Answer a question

First things first: this is not a duplicate of NumPy: calculate averages with NaNs removed, i'll explain why:

Suppose I have an array

a = array([1,2,3,4])

and I want to average over it with the weights

weights = [4,3,2,1]
output = average(a, weights=weights)
print output
     2.0

ok. So this is pretty straightforward. But now I have something like this:

a = array([1,2,nan,4])

calculating the average with the usual method yields of coursenan. Can I avoid this? In principle I want to ignore the nans, so I'd like to have something like this:

a = array([1,2,4])
weights = [4,3,1]
output = average(a, weights=weights)
print output
     1.75

Answers

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