How to delete columns in numpy.array
I would like to delete selected columns in a numpy.array . This is what I do:
n [397]: a = array([[ NaN, 2., 3., NaN],
.....: [ 1., 2., 3., 9]])
In [398]: print a
[[ NaN 2. 3. NaN]
[ 1. 2. 3. 9.]]
In [399]: z = any(isnan(a), axis=0)
In [400]: print z
[ True False False True]
In [401]: delete(a, z, axis = 1)
Out[401]:
array([[ 3., NaN],
[ 3., 9.]])
In this example my goal is to delete all the columns that contain NaN's. I expect the last command to result in:
array([[2., 3.],
[2., 3.]])
How can I do that?
Given its name, I think the standard way should be delete
:
import numpy as np
A = np.delete(A, 1, 0) # delete second row of A
B = np.delete(B, 2, 0) # delete third row of B
C = np.delete(C, 1, 1) # delete second column of C
According to numpy's documentation page, the parameters for numpy.delete
are as follow:
numpy.delete(arr, obj, axis=None)
-
arr
refers to the input array, -
obj
refers to which sub-arrays (e.g. column/row no. or slice of the array) and -
axis
refers to either column wise (axis = 1
) or row-wise (axis = 0
) delete operation.
Example from the numpy documentation:
>>> a = numpy.array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[12, 13, 14, 15]])
>>> numpy.delete(a, numpy.s_[1:3], axis=0) # remove rows 1 and 2
array([[ 0, 1, 2, 3],
[12, 13, 14, 15]])
>>> numpy.delete(a, numpy.s_[1:3], axis=1) # remove columns 1 and 2
array([[ 0, 3],
[ 4, 7],
[ 8, 11],
[12, 15]])
Another way is to use masked arrays:
import numpy as np
a = np.array([[ np.nan, 2., 3., np.nan], [ 1., 2., 3., 9]])
print(a)
# [[ NaN 2. 3. NaN]
# [ 1. 2. 3. 9.]]
The np.ma.masked_invalid method returns a masked array with nans and infs masked out:
print(np.ma.masked_invalid(a))
[[-- 2.0 3.0 --]
[1.0 2.0 3.0 9.0]]
The np.ma.compress_cols method returns a 2-D array with any column containing a masked value suppressed:
a=np.ma.compress_cols(np.ma.masked_invalid(a))
print(a)
# [[ 2. 3.]
# [ 2. 3.]]
See manipulating-a-maskedarray