numbagg
Fast N-dimensional aggregation functions with Numba
Decision gist · record as of 2026-08-14
Yes. Numbagg is worth installing if you work with large N-dimensional NumPy arrays and need fast aggregations or moving-window functions, especially on multi-core systems. Low install friction, active maintenance, no known vulnerabilities, and permissive licensing make it a safe choice. The JIT compilation overhead on first call is a minor drawback but typical for Numba-based libraries and acceptable for most scientific workflows.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- First execution of any function triggers JIT compilation, which adds startup latency.
- Low install friction with a pure Python wheel and only two runtime dependencies (numpy and numba).
License · maintenance · safety
(unclear) — BSD 3-Clause license with portions from Bottleneck (Simplified BSD). Permissive for commercial and private use; requires attribution and license notice in redistributions.
last release 2025-12-15 (242 days) · last repo commit 2026-08-14 · 243 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 974,005 downloads/mo, #4,601 on PyPI
Alternatives
Verify before relying
import numbagg
import numpy as np
arr = np.array([1.0, 2.0, np.nan, 4.0])
result = numbagg.nansum(arr)
# or for moving window:
window_result = numbagg.move_mean(arr, window=2)- Whether all listed functions (move_corr, group_nansum, etc.) are fully stable or still experimental.
- Exact memory overhead of JIT compilation relative to raw numpy for small arrays.
- Whether axis parameter behavior is documented for >3 dimensions in current release.
What it is and what it does
Numbagg is a library of fast aggregation and moving-window functions for N-dimensional NumPy arrays, written in Numba and exposed as generalized ufuncs. It includes standard reductions (nansum, nanmean, nanstd, nanvar), grouping operations (group_nansum, group_nanmean, etc.), moving-window statistics (move_mean, move_std, move_corr), and fill operations (ffill, bfill). The library is designed to outperform pandas and NumPy on multi-core systems through Numba's JIT compilation and parallelization, though single-threaded performance is comparable to NumPy and the first call to any function incurs JIT overhead.
It targets scientific and data-analysis workflows where performance on large arrays matters and flexible axis handling across many dimensions is needed. The main trade-off is that functions are JIT-compiled on first use, so initial calls are slower; subsequent calls are fast. Dependencies are minimal (numpy and numba), and the package supports Python 3.10 and later.
Use it for
- Compute moving averages and standard deviations on time-series data with NaN handling faster than pandas.
- Apply grouped aggregations (sum, mean, count) on multi-dimensional scientific data.
- Calculate rolling correlations and covariances on financial or climate datasets.
- Forward-fill or backward-fill missing values in large arrays with better performance than pandas.
- Parallelize custom reductions across multiple cores on arrays too large for pure NumPy.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Numbagg is worth installing if you work with large N-dimensional NumPy arrays and need fast aggregations or moving-window functions, especially on multi-core systems. Low install friction, active maintenance, no known vulnerabilities, and permissive licensing make it a safe choice. The JIT compilation overhead on first call is a minor drawback but typical for Numba-based libraries and acceptable for most scientific workflows.
Install
numbagg on PyPI
Before you install
Low install friction with a pure Python wheel and only two runtime dependencies (numpy and numba). Actively maintained with a recent release and no known vulnerabilities.
Requires Python 3.10 or later. First execution of any function triggers JIT compilation, which adds startup latency.
License in practice
BSD 3-Clause license with portions from Bottleneck (Simplified BSD). Permissive for commercial and private use; requires attribution and license notice in redistributions.
Quickstart
import numbagg
import numpy as np
arr = np.array([1.0, 2.0, np.nan, 4.0])
result = numbagg.nansum(arr)
# or for moving window:
window_result = numbagg.move_mean(arr, window=2)Verify before relying
- Whether all listed functions (move_corr, group_nansum, etc.) are fully stable or still experimental.
- Exact memory overhead of JIT compilation relative to raw numpy for small arrays.
- Whether axis parameter behavior is documented for >3 dimensions in current release.
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesnumpynumba |
| Maintenance | Actively maintained 242 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 974,005 / month, #4,601 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: Science/ResearchOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Scientific/Engineering |
Evidence: numbagg-0.9.4-py3-none-any.whl
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