Basic usage =========== .. contents:: Contents :depth: 2 :local: TextScrubber ------------ The :obj:`text_scrubber.text_scrubber.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 :obj:`text_scrubber.text_scrubber.TextScrubber` API reference. Geo --- The :obj:`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 :obj:`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'