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
• ...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 — one with a 1D array with no parallelization, and one with a 2D array with the potential for parallelization. Numbagg's relative performance is much higher where parallelization is possible.

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.)

Full benchmarks

[^1][^2][^3][^4][^5]

[^1]: Benchmarks were run on a Mac M1 laptop in December 2023 on numbagg's HEAD, pandas 2.1.1, bottleneck 1.3.7. The run in CI, though without demonstrating the full benefits of parallelization given GHA's low CPU count.

[^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 we focus on the benchmarks for larger arrays in order to reduce that impact. Any contributions to improve the benchmarks are welcome.

[^3]: Pandas doesn't have an equivalent move_exp_nancount function, so this is compared to a function which uses its sum function on an array of 1s. Similarly for group_nansum_of_squares, this requires two separate operations in pandas.

[^4]: anynan & allnan are also functions in numbagg, but not listed here as they require a different benchmark setup.

[^5]: nanmin, nanmax, nanargmin & nanargmax are not currently parallelized, so exhibit 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.

License

3-clause BSD. Includes portions of Bottleneck, which is distributed under a Simplified BSD license.