binpacking
Heuristic distribution of weighted items to bins (either a fixed number of bins or a fixed number of volume per bin). Data may be in form of list, dictionary, list of tuples or csv-file.
Decision gist · record as of 2026-08-14
Yes. The package solves a real, common problem with zero dependencies, low install friction, and a permissive license. The greedy algorithms are fast and adequate for most practical load-balancing and partitioning tasks. Maintenance is aging but the code is stable and the repository is not abandoned. Install it if you need to distribute weighted items across fixed bins or minimize bin count.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10+
- Low friction: pure Python wheel with no required dependencies.
- Maintenance status is aging—last release 204 days ago, but the repository is active and not archived.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use, modify, and distribute this package freely with minimal restrictions.
last release 2026-01-22 (204 days) · last repo commit 2026-01-22 · 97 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 265,679 downloads/mo, #8,319 on PyPI
Alternatives
Verify before relying
pip install binpacking
import binpacking
b = {'a': 10, 'b': 10, 'c': 11, 'd': 1, 'e': 2, 'f': 7}
bins = binpacking.to_constant_bin_number(b, 4)
print(bins)- How well the greedy algorithms perform compared to optimal solutions on typical problem sizes.
- Whether NumPy acceleration provides meaningful speedup for the datasets you plan to pack.
- Real-world accuracy of the Least Loaded Fit Decreasing and Longest Processing Time heuristics for your domain.
What it is and what it does
binpacking solves two classic bin packing problems using greedy heuristics. The first distributes items into exactly N bins with approximately balanced total weight per bin (useful for load balancing across a fixed number of workers). The second distributes items into the minimum number of bins, each respecting a maximum capacity constraint (useful for minimizing resource allocations). The package accepts input as lists, dictionaries, lists of tuples, or CSV files, and includes both pure Python and optional NumPy-accelerated implementations.
The algorithms are Least Loaded Fit Decreasing for constant-volume packing and Longest Processing Time for constant-bin-number packing. Both sort items by weight descending before placement. This is a straightforward, dependency-free tool for scheduling, resource allocation, and partitioning problems where exact optimality is less critical than a fast, reasonable solution.
Use it for
- Distribute jobs with known durations across a fixed number of CPU cores to minimize total runtime.
- Group files of different sizes into memory allocations to minimize the number of program runs needed.
- Pack items into containers or shipments with a maximum weight limit to minimize the number of containers.
- Partition data into batches for parallel processing while keeping batch sizes roughly equal.
- Schedule tasks across servers with fixed capacity to balance load.
- Bin CSV rows by a weight column for resource planning or logistics optimization.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package solves a real, common problem with zero dependencies, low install friction, and a permissive license. The greedy algorithms are fast and adequate for most practical load-balancing and partitioning tasks. Maintenance is aging but the code is stable and the repository is not abandoned. Install it if you need to distribute weighted items across fixed bins or minimize bin count.
Install
binpacking on PyPI
Before you install
Low friction: pure Python wheel with no required dependencies. Maintenance status is aging—last release 204 days ago, but the repository is active and not archived.
Requires Python 3.10+
License in practice
MIT license is permissive; you can use, modify, and distribute this package freely with minimal restrictions.
Quickstart
pip install binpacking
import binpacking
b = {'a': 10, 'b': 10, 'c': 11, 'd': 1, 'e': 2, 'f': 7}
bins = binpacking.to_constant_bin_number(b, 4)
print(bins)Verify before relying
- How well the greedy algorithms perform compared to optimal solutions on typical problem sizes.
- Whether NumPy acceleration provides meaningful speedup for the datasets you plan to pack.
- Real-world accuracy of the Least Loaded Fit Decreasing and Longest Processing Time heuristics for your domain.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Aging 204 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 265,679 / month, #8,319 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13 |
Evidence: binpacking-2.0.1-py3-none-any.whl
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