Michael Walker - Python Data Cleaning Cookbook: Modern techniques and Python tools to detect and remove dirty data to extract key insights
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- Book:Python Data Cleaning Cookbook: Modern techniques and Python tools to detect and remove dirty data to extract key insights
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Discover how to describe your data in detail, identify data issues, and find out how to solve them using commonly used techniques and tips and tricks
Key Features- Get well-versed with various data cleaning techniques to reveal key insights
- Manipulate data of different complexities to shape them into the right form as per your business needs
- Clean, monitor, and validate large data volumes to diagnose problems before moving on to data analysis
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.
Youll 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 them into a useful form. Youll 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 youve identified. Moving on, youll 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, youll 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, youll build functions and classes that you can reuse without modifying when you have new data.
By the end of this Python book, youll be equipped with all the key skills that you need to clean data and diagnose problems in it.
What you will learn- Find out how to read and analyze data from a variety of sources
- Produce summaries of the attributes of data frames, columns, and rows
- Filter data and select columns of interest that satisfy given criteria
- Address messy data issues, including working with dates and missing values
- Improve your productivity in Python pandas using method chaining
- Use visualizations to gain additional insights and identify potential data issues
- Enhance your ability to learn what is going on in your data
- Build user-defined functions and classes to automate data cleaning
This book is for anyone looking for ways to handle messy, duplicate, and poor data using different Python tools and techniques. The book takes a recipe-based approach to help you to learn how to clean and manage data. Working knowledge of Python programming is all you need to get the most out of the book.
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Modern techniques and Python tools to detect and remove dirty data and extract key insights
Michael Walker
BIRMINGHAMMUMBAI
Copyright 2020 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Commissioning Editor: Sunith Shetty
Acquisition Editor: Ali Abidi
Senior Editor: Mohammed Yusuf Imaratwale
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First published: December 2020
Production reference: 1091220
Published by Packt Publishing Ltd.
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ISBN 978-1-80056-566-1
www.packt.com
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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.
Meng-Chieh Ling has a Ph.D. in theoretical condensed matter physics from Karlsruhe Institute of Technology in Germany. He switched from physics to data science to pursue a successful career. After working for AGT International in Darmstadt for 2 years, he joined CHECK24 Fashion as a data scientist in Dsseldorf. His responsibilities include applying machine learning to improve the efficiency of data cleansing, automatic attributes tagging with deep learning, and developing image-based recommendation systems.
Sbastien Celles is a professor of applied physics at Poitiers Institute of Technology (the thermal science department). He has used Python for numerical simulations, data plotting, data predictions, and various other tasks since the early 2000s. He was a technical reviewer of the books Mastering Python for data science and Julia for Data Science: Explore data science from scratch with Julia
He is also the author of some Python packages available on PyPi, including openweathermap_requests, pandas_degreedays, pandas_confusion, python-constraint, python-server, and python-windrose.
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