resample using for subsample will regards NaN as zeros with sum #29382
Description
Problem description
I found the resample for subsampling to aggregate would take NaN(np.nan) as zero.
I have seen the similar question in Handling NaN during resampling #22065
I use .sum(), .sum(skipna=False), .apply(np.sum), and my function .apply(lambda x: np.sum(x.values) to test the same series. Only my function work out with my expected result.
my environment
numpy: 1.16.5
pandas: 0.25.1
import numpy as np
import pandas as pd
index = pd.date_range('1/1/2019', periods=12, freq='T')
series = pd.Series(range(12), index=index)
series[5:7] = np.nanseries
> 2019-01-01 00:00:00 0.0
> 2019-01-01 00:01:00 1.0
> 2019-01-01 00:02:00 2.0
> 2019-01-01 00:03:00 3.0
> 2019-01-01 00:04:00 4.0
> 2019-01-01 00:05:00 NaN
> 2019-01-01 00:06:00 NaN
> 2019-01-01 00:07:00 7.0
> 2019-01-01 00:08:00 8.0
> 2019-01-01 00:09:00 9.0
> 2019-01-01 00:10:00 10.0
> 2019-01-01 00:11:00 11.0
> Freq: T, dtype: float64
1 using .sum()
series.resample('2T').sum()
print(series.resample('2T').apply(lambda x: np.sum(x.values)))
> 2019-01-01 00:00:00 1.0
> 2019-01-01 00:02:00 5.0
> 2019-01-01 00:04:00 4.0
> 2019-01-01 00:06:00 7.0
> 2019-01-01 00:08:00 17.0
> 2019-01-01 00:10:00 21.0
> Freq: 2T, dtype: float64
2 using .sum(skipna=False) will raise an error
series.resample('2T').sum(skipna=False)
>UnsupportedFunctionCall: numpy operations are not valid with resample. Use .resample(...).sum() instead
3 using .apply(np.sum)
series.resample('2T').apply(np.sum)
> 2019-01-01 00:00:00 1.0
> 2019-01-01 00:02:00 5.0
> 2019-01-01 00:04:00 4.0
> 2019-01-01 00:06:00 7.0
> 2019-01-01 00:08:00 17.0
> 2019-01-01 00:10:00 21.0
> Freq: 2T, dtype: float64
4 using .apply(lambda x: np.sum(x.values))
series.resample('2T').apply(lambda x: np.sum(x.values))
> 2019-01-01 00:00:00 1.0
> 2019-01-01 00:02:00 5.0
> 2019-01-01 00:04:00 NaN
> 2019-01-01 00:06:00 NaN
> 2019-01-01 00:08:00 17.0
> 2019-01-01 00:10:00 21.0
> Freq: 2T, dtype: float64