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Scientific Computing with Python

Scientific Computing with Python

By : Führer, Claus Fuhrer, Solem, Verdier
4.5 (15)
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Scientific Computing with Python

Scientific Computing with Python

4.5 (15)
By: Führer, Claus Fuhrer, Solem, 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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About Packt
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References

7.2.3 Access to variables defined outside the local namespace

Python allows functions to access variables defined in any of its enclosing program units. These are called global variables, in contrast to local variables. The latter is only accessible within the function. For example, consider the following code:

import numpy as np # here the variable np is defined
def sqrt(x):
    return np.sqrt(x) # we use np inside the function

This feature should not be abused. The following code is an example of what not to do:

a = 3
def multiply(x):
    return a * x # bad style: access to the variable a defined outside

When changing the variable a, the function multiply tacitly changes its behavior:

a=3
multiply(4)  # returns 12
a=4  
multiply(4)  # returns 16

It is much better, in that case, to provide the variable as a parameter through the argument list:

def multiply(x, a):
    return a * x

Global variables can be useful when working with closures; see also the related example in Section 7.7: Anonymous...

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