lincc-frameworks / lincc-frameworks/nested-pandas
Can we convert dictionary-encoded parquet columns to Categorical Dtype?
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- Dominant language
- Python
- Stars
- 26
- Forks
- 8
- Avg merge
- 2d 2h
- Merged PRs (30d)
- 9
Description
For something like `band`, we know that there are only a few possible values, and parquet knows this too and does a dictionary-style encoding on those columns. Can we consider using this hint to convert those columns to Categorical Dtype and save space in-memory?
Is there an in-memory savings to be gained?
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
No files or tests are identified. Start by finding the parquet-reading entry point and reproduce the `band` example with dictionary-encoded columns; compare memory use before and after conversion to Categorical Dtype, then document the conditions and expected behavior for a completed change.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- pandas, python
- Domain
- data
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100