Python: The Ultimate Beginners Guide - Start Coding Today
It is easy to see that language has fantastic facilities for general use. The question is: why Python is the ideal language for scientific applications? The answers are many, and some might be summarized in this guide. Briefly, the first reason, and probably the main one, is: Python is an expressive language that is easy to translate the reasoning in an algorithm. In scientific applications, the argument is intrinsically complicated - this is the nature of science. It is an additional problem for the scientist has to worry about, beyond the basics of his research subject, the program correction in some relevant details: memory allocation, resource management, etc. Python automatically does it all very efficiently.
Python is extremely readable. It means it is very easy to understand programs written for some time. It is very common that programs in scientific activities are created from the evolution of previous algorithms. It is, therefore, critical to be able to understand what was done before. Once the keywords of Python are geared to the structure of programs (and not to tell the computer how to compile or interpret code snippets), no code snippets that are useless for reasoning.
Python has an active and vibrant community, spread all over the world. And being a free language, all its members are willing to contribute. It makes the documentation is abundant, and there are modules to perform virtually any task required. It is important: there is no time to reinvent the wheel, so when you can rely on active modules is great. But more than that, since Python programs are distributed as source code, anyone can change, correct and improve the algorithms. It makes the modules are mature and safe tested against various situations and many times. The achieved strength is a major factor.
Python is, moreover, a general purpose language. It is often necessary to deal with side tasks: search for data in a remote database, read a website graphically display the results, create a spreadsheet, etc. Specifically, scientific nature languages have a serious problem there, but since Python is used in virtually every type of task modules are ready to perform these tasks can become complicated. Again, it's one less worry for those who are developing scientific applications.
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