lintquarto: Package for running linters, static type checkers and code analysis tools on python code in quarto (.qmd) files.

[amyheather]

Submitting Author: (@amyheather)
All current maintainers: (@amyheather)
Package Name: lintquarto
One-Line Description of Package: Package for running linters, static type checkers and code analysis tools on python code in quarto (.qmd) files.
Repository Link: https://github.com/lintquarto/lintquarto
Version submitted: v0.5.0
EiC: TBD
Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
JOSS DOI: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD

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Code of Conduct & Commitment to Maintain Package

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Description

This package allows you to run linters, static type checkers, and code analysis tools on Python code embedded in Quarto (.qmd) files. Normally, these tools can't analyse code in Quarto files as they're meant for standard Python scripts, while Quarto files mix code with text and other formats. This package solves that by extracting the Python code from Quarto files with appropriate preservation of line numbers in the code so that the analysis tools can understand and evaluate it correctly.

Scope

• Please indicate which category or categories.
  Check out our package scope page to learn more about our
  scope. (If you are unsure of which category you fit, we suggest you make a pre-submission inquiry):

  • Data retrieval
  • Data extraction
  • Data processing/munging
  • Data deposition
  • Data validation and testing
  • Data visualization¹
  • Workflow automation
  • Citation management and bibliometrics
  • Scientific software wrappers
  • Database interoperability

Domain Specific

• Geospatial
• Education

Community Partnerships

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existing community please check below:

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• For all submissions, explain how and why the package falls under the categories you indicated above. In your explanation, please address the following points (briefly, 1-2 sentences for each):

While data validation and testing typically focuses on verifying the accuracy and reliability of data, code validation through linting (as well as checks using static type checkers and code analysis tools) is an important upstream step in scientific workflows. Ensuring code standardisation and quality helps improve clarity and maintainability, and may prevent the introduction of errors or inconsistencies into data processing pipelines. By enabling linting of python code within Quarto Markdown files, this package helps maintain high standards for both code and the data it produces or manipulates, supporting reproducible and reliable scientific research.

• Who is the target audience and what are scientific applications of this package?

The target audience is scientists, researchers, and analysts who use Quarto Markdown files to document and share computational analyses. It supports scientific workflows by ensuring that embedded Python code is robust, maintainable, and free of common errors.

• Are there other Python packages that accomplish the same thing? If so, how does yours differ?

While tools like nbqa allow running linters on Jupyter notebooks, there are currently no Python packages that support linters, static type checkers and code analysis tools to run on python code within .qmd files. This package fills that gap by specifically supporting Quarto Markdown.

• If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted:

N/A

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• contains a README with instructions for installing the development version.
• includes documentation with examples for all functions.
• contains a tutorial with examples of its essential functions and uses.
• has a test suite.
• has continuous integration setup, such as GitHub Actions CircleCI, and/or others.

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JOSS Checks

• The package has an obvious research application according to JOSS's definition in their submission requirements. Be aware that completing the pyOpenSci review process does not guarantee acceptance to JOSS. Be sure to read their submission requirements (linked above) if you are interested in submitting to JOSS.
• The package is not a "minor utility" as defined by JOSS's submission requirements: "Minor ‘utility’ packages, including ‘thin’ API clients, are not acceptable." pyOpenSci welcomes these packages under "Data Retrieval", but JOSS has slightly different criteria.
• The package contains a paper.md matching JOSS's requirements with a high-level description in the package root or in inst/.
• The package is deposited in a long-term repository with the DOI:

Note: JOSS accepts our review as theirs. You will NOT need to go through another full review. JOSS will only review your paper.md file. Be sure to link to this pyOpenSci issue when a JOSS issue is opened for your package. Also be sure to tell the JOSS editor that this is a pyOpenSci reviewed package once you reach this step.

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• Yes I am OK with reviewers submitting requested changes as issues to my repo. Reviewers will then link to the issues in their submitted review.

Confirm each of the following by checking the box.

• I have read the author guide.
• I expect to maintain this package for at least 2 years and can help find a replacement for the maintainer (team) if needed.

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Footnotes

1. Please fill out a pre-submission inquiry before submitting a data visualization package. ↩