Fast N-dimensional aggregation functions with Numba

# Numbagg: Fast N-dimensional aggregation functions with Numba

Fast, flexible N-dimensional array functions written with Numba and NumPy's generalized ufuncs.

## 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 1.17x 1.18x n/a 12.24x 4.36x n/a
ffill 1.17x 1.12x n/a 12.76x 4.34x n/a
group_nanall 1.44x n/a n/a 10.84x n/a n/a
group_nanany 1.20x n/a n/a 5.25x n/a n/a
group_nanargmax 2.88x n/a n/a 9.89x n/a n/a
group_nanargmin 2.82x n/a n/a 9.96x n/a n/a
group_nancount 1.01x n/a n/a 4.70x n/a n/a
group_nanfirst 1.39x n/a n/a 11.80x n/a n/a
group_nanlast 1.16x n/a n/a 5.36x n/a n/a
group_nanmax 1.14x n/a n/a 5.22x n/a n/a
group_nanmean 1.19x n/a n/a 5.64x n/a n/a
group_nanmin 1.13x n/a n/a 5.26x n/a n/a
group_nanprod 1.15x n/a n/a 4.95x n/a n/a
group_nanstd 1.18x n/a n/a 5.03x n/a n/a
group_nansum_of_squares 1.35x n/a n/a 8.11x n/a n/a
group_nansum 1.21x n/a n/a 5.95x n/a n/a
group_nanvar 1.19x n/a n/a 5.65x n/a n/a
move_corr 19.04x n/a n/a 92.48x n/a n/a
move_cov 14.58x n/a n/a 71.61x n/a n/a
move_exp_nancorr 6.73x n/a n/a 35.30x n/a n/a
move_exp_nancount 2.35x n/a n/a 10.56x n/a n/a
move_exp_nancov 5.77x n/a n/a 31.75x n/a n/a
move_exp_nanmean 2.03x n/a n/a 11.07x n/a n/a
move_exp_nanstd 1.89x n/a n/a 10.07x n/a n/a
move_exp_nansum 1.88x n/a n/a 9.70x n/a n/a
move_exp_nanvar 1.82x n/a n/a 9.71x n/a n/a
move_mean 3.82x 0.87x n/a 16.61x 4.01x n/a
move_std 5.96x 1.29x n/a 24.52x 6.04x n/a
move_sum 3.80x 0.83x n/a 15.95x 3.70x n/a
move_var 5.78x 1.27x n/a 25.41x 5.85x n/a
nanargmax[^5] 2.45x 1.00x n/a 2.16x 1.00x n/a
nanargmin[^5] 2.19x 1.01x n/a 2.05x 1.02x n/a
nancount 1.40x n/a 1.06x 11.00x n/a 4.16x
nanmax[^5] 3.26x 1.00x 0.11x 3.62x 3.24x 0.11x
nanmean 2.42x 0.98x 2.83x 13.58x 4.54x 13.13x
nanmin[^5] 3.27x 1.00x 0.11x 3.62x 3.24x 0.11x
nanquantile 0.94x n/a 0.78x 5.45x n/a 5.01x
nanstd 1.50x 1.51x 2.75x 8.29x 7.35x 13.27x
nansum 2.28x 0.97x 2.52x 17.71x 6.24x 16.05x
nanvar 1.50x 1.49x 2.81x 8.18x 6.97x 13.32x

### Full benchmarks

func shape size pandas bottleneck numpy numbagg pandas_ratio bottleneck_ratio numpy_ratio numbagg_ratio
bfill (1000,) 1000 0ms 0ms n/a 0ms 1.59x 0.03x n/a 1.00x
(10000000,) 10000000 20ms 20ms n/a 17ms 1.17x 1.18x n/a 1.00x
(100, 100000) 10000000 57ms 20ms n/a 5ms 12.24x 4.36x n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a 21ms n/a 5ms n/a 4.40x n/a 1.00x
(100, 1000, 1000) 100000000 n/a 248ms n/a 44ms n/a 5.70x n/a 1.00x
ffill (1000,) 1000 0ms 0ms n/a 0ms 1.53x 0.02x n/a 1.00x
(10000000,) 10000000 20ms 19ms n/a 17ms 1.17x 1.12x n/a 1.00x
(100, 100000) 10000000 56ms 19ms n/a 4ms 12.76x 4.34x n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a 19ms n/a 4ms n/a 4.33x n/a 1.00x
(100, 1000, 1000) 100000000 n/a 219ms n/a 42ms n/a 5.25x n/a 1.00x
group_nanall (1000,) 1000 0ms n/a n/a 0ms 1.79x n/a n/a 1.00x
(10000000,) 10000000 68ms n/a n/a 47ms 1.44x n/a n/a 1.00x
(100, 100000) 10000000 17ms n/a n/a 2ms 10.84x n/a n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a n/a n/a 1ms n/a n/a n/a 1.00x
group_nanany (1000,) 1000 0ms n/a n/a 0ms 1.78x n/a n/a 1.00x
(10000000,) 10000000 68ms n/a n/a 56ms 1.20x n/a n/a 1.00x
(100, 100000) 10000000 18ms n/a n/a 3ms 5.25x n/a n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a n/a n/a 3ms n/a n/a n/a 1.00x
group_nanargmax (1000,) 1000 1ms n/a n/a 0ms 17.60x n/a n/a 1.00x
(10000000,) 10000000 171ms n/a n/a 59ms 2.88x n/a n/a 1.00x
(100, 100000) 10000000 40ms n/a n/a 4ms 9.89x n/a n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a n/a n/a 4ms n/a n/a n/a 1.00x
group_nanargmin (1000,) 1000 1ms n/a n/a 0ms 17.56x n/a n/a 1.00x
(10000000,) 10000000 166ms n/a n/a 59ms 2.82x n/a n/a 1.00x
(100, 100000) 10000000 41ms n/a n/a 4ms 9.96x n/a n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a n/a n/a 4ms n/a n/a n/a 1.00x
group_nancount (1000,) 1000 0ms n/a n/a 0ms 1.68x n/a n/a 1.00x
(10000000,) 10000000 56ms n/a n/a 55ms 1.01x n/a n/a 1.00x
(100, 100000) 10000000 15ms n/a n/a 3ms 4.70x n/a n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a n/a n/a 3ms n/a n/a n/a 1.00x
group_nanfirst (1000,) 1000 0ms n/a n/a 0ms 1.88x n/a n/a 1.00x
(10000000,) 10000000 63ms n/a n/a 45ms 1.39x n/a n/a 1.00x
(100, 100000) 10000000 15ms n/a n/a 1ms 11.80x n/a n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a n/a n/a 1ms n/a n/a n/a 1.00x
group_nanlast (1000,) 1000 0ms n/a n/a 0ms 1.87x n/a n/a 1.00x
(10000000,) 10000000 62ms n/a n/a 53ms 1.16x n/a n/a 1.00x
(100, 100000) 10000000 15ms n/a n/a 3ms 5.36x n/a n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a n/a n/a 2ms n/a n/a n/a 1.00x
group_nanmax (1000,) 1000 0ms n/a n/a 0ms 1.89x n/a n/a 1.00x
(10000000,) 10000000 66ms n/a n/a 57ms 1.14x n/a n/a 1.00x
(100, 100000) 10000000 17ms n/a n/a 3ms 5.22x n/a n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a n/a n/a 3ms n/a n/a n/a 1.00x
group_nanmean (1000,) 1000 0ms n/a n/a 0ms 1.81x n/a n/a 1.00x
(10000000,) 10000000 67ms n/a n/a 57ms 1.19x n/a n/a 1.00x
(100, 100000) 10000000 19ms n/a n/a 3ms 5.64x n/a n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a n/a n/a 3ms n/a n/a n/a 1.00x
group_nanmin (1000,) 1000 0ms n/a n/a 0ms 1.84x n/a n/a 1.00x
(10000000,) 10000000 66ms n/a n/a 58ms 1.13x n/a n/a 1.00x
(100, 100000) 10000000 17ms n/a n/a 3ms 5.26x n/a n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a n/a n/a 3ms n/a n/a n/a 1.00x
group_nanprod (1000,) 1000 0ms n/a n/a 0ms 1.86x n/a n/a 1.00x
(10000000,) 10000000 63ms n/a n/a 55ms 1.15x n/a n/a 1.00x
(100, 100000) 10000000 16ms n/a n/a 3ms 4.95x n/a n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a n/a n/a 3ms n/a n/a n/a 1.00x
group_nanstd (1000,) 1000 0ms n/a n/a 0ms 1.73x n/a n/a 1.00x
(10000000,) 10000000 70ms n/a n/a 59ms 1.18x n/a n/a 1.00x
(100, 100000) 10000000 20ms n/a n/a 4ms 5.03x n/a n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a n/a n/a 4ms n/a n/a n/a 1.00x
group_nansum (1000,) 1000 0ms n/a n/a 0ms 1.89x n/a n/a 1.00x
(10000000,) 10000000 67ms n/a n/a 56ms 1.21x n/a n/a 1.00x
(100, 100000) 10000000 19ms n/a n/a 3ms 5.95x n/a n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a n/a n/a 3ms n/a n/a n/a 1.00x
group_nanvar (1000,) 1000 0ms n/a n/a 0ms 1.71x n/a n/a 1.00x
(10000000,) 10000000 69ms n/a n/a 58ms 1.19x n/a n/a 1.00x
(100, 100000) 10000000 20ms n/a n/a 4ms 5.65x n/a n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a n/a n/a 3ms n/a n/a n/a 1.00x
group_nansum_of_squares (1000,) 1000 0ms n/a n/a 0ms 2.36x n/a n/a 1.00x
(10000000,) 10000000 75ms n/a n/a 55ms 1.35x n/a n/a 1.00x
(100, 100000) 10000000 26ms n/a n/a 3ms 8.11x n/a n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a n/a n/a 3ms n/a n/a n/a 1.00x
move_corr (1000,) 1000 0ms n/a n/a 0ms 10.85x n/a n/a 1.00x
(10000000,) 10000000 909ms n/a n/a 48ms 19.04x n/a n/a 1.00x
(100, 100000) 10000000 869ms n/a n/a 9ms 92.48x n/a n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a n/a n/a 9ms n/a n/a n/a 1.00x
(100, 1000, 1000) 100000000 n/a n/a n/a 79ms n/a n/a n/a 1.00x
move_cov (1000,) 1000 0ms n/a n/a 0ms 10.05x n/a n/a 1.00x
(10000000,) 10000000 623ms n/a n/a 43ms 14.58x n/a n/a 1.00x
(100, 100000) 10000000 603ms n/a n/a 8ms 71.61x n/a n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a n/a n/a 8ms n/a n/a n/a 1.00x
(100, 1000, 1000) 100000000 n/a n/a n/a 72ms n/a n/a n/a 1.00x
move_mean (1000,) 1000 0ms 0ms n/a 0ms 1.84x 0.03x n/a 1.00x
(10000000,) 10000000 120ms 27ms n/a 31ms 3.82x 0.87x n/a 1.00x
(100, 100000) 10000000 113ms 27ms n/a 7ms 16.61x 4.01x n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a 27ms n/a 7ms n/a 3.96x n/a 1.00x
(100, 1000, 1000) 100000000 n/a 296ms n/a 58ms n/a 5.08x n/a 1.00x
move_std (1000,) 1000 0ms 0ms n/a 0ms 2.21x 0.08x n/a 1.00x
(10000000,) 10000000 178ms 39ms n/a 30ms 5.96x 1.29x n/a 1.00x
(100, 100000) 10000000 157ms 39ms n/a 6ms 24.52x 6.04x n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a 39ms n/a 7ms n/a 5.88x n/a 1.00x
(100, 1000, 1000) 100000000 n/a 411ms n/a 58ms n/a 7.13x n/a 1.00x
move_sum (1000,) 1000 0ms 0ms n/a 0ms 1.81x 0.02x n/a 1.00x
(10000000,) 10000000 121ms 26ms n/a 32ms 3.80x 0.83x n/a 1.00x
(100, 100000) 10000000 113ms 26ms n/a 7ms 15.95x 3.70x n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a 26ms n/a 7ms n/a 3.59x n/a 1.00x
(100, 1000, 1000) 100000000 n/a 281ms n/a 59ms n/a 4.77x n/a 1.00x
move_var (1000,) 1000 0ms 0ms n/a 0ms 2.04x 0.08x n/a 1.00x
(10000000,) 10000000 168ms 37ms n/a 29ms 5.78x 1.27x n/a 1.00x
(100, 100000) 10000000 161ms 37ms n/a 6ms 25.41x 5.85x n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a 37ms n/a 6ms n/a 5.85x n/a 1.00x
(100, 1000, 1000) 100000000 n/a 398ms n/a 56ms n/a 7.07x n/a 1.00x
move_exp_nancorr (1000,) 1000 0ms n/a n/a 0ms 7.27x n/a n/a 1.00x
(10000000,) 10000000 464ms n/a n/a 69ms 6.73x n/a n/a 1.00x
(100, 100000) 10000000 471ms n/a n/a 13ms 35.30x n/a n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a n/a n/a 13ms n/a n/a n/a 1.00x
(100, 1000, 1000) 100000000 n/a n/a n/a 111ms n/a n/a n/a 1.00x
move_exp_nancount (1000,) 1000 0ms n/a n/a 0ms 2.04x n/a n/a 1.00x
(10000000,) 10000000 77ms n/a n/a 33ms 2.35x n/a n/a 1.00x
(100, 100000) 10000000 69ms n/a n/a 7ms 10.56x n/a n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a n/a n/a 6ms n/a n/a n/a 1.00x
(100, 1000, 1000) 100000000 n/a n/a n/a 59ms n/a n/a n/a 1.00x
move_exp_nancov (1000,) 1000 0ms n/a n/a 0ms 7.07x n/a n/a 1.00x
(10000000,) 10000000 298ms n/a n/a 52ms 5.77x n/a n/a 1.00x
(100, 100000) 10000000 333ms n/a n/a 10ms 31.75x n/a n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a n/a n/a 10ms n/a n/a n/a 1.00x
(100, 1000, 1000) 100000000 n/a n/a n/a 87ms n/a n/a n/a 1.00x
move_exp_nanmean (1000,) 1000 0ms n/a n/a 0ms 1.40x n/a n/a 1.00x
(10000000,) 10000000 67ms n/a n/a 33ms 2.03x n/a n/a 1.00x
(100, 100000) 10000000 74ms n/a n/a 7ms 11.07x n/a n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a n/a n/a 7ms n/a n/a n/a 1.00x
(100, 1000, 1000) 100000000 n/a n/a n/a 60ms n/a n/a n/a 1.00x
move_exp_nanstd (1000,) 1000 0ms n/a n/a 0ms 2.33x n/a n/a 1.00x
(10000000,) 10000000 88ms n/a n/a 46ms 1.89x n/a n/a 1.00x
(100, 100000) 10000000 95ms n/a n/a 9ms 10.07x n/a n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a n/a n/a 9ms n/a n/a n/a 1.00x
(100, 1000, 1000) 100000000 n/a n/a n/a 78ms n/a n/a n/a 1.00x
move_exp_nansum (1000,) 1000 0ms n/a n/a 0ms 1.36x n/a n/a 1.00x
(10000000,) 10000000 62ms n/a n/a 33ms 1.88x n/a n/a 1.00x
(100, 100000) 10000000 71ms n/a n/a 7ms 9.70x n/a n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a n/a n/a 6ms n/a n/a n/a 1.00x
(100, 1000, 1000) 100000000 n/a n/a n/a 60ms n/a n/a n/a 1.00x
move_exp_nanvar (1000,) 1000 0ms n/a n/a 0ms 1.40x n/a n/a 1.00x
(10000000,) 10000000 77ms n/a n/a 42ms 1.82x n/a n/a 1.00x
(100, 100000) 10000000 84ms n/a n/a 9ms 9.71x n/a n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a n/a n/a 9ms n/a n/a n/a 1.00x
(100, 1000, 1000) 100000000 n/a n/a n/a 73ms n/a n/a n/a 1.00x
nanargmax[^5] (1000,) 1000 0ms 0ms n/a 0ms 13.07x 0.21x n/a 1.00x
(10000000,) 10000000 31ms 12ms n/a 12ms 2.45x 1.00x n/a 1.00x
(100, 100000) 10000000 28ms 13ms n/a 13ms 2.16x 1.00x n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a 13ms n/a 13ms n/a 1.05x n/a 1.00x
(100, 1000, 1000) 100000000 n/a 133ms n/a 127ms n/a 1.05x n/a 1.00x
nanargmin[^5] (1000,) 1000 0ms 0ms n/a 0ms 12.72x 0.21x n/a 1.00x
(10000000,) 10000000 27ms 13ms n/a 12ms 2.19x 1.01x n/a 1.00x
(100, 100000) 10000000 26ms 13ms n/a 12ms 2.05x 1.02x n/a 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a 13ms n/a 13ms n/a 1.05x n/a 1.00x
(100, 1000, 1000) 100000000 n/a 135ms n/a 129ms n/a 1.05x n/a 1.00x
nancount (1000,) 1000 0ms n/a 0ms 0ms 2.24x n/a 0.05x 1.00x
(10000000,) 10000000 5ms n/a 4ms 3ms 1.40x n/a 1.06x 1.00x
(100, 100000) 10000000 9ms n/a 3ms 1ms 11.00x n/a 4.16x 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a n/a 4ms 1ms n/a n/a 3.58x 1.00x
(100, 1000, 1000) 100000000 n/a n/a 45ms 7ms n/a n/a 6.74x 1.00x
nanmax[^5] (1000,) 1000 0ms 0ms 0ms 0ms 8.21x 0.21x 0.38x 1.00x
(10000000,) 10000000 41ms 12ms 1ms 13ms 3.26x 1.00x 0.11x 1.00x
(100, 100000) 10000000 45ms 41ms 1ms 13ms 3.62x 3.24x 0.11x 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a 40ms 1ms 12ms n/a 3.31x 0.12x 1.00x
(100, 1000, 1000) 100000000 n/a 402ms 15ms 121ms n/a 3.31x 0.12x 1.00x
nanmean (1000,) 1000 0ms 0ms 0ms 0ms 1.32x 0.02x 0.20x 1.00x
(10000000,) 10000000 23ms 9ms 27ms 10ms 2.42x 0.98x 2.83x 1.00x
(100, 100000) 10000000 28ms 9ms 27ms 2ms 13.58x 4.54x 13.13x 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a 9ms 27ms 2ms n/a 4.56x 13.69x 1.00x
(100, 1000, 1000) 100000000 n/a 91ms 310ms 17ms n/a 5.39x 18.39x 1.00x
nanmin[^5] (1000,) 1000 0ms 0ms 0ms 0ms 8.09x 0.21x 0.38x 1.00x
(10000000,) 10000000 41ms 12ms 1ms 13ms 3.27x 1.00x 0.11x 1.00x
(100, 100000) 10000000 45ms 41ms 1ms 13ms 3.62x 3.24x 0.11x 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a 40ms 1ms 12ms n/a 3.28x 0.12x 1.00x
(100, 1000, 1000) 100000000 n/a 401ms 15ms 122ms n/a 3.30x 0.12x 1.00x
nanquantile (1000,) 1000 0ms n/a 0ms 0ms 1.46x n/a 0.57x 1.00x
(10000000,) 10000000 186ms n/a 155ms 198ms 0.94x n/a 0.78x 1.00x
(100, 100000) 10000000 197ms n/a 181ms 36ms 5.45x n/a 5.01x 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a n/a 425ms 34ms n/a n/a 12.50x 1.00x
(100, 1000, 1000) 100000000 n/a n/a 4254ms 331ms n/a n/a 12.85x 1.00x
nanstd (1000,) 1000 0ms 0ms 0ms 0ms 1.06x 0.06x 0.46x 1.00x
(10000000,) 10000000 29ms 29ms 53ms 19ms 1.50x 1.51x 2.75x 1.00x
(100, 100000) 10000000 33ms 29ms 53ms 4ms 8.29x 7.35x 13.27x 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a 28ms 55ms 4ms n/a 7.25x 14.43x 1.00x
(100, 1000, 1000) 100000000 n/a 294ms 600ms 37ms n/a 8.02x 16.35x 1.00x
nansum (1000,) 1000 0ms 0ms 0ms 0ms 1.28x 0.02x 0.08x 1.00x
(10000000,) 10000000 22ms 9ms 24ms 10ms 2.28x 0.97x 2.52x 1.00x
(100, 100000) 10000000 27ms 9ms 24ms 2ms 17.71x 6.24x 16.05x 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a 9ms 25ms 1ms n/a 6.05x 16.66x 1.00x
(100, 1000, 1000) 100000000 n/a 90ms 282ms 13ms n/a 6.71x 21.07x 1.00x
nanvar (1000,) 1000 0ms 0ms 0ms 0ms 1.08x 0.06x 0.45x 1.00x
(10000000,) 10000000 28ms 28ms 53ms 19ms 1.50x 1.49x 2.81x 1.00x
(100, 100000) 10000000 33ms 28ms 54ms 4ms 8.18x 6.97x 13.32x 1.00x
(10, 10, 10, 10, 1000) 10000000 n/a 28ms 56ms 4ms n/a 7.13x 14.28x 1.00x
(100, 1000, 1000) 100000000 n/a 281ms 601ms 32ms n/a 8.71x 18.65x 1.00x

[^1][^2][^3][^4]

[^1]: Benchmarks were run on a Mac M1 laptop in December 2023 on numbagg's HEAD, pandas 2.1.1, bottleneck 1.3.7, numpy 1.25.2, 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 1s. 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:

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 to handle the axis argument in reduction functions.
• It rewrites plain functions into gufuncs, 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), and we hope that others will follow.

## Project details

Uploaded Source
Uploaded Python 3