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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312
9
章 プロットと可視化
図9-28 曜日ごとのtip_pct の箱ひげ図
さらに汎用的な
seaborn.FacetGrid
クラスを使うと、カスタマイズしたファセットグリッドプロット
を作成することもできます。詳しくは、
seaborn
のドキュメント（
https://seaborn.pydata.org/
）を参照
してください。
9.3
その他の
Python
用可視化ツール
オープンソースの世界ではよくあることですが、
Python
で画像を作成する場合、選択肢が多すぎ
ます（多すぎてリストアップしきれません）。
2010
年以降、ウェブ公開用の対話的なグラフィック作成
環境に焦点を当てて、多くの開発がなされてきました。
Bokeh
（
http://bokeh.pydata.org/
）や
Plotly
（
https://github.com/plotly/plotly.py
）などのツールを用いて、
Python
でウェブブラウザ向けに動的、
対話的に操作で
きる画像を作成することが可能になっています。
印刷用やウェブ用の静的な画像を作成する場合は、まずデフォルトとして
Matplotlib
を使い、必要に
応じて
pandas
や
seaborn
のようなアドオンライブラリを使うようにすることをお勧めします。それ以外
の要件でデータの可視化が必要になった場合は、世の中にある、他の可視化ツールのどれかを使える
ようになるのがよいでしょう。
Python
の可視化ツールのエコシステムは、今も、そして将来も拡大し
ながら、新しいものを生み出して ...
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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.