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  • Book Overview & Buying Scientific Computing with Python
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Scientific Computing with Python

Scientific Computing with Python - Second Edition

By : Claus Führer, Claus Fuhrer, Jan Erik Solem, Olivier Verdier
4.5 (14)
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Scientific Computing with Python

Scientific Computing with Python

4.5 (14)
By: Claus Führer, Claus Fuhrer, Jan Erik Solem, Olivier Verdier

Overview of this book

Python has tremendous potential within the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user interfaces, efficient data processing, and parallel computing to help you perform mathematical and scientific computing efficiently using Python. This book will help you to explore new Python syntax features and create different models using scientific computing principles. The book presents Python alongside mathematical applications and demonstrates how to apply Python concepts in computing with the help of examples involving Python 3.8. You'll use pandas for basic data analysis to understand the modern needs of scientific computing, and cover data module improvements and built-in features. You'll also explore numerical computation modules such as NumPy and SciPy, which enable fast access to highly efficient numerical algorithms. By learning to use the plotting module Matplotlib, you will be able to represent your computational results in talks and publications. A special chapter is devoted to SymPy, a tool for bridging symbolic and numerical computations. By the end of this Python book, you'll have gained a solid understanding of task automation and how to implement and test mathematical algorithms within the realm of scientific computing.
Table of Contents (23 chapters)
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20
About Packt
22
References

15.3.2 Timing with the Python module timeit

Python provides the module timeit, which can be used to measure execution time. It requires that, first, a time object is constructed. It is constructed from two strings: a string with setup commands and a string with the commands to be executed.

We take the same four alternatives as in the preceding example. The array and function definitions are written now in a string called setup_statements and four timing objects are constructed as follows:

import timeit
setup_statements="""
from scipy import zeros
from numpy import where
A=zeros((1000,1000))
A[57,63]=10.

def find_elements_1(A):
    b = []
    n, m = A.shape
    for i in range(n):
        for j in range(m):
            if abs(A[i, j]) > 1.e-10:
               b.append(A[i, j])
    return b

def find_elements_2(A):
    return [a for a in A.reshape((-1,)) if abs(a) > 1.e-10]

def find_elements_3(A):
    return [a for a in A.flatten() if...
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Scientific Computing with Python
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