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NumPy Beginner's Guide

NumPy Beginner's Guide

By : Ivan Idris
4.2 (14)
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NumPy Beginner's Guide

NumPy Beginner's Guide

4.2 (14)
By: Ivan Idris

Overview of this book

NumPy is an extension to, and the fundamental package for scientific computing with Python. In today's world of science and technology, it is all about speed and flexibility. When it comes to scientific computing, NumPy is on the top of the list. NumPy Beginner's Guide will teach you about NumPy, a leading scientific computing library. NumPy replaces a lot of the functionality of Matlab and Mathematica, but in contrast to those products, is free and open source. Write readable, efficient, and fast code, which is as close to the language of mathematics as is currently possible with the cutting edge open source NumPy software library. Learn all the ins and outs of NumPy that requires you to know basic Python only. Save thousands of dollars on expensive software, while keeping all the flexibility and power of your favourite programming language.You will learn about installing and using NumPy and related concepts. At the end of the book we will explore some related scientific computing projects. This book will give you a solid foundation in NumPy arrays and universal functions. Through examples, you will also learn about plotting with Matplotlib and the related SciPy project. NumPy Beginner's Guide will help you be productive with NumPy and have you writing clean and fast code in no time at all.
Table of Contents (19 chapters)
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Numpy Beginner's Guide Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Random numbers


Random numbers are used in Monte Carlo methods, stochastic calculus, and more. Real random numbers are hard to generate, so in practice we use pseudo random numbers. Pseudo random numbers are random enough for most intents and purposes, except for some very special cases. The functions related to random numbers can be found in the NumPy random module. The core random number generator is based on the Mersenne Twister algorithm. Random numbers can be generated from discrete or continuous distributions. The distribution functions have an optional size parameter, which tells NumPy how many numbers to generate. You can specify either an integer or a tuple as size. This will result in an array filled with random numbers of appropriate shape. Discrete distributions include the geometric, hypergeometric, and binomial distributions.

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