Learn how to use the Enum data type in Polars to handle categorical data with better performance and consistency.
In this video, you’ll see how Enums help you define fixed categories, control sort order, and perform fast comparisons—all while keeping your data pipelines clean and predictable.
🔹 What you’ll learn:
• How to define Enum columns in Polars
• Converting string columns to Enum types
• Controlling sort order with predefined categories
• Using comparison operators
• Filtering and conditional logic with Enums
• Key differences between Enum and Categorical types
🔹 Why use Enums?
• Faster than string comparisons
• Enforces consistent category values
• Built-in ordering for reliable sorting
• Memory efficient for repeated values
• Ideal for structured data pipelines
🔹 Example use cases:
• Priority levels (low → medium → high)
• Status tracking (pending → completed)
• Survey or form responses
• Feature engineering for machine learning
• Data validation workflows
🔹 Example:
pl.Enum(["low", "medium", "high"])
🔹 Code:
GitHub repo: https://github.com/hthomas229/PurpleCrown
Polars is designed for modern data workloads with fast execution, efficient memory usage, and scalable performance.
By the end of this video, you’ll be able to confidently use Enums to build cleaner and more reliable data workflows.
