Python package that offers text scrubbing functionality, providing building blocks for string cleaning as well as normalizing geographical text (countries/states/cities)
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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://sybrenjansen.github.io/text-scrubber/.
The TextScrubber class cleans a single or a collection of strings. It can be easily constructed and configured with
building blocks:
.. code-block:: python
from text_scrubber import TextScrubber
ts = (TextScrubber().to_ascii()
.lowercase()
.tokenize()
.remove_stop_words()
.join())
which can then be used as:
.. code-block:: python
ts.transform('héLlô there, WòrlD') # outputs 'hello world'
or with an iterable of input:
.. code-block:: python
ts.transform(['héLlô there, WòrlD', 'slímm̀er ÀI']) # outputs ['hello world', 'slimmer AI']
For a complete list of building blocks please refer to the TextScrubber API reference.
The text_scrubber.geo module contains functions to normalize geographical data which deal with spelling errors,
country name variations, etc.:
.. code-block:: python
from text_scrubber.geo import normalize_country, normalize_region, normalize_city
"""
Countries
"""
normalize_country('Peoples rep. of China')
# [Location(canonical_name='China', matched_name='Peoples Republic of China', country=None,
# score=1.0)]
normalize_country('Deutschland')
# [Location(canonical_name='Germany', matched_name='Deutschland', country=None, score=1.0)]
normalize_country('st Nevis and Kitties')
# [Location(canonical_name='Saint Kitts and Nevis', matched_name='Saint Kitts and Nevis',
# country=None, score=0.75)]
normalize_country('ira')
# [Location(canonical_name='Iran', matched_name='Iran', country=None, score=0.857...),
# Location(canonical_name='Iraq', matched_name='Iraq', country=None, score=0.857...)]
"""
Cities
"""
normalize_city('Leibnitz', ['Austria'])
# [Location(canonical_name='Leibnitz', matched_name='Leibnitz', country='Austria', score=1.0)]
normalize_city('heidelberg')
# [Location(canonical_name='Heidelberg', matched_name='Heidelberg', country='Germany',
# score=1.0),
# Location(canonical_name='Heidelberg', matched_name='Heidelberg', country='South Africa',
# score=1.0),
# Location(canonical_name='Heidelberg', matched_name='Heidelberg', country='United States',
# score=1.0)]
normalize_city('ohioo', ['US'])
# [Location(canonical_name='Ohio', matched_name='Ohio', country='United States',
# score=0.888...)]
normalize_city('Madri', ['Spain', 'US', 'Brazil'])
# [Location(canonical_name='Madrid', matched_name='Madrid', country='Spain',
# score=0.909...),
# Location(canonical_name='Madrid', matched_name='Madrid', country='United States',
# score=0.909...),
# Location(canonical_name='Mari', matched_name='Mari', country='Brazil',
# score=0.888...)]
"""
Regions
"""
normalize_region('triangle park', ['US'])
# [Location(canonical_name='The Triangle Park', matched_name='The Triangle Park',
# country='United States', score=1.0)]
normalize_region('Fur', ['Denmark'])
# [Location(canonical_name='Fur', matched_name='Fur', country='Denmark', score=1.0)]
normalize_region('texel', ['NL'])
# [Location(canonical_name='Texel', matched_name='Texel', country='Netherlands', score=1.0)]
Each of the above normalization functions return the canonical name, matched name, the match score, and when normalizing
cities or regions it will also contain the corresponding country. The difference between canonical and matched name
stems from the fact that some countries, cities, or regions can have alternative names. E.g., NYC maps to
New York City. When the query was NYCC the canonical name will be New York City, but the matched name
NYC. The match scores are always between 0.0 and 1.0, where 1.0 is a perfect match. If a known mapping exists, like
Deutschland to Germany, then the match score will be 1.0.
.. note::
When normalizing a country or finding countries in a string, the ``country`` attribute of a ``LocationMatch`` object
is always ``None``. The normalized name can be found using the ``canonical_name`` attribute.
The text_scrubber.geo module also contains functions to find the name of places (country, region, and city) in
text dealing with spelling errors, country name variations, etc.:
.. code-block:: python
from text_scrubber.geo import (find_city_in_string, find_country_in_string,
find_region_in_string)
"""
Countries
"""
find_country_in_string("Institute of German study, Accra, Ghana")
# [ExtractedLocation(location=Location(canonical_name='Ghana', matched_name='Ghana',
# country=None, score=1.0),
# substring='Ghana', substring_range=Range(start=34, end=39)),
# ExtractedLocation(location=Location(canonical_name='Germany', matched_name='Germany',
# country=None, score=0.923...),
# substring='German', substring_range=Range(start=13, end=19))]
find_country_in_string("Peking University, 5 Yiheyuan Rd, "
"Haidian District, Beijing, CH, 100871")
# This was a trick question though, as CH=Switzerland. China is CN
# [ExtractedLocation(location=Location(canonical_name='Switzerland', matched_name='CH',
# country=None, score=1.0),
# substring='CH', substring_range=Range(start=61, end=63))]
"""
Cities
"""
find_city_in_string("Météorage Pau France", {"France"})
# [ExtractedLocation(location=Location(canonical_name='Pau', matched_name='Pau',
# country='France', score=1.0),
# substring='Pau', substring_range=Range(start=10, end=13)),
# ExtractedLocation(location=Location(canonical_name='La Frasnée', matched_name='Фране',
# country='France', score=0.909...),
# substring='France', substring_range=Range(start=14, end=20))]
find_city_in_string("Bavarian Environment Agency, Hans Högn Straße 12, "
"95030 Hof Saale, Bavaria, Germany", {"Germany"})
# [ExtractedLocation(location=Location(canonical_name='Hof', matched_name='Hof',
# country='Germany', score=1.0),
# substring='Hof', substring_range=Range(start=56, end=59)),
# ExtractedLocation(location=Location(canonical_name='Saal', matched_name='Saal',
# country='Germany', score=0.888...),
# substring='Saale', substring_range=Range(start=60, end=65)),
# ExtractedLocation(location=Location(canonical_name='Trassem', matched_name='Trassem',
# country='Germany', score=0.857...),
# substring='Straße', substring_range=Range(start=39, end=45))]
"""
Regions
"""
find_region_in_string("Fur Museum, 7884 Fur, Denmark.", {"Denmark"})
# [ExtractedLocation(location=Location(canonical_name='Fur', matched_name='Fur',
# country='Denmark', score=1.0),
# substring='Fur', substring_range=Range(start=0, end=3)),
# ExtractedLocation(location=Location(canonical_name='Fur', matched_name='Fur',
# country='Denmark', score=1.0),
# substring='Fur', substring_range=Range(start=17, end=20)),
# ExtractedLocation(location=Location(canonical_name='Kingdom of Denmark',
# matched_name='Denmark', country='Denmark', score=1.0),
# substring='Denmark', substring_range=Range(start=22, end=29))]
find_region_in_string("Department of Biological Oceanography, Royal Netherlands Institute "
"for Sea Research (NIOZ), Texel, The Netherlands", {"Netherlands"})
# [ExtractedLocation(location=Location(canonical_name='Kingdom of the Netherlands',
# matched_name='Netherlands', country='Netherlands',
# score=1.0),
# substring='Netherlands', substring_range=Range(start=45, end=56)),
# ExtractedLocation(location=Location(canonical_name='Texel', matched_name='Texel',
# country='Netherlands', score=1.0),
# substring='Texel', substring_range=Range(start=92, end=97)),
# ExtractedLocation(location=Location(canonical_name='Kingdom of the Netherlands',
# matched_name='Netherlands', country='Netherlands',
# score=1.0),
# substring='Netherlands', substring_range=Range(start=103, end=114))]
.. note::
Whenever a country is considered part of another country ``normalize_country`` will return the latter.
E.g., ``Puerto Rico`` is mapped to ``United States`` and ``Greenland`` to ``Denmark``.
Resource loading
Resources for cities and regions aren't all loaded when you import ``TextScrubber``, they're loaded on the fly per
country. This means that the first time you do a query it can take a while. The second time around the same query will
be much faster, as will all other queries involving the same countr(y)(ies). You can load in resources per country in
advance by using:
.. code-block:: python
from text_scrubber.geo import (add_city_resources, add_region_resources,
normalize_country_to_country_codes)
country_codes = normalize_country_to_country_codes(['Netherlands', 'China', 'USA'])
add_city_resources(country_codes)
add_region_resources(country_codes, progress_bar=True)
.. note::
Whenever a country is considered part of another country ``normalize_country_to_country_codes`` returns both.
Cleaning
~~~~~~~~
There are clean functions available for countries/regions/cities, which all follow the same cleaning pipeline:
.. code-block:: python
from text_scrubber.geo import clean_country, clean_region, clean_city
clean_country('cent afr rep.') # 'central african republic'
clean_region('Hyōgo') # 'hyogo'
clean_city('płońsk') # 'plonsk'
clean_city('neustadt/westerwald') # 'neustadt westerwald'
Documentation
-------------
If you want to build the documentation, please install the documentation dependencies by executing:
.. code-block:: bash
pip install .[docs]
Documentation can be build by executing:
.. code-block:: bash
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:
.. code-block:: bash
make html
in the ``docs`` folder.