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
章 はじめに
1.3
必須の
Python
ライブラリ
Python
のデータ分析界隈や、この本で使うライブラリに詳しくない人のために、それらの一部につ
いて簡潔に概要を説明します。
1.3.1
NumPy
NumPy
（
http://numpy.org
）は
Numerical Python
の略で、長い間
Python
における数値計算の基盤と
なっています。
NumPy
は、データ構造やアルゴリズムを提供し、また、
Python
における数値データを
扱うほとんどのアプリケーションとの連携に必要なグルーとなっているライブラリです。特に、以下を
提供しています。
●
高速で効率的な多次元配列オブジェクトndarray
●
配列間における要素レベルの計算や、配列同士で数学的演算を行う関数
●
配列ベースのデータセットをディスクに読み書きするツール
●
線形代数演算、フーリエ変換、乱数の生成
●
Python
拡張やネイティブの
C
、
C++
コードが
NumPy
のデータ構造や計算機能にアクセスできる
ようにするための、安定した
C
言語の
API
NumPy
は、高速な配列処理能力を
Python
に与えるだけでなく、データ分析において、アルゴリズ
ムやライブラリ間でデータを受け渡しするためのデータコンテナとしての役割も担っています。数値
データを扱う場合、
NumPy
の配列は
Python
組み込みのデータ構造よりも効率的にデータの格納・操
作をすることができます。したがって多くの
Python
の数値計算ツールは、
NumPy
の配列を主要なデー
タ構造とし
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