Visualizing public transport using Python
I will demonstrate the power of the datashader library to make interactive visualizations of large datasets with little effort through an example of public transport data. I will also briefly touch upon the topics of open data, GTFS public transport schedules and using undocumented APIs. The presented approach can easily be used to plot a wide range of large-ish geo-datasets, such as traffic data, location history, industrial activity, demographics or voting patterns.
Analyst at the Central Bank of Hungary mostly doing applied econometrics. An R user during the day and a Python user at night. Studied at Corvinus University of Budapest, Central European University and the Heller Farkas College of Advanced Financial Studies (also held multiple introductory Python courses there). Have eclectic interests including data visualization, functional programming, behavioral economics, Bayesian econometrics and American football.