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Generalizing into ranges

K-anonymity can be a good privacy model for specific datasets that don't have many dimensions. The two main anonymization techniques used to transform a dataset into a k-anonymous table are generalization and suppression.

In this exercise, you will transform a satisfaction rating dataset to a 3-anonymous table containing possible sensitive attributes like satisfaction_rate and work_hours. Some combinations appear less than three times. Fix that to make the DataFrame 3-anonymous.

The DataFrame is available as employees. A k value of 3 is also available.

This exercise is part of the course

Data Privacy and Anonymization in Python

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Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Calculate how many unique combinations are for BirthYear and Department
print(employees.groupby(['birth_year','department']).____)
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