Code for managing large data sets in Python, usually with Pandas. These scripts mostly merge, filter, inspect, and count things. Developed for a charter school database of 10K+ units based on web-crawling and federal data sources (CCD, ACS, etc).
Replication code for "Sorting Schools: A Computational Analysis of Charter School Identities and Stratification" research article by Jaren Haber, UC Berkeley. Paper investigates the relationships between charter school and school district poverty & race, on one hand, and school ideology and academic performance, on the other.
Code and data for the research team scraping charter websites using scrapy, requests, Selenium, and wget with Python, shell, and Docker. This is the foundation of analyses into charter schools' linguistic strategies and social implications.
Code that examines geographic patterns in charter school proliferation, size, performance, and especially ideology within race- and class-structured school districts and Census tracts. Key packages include matplotlib, folium, and geoplotlib.