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NumPy Essentials

You're reading from  NumPy Essentials

Product type Book
Published in Apr 2016
Publisher
ISBN-13 9781784393670
Pages 156 pages
Edition 1st Edition
Languages
Authors (3):
Leo (Liang-Huan) Chin Leo (Liang-Huan) Chin
Profile icon Leo (Liang-Huan) Chin
Tanmay Dutta Tanmay Dutta
Profile icon Tanmay Dutta
Shane Holloway Shane Holloway
Profile icon Shane Holloway
View More author details

Table of Contents (16) Chapters

NumPy Essentials
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. An Introduction to NumPy 2. The NumPy ndarray Object 3. Using NumPy Arrays 4. NumPy Core and Libs Submodules 5. Linear Algebra in NumPy 6. Fourier Analysis in NumPy 7. Building and Distributing NumPy Code 8. Speeding Up NumPy with Cython 9. Introduction to the NumPy C-API 10. Further Reading

Hello world in Cython


Cython programs look quite similar to Python ones, mostly with added type information. Let's have a look at a simple program that computes the n th Fibonacci number given n:

defcompute_fibonacchi(n): 
    """ 
    Computes fibonacchi sequence 
 
    """ 
 
    a = 1 
    b = 1 
    intermediate = 0  
    for x in xrange(n): 
intermediate = a 
        a = a + b 
        b = intermediate 
    return a 

Let's study this program to understand what is going on under the hood when you call this function with some numeric output; let's say compute_fibonacchi(3).

As we know, Python is an interpreted and dynamic language, which means you do not need to declare variables before using them. This means that, at the start of a function call, the Python interpreter is agnostic about the type of value that n will hold. When you call the function with some integral value, Python does the type inference automatically...

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