How To Create A 2D NumPy Array In Python [6 Methods]

In this NumPy blog, I will explain how to create a 2D NumPy array in Python using various functions with some illustrative examples. I will also explain how to check if the array is of 2nd dimension or not, and what is its shape and size.

To create a 2D NumPy array in Python, you can utilize various methods provided by the NumPy library. Use np.array with a list of lists for custom values, np.zeros or np.ones for arrays of zeros or ones respectively, np.full to fill with a specific value, np.arange combined with np.reshape for sequential values in a 2D format, and np.random for arrays with random numbers.

Table of Contents

Methods to create a 2D NumPy array in Python

There are six different methods to create a 2D NumPy array in Python:

Using np.array
Using np.zeros
Using np.ones
Using np.full
Using np.arange with np.reshape
Using np.random

Let’s see them one by one using some illustrative examples:

Before this, we will see the process we need to follow to confirm that whatever we have created is a 2D NumPy array in Python.

import numpy as np	Imports the NumPy library and renames it as ‘np’.
print(type(array_name))	Outputs the data type of the variable ‘array_name’ (e.g., NumPy array).
print(np.ndim(array_name))	Prints the number of dimensions (e.g., 1D, 2D) of ‘array_name’.
print(array_name.shape)	Shows the dimensions of the array ‘array_name’ (e.g., (rows, columns)).
print(array_name.size)	Displays the total number of elements in the array ‘array_name’.

List of functions needed to check if the created array is a 2D array or not.

Method 1: np 2d array in Python with the np.array() function.

The np.array() function creates a 2D array by passing a list of lists, allowing for manual specification of array contents in Python. For instance:

import numpy as np

Data = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
print("The array is:\n", Data)
print(type(Data))
print(np.ndim(Data))
print(Data.size)
print(Data.shape)

Output: This will create a 2D array in Python with two rows and four columns.

The array is:
 [[1 2 3 4]
 [5 6 7 8]]
<class 'numpy.ndarray'>
2
8
(2, 4)

We can create a 2D NumPy array in Python by manually specifying array contents using np.array.

Method 2: Create a 2d NumPy array using np.zeros() function

The np.zeros() function in NumPy Python generates a 2D array filled entirely with zeros, useful for initializing arrays with a specific shape and size. For example:

import numpy as np

Data = np.zeros((2, 3))
print("The array is:\n", Data)
print(type(Data))
print(np.ndim(Data))
print(Data.size)
print(Data.shape)

Output: This code creates a 2×3 array filled with zeros through Python NumPy.

The array is:
 [[0. 0. 0.]
 [0. 0. 0.]]
<class 'numpy.ndarray'>
2
6
(2, 3)

This way, to create a 2D NumPy array in Python, we can use the np.zeros function.

Method 3: NumPy 2D array initialize using np.ones() function

The np.ones() function creates a 2D array in Python where all elements are ones, handy for initializing arrays of a specific shape with a default value of one.

import numpy as np

Data = np.ones((2, 3))
print("The array is:\n", Data)
print(type(Data))
print(np.ndim(Data))
print(Data.size)
print(Data.shape)

Output: This creates a 2×3 array filled with ones in Python.

The array is:
 [[1. 1. 1.]
 [1. 1. 1.]]
<class 'numpy.ndarray'>
2
6
(2, 3)

We can create a 2D NumPy array in Python using the np.ones function.

Method 4: How to create a 2d NumPy array using np.full() function

The np.full() function forms a 2D array filled with a specified value, providing control over the initial content of the array in Python.

import numpy as np

Data = np.full((2, 3), 7)
print("The array is:\n", Data)
print(type(Data))
print(np.ndim(Data))
print(Data.size)
print(Data.shape)

Output: This creates a 2×3 array where each element is 7 through Python.

The array is:
 [[7 7 7]
 [7 7 7]]
<class 'numpy.ndarray'>
2
6
(2, 3)

This way, we can create a 2D NumPy array in Python using np.full function.

Method 5: NumPy arange 2D array using np.arange with np.reshape

The np.arange with np.reshape function creates a 1D array with a range of numbers and reshapes it into a 2D array in Python NumPy, offering a way to generate sequential data and format it as needed.

import numpy as np

Data = np.arange(6).reshape(2, 3)
print("The array is:\n", Data)
print(type(Data))
print(np.ndim(Data))
print(Data.size)
print(Data.shape)

Output: This code creates a 2×3 array with values from 0 to 5 in Python.

The array is:
 [[0 1 2]
 [3 4 5]]
<class 'numpy.ndarray'>
2
6
(2, 3)

how to create a 2d array in numpy in Python

We can create a 2D NumPy array in Python, using the np.arange with np.reshape function.

Method 6: 2D array using numpy.random function

The np.random() function generates a 2D array with random values, ideal for simulations or when random initial data is required.

import numpy as np

Data = np.random.random((2, 3))
print("The array is:\n", Data)
print(type(Data))
print(np.ndim(Data))
print(Data.size)
print(Data.shape)

Output: This creates a 2×3 array with random floating-point numbers through Python.

The array is:
 [[0.29021833 0.2852124  0.78524396]
 [0.88524634 0.80241381 0.22616022]]
<class 'numpy.ndarray'>
2
6
(2, 3)

This way we can create a 2D NumPy array in Python using np.random function.

Conclusion

This article explains how to create a 2D NumPy array in Python using six different methods with examples. Whether we need to manually specify array contents using np.array, initialize arrays with zeros using np.zeros, ones using np.ones, or a specific value using np.full, generate sequential data using np.arange with np.reshape, or create arrays with random elements using np.random, NumPy provides a straightforward and efficient solution.

Understanding these methods is essential for anyone working in data science, scientific computing, or any field that requires efficient and effective numerical computation in Python.