shops 0.0.3

Provides data about shops in a given location, based on OpenStreetMap data.

Shops Package

Provides data about shoplike objecs in a given location, based on OpenStreetMap data.

Remember about license, see https://www.openstreetmap.org/copyright

import shops
import os

# data from https://download.geofabrik.de/europe/andorra.html
# you can try north-america/us code standing for
# https://download.geofabrik.de/north-america/us.html
# but in such case you should expect much longer processing
location_code = "europe/andorra"
# pernament location is better, this is used in example as it likely to exist on almost any Linux
path_processing_directory = "/tmp/ATP"
if os.path.isdir(path_processing_directory) == False:
    os.mkdir(path_processing_directory)
for entry in shops.osm.list_shops(location_code, path_processing_directory):
    print(entry)

It will provide filtered OSM data, in a form of yielded dicts, each with tags, center and osm_link fields, center being a dict with lat and lon fields:

...
{'tags': {'amenity': 'restaurant', 'building': 'yes', 'name': "Vermutería Arrosseria del Poble D'Auvinya"}, 'center': {'lat': 42.45450165, 'lon': 1.4926907}, 'osm_link': 'https://www.openstreetmap.org/way/1236442460'}
{'tags': {'access': 'yes', 'amenity': 'charging_station', 'capacity': '8', 'fee': 'yes', 'name': '313 Aparcament telecabina del Tarter', 'operator': "Forces Elèctriques d'Andorra", 'payment:app': 'yes', 'payment:electromaps': 'yes'}, 'center': {'lat': 42.57838705, 'lon': 1.6470569}, 'osm_link': 'https://www.openstreetmap.org/way/1270600406'}
{'tags': {'building': 'commercial', 'shop': 'car_repair'}, 'center': {'lat': 42.5531873, 'lon': 1.50726665}, 'osm_link': 'https://www.openstreetmap.org/way/1276422261'}
{'tags': {'addr:city': 'Incles', 'addr:postcode': 'AD100', 'addr:street': "Camí Pont D'Incles", 'amenity': 'restaurant', 'building': 'yes', 'name': 'Lamont'}, 'center': {'lat': 42.5831009, 'lon': 1.6640846}, 'osm_link': 'https://www.openstreetmap.org/way/1286995487'}
{'tags': {'addr:city': 'El Tarter', 'addr:postcode': 'AD100', 'addr:street': 'CG-2', 'amenity': 'restaurant', 'building': 'yes', 'cuisine': 'bar&grill', 'name': 'The Boss', 'name:ca': 'The Boss'}, 'center': {'lat': 42.57929245, 'lon': 1.64093665}, 'osm_link': 'https://www.openstreetmap.org/way/1288987664'}
{'tags': {'building': 'yes', 'office': 'government', 'type': 'multipolygon'}, 'center': {'lat': 42.50669325, 'lon': 1.5222641000000001}, 'osm_link': 'https://www.openstreetmap.org/relation/14084224'}
{'tags': {'amenity': 'bank', 'building': 'yes', 'name': 'Andbank', 'name:ca': 'Andbank', 'type': 'multipolygon'}, 'center': {'lat': 42.51118895, 'lon': 1.5346714750000001}, 'osm_link': 'https://www.openstreetmap.org/relation/14119424'}

Installation

pip install shops

It is uploaded to pypi.org

Behind scenes

Data is downloaded, preprocessed and output cached within specified folder.

First run will take long time especially for longer datasets.

Run tests

python3 -m unittest

Contributions

Bug reports, benchmarks, ideas, pull requests, suggestions and maybe thanks are welcome on the issue tracker!

I am especially looking for ways to make this code faster.

Note that for larger code changes opening issue first, before sending patch, may be a good idea.