# Selecting specific rows and columns from NumPy array

101

I've been going crazy trying to figure out what stupid thing I'm doing wrong here.

I'm using NumPy, and I have specific row indices and specific column indices that I want to select from. Here's the gist of my problem:

``````import numpy as np

a = np.arange(20).reshape((5,4))
# array([[ 0,  1,  2,  3],
#        [ 4,  5,  6,  7],
#        [ 8,  9, 10, 11],
#        [12, 13, 14, 15],
#        [16, 17, 18, 19]])

# If I select certain rows, it works
print a[[0, 1, 3], :]
# array([[ 0,  1,  2,  3],
#        [ 4,  5,  6,  7],
#        [12, 13, 14, 15]])

# If I select certain rows and a single column, it works
print a[[0, 1, 3], 2]
# array([ 2,  6, 14])

# But if I select certain rows AND certain columns, it fails
print a[[0,1,3], [0,2]]
# Traceback (most recent call last):
#   File "<stdin>", line 1, in <module>
# ValueError: shape mismatch: objects cannot be broadcast to a single shape
``````

Why is this happening? Surely I should be able to select the 1st, 2nd, and 4th rows, and 1st and 3rd columns? The result I'm expecting is:

``````a[[0,1,3], [0,2]] => [[0,  2],
[4,  6],
[12, 14]]
``````

This question is tagged with `python` `arrays` `numpy` `multidimensional-array` `numpy-slicing`

89

Fancy indexing requires you to provide all indices for each dimension. You are providing 3 indices for the first one, and only 2 for the second one, hence the error. You want to do something like this:

``````>>> a[[[0, 0], [1, 1], [3, 3]], [[0,2], [0,2], [0, 2]]]
array([[ 0,  2],
[ 4,  6],
[12, 14]])
``````

``````>>> a[[[0], [1], [3]], [0, 2]]
array([[ 0,  2],
[ 4,  6],
[12, 14]])
``````

This is much simpler to do if you index with arrays, not lists:

``````>>> row_idx = np.array([0, 1, 3])
>>> col_idx = np.array([0, 2])
>>> a[row_idx[:, None], col_idx]
array([[ 0,  2],
[ 4,  6],
[12, 14]])
``````

95

As Toan suggests, a simple hack would be to just select the rows first, and then select the columns over that.

``````>>> a[[0,1,3], :]            # Returns the rows you want
array([[ 0,  1,  2,  3],
[ 4,  5,  6,  7],
[12, 13, 14, 15]])
>>> a[[0,1,3], :][:, [0,2]]  # Selects the columns you want as well
array([[ 0,  2],
[ 4,  6],
[12, 14]])
``````

###  The built-in method: `np.ix_`

I recently discovered that numpy gives you an in-built one-liner to doing exactly what @Jaime suggested, but without having to use broadcasting syntax (which suffers from lack of readability). From the docs:

Using ix_ one can quickly construct index arrays that will index the cross product. `a[np.ix_([1,3],[2,5])]` returns the array `[[a[1,2] a[1,5]], [a[3,2] a[3,5]]]`.

So you use it like this:

``````>>> a = np.arange(20).reshape((5,4))
>>> a[np.ix_([0,1,3], [0,2])]
array([[ 0,  2],
[ 4,  6],
[12, 14]])
``````

And the way it works is that it takes care of aligning arrays the way Jaime suggested, so that broadcasting happens properly:

``````>>> np.ix_([0,1,3], [0,2])
(array([[0],
[1],
[3]]), array([[0, 2]]))
``````

Also, as MikeC says in a comment, `np.ix_` has the advantage of returning a view, which my first (pre-edit) answer did not. This means you can now assign to the indexed array:

``````>>> a[np.ix_([0,1,3], [0,2])] = -1
>>> a
array([[-1,  1, -1,  3],
[-1,  5, -1,  7],
[ 8,  9, 10, 11],
[-1, 13, -1, 15],
[16, 17, 18, 19]])
``````