A fast, safe, and intuitive DataFrame library.
C-Rank Grade: A (Welcoming) - 64/100
External PR Merge Rate: 56%
Response Time: -
First Timer Success: 80%
DataHaskell/dataframe has a recorded first-timer success rate of 80.0%. Repositories ranked A typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in DataHaskell/dataframe respond to incoming external pull requests in approximately 0.0 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 63.5 places DataHaskell/dataframe in the A tier.
The external contributor pull request merge rate for DataHaskell/dataframe is 56.0%, based on public PR activity from non-core contributors.
DataHaskell/dataframe currently has 4 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Balanced & professional review environment in DataHaskell/dataframe. Maintains strict focus on technical quality and test standards.
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/DataHaskell/dataframe?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
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Detected: UTC
Maintainers are most responsive around 06:00 UTC (06:00 UTC in your timezone). Recommended submission window is Wednesday - Friday from 06:00 to 10:00 UTC (06:00 - 10:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~16 hours (70% approval). Large PRs (>500 lines) take ~115 hours (25% approval). Keep your diff compact for rapid review.
We use loop structures to decode bytes: littleEndianWord64 :: BS.ByteString -> Word64 littleEndianWord64 bytes = foldr (.|.) 0 ( zipWith (\b i -> fromIntegral b `shiftL` i) (BS.unpack $ BS.take 8 bytes) [0, 8 ..] ) These loops can be unrolled to something like https://hackage.haskell.org/package/binary-0.8.9.3/docs/src/Data.Binary.Get.html#word64be
When reading Parquet we make an immutable intermediate data structures and accumulate them inti the final result. This is extremely expensive and slow. Since we know the number of rows/types we are reading ahead of time we can optimize the read time by: preallocating mutable buffers representing missingness with a validity bitmap rather than unboxing things into Maybe avoiding linked lists as an intermediate structure wherever possible These should be fairly simple changes. They will also help us clean up the parquet implementation as we go. Original discussion: #147 Similar issue: #133
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When evaluating whether to contribute to DataHaskell/dataframe, 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 DataHaskell/dataframe acknowledge new external contributions in approximately -. Out of all submitted pull requests from non-core authors in the last 180-day window, 56.0% were successfully merged into the primary branch.
DataHaskell/dataframe has a recorded first-timer success rate of 80.0%. Repositories ranked Welcoming typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~-. 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 63.5 places DataHaskell/dataframe in the Welcoming tier.
The external pull request merge rate is 56.0%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, DataHaskell/dataframe currently has 4 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.
All metrics displayed for DataHaskell/dataframe 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.