Can we change array size in python?

In this article, we will learn how to change the size of a numpy array in Python.

First, let’s understand what a numpy array is.
A NumPy array is a part of the NumPy library which is an array processing package.

import numpy as np
eg_arr = np.array[[[1,2],[3,4]]]
print[eg_arr]

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Using np.array, we store an array of shape [2,2] and size 4 in the variable eg_arr.

Now, let’s see how can we change the size of the array.

Changing size of numpy Array in Python

Size of a numpy array can be changed by using resize[] function of the NumPy library.

numpy.ndarray.resize[] takes these parameters-

1. New size of the array
2. refcheck- It is a boolean that checks the reference count. It checks if the array buffer is referenced to any other object. By default, it is set to True. You can also set it to False if you haven’t referenced the array to any other object.

During resizing, if the size of the new array is greater than the given size, then the array is filled with 0’s. Also, it resizes the array in-place.

Now let’s understand it with some examples.

Changing size of array with numpy.resize[]

Example 1 –

import numpy as np
cd = np.array[[2,4,6,8]]
cd.resize[[3,4],refcheck=False]
print[cd]

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The resize function changes the shape of the array from [4,] to [3,4]. Since the size of the new array is greater, the array is filled with 0’s.
So this gives us the following output-

Example 2 –

import numpy as np 
cd2 = np.array[[[1,2],[3,4]]] 
cd2.resize[[5,6],refcheck=False]
print[cd2]

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The resize function changes the array from [2,2] to [5,6] and fills the remaining portion of the array with 0’s.
Here’s the output-

import numpy as np
cd3=np.array[[[1,2],[3,4]]]
cd3.resize[[2,1],refcheck=False]
print[cd3]

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Here, the size of the new array is smaller, so this gives the following output-

I hope you all liked the article!

Also read-
Understanding NumPy array dimensions in Python

In Python, if the input is a numpy array, you can use np.lib.pad to pad zeros around it -

import numpy as np

A = np.array[[[1, 2 ],[2, 3]]]   # Input
A_new = np.lib.pad[A, [[0,1],[0,2]], 'constant', constant_values=[0]] # Output

Sample run -

In [7]: A  # Input: A numpy array
Out[7]: 
array[[[1, 2],
       [2, 3]]]

In [8]: np.lib.pad[A, [[0,1],[0,2]], 'constant', constant_values=[0]]
Out[8]: 
array[[[1, 2, 0, 0],
       [2, 3, 0, 0],
       [0, 0, 0, 0]]]  # Zero padded numpy array

If you don't want to do the math of how many zeros to pad, you can let the code do it for you given the output array size -

In [29]: A
Out[29]: 
array[[[1, 2],
       [2, 3]]]

In [30]: new_shape = [3,4]

In [31]: shape_diff = np.array[new_shape] - np.array[A.shape]

In [32]: np.lib.pad[A, [[0,shape_diff[0]],[0,shape_diff[1]]], 
                              'constant', constant_values=[0]]
Out[32]: 
array[[[1, 2, 0, 0],
       [2, 3, 0, 0],
       [0, 0, 0, 0]]]

Or, you can start off with a zero initialized output array and then put back those input elements from A -

In [38]: A
Out[38]: 
array[[[1, 2],
       [2, 3]]]

In [39]: A_new = np.zeros[new_shape,dtype = A.dtype]

In [40]: A_new[0:A.shape[0],0:A.shape[1]] = A

In [41]: A_new
Out[41]: 
array[[[1, 2, 0, 0],
       [2, 3, 0, 0],
       [0, 0, 0, 0]]]

In MATLAB, you can use padarray -

A_new  = padarray[A,[1 2],'post']

Sample run -

>> A
A =
     1     2
     2     3
>> A_new = padarray[A,[1 2],'post']
A_new =
     1     2     0     0
     2     3     0     0
     0     0     0     0

[[1 2 3]
 [4 5 6]]

[[1 2 3 4]
 [5 6 0 0]
 [0 0 0 0]]

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