July 2017
Intermediate to advanced
398 pages
11h 54m
Chinese
Content preview from Python数据处理
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数据探索和分析
｜
193
根据你的数据和问题的不同，你可能会发现一些特性和库比其他特性和库更
有用，但是我们强烈建议你找到使用不同工具来实验的方式。同数据分析本
身一样，这会加深你对
Python
和数据分析库的理解。
agate-stat
库有一些有趣的统计方法我们还没有探索过。你可以通过
GitHub
跟踪最新的
发布和功能（
https://github.com/onyxfish/agate-stats
）。
除此之外，我们建议你继续探索
numpy
。你可以使用
numpy
来计算百分位数值
（
https://docs.
scipy.org/doc/numpy-dev/reference/generated/numpy.percentile.html
）。你同样可以更深入地
使用
scipy
，使用
z
得分统计方法来确定离群值（
https://docs.scipy.org/doc/scipy/reference/
generated/scipy.stats.mstats.zscore.html
）。
如果你有时间敏感的数据，
numpy
能够计算数据之间列与列的变化，从而探索随时间推移数
据的变化（
https://docs.scipy.org/doc/numpy/reference/generated/numpy.diff.html
）。
agate
同样
可以计算时间相关数据中列的变化
（
http://agate.readthedocs.io/en/1.0.0/tutorial.html#computing-
new-columns ...
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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.