Question

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avarage of a number of arrays with numpy without considering zero values

I am working on numpy and I have a number of arrays with the same size and shape. They are 500*500. It has some Null values. I want to have an array that is result of one by one element average of my original arrays. For example:

A=[ 1 Null 8 Null; Null 4 6 1]
B=[ 8 5 8 Null; 5 9 5 3]

the resulting array should be like:

C=[ 4.5 5 8 Null; 5 6.5 5.5 2]

How can I do that?

Answer 1

Update: As of NumPy 1.8, you could use np.nanmean instead of scipy.stats.nanmean.

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If you have scipy, you could use scipy.stats.nanmean:

In [2]: import numpy as np

In [45]: import scipy.stats as stats

In [3]: nan = np.nan

In [43]: A = np.array([1, nan, 8, nan, nan, 4, 6, 1])   
In [44]: B = np.array([8, 5, 8, nan, 5, 9, 5, 3])  
In [46]: C = np.array([A, B])    
In [47]: C
Out[47]: 
array([[  1.,  nan,   8.,  nan,  nan,   4.,   6.,   1.],
       [  8.,   5.,   8.,  nan,   5.,   9.,   5.,   3.]])

In [48]: stats.nanmean(C)
Warning: invalid value encountered in divide
Out[48]: array([ 4.5,  5. ,  8. ,  nan,  5. ,  6.5,  5.5,  2. ])

You can find other numpy-only (masked-array) solutions, here. Namely,

In [60]: C = np.array([A, B])    
In [61]: C = np.ma.masked_array(C, np.isnan(C))    
In [62]: C
Out[62]: 
masked_array(data =
 [[1.0 -- 8.0 -- -- 4.0 6.0 1.0]
 [8.0 5.0 8.0 -- 5.0 9.0 5.0 3.0]],
             mask =
 [[False  True False  True  True False False False]
 [False False False  True False False False False]],
       fill_value = 1e+20)

In [63]: np.mean(C, axis = 0)
Out[63]: 
masked_array(data = [4.5 5.0 8.0 -- 5.0 6.5 5.5 2.0],
             mask = [False False False  True False False False False],
       fill_value = 1e+20)

In [66]: np.ma.filled(np.mean(C, axis = 0), nan)
Out[67]: array([ 4.5,  5. ,  8. ,  nan,  5. ,  6.5,  5.5,  2. ])