QuantEcon / QuantEcon/QuantEcon.py
ENH: Fill the 58 missing public API docstrings
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 2.4k
- Forks
- 2.3k
- Avg merge
- 3d 3h
- Merged PRs (30d)
- 3
Description
Problem
Public docstring coverage is 239 of 297 public non-dunder definitions (80.5%). The 58 gaps concentrate in two modules:
| Module | Missing |
|---|---|
_graph_tools.py |
17 (mostly properties) |
markov/core.py |
16 |
_arma.py, _kalman.py, distributions.py, util/timing.py |
4 each |
markov/ddp.py, random/utilities.py, others |
3 each |
Where docstrings exist they are numpydoc-conformant and generally good; this is about completeness, not quality.
Good for outside contributors, especially anyone with domain knowledge of the graph and Markov chain code, since several gaps are on properties whose meaning is not obvious from the name. Worth splitting into per-module sub-issues so two people do not collide.
Acceptance criteria
- All 58 public definitions carry a numpydoc-conformant docstring
- Parameters, returns and raised exceptions documented where non-obvious; examples for the public entry points among them
- The Sphinx build produces no new warnings
- A docstring-coverage check considered for CI to prevent regression
From the July 2026 technical-debt audit (AI-assisted; claims verified against 28d4b3b on 2026-07-25).
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 public definitions in _graph_tools.py and markov/core.py, then inspect the remaining listed modules and compare nearby docstrings for numpydoc conventions. Complete the 58 missing docstrings, adding parameters, returns, exceptions, and examples where required, then verify that the Sphinx build produces no new warnings.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- documentation
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 48/100