Good ways to "expand" a numpy ndarray?

Question

Are there good ways to "expand" a numpy ndarray? Say I have an ndarray like this:

[[1 2]  [3 4]] 

And I want each row to contains more elements by filling zeros:

[[1 2 0 0 0]  [3 4 0 0 0]] 

I know there must be some brute-force ways to do so (say construct a bigger array with zeros then copy elements from old smaller arrays), just wondering are there pythonic ways to do so. Tried numpy.reshape but didn't work:

import numpy as np a = np.array([[1, 2], [3, 4]]) np.reshape(a, (2, 5)) 

Numpy complains that: ValueError: total size of new array must be unchanged

Answer 1

You can use numpy.pad, as follows:

>>> import numpy as np >>> a=[[1,2],[3,4]] >>> np.pad(a, ((0,0),(0,3)), mode='constant', constant_values=0) array([[1, 2, 0, 0, 0],        [3, 4, 0, 0, 0]]) 

Here np.pad says, "Take the array a and add 0 rows above it, 0 rows below it, 0 columns to the left of it, and 3 columns to the right of it. Fill these columns with a constant specified by constant_values".

Answer 2

There are the index tricks r_ and c_.

>>> import numpy as np >>> a = np.array([[1, 2], [3, 4]]) >>> z = np.zeros((2, 3), dtype=a.dtype) >>> np.c_[a, z] array([[1, 2, 0, 0, 0],        [3, 4, 0, 0, 0]]) 

If this is performance critical code, you might prefer to use the equivalent np.concatenate rather than the index tricks.

>>> np.concatenate((a,z), axis=1) array([[1, 2, 0, 0, 0],        [3, 4, 0, 0, 0]]) 

There are also np.resize and np.ndarray.resize, but they have some limitations (due to the way numpy lays out data in memory) so read the docstring on those ones. You will probably find that simply concatenating is better.

By the way, when I've needed to do this I usually just do it the basic way you've already mentioned (create an array of zeros and assign the smaller array inside it), I don't see anything wrong with that!