{ "info": { "author": "Yaser Martinez Palenzuela", "author_email": "yaser.martinez@gmail.com", "bugtrack_url": null, "classifiers": [ "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6" ], "description": "===============================\ngeotext\n===============================\n\n.. image:: https://img.shields.io/pypi/v/geotext.svg\n :target: https://pypi.python.org/pypi/geotext\n\n.. image:: https://img.shields.io/pypi/pyversions/geotext.svg\n :target: https://pypi.python.org/pypi/geotext\n\n.. image:: https://travis-ci.org/elyase/geotext.png?branch=master\n :target: https://travis-ci.org/elyase/geotext\n\n\nGeotext extracts country and city mentions from text\n\n* Free software: MIT license\n* Documentation: https://geotext.readthedocs.org.\n\nUsage\n-----\n.. code-block:: python\n\n from geotext import GeoText\n\n places = GeoText(\"London is a great city\")\n places.cities\n # \"London\"\n\n # filter by country code\n result = geotext.GeoText('I loved Rio de Janeiro and Havana', 'BR').cities\n # 'Rio de Janeiro'\n\n GeoText('New York, Texas, and also China').country_mentions\n # OrderedDict([(u'US', 2), (u'CN', 1)])\n\nInstallation\n------------\n.. code-block:: bash\n\n pip install https://github.com/elyase/geotext/archive/master.zip\n\n\nFeatures\n--------\n- No external dependencies\n- Fast\n- Data from http://www.geonames.org licensed under the Creative Commons Attribution 3.0 License.\n\nSimilar projects\n----------------\n`geography\n`_: geography is more advanced and bigger in scope compared to geotext and can do everything geotext does. 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