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Book details
File Size: 5.22 MB
Format: pdf
Print Length: 388 pages
Simultaneous Device Usage: Up to 4 simultaneous devices, per publisher limits
Publisher: CRC Press; 1 edition (March 15, 2019)
Publication Date: March 15, 2019
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B07LB7QH1B
Introduction to Python for Science and Engineering
This guide offers a quick and incisive introduction to Python programming for anyone. The author has carefully developed a concise approach to using Python in any discipline of science and engineering, with plenty of examples, practical hints, and insider tips.
Readers will see why Python is such a widely appealing program, and learn the basics of syntax, data structures, input and output, plotting, conditionals and loops, user-defined functions, curve fitting, numerical routines, animation, and visualization. The author teaches by example and assumes no programming background for the reader.
From the introduction:
In writing this text, I assume that the reader:
* has never programmed before;
* is not familiar with programming environments;
* is familiar with how to get around a Mac or PC at a very basic level; and
* is competent in basic algebra, and for Chapters 8 and 9, calculus, linear algebra, ordinary differential equations, and Fourier analysis. The other chapters, including 10–12, require only basic algebra skills.
This book introduces, in some depth, four Python packages that are important for scientific applications:
NumPy, short for Numerical Python, provides Python with a multi- dimensional array object (like a vector or matrix) that is at the cen- ter of virtually all fast numerical processing in scientific Python.
It is both versatile and powerful, enabling fast numerical compu- tation that, in some cases, approaches speeds close to those of a compiled language like C, C++, or Fortran.
SciPy, short for Scientific Python, provides access through a Python interface to a very broad spectrum of scientific and numerical soft- ware written in C, C++, and Fortran. These include routines to numerically differentiate and integrate functions, solve differen- tial equations, diagonalize matrices, take discrete Fourier trans- forms, perform least-squares fitting, as well as many other numerical tasks.
matplotlib is a powerful plotting package written for Python and capable of producing publication-quality plots. While there are other Python plotting packages available, matplotlib is the most widely used and is the de facto standard.
Pandas is a powerful package for manipulating and analyzing data formatted and labeled in a manner similar to a spreadsheet (think Excel). Pandas is very useful for handling data produced in exper- iments, and is particularly adept at manipulating large data sets in different ways.