Question

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NumPy: calculate averages with NaNs removed

I have several identically-shaped numpy arrays. I want to take their pointwise average with a small twist: a np.nan value should be ignored in the averaging. In other words, average(np.array([1,2,3]), np.array([5,np.nan,7]), np.array([np.nan, 4, 2]) should equal np.array([3,3,4]).

Of course, I can do that by iterating through the elements within each numpy array, but I was hoping to avoid it. Is there a better way to implement this function?

(Python 3, but I doubt it matters.)

Answer 1

You can use scipy.stat‘s nanmean:

import numpy as np
from scipy.stats import nanmean
s = np.array([[1.0, 2.0, 3.0], [5.0, np.nan, 7.0], [np.nan, 4.0, 2.0]])

In [4]: nanmean(s)
Out[4]: array([ 3.,  3.,  4.])

@Dougal points out in the comments that the bottleneck package, which has significantly faster implementations of several numpy/scipy functions, includes an nanmean.