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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96
4
章
NumPy
の基礎：配列とベクトル演算
本書では
NumPy
の呼び出しを
import numpy as np
に統一しました。もちろん
from numpy
import *
という形で記述することもでき、この形式で呼び出すと毎回の関数呼び出しに
np.
を付ける必要はなくなります。しかしここで読者に強調しておきたいのは、
import numpy
as np
という記法の習慣化とその利点です。
numpy
という名前空間には数多くの関数名が定義
されており、例えば
min
や
max
などのように、
Python
組み込み関数と名前が重なるものが多
く存在します。そのためスコープを分けておくことが重要になります。
ndarray
は次のような特徴を持ちます。まず
ndarray
の多次元配列要素はすべて同じ型である必要が
あります。次に、
ndarray
配列には
shape
と
dtype
という属性があり、それぞれの配列変数ごとに固有
の値を持ちます。
shape
はその配列の次元ごとの要素数を格納するタプルです。
dtype
は配列要素に期
待する型を示します。
次の例を確認してみましょう。先ほど定義した
data
はまず外側が
2
要素、その内側に
3
要素の配列を
持つ配列で、最も内側の要素の型は
float64
です。
In [17]: data.shape
Out[17]: (2, 3)
In [18]: data.dtype
Out[18]: dtype('float64')
この章では
ndarray
の基礎として、後続の章を読み進める
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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.