replace zeroes in numpy array with the median value
This solution takes advantage of numpy.median:
import numpy as npfoo_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 elementsm = np.median(foo[foo > 0])# Assign the median to the zero elements foo[foo == 0] = mJust a note of caution, the median for your array (with no zeroes) is 23.5 but as written this sticks in 23.
foo2 = foo[:]foo2[foo2 == 0] = nz_values[middle]Instead of foo2, you could just update foo if you want. Numpy's smart array syntax can combine a few lines of the code you made. For example, instead of,
nonzero_values = foo[0::] > 0nz_values = foo[nonzero_values]You can just do
nz_values = foo[foo > 0]You can find out more about "fancy indexing" in the documentation.