Different Ways to Create Numpy Arrays in Python
Last Updated :
23 Jul, 2025
Creating NumPy arrays is a fundamental aspect of working with numerical data in Python. NumPy provides various methods to create arrays efficiently, catering to different needs and scenarios. In this article, we will see how we can create NumPy arrays using different ways and methods.
Ways to Create Numpy Arrays
Below are some of the ways by which we can create NumPy Arrays in Python:
Create Numpy Arrays Using Lists or Tuples
The simplest way to create a NumPy array is by passing a Python list or tuple to the numpy.array() function. This method creates a one-dimensional array.
Python3
import numpy as np
my_list = [1, 2, 3, 4, 5]
numpy_array = np.array(my_list)
print("Simple NumPy Array:",numpy_array)
Initialize a Python NumPy Array Using Special Functions
NumPy provides several built-in functions to generate arrays with specific properties.
- np.zeros(): Creates an array filled with zeros.
- np.ones(): Creates an array filled with ones.
- np.full(): Creates an array filled with a specified value.
- np.arange(): Creates an array with values that are evenly spaced within a given range.
- np.linspace(): Creates an array with values that are evenly spaced over a specified interval.
Python3
import numpy as np
zeros_array = np.zeros((2, 3))
ones_array = np.ones((3, 3))
constant_array = np.full((2, 2), 7)
range_array = np.arange(0, 10, 2) # start, stop, step
linspace_array = np.linspace(0, 1, 5) # start, stop, num
print("Zero Array:","\n",zeros_array)
print("Ones Array:","\n",ones_array)
print("Constant Array:","\n",constant_array)
print("Range Array:","\n",range_array)
print("Linspace Array:","\n",linspace_array)
OutputZero Array
[[0. 0. 0.]
[0. 0. 0.]]
Zero Array
[[1. 1. 1.]
[1. 1. 1.]
[1. 1. 1.]]
Constant Array
[[7 7]
[7 7]]
Range Array
[0 2 4 6 8]
Linspace Array
[0. 0.25 0.5 0.75 1. ]
Create Python Numpy Arrays Using Random Number Generation
NumPy provides functions to create arrays filled with random numbers.
- np.random.rand(): Creates an array of specified shape and fills it with random values sampled from a uniform distribution over [0, 1).
- np.random.randn(): Creates an array of specified shape and fills it with random values sampled from a standard normal distribution.
- np.random.randint(): Creates an array of specified shape and fills it with random integers within a given range.
Python3
import numpy as np
random_array = np.random.rand(2, 3)
normal_array = np.random.randn(2, 2)
randint_array = np.random.randint(1, 10, size=(2, 3))
print(random_array)
print(normal_array)
print(randint_array)
Output[[0.87948864 0.55022063 0.29237533]
[0.99475413 0.76666244 0.55240304]]
[[ 1.77971899 0.67837749]
[ 0.33101208 -1.04029635]]
[[6 6 3]
[8 5 8]]
Create Python Numpy Arrays Using Matrix Creation Routines
NumPy provides functions to create specific types of matrices.
- np.eye(): Creates an identity matrix of specified size.
- np.diag(): Constructs a diagonal array.
- np.zeros_like(): Creates an array of zeros with the same shape and type as a given array.
- np.ones_like(): Creates an array of ones with the same shape and type as a given array.
Python3
import numpy as np
identity_matrix = np.eye(3)
diagonal_array = np.diag([1, 2, 3])
zeros_like_array = np.zeros_like(diagonal_array)
ones_like_array = np.ones_like(diagonal_array)
print(identity_matrix)
print(diagonal_array)
print(zeros_like_array)
print(ones_like_array)
Output[[1. 0. 0.]
[0. 1. 0.]
[0. 0. 1.]]
[[1 0 0]
[0 2 0]
[0 0 3]]
[[0 0 0]
[0 0 0]
[0 0 0]]
[[1 1 1]
[1 1 1]
[1 1 1]]
Explore
Introduction
Creating NumPy Array
NumPy Array Manipulation