pandas Groupby回报平均值但！排除NaN [英] Pandas Groupby Return Average BUT! exclude NaN

问题描述

因此，我试图利用pandas groupby函数并减少我拥有的大数据框.这是一个示例:

So Im trying to make sense of the pandas groupby function and to reduce a large data frame I have. Here is an example:

                             A        B
2016-09-23 19:36:08+00:00   NaN     34.0
2016-09-23 19:36:11+00:00   NaN     33.0
2016-09-23 19:36:12+00:00   24.1    NaN
2016-09-23 19:36:14+00:00   NaN     34.0
2016-09-23 19:36:17+00:00   NaN     34.0
2016-09-23 19:36:20+00:00   NaN     34.0
2016-09-23 19:36:22+00:00   24.2    NaN
2016-09-23 19:36:23+00:00   NaN     34.0
2016-09-23 19:36:26+00:00   NaN     34.0
2016-09-23 19:36:29+00:00   NaN     34.0
2016-09-23 19:36:32+00:00   24.1    NaN
2016-09-23 19:36:33+00:00   NaN     34.0
2016-09-23 19:37:00+00:00   NaN     34.0
2016-09-23 19:37:02+00:00   24.1    NaN

因此，我有2个数据系列"A"和"B"，它们以不同的采样率以其采样时间作为原始数据帧的索引进行采样.

So I have 2 data series "A" and "B" that were sampled at different rates with their sampling time as the index of the original data frame.

我现在想按日期/小时/分钟对数据框的行进行分组，并返回每分钟数据的平均值.在这里，平均值应该忽略数据框中的缺失值.

I would like to now group the rows of the data frame by date/hour/minute and return the average of the data per minute. Here the average should ignore the missing values in the data frame.

例如，我将返回如下内容:

So for example, I would return something like this:

                             A        B
2016-09-23 19:36:00+00:00   24      34.0
2016-09-23 19:37:00+00:00   24.1    33.0

是否可以使用内置的熊猫函数来做到这一点?

Is it possible to do this with a built in pandas function?

推荐答案

我认为您需要

I think you need resample with Resampler.mean, which compute mean of groups, excluding missing values:

print (df.resample('1Min').mean())
                             A          B
2016-09-23 19:36:00  24.133333  33.888889
2016-09-23 19:37:00  24.100000  34.000000

使用groupby的另一种解决方案:

Another solution with groupby:

print (df.groupby([pd.TimeGrouper('1Min')]).mean())
                             A          B
2016-09-23 19:36:00  24.133333  33.888889
2016-09-23 19:37:00  24.100000  34.000000

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