pandas rolling apply to allow nan

Question

I have a very simple Pandas Series:

xx = pd.Series([1, 2, np.nan, np.nan, 3, 4, 5])

If I run this I get what I want:

>>> xx.rolling(3,1).mean()
0    1.0
1    1.5
2    1.5
3    2.0
4    3.0
5    3.5
6    4.0

But if I have to use .apply() I cannot get it to work by ignoring NaNs in the mean() operation:

>>> xx.rolling(3,1).apply(np.mean)
0    1.0
1    1.5
2    NaN
3    NaN
4    NaN
5    NaN
6    4.0

>>> xx.rolling(3,1).apply(lambda x : np.mean(x))
0    1.0
1    1.5
2    NaN
3    NaN
4    NaN
5    NaN
6    4.0

What should I do in order to both use .apply() and have the result in the first output? My actual problem is more complicated that I have to use .apply() to realize but it boils down to this issue.

Answer 1

You can use np.nanmean()

xx.rolling(3,1).apply(lambda x : np.nanmean(x))
Out[59]: 
0    1.0
1    1.5
2    1.5
3    2.0
4    3.0
5    3.5
6    4.0
dtype: float64

If you have to process the nans explicitly, you can do:

xx.rolling(3,1).apply(lambda x : np.mean(x[~np.isnan(x)]))
Out[94]: 
0    1.0
1    1.5
2    1.5
3    2.0
4    3.0
5    3.5
6    4.0
dtype: float64