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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122
4
章
NumPy
の基礎：配列とベクトル演算
In [185]: arr.cumsum()
Out[185]: array([ 0, 1, 3, 6, 10, 15, 21, 28])
多次元配列の場合、これらの累積関数は引数と同一サイズの配列を返しますが、途中経過の値とし
て返されるのは、指定された軸に沿った方向に計算されたものになります。
In [186]: arr = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8]])
In [187]: arr
Out[187]:
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
In [188]: arr.cumsum(axis=0)
Out[188]:
array([[ 0, 1, 2],
[ 3, 5, 7],
[ 9, 12, 15]])
In [189]: arr.cumpro
d(axis=1)
Out[189]:
array([[ 0, 0, 0],
[ 3, 12, 60],
[ 6, 42, 336]])
これらの関数を表4-5にまとめました。利用例は後の章で紹介していきます。
表4-5 基本的なndarrayの統計関数
関数 定義
sum
配列の和を、指定された軸に沿って取る。長さ
0
の配列に対しては
0
を返す。
mean
算術平均。長さ
0
の配列に対しては
NaN
を返す。
std
,
var
標準偏差、分散。自由度のデフォルト値は
n
で、任意の値を指定可能。
min
,
max
最小値、最大値。
argmin ...
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