Quickstart to anonym your data set
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.. automodule:: anonym.anonym.anonym.anonymize
    :members:
    :undoc-members:


Anonymize data set with user defined specifications
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The ``anonym`` library automatically anonymize all available data in a data set.
Because of the NER approach, not all columns can be correctly anonymized. We can manually
control this by specifying the entity for each column. In the example below we will demonstrate how
to force some of the columns into PERSON or DATE while other needs to remain untouched.
Note that all columns that are not specified are automatically detected with entities and faked consistently.

.. code:: python

	# Filepath
	filepath=r'C:\temp\extern_people_per_dep.csv'
	filepath_fake=r'C:\temp\extern_people_per_dep_fake.csv'

	# Import library
	from anonym import anonym
	# Initialize
	model = anonym(verbose='info')
	# Import data set
	df = model.import_data(filepath)

	# Set column names that needs to remain untoched
	do_not_fake=['Functie', 'ID (functie)']

	# Force the following columns to be catagorized as a specific Entity
	fakeit = {'Budgethouder':'PERSON',
		  'Behoeftesteller': 'PERSON',
		  'Project- afdeling': 'ORG',
		  'Financieringsbron': 'EVENT',
		  'Naam': 'PERSON',
		  'Startdatum': 'DATE',
		  'Einddatum': 'DATE',
		  'Mogelijke Einddatum': 'DATE',
		  'Mogelijke Einddatum': 'DATE',
		  'Totaal verpl.': 'MONEY',
		  'Kasrealisatie': 'MONEY',
		  }

	# Run model
	df_fake = model.anonymize(df, fakeit=fakeit, do_not_fake=do_not_fake)

	# Export to csv
	model.to_csv(df_fake, filepath_fake)