An Introduction To Python For Scientific Computing

An Introduction To Python For Scientific Computing

This text covers standard modules pre-loaded in Python, including packages for common mathematical and numerical routines.

Tag(s): Python

Publication date: 31 Dec 2014

ISBN-10: n/a

ISBN-13: n/a

Paperback: n/a

Views: 5,183

Type: N/A

Publisher: n/a

License: n/a

Post time: 01 Feb 2016 08:24:13

An Introduction To Python For Scientific Computing

An Introduction To Python For Scientific Computing This text covers standard modules pre-loaded in Python, including packages for common mathematical and numerical routines.
Tag(s): Python
Publication date: 31 Dec 2014
ISBN-10: n/a
ISBN-13: n/a
Paperback: n/a
Views: 5,183
Document Type: N/A
Publisher: n/a
License: n/a
Post time: 01 Feb 2016 08:24:13
Excerpts from the Overview:
Python is an extremely usable, high-level programming language that is quickly becoming a standard in scientific computing. It is open source, completely standardized across different platforms (Windows / MacOS / Linux), immensely flexible, and easy to use and learn. Programs written in Python are highly readable and often much shorter than comparable programs written in other languages like C or Fortran. Moreover, Python comes pre-loaded with standard modules that provide a huge array of functions and algorithms, for tasks like parsing text data, manipulating and finding files on disk, reading/writing compressed files, and downloading data from web servers. Python is also capable of all of the complex techniques that advanced programmers expect, like object orientation.

Python is somewhat different than languages like C, C++, or Fortran. In the latter, source code must first be compiled to an executable format before it can be run. In Python, there is no compilation step; instead, source code is interpreted on the fly in a line-by-line basis. That is, Python executes code as if it were a script. The main advantage of an interpreted language is that it is flexible; variables do not need to be declared ahead of time, and the program can adapt on-the-fly. The main disadvantage, however, is that numerically-intensive programs written in Python typically run slower than those in compiled languages. This would seem to make Python a poor choice for scientific computing; however, time-intensive subroutines can be compiled in C or Fortran and imported into Python in such a manner that they appear to behave just like normal Python functions.




About The Author(s)


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M. Scott Shell

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