Create Numpy Array of different shapes & initialize with identical values using numpy.full() in Python

Creating Numpy Array of different shapes & initialize with identical values using numpy.full()

In this article we will see how we can create a numpy array of different shapes but initialized with identical values. So, let’s start the explore the concept to understand it well.

numpy.full() :

Numpy module provides a function numpy.full() to create a numpy array of given shape and initialized with a given value.

Syntax : numpy.full(shape, given_value, dtype=None, order='C')

Where,

• shape : Represents shape of the array.
• given_value : Represents initialization value.
• dtype : Represents the datatype of elements(Optional).

But to use Numpy we have to include following module i.e.

import numpy as np

Example-1 : Create a 1D Numpy Array of length 8 and all elements initialized with value 2

Here array length is 8 and array elements to be initialized with 2.

Let’s see the below the program.

import numpy as np
# 1D Numpy Array created of length 8 & all elements initialized with value 2
sample_arr = np.full(8,2)
print(sample_arr)

Output :
[2 2 2 2 2 2 2 2]

Example-2 : Create a 2D Numpy Array of 3 rows | 4 columns and all elements initialized with value 5

Here 2D array of row 3 and column 4 and array elements to be initialized with 5.

Let’s see the below the program.

import numpy as np
#Create a 2D Numpy Array of 3 rows & 4 columns. All intialized with value 5
sample_arr = np.full((3,4), 5)
print(sample_arr)

Output :
[[5 5 5 5]
[5 5 5 5]
[5 5 5 5]]

import numpy as np
# Create a 3D Numpy array & all elements initialized with value 1
sample_arr = np.full((3,3,4), 1)
print(sample_arr)

Output :

[[[1 1 1 1]
[1 1 1 1]
[1 1 1 1]]

[[1 1 1 1]
[1 1 1 1]
[1 1 1 1]]

[[1 1 1 1]
[1 1 1 1]
[1 1 1 1]]]

import numpy as np
# 1D Numpy array craeted & all float elements initialized with value 4
sample_arr = np.full(8, 4, dtype=float)
print(sample_arr)

Output :
[4. 4. 4. 4. 4. 4. 4. 4.]