Hadley Whickham wrote a famous paper (for a certain definition of famous) about the importance of tidy data when doing data analysis. I want to talk a bit about that, using an example from a StackOverflow post, with a solution using pandas. The principles of tidy data aren’t language specific. A tidy dataset must satisfy three criteria (page 4 in Whickham’s paper): Each variable forms a column. Each observation forms a row. Each type of observational unit forms a table. In this StackOverflow post, the asker had some data NBA games, and wanted to know the number of days since a team last played. Here’s the example data: ...

As a graduate student, you read a lot of journal articles… a lot. With the material in the articles being as difficult as it is, I didn’t want to worry about organizing everything as well. That’s why I wrote this script to help (I may have also been procrastinating from studying for my qualifiers). This was one of my earliest little projects, so I’m not claiming that this is the best way to do anything. ...

Last time, we used Python to fetch some data from the Current Population Survey. Today, we’ll work on parsing the files we just downloaded. We downloaded two types of files last time: CPS monthly tables: a fixed-width format text file with the actual data Data Dictionaries: a text file describing the layout of the monthly tables Our goal is to parse the monthly tables. Here’s the first two lines from the unzipped January 1994 file: ...

The Current Population Survey is an important source of data for economists. It’s modern form took shape in the 70’s and unfortunately the data format and distribution shows its age. Some centers like IPUMS have attempted to put a nicer face on accessing the data, but they haven’t done everything yet. In this series I’ll describe methods I used to fetch, parse, and analyze CPS data for my second year paper. Today I’ll describe fetching the data. Everything is available at the paper’s GitHub Repository. ...