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.
ベストアンサー1
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.