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Polars Cookbook

You're reading from   Polars Cookbook Over 60 practical recipes to transform, manipulate, and analyze your data using Python Polars 1.x

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781805121152
Length 394 pages
Edition 1st Edition
Languages
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Author (1):
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Yuki Kakegawa Yuki Kakegawa
Author Profile Icon Yuki Kakegawa
Yuki Kakegawa
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Table of Contents (15) Chapters Close

Preface 1. Chapter 1: Getting Started with Python Polars FREE CHAPTER 2. Chapter 2: Reading and Writing Files 3. Chapter 3: An Introduction to Data Analysis in Python Polars 4. Chapter 4: Data Transformation Techniques 5. Chapter 5: Handling Missing Data 6. Chapter 6: Performing String Manipulations 7. Chapter 7: Working with Nested Data Structures 8. Chapter 8: Reshaping and Tidying Data 9. Chapter 9: Time Series Analysis 10. Chapter 10: Interoperability with Other Python Libraries 11. Chapter 11: Working with Common Cloud Data Sources 12. Chapter 12: Testing and Debugging in Polars 13. Index 14. Other Books You May Enjoy

Masking sensitive data

Security and privacy are crucial when working with data. There is some data that only certain people are allowed to see, such as your social security number, driver’s license number, and medical records. This data should be treated with care and be protected appropriately.

The best scenario is that you don’t store these kinds of data, or the process of masking data has been taken care of before the data gets to you. However, it’s always good to know how to work with and hide this kind of information. There are some ways you can mask your data, including replacing or randomizing values, hashing, and encryption.

In this recipe, we’ll cover how to mask our data by replacing values as well as hashing them.

How to do it...

Here is how to mask sensitive data:

  1. Create a column called SSN (which stands for social security number).
    1. Create a function to generate random numbers:
    import random
    def get_random_nums(num_list, length...
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