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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220
7
章 データのクリーニングと前処理
In [63]: data.replace([-999, -1000], np.nan)
Out[63]:
0 1.0
1 NaN
2 2.0
3 NaN
4 NaN
5 3.0
dtype: float64
置き換えたい値ごとに代わりに入れる値が異なる場合は、第
2
引数の置き換え後の値もリストで指定
します。
In [64]: data.replace([-999, -1000], [np.nan, 0])
Out[64]:
0 1.0
1 NaN
2 2.0
3 NaN
4 0.0
5 3.0
dtype: float64
もっとわかりやすくするため、置き換え前後の値をディクショナリで指定することも可能です。
In [65]: data.replace({-999: np.nan, -1000: 0})
Out[65]:
0 1.0
1 NaN
2
2.0
3 NaN
4 0.0
5 3.0
dtype: float64
data.replace
メソッドは
data.str.replace
メソッドとは異なります。
data.str.replace
は要
素ごとに文字列の置き換えを行うメソッドで、この章の後の節で、シリーズ（
Series
）オブ
ジェクトが持つ他の文字列処理メソッドと一緒に紹介します。
7.2.4
軸のインデックスの名前を変更する
シリーズ内の各値だけでなく、軸のラベルも同様に変換できます。軸のラベルの変換にも、異なるラ
ベルを持った新たなオブジェクトを生成するための、ある種の関数やマッピ
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