July 2017
Intermediate to advanced
398 pages
11h 54m
Chinese
Content preview from Python数据处理
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154
｜
第
8
章
都在
0.3
和
0.4
之间，那么你就知道，没在这个范围内的得分可能就是离群值）。
如果想对同样的数据做标准化，应该怎么做呢？举个例子，你可以将数据标准化，计算出
每分钟的平均得分。然后你可以将平均得分作图，查看分布情况。哪些球队每分钟得分较
高？有没有离群值？
你还可以计算标准差来查看分布情况。在第
9
章中我们会更全面地介绍标准化，但主要问
题就是：数据的正常范围是什么？这个范围之外都有哪些数据？数据有没有什么规律？
可以看出，归一化和标准化是不同的。但二者通常都可以让研究人员或调查人员确定数据
的分布，并明白该分布对后续研究或计算的含义。
数据标准化和归一化有时还需要删除离群值，这样你才能更好地发现数据的规律和分布。
回头看前面的球队例子，如果你从整个联赛中删除顶级得分球员的得分，球队的成绩是否
发生了巨大的变化？如果一名球员得到了所在球队一半的得分，那么回答是“是的”，这
会使球队成绩发生巨大的变化。
与此类似，如果某支球队总是大比分胜出，从联赛数据中剔除这支队伍，可能会大幅改变
平均得分及其分布情况。你可以使用归一化、标准化和剔除离群值的方法来帮你找到问题
的答案，这取决于你要解决的问题。
8.2
数据存储
我们已经讲过几种数据存储的方法，现在有了可用的数据，我们先来复习一下这些方法。
如果你正在使用数据库、知道预期的表格格式，并想要保存已经清洗过的数据，那么你应
该继续使用第
6
章讲过的
Python
库来连接数据库并保存数据。对于这些
Python
库中的大
部分库，你都可以使用游标直接向数据库提交。 ...
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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.