What is Python and why python in Bioinformatics?

What is Python and why python in Bioinformatics?

Python is a general-purpose high-level programming language whose design philosophy emphasizes code readability. Python aims to combine "remarkable power with very clear syntax", and its standard library is large and comprehensive. Its use of indentation for block delimiters is unusual among popular programming languages.

Source: http://en.wikipedia.org/wiki/Python_(programming_language)

Because scientists have long relied on the open availability of each other's research results, it was only natural that they would turn to Open Source software when it came time to apply computer processes to the study of biological processes. One of the first Open Source languages to gain popularity among biologists was Perl. Perl gained a foothold in bioinformatics based on its strong text processing facilities, which were ideally suited to analyzing early sequence data. To its credit, Perl has a history of successful use in bioinformatics and is still a very useful tool for biological research.
In comparison to Perl, Python is a relative newcomer to bioinformatics, but is steadily gaining in popularity. A few of the reasons for this popularity are the:

• Readability of Python code
• Ability to development applications quickly
• Powerful standard library of functionality
• Scalability from very small to very large programs

The Python language was designed to be as simple and accessible as possible, without giving up any of the power needed to develop sophisticated applications. Python's clean, consistent syntax leaves it free from the subtleties and nuances that can make other languages difficult to learn and programs written in those languages difficult to comprehend.
Python's dynamic nature adds to its accessibility. For example, Python doesn't require you to declare variables before you use them, and the same variable can refer to objects of different types over the course of its existence. Python can be also be used interactively, allowing you to familiarize yourself with the language of any Python modules in an interactive session where each command produces immediate results.
Python also has excellent support for the object-oriented style of programming. The basic idea is that object-orientation often provides a better way to organize the data and functionality within your programs. As the data and analytical techniques used in bioinformatics have become more complex, the value of object-oriented language features has risen.
In addition, Python integrates well with systems written in other languages, such as C, C++, Java and Fortran. One of the main benefits of C is speed. When a programmer needs an algorithm to run as fast as possible, they can code it in C or C++ and make it available to Python as an extension module. To the programmer, these are indistinguishable from pure Python modules. Similar utilities exist that make the large body of scientific algorithms coded in Fortran accessible to Python programs.
Java has become popular as a cross-platform and Web development language. The Python interpreter is now available in two variations: one version written in C, and the other version, known as Jython, written in Java. Jython allows Java programmers to write programs using the Python syntax and dynamic language features, and it allows Python programmers to use existing code developed in Java. These are just a few examples of the many ways Python is able to leverage and extend existing code written in other languages.
So while Perl is more well established in the bioinformatics community, many biologists and bioinformaticians are also turning to Python as it gains in popularity.

Source:

Beginning Python for Bioinformatics

by Patrick O'Brien

http://onlamp.com/pub/a/python/2002/10/17/biopython.html

you can download this from :
http://groups.google.com/group/bio-bio-1/files?upload=1