Python's support for scientific computing is composed of a number of packages and APIs for different functionalities required for scientific computing. For each category, we have multiple options and a most popular choice. The following are the examples of Python scientific computing options:
Chart plotting: At present, the most popular two-dimensional chart plotting package is matplotlib. There are several other plotting packages, such as Visvis, Plotly, HippoDraw, Chaco, MayaVI, Biggles, Pychart, and Bokeh. There are some packages that are built on top of matplotlib to provide enhanced functionality, such as Seaborn and Prettyplotlib.
Optimization: The SciPy stack has an optimization package. The other choices for the optimization functionality are OpenOpt and CVXOpt.
Advanced data analysis: Python supports integration with the R statistical package for advanced data analysis using RPy or the RSPlus-Python interface. There is a Python-based library for performing...