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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时间序列
|
369
使用 pandas.Grouper 存在一个限制，即必须使用时间作为
Series
或
DataFrame
的索引。
11.7
移动窗口函数
在移动窗口或指数衰减权重上进行统计或运行其他函数，也是一类常见于时间序列的数
组变换。这对于圆滑噪声数据或不连续数据很有帮助。我将其称为移动窗口函数，尽管
其中还包括没有固定长度窗口的函数（比如指数加权移动均值）。与其他统计函数一样，
移动窗口函数也会自动排除缺失数据。
开始之前，我们加载一些时间序列数据，并将其重采样为工作日频率：
现在导入 rolling 运算符，它与 resample 和 groupby 很像。通过一个 window（表示周
期的数量，见图
11-4
），可以在
Series
或
DataFrame
上调用它：
图 11-4：苹果公司股价的 250 日均线
370
|
第
11
章
表达式 rolling(250) 与 groupby 很像，但它不是进行分组，而是创建一个可以按照
250
日分组的移动窗口对象。然后，我们就得到了苹果公司股价的
250
日移动窗口。
默认情况下，滚动函数需要窗口中所有的值为非
NA
值，可以修改此行为以解决缺失
数据的问题。特别是，实际上在时间序列开始处，数据量就低于 window 周期数（如
图
11-5
所 示 ）：
图 11-5 ...
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