Python Data Cleaning Cookbook, Second Edition

eBook Details:

• Paperback: 486 pages
• Publisher: WOW! eBook; 2nd edition (May 31, 2024)
• Language: English
• ISBN-10: 1803239875
• ISBN-13: 978-1803239873

eBook Description:

Python Data Cleaning Cookbook, Second Edition: 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.

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 Python Data Cleaning Cookbook, 2nd Edition 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.

• 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

By the end of this Python Data Cleaning Cookbook, Second Edition book, you’ll know how to clean data and diagnose problems within it.