numpy-groupies
Optimised tools for group-indexing operations: aggregated sum and more.
Decision gist · record as of 2026-08-14
Yes. Low install friction, no mandatory dependencies, active maintenance, permissive license, and a focused API for group aggregation operations. Install if you need group-based array reductions without the overhead of pandas or other dataframe libraries. The 449-day gap since last release is notable but not disqualifying given the active repository and stable API.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction: pure Python wheel with only numpy as an optional runtime dependency.
- Last release 449 days ago; repository active with recent commits and 210 stars.
License · maintenance · safety
permissive license (permissive) — BSD 2-Clause permissive license. No restrictions on commercial or private use; redistribution requires retaining copyright notice and disclaimer.
last release 2025-05-22 (449 days) · last repo commit 2026-06-30 · 210 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 357,042 downloads/mo, #7,276 on PyPI
Alternatives
Verify before relying
pip install numpy_groupies
import numpy as np
import numpy_groupies as npg
group_idx = np.array([3, 0, 0, 1, 0, 3, 5, 5, 0, 4])
a = np.array([13.2, 3.5, 3.5, -8.2, 3.0, 13.4, 99.2, -7.1, 0.0, 53.7])
result = npg.aggregate(group_idx, a, func='sum', fill_value=0)- Whether all aggregation functions (sum, mean, std, min, max, etc.) are available in all backends or only in specific implementations
- Performance characteristics and memory usage for large arrays or many groups
- Whether the package is actively maintained or in maintenance-only mode given the 449-day gap since last release
What it is and what it does
numpy-groupies provides a small library of optimized tools for group-indexing operations on arrays. The core function, `aggregate`, takes an array of values and an array of group labels, then computes a reduction (sum, mean, standard deviation, min, max, or custom function) for each group. It also supports cumulative operations like cumsum and cumprod that preserve the input size, and boolean aggregations like 'all' and 'any'.
The package is conceptually similar to pandas groupby, MATLAB's accumarray, or the MapReduce paradigm. It handles various input shapes: 1D arrays with matching group indices, multidimensional arrays with axis-specific grouping, and 2D group index arrays for complex grouping patterns. The implementation is pure Python with numpy as an optional dependency, making it lightweight and easy to install.
Use it for
- Compute statistics (sum, mean, variance) for data points belonging to labeled groups without loading a full dataframe library
- Build histograms or frequency counts by aggregating values into bins defined by group indices
- Perform cumulative operations within groups (e.g., running sum per group) while preserving input array size
- Aggregate multidimensional scientific data along a specific axis by group membership
- Handle NaN values selectively using nan-aware aggregation functions (nansum, nanmean, etc.)
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Low install friction, no mandatory dependencies, active maintenance, permissive license, and a focused API for group aggregation operations. Install if you need group-based array reductions without the overhead of pandas or other dataframe libraries. The 449-day gap since last release is notable but not disqualifying given the active repository and stable API.
Install
numpy-groupies on PyPI
Before you install
Low friction: pure Python wheel with only numpy as an optional runtime dependency. Last release 449 days ago; repository active with recent commits and 210 stars.
License in practice
BSD 2-Clause permissive license. No restrictions on commercial or private use; redistribution requires retaining copyright notice and disclaimer.
Quickstart
pip install numpy_groupies
import numpy as np
import numpy_groupies as npg
group_idx = np.array([3, 0, 0, 1, 0, 3, 5, 5, 0, 4])
a = np.array([13.2, 3.5, 3.5, -8.2, 3.0, 13.4, 99.2, -7.1, 0.0, 53.7])
result = npg.aggregate(group_idx, a, func='sum', fill_value=0)Verify before relying
- Whether all aggregation functions (sum, mean, std, min, max, etc.) are available in all backends or only in specific implementations
- Performance characteristics and memory usage for large arrays or many groups
- Whether the package is actively maintained or in maintenance-only mode given the 449-day gap since last release
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagenumpy |
| Maintenance | Actively maintained 449 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 357,042 / month, #7,276 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Software Development :: Libraries |
Evidence: numpy_groupies-0.11.3-py3-none-any.whl
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See also flox · ipfn · lovely-numpy · numbagg · Bottleneck · fast-array-utils · ndindex · snuggs · xarray · momentchi2