Book Image

Applying Math with Python

By : Sam Morley
Book Image

Applying Math with Python

By: Sam Morley

Overview of this book

Python, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain. The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You'll start by developing core skills and learning about packages covered in Python’s scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you'll discover Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code. By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.
Table of Contents (12 chapters)

Generating random data

Many tasks involve generating large quantities of random numbers, which, in their most basic form, are either integers or floating-point numbers (double precision) lying in the range 0 ≤ x < 1. Ideally, these numbers should be selected uniformly, so that if we draw a large quantity of such numbers, they should be distributed roughly evenly across the range 0 ≤ x < 1.

In this recipe, we will see how to generate large quantities of random integers and floating-point numbers using NumPy, and show the distribution of these numbers using a histogram.

Getting ready

Before we start, we need to import the default_rng routine from the NumPy random module and create an instance of the default random number generator to use in the recipe:

from numpy.random import default_rng
rng = default_rng(12345) # changing seed for reproducibility

We have discussed this process in the Selecting items at random recipe.