Python Data Cleaning Cookbook: Prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn, and OpenAI

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

1. Anticipating Data Cleaning Issues When Importing Tabular Data with pandas
2. Anticipating Data Cleaning Issues When Working with HTML, JSON, and Spark Data
3. Taking the Measure of Your Data
4. Identifying Outliers in Subsets of Data
5. Using Visualizations for the Identification of Unexpected Values
6. Cleaning and Exploring Data with Series Operations
7. Identifying and Fixing Missing Values
8. Encoding, Transforming, and Scaling Features
9. Fixing Messy Data When Aggregating
10. Addressing Data Issues When Combining DataFrames
11. Tidying and Reshaping Data
12. Automate Data Cleaning with User-Defined Functions, Classes, and Pipelines

Michael Walker has worked as a data analyst for over 30 years at a variety of educational institutions. He is currently the CIO at College Unbound in Providence, Rhode Island, in the United States. He has also taught data science, research methods, statistics, and computer programming to undergraduates since 2006.