Validating Data
This unit focuses on making data validation an ordinary part of scientific Python work. You will start with explicit guard clauses and object-level checks, then compare runtime validation libraries for functions, domain objects, DataFrames, structured messages, LLM workflows, and command-line tools.
By the end of the unit, you should be able to choose an appropriate validation boundary, write checks that fail clearly, and use Python validation frameworks to keep invalid data from spreading through an analysis pipeline.
Sessions
Data Validation Patterns
Practice guard clauses, dataclass hooks, and Pydantic validators that fail fast on invalid scientific inputs.
Run-Time Data Validation Frameworks in Python
Compare beartype, attrs, and Pydantic for enforcing Python type and value constraints at runtime.
Specialized Data Validation Frameworks
Apply validation ideas to DataFrames, LLM inputs, JSON messages, and command-line interfaces.