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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4.1
NumPy ndarray
：多次元配列オブジェクト
107
このように範囲指定でスライスしていく場合、得られるのは元の配列と同次元のビューです。一方、
スライスだけでなくスカラー値を交えてインデックス指定した場合、スライスは元の配列よりも次元が
下がったものになります。
例えば、行には
2
番目の行（
1
行分）をスカラー値で指定し、列には先頭から
2
つ分をスライスで指定
してみましょう。
In [93]: arr2d[1, :2]
Out[93]: array([4, 5])
同様に、
3
列目だけ欲しい、ただし行は先頭
2
行でよい、という場合には次のようにします。
In [94]: arr2d[:2, 2]
Out[94]: array([3, 6])
この概念を図4-2に示したのでご覧ください。なお、単独のコロン（
:
）が軸全体を意味することを利
用し、次のようにして内側の次元のみで切り出すことができます。
In [95]: arr2d[:, :1]
Out[95]:
array([[1],
[4],
[7]])
スライス表記へのスカラー値の代入は、その範囲の要素すべてに対する代入を意味します。
In [96]: arr2d[:2, 1:] = 0
In [97]: arr2d
Out[97]:
array([[1, 0, 0],
[4, 0, 0],
[7, 8, 9]])
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