Anonymizer is a Python package that generates fake data for you. It internally makes use of the Faker package, and allows you to keep track of the mapping between your original and fake data. This will be especially useful when you are anonymizing data in pandas data frames.
_____ .__
/ _ \ ____ ____ ____ ___.__. _____ |__|________ ____ _______
/ /_\ \ / \ / _ \ / \< | | / \ | |\___ /_/ __ \\_ __ \
/ | \| | \( <_> )| | \\___ || Y Y \| | / / \ ___/ | | \/
\____|__ /|___| / \____/ |___| // ____||__|_| /|__|/_____ \ \___ >|__|
\/ \/ \/ \/ \/ \/ \/
names = ['Kevin Bell', 'Ricky Sheppard', 'James Hill MD']
anonymizer = Anonymizer()
anonymizer.get_anonymized_name('Ghajinikanth Zuckerberg')
# 'Catherine Parker'
anonymizer.get_original_name('Catherine Parker')
# 'Ghajinikanth Zuckerberg'
anonymizer.get_anonymized_name('Ghajinikanth Zuckerberg') # First Call
# 'Catherine Parker'
anonymizer.get_anonymized_name('Ghajinikanth Zuckerberg') # Second Call
# 'Catherine Parker'
anonymizer.get_anonymized_names(names)
# ['Leslie Adams', 'Michelle Burke', 'Annette Maxwell']
anonymizer.get_original_names(anonymizedNames)
# ['Kevin Bell', 'Ricky Sheppard', 'James Hill MD']
address_anonymizer = Anonymizer(faker_type=FakerType.ADDRESS)
address_anonymizer.get_anonymized_name('74437 Alexandra Well\nSouth Jade, CT 40282')
# 'USNS Hernandez\nFPO AA 32353'
df['Column']
# 0 None
# 1 None
# 2 Marcus Smith
# 3 Sherry Parsons
# 4 Marcus Smith
# Name: Author, dtype: object
anonymizer = Anonymizer(faker_type=FakerType.NAME)
df['Column'].apply(lambda s : anonymizer.get_anonymized_name(s) if s is not None else None)
# 0 None
# 1 None
# 2 Kelly Walker
# 3 Yolanda Hawkins
# 4 Kelly Walker
# Name: Author, dtype: object