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Example Config: Data Science / Data Engineering #87

@Kilo59

Description

@Kilo59

Domain Configuration: Data Science / Data Engineering

As part of #83, we are building out curated Ruff configurations to live in the configs/ directory of the repository. This issue tracks the creation and refinement of the Data Science / Data Engineering domain configuration.

Proposed Path

configs/data-science-engineering/ruff.toml

Goals

  • Focus on checks specifically built for data science tooling and data processing logic.
  • Useful rules to consider: NPY (NumPy-specific rules), PD (pandas-vet), and potentially CPY (copyright) for enterprise notebooks.
  • Implement config settings to allow seamless linting of Jupyter Notebooks (.ipynb).

💬 Call for Contributions

We strongly encourage data scientists and engineers to jump into this discussion! What Ruff linting rules or notebook configurations save you the most time? Are there specific pandas-vet rules you always ignore or always enable?

Please comment your recommendations below, or feel free to submit a PR introducing the configs/data-science-engineering/ruff.toml file yourself!

(Also, if you have ideas for entirely new domains, feel free to open a separate issue proposing them!)

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config-recipeDomain-specific configuration examples and recipesgood first issueGood for newcomershelp wantedExtra attention is needed

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