convert nan value to zero


Question

I have a 2D numpy array. Some of the values in this array are NaN. I want to perform certain operations using this array. For example consider the array:

[[   0.   43.   67.    0.   38.]
 [ 100.   86.   96.  100.   94.]
 [  76.   79.   83.   89.   56.]
 [  88.   NaN   67.   89.   81.]
 [  94.   79.   67.   89.   69.]
 [  88.   79.   58.   72.   63.]
 [  76.   79.   71.   67.   56.]
 [  71.   71.   NaN   56.  100.]]

I am trying to take each row, one at a time, sort it in reversed order to get max 3 values from the row and take their average. The code I tried is:

# nparr is a 2D numpy array
for entry in nparr:
    sortedentry = sorted(entry, reverse=True)
    highest_3_values = sortedentry[:3]
    avg_highest_3 = float(sum(highest_3_values)) / 3

This does not work for rows containing NaN. My question is, is there a quick way to convert all NaN values to zero in the 2D numpy array so that I have no problems with sorting and other things I am trying to do.

1
61
6/17/2016 3:08:18 PM

Accepted Answer

This should work:

from numpy import *

a = array([[1, 2, 3], [0, 3, NaN]])
where_are_NaNs = isnan(a)
a[where_are_NaNs] = 0

In the above case where_are_NaNs is:

In [12]: where_are_NaNs
Out[12]: 
array([[False, False, False],
       [False, False,  True]], dtype=bool)
94
8/15/2014 12:31:24 PM

Where A is your 2D array:

import numpy as np
A[np.isnan(A)] = 0

The function isnan produces a bool array indicating where the NaN values are. A boolean array can by used to index an array of the same shape. Think of it like a mask.


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