7 10.0 1990s_in_music Music_genre other
236918.0 1.2653094e7 14.0 Concert Music_genre other
41536.0 1.2653094e7 11.0 Duke_Ellington Music_genre link
2.5276055e7 1.2653094e7 16.0 Ariana_Grande Music_genre link
363651.0 1.2653094e7 10.0 Dark_wave Music_genre link
183304.0 1.2653094e7 11.0 Dub_(music) Music_genre link
4637590.0 1.2653094e7 16.0 Bob_Dylan Music_genre link
83688.0 1.2653094e7 16.0 Beyoncé Music_genre link
3.0528002e7 1.2653094e7 17.0 Ed_Sheeran Music_genre link
8239846.0 1.2653094e7 10.0 Bob_Marley Music_genre link
880.0 1.2653094e7 10.0 ABBA Music_genre link
5261.0 1.2653094e7 13.0 Celtic_music Music_genre other
2.7005455e7 1.2653094e7 12.0 Bruno_Mars Music_genre link
1.0232935e7 1.2653094e7 10.0 Diatonic_and_chromatic Music_genre other
7966.0 1.2653094e7 25.0 Disco Music_genre link
413723.0 1.2653094e7 12.0 Heavy_metal_subgenres Music_genre link
168377.0 1.2653094e7 40.0 Folk_rock Music_genre other
1.1655198e7 1.2653094e7 15.0 Ishkur's_Guide_to_Electronic_Music Music_genre link
3.0863005e7 1.2653094e7 13.0 List_of_Christian_bands_and_artists_by_genre Music_genre link
3.1976854e7 1.2653094e7 11.0 Japanese_Girl Music_genre link
10778.0 1.2653094e7 60.0 Funk Music_genre link
171111.0 1.2653094e7 11.0 Honky-tonk Music_genre link
4.1518485e7 1.2653094e7 10.0 Hozier_(musician) Music_genre other
1.198307e7 1.2653094e7 17.0 Johnny_Cash Music_genre link
3.1919748e7 1.2653094e7 31.0 FIFA_12 Music_genre link
172830.0 1.2653094e7 11.0 Fado Music_genre link
1.6477368e7 1.2653094e7 18.0 Katy_Perry Music_genre link
2319440.0 1.2653094e7 15.0 List_of_saxophonists Music_genre link
6921880.0 1.2653094e7 11.0 List_of_composers_by_name Music_genre other
2878021.0 1.2653094e7 16.0 List_of_country_genres Music_genre link
2.399895e7 1.2653094e7 28.0 Lists_of_musicians Music_genre link
559487.0 1.2653094e7 20.0 List_of_styles_of_music:_S–Z Music_genre link
559484.0 1.2653094e7 70.0 List_of_styles_of_music:_A–F Music_genre link
417829.0 1.2653094e7 18.0 List_of_all-female_bands Music_genre link
275671.0 1.2653094e7 44.0 List_of_electronic_music_genres Music_genre link
1.449809e7 1.2653094e7 13.0 Fall_Out_Boy Music_genre link
4429395.0 1.2653094e7 20.0 Eminem Music_genre link
973905.0 1.2653094e7 24.0 Genealogy_of_musical_genres Music_genre link
3.6042633e7 1.2653094e7 12.0 Electro_house Music_genre other
682482.0 1.2653094e7 53.0 Human Music_genre link
2.9909823e7 1.2653094e7 39.0 Kendrick_Lamar Music_genre link
7653811.0 1.2653094e7 19.0 Hip_hop_(disambiguation) Music_genre link
11181.0 1.2653094e7 10.0 Frank_Sinatra Music_genre link
3.3209238e7 1.2653094e7 13.0 Lana_Del_Rey Music_genre link
629945.0 1.2653094e7 29.0 K-pop Music_genre link
1.8945847e7 1.2653094e7 225.0 Hip_hop_music Music_genre link
2527136.0 1.2653094e7 14.0 Jazz_poetry Music_genre link
124802.0 1.2653094e7 38.0 Hard_rock Music_genre link
44706.0 1.2653094e7 283.0 Genre Music_genre link
73010.0 1.2653094e7 23.0 Hardcore_punk Music_genre link
1.7782843e7 1.2653094e7 22.0 Lady_Gaga Music_genre link
9355587.0 1.2653094e7 154.0 Example_(musician) Music_genre link
15613.0 1.2653094e7 218.0 Jazz Music_genre link
547533.0 1.2653094e7 10.0 Crossover_(music) Music_genre link
2.4686326e7 1.2653094e7 15.0 21st-century_classical_music Music_genre link
7885.0 1.2653094e7 15.0 Dance Music_genre other
3.3269956e7 1.2653094e7 24.0 5ive_(disambiguation) Music_genre other
167409.0 1.2653094e7 11.0 Alternative_rock Music_genre other
1.8127544e7 1.2653094e7 11.0 Ah_Me,_Ah_My Music_genre link
386347.0 1.2653094e7 15.0 Anti-folk Music_genre link
3.4953684e7 1.2653094e7 17.0 Charli_XCX Music_genre link
392811.0 1.2653094e7 23.0 African-American_music Music_genre other
2468299.0 1.2653094e7 13.0 CD-Text Music_genre link
3603298.0 1.2653094e7 150.0 Art_Official_Intelligence:_Mosaic_Thump Music_genre link
66038.0 1.2653094e7 28.0 Breakbeat Music_genre link
368323.0 1.2653094e7 12.0 Cockney_Rejects Music_genre link
255791.0 1.2653094e7 56.0 Art_music Music_genre link
461637.0 1.2653094e7 10.0 Cumbia Music_genre link
3352.0 1.2653094e7 118.0 Blues Music_genre link
149681.0 1.2653094e7 10.0 Beck Music_genre link
214666.0 1.2653094e7 402.0 List_of_music_styles Music_genre link
5347350.0 1.2653094e7 27.0 List_of_popular_music_genres Music_genre link
2.7052778e7 1.2653094e7 54.0 List_of_genres Music_genre link
3142048.0 1.2653094e7 29.0 List_of_jazz_genres Music_genre link
559485.0 1.2653094e7 14.0 List_of_styles_of_music:_G–M Music_genre link
413631.0 1.2653094e7 12.0 List_of_blues_genres Music_genre link
303261.0 1.2653094e7 10.0 Top_40 Music_genre other
147687.0 1.2653094e7 10.0 Stevie_Wonder Music_genre link
31056.0 1.2653094e7 10.0 The_Rolling_Stones Music_genre link
29812.0 1.2653094e7 84.0 The_Beatles Music_genre link
3208697.0 1.2653094e7 16.0 Youth_subculture Music_genre link
null 1.2653094e7 2746.0 other-empty Music_genre other
28261.0 1.2653094e7 38.0 Samba Music_genre other
1795886.0 1.2653094e7 11.0 Record_chart Music_genre other
21151.0 1.2653094e7 12.0 New_wave_music Music_genre link
248462.0 1.2653094e7 20.0 Music_of_Colombia Music_genre link
7504750.0 1.2653094e7 21.0 Music_festival Music_genre other
1.932133e7 1.2653094e7 41.0 Nightclub Music_genre link
199630.0 1.2653094e7 11.0 Pop_punk Music_genre link
171080.0 1.2653094e7 18.0 Music_of_the_United_States Music_genre link
18313.0 1.2653094e7 14.0 Louis_Armstrong Music_genre link
394633.0 1.2653094e7 16.0 Reggaeton Music_genre link
4635444.0 1.2653094e7 16.0 March_(music) Music_genre other
565560.0 1.2653094e7 11.0 Protopunk Music_genre other
37735.0 1.2653094e7 10.0 Melody Music_genre other
26168.0 1.2653094e7 59.0 Rhythm_and_blues Music_genre link
3.1772741e7 1.2653094e7 14.0 One_Direction Music_genre link
2.1065992e7 1.2653094e7 19.0 Sex_(The_Necks_album) Music_genre link
4.3272496e7 1.2653094e7 18.0 Meghan_Trainor Music_genre link
2.719197e7 1.2653094e7 15.0 Moombahton Music_genre other
3403168.0 1.2653094e7 35.0 Outline_of_music Music_genre link
null 1.2653094e7 359.0 other-yahoo Music_genre other
2.888765e7 2.8887473e7 11.0 Music_Group_(company) Music_group_(disambiguation) link
20180.0 2.8887473e7 69.0 Musical_ensemble Music_group_(disambiguation) link
897299.0 232692.0 11.0 Stan_Laurel Music_hall link
103067.0 232692.0 15.0 Stand-up_comedy Music_hall link
1.2892136e7 232692.0 10.0 Pack_Up_Your_Troubles_in_Your_Old_Kit-Bag Music_hall link
2.0715761e7 232692.0 103.0 The_boy_Jones Music_hall link
9206390.0 232692.0 11.0 Sunny_Afternoon Music_hall link
3158351.0 232692.0 47.0 The_Kinks Music_hall link
470943.0 232692.0 16.0 The_Triplets_of_Belleville Music_hall link
326433.0 232692.0 32.0 Variety_show Music_hall link
null 232692.0 417.0 other-empty Music_hall other
2246663.0 232692.0 45.0 Martha_My_Dear Music_hall link
24864.0 232692.0 23.0 Professional_wrestling Music_hall link
1.5580374e7 232692.0 35.0 Main_Page Music_hall other
5106604.0 232692.0 87.0 Seymour_Hicks Music_hall link
556635.0 232692.0 27.0 When_I'm_Sixty-Four Music_hall link
3.8027034e7 232692.0 46.0 Songs_of_the_First_World_War Music_hall link
null 232692.0 93.0 other-bing Music_hall other
48235.0 232692.0 51.0 Vaudeville Music_hall link
null 232692.0 103.0 other-other Music_hall other
null 232692.0 77.0 other-yahoo Music_hall other
null 232692.0 2106.0 other-google Music_hall other
null 232692.0 231.0 other-wikipedia Music_hall other
null 232692.0 15.0 other-twitter Music_hall other
2260734.0 232692.0 11.0 List_of_musical_forms_by_era Music_hall link
2861.0 232692.0 15.0 Advertising Music_hall link
7566837.0 232692.0 16.0 Bioscope_show Music_hall link
100096.0 232692.0 14.0 Edwardian_era Music_hall link
605891.0 232692.0 18.0 David_Bowie_(1967_album) Music_hall link
36999.0 232692.0 25.0 Carry_On_(franchise) Music_hall link
5142.0 232692.0 63.0 Charlie_Chaplin Music_hall link
95805.0 232692.0 13.0 Leslie_Phillips Music_hall link
1163667.0 232692.0 10.0 I'm_Henery_the_Eighth,_I_Am Music_hall link
1932690.0 232692.0 54.0 Honey_Pie Music_hall link
1468518.0 232692.0 34.0 Her_Majesty_(song) Music_hall link
43492.0 232692.0 10.0 Ian_Dury Music_hall link
6835232.0 232692.0 15.0 Holiday_(Bee_Gees_song) Music_hall link
1.871959e7 232692.0 13.0 I_Do_Like_To_be_Beside_the_Seaside Music_hall link
1084094.0 232692.0 28.0 It's_a_Long_Way_to_Tipperary Music_hall link
3832925.0 232692.0 25.0 Good_Old-Fashioned_Lover_Boy Music_hall link
8786.0 232692.0 19.0 David_Bowie Music_hall link
5130871.0 232692.0 52.0 America's_Most_Endangered_Places Music_hall other
428611.0 232692.0 14.0 Can-can Music_hall link
1.4923927e7 232692.0 11.0 Concert_saloon Music_hall link
3.0995031e7 232692.0 23.0 American_burlesque Music_hall link
null 7570941.0 39.0 other-google Music_history_of_Barbados other
null 2735439.0 49.0 other-google Music_history_of_Hungary other
null 2735439.0 10.0 other-empty Music_history_of_Hungary other
null 3430507.0 18.0 other-empty Music_history_of_Portugal other
387719.0 3430507.0 13.0 Music_of_Portugal Music_history_of_Portugal link
null 3430507.0 80.0 other-google Music_history_of_Portugal other
null 1616933.0 158.0 other-empty Music_history_of_the_United_States other
null 1616933.0 1935.0 other-google Music_history_of_the_United_States other
null 1616933.0 18.0 other-wikipedia Music_history_of_the_United_States other
null 1616933.0 40.0 other-yahoo Music_history_of_the_United_States other
171080.0 1616933.0 25.0 Music_of_the_United_States Music_history_of_the_United_States link
null 1616933.0 36.0 other-other Music_history_of_the_United_States other
null 1616933.0 49.0 other-bing Music_history_of_the_United_States other
246497.0 1616933.0 10.0 American_folk_music Music_history_of_the_United_States link
1.8985287e7 1616933.0 57.0 Culture_of_the_United_States Music_history_of_the_United_States link
2.3932051e7 3.1437105e7 80.0 1960s_in_music Music_history_of_the_United_States_in_the_1960s link
null 3.1437105e7 103.0 other-empty Music_history_of_the_United_States_in_the_1960s other
null 3.1437105e7 23.0 other-yahoo Music_history_of_the_United_States_in_the_1960s other
null 3.1437105e7 12.0 other-wikipedia Music_history_of_the_United_States_in_the_1960s other
null 3.1437105e7 1679.0 other-google Music_history_of_the_United_States_in_the_1960s other
8544676.0 3.1437105e7 46.0 Counterculture_of_the_1960s Music_history_of_the_United_States_in_the_1960s link
null 3.1437105e7 82.0 other-bing Music_history_of_the_United_States_in_the_1960s other
null 3.1437105e7 16.0 other-other Music_history_of_the_United_States_in_the_1960s other
null 411041.0 16.0 other-other Music_history_of_the_United_States_in_the_1980s other
1616933.0 411041.0 10.0 Music_history_of_the_United_States Music_history_of_the_United_States_in_the_1980s link
null 411041.0 36.0 other-bing Music_history_of_the_United_States_in_the_1980s other
null 411041.0 11.0 other-yahoo Music_history_of_the_United_States_in_the_1980s other
23726.0 411041.0 15.0 Pixies Music_history_of_the_United_States_in_the_1980s other
411040.0 411041.0 11.0 Music_history_of_the_United_States_in_the_1970s Music_history_of_the_United_States_in_the_1980s link
1.9753121e7 411041.0 79.0 1980s_in_music Music_history_of_the_United_States_in_the_1980s link
null 411041.0 51.0 other-empty Music_history_of_the_United_States_in_the_1980s other
null 411041.0 593.0 other-google Music_history_of_the_United_States_in_the_1980s other
null 411041.0 11.0 other-wikipedia Music_history_of_the_United_States_in_the_1980s other
null 1369822.0 23.0 other-google Music_in_Adygea other
247772.0 1369822.0 12.0 Music_of_Russia Music_in_Adygea other
407750.0 1369822.0 11.0 Adygea Music_in_Adygea other
735530.0 1284752.0 27.0 Bashkortostan Music_in_Bashkortostan link
61024.0 3.8584535e7 33.0 Charleston,_South_Carolina Music_in_Charleston link
null 3.8584535e7 82.0 other-google Music_in_Charleston other
null 3.8584535e7 21.0 other-empty Music_in_Charleston other
null 2.5941812e7 28.0 other-google Music_in_Colonial_Mexico other
751099.0 1499681.0 28.0 Dagestan Music_in_Dagestan other
null 1499681.0 10.0 other-google Music_in_Dagestan other
null 1.1374661e7 19.0 other-google Music_in_Darkness other
null 1.1374661e7 13.0 other-wikipedia Music_in_Darkness other
null 1.1374661e7 10.0 other-other Music_in_Darkness other
1.3075438e7 1.1374661e7 62.0 Ingmar_Bergman_filmography Music_in_Darkness link
null 2.5548658e7 77.0 other-empty Music_in_Dollhouse other
null 2.5548658e7 11.0 other-bing Music_in_Dollhouse other
1.4014034e7 2.5548658e7 75.0 Dollhouse_(TV_series) Music_in_Dollhouse link
null 2.5548658e7 253.0 other-google Music_in_Dollhouse other
null 2.5548658e7 11.0 other-wikipedia Music_in_Dollhouse other
null 1.6378289e7 45.0 other-google Music_in_Dresden other
null 1264053.0 33.0 other-google Music_in_High_Places other
1264134.0 1264053.0 22.0 Here's_to_the_Mourning Music_in_High_Places link
1136526.0 1264053.0 20.0 Unwritten_Law Music_in_High_Places link
2.4597057e7 1264053.0 10.0 Unwritten_Law_discography Music_in_High_Places link
1179127.0 1499731.0 14.0 Kuban_Cossacks Music_in_Krasnodar_Krai other
474125.0 1499731.0 13.0 Krasnodar_Krai Music_in_Krasnodar_Krai link
8262427.0 5563451.0 20.0 Leeds Music_in_Leeds link
5563531.0 5563451.0 15.0 List_of_bands_originating_in_Leeds Music_in_Leeds link
null 5563451.0 11.0 other-wikipedia Music_in_Leeds other
null 5563451.0 311.0 other-google Music_in_Leeds other
null 5563451.0 124.0 other-empty Music_in_Leeds other
473991.0 1499616.0 13.0 Mordovia Music_in_Mordovia other
null 3087094.0 10.0 other-empty Music_in_Mouth other
4455620.0 3087094.0 12.0 Neither_Am_I Music_in_Mouth link
2.5688717e7 3087094.0 14.0 Bell_X1_discography Music_in_Mouth link
2809365.0 3087094.0 41.0 Bell_X1_(band) Music_in_Mouth link
277952.0 4.2439092e7 34.0 Rita_Hayworth Music_in_My_Heart link
null 4.2439092e7 20.0 other-google Music_in_My_Heart other
174689.0 1492279.0 80.0 Nenets_people Music_in_Nenets_Autonomous_Okrug other
null 3.9884449e7 227.0 other-google Music_in_Paris other
309852.0 3.9884449e7 10.0 List_of_cultural_and_regional_genres_of_music Music_in_Paris other
null 3.9884449e7 24.0 other-empty Music_in_Paris other
22989.0 3.9884449e7 36.0 Paris Music_in_Paris link
null 3.9530149e7 86.0 other-google Music_in_Varanasi other
null 3.9530149e7 16.0 other-empty Music_in_Varanasi other
4108684.0 2129287.0 19.0 WWF_The_Music,_Vol._5 Music_in_professional_wrestling link
1.4521129e7 2129287.0 17.0 The_Time_Is_Now_(John_Cena_song) Music_in_professional_wrestling link
3174133.0 2129287.0 12.0 WWE_Originals Music_in_professional_wrestling link
1.4681044e7 2129287.0 14.0 The_Bella_Twins Music_in_professional_wrestling link
1954598.0 2129287.0 14.0 You_Can't_See_Me Music_in_professional_wrestling link
null 2129287.0 115.0 other-empty Music_in_professional_wrestling other
4373640.0 2129287.0 12.0 Roman_Reigns Music_in_professional_wrestling link
3.5347635e7 2129287.0 16.0 Sasha_Banks Music_in_professional_wrestling link
611396.0 2129287.0 36.0 Rick_Derringer Music_in_professional_wrestling link
2121727.0 2129287.0 17.0 Music_at_sporting_events Music_in_professional_wrestling link
4395990.0 2129287.0 11.0 Piledriver:_The_Wrestling_Album_2 Music_in_professional_wrestling link
665823.0 2129287.0 13.0 Randy_Orton Music_in_professional_wrestling link
303225.0 2129287.0 67.0 Triple_H Music_in_professional_wrestling link
null 2129287.0 18.0 other-bing Music_in_professional_wrestling other
655575.0 2129287.0 24.0 Sting_(wrestler) Music_in_professional_wrestling link
4395892.0 2129287.0 12.0 The_Wrestling_Album Music_in_professional_wrestling link
2.5816978e7 2129287.0 40.0 WWE_The_Music:_A_New_Day,_Vol._10 Music_in_professional_wrestling link
4.345633e7 2129287.0 31.0 WWE_Music_Group_discography Music_in_professional_wrestling link
6434529.0 2129287.0 332.0 WWE_Music_Group Music_in_professional_wrestling link
345792.0 2129287.0 24.0 The_Undertaker Music_in_professional_wrestling link
null 2129287.0 12.0 other-other Music_in_professional_wrestling other
4.0532625e7 2129287.0 10.0 Alexander_Rusev Music_in_professional_wrestling link
156126.0 2129287.0 19.0 Dwayne_Johnson Music_in_professional_wrestling link
2321041.0 2129287.0 10.0 Gravity_(Our_Lady_Peace_album) Music_in_professional_wrestling other
2710336.0 2129287.0 52.0 Jim_Johnston_(composer) Music_in_professional_wrestling link
4786864.0 2129287.0 39.0 Entrance_music Music_in_professional_wrestling link
null 2129287.0 14.0 other-yahoo Music_in_professional_wrestling other
null 2129287.0 460.0 other-google Music_in_professional_wrestling other
null 2129287.0 28.0 other-wikipedia Music_in_professional_wrestling other
4.1413713e7 2129287.0 14.0 Lana_(wrestling) Music_in_professional_wrestling link
345802.0 2129287.0 17.0 John_Cena Music_in_professional_wrestling link
1.7487089e7 2129287.0 31.0 List_of_Total_Nonstop_Action_Wrestling_albums Music_in_professional_wrestling link
289900.0 2.0009687e7 93.0 Castlevania Music_in_the_Castlevania_series link
2852745.0 2.0009687e7 12.0 Castlevania:_Portrait_of_Ruin Music_in_the_Castlevania_series link
null 2.0009687e7 16.0 other-empty Music_in_the_Castlevania_series other
null 2.0009687e7 218.0 other-google Music_in_the_Castlevania_series other
null 1182126.0 16.0 other-wikipedia Music_in_the_Chechen_Republic other
null 1182126.0 34.0 other-google Music_in_the_Chechen_Republic other
247772.0 1182126.0 16.0 Music_of_Russia Music_in_the_Chechen_Republic other
6427285.0 1182126.0 10.0 Makka_Sagaipova Music_in_the_Chechen_Republic other
null 1.1083892e7 23.0 other-google Music_in_the_Community other
474004.0 1518955.0 17.0 Komi_Republic Music_in_the_Komi_Republic other
null 5520065.0 104.0 other-google Music_in_the_Parks other
3935908.0 5520065.0 11.0 Hersheypark_Arena Music_in_the_Parks link
57905.0 1284783.0 19.0 Sakha_Republic Music_in_the_Sakha_Republic link
483558.0 1284783.0 58.0 Yakuts Music_in_the_Sakha_Republic link
null 1284764.0 98.0 other-google Music_in_the_Tyva_Republic other
3309582.0 1284764.0 15.0 Acoustic_scale Music_in_the_Tyva_Republic link
null 1284764.0 10.0 other-bing Music_in_the_Tyva_Republic other
483570.0 1284764.0 25.0 Tuvans Music_in_the_Tyva_Republic other
719202.0 1284764.0 10.0 Igil Music_in_the_Tyva_Republic other
1616030.0 1284764.0 49.0 Tuva Music_in_the_Tyva_Republic other
null 1284764.0 37.0 other-empty Music_in_the_Tyva_Republic other
1946204.0 null 31.0 Music_industry Music_industry_of_Asia redlink
1946204.0 3.9326699e7 73.0 Music_industry Music_industry_of_East_Asia link
414082.0 3.9326699e7 28.0 J-pop Music_industry_of_East_Asia link
null 3.9326699e7 53.0 other-empty Music_industry_of_East_Asia other
null 3.9326699e7 186.0 other-google Music_industry_of_East_Asia other
null 3.9326699e7 10.0 other-twitter Music_industry_of_East_Asia other
629945.0 3.9326699e7 37.0 K-pop Music_industry_of_East_Asia link
1946204.0 null 62.0 Music_industry Music_industry_of_Europe redlink
1946204.0 null 92.0 Music_industry Music_industry_of_North_America redlink
1946204.0 null 10.0 Music_industry Music_industry_of_South_America redlink
null 1.7025056e7 77.0 other-google Music_informatics other
null 261193.0 706.0 other-google Music_information_retrieval other
null 261193.0 14.0 other-wikipedia Music_information_retrieval other
2.1189305e7 261193.0 11.0 Audio_engineer Music_information_retrieval link
1.4004969e7 261193.0 11.0 Audio_mining Music_information_retrieval link
125297.0 261193.0 11.0 Dynamic_programming Music_information_retrieval link
42253.0 261193.0 19.0 Data_mining Music_information_retrieval other
3267504.0 261193.0 19.0 Music_OCR Music_information_retrieval link
1708126.0 261193.0 15.0 Pitch_detection_algorithm Music_information_retrieval link
null 261193.0 177.0 other-empty Music_information_retrieval other
null 261193.0 27.0 other-bing Music_information_retrieval other
null 261193.0 60.0 other-other Music_information_retrieval other
300730.0 261193.0 24.0 Mel-frequency_cepstrum Music_information_retrieval link
1.7025056e7 261193.0 11.0 Music_informatics Music_information_retrieval other
null 261193.0 18.0 other-yahoo Music_information_retrieval other
null 981637.0 10.0 other-yahoo Music_library other
null 981637.0 18.0 other-wikipedia Music_library other
null 981637.0 205.0 other-google Music_library other
9305406.0 981637.0 77.0 Production_music Music_library other
null 981637.0 13.0 other-bing Music_library other
null 981637.0 17.0 other-other Music_library other
null 981637.0 50.0 other-empty Music_library other
null 4.2280305e7 14.0 other-google Music_massage_therapy other
null 4530556.0 12.0 other-google Music_of_Abruzzo other
null 3721528.0 40.0 other-google Music_of_Adelaide other
1148.0 3721528.0 30.0 Adelaide Music_of_Adelaide link
null 412891.0 47.0 other-empty Music_of_Alabama other
null 412891.0 36.0 other-yahoo Music_of_Alabama other
null 412891.0 402.0 other-google Music_of_Alabama other
null 412891.0 24.0 other-wikipedia Music_of_Alabama other
null 412891.0 15.0 other-other Music_of_Alabama other
1291814.0 412891.0 14.0 Music_of_Alaska Music_of_Alabama link
null 412891.0 39.0 other-bing Music_of_Alabama other
null 1291814.0 16.0 other-bing Music_of_Alaska other
624.0 1291814.0 17.0 Alaska Music_of_Alaska link
null 1291814.0 18.0 other-empty Music_of_Alaska other
null 1291814.0 19.0 other-wikipedia Music_of_Alaska other
null 1291814.0 279.0 other-google Music_of_Alaska other
null 1291814.0 11.0 other-yahoo Music_of_Alaska other
null 999857.0 98.0 other-google Music_of_Alberta other
null 999857.0 12.0 other-wikipedia Music_of_Alberta other
248269.0 999857.0 13.0 Music_of_Canada Music_of_Alberta link
null 243022.0 15.0 other-yahoo Music_of_Algeria other
358.0 243022.0 26.0 Algeria Music_of_Algeria link
null 243022.0 28.0 other-bing Music_of_Algeria other
null 243022.0 69.0 other-other Music_of_Algeria other
1424498.0 243022.0 15.0 Andalusian_classical_music Music_of_Algeria link
1116955.0 243022.0 14.0 Islamic_music Music_of_Algeria link
451841.0 243022.0 16.0 Music_of_North_Africa Music_of_Algeria link
null 243022.0 135.0 other-empty Music_of_Algeria other
null 243022.0 33.0 other-wikipedia Music_of_Algeria other
null 243022.0 449.0 other-google Music_of_Algeria other
null 561191.0 49.0 other-google Music_of_Andorra other
600.0 561191.0 28.0 Andorra Music_of_Andorra link
null 561191.0 69.0 other-empty Music_of_Andorra other
null 364942.0 27.0 other-empty Music_of_Anguilla other
null 364942.0 26.0 other-google Music_of_Anguilla other
1217.0 364942.0 72.0 Anguilla Music_of_Anguilla link
null 1501429.0 16.0 other-google Music_of_Anhui other
154175.0 987406.0 18.0 Calypso_music Music_of_Antigua_and_Barbuda link
951.0 987406.0 18.0 Antigua_and_Barbuda Music_of_Antigua_and_Barbuda link
9887612.0 987406.0 35.0 Benna_(genre) Music_of_Antigua_and_Barbuda link
7504750.0 987406.0 16.0 Music_festival Music_of_Antigua_and_Barbuda link
null 987406.0 22.0 other-other Music_of_Antigua_and_Barbuda other
null 987406.0 17.0 other-bing Music_of_Antigua_and_Barbuda other
null 987406.0 248.0 other-google Music_of_Antigua_and_Barbuda other
null 987406.0 110.0 other-empty Music_of_Antigua_and_Barbuda other
null 987406.0 21.0 other-wikipedia Music_of_Antigua_and_Barbuda other
3181553.0 987406.0 26.0 Banjar Music_of_Antigua_and_Barbuda other
null 1400826.0 25.0 other-google Music_of_Aquitaine other
403097.0 247169.0 20.0 Culture_of_Argentina Music_of_Argentina link
309852.0 247169.0 10.0 List_of_cultural_and_regional_genres_of_music Music_of_Argentina link
2569276.0 247169.0 11.0 Argentine_cumbia Music_of_Argentina link
1.8951905e7 247169.0 80.0 Argentina Music_of_Argentina link
58895.0 247169.0 48.0 Latin_American_music Music_of_Argentina link
null 247169.0 60.0 other-yahoo Music_of_Argentina other
null 247169.0 244.0 other-empty Music_of_Argentina other
null 247169.0 126.0 other-wikipedia Music_of_Argentina other
null 247169.0 2174.0 other-google Music_of_Argentina other
null 247169.0 115.0 other-bing Music_of_Argentina other
376118.0 247169.0 21.0 Tango_music Music_of_Argentina link
null 247169.0 39.0 other-other Music_of_Argentina other
null 410801.0 35.0 other-other Music_of_Armenia other
null 410801.0 16.0 other-bing Music_of_Armenia other
241240.0 410801.0 13.0 Sabre_Dance Music_of_Armenia link
8473458.0 410801.0 10.0 Armenian_dance Music_of_Armenia link
1.0918072e7 410801.0 56.0 Armenia Music_of_Armenia link
865062.0 410801.0 25.0 Duduk Music_of_Armenia link
232847.0 410801.0 15.0 Aram_Khachaturian Music_of_Armenia link
413618.0 410801.0 10.0 Music_of_Iran Music_of_Armenia link
2058881.0 410801.0 39.0 Middle_Eastern_music Music_of_Armenia link
92149.0 410801.0 34.0 Oud Music_of_Armenia other
2.3931407e7 410801.0 10.0 Pop-folk Music_of_Armenia link
285300.0 410801.0 12.0 Qanun_(instrument) Music_of_Armenia link
null 410801.0 208.0 other-empty Music_of_Armenia other
387816.0 410801.0 12.0 Armenians Music_of_Armenia link
2049464.0 410801.0 15.0 Culture_of_Armenia Music_of_Armenia link
732267.0 410801.0 16.0 Komitas Music_of_Armenia link
null 410801.0 676.0 other-google Music_of_Armenia other
null 410801.0 68.0 other-wikipedia Music_of_Armenia other
null 410801.0 19.0 other-yahoo Music_of_Armenia other
null 2591959.0 11.0 other-empty Music_of_Arunachal_Pradesh other
null 2591959.0 13.0 other-wikipedia Music_of_Arunachal_Pradesh other
null 2591959.0 70.0 other-google Music_of_Arunachal_Pradesh other
null 1049220.0 141.0 other-empty Music_of_Assam other
null 1049220.0 312.0 other-google Music_of_Assam other
null 1049220.0 28.0 other-other Music_of_Assam other
602639.0 1049220.0 13.0 Northeast_India Music_of_Assam link
171300.0 3.489527e7 13.0 Southern_hip_hop Music_of_Atlanta link
null 3.489527e7 42.0 other-empty Music_of_Atlanta other
null 3.489527e7 596.0 other-google Music_of_Atlanta other
2206945.0 3.489527e7 24.0 Atlanta_hip_hop Music_of_Atlanta link
3138.0 3.489527e7 178.0 Atlanta Music_of_Atlanta link
null 3.489527e7 12.0 other-other Music_of_Atlanta other
null 3.489527e7 21.0 other-bing Music_of_Atlanta other
null 308391.0 865.0 other-google Music_of_Austria other
null 308391.0 52.0 other-wikipedia Music_of_Austria other
null 308391.0 143.0 other-empty Music_of_Austria other
null 308391.0 41.0 other-yahoo Music_of_Austria other
55866.0 308391.0 48.0 Vienna Music_of_Austria link
null 308391.0 50.0 other-bing Music_of_Austria other
null 308391.0 19.0 other-other Music_of_Austria other
2.6964606e7 308391.0 67.0 Austria Music_of_Austria link
2089641.0 1400477.0 17.0 French_folk_music Music_of_Auvergne link
null 1400477.0 25.0 other-google Music_of_Auvergne other
null 309778.0 415.0 other-google Music_of_Azerbaijan other
null 309778.0 36.0 other-wikipedia Music_of_Azerbaijan other
440310.0 309778.0 24.0 Music_of_Asia Music_of_Azerbaijan link
null 309778.0 155.0 other-empty Music_of_Azerbaijan other
746.0 309778.0 26.0 Azerbaijan Music_of_Azerbaijan link
null 309778.0 17.0 other-other Music_of_Azerbaijan other
3470781.0 309778.0 10.0 Mugham Music_of_Azerbaijan link
null 309778.0 26.0 other-bing Music_of_Azerbaijan other
null 309778.0 14.0 other-yahoo Music_of_Azerbaijan other
58481.0 1923094.0 27.0 Badakhshan Music_of_Badakhshan link
null 412724.0 64.0 other-empty Music_of_Bahrain other
null 412724.0 111.0 other-google Music_of_Bahrain other
1.8933277e7 412724.0 13.0 Bahrain Music_of_Bahrain link
2.6997138e7 2753032.0 39.0 Baltimore Music_of_Baltimore link
null 2753032.0 14.0 other-yahoo Music_of_Baltimore other
null 2753032.0 32.0 other-empty Music_of_Baltimore other
null 2753032.0 348.0 other-google Music_of_Baltimore other
null 2753032.0 15.0 other-wikipedia Music_of_Baltimore other
1.0721944e7 2753032.0 10.0 Spiderman_of_the_Rings Music_of_Baltimore link
null 2753032.0 17.0 other-bing Music_of_Baltimore other
null 2753032.0 11.0 other-other Music_of_Baltimore other
null 245850.0 49.0 other-other Music_of_Bangladesh other
null 245850.0 12.0 other-bing Music_of_Bangladesh other
null 245850.0 26.0 other-yahoo Music_of_Bangladesh other
3454.0 245850.0 26.0 Bangladesh Music_of_Bangladesh link
null 245850.0 64.0 other-wikipedia Music_of_Bangladesh other
null 245850.0 743.0 other-google Music_of_Bangladesh other
null 245850.0 238.0 other-empty Music_of_Bangladesh other
440310.0 245850.0 25.0 Music_of_Asia Music_of_Bangladesh link
519963.0 245850.0 13.0 Baul Music_of_Bangladesh link
null 4532941.0 11.0 other-wikipedia Music_of_Basilicata other
null 4532941.0 15.0 other-google Music_of_Basilicata other
null 8468129.0 713.0 other-twitter Music_of_Battlestar_Galactica_(2004_TV_series) other
null 8468129.0 34.0 other-wikipedia Music_of_Battlestar_Galactica_(2004_TV_series) other
null 8468129.0 1586.0 other-google Music_of_Battlestar_Galactica_(2004_TV_series) other
null 8468129.0 478.0 other-empty Music_of_Battlestar_Galactica_(2004_TV_series) other
3604689.0 8468129.0 36.0 Battlestar_Galactica_(miniseries) Music_of_Battlestar_Galactica_(2004_TV_series) other
2.1304123e7 8468129.0 13.0 Battlestar_Galactica_(season_2) Music_of_Battlestar_Galactica_(2004_TV_series) other
413556.0 8468129.0 26.0 All_Along_the_Watchtower Music_of_Battlestar_Galactica_(2004_TV_series) other
null 8468129.0 36.0 other-yahoo Music_of_Battlestar_Galactica_(2004_TV_series) other
null 8468129.0 33.0 other-bing Music_of_Battlestar_Galactica_(2004_TV_series) other
null 8468129.0 41.0 other-other Music_of_Battlestar_Galactica_(2004_TV_series) other
2391393.0 8468129.0 17.0 Richard_Gibbs Music_of_Battlestar_Galactica_(2004_TV_series) other
2700625.0 8468129.0 89.0 Bear_McCreary Music_of_Battlestar_Galactica_(2004_TV_series) other
3604726.0 8468129.0 145.0 Battlestar_Galactica_(2004_TV_series) Music_of_Battlestar_Galactica_(2004_TV_series) other
2821280.0 8468129.0 44.0 Kara_Thrace Music_of_Battlestar_Galactica_(2004_TV_series) other
5457957.0 8468129.0 14.0 Shape_of_Things_to_Come Music_of_Battlestar_Galactica_(2004_TV_series) other
247087.0 373626.0 20.0 Music_of_the_Netherlands Music_of_Belgium link
null 373626.0 108.0 other-empty Music_of_Belgium other
null 373626.0 16.0 other-yahoo Music_of_Belgium other
null 373626.0 1081.0 other-google Music_of_Belgium other
null 373626.0 46.0 other-wikipedia Music_of_Belgium other
143432.0 373626.0 31.0 Culture_of_Belgium Music_of_Belgium link
6180884.0 373626.0 20.0 French_pop_music Music_of_Belgium link
10878.0 373626.0 12.0 Flanders Music_of_Belgium link
3343.0 373626.0 47.0 Belgium Music_of_Belgium link
3.087116e7 373626.0 55.0 New_Beat Music_of_Belgium link
null 373626.0 33.0 other-bing Music_of_Belgium other
null 373626.0 22.0 other-other Music_of_Belgium other
null 306641.0 32.0 other-bing Music_of_Belize other
null 306641.0 25.0 other-wikipedia Music_of_Belize other
null 306641.0 408.0 other-google Music_of_Belize other
null 306641.0 134.0 other-empty Music_of_Belize other
null 306641.0 15.0 other-yahoo Music_of_Belize other
3458.0 306641.0 17.0 Belize Music_of_Belize link
5052533.0 951080.0 19.0 Rabindranath_Tagore Music_of_Bengal link
1158934.0 951080.0 11.0 Rabindra_Sangeet Music_of_Bengal link
245850.0 951080.0 11.0 Music_of_Bangladesh Music_of_Bengal link
null 951080.0 36.0 other-other Music_of_Bengal other
null 951080.0 157.0 other-empty Music_of_Bengal other
null 951080.0 42.0 other-wikipedia Music_of_Bengal other
null 951080.0 801.0 other-google Music_of_Bengal other
null 951080.0 11.0 other-yahoo Music_of_Bengal other
5985171.0 951080.0 10.0 Culture_of_West_Bengal Music_of_Bengal link
2.2612537e7 2.2648389e7 21.0 Terry_Riley:_Cadenza_on_the_Night_Plain Music_of_Bill_Evans link
null 2.2648389e7 17.0 other-google Music_of_Bill_Evans other
null 5508131.0 396.0 other-empty Music_of_Bollywood other
53207.0 5508131.0 42.0 Record_producer Music_of_Bollywood other
2.2138795e7 5508131.0 25.0 Sad_Hindi_songs Music_of_Bollywood other
4246.0 5508131.0 96.0 Bollywood Music_of_Bollywood link
5925126.0 5508131.0 18.0 Binaca_Geetmala Music_of_Bollywood other
406271.0 5508131.0 22.0 Filmi Music_of_Bollywood link
2.2448394e7 5508131.0 82.0 Hindi_dance_songs Music_of_Bollywood other
null 5508131.0 123.0 other-wikipedia Music_of_Bollywood other
null 5508131.0 1527.0 other-google Music_of_Bollywood other
5968908.0 5508131.0 27.0 Filmi_Devotional_songs Music_of_Bollywood other
222789.0 5508131.0 74.0 Lata_Mangeshkar Music_of_Bollywood other
1346834.0 5508131.0 11.0 K._L._Saigal Music_of_Bollywood other
334547.0 5508131.0 14.0 Kishore_Kumar Music_of_Bollywood link
null 5508131.0 59.0 other-bing Music_of_Bollywood other
null 5508131.0 76.0 other-other Music_of_Bollywood other
1.5580374e7 5508131.0 14.0 Main_Page Music_of_Bollywood other
14535.0 5508131.0 49.0 Music_of_India Music_of_Bollywood link
null 5508131.0 36.0 other-yahoo Music_of_Bollywood other
2173379.0 373624.0 11.0 Šaban_Šaulić Music_of_Bosnia_and_Herzegovina link
null 373624.0 11.0 other-yahoo Music_of_Bosnia_and_Herzegovina other
null 373624.0 98.0 other-empty Music_of_Bosnia_and_Herzegovina other
null 373624.0 364.0 other-google Music_of_Bosnia_and_Herzegovina other
null 373624.0 25.0 other-wikipedia Music_of_Bosnia_and_Herzegovina other
3463.0 373624.0 38.0 Bosnia_and_Herzegovina Music_of_Bosnia_and_Herzegovina link
1444274.0 373624.0 29.0 Music_of_Southeastern_Europe Music_of_Bosnia_and_Herzegovina link
1532326.0 373624.0 18.0 Narodna_muzika Music_of_Bosnia_and_Herzegovina link
null 373624.0 16.0 other-other Music_of_Bosnia_and_Herzegovina other
1471762.0 373624.0 13.0 Bosnian_rock Music_of_Bosnia_and_Herzegovina link
397584.0 373624.0 12.0 Culture_of_Bosnia_and_Herzegovina Music_of_Bosnia_and_Herzegovina link
3464.0 987389.0 33.0 Botswana Music_of_Botswana link
null 987389.0 22.0 other-wikipedia Music_of_Botswana other
null 987389.0 264.0 other-google Music_of_Botswana other
null 987389.0 34.0 other-other Music_of_Botswana other
null 987389.0 18.0 other-bing Music_of_Botswana other
null 987389.0 106.0 other-empty Music_of_Botswana other
null 432369.0 89.0 other-empty Music_of_Brittany other
4.2647129e7 432369.0 29.0 Tri_Martolod Music_of_Brittany link
5261.0 432369.0 28.0 Celtic_music Music_of_Brittany link
468295.0 432369.0 39.0 Denez_Prigent Music_of_Brittany link
3239660.0 432369.0 12.0 Culture_of_Brittany Music_of_Brittany link
1687254.0 432369.0 10.0 Fest_Noz Music_of_Brittany link
550629.0 432369.0 20.0 Bagad Music_of_Brittany link
38748.0 432369.0 13.0 Brittany Music_of_Brittany link
2089641.0 432369.0 26.0 French_folk_music Music_of_Brittany link
null 432369.0 18.0 other-bing Music_of_Brittany other
null 432369.0 32.0 other-other Music_of_Brittany other
null 432369.0 397.0 other-google Music_of_Brittany other
null 432369.0 36.0 other-wikipedia Music_of_Brittany other
null 987349.0 18.0 other-wikipedia Music_of_Brunei other
null 987349.0 296.0 other-google Music_of_Brunei other
3559333.0 987349.0 33.0 Culture_of_Brunei Music_of_Brunei link
null 987349.0 90.0 other-empty Music_of_Brunei other
987349.0 987349.0 56.0 Music_of_Brunei Music_of_Brunei other
3466.0 987349.0 43.0 Brunei Music_of_Brunei link
1824190.0 373849.0 14.0 Bulgarian_State_Television_Female_Vocal_Choir Music_of_Bulgaria link
251620.0 373849.0 17.0 Culture_of_Bulgaria Music_of_Bulgaria link
1749887.0 373849.0 16.0 Bulgarian_dances Music_of_Bulgaria link
4527.0 373849.0 16.0 Béla_Bartók Music_of_Bulgaria link
3228403.0 373849.0 19.0 Ghost_in_the_Shell_(film) Music_of_Bulgaria other
null 373849.0 1203.0 other-google Music_of_Bulgaria other
null 373849.0 81.0 other-wikipedia Music_of_Bulgaria other
null 373849.0 15.0 other-yahoo Music_of_Bulgaria other
1532326.0 373849.0 18.0 Narodna_muzika Music_of_Bulgaria link
null 373849.0 31.0 other-bing Music_of_Bulgaria other
null 373849.0 63.0 other-other Music_of_Bulgaria other
null 373849.0 204.0 other-empty Music_of_Bulgaria other
1444274.0 373849.0 40.0 Music_of_Southeastern_Europe Music_of_Bulgaria link
null 987266.0 125.0 other-empty Music_of_Burma other
null 987266.0 36.0 other-wikipedia Music_of_Burma other
null 987266.0 543.0 other-google Music_of_Burma other
1018512.0 987266.0 21.0 Culture_of_Burma Music_of_Burma link
null 987266.0 38.0 other-other Music_of_Burma other
null 987266.0 18.0 other-bing Music_of_Burma other
null 987266.0 26.0 other-yahoo Music_of_Burma other
null 561089.0 74.0 other-google Music_of_Burundi other
null 561089.0 11.0 other-wikipedia Music_of_Burundi other
null 561089.0 50.0 other-empty Music_of_Burundi other
2.1490998e7 561089.0 18.0 Burundi Music_of_Burundi link
196953.0 561089.0 68.0 Kings_of_the_Wild_Frontier Music_of_Burundi link
741441.0 1284811.0 44.0 Buryatia Music_of_Buryatia link
null 1284811.0 10.0 other-google Music_of_Buryatia other
null 4497082.0 37.0 other-google Music_of_Calabria other
null 442836.0 21.0 other-google Music_of_Canada's_Prairie_Provinces other
null 2.3921367e7 22.0 other-empty Music_of_Canadian_cultures other
null 2.3921367e7 338.0 other-google Music_of_Canadian_cultures other
3.569409e7 2.3921367e7 19.0 Anthems_and_nationalistic_songs_of_Canada Music_of_Canadian_cultures link
248269.0 2.3921367e7 26.0 Music_of_Canada Music_of_Canadian_cultures link
10623.0 2.3921367e7 11.0 Folk_music Music_of_Canadian_cultures link
2700625.0 2.2645621e7 36.0 Bear_McCreary Music_of_Caprica link
null 2.2645621e7 37.0 other-google Music_of_Caprica other
8468129.0 2.2645621e7 12.0 Music_of_Battlestar_Galactica_(2004_TV_series) Music_of_Caprica link
null 2.2645621e7 17.0 other-empty Music_of_Caprica other
null 7339937.0 29.0 other-empty Music_of_Cardiff other
165147.0 7339937.0 35.0 Stereophonics Music_of_Cardiff link
5882.0 7339937.0 36.0 Cardiff Music_of_Cardiff link
2.3794044e7 7339937.0 10.0 Bullet_for_My_Valentine_discography Music_of_Cardiff other
2213065.0 7339937.0 187.0 Bullet_for_My_Valentine Music_of_Cardiff link
492820.0 7339937.0 45.0 Lostprophets Music_of_Cardiff link
null 7339937.0 143.0 other-google Music_of_Cardiff other
null 444625.0 75.0 other-google Music_of_Castile_and_León other
null 444625.0 15.0 other-empty Music_of_Castile_and_León other
105621.0 444625.0 12.0 Music_of_Spain Music_of_Castile_and_León other
292222.0 1.4164396e7 15.0 Stochastic Music_of_Changes link
null 1.4164396e7 12.0 other-other Music_of_Changes other
null 1.4164396e7 435.0 other-google Music_of_Changes other
null 1.4164396e7 22.0 other-wikipedia Music_of_Changes other
1.4163e7 1.4164396e7 39.0 List_of_compositions_by_John_Cage Music_of_Changes link
null 1.4164396e7 60.0 other-empty Music_of_Changes other
99234.0 1.4164396e7 32.0 Aleatoric_music Music_of_Changes link
65954.0 1.4164396e7 50.0 John_Cage Music_of_Changes link
null 333496.0 24.0 other-yahoo Music_of_Chile other
null 333496.0 63.0 other-wikipedia Music_of_Chile other
null 333496.0 1371.0 other-google Music_of_Chile other
null 333496.0 15.0 other-other Music_of_Chile other
1.5580374e7 333496.0 12.0 Main_Page Music_of_Chile other
null 333496.0 78.0 other-bing Music_of_Chile other
null 333496.0 188.0 other-empty Music_of_Chile other
5489.0 333496.0 18.0 Chile Music_of_Chile link
58895.0 333496.0 19.0 Latin_American_music Music_of_Chile link
null 1.4216009e7 109.0 other-google Music_of_Coal other
5399.0 1291889.0 58.0 Colorado Music_of_Colorado link
null 1291889.0 22.0 other-yahoo Music_of_Colorado other
null 1291889.0 782.0 other-google Music_of_Colorado other
null 1291889.0 22.0 other-wikipedia Music_of_Colorado other
null 1291889.0 53.0 other-empty Music_of_Colorado other
null 1291889.0 49.0 other-bing Music_of_Colorado other
912509.0 1291889.0 10.0 Post-hardcore Music_of_Colorado link
null 1291920.0 11.0 other-bing Music_of_Connecticut other
null 1291920.0 11.0 other-empty Music_of_Connecticut other
null 1291920.0 182.0 other-google Music_of_Connecticut other
null 1291920.0 30.0 other-wikipedia Music_of_Connecticut other
6591.0 1297518.0 24.0 Crete Music_of_Crete link
3237687.0 1297518.0 10.0 Greek_folk_music Music_of_Crete link
null 1297518.0 30.0 other-wikipedia Music_of_Crete other
null 1297518.0 185.0 other-google Music_of_Crete other
null 1297518.0 39.0 other-empty Music_of_Crete other
1.8170476e7 1297518.0 12.0 Psarantonis Music_of_Crete link
962945.0 1297518.0 21.0 Nikos_Xilouris Music_of_Crete link
null 1297518.0 27.0 other-other Music_of_Crete other
246225.0 1297518.0 11.0 Music_of_Greece Music_of_Crete link
1.5580374e7 1297518.0 15.0 Main_Page Music_of_Crete other
1408397.0 1297518.0 17.0 Nakshatra Music_of_Crete other
1532326.0 243011.0 11.0 Narodna_muzika Music_of_Croatia link
null 243011.0 19.0 other-bing Music_of_Croatia other
null 243011.0 30.0 other-other Music_of_Croatia other
474950.0 243011.0 34.0 Culture_of_Croatia Music_of_Croatia link
1.4203005e7 243011.0 14.0 Croatian_popular_music Music_of_Croatia link
1.2248405e7 243011.0 19.0 Croatian_art Music_of_Croatia link
1.0600968e7 243011.0 12.0 Music_of_Yugoslavia Music_of_Croatia link
2119998.0 243011.0 22.0 Oliver_Dragojević Music_of_Croatia link
1444274.0 243011.0 40.0 Music_of_Southeastern_Europe Music_of_Croatia link
null 243011.0 29.0 other-yahoo Music_of_Croatia other
null 243011.0 99.0 other-empty Music_of_Croatia other
null 243011.0 513.0 other-google Music_of_Croatia other
null 243011.0 43.0 other-wikipedia Music_of_Croatia other
null 240761.0 15.0 other-twitter Music_of_Cuba other
null 240761.0 4444.0 other-google Music_of_Cuba other
null 240761.0 247.0 other-wikipedia Music_of_Cuba other
1005480.0 240761.0 84.0 Afro-Cuban Music_of_Cuba link
5347660.0 240761.0 11.0 Cascara Music_of_Cuba link
513544.0 240761.0 12.0 Buena_Vista_Social_Club Music_of_Cuba link
1654043.0 240761.0 42.0 Afro-Caribbean_music Music_of_Cuba link
682524.0 240761.0 24.0 Culture_of_Cuba Music_of_Cuba link
516807.0 240761.0 15.0 Cuban_cuisine Music_of_Cuba link
1107981.0 240761.0 10.0 Afro-Cuban_jazz Music_of_Cuba link
7966.0 240761.0 31.0 Disco Music_of_Cuba other
285709.0 240761.0 16.0 Latin_jazz Music_of_Cuba other
1.5703993e7 240761.0 11.0 Gente_de_Zona Music_of_Cuba other
1751751.0 240761.0 17.0 Guaracha Music_of_Cuba link
null 240761.0 180.0 other-other Music_of_Cuba other
1.5580374e7 240761.0 25.0 Main_Page Music_of_Cuba other
25423.0 240761.0 11.0 Rock_music Music_of_Cuba other
240761.0 240761.0 26.0 Music_of_Cuba Music_of_Cuba other
null 240761.0 274.0 other-bing Music_of_Cuba other
957080.0 240761.0 17.0 Son_(music) Music_of_Cuba link
null 240761.0 208.0 other-yahoo Music_of_Cuba other
2.5179257e7 240761.0 20.0 Dance_in_Cuba Music_of_Cuba link
5042481.0 240761.0 116.0 Cuba Music_of_Cuba link
1.0891249e7 240761.0 17.0 Cuban_rumba Music_of_Cuba link
274927.0 240761.0 19.0 Clave_(rhythm) Music_of_Cuba link
1467094.0 240761.0 15.0 Cha-cha-cha_(dance) Music_of_Cuba other
3840752.0 240761.0 13.0 Cachao_López Music_of_Cuba link
9069468.0 240761.0 23.0 Cha-cha-cha_(music) Music_of_Cuba link
755312.0 240761.0 36.0 Buena_Vista_Social_Club_(album) Music_of_Cuba link
54403.0 240761.0 13.0 Dizzy_Gillespie Music_of_Cuba other
166331.0 240761.0 38.0 Celia_Cruz Music_of_Cuba link
58895.0 240761.0 55.0 Latin_American_music Music_of_Cuba link
9197579.0 240761.0 17.0 List_of_Caribbean_music_groups Music_of_Cuba link
1683462.0 240761.0 30.0 Rumba_(dance) Music_of_Cuba link
1.9261987e7 240761.0 27.0 Trova Music_of_Cuba link
null 240761.0 703.0 other-empty Music_of_Cuba other
33134.0 240761.0 12.0 World_music Music_of_Cuba other
null 2.3291343e7 14.0 other-empty Music_of_Dance_Dance_Revolution_(1998_video_game) other
null 2.3291343e7 17.0 other-wikipedia Music_of_Dance_Dance_Revolution_(1998_video_game) other
null 2.3291343e7 14.0 other-twitter Music_of_Dance_Dance_Revolution_(1998_video_game) other
null 2.3291343e7 70.0 other-google Music_of_Dance_Dance_Revolution_(1998_video_game) other
2.1011855e7 2.3291343e7 25.0 List_of_Dance_Dance_Revolution_songs Music_of_Dance_Dance_Revolution_(1998_video_game) link
1459689.0 2.3291343e7 232.0 Dance_Dance_Revolution_(1998_video_game) Music_of_Dance_Dance_Revolution_(1998_video_game) link
575919.0 1291938.0 12.0 Jade_Tree_(record_label) Music_of_Delaware link
null 1291938.0 10.0 other-empty Music_of_Delaware other
null 1291938.0 60.0 other-google Music_of_Delaware other
null 3.0865941e7 2241.0 other-google Music_of_Detroit other
null 3.0865941e7 123.0 other-wikipedia Music_of_Detroit other
1730307.0 3.0865941e7 14.0 List_of_Super_Bowl_halftime_shows Music_of_Detroit link
73010.0 3.0865941e7 11.0 Hardcore_punk Music_of_Detroit other
559332.0 3.0865941e7 25.0 List_of_hip_hop_genres Music_of_Detroit other
5027648.0 3.0865941e7 28.0 Detroit_Rock_City Music_of_Detroit link
8687.0 3.0865941e7 106.0 Detroit Music_of_Detroit link
180178.0 3.0865941e7 15.0 Detroit_techno Music_of_Detroit other
140308.0 3.0865941e7 29.0 Alice_Cooper Music_of_Detroit other
1242998.0 3.0865941e7 15.0 History_of_Detroit Music_of_Detroit link
1171542.0 3.0865941e7 20.0 Hitsville_U.S.A. Music_of_Detroit link
null 3.0865941e7 231.0 other-empty Music_of_Detroit other
168617.0 3.0865941e7 86.0 The_White_Stripes Music_of_Detroit link
307387.0 3.0865941e7 58.0 Music_of_Michigan Music_of_Detroit link
167396.0 3.0865941e7 100.0 Motown Music_of_Detroit link
null 3.0865941e7 103.0 other-bing Music_of_Detroit other
1384091.0 3.0865941e7 11.0 The_Electrifying_Mojo Music_of_Detroit link
685028.0 3.0865941e7 10.0 Super_Bowl_XL Music_of_Detroit link
null 3.0865941e7 44.0 other-other Music_of_Detroit other
null 3.0865941e7 32.0 other-facebook Music_of_Detroit other
1.5580374e7 3.0865941e7 10.0 Main_Page Music_of_Detroit other
3235772.0 3.0865941e7 27.0 Midwest_hip_hop Music_of_Detroit link
null 3.0865941e7 92.0 other-yahoo Music_of_Detroit other
null 467018.0 20.0 other-wikipedia Music_of_East_Timor other
null 467018.0 113.0 other-google Music_of_East_Timor other
376026.0 467018.0 29.0 Culture_of_East_Timor Music_of_East_Timor link
null 467018.0 134.0 other-empty Music_of_East_Timor other
1444274.0 2.880603e7 20.0 Music_of_Southeastern_Europe Music_of_Eastern_Europe link
33134.0 2.880603e7 27.0 World_music Music_of_Eastern_Europe link
null 306026.0 109.0 other-empty Music_of_Ecuador other
9334.0 306026.0 23.0 Ecuador Music_of_Ecuador link
null 306026.0 30.0 other-yahoo Music_of_Ecuador other
679331.0 306026.0 27.0 Culture_of_Ecuador Music_of_Ecuador link
null 306026.0 71.0 other-bing Music_of_Ecuador other
null 306026.0 13.0 other-other Music_of_Ecuador other
null 306026.0 941.0 other-google Music_of_Ecuador other
null 306026.0 32.0 other-wikipedia Music_of_Ecuador other
null 247746.0 101.0 other-wikipedia Music_of_Egypt other
null 247746.0 2575.0 other-google Music_of_Egypt other
8087628.0 247746.0 55.0 Egypt Music_of_Egypt link
5884148.0 247746.0 18.0 Baladi Music_of_Egypt link
1.1298796e7 247746.0 11.0 Ancient_Egyptian_cuisine Music_of_Egypt link
null 247746.0 426.0 other-empty Music_of_Egypt other
451841.0 247746.0 23.0 Music_of_North_Africa Music_of_Egypt link
3.2186235e7 247746.0 22.0 Music_in_the_Civilization_video_game_series Music_of_Egypt link
1427446.0 247746.0 10.0 Prehistoric_Egypt Music_of_Egypt link
855541.0 247746.0 20.0 Phrygian_dominant_scale Music_of_Egypt other
1.6173864e7 247746.0 10.0 Nefer Music_of_Egypt link
null 247746.0 100.0 other-other Music_of_Egypt other
3378045.0 247746.0 10.0 Public_holidays_in_Egypt Music_of_Egypt link
1.5580374e7 247746.0 11.0 Main_Page Music_of_Egypt other
18839.0 247746.0 44.0 Music Music_of_Egypt link
null 247746.0 154.0 other-bing Music_of_Egypt other
null 247746.0 69.0 other-yahoo Music_of_Egypt other
51218.0 247746.0 57.0 Culture_of_Egypt Music_of_Egypt link
1969914.0 247746.0 11.0 Coptic_music Music_of_Egypt link
8233.0 247746.0 10.0 Death_metal Music_of_Egypt other
1502321.0 247746.0 50.0 Ancient_music Music_of_Egypt link
8559295.0 247746.0 24.0 Egyptian_cuisine Music_of_Egypt link
null 4534705.0 23.0 other-google Music_of_Emilia-Romagna other
null 1.0645556e7 39.0 other-google Music_of_Epirus_(Greece) other
3237687.0 1.0645556e7 10.0 Greek_folk_music Music_of_Epirus_(Greece) other
9667732.0 1.0645556e7 14.0 Polyphonic_song_of_Epirus Music_of_Epirus_(Greece) link
null 1.0645556e7 19.0 other-empty Music_of_Epirus_(Greece) other
null 423133.0 113.0 other-empty Music_of_Equatorial_Guinea other
null 423133.0 178.0 other-google Music_of_Equatorial_Guinea other
null 423133.0 14.0 other-wikipedia Music_of_Equatorial_Guinea other
418571.0 423133.0 30.0 Culture_of_Equatorial_Guinea Music_of_Equatorial_Guinea link
9366.0 423133.0 63.0 Equatorial_Guinea Music_of_Equatorial_Guinea link
null 423133.0 17.0 other-bing Music_of_Equatorial_Guinea other
5073646.0 372892.0 12.0 Veljo_Tormis Music_of_Estonia link
null 372892.0 11.0 other-bing Music_of_Estonia other
null 372892.0 20.0 other-other Music_of_Estonia other
null 372892.0 104.0 other-empty Music_of_Estonia other
3.5061186e7 372892.0 11.0 Traffic_(Estonian_band) Music_of_Estonia link
null 372892.0 359.0 other-google Music_of_Estonia other
null 372892.0 39.0 other-wikipedia Music_of_Estonia other
2.8222445e7 372892.0 33.0 Estonia Music_of_Estonia link
1.2454919e7 372892.0 14.0 Culture_of_Estonia Music_of_Estonia link
2157363.0 247149.0 57.0 K'naan Music_of_Ethiopia link
1632417.0 247149.0 11.0 Tilahun_Gessesse Music_of_Ethiopia link
null 247149.0 253.0 other-empty Music_of_Ethiopia other
2194444.0 247149.0 146.0 Mulatu_Astatke Music_of_Ethiopia link
166141.0 247149.0 10.0 Music_of_Africa Music_of_Ethiopia link
null 247149.0 84.0 other-other Music_of_Ethiopia other
null 247149.0 77.0 other-bing Music_of_Ethiopia other
1.5580374e7 247149.0 10.0 Main_Page Music_of_Ethiopia other
187749.0 247149.0 85.0 Ethiopia Music_of_Ethiopia link
2022635.0 247149.0 13.0 Éthiopiques Music_of_Ethiopia link
null 247149.0 64.0 other-yahoo Music_of_Ethiopia other
null 247149.0 88.0 other-wikipedia Music_of_Ethiopia other
null 247149.0 1232.0 other-google Music_of_Ethiopia other
null 1.1214612e7 143.0 other-google Music_of_Final_Fantasy_III other
5137643.0 1.1214612e7 53.0 Music_of_Final_Fantasy_I_and_II Music_of_Final_Fantasy_III link
null 1.1214612e7 37.0 other-empty Music_of_Final_Fantasy_III other
1001643.0 1.1214612e7 28.0 Music_of_the_Final_Fantasy_series Music_of_Final_Fantasy_III link
null 1.1214612e7 10.0 other-other Music_of_Final_Fantasy_III other
66495.0 1.1214612e7 17.0 Final_Fantasy_III Music_of_Final_Fantasy_III link
52754.0 1.1209352e7 47.0 Final_Fantasy_IV Music_of_Final_Fantasy_IV link
null 1.1209352e7 296.0 other-google Music_of_Final_Fantasy_IV other
null 1.1209352e7 31.0 other-wikipedia Music_of_Final_Fantasy_IV other
1.1139384e7 1.1209352e7 12.0 Final_Fantasy_IV_(3D_remake) Music_of_Final_Fantasy_IV link
1001643.0 1.1209352e7 19.0 Music_of_the_Final_Fantasy_series Music_of_Final_Fantasy_IV link
1.1214612e7 1.1209352e7 51.0 Music_of_Final_Fantasy_III Music_of_Final_Fantasy_IV link
null 1.1209352e7 133.0 other-empty Music_of_Final_Fantasy_IV other
5130807.0 1.1209352e7 10.0 Music_of_Final_Fantasy_V Music_of_Final_Fantasy_IV link
1337053.0 1.1216149e7 27.0 Music_of_Final_Fantasy_X Music_of_Final_Fantasy_IX link
2159211.0 1.1216149e7 17.0 Music_of_the_Final_Fantasy_VII_series Music_of_Final_Fantasy_IX link
null 1.1216149e7 295.0 other-empty Music_of_Final_Fantasy_IX other
null 1.1216149e7 30.0 other-wikipedia Music_of_Final_Fantasy_IX other
null 1.1216149e7 618.0 other-google Music_of_Final_Fantasy_IX other
52758.0 1.1216149e7 90.0 Final_Fantasy_IX Music_of_Final_Fantasy_IX link
625605.0 1.1216149e7 15.0 Emiko_Shiratori Music_of_Final_Fantasy_IX link
2171749.0 1.1216149e7 12.0 Music_of_Final_Fantasy_VI Music_of_Final_Fantasy_IX link
1001643.0 1.1216149e7 37.0 Music_of_the_Final_Fantasy_series Music_of_Final_Fantasy_IX link
1.0091424e7 1.1216149e7 58.0 Music_of_Final_Fantasy_VIII Music_of_Final_Fantasy_IX link
null 1.1216149e7 22.0 other-bing Music_of_Final_Fantasy_IX other
null 1.1216149e7 11.0 other-yahoo Music_of_Final_Fantasy_IX other
4.2374187e7 1.1216149e7 17.0 List_of_Vietnamese_films_of_2014 Music_of_Final_Fantasy_IX other
null 2171749.0 43.0 other-wikipedia Music_of_Final_Fantasy_VI other
null 2171749.0 445.0 other-google Music_of_Final_Fantasy_VI other
null 2171749.0 146.0 other-empty Music_of_Final_Fantasy_VI other
5130807.0 2171749.0 39.0 Music_of_Final_Fantasy_V Music_of_Final_Fantasy_VI link
2159211.0 2171749.0 22.0 Music_of_the_Final_Fantasy_VII_series Music_of_Final_Fantasy_VI link
57677.0 2171749.0 13.0 Nobuo_Uematsu Music_of_Final_Fantasy_VI other
52755.0 2171749.0 139.0 Final_Fantasy_VI Music_of_Final_Fantasy_VI link
3.5792677e7 2171749.0 16.0 Dancing_Mad Music_of_Final_Fantasy_VI link
2756115.0 2171749.0 17.0 Shirō_Sagisu Music_of_Final_Fantasy_VI link
null 2171749.0 32.0 other-other Music_of_Final_Fantasy_VI other
1001643.0 2171749.0 48.0 Music_of_the_Final_Fantasy_series Music_of_Final_Fantasy_VI link
1.1209352e7 2171749.0 11.0 Music_of_Final_Fantasy_IV Music_of_Final_Fantasy_VI link
1.0091424e7 2171749.0 13.0 Music_of_Final_Fantasy_VIII Music_of_Final_Fantasy_VI link
1001643.0 1.0091424e7 42.0 Music_of_the_Final_Fantasy_series Music_of_Final_Fantasy_VIII link
1.1216149e7 1.0091424e7 22.0 Music_of_Final_Fantasy_IX Music_of_Final_Fantasy_VIII link
1.5580374e7 1.0091424e7 139.0 Main_Page Music_of_Final_Fantasy_VIII other
null 1.0091424e7 14.0 other-bing Music_of_Final_Fantasy_VIII other
null 1.0091424e7 29.0 other-other Music_of_Final_Fantasy_VIII other
1.4241133e7 1.0091424e7 34.0 Eyes_on_Me_(Faye_Wong_song) Music_of_Final_Fantasy_VIII link
null 1.0091424e7 777.0 other-google Music_of_Final_Fantasy_VIII other
null 1.0091424e7 124.0 other-wikipedia Music_of_Final_Fantasy_VIII other
52757.0 1.0091424e7 159.0 Final_Fantasy_VIII Music_of_Final_Fantasy_VIII link
null 1.0091424e7 16.0 other-yahoo Music_of_Final_Fantasy_VIII other
57677.0 1.0091424e7 35.0 Nobuo_Uematsu Music_of_Final_Fantasy_VIII link
2159211.0 1.0091424e7 67.0 Music_of_the_Final_Fantasy_VII_series Music_of_Final_Fantasy_VIII link
1337053.0 1.0091424e7 14.0 Music_of_Final_Fantasy_X Music_of_Final_Fantasy_VIII link
null 1.0091424e7 375.0 other-empty Music_of_Final_Fantasy_VIII other
1337053.0 7873264.0 75.0 Music_of_Final_Fantasy_X Music_of_Final_Fantasy_X-2 link
1.1216149e7 7873264.0 12.0 Music_of_Final_Fantasy_IX Music_of_Final_Fantasy_X-2 link
1001643.0 7873264.0 19.0 Music_of_the_Final_Fantasy_series Music_of_Final_Fantasy_X-2 link
null 7873264.0 465.0 other-google Music_of_Final_Fantasy_X-2 other
null 7873264.0 13.0 other-wikipedia Music_of_Final_Fantasy_X-2 other
null 7873264.0 70.0 other-empty Music_of_Final_Fantasy_X-2 other
398631.0 7873264.0 55.0 Final_Fantasy_X-2 Music_of_Final_Fantasy_X-2 link
419271.0 1.1245276e7 46.0 Final_Fantasy_XI Music_of_Final_Fantasy_XI link
4264381.0 1.1245276e7 12.0 Music_of_Final_Fantasy_XII Music_of_Final_Fantasy_XI link
1337053.0 1.1245276e7 14.0 Music_of_Final_Fantasy_X Music_of_Final_Fantasy_XI link
null 1.1245276e7 127.0 other-empty Music_of_Final_Fantasy_XI other
null 1.1245276e7 13.0 other-wikipedia Music_of_Final_Fantasy_XI other
null 1.1245276e7 194.0 other-google Music_of_Final_Fantasy_XI other
7873264.0 1.1245276e7 29.0 Music_of_Final_Fantasy_X-2 Music_of_Final_Fantasy_XI link
1001643.0 1.1245276e7 22.0 Music_of_the_Final_Fantasy_series Music_of_Final_Fantasy_XI link
2194883.0 1.1245276e7 17.0 The_Star_Onions Music_of_Final_Fantasy_XI link
1001643.0 2.719431e7 49.0 Music_of_the_Final_Fantasy_series Music_of_Final_Fantasy_XIII link
1636825.0 2.719431e7 135.0 Final_Fantasy_XIII Music_of_Final_Fantasy_XIII link
null 2.719431e7 10.0 other-wikipedia Music_of_Final_Fantasy_XIII other
null 2.719431e7 597.0 other-google Music_of_Final_Fantasy_XIII other
4264381.0 2.719431e7 57.0 Music_of_Final_Fantasy_XII Music_of_Final_Fantasy_XIII link
1337053.0 2.719431e7 16.0 Music_of_Final_Fantasy_X Music_of_Final_Fantasy_XIII link
3.8124492e7 2.719431e7 23.0 Music_of_Final_Fantasy_XIII-2 Music_of_Final_Fantasy_XIII link
2159211.0 2.719431e7 10.0 Music_of_the_Final_Fantasy_VII_series Music_of_Final_Fantasy_XIII link
391547.0 2.719431e7 14.0 Masashi_Hamauzu Music_of_Final_Fantasy_XIII link
4.3990854e7 2.719431e7 12.0 Music_of_Lightning_Returns:_Final_Fantasy_XIII Music_of_Final_Fantasy_XIII link
null 2.719431e7 216.0 other-empty Music_of_Final_Fantasy_XIII other
null 4.2517401e7 107.0 other-empty Music_of_Final_Fantasy_XIV other
4.3990854e7 4.2517401e7 36.0 Music_of_Lightning_Returns:_Final_Fantasy_XIII Music_of_Final_Fantasy_XIV link
3.8080015e7 4.2517401e7 156.0 Final_Fantasy_XIV:_A_Realm_Reborn Music_of_Final_Fantasy_XIV link
1001643.0 4.2517401e7 35.0 Music_of_the_Final_Fantasy_series Music_of_Final_Fantasy_XIV link
2.719431e7 4.2517401e7 15.0 Music_of_Final_Fantasy_XIII Music_of_Final_Fantasy_XIV link
null 4.2517401e7 661.0 other-google Music_of_Final_Fantasy_XIV other
null 4.2517401e7 20.0 other-wikipedia Music_of_Final_Fantasy_XIV other
2.3068765e7 4.2517401e7 69.0 Final_Fantasy_XIV Music_of_Final_Fantasy_XIV link
1.8933066e7 308346.0 55.0 Florida Music_of_Florida link
8233.0 308346.0 55.0 Death_metal Music_of_Florida other
2.1027603e7 308346.0 20.0 Culture_of_Florida Music_of_Florida link
171080.0 308346.0 17.0 Music_of_the_United_States Music_of_Florida link
null 308346.0 79.0 other-empty Music_of_Florida other
null 308346.0 1170.0 other-google Music_of_Florida other
null 308346.0 38.0 other-wikipedia Music_of_Florida other
null 308346.0 36.0 other-yahoo Music_of_Florida other
null 308346.0 23.0 other-other Music_of_Florida other
null 308346.0 44.0 other-bing Music_of_Florida other
3.031949e7 244703.0 49.0 Zaz_(singer) Music_of_France link
460074.0 244703.0 15.0 Zouk Music_of_France other
null 244703.0 347.0 other-bing Music_of_France other
1.5580374e7 244703.0 15.0 Main_Page Music_of_France other
154437.0 244703.0 10.0 Mireille_Mathieu Music_of_France link
455818.0 244703.0 13.0 Léo_Ferré Music_of_France link
null 244703.0 107.0 other-other Music_of_France other
98988.0 244703.0 46.0 Culture_of_France Music_of_France link
1028178.0 244703.0 10.0 Françoise_Hardy Music_of_France link
53185.0 244703.0 19.0 French_hip_hop Music_of_France link
244706.0 244703.0 133.0 French_music Music_of_France link
6180884.0 244703.0 64.0 French_pop_music Music_of_France link
2462478.0 244703.0 20.0 List_of_French_artists Music_of_France link
6181221.0 244703.0 13.0 French_popular_music Music_of_France link
4.2854258e7 244703.0 15.0 Kendji_Girac Music_of_France link
5843419.0 244703.0 128.0 France Music_of_France link
143486.0 244703.0 16.0 Indie_rock Music_of_France link
3.8384402e7 244703.0 33.0 French_electronic_music Music_of_France link
12343.0 244703.0 36.0 Guadeloupe Music_of_France other
null 244703.0 173.0 other-yahoo Music_of_France other
660446.0 244703.0 27.0 Vanessa_Paradis Music_of_France link
null 244703.0 658.0 other-empty Music_of_France other
61159.0 244703.0 10.0 Maurice_Chevalier Music_of_France link
null 244703.0 137.0 other-wikipedia Music_of_France other
null 244703.0 5065.0 other-google Music_of_France other
64963.0 244703.0 20.0 Édith_Piaf Music_of_France link
null 319399.0 16.0 other-empty Music_of_French_Polynesia other
null 319399.0 29.0 other-google Music_of_French_Polynesia other
10737.0 319399.0 23.0 French_Polynesia Music_of_French_Polynesia link
null 2.2598486e7 12.0 other-google Music_of_Fujian other
null 2.2598486e7 16.0 other-empty Music_of_Fujian other
null 988801.0 66.0 other-empty Music_of_Gabon other
12027.0 988801.0 19.0 Gabon Music_of_Gabon link
null 988801.0 113.0 other-google Music_of_Gabon other
null 988801.0 11.0 other-wikipedia Music_of_Gabon other
null 432418.0 65.0 other-empty Music_of_Galicia,_Cantabria_and_Asturias other
4243394.0 432418.0 18.0 Galician_people Music_of_Galicia,_Cantabria_and_Asturias other
158996.0 432418.0 35.0 Hurdy-gurdy Music_of_Galicia,_Cantabria_and_Asturias link
5261.0 432418.0 26.0 Celtic_music Music_of_Galicia,_Cantabria_and_Asturias link
null 432418.0 13.0 other-other Music_of_Galicia,_Cantabria_and_Asturias other
null 432418.0 25.0 other-bing Music_of_Galicia,_Cantabria_and_Asturias other
null 432418.0 31.0 other-wikipedia Music_of_Galicia,_Cantabria_and_Asturias other
null 432418.0 292.0 other-google Music_of_Galicia,_Cantabria_and_Asturias other
12837.0 432418.0 32.0 Galicia_(Spain) Music_of_Galicia,_Cantabria_and_Asturias link
362965.0 432418.0 20.0 Capriccio_Espagnol Music_of_Galicia,_Cantabria_and_Asturias other
null 432418.0 17.0 other-yahoo Music_of_Galicia,_Cantabria_and_Asturias other
null 244795.0 189.0 other-yahoo Music_of_Germany other
1195868.0 244795.0 53.0 Culture_of_Germany Music_of_Germany link
953401.0 244795.0 94.0 German_rock Music_of_Germany link
1587088.0 244795.0 21.0 List_of_German_musicians Music_of_Germany link
12636.0 244795.0 12.0 German_literature Music_of_Germany link
5103330.0 244795.0 19.0 German_art Music_of_Germany link
276919.0 244795.0 70.0 Krautrock Music_of_Germany other
262094.0 244795.0 43.0 Volksmusik Music_of_Germany link
null 244795.0 724.0 other-empty Music_of_Germany other
598055.0 244795.0 28.0 Volkstümliche_Musik Music_of_Germany link
636692.0 244795.0 18.0 Metalcore Music_of_Germany other
5243467.0 244795.0 14.0 Neue_Deutsche_Härte Music_of_Germany link
497068.0 244795.0 112.0 Ostalgie Music_of_Germany other
234666.0 244795.0 62.0 Blind_Guardian Music_of_Germany link
8203.0 244795.0 16.0 Deutschlandlied Music_of_Germany link
609351.0 244795.0 47.0 Accept_(band) Music_of_Germany link
11867.0 244795.0 245.0 Germany Music_of_Germany link
152735.0 244795.0 24.0 Germans Music_of_Germany link
2130627.0 244795.0 44.0 Grave_Digger_(band) Music_of_Germany link
757143.0 244795.0 67.0 Gamma_Ray_(band) Music_of_Germany link
1246795.0 244795.0 12.0 German_folklore Music_of_Germany link
720421.0 244795.0 22.0 List_of_rock_genres Music_of_Germany other
null 244795.0 339.0 other-bing Music_of_Germany other
null 244795.0 132.0 other-other Music_of_Germany other
1332497.0 244795.0 22.0 Rage_(German_band) Music_of_Germany link
1.5580374e7 244795.0 32.0 Main_Page Music_of_Germany other
378119.0 244795.0 15.0 Neue_Deutsche_Welle Music_of_Germany link
1130361.0 244795.0 32.0 Running_Wild_(band) Music_of_Germany link
299409.0 244795.0 75.0 Schlager_music Music_of_Germany link
244795.0 244795.0 69.0 Music_of_Germany Music_of_Germany link
4.1005564e7 244795.0 37.0 Milky_Chance Music_of_Germany other
null 244795.0 4975.0 other-google Music_of_Germany other
null 244795.0 249.0 other-wikipedia Music_of_Germany other
null 245844.0 49.0 other-wikipedia Music_of_Ghana other
null 245844.0 1018.0 other-google Music_of_Ghana other
null 245844.0 167.0 other-empty Music_of_Ghana other
788753.0 245844.0 19.0 Culture_of_Ghana Music_of_Ghana link
null 245844.0 37.0 other-yahoo Music_of_Ghana other
null 245844.0 52.0 other-bing Music_of_Ghana other
null 245844.0 46.0 other-other Music_of_Ghana other
1791457.0 245844.0 96.0 Music_of_West_Africa Music_of_Ghana link
12067.0 245844.0 24.0 Ghana Music_of_Ghana link
197453.0 1356974.0 100.0 Goa_trance Music_of_Goa link
1980910.0 1356974.0 24.0 Culture_of_Goa Music_of_Goa link
DataFrame in python
clicksPy = sqlContext.read.parquet("/datasets/wiki-clickstream")

# in Python you need to put the object int its own line like this to get the type information
clicksPy 

clicksPy.show()

+--------+--------+---+-------------------+-----------+-----+
| prev_id| curr_id|  n|         prev_title| curr_title| type|
+--------+--------+---+-------------------+-----------+-----+
|13710401|12653094| 12|Punk_rock_subgenres|Music_genre| link|
|   25423|12653094| 16|         Rock_music|Music_genre|other|
|  178244|12653094| 10|        Muse_(band)|Music_genre| link|
|  156547|12653094| 10|              Remix|Music_genre| link|
| 1564758|12653094| 73|           Pop_rock|Music_genre| link|
|   18839|12653094|203|              Music|Music_genre| link|
| 5079506|12653094| 10|         Pink_Floyd|Music_genre| link|
|   24624|12653094|167|          Pop_music|Music_genre| link|
|  379560|12653094| 15|       Musical_form|Music_genre| link|
|15580374|12653094|197|          Main_Page|Music_genre|other|
|24297671|12653094|862|      Popular_music|Music_genre| link|
|12653094|12653094| 23|        Music_genre|Music_genre|other|
|   25520|12653094| 90|             Reggae|Music_genre| link|
|   54783|12653094| 18|       Music_theory|Music_genre| link|
|  147311|12653094| 14|        Ray_Charles|Music_genre| link|
| 8886086|12653094| 11|                Oi!|Music_genre| link|
|   19499|12653094| 10|       Mariah_Carey|Music_genre| link|
|38954428|12653094| 23| Sam_Smith_(singer)|Music_genre| link|
| 2110323|12653094| 12|            Rihanna|Music_genre| link|
|    null|12653094|632|        other-other|Music_genre|other|
+--------+--------+---+-------------------+-----------+-----+
only showing top 20 rows

Now you can continue from the original python notebook tweeted by Michael.

Recall from the beginning of this notebook that this python databricks notebook was used in the talk by Michael Armbrust at Spark Summit East February 2016 shared from https://twitter.com/michaelarmbrust/status/699969850475737088

(watch now, if you haven't already!)

Michael Armbrust Spark Summit East

You Try!

Try to laoad a DataFrame in R from the parquet file just as we did for python. Read the docs in databricks guide first:

And see the R example in the Programming Guide:

library(SparkR)

# just a quick test
df <- createDataFrame(faithful)
head(df)

# Read in the Parquet file created above. Parquet files are self-describing so the schema is preserved.
# The result of loading a parquet file is also a DataFrame.
clicksR <- read.df("/datasets/wiki-clickstream", source = "parquet")
clicksR # in R you need to put the object int its own line like this to get the type information

head(clicksR)

display(clicksR)
prev_id curr_id n prev_title curr_title type
1.3710401e7 1.2653094e7 12.0 Punk_rock_subgenres Music_genre link
25423.0 1.2653094e7 16.0 Rock_music Music_genre other
178244.0 1.2653094e7 10.0 Muse_(band) Music_genre link
156547.0 1.2653094e7 10.0 Remix Music_genre link
1564758.0 1.2653094e7 73.0 Pop_rock Music_genre link
18839.0 1.2653094e7 203.0 Music Music_genre link
5079506.0 1.2653094e7 10.0 Pink_Floyd Music_genre link
24624.0 1.2653094e7 167.0 Pop_music Music_genre link
379560.0 1.2653094e7 15.0 Musical_form Music_genre link
1.5580374e7 1.2653094e7 197.0 Main_Page Music_genre other
2.4297671e7 1.2653094e7 862.0 Popular_music Music_genre link
1.2653094e7 1.2653094e7 23.0 Music_genre Music_genre other
25520.0 1.2653094e7 90.0 Reggae Music_genre link
54783.0 1.2653094e7 18.0 Music_theory Music_genre link
147311.0 1.2653094e7 14.0 Ray_Charles Music_genre link
8886086.0 1.2653094e7 11.0 Oi! Music_genre link
19499.0 1.2653094e7 10.0 Mariah_Carey Music_genre link
3.8954428e7 1.2653094e7 23.0 Sam_Smith_(singer) Music_genre link
2110323.0 1.2653094e7 12.0 Rihanna Music_genre link
null 1.2653094e7 632.0 other-other Music_genre other
null 1.2653094e7 20.0 other-facebook Music_genre other
62808.0 1.2653094e7 56.0 Soul_music Music_genre link
null 1.2653094e7 514.0 other-bing Music_genre other
162707.0 1.2653094e7 95.0 Singing Music_genre link
5422144.0 1.2653094e7 35.0 Taylor_Swift Music_genre link
27176.0 1.2653094e7 16.0 Ska Music_genre link
28830.0 1.2653094e7 21.0 Song Music_genre link
4.1884523e7 1.2653094e7 22.0 Vaporwave Music_genre other
295560.0 1.2653094e7 13.0 Style Music_genre link
424093.0 1.2653094e7 10.0 1990s_in_music Music_genre other
236918.0 1.2653094e7 14.0 Concert Music_genre other
41536.0 1.2653094e7 11.0 Duke_Ellington Music_genre link
2.5276055e7 1.2653094e7 16.0 Ariana_Grande Music_genre link
363651.0 1.2653094e7 10.0 Dark_wave Music_genre link
183304.0 1.2653094e7 11.0 Dub_(music) Music_genre link
4637590.0 1.2653094e7 16.0 Bob_Dylan Music_genre link
83688.0 1.2653094e7 16.0 Beyoncé Music_genre link
3.0528002e7 1.2653094e7 17.0 Ed_Sheeran Music_genre link
8239846.0 1.2653094e7 10.0 Bob_Marley Music_genre link
880.0 1.2653094e7 10.0 ABBA Music_genre link
5261.0 1.2653094e7 13.0 Celtic_music Music_genre other
2.7005455e7 1.2653094e7 12.0 Bruno_Mars Music_genre link
1.0232935e7 1.2653094e7 10.0 Diatonic_and_chromatic Music_genre other
7966.0 1.2653094e7 25.0 Disco Music_genre link
413723.0 1.2653094e7 12.0 Heavy_metal_subgenres Music_genre link
168377.0 1.2653094e7 40.0 Folk_rock Music_genre other
1.1655198e7 1.2653094e7 15.0 Ishkur's_Guide_to_Electronic_Music Music_genre link
3.0863005e7 1.2653094e7 13.0 List_of_Christian_bands_and_artists_by_genre Music_genre link
3.1976854e7 1.2653094e7 11.0 Japanese_Girl Music_genre link
10778.0 1.2653094e7 60.0 Funk Music_genre link
171111.0 1.2653094e7 11.0 Honky-tonk Music_genre link
4.1518485e7 1.2653094e7 10.0 Hozier_(musician) Music_genre other
1.198307e7 1.2653094e7 17.0 Johnny_Cash Music_genre link
3.1919748e7 1.2653094e7 31.0 FIFA_12 Music_genre link
172830.0 1.2653094e7 11.0 Fado Music_genre link
1.6477368e7 1.2653094e7 18.0 Katy_Perry Music_genre link
2319440.0 1.2653094e7 15.0 List_of_saxophonists Music_genre link
6921880.0 1.2653094e7 11.0 List_of_composers_by_name Music_genre other
2878021.0 1.2653094e7 16.0 List_of_country_genres Music_genre link
2.399895e7 1.2653094e7 28.0 Lists_of_musicians Music_genre link
559487.0 1.2653094e7 20.0 List_of_styles_of_music:_S–Z Music_genre link
559484.0 1.2653094e7 70.0 List_of_styles_of_music:_A–F Music_genre link
417829.0 1.2653094e7 18.0 List_of_all-female_bands Music_genre link
275671.0 1.2653094e7 44.0 List_of_electronic_music_genres Music_genre link
1.449809e7 1.2653094e7 13.0 Fall_Out_Boy Music_genre link
4429395.0 1.2653094e7 20.0 Eminem Music_genre link
973905.0 1.2653094e7 24.0 Genealogy_of_musical_genres Music_genre link
3.6042633e7 1.2653094e7 12.0 Electro_house Music_genre other
682482.0 1.2653094e7 53.0 Human Music_genre link
2.9909823e7 1.2653094e7 39.0 Kendrick_Lamar Music_genre link
7653811.0 1.2653094e7 19.0 Hip_hop_(disambiguation) Music_genre link
11181.0 1.2653094e7 10.0 Frank_Sinatra Music_genre link
3.3209238e7 1.2653094e7 13.0 Lana_Del_Rey Music_genre link
629945.0 1.2653094e7 29.0 K-pop Music_genre link
1.8945847e7 1.2653094e7 225.0 Hip_hop_music Music_genre link
2527136.0 1.2653094e7 14.0 Jazz_poetry Music_genre link
124802.0 1.2653094e7 38.0 Hard_rock Music_genre link
44706.0 1.2653094e7 283.0 Genre Music_genre link
73010.0 1.2653094e7 23.0 Hardcore_punk Music_genre link
1.7782843e7 1.2653094e7 22.0 Lady_Gaga Music_genre link
9355587.0 1.2653094e7 154.0 Example_(musician) Music_genre link
15613.0 1.2653094e7 218.0 Jazz Music_genre link
547533.0 1.2653094e7 10.0 Crossover_(music) Music_genre link
2.4686326e7 1.2653094e7 15.0 21st-century_classical_music Music_genre link
7885.0 1.2653094e7 15.0 Dance Music_genre other
3.3269956e7 1.2653094e7 24.0 5ive_(disambiguation) Music_genre other
167409.0 1.2653094e7 11.0 Alternative_rock Music_genre other
1.8127544e7 1.2653094e7 11.0 Ah_Me,_Ah_My Music_genre link
386347.0 1.2653094e7 15.0 Anti-folk Music_genre link
3.4953684e7 1.2653094e7 17.0 Charli_XCX Music_genre link
392811.0 1.2653094e7 23.0 African-American_music Music_genre other
2468299.0 1.2653094e7 13.0 CD-Text Music_genre link
3603298.0 1.2653094e7 150.0 Art_Official_Intelligence:_Mosaic_Thump Music_genre link
66038.0 1.2653094e7 28.0 Breakbeat Music_genre link
368323.0 1.2653094e7 12.0 Cockney_Rejects Music_genre link
255791.0 1.2653094e7 56.0 Art_music Music_genre link
461637.0 1.2653094e7 10.0 Cumbia Music_genre link
3352.0 1.2653094e7 118.0 Blues Music_genre link
149681.0 1.2653094e7 10.0 Beck Music_genre link
214666.0 1.2653094e7 402.0 List_of_music_styles Music_genre link
5347350.0 1.2653094e7 27.0 List_of_popular_music_genres Music_genre link
2.7052778e7 1.2653094e7 54.0 List_of_genres Music_genre link
3142048.0 1.2653094e7 29.0 List_of_jazz_genres Music_genre link
559485.0 1.2653094e7 14.0 List_of_styles_of_music:_G–M Music_genre link
413631.0 1.2653094e7 12.0 List_of_blues_genres Music_genre link
303261.0 1.2653094e7 10.0 Top_40 Music_genre other
147687.0 1.2653094e7 10.0 Stevie_Wonder Music_genre link
31056.0 1.2653094e7 10.0 The_Rolling_Stones Music_genre link
29812.0 1.2653094e7 84.0 The_Beatles Music_genre link
3208697.0 1.2653094e7 16.0 Youth_subculture Music_genre link
null 1.2653094e7 2746.0 other-empty Music_genre other
28261.0 1.2653094e7 38.0 Samba Music_genre other
1795886.0 1.2653094e7 11.0 Record_chart Music_genre other
21151.0 1.2653094e7 12.0 New_wave_music Music_genre link
248462.0 1.2653094e7 20.0 Music_of_Colombia Music_genre link
7504750.0 1.2653094e7 21.0 Music_festival Music_genre other
1.932133e7 1.2653094e7 41.0 Nightclub Music_genre link
199630.0 1.2653094e7 11.0 Pop_punk Music_genre link
171080.0 1.2653094e7 18.0 Music_of_the_United_States Music_genre link
18313.0 1.2653094e7 14.0 Louis_Armstrong Music_genre link
394633.0 1.2653094e7 16.0 Reggaeton Music_genre link
4635444.0 1.2653094e7 16.0 March_(music) Music_genre other
565560.0 1.2653094e7 11.0 Protopunk Music_genre other
37735.0 1.2653094e7 10.0 Melody Music_genre other
26168.0 1.2653094e7 59.0 Rhythm_and_blues Music_genre link
3.1772741e7 1.2653094e7 14.0 One_Direction Music_genre link
2.1065992e7 1.2653094e7 19.0 Sex_(The_Necks_album) Music_genre link
4.3272496e7 1.2653094e7 18.0 Meghan_Trainor Music_genre link
2.719197e7 1.2653094e7 15.0 Moombahton Music_genre other
3403168.0 1.2653094e7 35.0 Outline_of_music Music_genre link
null 1.2653094e7 359.0 other-yahoo Music_genre other
2.888765e7 2.8887473e7 11.0 Music_Group_(company) Music_group_(disambiguation) link
20180.0 2.8887473e7 69.0 Musical_ensemble Music_group_(disambiguation) link
897299.0 232692.0 11.0 Stan_Laurel Music_hall link
103067.0 232692.0 15.0 Stand-up_comedy Music_hall link
1.2892136e7 232692.0 10.0 Pack_Up_Your_Troubles_in_Your_Old_Kit-Bag Music_hall link
2.0715761e7 232692.0 103.0 The_boy_Jones Music_hall link
9206390.0 232692.0 11.0 Sunny_Afternoon Music_hall link
3158351.0 232692.0 47.0 The_Kinks Music_hall link
470943.0 232692.0 16.0 The_Triplets_of_Belleville Music_hall link
326433.0 232692.0 32.0 Variety_show Music_hall link
null 232692.0 417.0 other-empty Music_hall other
2246663.0 232692.0 45.0 Martha_My_Dear Music_hall link
24864.0 232692.0 23.0 Professional_wrestling Music_hall link
1.5580374e7 232692.0 35.0 Main_Page Music_hall other
5106604.0 232692.0 87.0 Seymour_Hicks Music_hall link
556635.0 232692.0 27.0 When_I'm_Sixty-Four Music_hall link
3.8027034e7 232692.0 46.0 Songs_of_the_First_World_War Music_hall link
null 232692.0 93.0 other-bing Music_hall other
48235.0 232692.0 51.0 Vaudeville Music_hall link
null 232692.0 103.0 other-other Music_hall other
null 232692.0 77.0 other-yahoo Music_hall other
null 232692.0 2106.0 other-google Music_hall other
null 232692.0 231.0 other-wikipedia Music_hall other
null 232692.0 15.0 other-twitter Music_hall other
2260734.0 232692.0 11.0 List_of_musical_forms_by_era Music_hall link
2861.0 232692.0 15.0 Advertising Music_hall link
7566837.0 232692.0 16.0 Bioscope_show Music_hall link
100096.0 232692.0 14.0 Edwardian_era Music_hall link
605891.0 232692.0 18.0 David_Bowie_(1967_album) Music_hall link
36999.0 232692.0 25.0 Carry_On_(franchise) Music_hall link
5142.0 232692.0 63.0 Charlie_Chaplin Music_hall link
95805.0 232692.0 13.0 Leslie_Phillips Music_hall link
1163667.0 232692.0 10.0 I'm_Henery_the_Eighth,_I_Am Music_hall link
1932690.0 232692.0 54.0 Honey_Pie Music_hall link
1468518.0 232692.0 34.0 Her_Majesty_(song) Music_hall link
43492.0 232692.0 10.0 Ian_Dury Music_hall link
6835232.0 232692.0 15.0 Holiday_(Bee_Gees_song) Music_hall link
1.871959e7 232692.0 13.0 I_Do_Like_To_be_Beside_the_Seaside Music_hall link
1084094.0 232692.0 28.0 It's_a_Long_Way_to_Tipperary Music_hall link
3832925.0 232692.0 25.0 Good_Old-Fashioned_Lover_Boy Music_hall link
8786.0 232692.0 19.0 David_Bowie Music_hall link
5130871.0 232692.0 52.0 America's_Most_Endangered_Places Music_hall other
428611.0 232692.0 14.0 Can-can Music_hall link
1.4923927e7 232692.0 11.0 Concert_saloon Music_hall link
3.0995031e7 232692.0 23.0 American_burlesque Music_hall link
null 7570941.0 39.0 other-google Music_history_of_Barbados other
null 2735439.0 49.0 other-google Music_history_of_Hungary other
null 2735439.0 10.0 other-empty Music_history_of_Hungary other
null 3430507.0 18.0 other-empty Music_history_of_Portugal other
387719.0 3430507.0 13.0 Music_of_Portugal Music_history_of_Portugal link
null 3430507.0 80.0 other-google Music_history_of_Portugal other
null 1616933.0 158.0 other-empty Music_history_of_the_United_States other
null 1616933.0 1935.0 other-google Music_history_of_the_United_States other
null 1616933.0 18.0 other-wikipedia Music_history_of_the_United_States other
null 1616933.0 40.0 other-yahoo Music_history_of_the_United_States other
171080.0 1616933.0 25.0 Music_of_the_United_States Music_history_of_the_United_States link
null 1616933.0 36.0 other-other Music_history_of_the_United_States other
null 1616933.0 49.0 other-bing Music_history_of_the_United_States other
246497.0 1616933.0 10.0 American_folk_music Music_history_of_the_United_States link
1.8985287e7 1616933.0 57.0 Culture_of_the_United_States Music_history_of_the_United_States link
2.3932051e7 3.1437105e7 80.0 1960s_in_music Music_history_of_the_United_States_in_the_1960s link
null 3.1437105e7 103.0 other-empty Music_history_of_the_United_States_in_the_1960s other
null 3.1437105e7 23.0 other-yahoo Music_history_of_the_United_States_in_the_1960s other
null 3.1437105e7 12.0 other-wikipedia Music_history_of_the_United_States_in_the_1960s other
null 3.1437105e7 1679.0 other-google Music_history_of_the_United_States_in_the_1960s other
8544676.0 3.1437105e7 46.0 Counterculture_of_the_1960s Music_history_of_the_United_States_in_the_1960s link
null 3.1437105e7 82.0 other-bing Music_history_of_the_United_States_in_the_1960s other
null 3.1437105e7 16.0 other-other Music_history_of_the_United_States_in_the_1960s other
null 411041.0 16.0 other-other Music_history_of_the_United_States_in_the_1980s other
1616933.0 411041.0 10.0 Music_history_of_the_United_States Music_history_of_the_United_States_in_the_1980s link
null 411041.0 36.0 other-bing Music_history_of_the_United_States_in_the_1980s other
null 411041.0 11.0 other-yahoo Music_history_of_the_United_States_in_the_1980s other
23726.0 411041.0 15.0 Pixies Music_history_of_the_United_States_in_the_1980s other
411040.0 411041.0 11.0 Music_history_of_the_United_States_in_the_1970s Music_history_of_the_United_States_in_the_1980s link
1.9753121e7 411041.0 79.0 1980s_in_music Music_history_of_the_United_States_in_the_1980s link
null 411041.0 51.0 other-empty Music_history_of_the_United_States_in_the_1980s other
null 411041.0 593.0 other-google Music_history_of_the_United_States_in_the_1980s other
null 411041.0 11.0 other-wikipedia Music_history_of_the_United_States_in_the_1980s other
null 1369822.0 23.0 other-google Music_in_Adygea other
247772.0 1369822.0 12.0 Music_of_Russia Music_in_Adygea other
407750.0 1369822.0 11.0 Adygea Music_in_Adygea other
735530.0 1284752.0 27.0 Bashkortostan Music_in_Bashkortostan link
61024.0 3.8584535e7 33.0 Charleston,_South_Carolina Music_in_Charleston link
null 3.8584535e7 82.0 other-google Music_in_Charleston other
null 3.8584535e7 21.0 other-empty Music_in_Charleston other
null 2.5941812e7 28.0 other-google Music_in_Colonial_Mexico other
751099.0 1499681.0 28.0 Dagestan Music_in_Dagestan other
null 1499681.0 10.0 other-google Music_in_Dagestan other
null 1.1374661e7 19.0 other-google Music_in_Darkness other
null 1.1374661e7 13.0 other-wikipedia Music_in_Darkness other
null 1.1374661e7 10.0 other-other Music_in_Darkness other
1.3075438e7 1.1374661e7 62.0 Ingmar_Bergman_filmography Music_in_Darkness link
null 2.5548658e7 77.0 other-empty Music_in_Dollhouse other
null 2.5548658e7 11.0 other-bing Music_in_Dollhouse other
1.4014034e7 2.5548658e7 75.0 Dollhouse_(TV_series) Music_in_Dollhouse link
null 2.5548658e7 253.0 other-google Music_in_Dollhouse other
null 2.5548658e7 11.0 other-wikipedia Music_in_Dollhouse other
null 1.6378289e7 45.0 other-google Music_in_Dresden other
null 1264053.0 33.0 other-google Music_in_High_Places other
1264134.0 1264053.0 22.0 Here's_to_the_Mourning Music_in_High_Places link
1136526.0 1264053.0 20.0 Unwritten_Law Music_in_High_Places link
2.4597057e7 1264053.0 10.0 Unwritten_Law_discography Music_in_High_Places link
1179127.0 1499731.0 14.0 Kuban_Cossacks Music_in_Krasnodar_Krai other
474125.0 1499731.0 13.0 Krasnodar_Krai Music_in_Krasnodar_Krai link
8262427.0 5563451.0 20.0 Leeds Music_in_Leeds link
5563531.0 5563451.0 15.0 List_of_bands_originating_in_Leeds Music_in_Leeds link
null 5563451.0 11.0 other-wikipedia Music_in_Leeds other
null 5563451.0 311.0 other-google Music_in_Leeds other
null 5563451.0 124.0 other-empty Music_in_Leeds other
473991.0 1499616.0 13.0 Mordovia Music_in_Mordovia other
null 3087094.0 10.0 other-empty Music_in_Mouth other
4455620.0 3087094.0 12.0 Neither_Am_I Music_in_Mouth link
2.5688717e7 3087094.0 14.0 Bell_X1_discography Music_in_Mouth link
2809365.0 3087094.0 41.0 Bell_X1_(band) Music_in_Mouth link
277952.0 4.2439092e7 34.0 Rita_Hayworth Music_in_My_Heart link
null 4.2439092e7 20.0 other-google Music_in_My_Heart other
174689.0 1492279.0 80.0 Nenets_people Music_in_Nenets_Autonomous_Okrug other
null 3.9884449e7 227.0 other-google Music_in_Paris other
309852.0 3.9884449e7 10.0 List_of_cultural_and_regional_genres_of_music Music_in_Paris other
null 3.9884449e7 24.0 other-empty Music_in_Paris other
22989.0 3.9884449e7 36.0 Paris Music_in_Paris link
null 3.9530149e7 86.0 other-google Music_in_Varanasi other
null 3.9530149e7 16.0 other-empty Music_in_Varanasi other
4108684.0 2129287.0 19.0 WWF_The_Music,_Vol._5 Music_in_professional_wrestling link
1.4521129e7 2129287.0 17.0 The_Time_Is_Now_(John_Cena_song) Music_in_professional_wrestling link
3174133.0 2129287.0 12.0 WWE_Originals Music_in_professional_wrestling link
1.4681044e7 2129287.0 14.0 The_Bella_Twins Music_in_professional_wrestling link
1954598.0 2129287.0 14.0 You_Can't_See_Me Music_in_professional_wrestling link
null 2129287.0 115.0 other-empty Music_in_professional_wrestling other
4373640.0 2129287.0 12.0 Roman_Reigns Music_in_professional_wrestling link
3.5347635e7 2129287.0 16.0 Sasha_Banks Music_in_professional_wrestling link
611396.0 2129287.0 36.0 Rick_Derringer Music_in_professional_wrestling link
2121727.0 2129287.0 17.0 Music_at_sporting_events Music_in_professional_wrestling link
4395990.0 2129287.0 11.0 Piledriver:_The_Wrestling_Album_2 Music_in_professional_wrestling link
665823.0 2129287.0 13.0 Randy_Orton Music_in_professional_wrestling link
303225.0 2129287.0 67.0 Triple_H Music_in_professional_wrestling link
null 2129287.0 18.0 other-bing Music_in_professional_wrestling other
655575.0 2129287.0 24.0 Sting_(wrestler) Music_in_professional_wrestling link
4395892.0 2129287.0 12.0 The_Wrestling_Album Music_in_professional_wrestling link
2.5816978e7 2129287.0 40.0 WWE_The_Music:_A_New_Day,_Vol._10 Music_in_professional_wrestling link
4.345633e7 2129287.0 31.0 WWE_Music_Group_discography Music_in_professional_wrestling link
6434529.0 2129287.0 332.0 WWE_Music_Group Music_in_professional_wrestling link
345792.0 2129287.0 24.0 The_Undertaker Music_in_professional_wrestling link
null 2129287.0 12.0 other-other Music_in_professional_wrestling other
4.0532625e7 2129287.0 10.0 Alexander_Rusev Music_in_professional_wrestling link
156126.0 2129287.0 19.0 Dwayne_Johnson Music_in_professional_wrestling link
2321041.0 2129287.0 10.0 Gravity_(Our_Lady_Peace_album) Music_in_professional_wrestling other
2710336.0 2129287.0 52.0 Jim_Johnston_(composer) Music_in_professional_wrestling link
4786864.0 2129287.0 39.0 Entrance_music Music_in_professional_wrestling link
null 2129287.0 14.0 other-yahoo Music_in_professional_wrestling other
null 2129287.0 460.0 other-google Music_in_professional_wrestling other
null 2129287.0 28.0 other-wikipedia Music_in_professional_wrestling other
4.1413713e7 2129287.0 14.0 Lana_(wrestling) Music_in_professional_wrestling link
345802.0 2129287.0 17.0 John_Cena Music_in_professional_wrestling link
1.7487089e7 2129287.0 31.0 List_of_Total_Nonstop_Action_Wrestling_albums Music_in_professional_wrestling link
289900.0 2.0009687e7 93.0 Castlevania Music_in_the_Castlevania_series link
2852745.0 2.0009687e7 12.0 Castlevania:_Portrait_of_Ruin Music_in_the_Castlevania_series link
null 2.0009687e7 16.0 other-empty Music_in_the_Castlevania_series other
null 2.0009687e7 218.0 other-google Music_in_the_Castlevania_series other
null 1182126.0 16.0 other-wikipedia Music_in_the_Chechen_Republic other
null 1182126.0 34.0 other-google Music_in_the_Chechen_Republic other
247772.0 1182126.0 16.0 Music_of_Russia Music_in_the_Chechen_Republic other
6427285.0 1182126.0 10.0 Makka_Sagaipova Music_in_the_Chechen_Republic other
null 1.1083892e7 23.0 other-google Music_in_the_Community other
474004.0 1518955.0 17.0 Komi_Republic Music_in_the_Komi_Republic other
null 5520065.0 104.0 other-google Music_in_the_Parks other
3935908.0 5520065.0 11.0 Hersheypark_Arena Music_in_the_Parks link
57905.0 1284783.0 19.0 Sakha_Republic Music_in_the_Sakha_Republic link
483558.0 1284783.0 58.0 Yakuts Music_in_the_Sakha_Republic link
null 1284764.0 98.0 other-google Music_in_the_Tyva_Republic other
3309582.0 1284764.0 15.0 Acoustic_scale Music_in_the_Tyva_Republic link
null 1284764.0 10.0 other-bing Music_in_the_Tyva_Republic other
483570.0 1284764.0 25.0 Tuvans Music_in_the_Tyva_Republic other
719202.0 1284764.0 10.0 Igil Music_in_the_Tyva_Republic other
1616030.0 1284764.0 49.0 Tuva Music_in_the_Tyva_Republic other
null 1284764.0 37.0 other-empty Music_in_the_Tyva_Republic other
1946204.0 null 31.0 Music_industry Music_industry_of_Asia redlink
1946204.0 3.9326699e7 73.0 Music_industry Music_industry_of_East_Asia link
414082.0 3.9326699e7 28.0 J-pop Music_industry_of_East_Asia link
null 3.9326699e7 53.0 other-empty Music_industry_of_East_Asia other
null 3.9326699e7 186.0 other-google Music_industry_of_East_Asia other
null 3.9326699e7 10.0 other-twitter Music_industry_of_East_Asia other
629945.0 3.9326699e7 37.0 K-pop Music_industry_of_East_Asia link
1946204.0 null 62.0 Music_industry Music_industry_of_Europe redlink
1946204.0 null 92.0 Music_industry Music_industry_of_North_America redlink
1946204.0 null 10.0 Music_industry Music_industry_of_South_America redlink
null 1.7025056e7 77.0 other-google Music_informatics other
null 261193.0 706.0 other-google Music_information_retrieval other
null 261193.0 14.0 other-wikipedia Music_information_retrieval other
2.1189305e7 261193.0 11.0 Audio_engineer Music_information_retrieval link
1.4004969e7 261193.0 11.0 Audio_mining Music_information_retrieval link
125297.0 261193.0 11.0 Dynamic_programming Music_information_retrieval link
42253.0 261193.0 19.0 Data_mining Music_information_retrieval other
3267504.0 261193.0 19.0 Music_OCR Music_information_retrieval link
1708126.0 261193.0 15.0 Pitch_detection_algorithm Music_information_retrieval link
null 261193.0 177.0 other-empty Music_information_retrieval other
null 261193.0 27.0 other-bing Music_information_retrieval other
null 261193.0 60.0 other-other Music_information_retrieval other
300730.0 261193.0 24.0 Mel-frequency_cepstrum Music_information_retrieval link
1.7025056e7 261193.0 11.0 Music_informatics Music_information_retrieval other
null 261193.0 18.0 other-yahoo Music_information_retrieval other
null 981637.0 10.0 other-yahoo Music_library other
null 981637.0 18.0 other-wikipedia Music_library other
null 981637.0 205.0 other-google Music_library other
9305406.0 981637.0 77.0 Production_music Music_library other
null 981637.0 13.0 other-bing Music_library other
null 981637.0 17.0 other-other Music_library other
null 981637.0 50.0 other-empty Music_library other
null 4.2280305e7 14.0 other-google Music_massage_therapy other
null 4530556.0 12.0 other-google Music_of_Abruzzo other
null 3721528.0 40.0 other-google Music_of_Adelaide other
1148.0 3721528.0 30.0 Adelaide Music_of_Adelaide link
null 412891.0 47.0 other-empty Music_of_Alabama other
null 412891.0 36.0 other-yahoo Music_of_Alabama other
null 412891.0 402.0 other-google Music_of_Alabama other
null 412891.0 24.0 other-wikipedia Music_of_Alabama other
null 412891.0 15.0 other-other Music_of_Alabama other
1291814.0 412891.0 14.0 Music_of_Alaska Music_of_Alabama link
null 412891.0 39.0 other-bing Music_of_Alabama other
null 1291814.0 16.0 other-bing Music_of_Alaska other
624.0 1291814.0 17.0 Alaska Music_of_Alaska link
null 1291814.0 18.0 other-empty Music_of_Alaska other
null 1291814.0 19.0 other-wikipedia Music_of_Alaska other
null 1291814.0 279.0 other-google Music_of_Alaska other
null 1291814.0 11.0 other-yahoo Music_of_Alaska other
null 999857.0 98.0 other-google Music_of_Alberta other
null 999857.0 12.0 other-wikipedia Music_of_Alberta other
248269.0 999857.0 13.0 Music_of_Canada Music_of_Alberta link
null 243022.0 15.0 other-yahoo Music_of_Algeria other
358.0 243022.0 26.0 Algeria Music_of_Algeria link
null 243022.0 28.0 other-bing Music_of_Algeria other
null 243022.0 69.0 other-other Music_of_Algeria other
1424498.0 243022.0 15.0 Andalusian_classical_music Music_of_Algeria link
1116955.0 243022.0 14.0 Islamic_music Music_of_Algeria link
451841.0 243022.0 16.0 Music_of_North_Africa Music_of_Algeria link
null 243022.0 135.0 other-empty Music_of_Algeria other
null 243022.0 33.0 other-wikipedia Music_of_Algeria other
null 243022.0 449.0 other-google Music_of_Algeria other
null 561191.0 49.0 other-google Music_of_Andorra other
600.0 561191.0 28.0 Andorra Music_of_Andorra link
null 561191.0 69.0 other-empty Music_of_Andorra other
null 364942.0 27.0 other-empty Music_of_Anguilla other
null 364942.0 26.0 other-google Music_of_Anguilla other
1217.0 364942.0 72.0 Anguilla Music_of_Anguilla link
null 1501429.0 16.0 other-google Music_of_Anhui other
154175.0 987406.0 18.0 Calypso_music Music_of_Antigua_and_Barbuda link
951.0 987406.0 18.0 Antigua_and_Barbuda Music_of_Antigua_and_Barbuda link
9887612.0 987406.0 35.0 Benna_(genre) Music_of_Antigua_and_Barbuda link
7504750.0 987406.0 16.0 Music_festival Music_of_Antigua_and_Barbuda link
null 987406.0 22.0 other-other Music_of_Antigua_and_Barbuda other
null 987406.0 17.0 other-bing Music_of_Antigua_and_Barbuda other
null 987406.0 248.0 other-google Music_of_Antigua_and_Barbuda other
null 987406.0 110.0 other-empty Music_of_Antigua_and_Barbuda other
null 987406.0 21.0 other-wikipedia Music_of_Antigua_and_Barbuda other
3181553.0 987406.0 26.0 Banjar Music_of_Antigua_and_Barbuda other
null 1400826.0 25.0 other-google Music_of_Aquitaine other
403097.0 247169.0 20.0 Culture_of_Argentina Music_of_Argentina link
309852.0 247169.0 10.0 List_of_cultural_and_regional_genres_of_music Music_of_Argentina link
2569276.0 247169.0 11.0 Argentine_cumbia Music_of_Argentina link
1.8951905e7 247169.0 80.0 Argentina Music_of_Argentina link
58895.0 247169.0 48.0 Latin_American_music Music_of_Argentina link
null 247169.0 60.0 other-yahoo Music_of_Argentina other
null 247169.0 244.0 other-empty Music_of_Argentina other
null 247169.0 126.0 other-wikipedia Music_of_Argentina other
null 247169.0 2174.0 other-google Music_of_Argentina other
null 247169.0 115.0 other-bing Music_of_Argentina other
376118.0 247169.0 21.0 Tango_music Music_of_Argentina link
null 247169.0 39.0 other-other Music_of_Argentina other
null 410801.0 35.0 other-other Music_of_Armenia other
null 410801.0 16.0 other-bing Music_of_Armenia other
241240.0 410801.0 13.0 Sabre_Dance Music_of_Armenia link
8473458.0 410801.0 10.0 Armenian_dance Music_of_Armenia link
1.0918072e7 410801.0 56.0 Armenia Music_of_Armenia link
865062.0 410801.0 25.0 Duduk Music_of_Armenia link
232847.0 410801.0 15.0 Aram_Khachaturian Music_of_Armenia link
413618.0 410801.0 10.0 Music_of_Iran Music_of_Armenia link
2058881.0 410801.0 39.0 Middle_Eastern_music Music_of_Armenia link
92149.0 410801.0 34.0 Oud Music_of_Armenia other
2.3931407e7 410801.0 10.0 Pop-folk Music_of_Armenia link
285300.0 410801.0 12.0 Qanun_(instrument) Music_of_Armenia link
null 410801.0 208.0 other-empty Music_of_Armenia other
387816.0 410801.0 12.0 Armenians Music_of_Armenia link
2049464.0 410801.0 15.0 Culture_of_Armenia Music_of_Armenia link
732267.0 410801.0 16.0 Komitas Music_of_Armenia link
null 410801.0 676.0 other-google Music_of_Armenia other
null 410801.0 68.0 other-wikipedia Music_of_Armenia other
null 410801.0 19.0 other-yahoo Music_of_Armenia other
null 2591959.0 11.0 other-empty Music_of_Arunachal_Pradesh other
null 2591959.0 13.0 other-wikipedia Music_of_Arunachal_Pradesh other
null 2591959.0 70.0 other-google Music_of_Arunachal_Pradesh other
null 1049220.0 141.0 other-empty Music_of_Assam other
null 1049220.0 312.0 other-google Music_of_Assam other
null 1049220.0 28.0 other-other Music_of_Assam other
602639.0 1049220.0 13.0 Northeast_India Music_of_Assam link
171300.0 3.489527e7 13.0 Southern_hip_hop Music_of_Atlanta link
null 3.489527e7 42.0 other-empty Music_of_Atlanta other
null 3.489527e7 596.0 other-google Music_of_Atlanta other
2206945.0 3.489527e7 24.0 Atlanta_hip_hop Music_of_Atlanta link
3138.0 3.489527e7 178.0 Atlanta Music_of_Atlanta link
null 3.489527e7 12.0 other-other Music_of_Atlanta other
null 3.489527e7 21.0 other-bing Music_of_Atlanta other
null 308391.0 865.0 other-google Music_of_Austria other
null 308391.0 52.0 other-wikipedia Music_of_Austria other
null 308391.0 143.0 other-empty Music_of_Austria other
null 308391.0 41.0 other-yahoo Music_of_Austria other
55866.0 308391.0 48.0 Vienna Music_of_Austria link
null 308391.0 50.0 other-bing Music_of_Austria other
null 308391.0 19.0 other-other Music_of_Austria other
2.6964606e7 308391.0 67.0 Austria Music_of_Austria link
2089641.0 1400477.0 17.0 French_folk_music Music_of_Auvergne link
null 1400477.0 25.0 other-google Music_of_Auvergne other
null 309778.0 415.0 other-google Music_of_Azerbaijan other
null 309778.0 36.0 other-wikipedia Music_of_Azerbaijan other
440310.0 309778.0 24.0 Music_of_Asia Music_of_Azerbaijan link
null 309778.0 155.0 other-empty Music_of_Azerbaijan other
746.0 309778.0 26.0 Azerbaijan Music_of_Azerbaijan link
null 309778.0 17.0 other-other Music_of_Azerbaijan other
3470781.0 309778.0 10.0 Mugham Music_of_Azerbaijan link
null 309778.0 26.0 other-bing Music_of_Azerbaijan other
null 309778.0 14.0 other-yahoo Music_of_Azerbaijan other
58481.0 1923094.0 27.0 Badakhshan Music_of_Badakhshan link
null 412724.0 64.0 other-empty Music_of_Bahrain other
null 412724.0 111.0 other-google Music_of_Bahrain other
1.8933277e7 412724.0 13.0 Bahrain Music_of_Bahrain link
2.6997138e7 2753032.0 39.0 Baltimore Music_of_Baltimore link
null 2753032.0 14.0 other-yahoo Music_of_Baltimore other
null 2753032.0 32.0 other-empty Music_of_Baltimore other
null 2753032.0 348.0 other-google Music_of_Baltimore other
null 2753032.0 15.0 other-wikipedia Music_of_Baltimore other
1.0721944e7 2753032.0 10.0 Spiderman_of_the_Rings Music_of_Baltimore link
null 2753032.0 17.0 other-bing Music_of_Baltimore other
null 2753032.0 11.0 other-other Music_of_Baltimore other
null 245850.0 49.0 other-other Music_of_Bangladesh other
null 245850.0 12.0 other-bing Music_of_Bangladesh other
null 245850.0 26.0 other-yahoo Music_of_Bangladesh other
3454.0 245850.0 26.0 Bangladesh Music_of_Bangladesh link
null 245850.0 64.0 other-wikipedia Music_of_Bangladesh other
null 245850.0 743.0 other-google Music_of_Bangladesh other
null 245850.0 238.0 other-empty Music_of_Bangladesh other
440310.0 245850.0 25.0 Music_of_Asia Music_of_Bangladesh link
519963.0 245850.0 13.0 Baul Music_of_Bangladesh link
null 4532941.0 11.0 other-wikipedia Music_of_Basilicata other
null 4532941.0 15.0 other-google Music_of_Basilicata other
null 8468129.0 713.0 other-twitter Music_of_Battlestar_Galactica_(2004_TV_series) other
null 8468129.0 34.0 other-wikipedia Music_of_Battlestar_Galactica_(2004_TV_series) other
null 8468129.0 1586.0 other-google Music_of_Battlestar_Galactica_(2004_TV_series) other
null 8468129.0 478.0 other-empty Music_of_Battlestar_Galactica_(2004_TV_series) other
3604689.0 8468129.0 36.0 Battlestar_Galactica_(miniseries) Music_of_Battlestar_Galactica_(2004_TV_series) other
2.1304123e7 8468129.0 13.0 Battlestar_Galactica_(season_2) Music_of_Battlestar_Galactica_(2004_TV_series) other
413556.0 8468129.0 26.0 All_Along_the_Watchtower Music_of_Battlestar_Galactica_(2004_TV_series) other
null 8468129.0 36.0 other-yahoo Music_of_Battlestar_Galactica_(2004_TV_series) other
null 8468129.0 33.0 other-bing Music_of_Battlestar_Galactica_(2004_TV_series) other
null 8468129.0 41.0 other-other Music_of_Battlestar_Galactica_(2004_TV_series) other
2391393.0 8468129.0 17.0 Richard_Gibbs Music_of_Battlestar_Galactica_(2004_TV_series) other
2700625.0 8468129.0 89.0 Bear_McCreary Music_of_Battlestar_Galactica_(2004_TV_series) other
3604726.0 8468129.0 145.0 Battlestar_Galactica_(2004_TV_series) Music_of_Battlestar_Galactica_(2004_TV_series) other
2821280.0 8468129.0 44.0 Kara_Thrace Music_of_Battlestar_Galactica_(2004_TV_series) other
5457957.0 8468129.0 14.0 Shape_of_Things_to_Come Music_of_Battlestar_Galactica_(2004_TV_series) other
247087.0 373626.0 20.0 Music_of_the_Netherlands Music_of_Belgium link
null 373626.0 108.0 other-empty Music_of_Belgium other
null 373626.0 16.0 other-yahoo Music_of_Belgium other
null 373626.0 1081.0 other-google Music_of_Belgium other
null 373626.0 46.0 other-wikipedia Music_of_Belgium other
143432.0 373626.0 31.0 Culture_of_Belgium Music_of_Belgium link
6180884.0 373626.0 20.0 French_pop_music Music_of_Belgium link
10878.0 373626.0 12.0 Flanders Music_of_Belgium link
3343.0 373626.0 47.0 Belgium Music_of_Belgium link
3.087116e7 373626.0 55.0 New_Beat Music_of_Belgium link
null 373626.0 33.0 other-bing Music_of_Belgium other
null 373626.0 22.0 other-other Music_of_Belgium other
null 306641.0 32.0 other-bing Music_of_Belize other
null 306641.0 25.0 other-wikipedia Music_of_Belize other
null 306641.0 408.0 other-google Music_of_Belize other
null 306641.0 134.0 other-empty Music_of_Belize other
null 306641.0 15.0 other-yahoo Music_of_Belize other
3458.0 306641.0 17.0 Belize Music_of_Belize link
5052533.0 951080.0 19.0 Rabindranath_Tagore Music_of_Bengal link
1158934.0 951080.0 11.0 Rabindra_Sangeet Music_of_Bengal link
245850.0 951080.0 11.0 Music_of_Bangladesh Music_of_Bengal link
null 951080.0 36.0 other-other Music_of_Bengal other
null 951080.0 157.0 other-empty Music_of_Bengal other
null 951080.0 42.0 other-wikipedia Music_of_Bengal other
null 951080.0 801.0 other-google Music_of_Bengal other
null 951080.0 11.0 other-yahoo Music_of_Bengal other
5985171.0 951080.0 10.0 Culture_of_West_Bengal Music_of_Bengal link
2.2612537e7 2.2648389e7 21.0 Terry_Riley:_Cadenza_on_the_Night_Plain Music_of_Bill_Evans link
null 2.2648389e7 17.0 other-google Music_of_Bill_Evans other
null 5508131.0 396.0 other-empty Music_of_Bollywood other
53207.0 5508131.0 42.0 Record_producer Music_of_Bollywood other
2.2138795e7 5508131.0 25.0 Sad_Hindi_songs Music_of_Bollywood other
4246.0 5508131.0 96.0 Bollywood Music_of_Bollywood link
5925126.0 5508131.0 18.0 Binaca_Geetmala Music_of_Bollywood other
406271.0 5508131.0 22.0 Filmi Music_of_Bollywood link
2.2448394e7 5508131.0 82.0 Hindi_dance_songs Music_of_Bollywood other
null 5508131.0 123.0 other-wikipedia Music_of_Bollywood other
null 5508131.0 1527.0 other-google Music_of_Bollywood other
5968908.0 5508131.0 27.0 Filmi_Devotional_songs Music_of_Bollywood other
222789.0 5508131.0 74.0 Lata_Mangeshkar Music_of_Bollywood other
1346834.0 5508131.0 11.0 K._L._Saigal Music_of_Bollywood other
334547.0 5508131.0 14.0 Kishore_Kumar Music_of_Bollywood link
null 5508131.0 59.0 other-bing Music_of_Bollywood other
null 5508131.0 76.0 other-other Music_of_Bollywood other
1.5580374e7 5508131.0 14.0 Main_Page Music_of_Bollywood other
14535.0 5508131.0 49.0 Music_of_India Music_of_Bollywood link
null 5508131.0 36.0 other-yahoo Music_of_Bollywood other
2173379.0 373624.0 11.0 Šaban_Šaulić Music_of_Bosnia_and_Herzegovina link
null 373624.0 11.0 other-yahoo Music_of_Bosnia_and_Herzegovina other
null 373624.0 98.0 other-empty Music_of_Bosnia_and_Herzegovina other
null 373624.0 364.0 other-google Music_of_Bosnia_and_Herzegovina other
null 373624.0 25.0 other-wikipedia Music_of_Bosnia_and_Herzegovina other
3463.0 373624.0 38.0 Bosnia_and_Herzegovina Music_of_Bosnia_and_Herzegovina link
1444274.0 373624.0 29.0 Music_of_Southeastern_Europe Music_of_Bosnia_and_Herzegovina link
1532326.0 373624.0 18.0 Narodna_muzika Music_of_Bosnia_and_Herzegovina link
null 373624.0 16.0 other-other Music_of_Bosnia_and_Herzegovina other
1471762.0 373624.0 13.0 Bosnian_rock Music_of_Bosnia_and_Herzegovina link
397584.0 373624.0 12.0 Culture_of_Bosnia_and_Herzegovina Music_of_Bosnia_and_Herzegovina link
3464.0 987389.0 33.0 Botswana Music_of_Botswana link
null 987389.0 22.0 other-wikipedia Music_of_Botswana other
null 987389.0 264.0 other-google Music_of_Botswana other
null 987389.0 34.0 other-other Music_of_Botswana other
null 987389.0 18.0 other-bing Music_of_Botswana other
null 987389.0 106.0 other-empty Music_of_Botswana other
null 432369.0 89.0 other-empty Music_of_Brittany other
4.2647129e7 432369.0 29.0 Tri_Martolod Music_of_Brittany link
5261.0 432369.0 28.0 Celtic_music Music_of_Brittany link
468295.0 432369.0 39.0 Denez_Prigent Music_of_Brittany link
3239660.0 432369.0 12.0 Culture_of_Brittany Music_of_Brittany link
1687254.0 432369.0 10.0 Fest_Noz Music_of_Brittany link
550629.0 432369.0 20.0 Bagad Music_of_Brittany link
38748.0 432369.0 13.0 Brittany Music_of_Brittany link
2089641.0 432369.0 26.0 French_folk_music Music_of_Brittany link
null 432369.0 18.0 other-bing Music_of_Brittany other
null 432369.0 32.0 other-other Music_of_Brittany other
null 432369.0 397.0 other-google Music_of_Brittany other
null 432369.0 36.0 other-wikipedia Music_of_Brittany other
null 987349.0 18.0 other-wikipedia Music_of_Brunei other
null 987349.0 296.0 other-google Music_of_Brunei other
3559333.0 987349.0 33.0 Culture_of_Brunei Music_of_Brunei link
null 987349.0 90.0 other-empty Music_of_Brunei other
987349.0 987349.0 56.0 Music_of_Brunei Music_of_Brunei other
3466.0 987349.0 43.0 Brunei Music_of_Brunei link
1824190.0 373849.0 14.0 Bulgarian_State_Television_Female_Vocal_Choir Music_of_Bulgaria link
251620.0 373849.0 17.0 Culture_of_Bulgaria Music_of_Bulgaria link
1749887.0 373849.0 16.0 Bulgarian_dances Music_of_Bulgaria link
4527.0 373849.0 16.0 Béla_Bartók Music_of_Bulgaria link
3228403.0 373849.0 19.0 Ghost_in_the_Shell_(film) Music_of_Bulgaria other
null 373849.0 1203.0 other-google Music_of_Bulgaria other
null 373849.0 81.0 other-wikipedia Music_of_Bulgaria other
null 373849.0 15.0 other-yahoo Music_of_Bulgaria other
1532326.0 373849.0 18.0 Narodna_muzika Music_of_Bulgaria link
null 373849.0 31.0 other-bing Music_of_Bulgaria other
null 373849.0 63.0 other-other Music_of_Bulgaria other
null 373849.0 204.0 other-empty Music_of_Bulgaria other
1444274.0 373849.0 40.0 Music_of_Southeastern_Europe Music_of_Bulgaria link
null 987266.0 125.0 other-empty Music_of_Burma other
null 987266.0 36.0 other-wikipedia Music_of_Burma other
null 987266.0 543.0 other-google Music_of_Burma other
1018512.0 987266.0 21.0 Culture_of_Burma Music_of_Burma link
null 987266.0 38.0 other-other Music_of_Burma other
null 987266.0 18.0 other-bing Music_of_Burma other
null 987266.0 26.0 other-yahoo Music_of_Burma other
null 561089.0 74.0 other-google Music_of_Burundi other
null 561089.0 11.0 other-wikipedia Music_of_Burundi other
null 561089.0 50.0 other-empty Music_of_Burundi other
2.1490998e7 561089.0 18.0 Burundi Music_of_Burundi link
196953.0 561089.0 68.0 Kings_of_the_Wild_Frontier Music_of_Burundi link
741441.0 1284811.0 44.0 Buryatia Music_of_Buryatia link
null 1284811.0 10.0 other-google Music_of_Buryatia other
null 4497082.0 37.0 other-google Music_of_Calabria other
null 442836.0 21.0 other-google Music_of_Canada's_Prairie_Provinces other
null 2.3921367e7 22.0 other-empty Music_of_Canadian_cultures other
null 2.3921367e7 338.0 other-google Music_of_Canadian_cultures other
3.569409e7 2.3921367e7 19.0 Anthems_and_nationalistic_songs_of_Canada Music_of_Canadian_cultures link
248269.0 2.3921367e7 26.0 Music_of_Canada Music_of_Canadian_cultures link
10623.0 2.3921367e7 11.0 Folk_music Music_of_Canadian_cultures link
2700625.0 2.2645621e7 36.0 Bear_McCreary Music_of_Caprica link
null 2.2645621e7 37.0 other-google Music_of_Caprica other
8468129.0 2.2645621e7 12.0 Music_of_Battlestar_Galactica_(2004_TV_series) Music_of_Caprica link
null 2.2645621e7 17.0 other-empty Music_of_Caprica other
null 7339937.0 29.0 other-empty Music_of_Cardiff other
165147.0 7339937.0 35.0 Stereophonics Music_of_Cardiff link
5882.0 7339937.0 36.0 Cardiff Music_of_Cardiff link
2.3794044e7 7339937.0 10.0 Bullet_for_My_Valentine_discography Music_of_Cardiff other
2213065.0 7339937.0 187.0 Bullet_for_My_Valentine Music_of_Cardiff link
492820.0 7339937.0 45.0 Lostprophets Music_of_Cardiff link
null 7339937.0 143.0 other-google Music_of_Cardiff other
null 444625.0 75.0 other-google Music_of_Castile_and_León other
null 444625.0 15.0 other-empty Music_of_Castile_and_León other
105621.0 444625.0 12.0 Music_of_Spain Music_of_Castile_and_León other
292222.0 1.4164396e7 15.0 Stochastic Music_of_Changes link
null 1.4164396e7 12.0 other-other Music_of_Changes other
null 1.4164396e7 435.0 other-google Music_of_Changes other
null 1.4164396e7 22.0 other-wikipedia Music_of_Changes other
1.4163e7 1.4164396e7 39.0 List_of_compositions_by_John_Cage Music_of_Changes link
null 1.4164396e7 60.0 other-empty Music_of_Changes other
99234.0 1.4164396e7 32.0 Aleatoric_music Music_of_Changes link
65954.0 1.4164396e7 50.0 John_Cage Music_of_Changes link
null 333496.0 24.0 other-yahoo Music_of_Chile other
null 333496.0 63.0 other-wikipedia Music_of_Chile other
null 333496.0 1371.0 other-google Music_of_Chile other
null 333496.0 15.0 other-other Music_of_Chile other
1.5580374e7 333496.0 12.0 Main_Page Music_of_Chile other
null 333496.0 78.0 other-bing Music_of_Chile other
null 333496.0 188.0 other-empty Music_of_Chile other
5489.0 333496.0 18.0 Chile Music_of_Chile link
58895.0 333496.0 19.0 Latin_American_music Music_of_Chile link
null 1.4216009e7 109.0 other-google Music_of_Coal other
5399.0 1291889.0 58.0 Colorado Music_of_Colorado link
null 1291889.0 22.0 other-yahoo Music_of_Colorado other
null 1291889.0 782.0 other-google Music_of_Colorado other
null 1291889.0 22.0 other-wikipedia Music_of_Colorado other
null 1291889.0 53.0 other-empty Music_of_Colorado other
null 1291889.0 49.0 other-bing Music_of_Colorado other
912509.0 1291889.0 10.0 Post-hardcore Music_of_Colorado link
null 1291920.0 11.0 other-bing Music_of_Connecticut other
null 1291920.0 11.0 other-empty Music_of_Connecticut other
null 1291920.0 182.0 other-google Music_of_Connecticut other
null 1291920.0 30.0 other-wikipedia Music_of_Connecticut other
6591.0 1297518.0 24.0 Crete Music_of_Crete link
3237687.0 1297518.0 10.0 Greek_folk_music Music_of_Crete link
null 1297518.0 30.0 other-wikipedia Music_of_Crete other
null 1297518.0 185.0 other-google Music_of_Crete other
null 1297518.0 39.0 other-empty Music_of_Crete other
1.8170476e7 1297518.0 12.0 Psarantonis Music_of_Crete link
962945.0 1297518.0 21.0 Nikos_Xilouris Music_of_Crete link
null 1297518.0 27.0 other-other Music_of_Crete other
246225.0 1297518.0 11.0 Music_of_Greece Music_of_Crete link
1.5580374e7 1297518.0 15.0 Main_Page Music_of_Crete other
1408397.0 1297518.0 17.0 Nakshatra Music_of_Crete other
1532326.0 243011.0 11.0 Narodna_muzika Music_of_Croatia link
null 243011.0 19.0 other-bing Music_of_Croatia other
null 243011.0 30.0 other-other Music_of_Croatia other
474950.0 243011.0 34.0 Culture_of_Croatia Music_of_Croatia link
1.4203005e7 243011.0 14.0 Croatian_popular_music Music_of_Croatia link
1.2248405e7 243011.0 19.0 Croatian_art Music_of_Croatia link
1.0600968e7 243011.0 12.0 Music_of_Yugoslavia Music_of_Croatia link
2119998.0 243011.0 22.0 Oliver_Dragojević Music_of_Croatia link
1444274.0 243011.0 40.0 Music_of_Southeastern_Europe Music_of_Croatia link
null 243011.0 29.0 other-yahoo Music_of_Croatia other
null 243011.0 99.0 other-empty Music_of_Croatia other
null 243011.0 513.0 other-google Music_of_Croatia other
null 243011.0 43.0 other-wikipedia Music_of_Croatia other
null 240761.0 15.0 other-twitter Music_of_Cuba other
null 240761.0 4444.0 other-google Music_of_Cuba other
null 240761.0 247.0 other-wikipedia Music_of_Cuba other
1005480.0 240761.0 84.0 Afro-Cuban Music_of_Cuba link
5347660.0 240761.0 11.0 Cascara Music_of_Cuba link
513544.0 240761.0 12.0 Buena_Vista_Social_Club Music_of_Cuba link
1654043.0 240761.0 42.0 Afro-Caribbean_music Music_of_Cuba link
682524.0 240761.0 24.0 Culture_of_Cuba Music_of_Cuba link
516807.0 240761.0 15.0 Cuban_cuisine Music_of_Cuba link
1107981.0 240761.0 10.0 Afro-Cuban_jazz Music_of_Cuba link
7966.0 240761.0 31.0 Disco Music_of_Cuba other
285709.0 240761.0 16.0 Latin_jazz Music_of_Cuba other
1.5703993e7 240761.0 11.0 Gente_de_Zona Music_of_Cuba other
1751751.0 240761.0 17.0 Guaracha Music_of_Cuba link
null 240761.0 180.0 other-other Music_of_Cuba other
1.5580374e7 240761.0 25.0 Main_Page Music_of_Cuba other
25423.0 240761.0 11.0 Rock_music Music_of_Cuba other
240761.0 240761.0 26.0 Music_of_Cuba Music_of_Cuba other
null 240761.0 274.0 other-bing Music_of_Cuba other
957080.0 240761.0 17.0 Son_(music) Music_of_Cuba link
null 240761.0 208.0 other-yahoo Music_of_Cuba other
2.5179257e7 240761.0 20.0 Dance_in_Cuba Music_of_Cuba link
5042481.0 240761.0 116.0 Cuba Music_of_Cuba link
1.0891249e7 240761.0 17.0 Cuban_rumba Music_of_Cuba link
274927.0 240761.0 19.0 Clave_(rhythm) Music_of_Cuba link
1467094.0 240761.0 15.0 Cha-cha-cha_(dance) Music_of_Cuba other
3840752.0 240761.0 13.0 Cachao_López Music_of_Cuba link
9069468.0 240761.0 23.0 Cha-cha-cha_(music) Music_of_Cuba link
755312.0 240761.0 36.0 Buena_Vista_Social_Club_(album) Music_of_Cuba link
54403.0 240761.0 13.0 Dizzy_Gillespie Music_of_Cuba other
166331.0 240761.0 38.0 Celia_Cruz Music_of_Cuba link
58895.0 240761.0 55.0 Latin_American_music Music_of_Cuba link
9197579.0 240761.0 17.0 List_of_Caribbean_music_groups Music_of_Cuba link
1683462.0 240761.0 30.0 Rumba_(dance) Music_of_Cuba link
1.9261987e7 240761.0 27.0 Trova Music_of_Cuba link
null 240761.0 703.0 other-empty Music_of_Cuba other
33134.0 240761.0 12.0 World_music Music_of_Cuba other
null 2.3291343e7 14.0 other-empty Music_of_Dance_Dance_Revolution_(1998_video_game) other
null 2.3291343e7 17.0 other-wikipedia Music_of_Dance_Dance_Revolution_(1998_video_game) other
null 2.3291343e7 14.0 other-twitter Music_of_Dance_Dance_Revolution_(1998_video_game) other
null 2.3291343e7 70.0 other-google Music_of_Dance_Dance_Revolution_(1998_video_game) other
2.1011855e7 2.3291343e7 25.0 List_of_Dance_Dance_Revolution_songs Music_of_Dance_Dance_Revolution_(1998_video_game) link
1459689.0 2.3291343e7 232.0 Dance_Dance_Revolution_(1998_video_game) Music_of_Dance_Dance_Revolution_(1998_video_game) link
575919.0 1291938.0 12.0 Jade_Tree_(record_label) Music_of_Delaware link
null 1291938.0 10.0 other-empty Music_of_Delaware other
null 1291938.0 60.0 other-google Music_of_Delaware other
null 3.0865941e7 2241.0 other-google Music_of_Detroit other
null 3.0865941e7 123.0 other-wikipedia Music_of_Detroit other
1730307.0 3.0865941e7 14.0 List_of_Super_Bowl_halftime_shows Music_of_Detroit link
73010.0 3.0865941e7 11.0 Hardcore_punk Music_of_Detroit other
559332.0 3.0865941e7 25.0 List_of_hip_hop_genres Music_of_Detroit other
5027648.0 3.0865941e7 28.0 Detroit_Rock_City Music_of_Detroit link
8687.0 3.0865941e7 106.0 Detroit Music_of_Detroit link
180178.0 3.0865941e7 15.0 Detroit_techno Music_of_Detroit other
140308.0 3.0865941e7 29.0 Alice_Cooper Music_of_Detroit other
1242998.0 3.0865941e7 15.0 History_of_Detroit Music_of_Detroit link
1171542.0 3.0865941e7 20.0 Hitsville_U.S.A. Music_of_Detroit link
null 3.0865941e7 231.0 other-empty Music_of_Detroit other
168617.0 3.0865941e7 86.0 The_White_Stripes Music_of_Detroit link
307387.0 3.0865941e7 58.0 Music_of_Michigan Music_of_Detroit link
167396.0 3.0865941e7 100.0 Motown Music_of_Detroit link
null 3.0865941e7 103.0 other-bing Music_of_Detroit other
1384091.0 3.0865941e7 11.0 The_Electrifying_Mojo Music_of_Detroit link
685028.0 3.0865941e7 10.0 Super_Bowl_XL Music_of_Detroit link
null 3.0865941e7 44.0 other-other Music_of_Detroit other
null 3.0865941e7 32.0 other-facebook Music_of_Detroit other
1.5580374e7 3.0865941e7 10.0 Main_Page Music_of_Detroit other
3235772.0 3.0865941e7 27.0 Midwest_hip_hop Music_of_Detroit link
null 3.0865941e7 92.0 other-yahoo Music_of_Detroit other
null 467018.0 20.0 other-wikipedia Music_of_East_Timor other
null 467018.0 113.0 other-google Music_of_East_Timor other
376026.0 467018.0 29.0 Culture_of_East_Timor Music_of_East_Timor link
null 467018.0 134.0 other-empty Music_of_East_Timor other
1444274.0 2.880603e7 20.0 Music_of_Southeastern_Europe Music_of_Eastern_Europe link
33134.0 2.880603e7 27.0 World_music Music_of_Eastern_Europe link
null 306026.0 109.0 other-empty Music_of_Ecuador other
9334.0 306026.0 23.0 Ecuador Music_of_Ecuador link
null 306026.0 30.0 other-yahoo Music_of_Ecuador other
679331.0 306026.0 27.0 Culture_of_Ecuador Music_of_Ecuador link
null 306026.0 71.0 other-bing Music_of_Ecuador other
null 306026.0 13.0 other-other Music_of_Ecuador other
null 306026.0 941.0 other-google Music_of_Ecuador other
null 306026.0 32.0 other-wikipedia Music_of_Ecuador other
null 247746.0 101.0 other-wikipedia Music_of_Egypt other
null 247746.0 2575.0 other-google Music_of_Egypt other
8087628.0 247746.0 55.0 Egypt Music_of_Egypt link
5884148.0 247746.0 18.0 Baladi Music_of_Egypt link
1.1298796e7 247746.0 11.0 Ancient_Egyptian_cuisine Music_of_Egypt link
null 247746.0 426.0 other-empty Music_of_Egypt other
451841.0 247746.0 23.0 Music_of_North_Africa Music_of_Egypt link
3.2186235e7 247746.0 22.0 Music_in_the_Civilization_video_game_series Music_of_Egypt link
1427446.0 247746.0 10.0 Prehistoric_Egypt Music_of_Egypt link
855541.0 247746.0 20.0 Phrygian_dominant_scale Music_of_Egypt other
1.6173864e7 247746.0 10.0 Nefer Music_of_Egypt link
null 247746.0 100.0 other-other Music_of_Egypt other
3378045.0 247746.0 10.0 Public_holidays_in_Egypt Music_of_Egypt link
1.5580374e7 247746.0 11.0 Main_Page Music_of_Egypt other
18839.0 247746.0 44.0 Music Music_of_Egypt link
null 247746.0 154.0 other-bing Music_of_Egypt other
null 247746.0 69.0 other-yahoo Music_of_Egypt other
51218.0 247746.0 57.0 Culture_of_Egypt Music_of_Egypt link
1969914.0 247746.0 11.0 Coptic_music Music_of_Egypt link
8233.0 247746.0 10.0 Death_metal Music_of_Egypt other
1502321.0 247746.0 50.0 Ancient_music Music_of_Egypt link
8559295.0 247746.0 24.0 Egyptian_cuisine Music_of_Egypt link
null 4534705.0 23.0 other-google Music_of_Emilia-Romagna other
null 1.0645556e7 39.0 other-google Music_of_Epirus_(Greece) other
3237687.0 1.0645556e7 10.0 Greek_folk_music Music_of_Epirus_(Greece) other
9667732.0 1.0645556e7 14.0 Polyphonic_song_of_Epirus Music_of_Epirus_(Greece) link
null 1.0645556e7 19.0 other-empty Music_of_Epirus_(Greece) other
null 423133.0 113.0 other-empty Music_of_Equatorial_Guinea other
null 423133.0 178.0 other-google Music_of_Equatorial_Guinea other
null 423133.0 14.0 other-wikipedia Music_of_Equatorial_Guinea other
418571.0 423133.0 30.0 Culture_of_Equatorial_Guinea Music_of_Equatorial_Guinea link
9366.0 423133.0 63.0 Equatorial_Guinea Music_of_Equatorial_Guinea link
null 423133.0 17.0 other-bing Music_of_Equatorial_Guinea other
5073646.0 372892.0 12.0 Veljo_Tormis Music_of_Estonia link
null 372892.0 11.0 other-bing Music_of_Estonia other
null 372892.0 20.0 other-other Music_of_Estonia other
null 372892.0 104.0 other-empty Music_of_Estonia other
3.5061186e7 372892.0 11.0 Traffic_(Estonian_band) Music_of_Estonia link
null 372892.0 359.0 other-google Music_of_Estonia other
null 372892.0 39.0 other-wikipedia Music_of_Estonia other
2.8222445e7 372892.0 33.0 Estonia Music_of_Estonia link
1.2454919e7 372892.0 14.0 Culture_of_Estonia Music_of_Estonia link
2157363.0 247149.0 57.0 K'naan Music_of_Ethiopia link
1632417.0 247149.0 11.0 Tilahun_Gessesse Music_of_Ethiopia link
null 247149.0 253.0 other-empty Music_of_Ethiopia other
2194444.0 247149.0 146.0 Mulatu_Astatke Music_of_Ethiopia link
166141.0 247149.0 10.0 Music_of_Africa Music_of_Ethiopia link
null 247149.0 84.0 other-other Music_of_Ethiopia other
null 247149.0 77.0 other-bing Music_of_Ethiopia other
1.5580374e7 247149.0 10.0 Main_Page Music_of_Ethiopia other
187749.0 247149.0 85.0 Ethiopia Music_of_Ethiopia link
2022635.0 247149.0 13.0 Éthiopiques Music_of_Ethiopia link
null 247149.0 64.0 other-yahoo Music_of_Ethiopia other
null 247149.0 88.0 other-wikipedia Music_of_Ethiopia other
null 247149.0 1232.0 other-google Music_of_Ethiopia other
null 1.1214612e7 143.0 other-google Music_of_Final_Fantasy_III other
5137643.0 1.1214612e7 53.0 Music_of_Final_Fantasy_I_and_II Music_of_Final_Fantasy_III link
null 1.1214612e7 37.0 other-empty Music_of_Final_Fantasy_III other
1001643.0 1.1214612e7 28.0 Music_of_the_Final_Fantasy_series Music_of_Final_Fantasy_III link
null 1.1214612e7 10.0 other-other Music_of_Final_Fantasy_III other
66495.0 1.1214612e7 17.0 Final_Fantasy_III Music_of_Final_Fantasy_III link
52754.0 1.1209352e7 47.0 Final_Fantasy_IV Music_of_Final_Fantasy_IV link
null 1.1209352e7 296.0 other-google Music_of_Final_Fantasy_IV other
null 1.1209352e7 31.0 other-wikipedia Music_of_Final_Fantasy_IV other
1.1139384e7 1.1209352e7 12.0 Final_Fantasy_IV_(3D_remake) Music_of_Final_Fantasy_IV link
1001643.0 1.1209352e7 19.0 Music_of_the_Final_Fantasy_series Music_of_Final_Fantasy_IV link
1.1214612e7 1.1209352e7 51.0 Music_of_Final_Fantasy_III Music_of_Final_Fantasy_IV link
null 1.1209352e7 133.0 other-empty Music_of_Final_Fantasy_IV other
5130807.0 1.1209352e7 10.0 Music_of_Final_Fantasy_V Music_of_Final_Fantasy_IV link
1337053.0 1.1216149e7 27.0 Music_of_Final_Fantasy_X Music_of_Final_Fantasy_IX link
2159211.0 1.1216149e7 17.0 Music_of_the_Final_Fantasy_VII_series Music_of_Final_Fantasy_IX link
null 1.1216149e7 295.0 other-empty Music_of_Final_Fantasy_IX other
null 1.1216149e7 30.0 other-wikipedia Music_of_Final_Fantasy_IX other
null 1.1216149e7 618.0 other-google Music_of_Final_Fantasy_IX other
52758.0 1.1216149e7 90.0 Final_Fantasy_IX Music_of_Final_Fantasy_IX link
625605.0 1.1216149e7 15.0 Emiko_Shiratori Music_of_Final_Fantasy_IX link
2171749.0 1.1216149e7 12.0 Music_of_Final_Fantasy_VI Music_of_Final_Fantasy_IX link
1001643.0 1.1216149e7 37.0 Music_of_the_Final_Fantasy_series Music_of_Final_Fantasy_IX link
1.0091424e7 1.1216149e7 58.0 Music_of_Final_Fantasy_VIII Music_of_Final_Fantasy_IX link
null 1.1216149e7 22.0 other-bing Music_of_Final_Fantasy_IX other
null 1.1216149e7 11.0 other-yahoo Music_of_Final_Fantasy_IX other
4.2374187e7 1.1216149e7 17.0 List_of_Vietnamese_films_of_2014 Music_of_Final_Fantasy_IX other
null 2171749.0 43.0 other-wikipedia Music_of_Final_Fantasy_VI other
null 2171749.0 445.0 other-google Music_of_Final_Fantasy_VI other
null 2171749.0 146.0 other-empty Music_of_Final_Fantasy_VI other
5130807.0 2171749.0 39.0 Music_of_Final_Fantasy_V Music_of_Final_Fantasy_VI link
2159211.0 2171749.0 22.0 Music_of_the_Final_Fantasy_VII_series Music_of_Final_Fantasy_VI link
57677.0 2171749.0 13.0 Nobuo_Uematsu Music_of_Final_Fantasy_VI other
52755.0 2171749.0 139.0 Final_Fantasy_VI Music_of_Final_Fantasy_VI link
3.5792677e7 2171749.0 16.0 Dancing_Mad Music_of_Final_Fantasy_VI link
2756115.0 2171749.0 17.0 Shirō_Sagisu Music_of_Final_Fantasy_VI link
null 2171749.0 32.0 other-other Music_of_Final_Fantasy_VI other
1001643.0 2171749.0 48.0 Music_of_the_Final_Fantasy_series Music_of_Final_Fantasy_VI link
1.1209352e7 2171749.0 11.0 Music_of_Final_Fantasy_IV Music_of_Final_Fantasy_VI link
1.0091424e7 2171749.0 13.0 Music_of_Final_Fantasy_VIII Music_of_Final_Fantasy_VI link
1001643.0 1.0091424e7 42.0 Music_of_the_Final_Fantasy_series Music_of_Final_Fantasy_VIII link
1.1216149e7 1.0091424e7 22.0 Music_of_Final_Fantasy_IX Music_of_Final_Fantasy_VIII link
1.5580374e7 1.0091424e7 139.0 Main_Page Music_of_Final_Fantasy_VIII other
null 1.0091424e7 14.0 other-bing Music_of_Final_Fantasy_VIII other
null 1.0091424e7 29.0 other-other Music_of_Final_Fantasy_VIII other
1.4241133e7 1.0091424e7 34.0 Eyes_on_Me_(Faye_Wong_song) Music_of_Final_Fantasy_VIII link
null 1.0091424e7 777.0 other-google Music_of_Final_Fantasy_VIII other
null 1.0091424e7 124.0 other-wikipedia Music_of_Final_Fantasy_VIII other
52757.0 1.0091424e7 159.0 Final_Fantasy_VIII Music_of_Final_Fantasy_VIII link
null 1.0091424e7 16.0 other-yahoo Music_of_Final_Fantasy_VIII other
57677.0 1.0091424e7 35.0 Nobuo_Uematsu Music_of_Final_Fantasy_VIII link
2159211.0 1.0091424e7 67.0 Music_of_the_Final_Fantasy_VII_series Music_of_Final_Fantasy_VIII link
1337053.0 1.0091424e7 14.0 Music_of_Final_Fantasy_X Music_of_Final_Fantasy_VIII link
null 1.0091424e7 375.0 other-empty Music_of_Final_Fantasy_VIII other
1337053.0 7873264.0 75.0 Music_of_Final_Fantasy_X Music_of_Final_Fantasy_X-2 link
1.1216149e7 7873264.0 12.0 Music_of_Final_Fantasy_IX Music_of_Final_Fantasy_X-2 link
1001643.0 7873264.0 19.0 Music_of_the_Final_Fantasy_series Music_of_Final_Fantasy_X-2 link
null 7873264.0 465.0 other-google Music_of_Final_Fantasy_X-2 other
null 7873264.0 13.0 other-wikipedia Music_of_Final_Fantasy_X-2 other
null 7873264.0 70.0 other-empty Music_of_Final_Fantasy_X-2 other
398631.0 7873264.0 55.0 Final_Fantasy_X-2 Music_of_Final_Fantasy_X-2 link
419271.0 1.1245276e7 46.0 Final_Fantasy_XI Music_of_Final_Fantasy_XI link
4264381.0 1.1245276e7 12.0 Music_of_Final_Fantasy_XII Music_of_Final_Fantasy_XI link
1337053.0 1.1245276e7 14.0 Music_of_Final_Fantasy_X Music_of_Final_Fantasy_XI link
null 1.1245276e7 127.0 other-empty Music_of_Final_Fantasy_XI other
null 1.1245276e7 13.0 other-wikipedia Music_of_Final_Fantasy_XI other
null 1.1245276e7 194.0 other-google Music_of_Final_Fantasy_XI other
7873264.0 1.1245276e7 29.0 Music_of_Final_Fantasy_X-2 Music_of_Final_Fantasy_XI link
1001643.0 1.1245276e7 22.0 Music_of_the_Final_Fantasy_series Music_of_Final_Fantasy_XI link
2194883.0 1.1245276e7 17.0 The_Star_Onions Music_of_Final_Fantasy_XI link
1001643.0 2.719431e7 49.0 Music_of_the_Final_Fantasy_series Music_of_Final_Fantasy_XIII link
1636825.0 2.719431e7 135.0 Final_Fantasy_XIII Music_of_Final_Fantasy_XIII link
null 2.719431e7 10.0 other-wikipedia Music_of_Final_Fantasy_XIII other
null 2.719431e7 597.0 other-google Music_of_Final_Fantasy_XIII other
4264381.0 2.719431e7 57.0 Music_of_Final_Fantasy_XII Music_of_Final_Fantasy_XIII link
1337053.0 2.719431e7 16.0 Music_of_Final_Fantasy_X Music_of_Final_Fantasy_XIII link
3.8124492e7 2.719431e7 23.0 Music_of_Final_Fantasy_XIII-2 Music_of_Final_Fantasy_XIII link
2159211.0 2.719431e7 10.0 Music_of_the_Final_Fantasy_VII_series Music_of_Final_Fantasy_XIII link
391547.0 2.719431e7 14.0 Masashi_Hamauzu Music_of_Final_Fantasy_XIII link
4.3990854e7 2.719431e7 12.0 Music_of_Lightning_Returns:_Final_Fantasy_XIII Music_of_Final_Fantasy_XIII link
null 2.719431e7 216.0 other-empty Music_of_Final_Fantasy_XIII other
null 4.2517401e7 107.0 other-empty Music_of_Final_Fantasy_XIV other
4.3990854e7 4.2517401e7 36.0 Music_of_Lightning_Returns:_Final_Fantasy_XIII Music_of_Final_Fantasy_XIV link
3.8080015e7 4.2517401e7 156.0 Final_Fantasy_XIV:_A_Realm_Reborn Music_of_Final_Fantasy_XIV link
1001643.0 4.2517401e7 35.0 Music_of_the_Final_Fantasy_series Music_of_Final_Fantasy_XIV link
2.719431e7 4.2517401e7 15.0 Music_of_Final_Fantasy_XIII Music_of_Final_Fantasy_XIV link
null 4.2517401e7 661.0 other-google Music_of_Final_Fantasy_XIV other
null 4.2517401e7 20.0 other-wikipedia Music_of_Final_Fantasy_XIV other
2.3068765e7 4.2517401e7 69.0 Final_Fantasy_XIV Music_of_Final_Fantasy_XIV link
1.8933066e7 308346.0 55.0 Florida Music_of_Florida link
8233.0 308346.0 55.0 Death_metal Music_of_Florida other
2.1027603e7 308346.0 20.0 Culture_of_Florida Music_of_Florida link
171080.0 308346.0 17.0 Music_of_the_United_States Music_of_Florida link
null 308346.0 79.0 other-empty Music_of_Florida other
null 308346.0 1170.0 other-google Music_of_Florida other
null 308346.0 38.0 other-wikipedia Music_of_Florida other
null 308346.0 36.0 other-yahoo Music_of_Florida other
null 308346.0 23.0 other-other Music_of_Florida other
null 308346.0 44.0 other-bing Music_of_Florida other
3.031949e7 244703.0 49.0 Zaz_(singer) Music_of_France link
460074.0 244703.0 15.0 Zouk Music_of_France other
null 244703.0 347.0 other-bing Music_of_France other
1.5580374e7 244703.0 15.0 Main_Page Music_of_France other
154437.0 244703.0 10.0 Mireille_Mathieu Music_of_France link
455818.0 244703.0 13.0 Léo_Ferré Music_of_France link
null 244703.0 107.0 other-other Music_of_France other
98988.0 244703.0 46.0 Culture_of_France Music_of_France link
1028178.0 244703.0 10.0 Françoise_Hardy Music_of_France link
53185.0 244703.0 19.0 French_hip_hop Music_of_France link
244706.0 244703.0 133.0 French_music Music_of_France link
6180884.0 244703.0 64.0 French_pop_music Music_of_France link
2462478.0 244703.0 20.0 List_of_French_artists Music_of_France link
6181221.0 244703.0 13.0 French_popular_music Music_of_France link
4.2854258e7 244703.0 15.0 Kendji_Girac Music_of_France link
5843419.0 244703.0 128.0 France Music_of_France link
143486.0 244703.0 16.0 Indie_rock Music_of_France link
3.8384402e7 244703.0 33.0 French_electronic_music Music_of_France link
12343.0 244703.0 36.0 Guadeloupe Music_of_France other
null 244703.0 173.0 other-yahoo Music_of_France other
660446.0 244703.0 27.0 Vanessa_Paradis Music_of_France link
null 244703.0 658.0 other-empty Music_of_France other
61159.0 244703.0 10.0 Maurice_Chevalier Music_of_France link
null 244703.0 137.0 other-wikipedia Music_of_France other
null 244703.0 5065.0 other-google Music_of_France other
64963.0 244703.0 20.0 Édith_Piaf Music_of_France link
null 319399.0 16.0 other-empty Music_of_French_Polynesia other
null 319399.0 29.0 other-google Music_of_French_Polynesia other
10737.0 319399.0 23.0 French_Polynesia Music_of_French_Polynesia link
null 2.2598486e7 12.0 other-google Music_of_Fujian other
null 2.2598486e7 16.0 other-empty Music_of_Fujian other
null 988801.0 66.0 other-empty Music_of_Gabon other
12027.0 988801.0 19.0 Gabon Music_of_Gabon link
null 988801.0 113.0 other-google Music_of_Gabon other
null 988801.0 11.0 other-wikipedia Music_of_Gabon other
null 432418.0 65.0 other-empty Music_of_Galicia,_Cantabria_and_Asturias other
4243394.0 432418.0 18.0 Galician_people Music_of_Galicia,_Cantabria_and_Asturias other
158996.0 432418.0 35.0 Hurdy-gurdy Music_of_Galicia,_Cantabria_and_Asturias link
5261.0 432418.0 26.0 Celtic_music Music_of_Galicia,_Cantabria_and_Asturias link
null 432418.0 13.0 other-other Music_of_Galicia,_Cantabria_and_Asturias other
null 432418.0 25.0 other-bing Music_of_Galicia,_Cantabria_and_Asturias other
null 432418.0 31.0 other-wikipedia Music_of_Galicia,_Cantabria_and_Asturias other
null 432418.0 292.0 other-google Music_of_Galicia,_Cantabria_and_Asturias other
12837.0 432418.0 32.0 Galicia_(Spain) Music_of_Galicia,_Cantabria_and_Asturias link
362965.0 432418.0 20.0 Capriccio_Espagnol Music_of_Galicia,_Cantabria_and_Asturias other
null 432418.0 17.0 other-yahoo Music_of_Galicia,_Cantabria_and_Asturias other
null 244795.0 189.0 other-yahoo Music_of_Germany other
1195868.0 244795.0 53.0 Culture_of_Germany Music_of_Germany link
953401.0 244795.0 94.0 German_rock Music_of_Germany link
1587088.0 244795.0 21.0 List_of_German_musicians Music_of_Germany link
12636.0 244795.0 12.0 German_literature Music_of_Germany link
5103330.0 244795.0 19.0 German_art Music_of_Germany link
276919.0 244795.0 70.0 Krautrock Music_of_Germany other
262094.0 244795.0 43.0 Volksmusik Music_of_Germany link
null 244795.0 724.0 other-empty Music_of_Germany other
598055.0 244795.0 28.0 Volkstümliche_Musik Music_of_Germany link
636692.0 244795.0 18.0 Metalcore Music_of_Germany other
5243467.0 244795.0 14.0 Neue_Deutsche_Härte Music_of_Germany link
497068.0 244795.0 112.0 Ostalgie Music_of_Germany other
234666.0 244795.0 62.0 Blind_Guardian Music_of_Germany link
8203.0 244795.0 16.0 Deutschlandlied Music_of_Germany link
609351.0 244795.0 47.0 Accept_(band) Music_of_Germany link
11867.0 244795.0 245.0 Germany Music_of_Germany link
152735.0 244795.0 24.0 Germans Music_of_Germany link
2130627.0 244795.0 44.0 Grave_Digger_(band) Music_of_Germany link
757143.0 244795.0 67.0 Gamma_Ray_(band) Music_of_Germany link
1246795.0 244795.0 12.0 German_folklore Music_of_Germany link
720421.0 244795.0 22.0 List_of_rock_genres Music_of_Germany other
null 244795.0 339.0 other-bing Music_of_Germany other
null 244795.0 132.0 other-other Music_of_Germany other
1332497.0 244795.0 22.0 Rage_(German_band) Music_of_Germany link
1.5580374e7 244795.0 32.0 Main_Page Music_of_Germany other
378119.0 244795.0 15.0 Neue_Deutsche_Welle Music_of_Germany link
1130361.0 244795.0 32.0 Running_Wild_(band) Music_of_Germany link
299409.0 244795.0 75.0 Schlager_music Music_of_Germany link
244795.0 244795.0 69.0 Music_of_Germany Music_of_Germany link
4.1005564e7 244795.0 37.0 Milky_Chance Music_of_Germany other
null 244795.0 4975.0 other-google Music_of_Germany other
null 244795.0 249.0 other-wikipedia Music_of_Germany other
null 245844.0 49.0 other-wikipedia Music_of_Ghana other
null 245844.0 1018.0 other-google Music_of_Ghana other
null 245844.0 167.0 other-empty Music_of_Ghana other
788753.0 245844.0 19.0 Culture_of_Ghana Music_of_Ghana link
null 245844.0 37.0 other-yahoo Music_of_Ghana other
null 245844.0 52.0 other-bing Music_of_Ghana other
null 245844.0 46.0 other-other Music_of_Ghana other
1791457.0 245844.0 96.0 Music_of_West_Africa Music_of_Ghana link
12067.0 245844.0 24.0 Ghana Music_of_Ghana link
197453.0 1356974.0 100.0 Goa_trance Music_of_Goa link
1980910.0 1356974.0 24.0 Culture_of_Goa Music_of_Goa link

033_OBO_LoadExtract

033_OBO_PipedRDD_RigorousBayesianABTesting