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NumPy Tutorial

Learn NumPy

  • NumPy is a Python library.
  • NumPy is used for working with arrays.
  • NumPy is short for "Numerical Python".

Learning by Reading

We have created 43 tutorial pages for you to learn more about NumPy.

Basic

Introduction
Getting Started
Creating Arrays
Array Indexing
Array Slicing
Data Types
Copy vs View
Array Shape
Array Reshape
Array Iterating
Array Join
Array Split
Array Search
Array Sort
Array Filter

Random

Random Intro
Data Distribution
Random Permutation
Seaborn Module
Normal Dist.
Binomial Dist.
Poisson Dist.
Uniform Dist.
Logistic Dist.
Multinomial Dist.
Exponential Dist.
Chi Square Dist.
Rayleigh Dist.
Pareto Dist.
Zipf Dist.

ufunc

ufunc Intro
Create Function
Simple Arithmetic
Rounding Decimals
Logs
Summations
Products
Differences
Finding LCM
Finding GCD
Trigonometric
Hyperbolic
Set Operations

Learning by Examples

In our "Try it Yourself" editor, you can use the NumPy module, and modify the code to see the result.

Example

Create a NumPy array:

import numpy as np

arr = np.array([1, 2, 3, 4, 5])

print(arr)

print(type(arr))

Learning by Exercises

Many chapters in this tutorial end with an exercise where you can check your level of knowledge.

NumPy Introduction

What is NumPy?

NumPy is a Python library used for working with arrays.

It also has functions for working in domain of linear algebra, fourier transform, and matrices.

NumPy was created in 2005 by Travis Oliphant. It is an open source project and you can use it freely.

NumPy stands for Numerical Python.

Why Use NumPy?

In Python we have lists that serve the purpose of arrays, but they are slow to process.

NumPy aims to provide an array object that is up to 50x faster than traditional Python lists.

The array object in NumPy is called ndarray, it provides a lot of supporting functions that make working with ndarray very easy.

Arrays are very frequently used in data science, where speed and resources are very important.

Data Science: is a branch of computer science where we study how to store, use and analyze data for deriving information from it.

Why is NumPy Faster Than Lists?

NumPy arrays are stored at one continuous place in memory unlike lists, so processes can access and manipulate them very efficiently.

This behavior is called locality of reference in computer science.

This is the main reason why NumPy is faster than lists. Also it is optimized to work with latest CPU architectures.

Which Language is NumPy written in?

NumPy is a Python library and is written partially in Python, but most of the parts that require fast computation are written in C or C++.

Where is the NumPy Codebase?

The source code for NumPy is located at this github repository https://github.com/numpy/numpy

# Example GitHub command
git clone https://github.com/numpy/numpy.git

github: enables many people to work on the same codebase.