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# `langchain_experimental.data_anonymizer.deanonymizer_mapping`.create_anonymizer_mapping

langchain_experimental.data_anonymizer.deanonymizer_mapping.create_anonymizer_mapping(original_text: str, analyzer_results: List[RecognizerResult], anonymizer_results: EngineResult, is_reversed: bool = False) → Dict[str, Dict[str, str]]
  Create or update the mapping used to anonymize and/or

    deanonymize a text.

  This method exploits the results returned by the analysis and anonymization processes.

  If is_reversed is True, it constructs a mapping from each original entity to its anonymized value.

  If is_reversed is False, it constructs a mapping from each anonymized entity back to its original text value.

  If there are multiple entities of the same type, the mapping will include a count to differentiate them. For example, if there are two names in the input text, the mapping will include NAME_1 and NAME_2.

  Example of mapping: {

  > “PERSON”: {

  >   “<original>”: “<anonymized>”, “John Doe”: “Slim Shady”

  > }, “PHONE_NUMBER”: {

  > > “111-111-1111”: “555-555-5555”

  }

  Parameters

    - original_text (str) –

    - analyzer_results (List[RecognizerResult]) –

    - anonymizer_results (EngineResult) –

    - is_reversed (bool) –

  Return type

    Dict[str, Dict[str, str]]