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Key benefits

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

Description

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.

Who is this book for?

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.

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 by 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

What do you get with a Packt Subscription?

Free for first 7 days. ₹800 p/m after that. Cancel any time!

Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!

50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

Thousands of reference materials covering every tech concept you need to stay up to date.

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Tej Jan 11, 2021

The best part of this book is "how it works" section which explains the working behind the code. First, you start off with writing the code and the explanation of the code is in "how it works". In case you run into any bug, this section should help. This book gives a brief overview of different strategies for data cleaning. A good one time read.

Amazon Verified review

Roy Dec 26, 2020

I am a University Professor of Business Analytics and my specialty is Data Science. I have rated this book with 5 stars because Python Data Cleaning Cookbook offers some educational journeys on data cleaning via some examples through the open-source programming of python. The book is excellent for data enthusiasts to experience data cleaning from the eye of an experience data scientist. I would not recommend the book as a definite guide for data cleaning, however, I would defintaly recommend it to people who would like to be exposed to real examples of data clening.

ACEgolden Feb 02, 2021

The book is a good start for data scientist or machine learning engineer that comes from more software engineer background. It breaks down the issues with data in real world, for examples, missing values and outliers. Furthermore provides receipt on how to deal with the issues, along with use cases and rough theory behind the code pieces. Readers can find this book as a quick hands-on toolbox to apply for their daily data science job for analyzing numerical values and categorical values. I would recommend this book.

Susmithakolli Mar 17, 2021

The book is well structured. The aspects covered in this book are valuable. I started implementing some of these concepts in my job and it is making my work easy and efficient.

Elsie Jan 27, 2021

This book offers insights that cannot be gained from a simple google search. The author not only provides sample code, "recipes," for how to clean your data with Python, but teaches an approach to data cleaning. And the best part is that it is written in an engaging and clear manner.

Python Data Cleaning Cookbook: Modern techniques and Python tools to detect and remove dirty data and extract key insights

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