numpy advanced indexing on multidimensional-array

Question

Say x is a 3x3 numpy array that contains the following:

import numpy as np
x = np.array([[ 1.,  2.,  3.],
              [ 4.,  5.,  6.],
              [ 7.,  8.,  9.]])

is there some indexing that can give me the following subarray:

array([[ 1.,  2.],
       [ 5.,  6.]])

Yes things like x[(0,2), 0:2] but to no avail. I could not get subsets like the one i mentioned. — J.Doe
– J.Doe, Commented Feb 4, 2017 at 4:38

MSeifert · Accepted Answer · 2017-02-04 04:18:08Z

4

You can use integer array indexing with a tuple of arrays:

>>> rows = np.array([[0, 0],
...                  [1, 1]], dtype=np.intp)
>>> columns = np.array([[0, 1],
...                     [1, 2]], dtype=np.intp)
>>> x[rows, columns]
array([[ 1.,  2.],
       [ 5.,  6.]])

answered Feb 4, 2017 at 4:18

MSeifert

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2 Comments

J.Doe Over a year ago

Thanks, actually based on your suggestion the following also seems to work (I'm looking for a one-liner): x[[[0, 0],[1, 1]], [[0, 1],[1, 2]]]

MSeifert Over a year ago

okay, I wasn't aware you were looking for short code so I chose to show the explicit one. It is a bit clearer what the rows represents and what the columns are. :)

Po Stevanus Andrianta · Accepted Answer · 2017-02-04 04:14:33Z

-3

you can use

x[:2,:2]

to solve your problem

answered Feb 4, 2017 at 4:14

Po Stevanus Andrianta

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