- Expressing schemas as code should feel familiar to Python users, regardless of the dataframe library they’re using.
- Data validation should be compatible with the different workflows and tools in in the data science and ML stack without a lot of setup or configuration.
- Defining custom validation rules should be easy.
- The validation interface should make the debugging process easier.
- Integration with existing code should be as seamless as possible.
- Extending the interface to other statistical data structures should be easy using a core set of building blocks and abstractions.