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and landmarks across the globe using\nthird-party geocoders and other data sources.\n\ngeopy includes geocoder classes for the `OpenStreetMap Nominatim`_,\n`Google Geocoding API (V3)`_, and many other geocoding services.\nThe full list is available on the `Geocoders doc section`_.\nGeocoder classes are located in `geopy.geocoders`_.\n\n.. _OpenStreetMap Nominatim: https://nominatim.org\n.. _Google Geocoding API (V3): https://developers.google.com/maps/documentation/geocoding/\n.. _Geocoders doc section: https://geopy.readthedocs.io/en/latest/#geocoders\n.. _geopy.geocoders: https://github.com/geopy/geopy/tree/master/geopy/geocoders\n\ngeopy is tested against CPython (versions 3.7, 3.8, 3.9, 3.10, 3.11, 3.12)\nand PyPy3. geopy 1.x line also supported CPython 2.7, 3.4 and PyPy2.\n\n© geopy contributors 2006-2018 (see AUTHORS) under the `MIT\nLicense \u003chttps://github.com/geopy/geopy/blob/master/LICENSE\u003e`__.\n\nInstallation\n------------\n\nInstall using `pip \u003chttp://www.pip-installer.org/en/latest/\u003e`__ with:\n\n::\n\n    pip install geopy\n\nOr, `download a wheel or source archive from\nPyPI \u003chttps://pypi.python.org/pypi/geopy\u003e`__.\n\nGeocoding\n---------\n\nTo geolocate a query to an address and coordinates:\n\n.. code:: pycon\n\n    \u003e\u003e\u003e from geopy.geocoders import Nominatim\n    \u003e\u003e\u003e geolocator = Nominatim(user_agent=\"specify_your_app_name_here\")\n    \u003e\u003e\u003e location = geolocator.geocode(\"175 5th Avenue NYC\")\n    \u003e\u003e\u003e print(location.address)\n    Flatiron Building, 175, 5th Avenue, Flatiron, New York, NYC, New York, ...\n    \u003e\u003e\u003e print((location.latitude, location.longitude))\n    (40.7410861, -73.9896297241625)\n    \u003e\u003e\u003e print(location.raw)\n    {'place_id': '9167009604', 'type': 'attraction', ...}\n\nTo find the address corresponding to a set of coordinates:\n\n.. code:: pycon\n\n    \u003e\u003e\u003e from geopy.geocoders import Nominatim\n    \u003e\u003e\u003e geolocator = Nominatim(user_agent=\"specify_your_app_name_here\")\n    \u003e\u003e\u003e location = geolocator.reverse(\"52.509669, 13.376294\")\n    \u003e\u003e\u003e print(location.address)\n    Potsdamer Platz, Mitte, Berlin, 10117, Deutschland, European Union\n    \u003e\u003e\u003e print((location.latitude, location.longitude))\n    (52.5094982, 13.3765983)\n    \u003e\u003e\u003e print(location.raw)\n    {'place_id': '654513', 'osm_type': 'node', ...}\n\nMeasuring Distance\n------------------\n\nGeopy can calculate geodesic distance between two points using the\n`geodesic distance\n\u003chttps://en.wikipedia.org/wiki/Geodesics_on_an_ellipsoid\u003e`_ or the\n`great-circle distance\n\u003chttps://en.wikipedia.org/wiki/Great-circle_distance\u003e`_,\nwith a default of the geodesic distance available as the function\n`geopy.distance.distance`.\n\nHere's an example usage of the geodesic distance, taking pair\nof :code:`(lat, lon)` tuples:\n\n.. code:: pycon\n\n    \u003e\u003e\u003e from geopy.distance import geodesic\n    \u003e\u003e\u003e newport_ri = (41.49008, -71.312796)\n    \u003e\u003e\u003e cleveland_oh = (41.499498, -81.695391)\n    \u003e\u003e\u003e print(geodesic(newport_ri, cleveland_oh).miles)\n    538.390445368\n\nUsing great-circle distance, also taking pair of :code:`(lat, lon)` tuples:\n\n.. code:: pycon\n\n    \u003e\u003e\u003e from geopy.distance import great_circle\n    \u003e\u003e\u003e newport_ri = (41.49008, -71.312796)\n    \u003e\u003e\u003e cleveland_oh = (41.499498, -81.695391)\n    \u003e\u003e\u003e print(great_circle(newport_ri, cleveland_oh).miles)\n    536.997990696\n\nDocumentation\n-------------\n\nMore documentation and examples can be found at\n`Read the Docs \u003chttp://geopy.readthedocs.io/en/latest/\u003e`__.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgeopy%2Fgeopy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgeopy%2Fgeopy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgeopy%2Fgeopy/lists"}