Addison-Wesley Data & Analytics Pandas for Everyone: Python Data Analysis, Paperback
Manage and Automate Data Analysis with Pandas in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex.
Addison-Wesley Data & Analytics Pandas for Everyone: Python Data Analysis, Paperback
Articolo #: 73518765

Addison-Wesley Data & Analytics Pandas for Everyone: Python Data Analysis, Paperback

Articolo #: 73518765

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Manage and Automate Data Analysis with Pandas in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex.
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Cosa salta all'occhio

Comprehensive Introduction
Covers the fundamentals of data analysis using Python and Pandas, making it ideal for beginners and professionals looking to enhance their data manipulation skills.
Practical Examples
Includes real-world examples and exercises that help readers apply theoretical concepts, ensuring a hands-on understanding of data analytics and boosting retention.
Accessible Language
Written in an easy-to-understand style, it simplifies complex topics, making it approachable even for those without a technical background, facilitating learning for a broader audience.

Dettagli del prodotto

Shop for Addison-Wesley Data & Analytics: Pandas for Everyone Python Data Analysis Edition 2 in Paperback at Ubuy San Marino. Get great deals and fast shipping worldwide.
  • Manage and automate data analysis with Pandas in Python
  • Learn practical knowledge and insight for solving real problems with Pandas
  • Updated coverage of plotting and the seaborn data visualization library
  • Includes examples, resources, and online bonus material
  • Covers data cleaning techniques, model fitting, and performance tips
  • Explore advanced features like linear modeling and unsupervised machine learning
LanguageEnglish
Series TitleAddison-Wesley Data & Analytics
PublisherPearson Education
Book FormatPaperback
Original LanguagesEnglish
Number of Pages512
AuthorAddison-Wesley Professional
Is Large PrintN
TitlePandas for Everyone: Python Data Analysis
ISBN-139780137891153
Publication DateDecember, 2022
ISBN-100137891156
Book formatPaperback
Fiction/nonfictionNon-Fiction
GenreComputing & Internet
Pages512
Reading levelProfessional and Scholarly
SubgenreLanguages
Edition2nd Edition
Original languagesEnglish
Item Weight1.5 lbs (680 grams)

A chi è consigliato?

Suitable For
  • Beginner Programmers

    Ideal for individuals new to programming, offering a clear introduction to Python and data analysis with practical examples.

  • Data Analysts

    Great for aspiring data analysts looking to enhance their skills in data manipulation and analysis using the Pandas library.

  • Academic Students

    Well-suited for students studying data science or analytics courses, providing foundational knowledge essential for academic success.

Not Suitable For
  • Advanced Users

    Not ideal for experienced data scientists who seek advanced techniques or complex data analysis beyond basic Pandas functionalities.

DESCRIZIONE DEL PRODOTTO

Addison-Wesley Data & Analytics Pandas for Everyone: Python Data Analysis, Paperback

Hai qualche domanda? chatta con noi

Domande e risposte dei clienti

  • Domanda: What key concepts are introduced in this book?

    Risposta: Key concepts such as using regularization to prevent data overfitting and unsupervised machine learning methods are introduced.
  • Domanda: What are some new features in the second edition of the book?

    Risposta: Extended coverage of plotting, expanded examples, and resources, updated Python 3.9 code, and packages coverage.
  • Domanda: What are some techniques covered for data cleaning in this book?

    Risposta: From basic string manipulation to applying functions across dataframes, powerful data cleaning techniques are demonstrated.

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