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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496
付録
A
NumPy
：応用編
さらに
r_
と
c_
はスライスを配列に変換することもできます。
In [52]: np.c_[1:6, -10:-5]
Out[52]:
array([[ 1, -10],
[ 2, -9],
[ 3, -8],
[ 4, -7],
[ 5, -6]])
c_
や
r_
を用いてできることについてさらに詳しく知りたい場合は、
docstring
を参照してください。
A.2.4
要素の繰り返し：
tile
と
repeat
配列の繰り返しや複製を用いてより大きな配列を作るためのツールとして、
NumPy
には、
repeat
と
tile
という
2
つの便利な関数が用意されています。
repeat
は配列内の各要素を指定した回数だけ複製
して、より大きな配列を作る関数です。
In [53]: arr = np.arange(3)
In [54]: arr
Out[54]: arra
y([0, 1, 2])
In [55]: arr.repeat(3)
Out[55]: array([0, 0, 0, 1, 1, 1, 2, 2, 2])
MATLAB
などの他の配列プログラミングフレームワークでは、配列の複製や繰り返しが多
用されます。しかし
NumPy
では、その必要はそれほどありません。その主な理由は、配列
の複製や繰り返しのニーズをよりよく満たす手段として、
NumPy
にはブロードキャストが
あるためです。ブロードキャストについては、次の節で取り上げます。
repeat
に
1
つの整数を渡すと、各要素がその回数だ
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