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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487
付録
A
NumPy
：応用編
この付録では、配列計算ライブラリ
NumPy
のさらに深い部分を見ていきます。具体的には、
ndarray
型の詳細な内部構造や、高度な配列操作やアルゴリズムなどです。
この付録には多岐にわたるトピックが含まれているため、頭から順に読んでいく必要はありません。
A.1
ndarray
オブジェクトの内部構造
NumPy
の
ndarray
は、同種のデータ（連続的なデータまたはストライドデータ
＊
1
）の塊を多次元配列
オブジェクトとして解釈する手段を与えてくれます。データの解釈方法はデータ型、つまり
dtype
に
よって決まります。格納されているデータは
dtype
の示す型、例えば浮動小数や整数、真偽値などの型
として扱われることになります。
ndarray
の柔軟性を高くしている点の
1
つは、配列オブジェクトがすべて、データの塊のストライド
ビューであることです。例えば、配列のビュー
arr[::2, ::-1]
はデータをコピーせずにどのようにし
て扱っているのか、不思議に思うかもしれません。実は、
ndarray
はメモリの塊と
dtype
のみからなっ
ているのではない、というのがその答えです。
ndarray
は「ストライド（歩幅）」情報を持っており、刻む
幅を変えながらメモリ内の配列を移動できるのです。より正確には、
ndarray
は内部的に次の要素から
なっています。
●
データへのポインタ：
RAM
内やメモリマップファイル内の一連のデータへのポインタ
●
データ型（
dtype
）：配列内の各値を格納
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