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Python Data Cleaning Cookbook: Prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn, and OpenAI
Author(s): Michael Walker
Series: EXPERT INSIGHT
Publisher: Packt Publishing, 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 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 Description
Jumping 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.
Table of Contents
Anticipating Data Cleaning Issues When Importing Tabular Data with pandas
Anticipating Data Cleaning Issues When Working with HTML, JSON, and Spark Data
Taking the Measure of Your Data
Identifying Outliers in Subsets of Data
Using Visualizations for the Identification of Unexpected Values
Cleaning and Exploring Data with Series Operations
Identifying and Fixing Missing Values
Encoding, Transforming, and Scaling Features
Fixing Messy Data When Aggregating
Addressing Data Issues When Combining DataFrames
Tidying and Reshaping Data
Automate Data Cleaning with User-Defined Functions, Classes, and Pipelines
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 Description
Jumping 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.
Table of Contents
Anticipating Data Cleaning Issues When Importing Tabular Data with pandas
Anticipating Data Cleaning Issues When Working with HTML, JSON, and Spark Data
Taking the Measure of Your Data
Identifying Outliers in Subsets of Data
Using Visualizations for the Identification of Unexpected Values
Cleaning and Exploring Data with Series Operations
Identifying and Fixing Missing Values
Encoding, Transforming, and Scaling Features
Fixing Messy Data When Aggregating
Addressing Data Issues When Combining DataFrames
Tidying and Reshaping Data
Automate Data Cleaning with User-Defined Functions, Classes, and Pipelines
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