binpacking

library2.0.1pypypi✓ verified 83d ago

The `binpacking` library provides heuristic algorithms for distributing weighted items into bins. It supports scenarios with either a fixed number of bins or a fixed volume per bin, accepting data as lists, dictionaries, tuples, or CSV files. The current version is 2.0.1, and it has an active release cadence, with major updates and bug fixes released periodically.

data

pip install binpacking

package ↗

INSTALL

IMPORT

SIG · BINPACKING

binpacking

datapythonv2.0.1

Install

2.5s avg

Import

15ms

Disk

74MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v2.0.1 · 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

musl

py 3.10–3.940 runs

installs and imports cleanly · install 0.0s · import 0.016s · 89.6MB

glibc

py 3.10–3.940 runs

installs and imports cleanly · install 2.5s · import 0.015s · 86MB

74MB installed

● package 74MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

to_constant_volume

✓ from binpacking import to_constant_volume

✗ import binpacking

to_constant_bin_number

✓ from binpacking import to_constant_bin_number

✗ import binpacking

load_csv

✓ from binpacking import load_csv

✗ import binpacking

This quickstart demonstrates the two primary bin packing functions: `to_constant_volume` for packing into bins with a maximum capacity, and `to_multiple_bins` for packing into a predefined number of bins. Items are provided as a dictionary where keys are item identifiers and values are their weights or sizes.

import binpacking # Example 1: Pack items into bins with a constant volume limit items_by_weight = {'itemA': 10, 'itemB': 20, 'itemC': 30, 'itemD': 40, 'itemE': 15} max_bin_volume = 50 bins_constant_volume = binpacking.to_constant_volume(items_by_weight, max_bin_volume) print(f"Bins (constant volume {max_bin_volume}): {bins_constant_volume}") # Example 2: Pack items into a fixed number of bins num_target_bins = 2 bins_multiple_bins = binpacking.to_multiple_bins(items_by_weight, num_target_bins) print(f"Bins (fixed number {num_target_bins}): {bins_multiple_bins}") # For NumPy accelerated functions, ensure it's installed via: pip install binpacking[numpy] # from binpacking.matrices import to_constant_volume_numpy # (advanced usage)

Debug

Known issues

breakingVersion 2.0.0 and above require Python 3.10 or newer. Older Python 3 versions (e.g., 3.8, 3.9) and Python 2.x are no longer supported.

fix

Upgrade your Python environment to 3.10 or later. If unable to upgrade, you must use `binpacking` version < 2.0.0 (e.g., `pip install binpacking==1.5.2`).

affects: >=2.0.0

breakingThe packaging system transitioned from `setup.py`/`setup.cfg` to `pyproject.toml` in version 2.0.0. This primarily affects maintainers or developers building from source, but it's part of the modernization effort.

fix

Ensure your build environment supports PEP 517/518 and `pyproject.toml` for packaging operations.

affects: >=2.0.0

gotchaVersion 2.0.0 had a bug related to PEP 695 generic syntax compatibility with certain Python 3.10/3.11 environments, which could lead to runtime errors.

fix

Always upgrade to version 2.0.1 or later (`pip install --upgrade binpacking`) to resolve this compatibility issue. Version 2.0.1 replaced PEP 695 syntax with TypeVar-based generics for broader compatibility.

affects: 2.0.0

gotchaTo leverage NumPy for accelerated performance, you must explicitly install `binpacking` with the `[numpy]` extra. Standard installation does not include NumPy, and any attempts to use NumPy-specific features will fail.

fix

Install with `pip install binpacking[numpy]`.

affects: >=2.0.0

deprecatedThe `pytest-runner` dependency was removed in v1.5.2 due to security concerns.

fix

Ensure `pytest-runner` is not a direct or indirect dependency in your project to avoid potential security risks. Upgrade to `binpacking` v1.5.2 or newer.

affects: All versions

Upgrade

Version history

2.0.1latest on PyPI · released Jan 22, 2026

Audit

Dependencies

numpyoptionalOptional dependency for accelerated performance on large datasets. Install with `pip install binpacking[numpy]`.

Agent activity

13 hits · last 30 days

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OpenAI (training)

Resources

packagehttps://pypi.org/project/binpacking/ ↗