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)

Manipulating data in DataFrames

Once we have data in a DataFrame, we often need to apply some simple transformations or filters to the data before we can perform any analysis. This could include, for example, filtering the rows that are missing data or applying a function to individual columns.

In this recipe, we will see how to perform some basic manipulation of DataFrame objects to prepare the data for analysis.

Getting ready

For this recipe, we will need the pandas package imported under the pdalias, the NumPy package imported under the npalias, and a default random number generator object from NumPy created using the following commands:

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

How to do it...

The following steps illustrate how to perform some basic filtering and manipulations on a pandas DataFrame:

  1. We will first create a sample DataFrame using random data:
three = rng.uniform...