November 2017
Intermediate to advanced
328 pages
6h 51m
Japanese
Content preview from 入門 PySpark ―PythonとJupyterで活用するSpark 2エコシステム
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194
9
章
Blaze
によるポリグロットパーシステンス
traffic\
.groupby('Fatal')['Age_of_car']\
.agg({
'Fatal_AvgAge': np.mean,
'Fatal_Count': np.count_nonzero
})
SQL なら次のようになるでしょう。
SELECT Fatal
, AVG(Age_of_car) AS Fatal_AvgAge
, COUNT(Age_of_car) AS Fatal_Count
FROM traffic
GROUP BY Fatal
9.4.5
結合
2つの
DataShape
を結合することも簡単です。同じ結果が得られる方法はいくつもありますが、こ
こではまずすべての交通違反を違反の種類（
violation
オブジェクト）ごとに、そしてシートベルト
（
belts
オブジェクト）に関わる交通違反を選択します。
violation = traffic[
['Stop_month','Stop_day','Stop_year',
'Stop_hr','Stop_min','Stop_sec','Violation_Type']]
belts = traffic[
['Stop_month','Stop_day','Stop_year',
'Stop_hr','Stop_min','Stop_sec','Belts']]
これで、6つの日時に関する列を使って 2つのオブジェクトを結合できます。
同じことは、単に
Violatio ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.