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Python DataFrame Data Masking Techniques - Gen. David L. - Medium
The article provides a comprehensive guide to protecting sensitive data using Python's Pandas library, covering four main techniques: masking, hashing, randomization, and encryption. Each method is ex
plained with practical code examples and use cases, helping developers choose the right approach for their specific data protection needs.

Reasons to Read -- Learn:

  • how to implement four different data protection techniques in Python, with complete code examples that you can directly apply to your own datasets containing sensitive information like SSNs and phone numbers.
  • how to choose the most appropriate data protection method based on specific scenarios - whether you need to preserve data structure (masking), ensure irreversibility (hashing), break data associations (randomization), or maintain ability to restore original data (encryption).
  • practical applications of Pandas and Python's cryptography libraries for handling sensitive data, including specific regex patterns for masking SSNs and phone numbers, and implementation of the Fernet encryption system.
  • publisher: @tubelwj
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