Partridge

Partridge is python library for working with GTFS feeds using pandas DataFrames.

The implementation of Partridge is heavily influenced by our experience at Remix ingesting, analyzing, and debugging thousands of GTFS feeds from hundreds of agencies.

At the core of Partridge is a dependency graph rooted at trips.txt. Disconnected data is pruned away according to this graph when reading the contents of a feed. The root node can optionally be filtered to create a view of the feed specific to your needs. It's most common to filter a feed down to specific dates (service_id), routes (route_id), or both.

Philosphy

The design of Partridge is guided by the following principles:

• as much as possible
  • favor speed
  • allow for extension
  • succeed lazily on expensive paths
  • fail eagerly on inexpensive paths
• as little as possible
  • do anything other than efficiently read GTFS files into DataFrames
  • take an opinion on the GTFS spec

Usage

import datetime
import partridge as ptg

path = 'path/to/sfmta-2017-08-22.zip'

service_ids_by_date = ptg.read_service_ids_by_date(path)

service_ids = service_ids_by_date[datetime.date(2017, 9, 25)]

feed = ptg.feed(path, view={
    'trips.txt': {
        'service_id': service_ids,
        'route_id': '12300', # 18-46TH AVENUE
    },
})

assert set(feed.trips.service_id) == service_ids
assert list(feed.routes.route_id) == ['12300']

# Buses running the 18 - 46th Ave line use 88 stops (on September 25, 2017, at least).
assert len(feed.stops) == 88

Features

• Surprisingly fast :)
• Load only what you need into memory
• Built-in support for resolving service dates
• Easily extended to support fields and files outside the official spec (TODO: document this)
• Handle nested folders and bad data in zips
• Predictable type conversions

Installation

pip install partridge

Thank You

I hope you find this library useful. If you have suggestions for improving Partridge, please open an issue on GitHub.