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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16.5 D3 のスケールを使った範囲の調整
D3
の線形スケールには、多くの便利なユーティリティメソッドと豊富な機能があります。おそ
らく数値マップが主力のスケールになりますが、
D3
ドキュメント（
https://github.com/d3/d3/
blob/master/CHANGES.md#scales-d3-scale
）を読んで線形スケールの柔軟性を十分理解するこ
とをお勧めします。この
Web
ページには、以下のようなほぼすべての定量的事例に対応する
D3
のその他の定量的スケールがあります。
•
指数スケール。線形スケールに似ているが指数変換を行う（
sqrt
など）。
•
対数スケール。線形スケールに似ているが対数変換を行う。
•
量子化スケール。線形スケールの変異形で離散範囲を扱う。つまり、入力は連続しているが、
出力は区分やグループに分ける（例えば、
[1, 2, 3, 4, 5]
）。
•
カラーパレットに使うことが多い分位スケールは量子化スケールに似ているが、離散的または
グループ化された定義域と範囲を扱う。
•
恒等スケール。同じ定義域と範囲を持つ線形スケール（かなり難解）。
定量的スケールは連続値数量の操作に最適ですが、離散定義域（名前やカテゴリなど）に基づい
た値を取得したいことも多いでしょう。
D3
には、このニーズを満たす特殊な序数スケールがあり
ます。
16.5.2
序数スケール
序数スケールは定義域として値の配列を取り、その配列を離散範囲や連続範囲にマッピングして
それぞれの値に対して
1
つのマッピングされた値を作成します。 ...
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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.