Lukas and I were trying to write a succinct comparison of the most popular packages that are typically used for data analysis. I think most people choose one based on what people around them use or what they learn in school ...
Easy-to-use and general-purpose machine learning in Python. Scikit-learn integrates machine learning algorithms in the tightly-knit scientific Python world, building upon numpy, scipy, and matplotlib. As a machine-learning module, it provides versatile tools for data mining and analysis in any field of science and engineering. It strives to be simple and efficient, accessible to everybody, and reusable in various contexts.
When it comes to analyzing Big Data, software packages such as Hadoop or the R statistical language come readily to mind. But at least one company, AppNexus, also relies on the Python programming language to help conduct heavy-duty data analysis.
Python provides employees with different backgrounds -- notably engineers, mathematicians and analysts -- a common, easy-to-understand language that can be used to prototype new functionality for the company. In addition, Python libraries such as SciPy, iPython and Pandas provide much of the mathematical functionality typically found in the R programming language.
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