df-anonymizer 0.1.3

A lightweight library for anonymizing and transforming data in pandas DataFrames, including masking, suppression, perturbation, permutation, generalization, and pseudonymization.

df-anonymizer

A lightweight Python library designed to apply privacy-preserving transformations on datasets in pandas.DataFrame format.
It is ideal for preparing data for research, analysis, reporting or machine learning while protecting sensitive personal information.

✨ Key Features

• Masking: Mask email addresses and identification numbers
• Pseudonymization: Generate unique pseudonyms with key mapping table
• Data perturbation: Add privacy noise to age, weight, height, etc.
• Data generalization: Bucket or reduce granularity for numeric and date values
• Suppression: Remove sensitive columns or filter out specific records
• Shuffling: Randomly reorder rows
• Evaluation: Compute the k-anonymity score for your dataset

All functions are optimized to work with pandas.DataFrame structures.

📦 Installation

pip install df-anonymizer

👉 Example

📌 Pseudonymization

import pandas as pd
from df_anonymizer import pseudonymization

df = pd.DataFrame({'NRIC': ['S1234567A', 'S2345678B', 'S3456789C']})
anon_df = pseudonymization(df, 'NRIC')
print(anon_df)

# Example output:
#        NRIC
# 0  abcd123456
# 1  efgh234567
# 2  ijkl345678

📌 Masking

from df_anonymizer import maskID, maskEmail

df_mask = pd.DataFrame({
    'ID': ['123456789', '987654321'],
    'Email': ['[e-mail]', '[e-mail]']
})
df_mask = maskID(df_mask, 'ID')
df_mask = maskEmail(df_mask, 'Email')
print(df_mask)

# Output:
#          ID              Email
# 0     ******789     a****@example.com
# 1     ******321     b**@example.com

Reference

1. Guide To Basic Anonymization