The-Strategy-Unit / The-Strategy-Unit/data_science
Python packagaing
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- Dominant language
- Jupyter Notebook
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- 11
- Forks
- 5
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- 5h 34m
- Merged PRs (30d)
- 1
Description
We've had a session about R packages and now it's time for one about Python packages, especially as we have more interested Pythonistas. This could extend to best-practice Python project structure in general.
Discussed a bit with @anyaferguson, @craig-parylo, @Nat-Stephenson and @tomjemmett in a beginner Python session.
Example resource: https://packaging.python.org/en/latest/
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Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the linked R packages issue and the Python Packaging User Guide. Define the scope for a Python packaging session, including whether it covers general project structure, and prepare materials that address the agreed topics.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- documentation
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100