Preparing employee data for safe release
When you deal with real data, you need to make sure that there's no way our customer's or other people's personal information can be traced or exposed. In this exercise, you'll use a simplified version of the IBM HR Analytics Employee dataset to practice suppression and generalization techniques.
To avoid leaking information about the dataset, you will replace the column names with numbers.
The DataFrame is loaded as hr
, use the console to explore it. numpy
is imported as np
.
This exercise is part of the course
Data Privacy and Anonymization in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Drop unique data and almost unique data
df_dropped = ____(["employee_number", "monthly_income", "monthly_rate", "daily_rate"], axis=1)
# Drop the rows with NaN values
df_cleaned = ____