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

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.

Licensed under: CC-BY-SA with attribution

Not affiliated with: Stack Overflow