November 2020
Intermediate to advanced
396 pages
8h 34m
Japanese
book
データサイエンスのための統計学入門 第2版 ―予測、分類、統計モデリング、統計的機械学習とR/Pythonプログラミング
by Peter Bruce, Andrew Bruce, Peter Gedeck, 黒川 利明, 大橋 真也
Content preview from データサイエンスのための統計学入門 第2版 ―予測、分類、統計モデリング、統計的機械学習とR/Pythonプログラミング
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42
■
1
章 探索的データ分析
df['All'] = df['All'] / sum(df['All'])
❹
perc_crosstab = df
❶
margins
キーワード引数が行と列の和を追加する
❷
列の和を無視したピボットテーブルのコピーを作る
❸
行を行の和で割る
❹
'All'
列をその和で割る
1.8.3
カテゴリデータと数量データ
箱 ひ げ 図（「1.5.1 パーセンタイルと箱ひげ図」参照）では、カテゴリ変数でグループ
分けされた数量変数の分布を簡単に比較できる。例えば、航空会社別にフライトの遅
延を比較したいとする。図 1-10 は次のコードにより、航空会社別の
1
か月の遅延パーセ
ントを示す。このパーセントは、遅延原因が航空会社によるもので、天候や航空管制に
よるものではない。
（
R
）
boxplot(pct_carrier_delay ~ airline, data=airline_stats, ylim=c(0, 50),
cex.axis=.6, ylab='Daily % of Delayed Flights')
pandas boxplot
メソッドは、
by
引数でデータセットをグループに分け、それぞれ
の箱ひげ図を作る。
（
Python
）
ax = airline_stats.boxplot(by='airline', column='pct_carrier_delay')
ax.set_xlabel('')
ax.set_ylabel('Daily % of Delayed Flights') ...
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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.