The Python environment

A feature of the Python environment is its interactive console allowing you to both use Python as a desktop programmable calculator and also as an environment to write and test snippets of code. The read-evaluate-print loop of the console is a very convenient way to interact with a larger code base, such as to run functions and methods or to create instances of classes. This is one of the major advantages of Python over compiled languages such as C/C++ or Java, where the write-compile-test-recompile cycle can increase development time considerably compared to Python's read - evaluate - print loop. Being able to type in expressions and get an immediate response can greatly speed up data science tasks.

There are some excellent distributions of Python apart from the official CPython version. Two of the most popular are Anaconda (https://www.continuum.io/downloads) and Canopy (https://www.enthought.com/products/canopy/). Most distributions come with their own developer environments. Both Canopy and Anaconda include libraries for scientific, machine learning, and other data applications. Most distributions come with an editor.

There are also a number of implementations of the Python console, apart from the CPython version. Most notable amongst these is the Ipython/Jupyter platform that includes a web-based computational environment.