将nan值转换为零 [英] convert nan value to zero

问题描述

我有一个二维的numpy数组.此数组中的某些值为NaN.我想使用此数组执行某些操作.例如考虑数组:

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.]]

我试图每次取一行，以相反的顺序对其进行排序，以从该行中获取最多3个值并取其平均值.我尝试的代码是:

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

这不适用于包含NaN的行.我的问题是，是否有一种快速的方法可以将2D numpy数组中的所有NaN值都转换为零，这样我在排序和其他尝试做的事情上都不会遇到问题.

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.

推荐答案

这应该有效:

from numpy import *

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

在上述情况下，_are_NaNs为:

In the above case where_are_NaNs is:

In [12]: where_are_NaNs
Out[12]: 
array([[False, False, False],
       [False, False,  True]], dtype=bool)

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