How to append values to multidimensional numpy array?

Question

How can I efficiently append values to a multidimensional numpy array?

import numpy as np

a = np.array([[1,2,3], [4,5,6]])
print(a)

I want to append np.NaN for k=2 times to each dimension/array of the outer array?

One option would be to use a loop - but I guess there must be something smarter (vectorized) in numpy

Expected result would be:

np.array([[1,2,3, np.NaN, np.NaN, ], [4,5,6, np.NaN, np.NaN, ]])

I.e. I am looking for a way to:

np.concatenate((a, np.NaN))

on all the inner dimensions.

A

np.append(a,  [[np.NaN, np.NaN]], axis=0)

fails with:

ValueError: all the input array dimensions for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 3 and the array at index 1 has size 2

With axis np.append is just a call to np.concatenate, as indicated by the traceback. You are trying to join a (2,3) and (1,2) on the first dimension. But the target is a (2,5), so you need to add a (2,2) on axis 1. — hpaulj
– hpaulj, Commented Mar 14, 2021 at 16:01

the23Effect · Accepted Answer · 2021-03-14 10:42:23Z

2

For your problem np.hstack() or np.pad() should do the job.

Using np.hstack():

k = 2
a_mat = np.array([[1,2,3], [4, 5, 6]])
nan_mat = np.zeros((a_mat.shape[0], k))
nan_mat.fill(np.nan)

a_mat = np.hstack((a_mat, nan_mat))

Using np.pad():

k = 2
padding_shape = [(0, 0), (0, k)]   # [(dim1_before_pads, dim1_after_pads)..]
a_mat = np.array([[1,2,3], [4, 5, 6]])
np.pad(a_mat, mode='constant', constant_values=np.nan)

Note: Incase you are using np.pad() for filling with np.nan, check this post out as well: about padding with np.nan

edited Mar 14, 2021 at 10:42

answered Mar 14, 2021 at 10:23

the23Effect

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