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Python Data Cleaning Cookbook - Second Edition (Early Access)
Author(s): Michael Walker
Publisher: Packt, Year: 2024
Learn the intricacies of data description, issue identification, and practical problem-solving, armed with essential techniques and expert tips.
Key Features
Get to grips with various data cleaning techniques to reveal key insights.
Manipulate data of different complexities to shape them into the right form according to your business needs..
Clean, monitor, and validate large data volumes to diagnose problems using cutting-edge methodologies including Machine learning and AI.
Book Description
Jumping into data analysis without proper data cleaning will certainly lead to incorrect results. The Python Data Cleaning Cookbook will show you tools and techniques for cleaning and handling data with Python for better outcomes. You will begin by getting familiar with the shape of data by using practices that can be deployed routinely with most data sources.
Fully updated to the latest version of Python and all relevant tools, this book will teach you how to manipulate data to get it into a useful form. The current edition emphasizes advanced techniques like machine learning and AI-specific approaches to data cleaning along with the conventional ones. You will 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. Next, you'll cover recipes for using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors and generate visualizations for exploratory data analysis (EDA) to identify 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 Data Cleaning book, you'll know how to clean data and diagnose problems within it.
What you will learn
Find out how to read and analyze data from a variety of sources
Produce summaries of the attributes of datasets, 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
Who this book is 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 with practical examples.
Working knowledge of Python programming is all you need to get the most out of the book.
Key Features
Get to grips with various data cleaning techniques to reveal key insights.
Manipulate data of different complexities to shape them into the right form according to your business needs..
Clean, monitor, and validate large data volumes to diagnose problems using cutting-edge methodologies including Machine learning and AI.
Book Description
Jumping into data analysis without proper data cleaning will certainly lead to incorrect results. The Python Data Cleaning Cookbook will show you tools and techniques for cleaning and handling data with Python for better outcomes. You will begin by getting familiar with the shape of data by using practices that can be deployed routinely with most data sources.
Fully updated to the latest version of Python and all relevant tools, this book will teach you how to manipulate data to get it into a useful form. The current edition emphasizes advanced techniques like machine learning and AI-specific approaches to data cleaning along with the conventional ones. You will 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. Next, you'll cover recipes for using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors and generate visualizations for exploratory data analysis (EDA) to identify 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 Data Cleaning book, you'll know how to clean data and diagnose problems within it.
What you will learn
Find out how to read and analyze data from a variety of sources
Produce summaries of the attributes of datasets, 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
Who this book is 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 with practical examples.
Working knowledge of Python programming is all you need to get the most out of the book.
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