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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5.2
pandas
の重要な機能
153
Utah 8 10 11
New York 12 14 15
In [114]: data.drop(['two', 'four'], axis='columns')
Out[114]:
one three
Ohio 0 2
Colorado 4 6
Utah 8 10
New York 12 14
drop
のようなシリーズやデータフレームのサイズを変更する関数の多くは、オブジェクトをインプ
レースで（直接置き換えながら）、新しいオブジェクトを戻さずに変更することもできます。
In [115]: obj.drop('c', inplace=True)
In [116]: obj
Out[116]:
a 0.0
b 1.0
d 3.
0
e 4.0
dtype: float64
inplace
を使うときは、削除したデータは完全になくなるので気を付けましょう。
5.2.3
インデックス参照、選択、フィルタリング
シリーズのインデックス参照（
obj[...]
）は、
NumPy
の配列のインデックス参照と同じように機能し
ます。ただし、シリーズでは整数値の指定だけではなく、シリーズのインデックス値を指定した参照も
できます。この例をいくつか示します。
In [117]: obj = pd.Series(np.arange(4.), index=['a', 'b', 'c', 'd'])
In [118]: obj
Out[118]:
a 0.0
b 1.0
c 2.0
d 3.0
dtype: float64
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