Jake VanderPlas - A Whirlwind Tour of Python

• 2016-08-10: First Release

Conceived in the late 1980s as a teaching and scripting language, Pythonhas since become an essential tool for many programmers, engineers,researchers, and data scientists across academia and industry. As anastronomer focused on building and promoting the free open tools fordata-intensive science, Ive found Python to be a near-perfect fit forthe types of problems I face day to day, whether its extracting meaningfrom large astronomical datasets, scraping and munging data sources fromthe Web, or automating day-to-day research tasks.

The appeal of Python is in its simplicity and beauty, as well as theconvenience of the large ecosystem of domain-specific tools that havebeen built on top of it. For example, most of the Python code inscientific computing and data science is built around a group of matureand useful packages:

• NumPy provides efficient storage and computation formultidimensional data arrays.

• SciPy contains a wide array of numerical tools suchas numerical integration and interpolation.

• Pandas provides a DataFrame object alongwith a powerful set of methods to manipulate, filter, group, andtransform data.

• Matplotlib provides a useful interface forcreation of publication-quality plots and figures.

• Scikit-Learn provides a uniform toolkit forapplying common machine learning algorithms to data.

• IPython/Jupyter provides an enhanced terminal andan interactive notebook environment that is useful for exploratoryanalysis, as well as creation of interactive, executable documents. Forexample, the manuscript for this report was composed entirely in Jupyter notebooks.

No less important are the numerous other tools and packages whichaccompany these: if there is a scientific or data analysis task you wantto perform, chances are someone has written a package that will do itfor you.

To tap into the power of this data science ecosystem, however, first requires familiarity with the Python language itself. I often encounterstudents and colleagues who have (sometimes extensive) backgrounds incomputing in some languageMATLAB, IDL, R, Java, C++, etc.and arelooking for a brief but comprehensive tour of the Python language thatrespects their level of knowledge rather than starting from ground zero.This report seeks to fill that niche.

As such, this report in no way aims to be a comprehensive introductionto programming, or a full introduction to the Python language itself; ifthat is what you are looking for, you might check out one of therecommended references listed in . Instead, this willprovide a whirlwind tour of some of Pythons essential syntax andsemantics, built-in data types and structures, function definitions,control flow statements, and other aspects of the language. My aim isthat readers will walk away with a solid foundation from which toexplore the data science stack just outlined.