How to delete columns in numpy.array

Question

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?

Answer 1

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.

Answer 2

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]])