Selecting specific rows and columns from NumPy array

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 wantarray([[ 0,  1,  2,  3],       [ 4,  5,  6,  7],       [12, 13, 14, 15]])>>> a[[0,1,3], :][:, [0,2]]  # Selects the columns you want as wellarray([[ 0,  2],       [ 4,  6],       [12, 14]])

[Edit] 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]])

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]])

That is of course a pain to write, so you can let broadcasting help you:

>>> 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]])

USE:

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

OR:

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