This module is a subclass of PIIGenerator, a class designed to handle the conversion and reversal of personally identifiable information (PII) in a DataFrame.
The EmailGenerator class has three important methods: fit, convert, and reverse_convert:
The fit method is used to fit the generator to the metadata, which includes identifying the columns in the DataFrame that contain email addresses.
The convert method removes the email columns from the DataFrame, while the reverse_convert method adds new email columns to the DataFrame.
Motivation and Context
The motivation is to provide a way to handle email addresses in a DataFrame.
This is particularly useful when dealing with datasets that contain sensitive information, such as email addresses, and need to be anonymized or de-identified.
How has this been tested?
Email Generator has been tested using a variety of test cases.
These tests include checking if the fit method correctly identifies the email columns in the DataFrame, and if the convert and reverse_convert methods correctly handle the email columns.
Types of changes
[ ] Maintenance (no change in code, maintain the project's CI, docs, etc.)
[ ] Bug fix (non-breaking change which fixes an issue)
[x] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
Checklist:
[x] My code follows the code style of this project.
[x] My change requires a change to the documentation.
Description
This module is a subclass of PIIGenerator, a class designed to handle the conversion and reversal of personally identifiable information (PII) in a DataFrame.
The EmailGenerator class has three important methods:
fit,convert, andreverse_convert:fitmethod is used to fit the generator to the metadata, which includes identifying the columns in the DataFrame that contain email addresses.convertmethod removes the email columns from the DataFrame, while thereverse_convertmethod adds new email columns to the DataFrame.Motivation and Context
The motivation is to provide a way to handle email addresses in a DataFrame.
This is particularly useful when dealing with datasets that contain sensitive information, such as email addresses, and need to be anonymized or de-identified.
How has this been tested?
Email Generator has been tested using a variety of test cases.
These tests include checking if the fit method correctly identifies the email columns in the DataFrame, and if the convert and reverse_convert methods correctly handle the email columns.
Types of changes
Checklist: