Introduction
Have you ever performed any of these two libraries in your Python applications? If not, and you want to know about these libraries, then don't worry, ninjas. Coding Ninjas got your back. We will clear all your doubts.
In this article, we will discuss the difference between NumPy and Pandas. We will discuss what they are. Libraries in Python are the most important asset. Because using the libraries, we can write simpler code and can get fast execution of it. Before moving on to the main topic, let us understand what NumPy and Pandas are and their features.
What is NumPy in Python?
In Python, we have lists that work as arrays for us. But when we perform operations on the lists with largesize data, it becomes slow. There NumPy comes into the picture. NumPy is an opensource library in Python. NumPy stands for Numerical Python. It is used for various purposes like data analysis, numerical computation, and scientific computation.
NumPy is very much faster than a list. NumPy provides an array object which is known as ndarray. This array object provides us with efficient computation and manipulation of multidimensional arrays. On these arrays, we can apply various functions that are provided by NumPy. These functions are:

Linear algebra,

Fourier transform,

Statistics functions, etc.
Let us look at some features of NumPy.
Features
There are different features of using NumPy:

NumPy supports vectorized operations: This is the most important feature of NumPy. It supports vectorized operations. Vectorized operations means that we can perform operations on the entire array at once. This thing makes them more efficient and faster than the traditional loops in Python.

NumPy can broadcast the arrays: NumPy provides the ability to broadcast the arrays. We can broadcast any of the arrays with any shape and size. It also allows us to perform operations on them. If we do the same thing with others, it requires more complex loops and indexing.

NumPy provides a random number generation ability: NumPy can help us to generate random numbers. Because NumPy comes up with several functions that can help us to generate random numbers. It can generate normally distributed random numbers, uniformly distributed random numbers, and so on.

NumPy can integrate with other libraries: NumPy allows it to integrate with other libraries. It is designed to integrate with other scientific libraries in Python. These libraries are SciPy, matplotlib, etc.
Let us understand NumPy with the help of an example.
Example of NumPy
Here is an example to understand the NumPy library in Python:
# Importing NumPy library
import numpy as np
# Creating a twodimensional array
# Shape of the array is (2, 3) and filling it with random values
myArr = np.random.rand(2, 3)
print("Array is: ",myArr)
# Finding the shape of the myArr
print("Shape of the Array is:",myArr.shape)
# Getting the number of dimensions of the myArr
print("Number of dimensions:",myArr.ndim)
# Finding the sum of all elements of the myArr
print("Sum of all elements is:",myArr.sum())
# Finding the total number of elements available in the myArr
print("Size of the Array is:",myArr.size)
# Finding the minimum and maximum values of the myArr
print("Minimum value is:",myArr.min(),"and Maximum value is:",myArr.max())
Output
Array is: [[0.74335629 0.63534874 0.70232367]
[0.62727798 0.40585586 0.4298033 ]]
Shape of the Array is: (2, 3)
Number of dimensions: 2
Sum of all elements is: 3.543965831067365
Size of the Array is: 6
Minimum value is: 0.40585585920675815 and Maximum value is: 0.7433562913101969
Also see, Swapcase in Python and Convert String to List Python.