🌊 Online machine learning in Python
C-Rank Grade: A (Welcoming) - 68/100
External PR Merge Rate: 82%
Response Time: 3h
First Timer Success: 71%
online-ml/river has a recorded first-timer success rate of 70.6%. Repositories ranked A typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in online-ml/river respond to incoming external pull requests in approximately 2.7 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 67.9 places online-ml/river in the A tier.
The external contributor pull request merge rate for online-ml/river is 81.9%, based on public PR activity from non-core contributors.
online-ml/river currently has 1 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Highly welcoming maintainers in online-ml/river. Prompt code reviews with positive guidance for new contributors.
Average Response Latency
Tracks hours until a maintainer leaves a review, comment, or PR response.
External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
Rate at which developers submitting their first repository PR succeed.
Show contributors that your repository actively reviews and merges external pull requests.
[](https://getmerged.abhishekco.de/online-ml/river?utm_source=github&utm_medium=badge)
Historical health & responsiveness evolution
External PR acceptance
First PR success rate
50% receive response in
90% receive response in
Maintainer review activity heatmap & prime window prediction
Calculating local time...
Calculating local time...
Detected: UTC
Maintainers are most responsive around 12:00 UTC (12:00 UTC in your timezone). Recommended submission window is Tuesday - Thursday from 10:00 to 14:00 UTC (10:00 - 14:00 UTC local).
Limited recent maintainer review activity recorded for this repository. Prediction based on heuristic baseline.
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~4 hours (96% approval). Large PRs (>500 lines) take ~13 hours (37% approval). Keep your diff compact for rapid review.
Enter your branch size and test coverage below to forecast review turnaround and get 1-click git splitting commands.
Alternative open-source projects with active maintainers in Python.
No repository description available.
A deep learning package for many-body potential energy representation and molecular dynamics
Agent-driven research knowledge base. Agents collect, search, and synthesize web research into a persistent, searchable wiki.
Motivation #1900 introduced dataframe-agnostic mini-batching via narwhals: inputs are wrapped at the method boundary, the numpy compute core stays untouched, and outputs are rebuilt in the caller's native backend (pandas / polars / pyarrow / nullable / arrow-backed pandas). The helpers live in river/utils/dataframe.py: into_frame / into_series — wrap native inputs to_numpy — extract a float64 array for the compute core to_native_frame / to_native_series — rebuild output in the caller's backend, preserving the pandas index So far only linear_model (GLM + LinearRegression, LogisticRegression, BayesianLinearRegression) uses this. Every other mini-batch method still hard-codes pandas (pd.DataFrame/pd.Series signatures, .values, .columns, etc.). The goal of this issue is to migrate all _many methods to narwhals so that any narwhals-supported backend works end to end. Scope Mini-batch methods to migrate (learn_many, predict_many, predict_proba_many, transform_many, and friends): Base
Rate what actually matters after opening a pull request here.
Rate your first-hand PR experience (review speed, maintainer responsiveness, and onboarding ease) to help other contributors.
When evaluating whether to contribute to online-ml/river, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.
Currently, maintainers of online-ml/river acknowledge new external contributions in approximately 2 hours. Out of all submitted pull requests from non-core authors in the last 180-day window, 81.9% were successfully merged into the primary branch.
online-ml/river has a recorded first-timer success rate of 70.6%. Repositories ranked Welcoming typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~2 hours. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 67.9 places online-ml/river in the Welcoming tier.
The external pull request merge rate is 81.9%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, online-ml/river currently has 1 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.
All metrics displayed for online-ml/river are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.