convert nan value to zero
How about nan_to_num()?
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.
You could use np.where
to find where you have NaN
:
import numpy as np
a = np.array([[ 0, 43, 67, 0, 38],
[ 100, 86, 96, 100, 94],
[ 76, 79, 83, 89, 56],
[ 88, np.nan, 67, 89, 81],
[ 94, 79, 67, 89, 69],
[ 88, 79, 58, 72, 63],
[ 76, 79, 71, 67, 56],
[ 71, 71, np.nan, 56, 100]])
b = np.where(np.isnan(a), 0, a)
In [20]: b
Out[20]:
array([[ 0., 43., 67., 0., 38.],
[ 100., 86., 96., 100., 94.],
[ 76., 79., 83., 89., 56.],
[ 88., 0., 67., 89., 81.],
[ 94., 79., 67., 89., 69.],
[ 88., 79., 58., 72., 63.],
[ 76., 79., 71., 67., 56.],
[ 71., 71., 0., 56., 100.]])
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)