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)

Getting ready

Unlike most of the recipes in this book, this recipe cannot be run in a Jupyter notebook since we will run the resulting app from the command line.

For this recipe, we will need to import the Faust package:

import faust

We will also need an instance of the default random number generator from the NumPy package:

from numpy.random import default_rng
rng = default_rng(12345)

We will also need to run an instance of a Kafka service on our local machine so that our Faust application can interact with the message broker.

Once you have downloaded Kafka and decompressed the downloaded source, navigate to the folder that the Kafka application can be found in. Open this folder in the terminal. Start the ZooKeeper server using the following command for Linux or Mac:

          bin/ config/

If you're on Windows, use the following command instead: