{"id":25028752,"url":"https://github.com/numbagg/numbagg","last_synced_at":"2025-05-15T09:07:16.441Z","repository":{"id":22169855,"uuid":"25501620","full_name":"numbagg/numbagg","owner":"numbagg","description":"Fast N-dimensional aggregation functions with Numba","archived":false,"fork":false,"pushed_at":"2025-05-06T04:38:24.000Z","size":496,"stargazers_count":218,"open_issues_count":11,"forks_count":16,"subscribers_count":5,"default_branch":"main","last_synced_at":"2025-05-06T05:29:55.585Z","etag":null,"topics":["numba","numpy","python"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/numbagg.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2014-10-21T04:10:26.000Z","updated_at":"2025-05-06T04:38:27.000Z","dependencies_parsed_at":"2023-10-05T00:00:29.000Z","dependency_job_id":"9240e746-efd5-4417-be2e-2d441b68b2df","html_url":"https://github.com/numbagg/numbagg","commit_stats":{"total_commits":105,"total_committers":9,"mean_commits":"11.666666666666666","dds":0.6761904761904762,"last_synced_commit":"2f7b3a8b517395e13165d5a58d58c6c45eef5536"},"previous_names":[],"tags_count":30,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/numbagg%2Fnumbagg","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/numbagg%2Fnumbagg/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/numbagg%2Fnumbagg/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/numbagg%2Fnumbagg/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/numbagg","download_url":"https://codeload.github.com/numbagg/numbagg/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252630697,"owners_count":21779414,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["numba","numpy","python"],"created_at":"2025-02-05T20:43:00.280Z","updated_at":"2025-05-15T09:07:16.389Z","avatar_url":"https://github.com/numbagg.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Numbagg: Fast N-dimensional aggregation functions with Numba\n\n[![GitHub Workflow CI Status](https://img.shields.io/github/actions/workflow/status/numbagg/numbagg/test.yaml?branch=main\u0026logo=github\u0026style=for-the-badge)](https://github.com/numbagg/numbagg/actions/workflows/test.yaml)\n[![PyPI Version](https://img.shields.io/pypi/v/numbagg?style=for-the-badge)](https://pypi.python.org/pypi/numbagg/)\n\nFast, flexible N-dimensional array functions written with\n[Numba](https://github.com/numba/numba) and NumPy's [generalized\nufuncs](http://docs.scipy.org/doc/numpy/reference/c-api.generalized-ufuncs.html).\n\n## Why use numbagg?\n\n### Performance\n\n- Outperforms pandas\n - On a single core, 2-10x faster for moving window functions, 1-2x faster for\n aggregation and grouping functions\n - When parallelizing with multiple cores, 4-30x faster\n- Outperforms bottleneck on multiple cores\n - On a single core, matches bottleneck\n - When parallelizing with multiple cores, 3-7x faster\n- Outperforms numpy on multiple cores\n - On a single core, matches numpy\n - When parallelizing with multiple cores, 5-15x faster\n- ...though numbagg's functions are JIT compiled, so the first run is much slower\n\n### Versatility\n\n- More functions (though bottleneck has some functions we don't have, and pandas' functions\n have many more parameters)\n- Functions work for \u003e3 dimensions. All functions take an arbitrary axis or\n tuple of axes to calculate over\n- Written in numba — way less code, simple to inspect, simple to improve\n\n## Functions \u0026 benchmarks\n\n### Summary benchmark\n\nTwo benchmarks summarize numbagg's performance — the first with a 1D array of 10M elements without\nparallelization, and a second with a 2D array of 100x10K elements with parallelization. Numbagg's relative\nperformance is much higher where parallelization is possible. A wider range of arrays is\nlisted in the full set of benchmarks below.\n\nThe values in the table are numbagg's performance as a multiple of other libraries for a\ngiven shaped array calculated over the final axis. (so 1.00x means numbagg is equal,\nhigher means numbagg is faster.)\n\n| func | 1D\u003cbr\u003epandas | 1D\u003cbr\u003ebottleneck | 1D\u003cbr\u003enumpy | 2D\u003cbr\u003epandas | 2D\u003cbr\u003ebottleneck | 2D\u003cbr\u003enumpy |\n| :------------------------ | -----------: | ---------------: | ----------: | -----------: | ---------------: | ----------: |\n| `bfill` | 1.06x | 1.13x | n/a | 11.11x | 5.04x | n/a |\n| `ffill` | 1.12x | 0.99x | n/a | 11.50x | 4.25x | n/a |\n| `group_nanall` | 1.38x | n/a | n/a | 7.77x | n/a | n/a |\n| `group_nanany` | 1.12x | n/a | n/a | 6.21x | n/a | n/a |\n| `group_nanargmax` | 1.16x | n/a | n/a | 6.81x | n/a | n/a |\n| `group_nanargmin` | 1.17x | n/a | n/a | 6.48x | n/a | n/a |\n| `group_nancount` | 1.05x | n/a | n/a | 4.94x | n/a | n/a |\n| `group_nanfirst` | 1.52x | n/a | n/a | 11.13x | n/a | n/a |\n| `group_nanlast` | 1.12x | n/a | n/a | 5.56x | n/a | n/a |\n| `group_nanmax` | 1.13x | n/a | n/a | 5.13x | n/a | n/a |\n| `group_nanmean` | 1.14x | n/a | n/a | 5.61x | n/a | n/a |\n| `group_nanmin` | 1.12x | n/a | n/a | 5.75x | n/a | n/a |\n| `group_nanprod` | 1.15x | n/a | n/a | 5.25x | n/a | n/a |\n| `group_nanstd` | 1.14x | n/a | n/a | 5.41x | n/a | n/a |\n| `group_nansum_of_squares` | 1.33x | n/a | n/a | 8.00x | n/a | n/a |\n| `group_nansum` | 1.18x | n/a | n/a | 5.63x | n/a | n/a |\n| `group_nanvar` | 1.13x | n/a | n/a | 4.88x | n/a | n/a |\n| `move_corr` | 16.42x | n/a | n/a | 115.76x | n/a | n/a |\n| `move_cov` | 12.30x | n/a | n/a | 86.56x | n/a | n/a |\n| `move_exp_nancorr` | 6.65x | n/a | n/a | 46.98x | n/a | n/a |\n| `move_exp_nancount` | 1.88x | n/a | n/a | 9.95x | n/a | n/a |\n| `move_exp_nancov` | 6.53x | n/a | n/a | 43.63x | n/a | n/a |\n| `move_exp_nanmean` | 1.61x | n/a | n/a | 10.65x | n/a | n/a |\n| `move_exp_nanstd` | 1.76x | n/a | n/a | 12.40x | n/a | n/a |\n| `move_exp_nansum` | 1.09x | n/a | n/a | 9.01x | n/a | n/a |\n| `move_exp_nanvar` | 1.77x | n/a | n/a | 11.41x | n/a | n/a |\n| `move_mean` | 6.03x | 1.34x | n/a | 26.60x | 6.25x | n/a |\n| `move_std` | 4.76x | 0.89x | n/a | 29.09x | 6.24x | n/a |\n| `move_sum` | 5.16x | 1.13x | n/a | 24.02x | 6.10x | n/a |\n| `move_var` | 5.45x | 1.05x | n/a | 29.54x | 6.05x | n/a |\n| `nanargmax`[^5] | 2.40x | 0.53x | n/a | 2.32x | 0.93x | n/a |\n| `nanargmin`[^5] | 2.35x | 0.50x | n/a | 2.53x | 1.00x | n/a |\n| `nancount` | 2.01x | n/a | 1.59x | 12.26x | n/a | 3.96x |\n| `nanmax`[^5] | 3.15x | 0.50x | 0.09x | 3.59x | 3.24x | 0.09x |\n| `nanmean` | 3.00x | 1.01x | 3.82x | 18.98x | 5.04x | 19.33x |\n| `nanmin`[^5] | 3.07x | 0.50x | 0.09x | 3.39x | 3.03x | 0.09x |\n| `nanquantile` | 0.69x | n/a | 0.53x | 4.94x | n/a | 4.33x |\n| `nanstd` | 1.63x | 1.61x | 3.39x | 12.39x | 10.18x | 22.03x |\n| `nansum` | 2.48x | 0.94x | 3.31x | 20.47x | 4.65x | 17.90x |\n| `nanvar` | 1.61x | 1.65x | 3.40x | 12.62x | 10.49x | 22.13x |\n\n### Full benchmarks\n\n\u003cdetails\u003e\n\n| func | shape | size | ndim | pandas | bottleneck | numpy | numbagg | pandas_ratio | bottleneck_ratio | numpy_ratio | numbagg_ratio |\n| :------------------------ | ---------------------: | --------: | ---: | -----: | ---------: | -----: | ------: | -----------: | ---------------: | ----------: | ------------: |\n| `bfill` | (1000,) | 1000 | 1 | 0ms | 0ms | n/a | 0ms | 0.38x | 0.01x | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 15ms | 16ms | n/a | 14ms | 1.06x | 1.13x | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 37ms | 17ms | n/a | 3ms | 11.11x | 5.04x | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 18ms | n/a | 3ms | n/a | 6.13x | n/a | 1.00x |\n| | (100, 1000, 1000) | 100000000 | 3 | n/a | 199ms | n/a | 31ms | n/a | 6.44x | n/a | 1.00x |\n| `ffill` | (1000,) | 1000 | 1 | 0ms | 0ms | n/a | 0ms | 0.37x | 0.01x | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 15ms | 14ms | n/a | 14ms | 1.12x | 0.99x | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 37ms | 14ms | n/a | 3ms | 11.50x | 4.25x | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 14ms | n/a | 3ms | n/a | 4.64x | n/a | 1.00x |\n| | (100, 1000, 1000) | 100000000 | 3 | n/a | 176ms | n/a | 31ms | n/a | 5.72x | n/a | 1.00x |\n| `group_nanall` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.72x | n/a | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 48ms | n/a | n/a | 35ms | 1.38x | n/a | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 18ms | n/a | n/a | 2ms | 7.77x | n/a | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 1ms | n/a | n/a | n/a | 1.00x |\n| `group_nanany` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.70x | n/a | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 49ms | n/a | n/a | 44ms | 1.12x | n/a | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 18ms | n/a | n/a | 3ms | 6.21x | n/a | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 2ms | n/a | n/a | n/a | 1.00x |\n| `group_nanargmax` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 1.07x | n/a | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 49ms | n/a | n/a | 42ms | 1.16x | n/a | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 17ms | n/a | n/a | 3ms | 6.81x | n/a | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 2ms | n/a | n/a | n/a | 1.00x |\n| `group_nanargmin` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 1.06x | n/a | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 49ms | n/a | n/a | 42ms | 1.17x | n/a | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 17ms | n/a | n/a | 3ms | 6.48x | n/a | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 2ms | n/a | n/a | n/a | 1.00x |\n| `group_nancount` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.66x | n/a | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 44ms | n/a | n/a | 42ms | 1.05x | n/a | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 13ms | n/a | n/a | 3ms | 4.94x | n/a | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 1ms | n/a | n/a | n/a | 1.00x |\n| `group_nanfirst` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.73x | n/a | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 52ms | n/a | n/a | 34ms | 1.52x | n/a | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 16ms | n/a | n/a | 1ms | 11.13x | n/a | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 1ms | n/a | n/a | n/a | 1.00x |\n| `group_nanlast` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.72x | n/a | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 47ms | n/a | n/a | 42ms | 1.12x | n/a | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 14ms | n/a | n/a | 2ms | 5.56x | n/a | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 1ms | n/a | n/a | n/a | 1.00x |\n| `group_nanmax` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.71x | n/a | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 48ms | n/a | n/a | 43ms | 1.13x | n/a | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 14ms | n/a | n/a | 3ms | 5.13x | n/a | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 2ms | n/a | n/a | n/a | 1.00x |\n| `group_nanmean` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.72x | n/a | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 50ms | n/a | n/a | 44ms | 1.14x | n/a | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 16ms | n/a | n/a | 3ms | 5.61x | n/a | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 2ms | n/a | n/a | n/a | 1.00x |\n| `group_nanmin` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.73x | n/a | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 48ms | n/a | n/a | 43ms | 1.12x | n/a | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 14ms | n/a | n/a | 2ms | 5.75x | n/a | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 2ms | n/a | n/a | n/a | 1.00x |\n| `group_nanprod` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.70x | n/a | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 48ms | n/a | n/a | 42ms | 1.15x | n/a | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 14ms | n/a | n/a | 3ms | 5.25x | n/a | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 1ms | n/a | n/a | n/a | 1.00x |\n| `group_nanstd` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.71x | n/a | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 51ms | n/a | n/a | 45ms | 1.14x | n/a | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 17ms | n/a | n/a | 3ms | 5.41x | n/a | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 2ms | n/a | n/a | n/a | 1.00x |\n| `group_nansum` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.74x | n/a | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 51ms | n/a | n/a | 43ms | 1.18x | n/a | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 16ms | n/a | n/a | 3ms | 5.63x | n/a | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 2ms | n/a | n/a | n/a | 1.00x |\n| `group_nanvar` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.70x | n/a | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 51ms | n/a | n/a | 45ms | 1.13x | n/a | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 16ms | n/a | n/a | 3ms | 4.88x | n/a | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 2ms | n/a | n/a | n/a | 1.00x |\n| `group_nansum_of_squares` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.88x | n/a | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 57ms | n/a | n/a | 43ms | 1.33x | n/a | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 22ms | n/a | n/a | 3ms | 8.00x | n/a | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 1ms | n/a | n/a | n/a | 1.00x |\n| `move_corr` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 2.68x | n/a | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 710ms | n/a | n/a | 43ms | 16.42x | n/a | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 683ms | n/a | n/a | 6ms | 115.76x | n/a | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 5ms | n/a | n/a | n/a | 1.00x |\n| | (100, 1000, 1000) | 100000000 | 3 | n/a | n/a | n/a | 49ms | n/a | n/a | n/a | 1.00x |\n| `move_cov` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 2.43x | n/a | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 490ms | n/a | n/a | 40ms | 12.30x | n/a | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 460ms | n/a | n/a | 5ms | 86.56x | n/a | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 4ms | n/a | n/a | n/a | 1.00x |\n| | (100, 1000, 1000) | 100000000 | 3 | n/a | n/a | n/a | 44ms | n/a | n/a | n/a | 1.00x |\n| `move_mean` | (1000,) | 1000 | 1 | 0ms | 0ms | n/a | 0ms | 0.46x | 0.01x | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 92ms | 21ms | n/a | 15ms | 6.03x | 1.34x | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 88ms | 21ms | n/a | 3ms | 26.60x | 6.25x | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 20ms | n/a | 3ms | n/a | 6.66x | n/a | 1.00x |\n| | (100, 1000, 1000) | 100000000 | 3 | n/a | 228ms | n/a | 32ms | n/a | 7.12x | n/a | 1.00x |\n| `move_std` | (1000,) | 1000 | 1 | 0ms | 0ms | n/a | 0ms | 0.53x | 0.02x | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 141ms | 26ms | n/a | 30ms | 4.76x | 0.89x | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 123ms | 26ms | n/a | 4ms | 29.09x | 6.24x | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 26ms | n/a | 4ms | n/a | 7.37x | n/a | 1.00x |\n| | (100, 1000, 1000) | 100000000 | 3 | n/a | 291ms | n/a | 37ms | n/a | 7.82x | n/a | 1.00x |\n| `move_sum` | (1000,) | 1000 | 1 | 0ms | 0ms | n/a | 0ms | 0.46x | 0.01x | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 95ms | 21ms | n/a | 18ms | 5.16x | 1.13x | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 83ms | 21ms | n/a | 3ms | 24.02x | 6.10x | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 21ms | n/a | 3ms | n/a | 6.79x | n/a | 1.00x |\n| | (100, 1000, 1000) | 100000000 | 3 | n/a | 227ms | n/a | 31ms | n/a | 7.29x | n/a | 1.00x |\n| `move_var` | (1000,) | 1000 | 1 | 0ms | 0ms | n/a | 0ms | 0.50x | 0.02x | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 131ms | 25ms | n/a | 24ms | 5.45x | 1.05x | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 122ms | 25ms | n/a | 4ms | 29.54x | 6.05x | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 25ms | n/a | 4ms | n/a | 7.12x | n/a | 1.00x |\n| | (100, 1000, 1000) | 100000000 | 3 | n/a | 275ms | n/a | 36ms | n/a | 7.69x | n/a | 1.00x |\n| `move_exp_nancorr` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 2.33x | n/a | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 344ms | n/a | n/a | 52ms | 6.65x | n/a | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 338ms | n/a | n/a | 7ms | 46.98x | n/a | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 6ms | n/a | n/a | n/a | 1.00x |\n| | (100, 1000, 1000) | 100000000 | 3 | n/a | n/a | n/a | 55ms | n/a | n/a | n/a | 1.00x |\n| `move_exp_nancount` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.57x | n/a | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 51ms | n/a | n/a | 27ms | 1.88x | n/a | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 47ms | n/a | n/a | 5ms | 9.95x | n/a | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 4ms | n/a | n/a | n/a | 1.00x |\n| | (100, 1000, 1000) | 100000000 | 3 | n/a | n/a | n/a | 40ms | n/a | n/a | n/a | 1.00x |\n| `move_exp_nancov` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 2.19x | n/a | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 215ms | n/a | n/a | 33ms | 6.53x | n/a | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 234ms | n/a | n/a | 5ms | 43.63x | n/a | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 5ms | n/a | n/a | n/a | 1.00x |\n| | (100, 1000, 1000) | 100000000 | 3 | n/a | n/a | n/a | 43ms | n/a | n/a | n/a | 1.00x |\n| `move_exp_nanmean` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.39x | n/a | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 47ms | n/a | n/a | 30ms | 1.61x | n/a | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 52ms | n/a | n/a | 5ms | 10.65x | n/a | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 4ms | n/a | n/a | n/a | 1.00x |\n| | (100, 1000, 1000) | 100000000 | 3 | n/a | n/a | n/a | 43ms | n/a | n/a | n/a | 1.00x |\n| `move_exp_nanstd` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.68x | n/a | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 64ms | n/a | n/a | 36ms | 1.76x | n/a | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 74ms | n/a | n/a | 6ms | 12.40x | n/a | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 5ms | n/a | n/a | n/a | 1.00x |\n| | (100, 1000, 1000) | 100000000 | 3 | n/a | n/a | n/a | 44ms | n/a | n/a | n/a | 1.00x |\n| `move_exp_nansum` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.38x | n/a | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 36ms | n/a | n/a | 33ms | 1.09x | n/a | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 43ms | n/a | n/a | 5ms | 9.01x | n/a | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 4ms | n/a | n/a | n/a | 1.00x |\n| | (100, 1000, 1000) | 100000000 | 3 | n/a | n/a | n/a | 42ms | n/a | n/a | n/a | 1.00x |\n| `move_exp_nanvar` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.40x | n/a | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 56ms | n/a | n/a | 32ms | 1.77x | n/a | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 64ms | n/a | n/a | 6ms | 11.41x | n/a | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 4ms | n/a | n/a | n/a | 1.00x |\n| | (100, 1000, 1000) | 100000000 | 3 | n/a | n/a | n/a | 46ms | n/a | n/a | n/a | 1.00x |\n| `nanargmax`[^5] | (1000,) | 1000 | 1 | 0ms | 0ms | n/a | 0ms | 17.65x | 0.17x | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 24ms | 5ms | n/a | 10ms | 2.40x | 0.53x | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 25ms | 10ms | n/a | 11ms | 2.32x | 0.93x | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 11ms | n/a | 11ms | n/a | 1.00x | n/a | 1.00x |\n| | (100, 1000, 1000) | 100000000 | 3 | n/a | 107ms | n/a | 108ms | n/a | 0.99x | n/a | 1.00x |\n| `nanargmin`[^5] | (1000,) | 1000 | 1 | 0ms | 0ms | n/a | 0ms | 17.72x | 0.17x | n/a | 1.00x |\n| | (10000000,) | 10000000 | 1 | 25ms | 5ms | n/a | 11ms | 2.35x | 0.50x | n/a | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 25ms | 10ms | n/a | 10ms | 2.53x | 1.00x | n/a | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 11ms | n/a | 11ms | n/a | 1.00x | n/a | 1.00x |\n| | (100, 1000, 1000) | 100000000 | 3 | n/a | 108ms | n/a | 108ms | n/a | 1.00x | n/a | 1.00x |\n| `nancount` | (1000,) | 1000 | 1 | 0ms | n/a | 0ms | 0ms | 0.77x | n/a | 0.02x | 1.00x |\n| | (10000000,) | 10000000 | 1 | 3ms | n/a | 3ms | 2ms | 2.01x | n/a | 1.59x | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 8ms | n/a | 3ms | 1ms | 12.26x | n/a | 3.96x | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | 3ms | 1ms | n/a | n/a | 3.97x | 1.00x |\n| | (100, 1000, 1000) | 100000000 | 3 | n/a | n/a | 33ms | 7ms | n/a | n/a | 5.07x | 1.00x |\n| `nanmax`[^5] | (1000,) | 1000 | 1 | 0ms | 0ms | 0ms | 0ms | 11.07x | 0.17x | 0.55x | 1.00x |\n| | (10000000,) | 10000000 | 1 | 32ms | 5ms | 1ms | 10ms | 3.15x | 0.50x | 0.09x | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 36ms | 33ms | 1ms | 10ms | 3.59x | 3.24x | 0.09x | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 32ms | 1ms | 10ms | n/a | 3.24x | 0.10x | 1.00x |\n| | (100, 1000, 1000) | 100000000 | 3 | n/a | 320ms | 11ms | 98ms | n/a | 3.26x | 0.11x | 1.00x |\n| `nanmean` | (1000,) | 1000 | 1 | 0ms | 0ms | 0ms | 0ms | 0.39x | 0.00x | 0.05x | 1.00x |\n| | (10000000,) | 10000000 | 1 | 17ms | 6ms | 21ms | 6ms | 3.00x | 1.01x | 3.82x | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 21ms | 5ms | 21ms | 1ms | 18.98x | 5.04x | 19.33x | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 5ms | 21ms | 1ms | n/a | 6.10x | 23.77x | 1.00x |\n| | (100, 1000, 1000) | 100000000 | 3 | n/a | 54ms | 258ms | 8ms | n/a | 7.00x | 33.59x | 1.00x |\n| `nanmin`[^5] | (1000,) | 1000 | 1 | 0ms | 0ms | 0ms | 0ms | 10.86x | 0.17x | 0.55x | 1.00x |\n| | (10000000,) | 10000000 | 1 | 33ms | 5ms | 1ms | 11ms | 3.07x | 0.50x | 0.09x | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 36ms | 32ms | 1ms | 11ms | 3.39x | 3.03x | 0.09x | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 32ms | 1ms | 10ms | n/a | 3.12x | 0.10x | 1.00x |\n| | (100, 1000, 1000) | 100000000 | 3 | n/a | 320ms | 11ms | 102ms | n/a | 3.12x | 0.11x | 1.00x |\n| `nanquantile` | (1000,) | 1000 | 1 | 0ms | n/a | 0ms | 0ms | 0.56x | n/a | 0.21x | 1.00x |\n| | (10000000,) | 10000000 | 1 | 114ms | n/a | 87ms | 164ms | 0.69x | n/a | 0.53x | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 131ms | n/a | 115ms | 27ms | 4.94x | n/a | 4.33x | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | 315ms | 19ms | n/a | n/a | 16.51x | 1.00x |\n| | (100, 1000, 1000) | 100000000 | 3 | n/a | n/a | 3118ms | 165ms | n/a | n/a | 18.88x | 1.00x |\n| `nanstd` | (1000,) | 1000 | 1 | 0ms | 0ms | 0ms | 0ms | 0.31x | 0.02x | 0.14x | 1.00x |\n| | (10000000,) | 10000000 | 1 | 21ms | 20ms | 43ms | 13ms | 1.63x | 1.61x | 3.39x | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 24ms | 20ms | 43ms | 2ms | 12.39x | 10.18x | 22.03x | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 20ms | 46ms | 1ms | n/a | 14.17x | 32.66x | 1.00x |\n| | (100, 1000, 1000) | 100000000 | 3 | n/a | 202ms | 513ms | 13ms | n/a | 16.08x | 40.78x | 1.00x |\n| `nansum` | (1000,) | 1000 | 1 | 0ms | 0ms | 0ms | 0ms | 0.46x | 0.01x | 0.03x | 1.00x |\n| | (10000000,) | 10000000 | 1 | 14ms | 5ms | 19ms | 6ms | 2.48x | 0.94x | 3.31x | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 22ms | 5ms | 19ms | 1ms | 20.47x | 4.65x | 17.90x | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 5ms | 20ms | 1ms | n/a | 6.21x | 22.95x | 1.00x |\n| | (100, 1000, 1000) | 100000000 | 3 | n/a | 53ms | 226ms | 8ms | n/a | 6.98x | 29.90x | 1.00x |\n| `nanvar` | (1000,) | 1000 | 1 | 0ms | 0ms | 0ms | 0ms | 0.32x | 0.02x | 0.13x | 1.00x |\n| | (10000000,) | 10000000 | 1 | 21ms | 21ms | 44ms | 13ms | 1.61x | 1.65x | 3.40x | 1.00x |\n| | (100, 100000) | 10000000 | 2 | 25ms | 21ms | 43ms | 2ms | 12.62x | 10.49x | 22.13x | 1.00x |\n| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 20ms | 46ms | 1ms | n/a | 14.02x | 32.28x | 1.00x |\n| | (100, 1000, 1000) | 100000000 | 3 | n/a | 202ms | 503ms | 13ms | n/a | 15.68x | 38.98x | 1.00x |\n\n\u003c/details\u003e\n\n[^1][^2][^3][^4]\n\n[^1]:\n Benchmarks were run on a Mac M3 Max laptop in September 2024 on numbagg's HEAD,\n pandas 2.2.2, bottleneck 1.4.0 numpy 2.0.1, with `python\nnumbagg/test/run_benchmarks.py -- --benchmark-max-time=10`. They run in CI,\n though GHA's low CPU count means we don't see the full benefits of\n parallelization.\n\n[^2]:\n While we separate the setup and the running of the functions, pandas still\n needs to do some work to create its result dataframe, and numbagg does some\n checks in python which bottleneck does in C or doesn't do. So use benchmarks\n on larger arrays for our summary so we can focus on the computational speed,\n which doesn't asymptote away. Any contributions to improve the benchmarks are\n welcome.\n\n[^3]:\n In some instances, a library won't have the exact function — for example,\n pandas doesn't have an equivalent `move_exp_nancount` function, so we use\n its `sum` function on an array of `1`s. Similarly for\n `group_nansum_of_squares`, we use two separate operations.\n\n[^4]:\n `anynan` \u0026 `allnan` are also functions in numbagg, but not listed here as they\n require a different benchmark setup.\n\n[^5]:\n This function is not currently parallelized, so exhibits worse performance\n on parallelizable arrays.\n\n## Example implementation\n\nNumbagg makes it easy to write, in pure Python/NumPy, flexible aggregation\nfunctions accelerated by Numba. All the hard work is done by Numba's JIT\ncompiler and NumPy's gufunc machinery (as wrapped by Numba).\n\nFor example, here is how we wrote `nansum`:\n\n```python\nimport numpy as np\nfrom numbagg.decorators import ndreduce\n\n@ndreduce.wrap()\ndef nansum(a):\n asum = 0.0\n for ai in a.flat:\n if not np.isnan(ai):\n asum += ai\n return asum\n```\n\n## Implementation details\n\nNumbagg includes somewhat awkward workarounds for features missing from\nNumPy/Numba:\n\n- It implements its own cache for functions wrapped by Numba's\n `guvectorize`, because that decorator is rather slow.\n- It does its [own handling of array\n transposes](https://github.com/numbagg/numbagg/blob/e166adae94b3be35497dcdc22772026df75af253/numbagg/decorators.py#L170-L181)\n to handle the `axis` argument in reduction functions.\n- It [rewrites plain functions into\n gufuncs](https://github.com/numbagg/numbagg/blob/e166adae94b3be35497dcdc22772026df75af253/numbagg/transform.py),\n to allow writing a traditional function while retaining the multidimensional advantages of\n gufuncs.\n\nAlready some of the ideas here have flowed upstream to numba (for example, [an\naxis parameter](https://github.com/numpy/numpy/issues/5197)), and we hope\nthat others will follow.\n\n## License\n\n3-clause BSD. Includes portions of Bottleneck, which is distributed under a\nSimplified BSD license.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnumbagg%2Fnumbagg","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnumbagg%2Fnumbagg","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnumbagg%2Fnumbagg/lists"}