Skip to main content

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

Project description

=========
Partridge
=========


.. image:: https://img.shields.io/pypi/v/partridge.svg
:target: https://pypi-hypernode.com/pypi/partridge

.. image:: https://img.shields.io/travis/remix/partridge.svg
:target: https://travis-ci.org/remix/partridge


Partridge is python library for working with `GTFS <https://developers.google.com/transit/gtfs/>`__ feeds using `pandas <https://pandas.pydata.org/>`__ DataFrames.

Partridge is heavily influenced by our experience at `Remix <https://www.remix.com/>`__ analyzing and debugging every GTFS feed we could find.

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.

Feeds can also be filtered to create a view specific to your needs. It's most common to filter a feed down to specific dates (``service_id``) or routes (``route_id``), but any field can be filtered.

.. figure:: dependency-graph.png
:alt: dependency graph


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


Installation
------------

.. code:: console

pip install partridge


Usage
-----

**Setup**

.. code:: python

import partridge as ptg

inpath = 'path/to/caltrain-2017-07-24/'


Inspecting the calendar
~~~~~~~~~~~~~~~~~~~~~~~


**The date with the most trips**

.. code:: python

date, service_ids = ptg.read_busiest_date(inpath)
# datetime.date(2017, 7, 17), frozenset({'CT-17JUL-Combo-Weekday-01'})


**The week with the most trips**


.. code:: python

service_ids_by_date = ptg.read_busiest_week(inpath)
# {datetime.date(2017, 7, 17): frozenset({'CT-17JUL-Combo-Weekday-01'}),
# datetime.date(2017, 7, 18): frozenset({'CT-17JUL-Combo-Weekday-01'}),
# datetime.date(2017, 7, 19): frozenset({'CT-17JUL-Combo-Weekday-01'}),
# datetime.date(2017, 7, 20): frozenset({'CT-17JUL-Combo-Weekday-01'}),
# datetime.date(2017, 7, 21): frozenset({'CT-17JUL-Combo-Weekday-01'}),
# datetime.date(2017, 7, 22): frozenset({'CT-17JUL-Caltrain-Saturday-03'}),
# datetime.date(2017, 7, 23): frozenset({'CT-17JUL-Caltrain-Sunday-01'})}


**Dates with active service**

.. code:: python

service_ids_by_date = ptg.read_service_ids_by_date(path)

date, service_ids = min(service_ids_by_date.items())
# (datetime.date(2017, 7, 15), frozenset({'CT-17JUL-Caltrain-Saturday-03'}))

date, service_ids = max(service_ids_by_date.items())
# (datetime.date(2019, 7, 20), frozenset({'CT-17JUL-Caltrain-Saturday-03'}))


**Dates with identical service**


.. code:: python

dates_by_service_ids = ptg.read_dates_by_service_ids(inpath)

busiest_date, busiest_service = ptg.read_busiest_date(inpath)
dates = dates_by_service_ids[busiest_service]

min(dates), max(dates)
# datetime.date(2017, 7, 17), datetime.date(2019, 7, 19)


Reading a feed
~~~~~~~~~~~~~~



.. code:: python

_date, service_ids = ptg.read_busiest_date(inpath)

view = {
'trips.txt': {'service_id': service_ids},
'stops.txt': {'stop_name': 'Gilroy Caltrain'},
}

feed = ptg.load_feed(path, view)


Extracting a new feed
~~~~~~~~~~~~~~~~~~~~~

.. code:: python

outpath = 'gtfs-slim.zip'

date, service_ids = ptg.read_busiest_date(inpath)
view = {'trips.txt': {'service_id': service_ids}}

ptg.extract_feed(inpath, outpath, view)
feed = ptg.load_feed(outpath)

assert service_ids == set(feed.trips.service_id)


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

Thank You
---------

I hope you find this library useful. If you have suggestions for
improving Partridge, please open an `issue on
GitHub <https://github.com/remix/partridge/issues>`__.


History
=======

1.0.0 (2018-12-18)
------------------

This release is a combination of major internal refactorings and some minor interface changes. Overall, you should expect your upgrade from pre-1.0 versions to be relatively painless. A big thank you to @genhernandez and @csb19815 for their valuable design feedback.

Here is a list of interface changes:

* The class ``partridge.gtfs.feed`` has been renamed to ``partridge.gtfs.Feed``.
* The public interface for instantiating feeds is ``partridge.load_feed``. This function replaces the previously undocumented function ``partridge.get_filtered_feed``.
* A new function has been added for identifying the busiest week in a feed: ``partridge.read_busiest_date``
* The public function ``partridge.get_representative_feed`` has been removed in favor of using ``partridge.read_busiest_date`` directly.
* The public function ``partridge.writers.extract_feed`` is now available via the top level module: ``partridge.extract_feed``.

Miscellaneous minor changes:

* Character encoding detection is now done by the ``cchardet`` package instead of ``chardet``. ``cchardet`` is faster, but may not always return the same result as ``chardet``.
* Zip files are unpacked into a temporary directory instead of reading directly from the zip. These temporary directories are cleaned up when the feed is garbage collected or when the process exits.
* The code base is now annotated with type hints and the build runs ``mypy`` to verify the types.
* DataFrames are cached in a dictionary instead of the ``functools.lru_cache`` decorator.
* The ``partridge.extract_feed`` function now writes files concurrently to improve performance.


0.11.0 (2018-08-01)
-------------------

* Fix major performance issue related to encoding detection. Thank you to @cjer for reporting the issue and advising on a solution.


0.10.0 (2018-04-30)
-------------------

* Improved handling of non-standard compliant file encodings
* Only require functools32 for Python < 3
* ``ptg.parsers.parse_date`` no longer accepts dates, only strings


0.9.0 (2018-03-24)
------------------

* Improves read time for large feeds by adding LRU caching to ``ptg.parsers.parse_time``.


0.8.0 (2018-03-14)
------------------

* Gracefully handle completely empty files. This change unifies the behavior of reading from a CSV with a header only (no data rows) and a completely empty (zero bytes) file in the zip.


0.7.0 (2018-03-09)
------------------

* Fix handling of nested folders and zip containing nested folders.
* Add ``ptg.get_filtered_feed`` for multi-file filtering.


0.6.1 (2018-02-24)
------------------

* Fix bug in ``ptg.read_service_ids_by_date``. Reported by @cjer in #27.


0.6.0 (2018-02-21)
------------------

* Published package no longer includes unnecessary fixtures to reduce the size.
* Naively write a feed object to a zip file with ``ptg.write_feed_dangerously``.
* Read the earliest, busiest date and its ``service_id``'s from a feed with ``ptg.read_busiest_date``.
* Bug fix: Handle ``calendar.txt``/``calendar_dates.txt`` entries w/o applicable trips.


0.6.0.dev1 (2018-01-23)
-----------------------

* Add support for reading files from a folder. Thanks again @danielsclint!


0.5.0 (2017-12-22)
------------------

* Easily build a representative view of a zip with ``ptg.get_representative_feed``. Inspired by `peartree <https://github.com/kuanb/peartree/blob/3bfc3f49ae6986d6020913b63c8ee32582b3dcc3/peartree/paths.py#L26>`_.
* Extract out GTFS zips by agency_id/route_id with ``ptg.extract_{agencies,routes}``.
* Read arbitrary files from a zip with ``feed.get('myfile.txt')``.
* Remove ``service_ids_by_date``, ``dates_by_service_ids``, and ``trip_counts_by_date`` from the feed class. Instead use ``ptg.{read_service_ids_by_date,read_dates_by_service_ids,read_trip_counts_by_date}``.


0.4.0 (2017-12-10)
------------------

* Add support for Python 2.7. Thanks @danielsclint!


0.3.0 (2017-10-12)
------------------

* Fix service date resolution for raw_feed. Previously raw_feed considered all days of the week from calendar.txt to be active regardless of 0/1 value.


0.2.0 (2017-09-30)
------------------

* Add missing edge from fare_rules.txt to routes.txt in default dependency graph.


0.1.0 (2017-09-23)
------------------

* First release on PyPI.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

partridge-1.0.0.tar.gz (29.8 kB view details)

Uploaded Source

Built Distribution

partridge-1.0.0-py2.py3-none-any.whl (13.8 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file partridge-1.0.0.tar.gz.

File metadata

  • Download URL: partridge-1.0.0.tar.gz
  • Upload date:
  • Size: 29.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: Python-urllib/3.7

File hashes

Hashes for partridge-1.0.0.tar.gz
Algorithm Hash digest
SHA256 cb58b87c4950c60eb98ca3f45afcdbcd8bc0d0a0c4b190911d7d65ba0810fb95
MD5 98e0cce40f694314d7547252023946b2
BLAKE2b-256 6fbecd7fb93c7cb49293c9cf2e6fdcec7a56ad7fdf5bd14bb2a1c3e641d025bf

See more details on using hashes here.

File details

Details for the file partridge-1.0.0-py2.py3-none-any.whl.

File metadata

File hashes

Hashes for partridge-1.0.0-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 9c88522bdc2b61a71067f5ea104dbe9b0deb239f9be636368846a7231dd11dc0
MD5 427f1a6a665f772decec9f16c74a0287
BLAKE2b-256 19a63a26b6ffc3a317248a279f9c0057ae92e9f3432af50af8d233c217d80de3

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page