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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数据聚合与分组操作
|
315
10.3.3
示例：用指定分组的值填充缺失值
在清洗缺失数据时，有时你会用 dropna 将其删除，而有时则可能想用固定值或由数据
本身衍生出的值来填充空（
NA
）值。这时就得使用 fillna 这个工具了。在下面这个例
子中，我用平均值填充空值：
假设你需要对不同的分组填充不同的值。一种方法是将数据分组，并使用 apply 和一个
316
|
第
10
章
能够对各数据块调用 fillna 的函数。下面是一些有关美国若干州的示例数据，将其分
为东部地区和西部地区：
将其中一些值设置为缺失值：
数据聚合与分组操作
|
317
我们可以用分组平均值来填充
NA
值，如下所示：
存在另外一种情况，你可能已经在代码中预设了针对各组的填充值。由于分组都具有内
置的 name 属性，我们可以这样使用：
10.3.4
示例：随机采样和排列
假设你想要从一个大型数据集中随机抽取（进行替换或不替换）样本，以进行蒙特卡
罗模拟（
Monte Carlo simulation
）或其他工作。“抽取”的方式有很多，这里采用的是 ...
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