Python data cleaning cookbook

• Verfasst von: Walker, Michael [VerfasserIn]
• Titel: Python data cleaning cookbook
• Titelzusatz: prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn and OpenAI
• Verf.angabe: Michael Walker
• Ausgabe: Second edition.
• Verlagsort: Birmingham, UK
• Verlag: Packt Publishing Ltd.
• Jahr: 2024
• Umfang: 1 online resource (486 pages)
• Illustrationen: illustrations
• Gesamttitel/Reihe: Expert insight
• Fussnoten: Includes index
• ISBN: 978-1-80323-987-3
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Analyse de variance
• Sach-SW: Bases de données ; Gestion
• Sach-SW: Exploration de données (Informatique)
• Sach-SW: Python (Langage de programmation)
• K10plus-PPN: 1892757575
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m454441993X
• Lokale URL Inst.: Zum Volltext

Abstract

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.