python-citybikes 0.3.3

About Citybikes

Citybikes is a project about making bike sharing data available to both users and developers. It has a nice and easy to use API, but this package might be useful to projects consuming the API.

python-citybikes must not be confused with pybikes:

• pybikes is a set of tools to access bike sharing data directly from the providers. It’s the library powering the project and the API. If a network is not supported or does not correctly work, it’s there where the issue or the contribution must be sent.

• python-citybikes is a python wrapper around the Citybikes API.

Installation

$ pip install python-citybikes

Usage

First instantiate a client

>>>> import citybikes
>>>> client = citybikes.Client()

Get the full list of networks

>>>> networks = list(client.networks)
>>>> len(networks)
458
>>>> networks[0]
{'name': 'Opole Bike', 'href': '/v2/networks/opole-bike', 'location': {'lat
itude': 50.6645, 'city': 'Opole', 'country': 'PL', 'longitude': 17.9276}, '
id': 'opole-bike', 'company': ['Nextbike GmbH']}

Get stations from a network

>>>> len(networks[0].stations)
16
>>>> list(networks[0].stations)[0]
{'timestamp': '2016-11-22T16:05:44.318000Z', 'id': 'd8c9f66260759aeb27445b2
cddf2d6b9', 'name': 'Pętla Autobusowa - Dambonia', 'free_bikes': 7, 'empty_
slots': 3, 'latitude': 50.661775266224, 'extra': {'bike_uids': ['60128', '6
0108', '60063', '60062', '60052', '60037', '60190'], 'number': '6011', 'slo
ts': 10, 'uid': '132115'}, 'longitude': 17.888891100884}

Instantiate a network by id directly

>>>> bicing = citybikes.Network(client, uid='bicing')
>>>> bicing['name']
'Bicing'
>>>> len(bicing.stations)
465

Get a network ordered by distance to lat, lng

>>>> # Lets get the nearest network to NY lat, lng
>>>> net, dist = next(iter(client.networks.near(40.7128, -74.0059)))
>>>> net
{'href': '/v2/networks/citi-bike-nyc', 'id': 'citi-bike-nyc', 'gbfs_href':
'https://gbfs.citibikenyc.com/gbfs/gbfs.json', 'location': {'latitude': 40.
7143528, 'country': 'US', 'longitude': -74.00597309999999, 'city': 'New Yor
k, NY'}, 'company': ['NYC Bike Share, LLC', 'Motivate International, Inc.',
'PBSC Urban Solutions'], 'name': 'Citi Bike'}

Get stations from a network ordered by distance to lat, lng

>>>> # Now, get stations ordered by distance to Manhattan center
>>>> sts = net.stations.near(40.7831, -73.9712)
>>>> for s, dist in sts[:5]:
...     print(s['name'])
...
W 82 St & Central Park West
Central Park West & W 85 St
W 84 St & Columbus Ave
Central Park West & W 76 St
W 89 St & Columbus Ave