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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126
4
章
NumPy
の基礎：配列とベクトル演算
を区別するのに、キーワードを指定しておきます。
In [216]: np.savez('array_archive.npz', a=arr, b=arr)
拡張子
.npz
のファイルを読み込んで
ndarray
を取り出す際には、ディクショナリから要素を取り出す
ときと同じように、キーワードを指定します。この読み込みは遅延読み込みであり、データは参照され
た時点で初めて実際にロードされます。
In [217]: arch = np.load('array_archive.npz')
In [218]: arch['b']
Out[218]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
データを圧縮して保管したい場合、
numpy.savez_compressed
を用いることができます。
In [219]: np.savez_compressed('arrays_compressed.npz', a=arr, b=arr)
4.5
行列計算
行列の計算（線形代数）には行列の積、行列の分解、行列式の計算、正方行列にまつわる各種の
計算などがあり、これらは
NumPy
を含むあらゆる配列計算ライブラリにとって重要な機能の一部で
す。
MATLAB
などの他の言語と比較して、
NumPy
では演算子
*
（アスタリスク）の定義が異なります。
MATLAB
では演算子
*
を
2
次元配列同士の内積（ドット積）として用いますが、
NumPy
では対応する要
素ごとを掛け合 ...
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