text-scrubber 0.2.0

Python package that offers text scrubbing functionality, providing building blocks for string cleaning as well as normalizing geographical text (countries/states/cities)

text-scrubber is a Python package that offers text scrubbing functionality, providing building blocks for string cleaning as well as normalizing geographical text (countries/states/cities).

Full documentation is available at https://slimmer-ai.github.io/text-scrubber/.

TextScrubber

The TextScrubber class cleans a single or a collection of strings. It can be easily constructed and configured with building blocks:

from text_scrubber import TextScrubber

ts = (TextScrubber().to_ascii()
                    .lowercase()
                    .tokenize()
                    .remove_stop_words()
                    .join())

which can then be used as

ts.transform('héLlô there, WòrlD')  # outputs 'hello world'

or

ts.transform(['héLlô there, WòrlD', 'slímm̀er ÀI'])  # outputs ['hello world', 'slimmer AI']

Geo

The geo module contains functions to normalize geographical data which deal with spelling errors, country name variations, etc.:

from text_scrubber.geo import normalize_country, normalize_state, normalize_city

# Countries
normalize_country('Peoples rep. of China')  # ['China']
normalize_country('Deutschland')            # ['Germany']
normalize_country('st Nevis and Kitties')   # ['Saint Kitts and Nevis']
normalize_country('ira')                    # ['Iran', 'Iraq']

# States
normalize_state('Qld')         # [('Queensland', 'Australia')]
normalize_state('AR')          # [('Arkansas', 'United States'), ('Arunachal Pradesh', 'India')]
normalize_state('King Kong')   # [('Hong Kong', 'China')]

# Cities
normalize_city('Leibnitz')    # [('Leibnitz', 'Austria')]
normalize_city('heidelberg')  # [('Heidelberg', 'Australia'), ('Heidelberg', 'Germany'),
                              #  ('Heidelberg', 'South Africa'), ('Heidelberg', 'United States')]
normalize_city('texas')       # [('Texas City', 'United States')]
normalize_city('Pari')        # [('Parai', 'Brazil'), ('Paris', 'Canada'), ('Paris', 'France'),
                              #  ('Paris', 'United States'), ('Parit', 'Malaysia'),
                              #  ('Pariz', 'Czech Republic')]

Documentation

If you want to build the documentation, please install the documentation dependencies by executing:

pip install .[docs]

Documentation can be build by executing:

python setup.py build_docs

Documentation can also be build from the docs folder directly. In that case text-scrubber should be installed and available in your current working environment. Execute:

make html

in the docs folder.