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.
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 : where_are_NaNs Out: array([[False, False, False], [False, False, True]], dtype=bool)
A is your 2D array:
import numpy as np A[np.isnan(A)] = 0
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.