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Autor(en): 
  • Susan E. McGregor
  • Practical Python Data Wrangling and Data Quality: Getting Started with Reading, Cleaning, and Analyzing Data 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Dezember 2021  
    Genre:  EDV / Informatik 
     
    analyzing data with python / APIs / COMPUTERS / Data Science / Data Analytics / data cleaning / data engineering python / data equity / Data ethics / Data Mining
    ISBN:  9781492091509 
    EAN-Code: 
    9781492091509 
    Verlag:  O'Reilly 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 233 mm / B 178 mm / D 28 mm 
    Gewicht:  674 gr 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:

    The world around us is full of data that holds unique insights and valuable stories, and this book will help you uncover them. Whether you already work with data or want to learn more about its possibilities, the examples and techniques in this practical book will help you more easily clean, evaluate, and analyze data so that you can generate meaningful insights and compelling visualizations.

    Complementing foundational concepts with expert advice, author Susan E. McGregor provides the resources you need to extract, evaluate, and analyze a wide variety of data sources and formats, along with the tools to communicate your findings effectively. This book delivers a methodical, jargon-free way for data practitioners at any level, from true novices to seasoned professionals, to harness the power of data.

    • Use Python 3.8+ to read, write, and transform data from a variety of sources
    • Understand and use programming basics in Python to wrangle data at scale
    • Organize, document, and structure your code using best practices
    • Collect data from structured data files, web pages, and APIs
    • Perform basic statistical analyses to make meaning from datasets
    • Visualize and present data in clear and compelling ways

      



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