narrower dtypes in `numpy.lib._function_base_impl`
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- python
- Domain
- developer-experience
Research direction
Start in the numpy.lib._function_base_impl stubs and review the unchecked functions listed in the issue, noting how their current dtype annotations are specified. The work is complete when the listed functions have appropriately narrower dtypes, while corrcoef and cov remain coordinated with #526.
Written by the indexing model from the issue text.
Description
-
angle -
append -
asarray_chkfinite -
average -
bartlett -
bincount -
blackman -
copy -
corrcoef- #526 -
cov- #526 -
delete -
diff -
digitize -
extract -
flip -
gradient -
hamming -
hanning -
i0 -
insert -
interp -
iterable -
kaiser -
median -
meshgrid -
percentile -
piecewise -
place -
quantile -
rot90 -
select -
sinc -
sort_complex -
trapezoid -
trapz -
trim_zeros -
unwrap -
vectorize
- Dominant language
- Python
- Stars
- 79
- Forks
- 8
- PR merge metrics
- No merged PRs in 30d
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.
More from numpy/numtype
-
topic: documentation
Difficulty 2/5 1-3 hours Newbie friendliness 68/100
-
topic: documentation
Difficulty 4/5 3-5 days Newbie friendliness 35/100
-
blame: Pyright tool: basedpyright
-
numpy.generic stubs: Incomplete
Difficulty 3/5 1-2 days Newbie friendliness 35/100
-
_numtype stubs: Refactor
Difficulty 3/5 1-2 days Newbie friendliness 45/100
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