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Python Data Analysis - Second Edition
Learn how to apply powerful data analysis techniques with popular open source Python modules
Python Data Analysis - Second Edition
Item #: 13658994

Python Data Analysis - Second Edition

Item #: 13658994

€ 70

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Learn how to apply powerful data analysis techniques with popular open source Python modules
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What Stands Out

Comprehensive Coverage
This edition thoroughly explores Python tools and libraries for data analysis, ensuring users gain a robust understanding of effective techniques for extracting insights from complex datasets.
Practical Examples
Includes real-world case studies and hands-on projects, helping readers apply concepts immediately, enhancing learning through practice and ensuring better retention of knowledge.
Updated Techniques
Features the latest updates in Python data analysis practices, ensuring users stay ahead with current methodologies and tools, making it invaluable for both beginners and seasoned analysts.

Product Details

Get the revised edition of Python Data Analysis book and analyze data with Python. Order now on Ubuy Italy for reliable online shopping.
Publisher Packt Publishing
Publication date March 27, 2017
Edition 2nd Revised edition
Language English
Print length 330 pages
ISBN-10 1787127486
ISBN-13 978-1787127487
Item Weight 1.25 pounds (570 grams)
Dimensions 7.5 x 0.75 x 9.25 inches (19.1 x 1.9 x 23.5 cm)

Who Should Buy?

Suitable For
  • Data Analysts

    This edition provides insights into data manipulation techniques, ideal for professionals looking to enhance their analytical skills.

  • Students Learning Python

    Perfect for students new to Python who want to focus on practical data analysis applications alongside programming fundamentals.

  • Business Intelligence Professionals

    Offers practical tools and techniques for extracting actionable insights from data to support business decision-making.

Not Suitable For
  • Complete Beginners

    Individuals with no programming background may find the content too advanced or challenging to follow effectively.

Product Description

Python Data Analysis - Second Edition

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Data Modeling & Design Editorial Review

**** "Python Data Analysis - Second Edition" has garnered significant appreciation from its readers for its comprehensive approach to data manipulation and analysis using Python. Enthusiasts embarking on the journey of data analysis, especially those dealing with geospatial datasets or specific fields like sports analytics, will find the book to be an invaluable resource. Its detailed exploration of diverse data analysis techniques, including an introduction to learning algorithms for tasks like sentiment analysis, positions this book as a strong foundation for newcomers and intermediate users alike. Readers have praised the clarity with which the authors present information about various Python libraries tailored for different data analysis tasks. This organization not only aids comprehension but also enhances practical application, allowing users to quickly find the tools they need for their specific projects. The book does not shy away from addressing the complexities of handling large datasets, equipping readers with strategies to optimize performance as their data needs grow. While some have noted that certain chapters are so rich in content that they could warrant standalone books, this breadth of information ultimately contributes to a well-rounded educational experience. In summary, "Python Data Analysis - Second Edition" stands out as a crucial text for anyone looking to deepen their understanding of data analysis through Python, making it a recommended read for both novices and those looking to refine their skills in the domain. **

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Pros

  • Comprehensive foundation for data manipulation and analysis in Python.
  • Strong focus on practical application with various Python libraries categorized by use.
  • Insightful discussions on advanced topics, such as learning algorithms for sentiment analysis.
  • Offers strategies for optimizing performance with large datasets.

Cons

  • Some chapters may feel densely packed with information, potentially requiring further exploration.

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