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Ivan Idris, "NumPy Cookbook - Second Edition: Over 90 fascinating recipes to learn and perform mathematical, scientific, and engineering Python computations with NumPy"
English | 2015 | pages: 258 | ISBN: 1784390941 | PDF | 15,1 mb
Over 90 fascinating recipes to learn and perform mathematical, scientific, and engineering Python computations with NumPy
About This Book
- Perform high-performance calculations with clean and efficient NumPy code
- Simplify large data sets by analysing them with statistical functions
- A solution-based guide packed with engaging recipes to execute complex linear algebra and mathematical computations
Who This Book Is For
If you are a Python developer with some experience of working on scientific, mathematical, and statistical applications and want to gain an expert understanding of NumPy programming in relation to science, math, and finance using practical recipes, then this book is for you.
What You Will Learn
- Learn advanced indexing and linear algebra
- Deal with missing stock price data using masked arrays
- Explore everything you need to know about image processing
- Dive into broadcasting and histograms
- Profile NumPy code and visualize the results
- Speed up your code with Cython
- Use universal functions and interoperability features
- Analyze your performance using Quality Assurance
- Learn about exploratory and predictive data analysis with NumPy
In Detail
NumPy has the ability to give you speed and high productivity. High performance calculations can be done easily with clean and efficient code, and it allows you to execute complex algebraic and mathematical computations in no time.
This book will give you a solid foundation in NumPy arrays and universal functions. Starting with the installation and configuration of IPython, you'll learn about advanced indexing and array concepts along with commonly used yet effective functions. You will then cover practical concepts such as image processing, special arrays, and universal functions. You will also learn about Descriptionting with MatDescriptionlib and the related SciPy project with the help of examples. At the end of the book, you will study how to explore atmospheric pressure and its related techniques. By the time you finish this book, you'll be able to write clean and fast code with NumPy.
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