Book Image

The Python Workshop

By : Olivier Pons, Andrew Bird, Dr. Lau Cher Han, Mario Corchero Jiménez, Graham Lee, Corey Wade
Book Image

The Python Workshop

By: Olivier Pons, Andrew Bird, Dr. Lau Cher Han, Mario Corchero Jiménez, Graham Lee, Corey Wade

Overview of this book

Have you always wanted to learn Python, but never quite known how to start? More applications than we realize are being developed using Python because it is easy to learn, read, and write. You can now start learning the language quickly and effectively with the help of this interactive tutorial. The Python Workshop starts by showing you how to correctly apply Python syntax to write simple programs, and how to use appropriate Python structures to store and retrieve data. You'll see how to handle files, deal with errors, and use classes and methods to write concise, reusable, and efficient code. As you advance, you'll understand how to use the standard library, debug code to troubleshoot problems, and write unit tests to validate application behavior. You'll gain insights into using the pandas and NumPy libraries for analyzing data, and the graphical libraries of Matplotlib and Seaborn to create impactful data visualizations. By focusing on entry-level data science, you'll build your practical Python skills in a way that mirrors real-world development. Finally, you'll discover the key steps in building and using simple machine learning algorithms. By the end of this Python book, you'll have the knowledge, skills and confidence to creatively tackle your own ambitious projects with Python.
Table of Contents (13 chapters)

Introduction

In Chapter 9, Practical Python – Advanced Topics, you looked at how to use GitHub to collaborate with team members. You also used conda to document and set up the dependencies for Python programs and docker to create reproducible Python environments to run our code.

We now shift gears to data science. Data science is booming like never before. Data scientists have become among the most sought-after practitioners in the world today. Most leading corporations have data scientists to analyze and explain their data.

Data analytics focuses on the analysis of big data. As each day goes by, there is more data than ever before — far too much for any human to analyze by sight. Leading Python developers such as Wes McKinney and Travis Oliphant addressed the gap by creating specialized Python libraries, in particular, pandas and NumPy to handle big data.

Taken together, pandas and NumPy are masterful at handling big data. They are built for speed, efficiency...