As part of the best-selling Pocket Primer series, this book is designed to provide an introduction to Python tools which are used by data scientists. It includes coverage of fundamental aspects of NumPy and Pandas, how to write regular expressions, and how to perform data cleaning tasks. The first chapter contains a quick tour of basic Python, followed by a chapter introducing NumPy, and followed by a chapter on Pandas. Chapter 4 provides a high-level view of Sklearn and SciPy. Chapter 5 contains an assortment of data cleaning tasks that are solved via Python and the awk programming language. Chapter 6 delves into data visualization with Matplotlib, Seaborn, and Bokeh. Next, one appendix explores issues that can arise with data, followed by an appendix on awk. Numerous code samples are used to illustrate concepts. Companion files with source code are available for downloading from the publisher.

Features

• Features coverage of fundamental aspects of NumPy and Pandas, how to write regular expressions, and how to perform data cleaning tasks
• Demonstrates concepts using numerous code samples throughout
• Includes companion files with source code

Table of Contents

1: Introduction to Python

2: Introduction to NumPy

3: Introduction to Pandas

4: Working with Sklearn and SciPy

5: Data Cleaning Tasks

6: Data Visualization

Appendices: A. Working with Data

Appendices B: Working with awk

About the Author

Oswald Campesato (San Francisco, CA) is an adjunct instructor at UC-Santa Clara and specializes in Deep Learning, Java, Android, and Python. He is the author/co-author of over twenty-five books including Data Wrangling, Python 3 for Machine Learning, and the NLP Using R Pocket Primer (all Mercury Learning).