binpacking 2.0.1

Requirements

• Python 3.10+

• No dependencies (NumPy optional for large datasets)

Install

pip install binpacking

For optional NumPy acceleration:

pip install binpacking[numpy]

Quick Start

import binpacking

# Distribute items to 4 bins with balanced weights
b = {'a': 10, 'b': 10, 'c': 11, 'd': 1, 'e': 2, 'f': 7}
bins = binpacking.to_constant_bin_number(b, 4)
print(bins)
# [{'c': 11}, {'b': 10}, {'a': 10}, {'f': 7, 'e': 2, 'd': 1}]

# Distribute items to bins with max volume 11
values = [10, 10, 11, 1, 2, 7]
bins = binpacking.to_constant_volume(values, 11)
print(bins)
# [[11], [10], [10], [7, 2, 1]]

Use Cases

Consider you have a list of items, each carrying a weight w_i. Typical questions are:

1. How can we distribute the items to a minimum number of bins N of equal volume V?

2. How can we distribute the items to exactly N bins where each carries items that sum up to approximately equal weight?

Example 1: You have files of different sizes to load into memory, but only 8GB of RAM. How do you group files to minimize the number of program runs? → Use to_constant_volume.

Example 2: You have jobs with known durations and a 4-core CPU. How do you distribute jobs so all cores finish at approximately the same time? → Use to_constant_bin_number.

Input Formats

Both algorithms accept:

• Lists of weights: [10, 10, 11, 1, 2, 7]

• Dictionaries with weights as values: {'a': 10, 'b': 10, ...}

• Lists of tuples with weight_pos parameter: [('item1', 10), ('item2', 5)]

Command Line Interface

The binpacking command processes CSV files:

$ binpacking -h
usage: binpacking [-h] [-f FILEPATH] [-V V_MAX] [-N N_BIN] [-c WEIGHT_COLUMN]
                  [-H] [-d DELIM] [-q QUOTECHAR] [-l LOWER_BOUND]
                  [-u UPPER_BOUND] [--use-numpy] [-o OUTPUT_DIR]

Bin-pack CSV rows by weight column

options:
  -h, --help            show this help message and exit
  -f, --filepath        path to the csv-file to be bin-packed
  -V, --volume          maximum volume per bin (constant volume algorithm)
  -N, --n-bin           number of bins (constant bin number algorithm)
  -c, --weight-column   column number or name where the weight is stored
  -H, --has-header      set if the csv-file has a header row
  -d, --delimiter       delimiter in the csv-file (use "tab" for tabs)
  -q, --quotechar       quote character in the csv-file
  -l, --lower-bound     exclude weights below this bound
  -u, --upper-bound     exclude weights above this bound
  --use-numpy           use NumPy-accelerated algorithms
  -o, --output-dir      output directory (default: current working directory)

Examples

In the repository’s directory:

cd examples_and_resources/

# Constant volume: pack words into bins of max weight 2000
binpacking -f hamlet_word_count.csv -V 2000 -H -c count -l 10 -u 1000

# Constant bin number: distribute to exactly 4 bins
binpacking -f hamlet_word_count.csv -N 4 -H -c count

# Output to specific directory
binpacking -f hamlet_word_count.csv -N 4 -H -c count -o /tmp/output/

In Python:

import binpacking

b = {'a': 10, 'b': 10, 'c': 11, 'd': 1, 'e': 2, 'f': 7}
bins = binpacking.to_constant_bin_number(b, 4)
print("===== dict\n", b, "\n", bins)

b = list(b.values())
bins = binpacking.to_constant_volume(b, 11)
print("===== list\n", b, "\n", bins)

NumPy Acceleration

For large datasets, use the NumPy-accelerated versions:

from binpacking.numpy import to_constant_volume, to_constant_bin_number

bins = to_constant_volume(large_list, V_max)
bins = to_constant_bin_number(large_list, N_bin)

Or via CLI with --use-numpy.

Algorithms

• Constant Volume: Least Loaded Fit Decreasing — items sorted by weight (descending), each placed in the emptiest bin that fits.

• Constant Bin Number: Longest Processing Time (LPT) — items sorted by weight (descending), each placed in the bin with lowest total weight.

See examples_and_resources/efficiency_analyses/ for benchmarks and analysis.

Related packages

• prtpy by Erel Segal-Halevi.

• numberpartitioning by Søren Fuglede Jørgensen.