Find faster way to infer categories in `PointsModel.parse()`
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- Dominant language
- Python
- Stars
- 394
- Forks
- 95
- Avg merge
- 4d 3h
- Merged PRs (30d)
- 7
Description
The fix in https://github.com/scverse/spatialdata/pull/1010 introduces a performance regression. We should investigate faster ways to find categories. A solution is to ask the user to set the categories explicitly by converting string columns to Categorical before parsing it with PointsModel.parse().
Contributor guide
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 performance-regressing fix in pull request #1010 and the PointsModel.parse() entry point. Compare category inference with the proposed explicit Categorical input approach; done means identifying and validating a faster category-discovery path without the regression.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data, performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100