NumPy Tutorial – Python Library

Last Updated : 29 Apr, 2025

NumPy (short for Numerical Python ) is one of the most fundamental libraries in Python for scientific computing. It provides support for large, multi-dimensional arrays and matrices along with a collection of mathematical functions to operate on arrays.

At its core it introduces the ndarray (n-dimensional array) object which allows us to store and manipulate large datasets in a memory-efficient manner. Unlike Python’s built-in lists, NumPy arrays are homogeneous and enable faster operations.

Important Facts to Know :

Vectorized Operations: NumPy operations are faster than Python lists because they use optimized C-based functions.
Broadcasting Feature: NumPy allows operations between arrays of different shapes without explicit looping known as broadcasting making it easier to handle large datasets.

What is NumPy Used for?

With NumPy, you can perform a wide range of numerical operations, including:

Creating and manipulating arrays.
Performing element-wise and matrix operations.
Generating random numbers and statistical calculations.
Conducting linear algebra operations.
Working with Fourier transformations.
Handling missing values efficiently in datasets.

Why Learn NumPy?

NumPy speeds up math operations like addition and multiplication on large groups of numbers compared to regular Python..
It’s good for handling large lists of numbers (arrays), so you don’t have to write complicated loops.
It gives ready-to-use functions for statistics, algebra and random numbers.
Libraries like Pandas, SciPy, TensorFlow and many others are built on top of NumPy.
NumPy uses less memory and stores data more efficiently, which matters when working with lots of data.

NumPy Basics

This section covers the fundamentals of NumPy, including installation, importing the library and understanding its core functionalities. You will learn about the advantages of NumPy over Python lists and how to set up your environment for efficient numerical computing.

NumPy Arrays

NumPy arrays (ndarrays) are the backbone of the library. This section covers how to create and manipulate arrays effectively for data storage and processing

Mathematical Operations in NumPy

This section covers essential mathematical functions for array computations, including basic arithmetic, aggregation and mathematical transformations.

Basic Arithmetic Operations
Aggregation Functions (sum, mean, max, min)
Universal Functions in Numpy
Mathematical Functions in Numpy

Linear Algebra with NumPy

NumPy provides built-in functions for linear algebra operations essential for scientific computing and machine learning applications.

Random Number Generation and Statistics

NumPy’s random module provides a list of functions for generating random numbers, which are essential for simulations, cryptography and machine learning applications. It supports various probability distributions, such as normal, uniform and Poisson and enable statistical analysis.

Advanced NumPy Operations

This section covers advanced NumPy techniques to enhance performance and handle complex computations. It includes vectorized operations for speed optimization, memory management strategies and integration with Pandas for efficient data analysis.