Replace zeros in a numpy array with a median value

Question

I have a numpy array like this:

foo_array = [38,26,14,55,31,0,15,8,0,0,0,18,40,27,3,19,0,49,29,21,5,38,29,17,16] 

I want to replace all zeros with the median value of the entire array (where zero values ​​should not be included in the calculation of the median)

So far this has been going on:

 foo_array = [38,26,14,55,31,0,15,8,0,0,0,18,40,27,3,19,0,49,29,21,5,38,29,17,16] foo = np.array(foo_array) foo = np.sort(foo) print "foo sorted:",foo #foo sorted: [ 0 0 0 0 0 3 5 8 14 15 16 17 18 19 21 26 27 29 29 31 38 38 40 49 55] nonzero_values = foo[0::] > 0 nz_values = foo[nonzero_values] print "nonzero_values?:",nz_values #nonzero_values?: [ 3 5 8 14 15 16 17 18 19 21 26 27 29 29 31 38 38 40 49 55] size = np.size(nz_values) middle = size / 2 print "median is:",nz_values[middle] #median is: 26 

Is there a smart way to achieve this using numpy syntax?

thanks

Answer 1

This solution uses numpy.median :

 import numpy as np foo_array = [38,26,14,55,31,0,15,8,0,0,0,18,40,27,3,19,0,49,29,21,5,38,29,17,16] foo = np.array(foo_array) # Compute the median of the non-zero elements m = np.median(foo[foo > 0]) # Assign the median to the zero elements foo[foo == 0] = m 

Just be careful, the median for your array (without zeros) is 23.5, but as written, these are sticks at 23.

Answer 2

 foo2 = foo[:] foo2[foo2 == 0] = nz_values[middle] 

Instead of foo2 you can just update foo if you want. The Numpy Smart Array Simulator can combine multiple lines of code that you did. For example, instead of

 nonzero_values = foo[0::] > 0 nz_values = foo[nonzero_values] 

You can just do

 nz_values = foo[foo > 0] 

You can learn more about the "indexing fantasy" in the documentation .