resample using for subsample will regards NaN as zeros with sum


#### 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](https://github.com/pandas-dev/pandas/issues/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

```python
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.nan
```

```
series
> 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
```


















