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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7.12
D3
によるフライトの可視化
147
#
降順に利用の多い経由地を表示する
display(filteredPaths.groupBy("v1.id", "v1.City").count().
orderBy(desc("count")).limit(10))
出力を棒グラフにすると次のようになります。
7.12
D3
によるフライトの可視化
このデータセット中のフライトパスと経路を強力な可視化機能で楽しんでみたいなら、Databricks
ノートブック中の Airports D3（https://mbostock.github.io/d3/talk/20111116/airports.html）が利用
できます。GraphFrameとDataFrame、そ し て D3による可視化を組み合わせれば、このデータセット
内での定刻どおりに到着したフライトや、予定よりも早く到着したすべてのフライトに対して、その接
続の様子をそのまま可視化できます。
青い円は頂点（空港）で、円の大きさはその空港に出入りしている辺（フライト）の数を示していま
す。黒い直線は辺とその両端の頂点を示します。枠外に出て行っている辺は、ハワイ州やアラスカ州に
ある空港につながっています。
この可視化を行うためには、まずノートブックに埋め込まれている
d3a
と呼ばれる
scala
のパッ
ケージを作成しなければなりません（このノートブックは http://bit.ly/2yrEqggからダウンロードでき
ま す ）。 こ こ で は Databr ...
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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.