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
95
In [10]: %time for _ in range(10): my_arr2 = my_arr * 2
CPU times: user 20 ms, sys: 50 ms, total: 70 ms
Wall time: 72.4 ms
In [11]: %time for _ in range(10): my_list2 = [x * 2 for x in my_list]
CPU times: user 760 ms, sys: 290 ms, total: 1.05 s
Wall time: 1.05 s
この例で示されるように、
NumPy
のアルゴリズムは
Python
標準で提供される同等機能と比較して
10
倍から
100
倍、あるいはそれ以上に高速に動作します。さらに要求するメモリ量も格段に少なく済む
という特徴があります。
4.1
NumPy ndarray
：多次元配列オブジェクト
ndarray
は
NumPy
の基本要素の
1
つであり、その名前は
N
次元配列オブジェクト（
N-dimensional
array
）に由来します。
ndarray
は
Python
環境における高速かつ柔軟な大規模データ処理を提供しま
す。まず
ndarray
の算術演算から見ていきましょう。
ndarray
に対する算術操作は、その配列要素すべ
てに作用します。
NumPy
が配列要素に一括計算する仕組みを見ていきたいと思います。これは標準
Python
環境でスカラー値を算術演 ...
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