Python Data Cleaning Cookbook

by Michael Walker

Getting clean data to reveal insights is essential, as directly jumping into data analysis without proper data cleaning may lead to incorrect results. This book shows you tools and techniques that you can apply to clean and handle data with Python. You'll begin by getting familiar with the shape of data by using practices that can be deployed routinely with most data sources. Then, the book teaches you how to manipulate data to get it into a useful form. You'll also learn how to filter and summarize data to gain insights and better understand what makes sense and what does not, along with discovering how to operate on data to address the issues you've identified. Moving on, you'll perform key tasks, such as handling missing values, validating errors, removing duplicate data, monitoring high volumes of data, and handling outliers and invalid dates. Next, you'll cover recipes on using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors, and generate visualizations for exploratory data analysis (EDA) to visualize unexpected values. Finally, you'll build functions and classes that you can reuse without modification when you have new data.

By the end of this Python book, you'll be equipped with all the key skills that you need to clean data and diagnose problems within it.

436 pages, Kindle Edition

First published December 11, 2020

About the author

Librarian Note: There is more than one author by this name in the Goodreads database.

Michael Walker has worked as a data analyst for over 30 years at a variety of educational institutions. He has also taught data science, research methods, statistics, and computer programming to undergraduates since 2006. He generates public sector and foundation reports and conducts analyses for publication in academic journals.

Reviews

[Matt Chequers · Rating 4 out of 5]

I think it's a good book. Shame for the authors that fast forward a few years and you can just use ChatGPT to get the info.

[Andrey Andreev · Rating 5 out of 5]

Applause for the author. For me, it turned out a good source for drawing knowledge. The material is structured and clear even if I run into of few chapter which maybe early for me but in general this book is must have for who somewhat works with data.