July 2018
Intermediate to advanced
596 pages
13h 35m
Japanese
Content preview from Pythonによるデータ分析入門 第2版 ―NumPy、pandasを使ったデータ処理
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315
10
章
データの集約とグループ演算
データをカテゴライズして、それぞれのグループに関数を適用することは、集約や変換と呼ばれ、
データ分析のワークフローの中で最も重要な部分です。データを読み込み、マージし、データセットを
準備した後に行う類似のタスクには、グループごとの統計計算を行うことや、（可能な場合には）レポー
ティングや可視化のためにピボットテーブルを使うこともあります。
pandas
は柔軟な
groupby
インタ
フェースを持っており、これを利用することで、データセットを自然な方法でスライスし、ダイシング
し集計することができます。
リレーショナルデータベースと
SQL
（「構造化問い合わせ言語」（
Structured Query Language
）の略）
が一般的なのは、データの連結やフィルタリング、変換、集約が容易であるからです。しかし、
SQL
のようなクエリ言語はグループ演算で実現できることに、ある程
度の制約があります。この後見ていく
ように
Python
と
pandas
の表現力を使えば、より複雑なグループ演算を行うことができます。そして、
その操作には
pandas
のオブジェクトや
NumPy
の配列を用いることができます。この章では、次に挙
げることを実現する方法を学びます。
●
pandas
のオブジェクトを
1
つあるいは複数のキー（関数や配列、データフレームの列名の形式で
指定）を使って分割する方法。
●
グループの要約統計量の計算方法。具体的には、個数のカウント、平均値、標準偏差や、ユー
ザが定義
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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.