Setting up Python environments for data mining

A computing setup conducive to advanced data mining requires a comfortable development environment and working libraries for data management, analytics, plotting, and deployment. The popular bundled Python distribution from Anaconda is a perfect fit for the job. It is targeted at scientists and engineers, and includes all the required packages to get started. Conda itself is a package manager for maintaining working Python environments and, of course, is included in the bundle. The package manager will allow you to install/remove combinations of libraries into segregated Python environments, all the while reconciling any version dependencies between the distinct libraries. 

It includes an integrated development environment called The Scientific Python Development Environment (Spyder) and a ready-to-use implementation of Jupyter Notebook interface. Both of these development environments use the interactive Python console called IPython. IPython gives you a live console for scripting. You can run a single line of code, check results, then run another line of code in same console in an interactive fashion. A few trial-and-error sessions with IPython will demonstrate very clearly why these Python tools are so beloved by practitioners working in a rapid prototyping environment.