Why is Python so powerful for scientific programming

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How can free software tools be better than commercial software tools?

Currently, I'm using Python as a scientific programming tool. Whenever I have used several free tools for scientific programming previously, I can not hide my regreat since free tools have always important disadvatages. Especailly, there is usually less documentation to use. However, rightnow I found that some free tools have both high compatibility with commercial best tools and better documentations to use. One of examples in scientific programming is Python with numpy, matplotlib libraries.

In Python, similar to the competitive commercial tool, debugging environment is avaliable if free Eclipse with PyDev is used. Moreover, spyder offers wonderful indegrated editting environment.

Another supporing environment is iPython with a option of pylab. It supports very wonderful scripting environment to support plot online. For python expert, it will be the best environment to test and run his scientific codes. I open iPython with pylab option whenever I use Python for scientific programming.

I think that the current commercially best computtiong tool is a good tool for engineering computation since it supports many engineering libraries for signal processing, probability, mechanical engineering. However, for scientific programming, a computational tool must offers flexible and extensionable environments. Therefore, Python is most appropriate for scientific computing. Organic and inorganic nano materials, information, and biology are the areas for new scientific research.

First of all, Python is success to combine basic programming language form and object oriented programming. Therefore, it is approprotate for both scientific programming and engineering programming where scientific programming and engineering programming requres object oriented approach and fortran like mathematical description. 

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