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NumPy Cookbook - Second Edition

Over 90 fascinating recipes to learn and perform mathematical, scientific, and engineering Python computations with NumPy About This BookPerform high-performance calculations with clean and efficient NumPy codeSimplify large data sets by analysing them with statistical functionsA solution-based guide packed with engaging recipes to execute complex linear algebra and mathematical computationsWho This Book Is ForIf 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 DetailNumPy 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 plotting with Matplotlib 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.

Paperback: 263 pages

Publisher: Packt Publishing - ebooks Account; 2nd Revised ed. edition (May 29, 2015)

Language: English

ISBN-10: 1784390941

ISBN-13: 978-1784390945

Product Dimensions: 7.5 x 0.6 x 9.2 inches

Shipping Weight: 1.2 pounds (View shipping rates and policies)

Average Customer Review: 3.8 out of 5 stars  See all reviews (4 customer reviews)

Best Sellers Rank: #1,933,156 in Books (See Top 100 in Books) #160 in Books > Computers & Technology > Hardware & DIY > Mainframes & Minicomputers #1101 in Books > Computers & Technology > Programming > Languages & Tools > Python #2263 in Books > Textbooks > Computer Science > Graphics & Visualization

When it comes to software, I learn best from evaluating examples, and then modifying those examples to fit my needs. The cookbook type format is great for that, and the NumPy book here is no exception.This book doesn't just give math related examples of what numpy can do, but it uses a number of other libraries, such as scipy, matplotlib, pillow to highlight numpy related features. There is even a section with exchanging data with MATLAB/Octave as well as R. A lot of examples involve image and audio processing which is a good illustration of numpy's capabilities.The part that I really enjoy is that there is a chapter dedicated to profiling and debugging, where it is explained how to evaluate how fast/efficient your code is written, and gives instructions to how to properly debug with ipython, which is something I haven't seen much of elsewhere.I only discussed a small number of chapters, but this book does a decent job covering numpy's capabilities, but it does an excellent job highlighting its interaction with other libraries, as well as best practices with profiling and testing.

Awesome book, with tons of useful information. I bought this book to help me with my stock analysis program. However, The plethora of information in this book on processing images, audio, and statistics have helped me with many other projects, including analyzing network traffic (Chapter 9 is a great help to enable fast processing with python). Being a secure development TDD guy, chapters 7 & 8 sealed the deal for me.

The book is just an introduction in Numpy. In the second edition two chapters are added; Latest and Greatest in Numpy and Exploratory and Predictive Data Analysis with NumPy Unfortunately the code for the examples con not be downloaded from the Packt webpage, so the examples can not be executed. The book describes the installation of IPython, matplotlib but the installation of Numpy is not described. Nevertheless the book also contains some useful examples.

This book has a lot of information in it and may be a great book if was titled "random python recipes". I was looking for a book on NumPy and while Bumpy is used throughout the book the there is no focus. I would not recommend this book

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