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Fast N-dimensional aggregation functions with Numba

Project description

Numbagg: Fast N-dimensional aggregation functions with Numba

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

Currently accelerated functions:

  • Array functions: allnan, anynan, count, nanargmax, nanargmin, nanmax, nanmean, nanstd, nanvar, nanmin, nansum
  • Moving window functions: move_exp_nanmean, move_mean, move_sum

Note: Only functions listed here (exposed in Numbagg's top level namespace) are supported as part of Numbagg's public API.

Easy to extend

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

def nansum(a):
    asum = 0.0
    for ai in a.flat:
        if not np.isnan(ai):
            asum += ai
    return asum

You are welcome to experiment with Numbagg's decorator functions, but these are not public APIs (yet): we reserve the right to change them at any time.

We'd rather get your pull requests to add new functions into Numbagg directly!

Advantages over Bottleneck

  • Way less code. Easier to add new functions. No ad-hoc templating system. No Cython!
  • Fast functions still work for >3 dimensions.
  • axis argument handles tuples of integers.

Most of the functions in Numbagg (including our test suite) are adapted from Bottleneck's battle-hardened implementations. Still, Numbagg is experimental, and probably not yet ready for production.


Initial benchmarks are quite encouraging. Numbagg/Numba has comparable (slightly better) performance than Bottleneck's hand-written C:

import numbagg
import numpy as np
import bottleneck

x = np.random.RandomState(42).randn(1000, 1000)
x[x < -1] = np.NaN

# timings with numba=0.41.0 and bottleneck=1.2.1

In [2]: %timeit numbagg.nanmean(x)
1.8 ms ± 92.3 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [3]: %timeit numbagg.nanmean(x, axis=0)
3.63 ms ± 136 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [4]: %timeit numbagg.nanmean(x, axis=1)
1.81 ms ± 41 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

In [5]: %timeit bottleneck.nanmean(x)
2.22 ms ± 119 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [6]: %timeit bottleneck.nanmean(x, axis=0)
4.45 ms ± 107 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [7]: %timeit bottleneck.nanmean(x, axis=1)
2.19 ms ± 13.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

Our approach

Numbagg includes somewhat awkward workarounds for features missing from NumPy/Numba:

I hope that the need for most of these will eventually go away. In the meantime, expect Numbagg to be tightly coupled to Numba and NumPy release cycles.


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

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