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Contributing to DataProfi

Getting Started

  1. Fork the repository
  2. Clone your fork
  3. Create a virtual environment and install dependencies:
python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"
  1. Run tests to verify setup:
pytest

Development Workflow

  1. Create a branch for your feature/fix
  2. Make your changes
  3. Run tests: pytest
  4. Run linter: ruff check .
  5. Submit a pull request

Project Structure

dataprofi/
  core/         - Shared types and configuration
  ingest/       - Data loading (CSV, JSON, API)
  profiler/     - Quality scoring, column profiling, timeseries, geo, correlations, schema
  cleaner/      - Cleaning pipeline (missing, duplicates, outliers, types, normalization)
  indexer/      - PostgreSQL index recommendation engine
  api/          - FastAPI server and Pydantic schemas

frontend/
  src/components/  - React components (one per tab)
  src/api/         - API client functions
  src/types/       - TypeScript type definitions

tests/            - pytest test suite
samples/          - Sample datasets for testing

Code Style

  • Python: follow ruff defaults, line length 100
  • TypeScript: strict mode, no any where avoidable
  • No emoji or unicode symbols in UI - use lucide-react icons
  • Comments only when the WHY is non-obvious - code should be self-documenting
  • No unnecessary abstractions - three similar lines beats a premature helper

Adding a New Profiler Module

  1. Create dataprofi/profiler/your_module.py
  2. Define dataclasses in dataprofi/core/types.py
  3. Add API endpoint in dataprofi/api/server.py
  4. Add frontend component in frontend/src/components/YourView.tsx
  5. Add API client function in frontend/src/api/client.ts
  6. Register the tab in frontend/src/App.tsx
  7. Write tests in tests/test_your_module.py

Reporting Issues

Use GitHub Issues. Include:

  • What you expected
  • What happened instead
  • Steps to reproduce
  • Sample data (if applicable)