July 2017
Intermediate to advanced
398 pages
11h 54m
Chinese
Content preview from Python数据处理
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34
第 3 章
供机器读取的数据
数据可以存储成许多不同的格式和文件类型。某些格式存储的数据很容易被机器处理，而
另一些格式存储的数据则容易被人工读取。微软的
Word
文档属于后者，而
CSV
、
JSON
和
XML
文件则属于前者。本章我们将学习如何读取那些容易被机器处理的文件，在第
4
章和第
5
章我们将讨论那些供人工读取的文件。
以易于机器理解的方式来存储数据的文件格式，通常被称作
机器可读的
（
machine readable
）。常见的机器可读格式包括：
•
逗号分隔值（
Comma-Separated Values
，
CSV
）
• JavaScript
对象符号（
JavaScript Object Notation
，
JSON
）
•
可扩展标记语言（
eXtensible Markup Language
，
XML
）
在口语和书面语中，提到这些数据格式时通常使用它们的短名字（如
CSV
）。
我们将使用这些缩写。
在寻找数据、向组织或机构发出数据请求时，你能找到最好的资源就是本章讲到的这些格
式。与易于人工读取的格式相比，这些格式更容易被
Python
脚本处理，在数据网站上通常
也很容易找到。
创建代码主文件夹
为了能够顺利完成本章的例子和代码，你需要将文件保存到本地计算机。你应该创建
一个文件夹（如果之前还没有创建的话），用来保存
Python
代码和数据文件。文件夹
的名字要直观，比如叫
data_wrangling
（数据处理）。然后在这个文件夹中创建一个子
文件夹，用来保存与本书相关的代码［比如叫
code
（代码）］。这有助于保持你的文件
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