How to fill 2d numpy array with some 2d numpy array

Question

I have a 2d numpy array which varies in size from one value to other.

How can I generalize the size by filling in zeros if the input value has less dimension than required?

Exmaple:

Input list with numpy array dimension:
[(40, 173),
(40, 14),
(40, 56),
(40, 173)]

And I want to have all the arrays to be (40, 173), where if it is has lesser size, than filling rest with zeros.

Start with a `np.zeros((40,173))` array, and copy the input array to the desired slice. — hpaulj, Jul 16 '20 at 01:50
How to how to copy it to desired size? I want (40, 173), but some input data is of smaller size. — Deep, Jul 16 '20 at 02:00

score 0 · Accepted Answer · answered Jul 16 '20 at 01:37

0

This is called zero-padding. Assuming you want to pad an array A:

np.pad(A, ((0, rows), (0, cols)), "constant")

You can set:

rows = 40 - A.shape[0]
cols = 173 - A.shape[1]

More info: Zero pad numpy array

answered Jul 16 '20 at 01:37

wjakobw

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Still returns same size array. Don't know if I am missing something – Deep Jul 16 '20 at 01:58
It's working now. I figured out that output of np.pad had to be saved – Deep Jul 16 '20 at 02:08

score 0 · Answer 2 · answered Jul 16 '20 at 03:06

0

Assigning to a zeros array:

In [160]: res = np.zeros((40,173),int)                                                               
In [161]: res[0:20, 0:30] = np.ones((20,30),int)

answered Jul 16 '20 at 03:06

hpaulj

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How to fill 2d numpy array with some 2d numpy array

2 Answers2