使用平均值填充 pandas 数据帧中的缺失值 [英] Fill in missing values in pandas dataframe using mean

问题描述

datetime
2012-01-01    125.5010
2012-01-02    NaN
2012-01-03    125.5010
2013-01-04    NaN
2013-01-05    125.5010
2013-02-28    125.5010
2014-02-28    125.5010
2016-01-02    125.5010
2016-01-04    125.5010
2016-02-28    NaN

我想填写missig通过使用从数据集计算出的气候学来计算此数据框中的值，即通过平均值$ code> 28th feb 28th feb 2016 c>其他年份。我如何做到这一点？

I would like to fill in the missig values in this dataframe by using a climatology computed from the dataset i.e fill in missing 28th feb 2016 value by averaging values of 28th feb from other years. How do i do this?

推荐答案

你可以使用 groupby by month 和 day 和 转换 与 fillna 意味着 ：

You can use groupby by month and day and transform with fillna mean:

print df.groupby([df.index.month, df.index.day]).transform(lambda x: x.fillna(x.mean()))
datetime           
2012-01-01  125.501
2012-01-02  125.501
2012-01-03  125.501
2013-01-04  125.501
2013-01-05  125.501
2013-02-28  125.501
2014-02-28  125.501
2016-01-02  125.501
2016-01-04  125.501
2016-02-28  125.501

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