Numpy mean of nonzero values

Question

I have a matrix of size N*M and I want to find the mean value for each row. The values are from 1 to 5 and entries that do not have any value are set to 0. However, when I w

Answer 1

Get the count of non-zeros in each row and use that for averaging the summation along each row. Thus, the implementation would look something like this -

np.true_divide(matrix.sum(1),(matrix!=0).sum(1))

If you are on an older version of NumPy, you can use float conversion of the count to replace np.true_divide, like so -

matrix.sum(1)/(matrix!=0).sum(1).astype(float)

Sample run -

In [160]: matrix
Out[160]: 
array([[0, 0, 1, 0, 2],
       [1, 0, 0, 2, 0],
       [0, 1, 1, 0, 0],
       [0, 2, 2, 2, 2]])

In [161]: np.true_divide(matrix.sum(1),(matrix!=0).sum(1))
Out[161]: array([ 1.5,  1.5,  1. ,  2. ])

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Another way to solve the problem would be to replace zeros with NaNs and then use np.nanmean, which would ignore those NaNs and in effect those original zeros, like so -

np.nanmean(np.where(matrix!=0,matrix,np.nan),1)

From performance point of view, I would recommend the first approach.