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https://github.com/numbagg/numbagg
Fast N-dimensional aggregation functions with Numba
https://github.com/numbagg/numbagg
numba numpy python
Last synced: about 4 hours ago
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Fast N-dimensional aggregation functions with Numba
- Host: GitHub
- URL: https://github.com/numbagg/numbagg
- Owner: numbagg
- License: other
- Created: 2014-10-21T04:10:26.000Z (over 10 years ago)
- Default Branch: main
- Last Pushed: 2025-01-28T02:02:45.000Z (9 days ago)
- Last Synced: 2025-01-28T03:19:30.789Z (9 days ago)
- Topics: numba, numpy, python
- Language: Python
- Homepage:
- Size: 413 KB
- Stars: 212
- Watchers: 5
- Forks: 16
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Numbagg: Fast N-dimensional aggregation functions with Numba
[](https://github.com/numbagg/numbagg/actions/workflows/test.yaml)
[](https://pypi.python.org/pypi/numbagg/)
Fast, flexible N-dimensional array functions written with
[Numba](https://github.com/numba/numba) and NumPy's [generalized
ufuncs](http://docs.scipy.org/doc/numpy/reference/c-api.generalized-ufuncs.html).
## Why use numbagg?
### Performance
- Outperforms pandas
- On a single core, 2-10x faster for moving window functions, 1-2x faster for
aggregation and grouping functions
- When parallelizing with multiple cores, 4-30x faster
- Outperforms bottleneck on multiple cores
- On a single core, matches bottleneck
- When parallelizing with multiple cores, 3-7x faster
- Outperforms numpy on multiple cores
- On a single core, matches numpy
- When parallelizing with multiple cores, 5-15x faster
- ...though numbagg's functions are JIT compiled, so the first run is much slower
### Versatility
- More functions (though bottleneck has some functions we don't have, and pandas' functions
have many more parameters)
- Functions work for >3 dimensions. All functions take an arbitrary axis or
tuple of axes to calculate over
- Written in numba — way less code, simple to inspect, simple to improve
## Functions & benchmarks
### Summary benchmark
Two benchmarks summarize numbagg's performance — the first with a 1D array of 10M elements without
parallelization, and a second with a 2D array of 100x10K elements with parallelization. Numbagg's relative
performance is much higher where parallelization is possible. A wider range of arrays is
listed in the full set of benchmarks below.
The values in the table are numbagg's performance as a multiple of other libraries for a
given shaped array calculated over the final axis. (so 1.00x means numbagg is equal,
higher means numbagg is faster.)
| func | 1D
pandas | 1D
bottleneck | 1D
numpy | 2D
pandas | 2D
bottleneck | 2D
numpy |
| :------------------------ | -----------: | ---------------: | ----------: | -----------: | ---------------: | ----------: |
| `bfill` | 1.06x | 1.13x | n/a | 11.11x | 5.04x | n/a |
| `ffill` | 1.12x | 0.99x | n/a | 11.50x | 4.25x | n/a |
| `group_nanall` | 1.38x | n/a | n/a | 7.77x | n/a | n/a |
| `group_nanany` | 1.12x | n/a | n/a | 6.21x | n/a | n/a |
| `group_nanargmax` | 1.16x | n/a | n/a | 6.81x | n/a | n/a |
| `group_nanargmin` | 1.17x | n/a | n/a | 6.48x | n/a | n/a |
| `group_nancount` | 1.05x | n/a | n/a | 4.94x | n/a | n/a |
| `group_nanfirst` | 1.52x | n/a | n/a | 11.13x | n/a | n/a |
| `group_nanlast` | 1.12x | n/a | n/a | 5.56x | n/a | n/a |
| `group_nanmax` | 1.13x | n/a | n/a | 5.13x | n/a | n/a |
| `group_nanmean` | 1.14x | n/a | n/a | 5.61x | n/a | n/a |
| `group_nanmin` | 1.12x | n/a | n/a | 5.75x | n/a | n/a |
| `group_nanprod` | 1.15x | n/a | n/a | 5.25x | n/a | n/a |
| `group_nanstd` | 1.14x | n/a | n/a | 5.41x | n/a | n/a |
| `group_nansum_of_squares` | 1.33x | n/a | n/a | 8.00x | n/a | n/a |
| `group_nansum` | 1.18x | n/a | n/a | 5.63x | n/a | n/a |
| `group_nanvar` | 1.13x | n/a | n/a | 4.88x | n/a | n/a |
| `move_corr` | 16.42x | n/a | n/a | 115.76x | n/a | n/a |
| `move_cov` | 12.30x | n/a | n/a | 86.56x | n/a | n/a |
| `move_exp_nancorr` | 6.65x | n/a | n/a | 46.98x | n/a | n/a |
| `move_exp_nancount` | 1.88x | n/a | n/a | 9.95x | n/a | n/a |
| `move_exp_nancov` | 6.53x | n/a | n/a | 43.63x | n/a | n/a |
| `move_exp_nanmean` | 1.61x | n/a | n/a | 10.65x | n/a | n/a |
| `move_exp_nanstd` | 1.76x | n/a | n/a | 12.40x | n/a | n/a |
| `move_exp_nansum` | 1.09x | n/a | n/a | 9.01x | n/a | n/a |
| `move_exp_nanvar` | 1.77x | n/a | n/a | 11.41x | n/a | n/a |
| `move_mean` | 6.03x | 1.34x | n/a | 26.60x | 6.25x | n/a |
| `move_std` | 4.76x | 0.89x | n/a | 29.09x | 6.24x | n/a |
| `move_sum` | 5.16x | 1.13x | n/a | 24.02x | 6.10x | n/a |
| `move_var` | 5.45x | 1.05x | n/a | 29.54x | 6.05x | n/a |
| `nanargmax`[^5] | 2.40x | 0.53x | n/a | 2.32x | 0.93x | n/a |
| `nanargmin`[^5] | 2.35x | 0.50x | n/a | 2.53x | 1.00x | n/a |
| `nancount` | 2.01x | n/a | 1.59x | 12.26x | n/a | 3.96x |
| `nanmax`[^5] | 3.15x | 0.50x | 0.09x | 3.59x | 3.24x | 0.09x |
| `nanmean` | 3.00x | 1.01x | 3.82x | 18.98x | 5.04x | 19.33x |
| `nanmin`[^5] | 3.07x | 0.50x | 0.09x | 3.39x | 3.03x | 0.09x |
| `nanquantile` | 0.69x | n/a | 0.53x | 4.94x | n/a | 4.33x |
| `nanstd` | 1.63x | 1.61x | 3.39x | 12.39x | 10.18x | 22.03x |
| `nansum` | 2.48x | 0.94x | 3.31x | 20.47x | 4.65x | 17.90x |
| `nanvar` | 1.61x | 1.65x | 3.40x | 12.62x | 10.49x | 22.13x |
### Full benchmarks
| func | shape | size | ndim | pandas | bottleneck | numpy | numbagg | pandas_ratio | bottleneck_ratio | numpy_ratio | numbagg_ratio |
| :------------------------ | ---------------------: | --------: | ---: | -----: | ---------: | -----: | ------: | -----------: | ---------------: | ----------: | ------------: |
| `bfill` | (1000,) | 1000 | 1 | 0ms | 0ms | n/a | 0ms | 0.38x | 0.01x | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 15ms | 16ms | n/a | 14ms | 1.06x | 1.13x | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 37ms | 17ms | n/a | 3ms | 11.11x | 5.04x | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 18ms | n/a | 3ms | n/a | 6.13x | n/a | 1.00x |
| | (100, 1000, 1000) | 100000000 | 3 | n/a | 199ms | n/a | 31ms | n/a | 6.44x | n/a | 1.00x |
| `ffill` | (1000,) | 1000 | 1 | 0ms | 0ms | n/a | 0ms | 0.37x | 0.01x | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 15ms | 14ms | n/a | 14ms | 1.12x | 0.99x | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 37ms | 14ms | n/a | 3ms | 11.50x | 4.25x | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 14ms | n/a | 3ms | n/a | 4.64x | n/a | 1.00x |
| | (100, 1000, 1000) | 100000000 | 3 | n/a | 176ms | n/a | 31ms | n/a | 5.72x | n/a | 1.00x |
| `group_nanall` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.72x | n/a | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 48ms | n/a | n/a | 35ms | 1.38x | n/a | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 18ms | n/a | n/a | 2ms | 7.77x | n/a | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 1ms | n/a | n/a | n/a | 1.00x |
| `group_nanany` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.70x | n/a | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 49ms | n/a | n/a | 44ms | 1.12x | n/a | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 18ms | n/a | n/a | 3ms | 6.21x | n/a | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 2ms | n/a | n/a | n/a | 1.00x |
| `group_nanargmax` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 1.07x | n/a | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 49ms | n/a | n/a | 42ms | 1.16x | n/a | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 17ms | n/a | n/a | 3ms | 6.81x | n/a | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 2ms | n/a | n/a | n/a | 1.00x |
| `group_nanargmin` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 1.06x | n/a | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 49ms | n/a | n/a | 42ms | 1.17x | n/a | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 17ms | n/a | n/a | 3ms | 6.48x | n/a | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 2ms | n/a | n/a | n/a | 1.00x |
| `group_nancount` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.66x | n/a | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 44ms | n/a | n/a | 42ms | 1.05x | n/a | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 13ms | n/a | n/a | 3ms | 4.94x | n/a | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 1ms | n/a | n/a | n/a | 1.00x |
| `group_nanfirst` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.73x | n/a | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 52ms | n/a | n/a | 34ms | 1.52x | n/a | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 16ms | n/a | n/a | 1ms | 11.13x | n/a | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 1ms | n/a | n/a | n/a | 1.00x |
| `group_nanlast` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.72x | n/a | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 47ms | n/a | n/a | 42ms | 1.12x | n/a | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 14ms | n/a | n/a | 2ms | 5.56x | n/a | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 1ms | n/a | n/a | n/a | 1.00x |
| `group_nanmax` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.71x | n/a | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 48ms | n/a | n/a | 43ms | 1.13x | n/a | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 14ms | n/a | n/a | 3ms | 5.13x | n/a | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 2ms | n/a | n/a | n/a | 1.00x |
| `group_nanmean` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.72x | n/a | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 50ms | n/a | n/a | 44ms | 1.14x | n/a | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 16ms | n/a | n/a | 3ms | 5.61x | n/a | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 2ms | n/a | n/a | n/a | 1.00x |
| `group_nanmin` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.73x | n/a | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 48ms | n/a | n/a | 43ms | 1.12x | n/a | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 14ms | n/a | n/a | 2ms | 5.75x | n/a | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 2ms | n/a | n/a | n/a | 1.00x |
| `group_nanprod` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.70x | n/a | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 48ms | n/a | n/a | 42ms | 1.15x | n/a | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 14ms | n/a | n/a | 3ms | 5.25x | n/a | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 1ms | n/a | n/a | n/a | 1.00x |
| `group_nanstd` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.71x | n/a | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 51ms | n/a | n/a | 45ms | 1.14x | n/a | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 17ms | n/a | n/a | 3ms | 5.41x | n/a | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 2ms | n/a | n/a | n/a | 1.00x |
| `group_nansum` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.74x | n/a | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 51ms | n/a | n/a | 43ms | 1.18x | n/a | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 16ms | n/a | n/a | 3ms | 5.63x | n/a | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 2ms | n/a | n/a | n/a | 1.00x |
| `group_nanvar` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.70x | n/a | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 51ms | n/a | n/a | 45ms | 1.13x | n/a | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 16ms | n/a | n/a | 3ms | 4.88x | n/a | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 2ms | n/a | n/a | n/a | 1.00x |
| `group_nansum_of_squares` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.88x | n/a | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 57ms | n/a | n/a | 43ms | 1.33x | n/a | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 22ms | n/a | n/a | 3ms | 8.00x | n/a | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 1ms | n/a | n/a | n/a | 1.00x |
| `move_corr` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 2.68x | n/a | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 710ms | n/a | n/a | 43ms | 16.42x | n/a | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 683ms | n/a | n/a | 6ms | 115.76x | n/a | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 5ms | n/a | n/a | n/a | 1.00x |
| | (100, 1000, 1000) | 100000000 | 3 | n/a | n/a | n/a | 49ms | n/a | n/a | n/a | 1.00x |
| `move_cov` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 2.43x | n/a | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 490ms | n/a | n/a | 40ms | 12.30x | n/a | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 460ms | n/a | n/a | 5ms | 86.56x | n/a | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 4ms | n/a | n/a | n/a | 1.00x |
| | (100, 1000, 1000) | 100000000 | 3 | n/a | n/a | n/a | 44ms | n/a | n/a | n/a | 1.00x |
| `move_mean` | (1000,) | 1000 | 1 | 0ms | 0ms | n/a | 0ms | 0.46x | 0.01x | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 92ms | 21ms | n/a | 15ms | 6.03x | 1.34x | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 88ms | 21ms | n/a | 3ms | 26.60x | 6.25x | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 20ms | n/a | 3ms | n/a | 6.66x | n/a | 1.00x |
| | (100, 1000, 1000) | 100000000 | 3 | n/a | 228ms | n/a | 32ms | n/a | 7.12x | n/a | 1.00x |
| `move_std` | (1000,) | 1000 | 1 | 0ms | 0ms | n/a | 0ms | 0.53x | 0.02x | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 141ms | 26ms | n/a | 30ms | 4.76x | 0.89x | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 123ms | 26ms | n/a | 4ms | 29.09x | 6.24x | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 26ms | n/a | 4ms | n/a | 7.37x | n/a | 1.00x |
| | (100, 1000, 1000) | 100000000 | 3 | n/a | 291ms | n/a | 37ms | n/a | 7.82x | n/a | 1.00x |
| `move_sum` | (1000,) | 1000 | 1 | 0ms | 0ms | n/a | 0ms | 0.46x | 0.01x | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 95ms | 21ms | n/a | 18ms | 5.16x | 1.13x | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 83ms | 21ms | n/a | 3ms | 24.02x | 6.10x | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 21ms | n/a | 3ms | n/a | 6.79x | n/a | 1.00x |
| | (100, 1000, 1000) | 100000000 | 3 | n/a | 227ms | n/a | 31ms | n/a | 7.29x | n/a | 1.00x |
| `move_var` | (1000,) | 1000 | 1 | 0ms | 0ms | n/a | 0ms | 0.50x | 0.02x | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 131ms | 25ms | n/a | 24ms | 5.45x | 1.05x | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 122ms | 25ms | n/a | 4ms | 29.54x | 6.05x | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 25ms | n/a | 4ms | n/a | 7.12x | n/a | 1.00x |
| | (100, 1000, 1000) | 100000000 | 3 | n/a | 275ms | n/a | 36ms | n/a | 7.69x | n/a | 1.00x |
| `move_exp_nancorr` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 2.33x | n/a | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 344ms | n/a | n/a | 52ms | 6.65x | n/a | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 338ms | n/a | n/a | 7ms | 46.98x | n/a | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 6ms | n/a | n/a | n/a | 1.00x |
| | (100, 1000, 1000) | 100000000 | 3 | n/a | n/a | n/a | 55ms | n/a | n/a | n/a | 1.00x |
| `move_exp_nancount` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.57x | n/a | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 51ms | n/a | n/a | 27ms | 1.88x | n/a | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 47ms | n/a | n/a | 5ms | 9.95x | n/a | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 4ms | n/a | n/a | n/a | 1.00x |
| | (100, 1000, 1000) | 100000000 | 3 | n/a | n/a | n/a | 40ms | n/a | n/a | n/a | 1.00x |
| `move_exp_nancov` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 2.19x | n/a | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 215ms | n/a | n/a | 33ms | 6.53x | n/a | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 234ms | n/a | n/a | 5ms | 43.63x | n/a | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 5ms | n/a | n/a | n/a | 1.00x |
| | (100, 1000, 1000) | 100000000 | 3 | n/a | n/a | n/a | 43ms | n/a | n/a | n/a | 1.00x |
| `move_exp_nanmean` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.39x | n/a | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 47ms | n/a | n/a | 30ms | 1.61x | n/a | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 52ms | n/a | n/a | 5ms | 10.65x | n/a | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 4ms | n/a | n/a | n/a | 1.00x |
| | (100, 1000, 1000) | 100000000 | 3 | n/a | n/a | n/a | 43ms | n/a | n/a | n/a | 1.00x |
| `move_exp_nanstd` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.68x | n/a | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 64ms | n/a | n/a | 36ms | 1.76x | n/a | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 74ms | n/a | n/a | 6ms | 12.40x | n/a | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 5ms | n/a | n/a | n/a | 1.00x |
| | (100, 1000, 1000) | 100000000 | 3 | n/a | n/a | n/a | 44ms | n/a | n/a | n/a | 1.00x |
| `move_exp_nansum` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.38x | n/a | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 36ms | n/a | n/a | 33ms | 1.09x | n/a | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 43ms | n/a | n/a | 5ms | 9.01x | n/a | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 4ms | n/a | n/a | n/a | 1.00x |
| | (100, 1000, 1000) | 100000000 | 3 | n/a | n/a | n/a | 42ms | n/a | n/a | n/a | 1.00x |
| `move_exp_nanvar` | (1000,) | 1000 | 1 | 0ms | n/a | n/a | 0ms | 0.40x | n/a | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 56ms | n/a | n/a | 32ms | 1.77x | n/a | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 64ms | n/a | n/a | 6ms | 11.41x | n/a | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | n/a | 4ms | n/a | n/a | n/a | 1.00x |
| | (100, 1000, 1000) | 100000000 | 3 | n/a | n/a | n/a | 46ms | n/a | n/a | n/a | 1.00x |
| `nanargmax`[^5] | (1000,) | 1000 | 1 | 0ms | 0ms | n/a | 0ms | 17.65x | 0.17x | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 24ms | 5ms | n/a | 10ms | 2.40x | 0.53x | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 25ms | 10ms | n/a | 11ms | 2.32x | 0.93x | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 11ms | n/a | 11ms | n/a | 1.00x | n/a | 1.00x |
| | (100, 1000, 1000) | 100000000 | 3 | n/a | 107ms | n/a | 108ms | n/a | 0.99x | n/a | 1.00x |
| `nanargmin`[^5] | (1000,) | 1000 | 1 | 0ms | 0ms | n/a | 0ms | 17.72x | 0.17x | n/a | 1.00x |
| | (10000000,) | 10000000 | 1 | 25ms | 5ms | n/a | 11ms | 2.35x | 0.50x | n/a | 1.00x |
| | (100, 100000) | 10000000 | 2 | 25ms | 10ms | n/a | 10ms | 2.53x | 1.00x | n/a | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 11ms | n/a | 11ms | n/a | 1.00x | n/a | 1.00x |
| | (100, 1000, 1000) | 100000000 | 3 | n/a | 108ms | n/a | 108ms | n/a | 1.00x | n/a | 1.00x |
| `nancount` | (1000,) | 1000 | 1 | 0ms | n/a | 0ms | 0ms | 0.77x | n/a | 0.02x | 1.00x |
| | (10000000,) | 10000000 | 1 | 3ms | n/a | 3ms | 2ms | 2.01x | n/a | 1.59x | 1.00x |
| | (100, 100000) | 10000000 | 2 | 8ms | n/a | 3ms | 1ms | 12.26x | n/a | 3.96x | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | 3ms | 1ms | n/a | n/a | 3.97x | 1.00x |
| | (100, 1000, 1000) | 100000000 | 3 | n/a | n/a | 33ms | 7ms | n/a | n/a | 5.07x | 1.00x |
| `nanmax`[^5] | (1000,) | 1000 | 1 | 0ms | 0ms | 0ms | 0ms | 11.07x | 0.17x | 0.55x | 1.00x |
| | (10000000,) | 10000000 | 1 | 32ms | 5ms | 1ms | 10ms | 3.15x | 0.50x | 0.09x | 1.00x |
| | (100, 100000) | 10000000 | 2 | 36ms | 33ms | 1ms | 10ms | 3.59x | 3.24x | 0.09x | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 32ms | 1ms | 10ms | n/a | 3.24x | 0.10x | 1.00x |
| | (100, 1000, 1000) | 100000000 | 3 | n/a | 320ms | 11ms | 98ms | n/a | 3.26x | 0.11x | 1.00x |
| `nanmean` | (1000,) | 1000 | 1 | 0ms | 0ms | 0ms | 0ms | 0.39x | 0.00x | 0.05x | 1.00x |
| | (10000000,) | 10000000 | 1 | 17ms | 6ms | 21ms | 6ms | 3.00x | 1.01x | 3.82x | 1.00x |
| | (100, 100000) | 10000000 | 2 | 21ms | 5ms | 21ms | 1ms | 18.98x | 5.04x | 19.33x | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 5ms | 21ms | 1ms | n/a | 6.10x | 23.77x | 1.00x |
| | (100, 1000, 1000) | 100000000 | 3 | n/a | 54ms | 258ms | 8ms | n/a | 7.00x | 33.59x | 1.00x |
| `nanmin`[^5] | (1000,) | 1000 | 1 | 0ms | 0ms | 0ms | 0ms | 10.86x | 0.17x | 0.55x | 1.00x |
| | (10000000,) | 10000000 | 1 | 33ms | 5ms | 1ms | 11ms | 3.07x | 0.50x | 0.09x | 1.00x |
| | (100, 100000) | 10000000 | 2 | 36ms | 32ms | 1ms | 11ms | 3.39x | 3.03x | 0.09x | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 32ms | 1ms | 10ms | n/a | 3.12x | 0.10x | 1.00x |
| | (100, 1000, 1000) | 100000000 | 3 | n/a | 320ms | 11ms | 102ms | n/a | 3.12x | 0.11x | 1.00x |
| `nanquantile` | (1000,) | 1000 | 1 | 0ms | n/a | 0ms | 0ms | 0.56x | n/a | 0.21x | 1.00x |
| | (10000000,) | 10000000 | 1 | 114ms | n/a | 87ms | 164ms | 0.69x | n/a | 0.53x | 1.00x |
| | (100, 100000) | 10000000 | 2 | 131ms | n/a | 115ms | 27ms | 4.94x | n/a | 4.33x | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | n/a | 315ms | 19ms | n/a | n/a | 16.51x | 1.00x |
| | (100, 1000, 1000) | 100000000 | 3 | n/a | n/a | 3118ms | 165ms | n/a | n/a | 18.88x | 1.00x |
| `nanstd` | (1000,) | 1000 | 1 | 0ms | 0ms | 0ms | 0ms | 0.31x | 0.02x | 0.14x | 1.00x |
| | (10000000,) | 10000000 | 1 | 21ms | 20ms | 43ms | 13ms | 1.63x | 1.61x | 3.39x | 1.00x |
| | (100, 100000) | 10000000 | 2 | 24ms | 20ms | 43ms | 2ms | 12.39x | 10.18x | 22.03x | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 20ms | 46ms | 1ms | n/a | 14.17x | 32.66x | 1.00x |
| | (100, 1000, 1000) | 100000000 | 3 | n/a | 202ms | 513ms | 13ms | n/a | 16.08x | 40.78x | 1.00x |
| `nansum` | (1000,) | 1000 | 1 | 0ms | 0ms | 0ms | 0ms | 0.46x | 0.01x | 0.03x | 1.00x |
| | (10000000,) | 10000000 | 1 | 14ms | 5ms | 19ms | 6ms | 2.48x | 0.94x | 3.31x | 1.00x |
| | (100, 100000) | 10000000 | 2 | 22ms | 5ms | 19ms | 1ms | 20.47x | 4.65x | 17.90x | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 5ms | 20ms | 1ms | n/a | 6.21x | 22.95x | 1.00x |
| | (100, 1000, 1000) | 100000000 | 3 | n/a | 53ms | 226ms | 8ms | n/a | 6.98x | 29.90x | 1.00x |
| `nanvar` | (1000,) | 1000 | 1 | 0ms | 0ms | 0ms | 0ms | 0.32x | 0.02x | 0.13x | 1.00x |
| | (10000000,) | 10000000 | 1 | 21ms | 21ms | 44ms | 13ms | 1.61x | 1.65x | 3.40x | 1.00x |
| | (100, 100000) | 10000000 | 2 | 25ms | 21ms | 43ms | 2ms | 12.62x | 10.49x | 22.13x | 1.00x |
| | (10, 10, 10, 10, 1000) | 10000000 | 5 | n/a | 20ms | 46ms | 1ms | n/a | 14.02x | 32.28x | 1.00x |
| | (100, 1000, 1000) | 100000000 | 3 | n/a | 202ms | 503ms | 13ms | n/a | 15.68x | 38.98x | 1.00x |
[^1][^2][^3][^4]
[^1]:
Benchmarks were run on a Mac M3 Max laptop in September 2024 on numbagg's HEAD,
pandas 2.2.2, bottleneck 1.4.0 numpy 2.0.1, with `python
numbagg/test/run_benchmarks.py -- --benchmark-max-time=10`. They run in CI,
though GHA's low CPU count means we don't see the full benefits of
parallelization.
[^2]:
While we separate the setup and the running of the functions, pandas still
needs to do some work to create its result dataframe, and numbagg does some
checks in python which bottleneck does in C or doesn't do. So use benchmarks
on larger arrays for our summary so we can focus on the computational speed,
which doesn't asymptote away. Any contributions to improve the benchmarks are
welcome.
[^3]:
In some instances, a library won't have the exact function — for example,
pandas doesn't have an equivalent `move_exp_nancount` function, so we use
its `sum` function on an array of `1`s. Similarly for
`group_nansum_of_squares`, we use two separate operations.
[^4]:
`anynan` & `allnan` are also functions in numbagg, but not listed here as they
require a different benchmark setup.
[^5]:
This function is not currently parallelized, so exhibits worse performance
on parallelizable arrays.
## Example implementation
Numbagg makes it easy to write, in pure Python/NumPy, flexible aggregation
functions accelerated by Numba. All the hard work is done by Numba's JIT
compiler and NumPy's gufunc machinery (as wrapped by Numba).
For example, here is how we wrote `nansum`:
```python
import numpy as np
from numbagg.decorators import ndreduce
@ndreduce.wrap()
def nansum(a):
asum = 0.0
for ai in a.flat:
if not np.isnan(ai):
asum += ai
return asum
```
## Implementation details
Numbagg includes somewhat awkward workarounds for features missing from
NumPy/Numba:
- It implements its own cache for functions wrapped by Numba's
`guvectorize`, because that decorator is rather slow.
- It does its [own handling of array
transposes](https://github.com/numbagg/numbagg/blob/e166adae94b3be35497dcdc22772026df75af253/numbagg/decorators.py#L170-L181)
to handle the `axis` argument in reduction functions.
- It [rewrites plain functions into
gufuncs](https://github.com/numbagg/numbagg/blob/e166adae94b3be35497dcdc22772026df75af253/numbagg/transform.py),
to allow writing a traditional function while retaining the multidimensional advantages of
gufuncs.
Already some of the ideas here have flowed upstream to numba (for example, [an
axis parameter](https://github.com/numpy/numpy/issues/5197)), and we hope
that others will follow.
## License
3-clause BSD. Includes portions of Bottleneck, which is distributed under a
Simplified BSD license.