November 2018
Intermediate to advanced
272 pages
6h 51m
Japanese
book
エレガントなSciPy ―Pythonによる科学技術計算
by Juan Nunez-Iglesias, Stéfan van der Walt, Harriet Dashnow, 山崎 邦子, 山崎 康宏
Content preview from エレガントなSciPy ―Pythonによる科学技術計算
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33
2.1 データの取得
この
2
つの関数を合わせると、
NumPy
を強力にしている以下の多くのこと（最初の
3
つは
1
章に
出てきましたよね）のよい見本となっています。
•
配列は、リストのような
1
次元にも、行列のような
2
次元にも、さらに多次元にもなる。こ
のため、多種多様な数値データを配列で表現できる。本章の例では、
2
次元配列を表す。
•
配列を用いると、複数の数値演算を一度に表現できる。
quantile_norm_log
のコードの
1
行目
では、
1
回の呼び出しで、
X
の全要素に
1
を足して対数を取っている。このような処理をベク
トル化と呼ぶ。
•
配列操作は、配列の軸に沿って行うことができる。
quantile_norm
のコードの
1
行目では、
np.sort
の
axis
パラメータを指定するだけで、各列のデータがソートされる。続いて、別の
axis
を指定し、各行に沿った平均を取っている。
•
配列が、科学
Python
エコシステムを支えている。
scipy.stats.rankdata
関数は、
Python
リ
ストではなく、
NumPy
配列に作用する。さらに、
Python
の科学ライブラリの多くが同様に
設計されている。
•
axis
キーワード引数を取らない関数でも、
NumPy
の
apply_along_axis
関数を使うと、軸に
沿った演算が可能になる。
•
配列は、ファンシーインデックス参照によって、多様なデータ操作をサポートしている（
Xn =
quantiles[ranks]
）。これはおそらく
NumPy
の最もクセのある仕様だが ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
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.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.