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)

Plotting data from a DataFrame

As with many mathematical problems, one of the first steps to find some way to visualize the problem and all the information is to formulate a strategy. For data-based problems, this usually means producing a plot of the data and visually inspecting it for trends, patterns, and the underlying structure. Since this is such a common operation, pandas provides a quick and simple interface for plotting data in various forms, using Matplotlib under the hood by default, directly from a Series or DataFrame.

In this recipe, we will see how to plot data directly from a DataFrame or Series to understand the underlying trends and structure.

Getting ready

For this recipe, we will need the pandas library import as pd, the NumPy library import as np, the matplotlib pyplot module imported as plt, and a default random number generator instance created using the following commands:

from numpy.random import default_rng
rng = default_rng...