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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4.3
ndarray
による配列指向プログラミング
123
Out[191]: 42
ここで紹介するのは
any
と
all
という
2
つの関数で、真偽値配列に適用する場合に特に便利なもので
す。
any
は要素に
1
つでも
True
があるかを確認するのに用いられ、
all
は要素のすべてが
True
であるか
を確認するのに用いられます。
In [192]: bools = np.array([False, False, True, False])
In [193]: bools.any()
Out[193]: True
In [194]: bools.all()
Out[194]: False
any
、
all
ともに真偽値以外の配列に対して適用することができます。この場合、
0
以外の値が
True
とみなされます
＊
1
。
4.3.4
ソート
Python
組み込みのリスト型と同様に、
NumPy
の
ndarray
も
sort
関数で並び変えることができます。
こ
れは元の
ndarray
を直接置換（
in-place
）します。
In [195]: arr = np.random.randn(6)
In [196]: arr
Out[196]: array([ 0.6095, -0.4938, 1.24 , -0.1357, 1.43 , -0.8469])
In [197]: arr.sort()
In [198]: arr
Out[198]: array([-0.8469, -0.4938, -0.1357, 0.6095, 1.24 , 1.43 ])
多次元配列 ...
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