August 2015
Intermediate to advanced
272 pages
5h 46m
Japanese
Content preview from Think Stats 第2版 ―プログラマのための統計入門
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96
7 章 変数間の関係
図
7-2
ジッターと透明化（左）と六角形ビン分割プロットによる散布図（右）
図
7-2
の右側は結果を表しています。
hexbin
の長所は、生成するファイルのサイ
ズについても時間についても、関係性もうまく表し、また大きなデータセットに対し
ても効果的な点です。逆に難点は、外れ値が見えなくなってしまう点です。
この話の教訓は、誤解を招きかねない余計なものをもたらさず、明確な関係を表す
散布図の作成は簡単でないということです。
7.2
関係を特徴付ける
散布図は、変数間関係の全般的な印象を与えますが、関係の性質についてより深い
洞察を与えてくれる可視化が他にもあります。その
1
つが、ある変数をビンに分けて、
他の変数のパーセンタイルをプロットするものです。
NumPy
と
pandas
では、そのようにデータをビンに分ける関数を用意しています。
df = df.dropna(subset=['htm3', 'wtkg2'])
bins = np.arange(135, 210, 5)
indices = np.digitize(df.htm3, bins)
groups = df.groupby(indices)
dropna
は、カラムの中で
nan
を持つ行を削除します。
arange
は
NumPy
配列のビ
ンを
135
から
210
の前まで
5
ずつ増やして作ります。
digitize
は、
df.htm3
の各値を含むビンのインデックスを計算します。結果は整数
インデックスの
NumPy
配列です。最小値のビンよりも低い値は、インデックス ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
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.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.