How can I ignore zeros when I take the median on columns of an array?

arrays, median, numpy, python, zero

Solution

Use masked arrays and `np.ma.median(axis=0).filled(0)` to get the medians of the columns.

In [1]: x = np.array([[10, 0, 10, 0], [1, 1, 0, 0], [9, 9, 9, 0], [0, 10, 1, 0]])
In [2]: y = np.ma.masked_where(x == 0, x)
In [3]: x
Out[3]: 
array([[10,  0, 10, 0],
       [ 1,  1,  0, 0],
       [ 9,  9,  9, 0],
       [ 0, 10,  1, 0]])
In [4]: y
Out[4]: 
masked_array(data =
 [[10 -- 10 --]
 [1 1 -- --]
 [9 9 9 --]
 [-- 10 1 --]],
             mask =
 [[False  True False True]
 [False False  True True]
 [False False False True]
 [ True False False True]],
       fill_value = 999999)
In [6]: np.median(x, axis=0)
Out[6]: array([ 5.,  5.,  5., 0.])
In [7]: np.ma.median(y, axis=0).filled(0)
Out[7]: 
array(data = [ 9.  9.  9., 0.])

Problem

I have a simple numpy array. ``` array([[10, 0, 10, 0], [ 1, 1, 0, 0] [ 9, 9, 9, 0] [ 0, 10, 1, 0]]) ``` I would like to take the median of each column, individually, of this array. However, there are a few `0` values in various places which I would like to ignore in the calculation of the medians. To further complicate, I would like to keep the columns with only `0` entries as having the median of `0`. In this manner, those columns would serve as a bit of a place holder, keeping the dimensions of the matrix the same. The numpy documentation doesn't have any argument that would work for what I want (maybe I am spoiled by the many switches we get with R!) `numpy.median(a, axis=None, out=None, overwrite_input=False)[source]` Can someone please shed some light on an effective way to do this, which is in line with the spirit of numpy? I could hack it out but in that case I feel like I've defeated the purpose of using numpy in the first place. Thanks in advance.

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