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：标准化和脚本化
｜
157
8.4
数据清洗脚本化
随着你的
Python
知识的逐步深化与丰富，你编写的
Python
代码也会逐渐变得复杂。现在
你可以编写函数、解析文件、导入并使用多个
Python
库，甚至还可以存储数据。是时候开
始将代码脚本化了。脚本化（
scripting
）的意思是，确定代码的结构，用于后续使用、学
习和分享。
以
UNICEF
数据为例。我们知道，
UNICEF
每隔几年会发布这些数据集，其中许多数据是
不变的。调查不太可能发生较大变化——它是建立在多年经验的基础之上。考虑到这些事
实，我们可以信任这些数据集有相当高的一致性。如果我们需要再次用到
UNICEF
数据，
可能至少可以复用第一次写的脚本中的一部分代码。
目前我们代码的结构比较简单，也缺少代码文档。除了可读性较差外，这样的代码还很难
复用。虽然现在我们可以看懂自己写的函数，但一年后我们还能准确地读懂并理解这些函
数吗？我们把这些函数发给同事，他们能看懂我们的笔记吗？在我们对这些问题做出肯定
的回答之前，最好一行代码也不要写。如果一年后我们无法读懂自己的代码，那么这些代
码是没有任何用处的，当发布新报告时会有人（很可能是我们自己）重新写这些代码。
Python
之禅不仅适用于编写代码，还适用于组织代码，函数、变量和类的命名，等等。最
好在选择命名上花点时间，判断哪些名字可以让你和他人都一目了然。注释和文档可以帮
助理解，但代码本身也应该具有较强的可读性。
经常有人称赞
Python
是最容易读懂的语言之一，即使是看不懂代码的人也能
读懂！保持代码语法简洁可读，这样解释代码功能的文档也不需要太长。 ...
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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.