How to set values in a 2d numpy array?

Question

I am working with a 2-d numpy array which looks like this:

array([[[0, 0, 0, 0],
    [0, 0, 0, 0],
    [0, 0, 0, 0],
    ..., 
    [0, 0, 0, 0],
    [0, 0, 0, 0],
    [0, 0, 0, 0]],

   [[0, 0, 0, 0],
    [0, 0, 0, 0],
    [0, 0, 0, 0],
    ..., 
    [0, 0, 0, 0],
    [0, 0, 0, 0],
    [0, 0, 0, 0]],

   [[0, 0, 0, 0],
    [0, 0, 0, 0],
    [0, 0, 0, 0],
    ..., 
    [0, 0, 0, 0],
    [0, 0, 0, 0],
    [0, 0, 0, 0]]])

So I have numpy inside numpy which has a list of four values (pixels in RGBA to be specific). I want to set all values to 0. What is the most pythonic way to do it? Thanks in advance!

That would be a 3D array. For example you obtain that from np.zeros((3,100,4), dtype=np.int8) — fferri
– fferri, Commented Apr 24, 2018 at 22:03
Hmm, you are correct! So here I am copying my numpy array from another numpy array and setting all values to zero. What is the most efficient way to do that? Can you suggest? — Infra Red Spectrum
– Infra Red Spectrum, Commented Apr 24, 2018 at 22:08

fferri · Accepted Answer · 2018-04-24 22:14:11Z

1

m[:] = 0

would set all values of your array m to zero.

If you need an array of zeros with the same shape and datatype of m, use:

np.zeros_like(m)

answered Apr 24, 2018 at 22:14

fferri

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tykom · Accepted Answer · 2018-04-24 22:14:05Z

0

import numpy as np

a = np.random.randn(10, 10)
b = np.zeros_like(a)

b will be an ndarray of exactly the same shape as the original, filled with 0.

answered Apr 24, 2018 at 22:14

tykom

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