python numpy weighted average with nans

First find out indices where the items are not nan, and then pass the filtered versions of a and weights to numpy.average:

>>> import numpy as np
>>> a = np.array([1,2,np.nan,4])
>>> weights = np.array([4,3,2,1])
>>> indices = np.where(np.logical_not(np.isnan(a)))[0]
>>> np.average(a[indices], weights=weights[indices])
1.75

As suggested by @mtrw in comments, it would be cleaner to use masked array here instead of index array:

>>> indices = ~np.isnan(a)
>>> np.average(a[indices], weights=weights[indices])
1.75

Alternatively, you can use a MaskedArray as such:

>>> import numpy as np

>>> a = np.array([1,2,np.nan,4])
>>> weights = np.array([4,3,2,1])
>>> ma = np.ma.MaskedArray(a, mask=np.isnan(a))
>>> np.ma.average(ma, weights=weights)
1.75