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DataExcept

Structured, hierarchical exception classes for data science, machine learning and data engineering workflows.

Instead of debugging a bare ValueError, you get an exception that says what actually went wrong, where, and with which value:

from dataexcept import ValidationError

raise ValidationError("age", -1)
# ValidationError: Validation failed for field 'age': -1

Installation

pip install DataExcept

Where to go next

  • Command-Line Interface — inspect the exported exception classes and check the installed version.
  • Logging Helpers — log exceptions with structured context and re-raise without losing the traceback.
  • Advanced Usage — derive your own project-specific errors from the provided base classes.
  • API Reference — every exception and helper, generated from the source.

Local Lambda demo

The repository ships with a .env.example and a matching make target, so you can run the mocked Lambda workflow without touching real infrastructure:

make lambda-demo

The target copies .env.example to .env if it is missing, then runs python -m examples.lambda_main.