I'm excited to share Pandera, an open source framework for data testing, built for data scientists and ML engineers:
https://www.union.ai/pandera
I’ve been working with data and building models for a decade, and one of the biggest pain points for me is working with low-quality data. I got burned by incorrect data types and unexpected values so many times that I built a Pandera to help you safeguard your pipelines from silent data bugs.
You can create schemas for your dataframe-like objects, which can be validated at run-time, in your unit tests via property-based testing, or in your type-linting hooks via mypy.
I'm excited to share Pandera, an open source framework for data testing, built for data scientists and ML engineers: https://www.union.ai/pandera
I’ve been working with data and building models for a decade, and one of the biggest pain points for me is working with low-quality data. I got burned by incorrect data types and unexpected values so many times that I built a Pandera to help you safeguard your pipelines from silent data bugs.
You can create schemas for your dataframe-like objects, which can be validated at run-time, in your unit tests via property-based testing, or in your type-linting hooks via mypy.
Give it a try here: https://pandera.readthedocs.io/en/stable/try_pandera.html