July 2017
Intermediate to advanced
398 pages
11h 54m
Chinese
Content preview from Python数据处理
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数据清洗：研究、匹配与格式化
｜
123
仔细观察文件及其包含的数据，我们从这里开始数据清洗过程。数据清洗的第一步通常是
简单的目视分析。我们仔细观察文件，看看能发现什么！
7.2.1
找出需要清洗的数据
数据清洗的第一步是，观察数据字段，仔细寻找不一致的地方。如果在数据清洗之初就可
以使数据看起来更加干净的话，你会更容易找到在数据归一化过程中需要解决的最初问题。
我们来看一下
mn.csv
文件。文件中包含原始数据，并用首字母缩写作为标题，这些缩写的
含义可能很好翻译。我们来看一下
mn.csv
文件的列标题：
"","HH1","HH2","LN","MWM1","MWM2", ...
每一项都代表调查中的一个问题或数据，我们想要的是可读性更强的版本。通过谷歌
搜索，我们在分享
MICS
数据的世界银行网站（
http://microdata.worldbank.org/index.php/
catalog/1794/datafile/F5
）上找到了这些标题的具体含义。
花点时间研究世界银行网站上有没有缩写一览表，可以帮你更好地完成数据
清洗工作。你还可以给该机构打电话，询问是否有易于使用的缩写列表。
利用在第
11
章即将学到的一些网络抓取技术，我们可以得到一个
CSV
文件，里面包含这
些标题及其英文释义，以及世界银行在采集这些
MICS
数据时所问的问题。我们已将网
络抓取生成的新标题放在本书仓库中（
mn-headers.csv
）。我们希望将这些标题与调查数据
一一对应，这样就有了可读性较强的问题和答案。我们有几种方法可以做到这一点。 ...
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