add_recognizer(recognizer)
Add a recognizer to the analyzer
anonymize(text[, language, allow_list])
Anonymize text
deanonymize(text_to_deanonymize[, ...])
Deanonymize text
load_deanonymizer_mapping(file_path)
Load the deanonymizer mapping from a JSON or YAML file.
reset_deanonymizer_mapping()
Reset the deanonymizer mapping
save_deanonymizer_mapping(file_path)
Save the deanonymizer mapping to a JSON or YAML file.
__init__(analyzed_fields: Optional[List[str]] = None, operators: Optional[Dict[str, OperatorConfig]] = None, languages_config: Dict = {'models': [{'lang_code': 'en', 'model_name': 'en_core_web_lg'}], 'nlp_engine_name': 'spacy'}, add_default_faker_operators: bool = True, faker_seed: Optional[int] = None)[source]¶
Parameters
analyzed_fields – List of fields to detect and then anonymize.
Defaults to all entities supported by Microsoft Presidio.
operators – Operators to use for anonymization.
Operators allow for custom anonymization of detected PII.
Learn more:
https://microsoft.github.io/presidio/tutorial/10_simple_anonymization/
languages_config – Configuration for the NLP engine.
First language in the list will be used as the main language
in self.anonymize(…) when no language is specified.
Learn more:
https://microsoft.github.io/presidio/analyzer/customizing_nlp_models/
faker_seed – Seed used to initialize faker.
Defaults to None, in which case faker will be seeded randomly
and provide random values.
add_operators(operators: Dict[str, OperatorConfig]) → None¶
Add operators to the anonymizer
Parameters