Thursday, 2 October 2014

Top 6 Python Scientific Packages

If Python is your thing and you're looking for some motivation when down and out consider these 6 amazing scientific packages that are powered by Python, and you'll be satisfied that you took the right decision when considering the language as your first choice. These scientific packages are a live testament to how useful Python could be as a programming language.

Astropy, Biopython, NetworkX, SciPy, scikit-learn, SymPy, Python


The Astropy Project is a collection of software packages written in the Python programming language and designed for use in astronomy. The software was created as part of an unprecedented, professional community effort to develop a single, free, core package for astronomical utilities due to the increasingly widespread usage of Python by astronomers, and to foster interoperability between various extant Python astronomy packages.


The Biopython Project is an international association of developers of non-commercial Python tools for computational molecular biology, as well as bioinformatics. Biopython is one of a number of Bio projects designed to reduce code duplication.


NetworkX is a Python library for studying graphs and networks. NetworkX is free software released under the BSD-new license. NetworkX is suitable for operation on large real-world graphs: eg, graphs in excess of 10 million nodes and 100 million edges


SciPy is a computing environment and open source ecosystem of software for the Python programming language used by scientists, analysts and engineers doing scientific computing and technical computing.


scikit-learn is an open source machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, logistic regression, naive Bayes, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.


SymPy is a Python library for symbolic computation. It provides computer algebra capabilities either as a standalone application, as a library to other applications, or live on the web as SymPy Live. SymPy is trivial to install and to inspect because is written entirely in Python and because it does not depend on any additional libraries.
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