Authors

Ivan Idris

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Ivan Idris has an MSc in experimental physics. His graduation thesis had a strong emphasis on applied computer science. After graduating, he worked for several companies as a Java developer, data warehouse developer, and QA analyst. His main professional interests are business intelligence, big data, and cloud computing. Ivan Idris enjoys writing clean, testable code and interesting technical articles. Ivan Idris is the author of NumPy 1.5. Beginner's Guide and NumPy Cookbook by Packt Publishing.

Books from Ivan Idris

  1. Instant Pygame for Python Game Development How-to
    $9.99
    Create engaging and fun games with Pygame, Python's Game development library
  2. Python Data Analysis Cookbook
    $39.99
    Over 140 practical recipes to help you make sense of your data with ease and build production-ready data apps
  3. Python Data Analysis
    $29.99
    Learn how to apply powerful data analysis techniques with popular open source Python modules
  4. NumPy Cookbook - Second Edition
    $35.99
    Over 90 fascinating recipes to learn and perform mathematical, scientific, and engineering Python computations with NumPy
  5. NumPy Cookbook
    $26.99
    If you’re a Python developer with basic NumPy skills, the 70+ recipes in this brilliant cookbook will boost your skills in no time. Learn to raise productivity levels and code faster and cleaner with the open source mathematical library.
  6. NumPy Beginner's Guide - Second Edition
    $26.99
    An action packed guide using real world examples of the easy to use, high performance, free open source NumPy mathematical library.
  7. NumPy 1.5 Beginner's Guide
    $26.99
    An action-packed guide for the easy-to-use, high performance, Python based free open source NumPy mathematical library using real-world examples with this book and ebook
  8. Learning NumPy Array
    $16.99
    Supercharge your scientific Python computations by understanding how to use the NumPy library effectively
  9. NumPy: Beginner's Guide - Third Edition
    $35.99
    Build efficient, high-speed programs using the high-performance NumPy mathematical library