Python Data Cleaning Cookbook
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Python Data Cleaning Cookbook
Author: Michael Walker
language: en
Publisher: Packt Publishing Ltd
Release Date: 2020-12-11
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 FeaturesGet well-versed with various data cleaning techniques to reveal key insightsManipulate data of different complexities to shape them into the right form as per your business needsClean, monitor, and validate large data volumes to diagnose problems before moving on to data analysisBook 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. What you will learnFind out how to read and analyze data from a variety of sourcesProduce summaries of the attributes of data frames, columns, and rowsFilter data and select columns of interest that satisfy given criteriaAddress messy data issues, including working with dates and missing valuesImprove your productivity in Python pandas by using method chainingUse visualizations to gain additional insights and identify potential data issuesEnhance your ability to learn what is going on in your dataBuild user-defined functions and classes to automate data cleaningWho 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. Working knowledge of Python programming is all you need to get the most out of the book.
Python Data Cleaning Cookbook
Author: Michael Walker
language: en
Publisher: Packt Publishing Ltd
Release Date: 2024-05-31
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 new techniques for data preprocessing and cleaning for machine learning and NLP models Use new and updated AI tools and techniques for data cleaning tasks Clean, monitor, and validate large data volumes to diagnose problems using cutting-edge methodologies including Machine learning and AI Book DescriptionJumping into data analysis without proper data cleaning will certainly lead to incorrect results. The Python Data Cleaning Cookbook - Second Edition will show you tools and techniques for cleaning and handling data with Python for better outcomes. Fully updated to the latest version of Python and all relevant tools, this book will teach you how to manipulate and clean data to get it into a useful form. he current edition focuses on advanced techniques like machine learning and AI-specific approaches and tools for data cleaning along with the conventional ones. The book also delves into tips and techniques to process and clean data for ML, AI, and NLP models. 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 Using OpenAI tools for various data cleaning tasks Producing summaries of the attributes of datasets, columns, and rows Anticipating data-cleaning issues when importing tabular data into pandas Applying validation techniques for imported tabular data Improving your productivity in pandas by using method chaining Recognizing and resolving common issues like dates and IDs Setting up indexes to streamline data issue identification Using data cleaning to prepare your data for ML and AI models 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.
Python Data Cleaning Cookbook - Second Edition
The book shows you how to clean, wrangle, and view data from multiple perspectives, including dataset and column attributes.