An efficient way to calculate the mean of each column or row of non-zero elements
arrays, numpy, python
Solution
Since the values to discard are 0, you can compute the mean manually by doing the sum along an axis and then dividing by the number of non zeros elements (along the same axis):
a = np.array([[8.,9,7,0], [0,0,5,6]])
a.sum(1)/(a != 0).sum(1)
results in:
array([ 8. , 5.5])
as you can see, the zeros are not considered in the mean.
Problem
I have a numpy array for ratings given by users on movies. The rating is between 1 and 5, while 0 means that a user does not rate on a movie. I want to calculate the average rating of each movie, and the average rating of each user. In other words, I will calculate the mean of each column or row of non-zero elements. Is there an efficient numpy array function to handle this case? I know manually iterating ratings by columns or rows can solve the problem. Thanks in advance!