August 2017
Intermediate to advanced
500 pages
12h 3m
Japanese
Content preview from PythonとJavaScriptではじめるデータビジュアライゼーション
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168
7 章 NumPy 入門
7.1
NumPy
配列
NumPy
のすべては、同種の多次元配列
ndarray
オブジェクトを中心に構築されています。配列
についても対する操作はとても高速なコンパイル済みライブラリを使って実行するので、
NumPy
はネイティブな
Python
よりもはるかに性能が優れています。特に、
Python
の
int
や
float
と同
様に、この配列に標準的な演算を実行します。次のコードでは、
2
つの整数を加えるのと同じくら
い簡単に短時間で配列全体をその配列自体に追加します（各々の要素ごとの和）。
import numpy as np #
❶
a = np.array([1, 2, 3]) #
❷
a + a
# array([2, 4, 6])
を出力する
❶
NumPy
ライブラリを使う標準的な方法。
"from numpy import *"
よりずっとよい
†
。
❷ 数値の
Python
リストを自動的に変換する。
水面下で
NumPy
は並列計算をします。例えば、大規模行列（
2
次元配列）を許容範囲の時間で
処理します。
NumPy
の
ndarray
の主要なプロパティは次元数（
ndim
）、形状（
shape
）、数値型（
dtype
）で
す。数値の同じ配列の形状を必要に応じて作り直すことができ、その際は配列の次元数が変わる
場合もあります。小さな要素が
8
個の配列の形状を変換する例を紹介します。まず
print_array_
details
メソッドを使って主要な配列プロパティを出力します。
def print_array_details(a): ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.