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Extract countries, regions and cities from a URL or text

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This project is no longer being maintained and has been archived. Please check the Forks list for newer versions.


We are aware of two 3rd party forks for this library: - [Maintained] recently revived, this project will be ensuring maintanance of Geograpy.Thanks to @WolfgangFahl for getting in touch about maintaining this. - [Outdated] This fork fixes issues with newer versions of nltk. A rewrite that fixes more issues is available here, please use it instead:


Extract place names from a URL or text, and add context to those names -- for example distinguishing between a country, region or city.

Install & Setup

Grab the package using

(this will take a few minutes)
pip install geograpy

Geograpy uses NLTK for entity recognition, so you'll also need to download the models we're using. Fortunately there's a command that'll take care of this for you.


Basic Usage

Import the module, give some text or a URL, and presto.

import geograpy
url = ''
places = geograpy.get_place_context(url=url)

Now you have access to information about all the places mentioned in the linked article.

  • places.countries
    contains a list of country names
  • places.regions
    contains a list of region names
  • places.cities
    contains a list of city names
  • places.other
    lists everything that wasn't clearly a country, region or city

Note that the

list might be useful for shorter texts, to pull out information like street names, points of interest, etc, but at the moment is a bit messy when scanning longer texts that contain possessive forms of proper nouns (like "Russian" instead of "Russia").

But Wait, There's More

In addition to listing the names of discovered places, you'll also get some information about the relationships between places.

  • places.country_regions
    regions broken down by country
  • places.country_cities
    cities broken down by country
  • places.address_strings
    city, region, country strings useful for geocoding

Last But Not Least

While a text might mention many places, it's probably focused on one or two, so Geograpy also breaks down countries, regions and cities by number of mentions.

  • places.country_mentions
  • places.region_mentions
  • places.city_mentions

Each of these returns a list of tuples. The first item in the tuple is the place name and the second item is the number of mentions. For example:

[('Russian Federation', 14), (u'Ukraine', 11), (u'Lithuania', 1)]  

If You're Really Serious

You can of course use each of Geograpy's modules on their own. For example:

from geograpy import extraction

e = extraction.Extractor(url='') e.find_entities()

You can now access all of the places found by the Extractor

print e.places

Place context is handled in the

module. For example:
from geograpy import places

pc = places.PlaceContext(['Cleveland', 'Ohio', 'United States'])

pc.set_countries() print pc.countries #['United States']

pc.set_regions() print pc.regions #['Ohio']

pc.set_cities() print pc.cities #['Cleveland']

print pc.address_strings #['Cleveland, Ohio, United States']

And of course all of the other information shown above (

etc) is available after the corresponding
method is called.


Geograpy uses the following excellent libraries:

Geograpy uses the following data sources:

Hat tip to Chris Albon for the name.

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