033_OBO_xx1_OBOnlineExample(Python)

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ScaDaMaLe Course site and book

Old Bailey Online Analysis - for time-varying Bayesian Binomial Partition Models

Benny Avelin and Raazesh Sainudiin

Load Data

Check that the csv file is there in dbfs, otherwise go to Step 0 at the bottom of this notebook.

%fs
ls "dbfs:/tmp/obo.csv"
 
path
name
size
1
dbfs:/tmp/obo.csv
obo.csv
10208575

Showing all 1 rows.

%fs
head "dbfs:/tmp/obo.csv"
[Truncated to first 65536 bytes] breakingPeace,corporal,damage,death,deception,guilty,id,imprison,kill,miscPunish,miscVerdict,miscellaneous,noPunish,notGuilty,royalOffences,sexual,specialVerdict,theft,transport,violentTheft 0,0,0,1,0,1,t18361024-2239,0,0,0,0,0,0,0,0,0,0,0,0,1 0,0,0,1,0,2,t18361024-2240,0,0,0,0,0,0,0,0,0,0,0,0,1 0,0,0,0,0,1,t18361024-2241,1,0,0,0,0,0,0,1,0,0,0,0,0 0,0,0,0,0,0,t18361024-2242,0,0,0,0,1,0,1,0,0,0,0,0,0 0,0,0,0,0,0,t18361024-2243,0,0,0,0,1,0,1,0,0,0,0,0,0 0,0,0,0,0,1,t18361024-2244,1,0,0,0,0,0,0,0,1,0,0,0,0 0,0,0,0,0,1,t18361024-2245,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18361024-2246,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,1,t18361024-2247,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18361024-2248,0,0,0,0,0,0,1,0,0,0,1,1,0 0,0,0,0,0,0,t18361024-2249,0,0,0,0,0,0,1,0,0,0,1,0,0 0,0,0,0,0,1,t18361024-2250,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,1,t18361024-2251,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,0,t18361024-2252,0,0,0,0,0,0,1,0,0,0,1,0,0 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0,0,0,0,0,0,t18370227-872,0,0,0,0,0,0,1,0,0,0,1,0,0 0,0,0,0,0,0,t18370227-873,0,0,0,0,0,0,1,0,0,0,1,0,0 0,0,0,0,0,1,t18370227-874,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,0,t18370227-875,0,0,0,0,0,0,1,0,0,0,1,0,0 0,0,0,0,0,0,t18370227-876,0,0,0,0,0,0,1,0,0,0,1,0,0 0,0,0,0,1,1,t18370227-877,1,0,0,0,0,0,0,0,0,0,0,0,0 0,0,0,0,0,1,t18370227-878,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370227-879,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370227-880,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370227-881,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370227-882,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370227-883,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,2,t18370227-884,1,0,0,0,0,0,1,0,0,0,2,0,0 0,0,0,0,0,1,t18370227-885,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370227-886,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,0,t18370227-887,0,0,0,0,0,0,1,0,0,0,1,0,0 0,0,0,0,0,2,t18370227-888,1,0,0,0,0,1,0,0,0,0,1,0,0 0,0,0,0,0,0,t18370227-890,0,0,0,0,0,0,1,0,0,0,1,0,0 0,0,0,0,0,1,t18370227-891,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370227-892,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370227-893,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370227-894,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,0,t18370227-895,0,0,0,0,0,0,1,0,0,0,1,0,0 0,0,0,0,0,1,t18370227-896,0,1,1,0,0,0,0,0,0,0,0,0,0 0,0,0,0,0,1,t18370227-897,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,0,t18370227-898,0,0,0,0,0,0,1,0,0,0,1,0,0 0,0,0,0,0,1,t18370227-899,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,1,t18370227-900,1,0,0,0,0,0,1,0,0,0,2,0,0 0,0,0,0,0,1,t18370227-901,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,0,t18370227-902,0,0,0,0,0,0,1,0,0,0,1,0,0 0,0,0,0,0,0,t18370227-903,0,0,0,0,0,0,1,0,0,0,1,0,0 0,0,0,0,0,2,t18370227-904,1,0,0,0,0,0,1,0,0,0,2,0,0 0,0,0,0,0,1,t18370227-905,0,0,0,0,0,0,1,0,0,0,2,0,0 0,0,0,0,0,1,t18370227-906,0,0,0,0,0,0,1,0,0,0,1,1,0 0,0,0,0,0,1,t18370227-907,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,1,1,t18370227-908,1,0,0,0,0,0,0,0,0,0,0,0,0 0,0,0,0,1,1,t18370227-909,1,0,0,0,0,0,0,0,0,0,0,0,0 0,0,0,0,0,1,t18370227-910,0,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370227-911,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,1,t18370227-912,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,1,0,1,t18370403-913,0,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,1,0,1,t18370403-914,0,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,1,0,1,t18370403-915,0,0,0,0,0,0,0,0,0,0,1,0,0 1,0,0,1,0,1,t18370403-916,0,0,0,0,0,0,0,0,0,0,0,0,0 0,0,0,1,0,2,t18370403-917,0,2,0,0,0,0,0,0,0,0,0,1,0 0,0,0,0,0,0,t18370403-919,0,0,0,0,1,0,1,0,0,0,0,0,0 0,0,0,0,0,2,t18370403-920,0,0,0,0,1,1,1,0,0,0,0,0,0 0,0,0,0,0,1,t18370403-921,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,1,t18370403-922,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,1,t18370403-923,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-924,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,1,t18370403-925,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,1,t18370403-926,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-927,0,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-928,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,1,t18370403-929,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,2,t18370403-930,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-931,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-932,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,0,t18370403-933,0,0,0,0,0,0,1,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-934,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,1,t18370403-935,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,1,t18370403-936,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,1,t18370403-937,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,0,t18370403-938,0,0,0,0,0,0,1,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-939,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,1,t18370403-940,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-941,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,0,t18370403-942,0,0,0,0,0,0,1,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-943,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,1,t18370403-944,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-945,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-946,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-947,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,0,t18370403-948,0,0,0,0,0,0,1,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-948a,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,0,t18370403-949,0,0,0,0,0,0,1,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-951,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,1,t18370403-952,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,1,1,t18370403-953,0,0,0,0,0,0,0,0,0,0,0,1,0 0,0,0,0,0,2,t18370403-954,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,1,t18370403-955,1,0,0,0,0,0,1,0,0,0,2,0,0 0,0,0,0,0,1,t18370403-956,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-957,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-958,0,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-959,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,1,t18370403-960,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,0,t18370403-961,0,0,0,0,0,0,1,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-962,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-963,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,1,t18370403-964,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,0,t18370403-965,0,0,0,0,0,0,1,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-966,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-967,0,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-968,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-969,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,1,t18370403-970,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,1,t18370403-971,0,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-972,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-973,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-974,1,0,0,0,0,0,0,0,0,0,1,0,0 0,1,0,0,0,2,t18370403-975,2,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-976,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,1,t18370403-977,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-978,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,0,t18370403-979,0,0,0,0,0,0,1,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-980,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,1,t18370403-981,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-982,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-983,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-984,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-985,0,0,0,0,0,0,0,0,0,0,1,1,0 0,0,0,0,0,2,t18370403-986,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,0,t18370403-987,0,0,0,0,0,0,1,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-988,1,0,0,0,0,0,0,0,0,0,1,0,0 0,0,0,0,0,1,t18370403-989,0,0,0,0,0,0,1,0,0,0,1,1,0 0,0,0,0,0,1,t18370403-990,0,0,0,0,0,0,0,0,0,0,1,1,0
oboDF = spark.read.format("csv").option("header", "true").load("dbfs:/tmp/obo.csv")
display(oboDF)
 
breakingPeace
corporal
damage
death
deception
guilty
id
imprison
kill
miscPunish
miscVerdict
miscellaneous
noPunish
notGuilty
royalOffences
sexual
specialVerdict
theft
transport
violentTheft
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
0
0
1
0
1
t18361024-2239
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
2
t18361024-2240
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
t18361024-2241
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
t18361024-2242
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
t18361024-2243
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
1
t18361024-2244
1
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
t18361024-2245
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
t18361024-2246
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
1
t18361024-2247
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
t18361024-2248
0
0
0
0
0
0
1
0
0
0
1
1
0
0
0
0
0
0
0
t18361024-2249
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
0
1
t18361024-2250
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
1
t18361024-2251
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
t18361024-2252
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
0
1
t18361024-2253
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
t18361024-2254
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
t18361024-2255
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0

Truncated results, showing first 1000 rows.

Step 0: Download CSV and Copy to dbfs

%sh
wget http://lamastex.org/datasets/public/OldBailey/oboOffencePunnishmentCountsFrom-sds-2-2-ApacheSparkScalaProcessingOfOBOXMLDoneByRaazOn20180405.csv
--2018-04-30 07:03:52-- http://lamastex.org/datasets/public/OldBailey/oboOffencePunnishmentCountsFrom-sds-2-2-ApacheSparkScalaProcessingOfOBOXMLDoneByRaazOn20180405.csv Resolving lamastex.org (lamastex.org)... 166.62.28.100 Connecting to lamastex.org (lamastex.org)|166.62.28.100|:80... connected. HTTP request sent, awaiting response... 200 OK Length: 10208575 (9.7M) [text/csv] Saving to: ‘oboOffencePunnishmentCountsFrom-sds-2-2-ApacheSparkScalaProcessingOfOBOXMLDoneByRaazOn20180405.csv’ 0K .......... .......... .......... .......... .......... 0% 127K 78s 50K .......... .......... .......... .......... .......... 1% 253K 58s 100K .......... .......... .......... .......... .......... 1% 31.1M 39s 150K .......... .......... .......... .......... .......... 2% 254K 39s 200K .......... .......... .......... .......... .......... 2% 8.40M 31s 250K .......... .......... .......... .......... .......... 3% 261K 32s 300K .......... .......... .......... .......... .......... 3% 23.6M 27s 350K .......... .......... .......... .......... .......... 4% 12.2M 24s 400K .......... .......... .......... .......... .......... 4% 261K 25s 450K .......... .......... .......... .......... .......... 5% 2.81M 23s 500K .......... .......... .......... .......... .......... 5% 35.5M 21s 550K .......... .......... .......... .......... .......... 6% 52.2M 19s 600K .......... .......... .......... .......... .......... 6% 283K 20s 650K .......... .......... .......... .......... .......... 7% 33.6M 18s 700K .......... .......... .......... .......... .......... 7% 2.90M 17s 750K .......... .......... .......... .......... .......... 8% 31.4M 16s 800K .......... .......... .......... .......... .......... 8% 35.1M 15s 850K .......... .......... .......... .......... .......... 9% 34.3M 14s 900K .......... .......... .......... .......... .......... 9% 286K 15s 950K .......... .......... .......... .......... .......... 10% 32.9M 14s 1000K .......... .......... .......... .......... .......... 10% 33.4M 13s 1050K .......... .......... .......... .......... .......... 11% 3.14M 13s 1100K .......... .......... .......... .......... .......... 11% 30.4M 12s 1150K .......... .......... .......... .......... .......... 12% 25.3M 12s 1200K .......... .......... .......... .......... .......... 12% 25.1M 11s 1250K .......... .......... .......... .......... .......... 13% 32.9M 11s 1300K .......... .......... .......... .......... .......... 13% 37.4M 10s 1350K .......... .......... .......... .......... .......... 14% 292K 11s 1400K .......... .......... .......... .......... .......... 14% 31.5M 10s 1450K .......... .......... .......... .......... .......... 15% 21.9M 10s 1500K .......... .......... .......... .......... .......... 15% 39.5M 10s 1550K .......... .......... .......... .......... .......... 16% 3.60M 9s 1600K .......... .......... .......... .......... .......... 16% 9.47M 9s 1650K .......... .......... .......... .......... .......... 17% 36.4M 9s 1700K .......... .......... .......... .......... .......... 17% 35.0M 8s 1750K .......... .......... .......... .......... .......... 18% 32.6M 8s 1800K .......... .......... .......... .......... .......... 18% 16.6M 8s 1850K .......... .......... .......... .......... .......... 19% 36.5M 8s 1900K .......... .......... .......... .......... .......... 19% 38.2M 7s 1950K .......... .......... .......... .......... .......... 20% 6.83M 7s 2000K .......... .......... .......... .......... .......... 20% 317K 8s 2050K .......... .......... .......... .......... .......... 21% 31.9M 7s 2100K .......... .......... .......... .......... .......... 21% 27.9M 7s 2150K .......... .......... .......... .......... .......... 22% 22.1M 7s 2200K .......... .......... .......... .......... .......... 22% 25.2M 7s 2250K .......... .......... .......... .......... .......... 23% 4.88M 7s 2300K .......... .......... .......... .......... .......... 23% 10.8M 6s 2350K .......... .......... .......... .......... .......... 24% 23.8M 6s 2400K .......... .......... .......... .......... .......... 24% 27.6M 6s 2450K .......... .......... .......... .......... .......... 25% 23.8M 6s 2500K .......... .......... .......... .......... .......... 25% 23.7M 6s 2550K .......... .......... .......... .......... .......... 26% 30.7M 6s 2600K .......... .......... .......... .......... .......... 26% 3.88M 6s 2650K .......... .......... .......... .......... .......... 27% 289M 5s 2700K .......... .......... .......... .......... .......... 27% 374K 6s 2750K .......... .......... .......... .......... .......... 28% 49.1M 6s 2800K .......... .......... .......... .......... .......... 28% 46.2M 5s 2850K .......... .......... .......... .......... .......... 29% 2.99M 5s 2900K .......... .......... .......... .......... .......... 29% 36.8M 5s 2950K .......... .......... .......... .......... .......... 30% 34.1M 5s 3000K .......... .......... .......... .......... .......... 30% 29.7M 5s 3050K .......... .......... .......... .......... .......... 31% 34.3M 5s 3100K .......... .......... .......... .......... .......... 31% 30.7M 5s 3150K .......... .......... .......... .......... .......... 32% 41.1M 5s 3200K .......... .......... .......... .......... .......... 32% 27.9M 5s 3250K .......... .......... .......... .......... .......... 33% 6.14M 4s 3300K .......... .......... .......... .......... .......... 33% 32.8M 4s 3350K .......... .......... .......... .......... .......... 34% 13.4M 4s 3400K .......... .......... .......... .......... .......... 34% 30.1M 4s 3450K .......... .......... .......... .......... .......... 35% 41.2M 4s 3500K .......... .......... .......... .......... .......... 35% 2.94M 4s 3550K .......... .......... .......... .......... .......... 36% 49.2M 4s 3600K .......... .......... .......... .......... .......... 36% 44.0M 4s 3650K .......... .......... .......... .......... .......... 37% 43.2M 4s 3700K .......... .......... .......... .......... .......... 37% 42.1M 4s 3750K .......... .......... .......... .......... .......... 38% 42.0M 4s 3800K .......... .......... .......... .......... .......... 38% 34.0M 4s 3850K .......... .......... .......... .......... .......... 39% 50.9M 3s 3900K .......... .......... .......... .......... .......... 39% 387K 4s 3950K .......... .......... .......... .......... .......... 40% 32.8M 4s 4000K .......... .......... .......... .......... .......... 40% 26.7M 3s 4050K .......... .......... .......... .......... .......... 41% 38.8M 3s 4100K .......... .......... .......... .......... .......... 41% 3.60M 3s 4150K .......... .......... .......... .......... .......... 42% 37.9M 3s 4200K .......... .......... .......... .......... .......... 42% 26.2M 3s 4250K .......... .......... .......... .......... .......... 43% 30.6M 3s 4300K .......... .......... .......... .......... .......... 43% 33.7M 3s 4350K .......... .......... .......... .......... .......... 44% 34.2M 3s 4400K .......... .......... .......... .......... .......... 44% 26.0M 3s 4450K .......... .......... .......... .......... .......... 45% 30.7M 3s 4500K .......... .......... .......... .......... .......... 45% 40.9M 3s 4550K .......... .......... .......... .......... .......... 46% 30.5M 3s 4600K .......... .......... .......... .......... .......... 46% 15.6M 3s 4650K .......... .......... .......... .......... .......... 47% 31.5M 3s 4700K .......... .......... .......... .......... .......... 47% 3.47M 3s 4750K .......... .......... .......... .......... .......... 48% 38.2M 3s 4800K .......... .......... .......... .......... .......... 48% 36.2M 3s 4850K .......... .......... .......... .......... .......... 49% 22.0M 2s 4900K .......... .......... .......... .......... .......... 49% 26.7M 2s 4950K .......... .......... .......... .......... .......... 50% 19.0M 2s 5000K .......... .......... .......... .......... .......... 50% 24.1M 2s 5050K .......... .......... .......... .......... .......... 51% 34.4M 2s 5100K .......... .......... .......... .......... .......... 51% 8.93M 2s 5150K .......... .......... .......... .......... .......... 52% 28.7M 2s 5200K .......... .......... .......... .......... .......... 52% 20.7M 2s 5250K .......... .......... .......... .......... .......... 53% 38.8M 2s 5300K .......... .......... .......... .......... .......... 53% 40.1M 2s 5350K .......... .......... .......... .......... .......... 54% 35.9M 2s 5400K .......... .......... .......... .......... .......... 54% 37.8M 2s 5450K .......... .......... .......... .......... .......... 55% 38.0M 2s 5500K .......... .......... .......... .......... .......... 55% 423K 2s 5550K .......... .......... .......... .......... .......... 56% 35.4M 2s 5600K .......... .......... .......... .......... .......... 56% 34.3M 2s 5650K .......... .......... .......... .......... .......... 57% 26.3M 2s 5700K .......... .......... .......... .......... .......... 57% 35.3M 2s 5750K .......... .......... .......... .......... .......... 58% 41.3M 2s 5800K .......... .......... .......... .......... .......... 58% 4.04M 2s 5850K .......... .......... .......... .......... .......... 59% 27.7M 2s 5900K .......... .......... .......... .......... .......... 59% 19.8M 2s 5950K .......... .......... .......... .......... .......... 60% 27.5M 2s 6000K .......... .......... .......... .......... .......... 60% 28.1M 2s 6050K .......... .......... .......... .......... .......... 61% 25.7M 2s 6100K .......... .......... .......... .......... .......... 61% 22.7M 2s 6150K .......... .......... .......... .......... .......... 62% 30.2M 2s 6200K .......... .......... .......... .......... .......... 62% 18.1M 2s 6250K .......... .......... .......... .......... .......... 63% 29.4M 2s 6300K .......... .......... .......... .......... .......... 63% 25.3M 1s 6350K .......... .......... .......... .......... .......... 64% 28.4M 1s 6400K .......... .......... .......... .......... .......... 64% 36.6M 1s 6450K .......... .......... .......... .......... .......... 65% 38.4M 1s 6500K .......... .......... .......... .......... .......... 65% 43.2M 1s 6550K .......... .......... .......... .......... .......... 66% 42.8M 1s 6600K .......... .......... .......... .......... .......... 66% 9.42M 1s 6650K .......... .......... .......... .......... .......... 67% 18.2M 1s 6700K .......... .......... .......... .......... .......... 67% 8.86M 1s 6750K .......... .......... .......... .......... .......... 68% 9.37M 1s 6800K .......... .......... .......... .......... .......... 68% 10.7M 1s 6850K .......... .......... .......... .......... .......... 69% 23.5M 1s 6900K .......... .......... .......... .......... .......... 69% 30.6M 1s 6950K .......... .......... .......... .......... .......... 70% 41.2M 1s 7000K .......... .......... .......... .......... .......... 70% 29.3M 1s 7050K .......... .......... .......... .......... .......... 71% 35.4M 1s 7100K .......... .......... .......... .......... .......... 71% 34.4M 1s 7150K .......... .......... .......... .......... .......... 72% 37.1M 1s 7200K .......... .......... .......... .......... .......... 72% 18.2M 1s 7250K .......... .......... .......... .......... .......... 73% 31.5M 1s 7300K .......... .......... .......... .......... .......... 73% 19.7M 1s 7350K .......... .......... .......... .......... .......... 74% 45.3M 1s 7400K .......... .......... .......... .......... .......... 74% 37.2M 1s 7450K .......... .......... .......... .......... .......... 75% 31.1M 1s 7500K .......... .......... .......... .......... .......... 75% 47.0M 1s 7550K .......... .......... .......... .......... .......... 76% 43.1M 1s 7600K .......... .......... .......... .......... .......... 76% 45.7M 1s 7650K .......... .......... .......... .......... .......... 77% 42.6M 1s 7700K .......... .......... .......... .......... .......... 77% 45.1M 1s 7750K .......... .......... .......... .......... .......... 78% 478K 1s 7800K .......... .......... .......... .......... .......... 78% 31.2M 1s 7850K .......... .......... .......... .......... .......... 79% 15.6M 1s 7900K .......... .......... .......... .......... .......... 79% 26.8M 1s 7950K .......... .......... .......... .......... .......... 80% 26.1M 1s 8000K .......... .......... .......... .......... .......... 80% 24.2M 1s 8050K .......... .......... .......... .......... .......... 81% 39.4M 1s 8100K .......... .......... .......... .......... .......... 81% 45.8M 1s 8150K .......... .......... .......... .......... .......... 82% 16.6M 1s 8200K .......... .......... .......... .......... .......... 82% 23.4M 1s 8250K .......... .......... .......... .......... .......... 83% 36.8M 1s 8300K .......... .......... .......... .......... .......... 83% 2.04M 1s 8350K .......... .......... .......... .......... .......... 84% 38.1M 1s 8400K .......... .......... .......... .......... .......... 84% 31.4M 1s 8450K .......... .......... .......... .......... .......... 85% 36.8M 0s 8500K .......... .......... .......... .......... .......... 85% 40.6M 0s 8550K .......... .......... .......... .......... .......... 86% 43.1M 0s 8600K .......... .......... .......... .......... .......... 86% 40.3M 0s 8650K .......... .......... .......... .......... .......... 87% 39.6M 0s 8700K .......... .......... .......... .......... .......... 87% 23.7M 0s 8750K .......... .......... .......... .......... .......... 88% 38.3M 0s 8800K .......... .......... .......... .......... .......... 88% 28.9M 0s 8850K .......... .......... .......... .......... .......... 89% 36.8M 0s 8900K .......... .......... .......... .......... .......... 89% 28.3M 0s 8950K .......... .......... .......... .......... .......... 90% 30.1M 0s 9000K .......... .......... .......... .......... .......... 90% 37.1M 0s 9050K .......... .......... .......... .......... .......... 91% 27.0M 0s 9100K .......... .......... .......... .......... .......... 91% 26.4M 0s 9150K .......... .......... .......... .......... .......... 92% 28.5M 0s 9200K .......... .......... .......... .......... .......... 92% 45.0M 0s 9250K .......... .......... .......... .......... .......... 93% 6.96M 0s 9300K .......... .......... .......... .......... .......... 93% 40.4M 0s 9350K .......... .......... .......... .......... .......... 94% 31.3M 0s 9400K .......... .......... .......... .......... .......... 94% 8.33M 0s 9450K .......... .......... .......... .......... .......... 95% 36.9M 0s 9500K .......... .......... .......... .......... .......... 95% 28.7M 0s 9550K .......... .......... .......... .......... .......... 96% 30.4M 0s 9600K .......... .......... .......... .......... .......... 96% 36.0M 0s 9650K .......... .......... .......... .......... .......... 97% 38.0M 0s 9700K .......... .......... .......... .......... .......... 97% 38.1M 0s 9750K .......... .......... .......... .......... .......... 98% 39.0M 0s 9800K .......... .......... .......... .......... .......... 98% 35.7M 0s 9850K .......... .......... .......... .......... .......... 99% 33.0M 0s 9900K .......... .......... .......... .......... .......... 99% 34.6M 0s 9950K .......... ......... 100% 28.6M=2.8s 2018-04-30 07:03:55 (3.42 MB/s) - ‘oboOffencePunnishmentCountsFrom-sds-2-2-ApacheSparkScalaProcessingOfOBOXMLDoneByRaazOn20180405.csv’ saved [10208575/10208575]
%sh
pwd && ls
/databricks/driver conf derby.log eventlogs ganglia logs MRS_IsIt1or2Coins.samples oboOffencePunnishmentCountsFrom-sds-2-2-ApacheSparkScalaProcessingOfOBOXMLDoneByRaazOn20180405.csv
%fs
cp "file:/databricks/driver/oboOffencePunnishmentCountsFrom-sds-2-2-ApacheSparkScalaProcessingOfOBOXMLDoneByRaazOn20180405.csv" "dbfs:/tmp/obo.csv"
res4: Boolean = true
%fs
ls "dbfs:/tmp/"
 
path
name
size
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
dbfs:/tmp/IsIt1or2Coins
IsIt1or2Coins
6548544
dbfs:/tmp/hive/
hive/
0
dbfs:/tmp/obo.csv
obo.csv
10208575
dbfs:/tmp/simple
simple
8760
dbfs:/tmp/simple.c
simple.c
182
dbfs:/tmp/suckAs.sh
suckAs.sh
781
dbfs:/tmp/temporary-a2e8e1bb-9a0e-4166-955d-a37d2388205c/
temporary-a2e8e1bb-9a0e-4166-955d-a37d2388205c/
0
dbfs:/tmp/temporary-bd589b15-9006-4697-a3a2-97d9f23d1a97/
temporary-bd589b15-9006-4697-a3a2-97d9f23d1a97/
0
dbfs:/tmp/temporary-bee4dd42-8147-4a95-8bff-0fe554739814/
temporary-bee4dd42-8147-4a95-8bff-0fe554739814/
0
dbfs:/tmp/temporary-c112a7cd-1547-4a0e-89c0-511f3a9d003d/
temporary-c112a7cd-1547-4a0e-89c0-511f3a9d003d/
0
dbfs:/tmp/temporary-c1a6e85d-db56-4624-a417-d135e12c645a/
temporary-c1a6e85d-db56-4624-a417-d135e12c645a/
0
dbfs:/tmp/temporary-c5303e67-3920-4479-8f11-489187899040/
temporary-c5303e67-3920-4479-8f11-489187899040/
0
dbfs:/tmp/temporary-c6b0ce6f-fd0f-4d15-b112-c04489109310/
temporary-c6b0ce6f-fd0f-4d15-b112-c04489109310/
0
dbfs:/tmp/temporary-c7b324d6-27dd-44c2-aa31-742aa19d667f/
temporary-c7b324d6-27dd-44c2-aa31-742aa19d667f/
0
dbfs:/tmp/temporary-c9df7952-700a-4cf9-a9be-7353e8020f11/
temporary-c9df7952-700a-4cf9-a9be-7353e8020f11/
0
dbfs:/tmp/temporary-ca189a65-99ae-409b-9415-0c425820d65a/
temporary-ca189a65-99ae-409b-9415-0c425820d65a/
0
dbfs:/tmp/temporary-ca42504d-49f0-4495-b918-d053ce46ffe1/
temporary-ca42504d-49f0-4495-b918-d053ce46ffe1/
0

Showing all 39 rows.

Preparing the windowed version with ??

Scala is simpler here as done in next noteboo.

TODO Need to use the simpler method using dbfs FUSE that Ivan Sadikov showed earlier. However the results in the next notebook using suckAs.sh works and it's left for fun.