If no analyzed_fields are specified, by default the anonymizer will detect all supported formats. Below is the full list of them:['PERSON', 'EMAIL_ADDRESS', 'PHONE_NUMBER', 'IBAN_CODE', 'CREDIT_CARD', 'CRYPTO', 'IP_ADDRESS', 'LOCATION', 'DATE_TIME', 'NRP', 'MEDICAL_LICENSE', 'URL', 'US_BANK_NUMBER', 'US_DRIVER_LICENSE', 'US_ITIN', 'US_PASSPORT', 'US_SSN']Disclaimer: We suggest carefully defining the private data to be detected - Presidio doesn't work perfectly and it sometimes makes mistakes, so it's better to have more control over the data.anonymizer = PresidioAnonymizer()anonymizer.anonymize(    "My name is Slim Shady, call me at 313-666-7440 or email me at real.slim.shady@gmail.com")    'My name is Carla Fisher, call me at 001-683-324-0721x0644 or email me at krausejeremy@example.com'\
It may be that the above list of detected fields is not sufficient. For example, the already available PHONE_NUMBER field does not support polish phone numbers and confuses it with another field:anonymizer = PresidioAnonymizer()anonymizer.anonymize("My polish phone number is 666555444")    'My polish phone number is QESQ21234635370499'\
You can then write your own recognizers and add them to the pool of those present. How exactly to create recognizers is described in the Presidio documentation.# Define the regex pattern in a Presidio `Pattern` object:from presidio_analyzer import Pattern, PatternRecognizerpolish_phone_numbers_pattern = Pattern(    name="polish_phone_numbers_pattern",    regex="(?<!\w)(\(?(\+|00)?48\)?)?[ -]?\d{3}[ -]?\d{3}[ -]?\d{3}(?!\w)",    score=1,)# Define the recognizer with one or more patternspolish_phone_numbers_recognizer = PatternRecognizer(    supported_entity="POLISH_PHONE_NUMBER", patterns=[polish_phone_numbers_pattern])\
Now, we can add recognizer by calling add_recognizer method on the anonymizer:anonymizer.add_recognizer(polish_phone_numbers_recognizer)\
Open In Colab
Open In Colab ->: If no analyzed_fields are specified, by default the anonymizer will detect all supported formats. Below is the full list of them:['PERSON', 'EMAIL_ADDRESS', 'PHONE_NUMBER', 'IBAN_CODE', 'CREDIT_CARD', 'CRYPTO', 'IP_ADDRESS', 'LOCATION', 'DATE_TIME', 'NRP', 'MEDICAL_LICENSE', 'URL', 'US_BANK_NUMBER', 'US_DRIVER_LICENSE', 'US_ITIN', 'US_PASSPORT', 'US_SSN']Disclaimer: We suggest carefully defining the private data to be detected - Presidio doesn't work perfectly and it sometimes makes mistakes, so it's better to have more control over the data.anonymizer = PresidioAnonymizer()anonymizer.anonymize(    "My name is Slim Shady, call me at 313-666-7440 or email me at real.slim.shady@gmail.com")    'My name is Carla Fisher, call me at 001-683-324-0721x0644 or email me at krausejeremy@example.com'\
It may be that the above list of detected fields is not sufficient. For example, the already available PHONE_NUMBER field does not support polish phone numbers and confuses it with another field:anonymizer = PresidioAnonymizer()anonymizer.anonymize("My polish phone number is 666555444")    'My polish phone number is QESQ21234635370499'\
You can then write your own recognizers and add them to the pool of those present. How exactly to create recognizers is described in the Presidio documentation.# Define the regex pattern in a Presidio `Pattern` object:from presidio_analyzer import Pattern, PatternRecognizerpolish_phone_numbers_pattern = Pattern(    name="polish_phone_numbers_pattern",    regex="(?<!\w)(\(?(\+|00)?48\)?)?[ -]?\d{3}[ -]?\d{3}[ -]?\d{3}(?!\w)",    score=1,)# Define the recognizer with one or more patternspolish_phone_numbers_recognizer = PatternRecognizer(    supported_entity="POLISH_PHONE_NUMBER", patterns=[polish_phone_numbers_pattern])\
Now, we can add recognizer by calling add_recognizer method on the anonymizer:anonymizer.add_recognizer(polish_phone_numbers_recognizer)\