Python is easy to read and write and backed by a wonderful community. It is an incredibly useful tool for working with economic data. I’ve written a few examples of how this open-source programming language can be used to work with real-world economic data.

I use the miniconda distribution of Python, which already includes key packages (such as NumPy, Pandas, SciPy, etc) and tools (Jupyter notebook, Conda).

Guides: Python for Economic Analysis


Three-part guide to using the International Monetary Fund's Application Programming Interface to machine-read data.


Analyze and visualize the trade network for specific goods using a algorithms from the NetworkX package.


A guide to collecting data from the U.S. Bureau of Labor Statistics API using Python's requests package.

CPS micro

An example of reading U.S. Current Population Survey (CPS) microdata and benchmarking against published data.