Install & Compatibility
Where this runs
tested against v0.9.4 · pip install
no network on importno background threads
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
muslpy 3.10–3.920 runs
build_error
glibcpy 3.10–3.920 runs
installs and imports cleanly · install 7.0s · import 0.657s · 274MB
273MB installed
● package 273MB
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
nansum
✓ from numbagg import nansum
✗ import numbagg; result = numbagg.nansum(...)
This quickstart demonstrates how to use `numbagg` for basic array aggregation (nansum) and moving window calculations (move_mean) on NumPy arrays. Note that the first call to any numbagg function will incur JIT compilation overhead.
import numbagg
import numpy as np
a = np.array([1, 2, np.nan, 4, 5])
b = np.random.rand(10, 5)
# Calculate sum, ignoring NaNs
sum_result = numbagg.nansum(a)
print(f"nansum(a): {sum_result}")
# Calculate moving mean with a window of 3
moving_mean_result = numbagg.move_mean(b, window=3, axis=1)
print(f"move_mean(b, window=3, axis=1, shape): {moving_mean_result.shape}")
Debug
Known issues
gotchaNumbagg is currently considered experimental and not yet ready for production use, as stated on its PyPI page. While robust, its API or internal workings may evolve.fixMonitor official releases and change logs for updates on stability and production readiness.
affects: All versions up to 0.9.4
gotchaThe first call to any Numbagg function incurs a significant performance penalty due to Numba's Just-In-Time (JIT) compilation. Subsequent calls to the same function with compatible argument types will be much faster.fixConsider a warm-up run with dummy data if predictable performance from the first invocation is critical in a performance-sensitive application.
affects: All versions up to 0.9.4
deprecatedNumbagg's grouped calculation functions (e.g., `numbagg.grouped.group_nanmean`) might be deprecated in favor of using `flox` with `numbagg` as a backend. This is an ongoing discussion within the xarray community, with `flox` potentially offering better support for nD array grouped by 1D labels.fixFor new projects or complex grouped aggregations, consider evaluating `flox` (especially with its `numbagg` engine option) instead of directly using `numbagg.grouped` functions. Monitor `numbagg` and `xarray` (and `flox`) repositories for updates on this transition.
affects: Versions 0.9.x
gotchaThe `numbagg.decorators` module, used internally for creating JIT-compiled aggregation functions, is not part of Numbagg's public API. Its functions and signatures may change without prior notice.fixAvoid direct imports from `numbagg.decorators` for application code. Instead, use the top-level functions directly exposed by the `numbagg` package.
affects: All versions up to 0.9.4
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Version history
0.9.4latest on PyPI · released Dec 15, 2025
Audit
Dependencies
numbarequiredCore dependency for JIT compilation and array function acceleration.
numpyrequiredFundamental library for N-dimensional array operations.