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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

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.

Who is this book 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.

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

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Bryan Edwards Jul 29, 2024

Great book - the author does a great job explaining the various concepts, and the examples are very helpful

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N/A Jul 27, 2024

Brady Jul 01, 2024

Great resource for anyone working in data analysis or data science. This book is incredibly well-organized and provides practical, hands-on recipes for cleaning and preparing data using pandas, one of the most powerful Python libraries.What sets this book apart is its clear, step-by-step approach to tackling common data cleaning tasks. Each recipe is well-explained, with code snippets that are easy to follow and implement. The authors do an excellent job of breaking down complex concepts into manageable parts, making it accessible for both beginners and experienced users.I particularly appreciate the real-world examples and practical tips provided throughout the book. These examples not only help in understanding the concepts but also in applying them to real data challenges. The coverage of diverse topics—from handling missing data to dealing with duplicates and outliers—is comprehensive and thorough.Whether you're new to data cleaning or looking to refine your skills, this book is a fantastic guide. It has significantly improved my efficiency in preparing data for analysis, and I highly recommend it to anyone looking to enhance their data cleaning skills with pandas.

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Vishi kaushal Sep 09, 2024

This book cover all the concepts in details with proper exercise. You should go for this if you are starting your data journey.

Petar Dimov Sep 13, 2024

I recently finished reading the second edition of Mike Walker’s 𝗣𝘆𝘁𝗵𝗼𝗻 𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗖𝗼𝗼𝗸𝗯𝗼𝗼𝗸.💡 𝗜𝘁’𝘀 𝗼𝗳𝘁𝗲𝗻 𝘀𝗮𝗶𝗱 𝘁𝗵𝗮𝘁 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝗶𝘀 𝗺𝗼𝘀𝘁𝗹𝘆 𝗮𝗯𝗼𝘂𝘁 𝗰𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗱𝗮𝘁𝗮, 𝗮𝘀 𝗶𝗳 𝗶𝘁’𝘀 𝗮𝗻 𝘂𝗻𝗱𝗲𝘀𝗶𝗿𝗮𝗯𝗹𝗲 𝘁𝗮𝘀𝗸. However, this perspective overlooks its significance. Just as prepping ingredients is crucial in cooking or collecting materials is necessary for writing, 𝗱𝗮𝘁𝗮 𝗰𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗶𝘀 𝗮 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝗮𝘀𝗽𝗲𝗰𝘁 𝗼𝗳 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲. 𝗧𝗵𝗲 𝗯𝗲𝘁𝘁𝗲𝗿 𝘆𝗼𝘂 𝗮𝗿𝗲 𝗮𝘁 𝗶𝘁, 𝘁𝗵𝗲 𝗺𝗼𝗿𝗲 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗮𝗻𝗱 𝘃𝗮𝗹𝘂𝗮𝗯𝗹𝗲 𝘆𝗼𝘂𝗿 𝘄𝗼𝗿𝗸 𝗯𝗲𝗰𝗼𝗺𝗲𝘀.✍ 𝗜 𝗹𝗼𝘃𝗲𝗱 𝗵𝗼𝘄 𝘁𝗵𝗲 𝗯𝗼𝗼𝗸 𝗷𝘂𝗺𝗽𝘀 𝗿𝗶𝗴𝗵𝘁 𝗶𝗻, offering practical tasks for immediate results. The cookbook style makes it simple and actionable.🔧 𝗧𝗵𝗲 𝗯𝗼𝗼𝗸 𝗱𝗲𝗹𝘃𝗲𝗱 𝗶𝗻𝘁𝗼 𝘂𝘁𝗶𝗹𝗶𝘇𝗶𝗻𝗴 𝗢𝗽𝗲𝗻𝗔𝗜 𝘁𝗼𝗼𝗹𝘀 𝗳𝗼𝗿 𝗱𝗶𝘃𝗲𝗿𝘀𝗲 𝗱𝗮𝘁𝗮 𝗰𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝘁𝗮𝘀𝗸𝘀, showcasing how AI can enhance the efficiency and effectiveness of data preprocessing. It also emphasized summarizing dataset attributes, providing a comprehensive overview of data at both column and row levels.🚨 𝗪𝗮𝗹𝗸𝗲𝗿 𝗮𝗱𝗱𝗿𝗲𝘀𝘀𝗲𝗱 𝗮𝗻𝘁𝗶𝗰𝗶𝗽𝗮𝘁𝗶𝗻𝗴 𝗶𝘀𝘀𝘂𝗲𝘀 𝘄𝗶𝘁𝗵 𝘁𝗮𝗯𝘂𝗹𝗮𝗿 𝗱𝗮𝘁𝗮 𝗶𝗻 𝗽𝗮𝗻𝗱𝗮𝘀, helping readers proactively tackle common data import problems. 𝗘𝗻𝗵𝗮𝗻𝗰𝗶𝗻𝗴 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆 𝘄𝗶𝘁𝗵 𝗺𝗲𝘁𝗵𝗼𝗱 𝗰𝗵𝗮𝗶𝗻𝗶𝗻𝗴 𝗶𝗻 𝗽𝗮𝗻𝗱𝗮𝘀 was another key highlight, streamlining workflows and improving code readability.⚙️ 𝗧𝗵𝗲 𝗯𝗼𝗼𝗸 𝗰𝗼𝘃𝗲𝗿𝗲𝗱 𝗵𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗰𝗼𝗺𝗺𝗼𝗻 𝗱𝗮𝘁𝗮 𝗶𝘀𝘀𝘂𝗲𝘀, including problematic dates and IDs, and setting up indexes to quickly identify and assess data problems. 𝗜𝘁 𝗮𝗹𝘀𝗼 𝗲𝗺𝗽𝗵𝗮𝘀𝗶𝘇𝗲𝗱 𝗽𝗿𝗲𝗽𝗮𝗿𝗶𝗻𝗴 𝗱𝗮𝘁𝗮 𝗳𝗼𝗿 𝗠𝗟 𝗮𝗻𝗱 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀, ensuring that the data is clean and reliable for advanced analytics.✨ 𝗢𝘃𝗲𝗿𝗮𝗹𝗹, 𝗜 𝗳𝗼𝘂𝗻𝗱 𝘁𝗵𝗶𝘀 𝗯𝗼𝗼𝗸 𝗯𝗲𝗻𝗲𝗳𝗶𝗰𝗶𝗮𝗹 𝗮𝗻𝗱 𝗲𝗻𝗷𝗼𝘆𝗮𝗯𝗹𝗲. 𝗜𝘁 𝗿𝗲𝗹𝗶𝗲𝘃𝗲𝗱 𝘁𝗵𝗲 𝗯𝘂𝗿𝗱𝗲𝗻 𝗼𝗳 𝗱𝗮𝘁𝗮 𝗰𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗺𝗮𝗱𝗲 𝗶𝘁 𝗳𝗲𝗲𝗹 𝗹𝗶𝗸𝗲 𝗮 𝗳𝗹𝘂𝗶𝗱 𝗽𝗮𝗿𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀.

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

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