GTFS Kit is a Python 3.10+ library for analyzing General Transit Feed Specification (GTFS) data in memory without a database. It uses Pandas and GeoPandas to do the heavy lifting.

The functions/methods of GTFS Kit assume a valid GTFS feed but offer no inbuilt validation, because GTFS validation is complex and already solved by dedicated libraries. So unless you know what you're doing, use the Canonical GTFS Validator before you analyze a feed with GTFS Kit.

Install it from PyPI with UV, say, via uv add gtfs_kit.

In the Jupyter notebook notebooks/examples.ipynb, which is a Github-displayable export of the Marimo notebook notebooks/examples.py.

• Alex Raichev (2019-09), maintainer

The documentation is built via Sphinx from the source code in the docs directory then published to Github Pages at araichev.github.io/gtfs_kit_docs.

• This project's development status is Alpha. I use GTFS Kit at my job and change it breakingly to suit my needs.
• This project uses semantic versioning.
• I aim for GTFS Kit to handle the current GTFS. In particular, i avoid handling GTFS extensions. That is the most reasonable scope boundary i can draw at present, given this project's tiny budget. If you would like to fund this project to expand its scope, please email me.
• Thanks to MRCagney for periodically donating to this project.
• Constructive feedback and contributions are welcome. Please issue pull requests from a feature branch into the develop branch and include tests.
• GTFS time is measured relative to noon minus 12 hours, which can mess things up when crossing into daylight savings time. I don't think this issue causes any bugs in GTFS Kit, but you and i have been warned. Thanks to user Github user derhuerst for bringing this to my attention in closed Issue 8.
• With release 10.0.0, i removed the validation module validators.py to avoid duplicating the work of what is now the canonical feed validator (written in Java).
• I'll probably move to Polars + Polars ST for the next big release so the library can handle big feeds faster.

• Before pushing to master, export the example Marimo notebook to Jupyter via uv run marimo export ipynb notebooks/examples.py -o notebooks/examples.ipynb --include-outputs -f, because the docs refer to that Github-displayable version.
• After pushing to master, update the published docs via uv run make -C docs publish-docs-github