Over 70 interesting recipes for learning the Python open source mathematical library, NumPy
Overview
- Do high performance calculations with clean and efficient NumPy code
- Analyze large sets of data with statistical functions
- Execute complex linear algebra and mathematical computations
In Detail
Today's world of science and technology is all about speed and flexibility. When it comes to scientific computing, NumPy is on the top of the list. NumPy will give you both speed and high productivity.
"NumPy Cookbook" will teach you all about NumPy, a leading scientific computing library. NumPy replaces a lot of the functionality of Matlab and Mathematica, but in contrast to those products, it is free and open source.
"Numpy Cookbook" will teach you to write readable, efficient, and fast code that is as close to the language of Mathematics as much as possible with the cutting edge open source NumPy software library.
You will learn about installing and using NumPy and related concepts. At the end of the book, we will explore related scientific computing projects.
This book will give you a solid foundation in NumPy arrays and universal functions. You will also learn about plotting with Matplotlib and the related SciPy project through examples.
"NumPy Cookbook" will help you to be productive with NumPy and write clean and fast code.
What you will learn from this book
- Learn advanced Indexing and linear algebra
- Know reshaping automatically
- Dive into Broadcasting and Histograms
- Profile NumPy code and visualize your profiling results
- Speed up your code with Cython
- Use the array interface to expose foreign memory to NumPy
- Use universal functions and interoperability features
- Learn about Matplotlib and Scipy which is often used in conjunction with Numpy
Approach
Written in Cookbook style, the code examples will take your Numpy skills to the next level.
Who this book is written for
This book will take Python developers with basic Numpy skills to the next level through some practical recipes.
Ivan Idris
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. He enjoys writing clean, testable code, and interesting technical articles. He is the author of NumPy 1.5 Beginner's Guide. You can find more information and a blog with a few NumPy examples at ivanidris.net.
