The-Strategy-Unit / The-Strategy-Unit/data_science
Parallel processing in data analysis
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session: C&C ☕
- Dominant language
- Jupyter Notebook
- Stars
- 11
- Forks
- 5
- Avg merge
- 5h 34m
- Merged PRs (30d)
- 1
Description
- What is parallel processing?
- What is a rough rule of thumb for when it is worth it for common data tasks (reading many files, cleaning data, running many models)
- Examples using the furrr / future packages
- Some pitfalls – things that you can’t do
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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
Use the issue questions as the outline for the material, starting with the repository’s existing data-analysis documentation or notebooks. Explain when parallel processing is worthwhile, demonstrate furrr and future examples for the listed tasks, and document limitations and pitfalls; completion means all four requested areas are covered with runnable examples.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- r
- Domain
- data, documentation
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 55/100