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
121
この例のように、
np.where
関数に渡す引数は、必ずしも同一サイズの配列、もしくはスカラー値に
揃っている必要はありません。
4.3.2
数学関数、統計関数
NumPy
の統計関数は、
ndarray
配列全体、あるいは特定の軸を中心とした統計処理を提供します。
sum
や
mean
といった集計処理（これらは次元削減（配列の次数を下げる処理）とも呼ばれます）や標準偏
差を求める
std
は、
ndarray
のインスタンスメソッドとしても、
NumPy
のトップレベル関数としても呼
び出すことができます。
まず正規分布によるランダムデータを生成し、いくつかの統計値を計算してみましょう。
In [177]: arr = np.random.randn(5, 4)
In [178]: arr
Out[178]:
array([[ 2.1695, -0.1149, 2.0037,
0.0296],
[ 0.7953, 0.1181, -0.7485, 0.585 ],
[ 0.1527, -1.5657, -0.5625, -0.0327],
[-0.929 , -0.4826, -0.0363, 1.0954],
[ 0.9809, -0.5895, 1.5817, -0.5287]])
In [179]: arr.mean()
Out[179]: 0.19607051119998253
In [180]: np.mean(arr)
Out[180]: 0.19607051119998253 ...
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