How does Python compare to other solutions?
The original article can be found here at the Scipy introduction
Compiled languages: C, C++, Fortran...
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Pros:
Very fast. For heavy computations, it’s difficult to outperform these languages.
Cons:
Painful usage: no interactivity during development, mandatory compilation steps, verbose syntax, manual memory management. These are difficult languages for non programmers.
Matlab scripting language
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Pros:
Very rich collection of libraries with numerous algorithms, for many different domains. Fast execution because these libraries are often written in a compiled language.
Pleasant development environment: comprehensive and help, integrated editor, etc. Commercial support is available.
Cons:
Base language is quite poor and can become restrictive for advanced users.
Not free.
Julia
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Pros:
Fast code, yet interactive and simple.
Easily connects to Python or C.
Cons:
Ecosystem limited to numerical computing.Still young.
Other scripting languages: Scilab, Octave, R, IDL, etc.
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Pros:
Open-source, free, or at least cheaper than Matlab.
Some features can be very advanced (statistics in R, etc.)
Cons:
Fewer available algorithms than in Matlab, and the language is not more advanced. Some software are dedicated to one domain. Ex: Gnuplot to draw curves. These programs are very powerful, but they are restricted to a single type of usage, such as plotting.
Python
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Pros:
Very rich scientific computing libraries
Well thought out language, allowing to write very readable and well structured code: we “code what we think”.
Many libraries beyond scientific computing (web server, serial port access, etc.)
Free and open-source software, widely spread, with a vibrant community.
A variety of powerful environments to work in, such as IPython, Spyder, Jupyter notebooks,Pycharm
Cons:
Not all the algorithms that can be found in more specialized software or toolboxes.
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