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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9.2
pandas
と
seaborn
のプロット関数
309
図9-25 statsmodels のマクロ経済学データ（macrodata）の散布図行列
plot_kws
というキーワード引数を使っていることに気付いたかもしれません。この
plot_kws
引数を
使うと、対角線上以外の要素について、個々のプロット機能を呼び出す際に設定オプションを渡すこと
が可能です
＊
1
。さらに細かい設定オプションについては、
seaborn.pairplot
の
docstring
で調べてくだ
さい。
9.2.5
ファセットグリッドとカテゴリ型データ
他にもグループ化のための次元があるようなデータの場合は、どうなるでしょうか。多くのカテゴリ
変数を含んだデータを可視化する
1
つの方法は、特定の属性値でデータをまとめ、並べて表示できる
（特定の切り口でデータを見ることができる）ファセットグリッドを使うことです。
seaborn
には、さま
ざまなファセットプロットを簡単に作ることができる
factorplot
という便利な組み込みの関数がありま
＊
1
訳注：対角線要素については、
diag_kws
というキーワード引数で設定できます。
310
9
章 プロットと可視化
す（可視化結果は図9-26を参照してください）。
In [108]: sns.factorplot(x='day', ...
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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.