July 2017
Intermediate to advanced
398 pages
11h 54m
Chinese
Content preview from Python数据处理
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150
｜
第
7
章
读更多正则表达式的内容，我们推荐在
RegExr
网站（
http://www.regexr.com/
）上试用交互
式正则表达式解析器，还可以通读免费的正则表达式教程（
http://www.regular-expressions.
info/tutorial.html
）。
前面学过了这么多匹配方法，现在你可以轻松找出重复值。下面我们来看一下，在我们的
数据集中遇到重复值时可以有哪些做法。
7.2.7
如何处理重复记录
根据你的数据状态，你可能希望合并重复记录。如果你的数据集只有重复行，那就无需担
心数据存储的问题，这些数据已经包含在最终数据集内，你只需在清洗后的数据中删除或
舍弃这些行即可。但如果你要合并不同的数据集，并希望保存每一条重复数据，那你需要
思考用
Python
实现的最佳做法。
在第
9
章我们将会讲到合并数据的一般性方法，用到一些新库。但合并数据行还有简单的
方法，和你解析数据的方法相同。如果你用
DictReader
提取数据的话，我们用一个例子
来讲解这个方法。我们将合并男性数据集的一些数据行。这次我们希望基于家庭来合并数
据，所以我们关注的是每一家的调查，而不是每个人的调查：
from
csv
import
DictReader
mn_data_rdr = DictReader(open('data/unicef/mn.csv', 'rb'))
➊
mn_data = [d
for
d
in
mn_data_rdr]
def
combine_data_dict(data_rows): ...
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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.