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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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20
About Packt
22
References

2.1 Variables

Variables are references to Python objects. They are created by assignments, for example:

a = 1
diameter = 3.
height = 5.
cylinder = [diameter, height] # reference to a list

Variables take names that consist of any combination of capital and small letters, the underscore _, and digits. A variable name must not start with a digit. Note that variable names are case sensitive. A good naming of variables is an essential part of documenting your work, so we recommend that you use descriptive variable names.

Python has 33 reserved keywords, which cannot be used as variable names (see Table 2.1). Any attempt to use such a keyword as a variable name would raise a syntax error:

Table 2.1: Reserved Python keywords

As opposed to other programming languages, variables require no type declaration in Python. The type is automatically deduced:

x = 3 # integer (int)
y = 'sunny' # string (str)

You can create several variables with a multiple assignment statement:

a = b = c = 1 # a, b and c get the same value 1

Variables can also be altered after their definition:

a = 1 
a = a + 1 # a gets the value 2
a = 3 * a # a gets the value 6

The last two statements can be written by combining the two operations with an assignment directly by using increment operators:

a += 1 # same as a = a + 1 
a *= 3 # same as a = 3 * a
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