November 2023
Intermediate to advanced
512 pages
11h 53m
Chinese
Content preview from 利用 Python 进行数据分析：原书第 3 版
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234
第 8 章
数据规整：连接、联合和重塑
在许多应用中，数据可能分散在多个文件或数据库中，或者排列的形式不利于分析。本
章关注的是连接、联合、重塑数据的方法。
首先，我会介绍
pandas
的层次化索引，它广泛用于以上操作。然后，我将深入介绍一些
特殊的数据操作。在第
13
章，读者可以看到这些工具的具体运用。
8.1
层次化索引
层次化索引是
pandas
的一项重要功能，它使你能在一个轴上拥有多个（两个以上）索引
层级。用另一种说法，它使你能以低维度形式处理高维度数据。我们先来看一个简单的
例子：创建一个
Series
，并用一个由列表（或数组）构成的列表作为索引：
看到的结果是以 MultiIndex 作为索引的经过美化的
Series
视图。索引之间的“间隔”
表示“直接使用上面的标签”：
数据规整：连接、联合和重塑
|
235
对于层次化索引对象，可以使用部分索引，使用它选取数据子集更为简单：
在“内部”层级中进行选取也是可行的。这里我选取第二层索引值为
2
的数据：
层次化索引在数据重塑和基于分组的操作（如生成透视表）中发挥着关键的作用。例如，
可以通过 unstack 方法将下面的数据重排到
DataFrame
中：
236
|
第
8
章
unstack 的逆运算是 stack：
stack 和 unstack 将在
8.3
节中详细讲解。 ...
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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.