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
111
[ 1.0072, 0. , 0.275 , 0.2289],
[ 1.3529, 0.8864, 0. , 0. ],
[ 1.669 , 0. , 0. , 0.477 ],
[ 3.2489, 0. , 0. , 0.1241],
[ 0.3026, 0.5238, 0.0009, 1.3438],
[ 0. , 0. , 0. , 0. ]])
特定の行や列に対する操作も、
1
次元の真偽値配列を使えば簡単です。
In [115]: data[names != 'Joe'] = 7
In [116]: data
Out[116]:
array([[ 7. ,
7. , 7. , 7. ],
[ 1.0072, 0. , 0.275 , 0.2289],
[ 7. , 7. , 7. , 7. ],
[ 7. , 7. , 7. , 7. ],
[ 7. , 7. , 7. , 7. ],
[ 0.3026, 0.5238, 0.0009, 1.3438],
[ 0. , 0. , 0. , 0. ]])
後に見ていくように、この類の
2
次元データの操作は
pandas
環境で活用されます。
4.1.6
ファンシーインデックス参照
ファンシーインデックス参照とは、インデックス参照に整数配列を用いる方法のことです。次のよう
な
8
×
4
配列があるものとします。
In [117]: arr = np.empty((8, 4))
In [118]: ...
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