Add advanced indexing support
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- Dominant language
- Python
- Stars
- 668
- Forks
- 141
- Avg merge
- 2d 8h
- Merged PRs (30d)
- 4
Description
I would like to have support for advanced indexing for both retrieval and assignment. Ideally I was hoping to find something that could serve as a drop-in replacement for numpy.ndarray for these types of operations. Is this functionality something that would be in the scope of this library? Are there any thoughts on how it should be implemented?
On a related note I noticed that COO is currently immutable and thus doesn't doesn't allow item assignment. However I wonder if one could support assignment by having COO make an in-place copy of itself. Of course this will be extremely inefficient for updating a single element but when addressing a large number of elements in parallel the overhead from the copy should be more manageable. Of course in the documentation you could stress that setting elements of COO individually is not recommended.
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 current COO indexing and immutability entry points, then compare the requested retrieval and assignment behavior with NumPy advanced indexing. Define the supported semantics for both operations and determine whether in-place-copy assignment is in scope. Done means the project has an agreed implementation direction and corresponding behavior for advanced indexing.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- numpy, python
- Domain
- data
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100