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NumPy Tutorial - Python Library

Last Updated : 12 Aug, 2025

NumPy is a core Python library for numerical computing, built for handling large arrays
and matrices efficiently.

ndarray object – Stores homogeneous data in n-dimensional arrays for fast

processing.
Vectorized operations – Perform element-wise calculations without explicit loops.
Broadcasting – Apply operations across arrays of different shapes.
Linear algebra functions – Matrix multiplication, inversion, eigenvalues, etc.
Statistical tools – Mean, median, standard deviation, and more.
Fourier transforms – Fast computation for signal and image processing.
Integration with other libraries – Works seamlessly with Pandas, SciPy, and scikit-
learn.

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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
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NumPy over Python lists and how to set up your environment for efficient numerical
computing.

Introduction to NumPy
Installing NumPy
Understanding NumPy Arrays

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

Creating NumPy Arrays

Numpy Array Indexing and Slicing
Reshaping and Resizing Arrays
Stacking and Splitting Arrays
Broadcasting in NumPy

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

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functions for linear for youroperations
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Matrix Multiplication measurement,
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Matrix & vector products in Numpy
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Determinants and Inverse of a Matrix
Inner and Outer Functions
Dot and Vdot Functions
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Random Number Generation

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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.

Generating Random Numbers

Normal Distribution
Binomial Distribution
Poisson Distribution
Uniform Distribution
Exponential Distribution
Chi-square Distribution
Statistical Functions (mean, median, variance, standard deviation)

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.

Vectorized Operations for Performance Optimization

Broadcasting in Numpy
Sparse Matrices in Numpy
Working with Images in Numpy

NumPy Quiz
Test your knowledge of NumPy with this quiz, covering key topics such as array
operations, mathematical functions and broadcasting.
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NumPy Quiz

Refer to Practice Exercises, Questions and Solutions for hands-on-numpy problems.

Numpy Tutorial for Beginners | Learn Python From Scratch

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