Quinn is a Python library providing helper methods for PySpark to enhance developer productivity. It offers DataFrame validation functions, useful column functions/DataFrame transformations, and performant helper functions. The library is currently at version 0.10.3 and maintains an active release cadence.
pip install quinnVerified import paths — ran on the pinned version, not inferred.
This quickstart demonstrates how to initialize a SparkSession, create a DataFrame using `quinn`'s extended `create_df` method, apply a common DataFrame transformation like `snake_case_columns`, and utilize a Column extension such as `isTruthy`.
Users migrating from versions prior to 0.2.0 will need to update their import statements and potentially function calls to align with the new module structure. Refer to the GitHub releases for details.
Avoid using `print_athena_create_table`. Check the latest documentation for alternative methods to generate Athena table DDL or construct it manually.
For the main `quinn` library, prefer `import quinn` and then call functions as `quinn.function_name()`. For extensions, `from quinn.extensions import *` is often intended for the automatic patching, but be aware of the namespace pollution. If only specific extension functions are needed and not the automatic patching, consider importing them directly, though this might miss the intended auto-patching behavior.
Always remember that your DataFrame transformations won't run until an action is triggered. Use actions strategically for debugging (e.g., `df.show()`) and ensure your job design accounts for this lazy execution model.