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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3.9
DataFrame
のシナリオ─定刻フライトのパフォーマンス
51
ノートブック（DatabricksやJupyter、Apache Zeppelinなど）を使えば、クエリの実行とその結果
の可視化が容易になります。次の例で使っているのは Databricks notebookです。この Pythonのノー
トブックでは
%sql
関数を使ってノートブックのセル内でSQL 文を実行できます。
%sql
--
都市と出発地のコードごとにフライトの合計遅延時間（ワシントン州）
select a.City, f.origin, sum(f.delay) as Delays
from FlightPerformance f
join airports a
on a.IATA = f.origin
where a.State = 'WA'
group by a.City, f.origin
order by sum(f.delay) desc
これは以前のクエリと同じですが、フォーマットのおかげで読みやすくなっています。この Databricks
notebookの例では、このデータをすぐにバーチャートとして可視化できます。
3.9.3
フライトパフォーマンスデータの可視化
データの可視化を続けましょう。ただし今度は、米国内の週ごとにブレークダウンします。
%sql
52
3
章
DataFrame
--
（米国の）州ごとのフライトの合計遅延時間
select a.State, sum(f.delay) as Delays ...
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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.