Registry / data / python-constraint

python-constraint

library1.4.0pypypi✓ verified 87d ago

python-constraint is a module implementing support for handling Constraint Satisfaction Problems (CSPs) over finite domains. The current version, 2.5.0, offers enhanced features like efficient string-based constraints, negative value support, and performance improvements, with releases typically occurring several times a year.

data

pip install python-constraint

github ↗package ↗

INSTALL

IMPORT

SIG · PYTHON-CONSTRAINT

python-constraint

datapythonv1.4.0

Install

2.5s avg

Import

9ms

Disk

17MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v1.4.0 · 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 3.10–3.910 runs

installs and imports cleanly · install 0.0s · import 0.010s · 19.5MB

glibc

py 3.10–3.910 runs

installs and imports cleanly · install 2.5s · import 0.009s · 20MB

17MB installed

● package 17MB

Code

Verified usage

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

Problem

✓ from constraint import Problem

Constraint

✓ from constraint import Constraint

Base class for custom constraints, less commonly imported directly for basic use.

✓ from constraint import *

Commonly used for convenience to import Problem and various constraint types (e.g., AllDifferentConstraint).

This quickstart demonstrates how to define a simple Constraint Satisfaction Problem (CSP) using the `Problem` class, add variables with their respective finite domains, and apply a string-based constraint. Finally, it shows how to retrieve and iterate through all valid solutions.

from constraint import Problem problem = Problem() # Add variables with finite domains problem.addVariable('x', [1, 2, 3]) problem.addVariable('y', [1, 2, 3]) # Add a string-based constraint (preferred method since v2.1.0) # x * 2 == y problem.addConstraint('x * 2 == y', ('x', 'y')) # Find and print all solutions solutions = problem.getSolutions() # Example of printing solutions if solutions: print(f"Found {len(solutions)} solution(s):") for sol in solutions: print(sol) else: print("No solutions found.")

Debug

Known issues

deprecatedUsing lambda or function-based constraints directly with `addConstraint` is deprecated in favor of string-based constraints for better performance and readability.

fix

Rewrite constraints as Python-evaluable strings (e.g., `problem.addConstraint('x * 2 == y', ('x', 'y'))`) instead of lambda functions (e.g., `problem.addConstraint(lambda x, y: x*2 == y, ('x', 'y'))`).

affects: >=2.1.0

gotchaThe performance of `getSolutions()` can degrade exponentially with an increasing number of variables or larger domains, potentially leading to long computation times or memory exhaustion for complex problems.

fix

For large problems, consider reducing variable domains, adding more restrictive constraints early, or exploring alternative constraint satisfaction libraries optimized for specific problem types if `python-constraint` becomes too slow.

affects: All

gotchaWhen using string-based arithmetic constraints (e.g., 'x + y == z'), ensure that the domains of the involved variables contain only numeric types.

fix

If your variable domains include non-numeric types (e.g., strings), you must implement custom `Constraint` classes or filter domains to ensure type compatibility before applying arithmetic operations.

affects: All

Errors

Common errors & fixes

TypeError: unsupported operand type(s) for *: 'str' and 'int'

Attempting to apply an arithmetic constraint (especially string-based ones) to variables whose domains contain non-numeric values.

fix

Ensure that variables involved in arithmetic operations have domains consisting only of numeric types (integers, floats). If mixed types are needed, define a custom `Constraint` class to handle the specific logic.

No solutions found.

The set of variables and constraints defined in the problem are mutually exclusive, meaning no valid assignment of values satisfies all conditions simultaneously. The `getSolutions()` method will return an empty list.

fix

Carefully review your `addVariable` domains and `addConstraint` definitions. Look for contradictions or overly restrictive conditions. Simplify the problem or relax some constraints to test if solutions then become available.

NameError: name 'variable_name' is not defined (when using lambda)

When using a lambda or function as a constraint, the argument names in the function signature do not exactly match the variable names (as strings) passed in the tuple/list as the second argument to `addConstraint`.

fix

Ensure the argument names of your lambda or function precisely correspond to the string variable names in the tuple/list you provide to `addConstraint`. For example, for `addConstraint(lambda x, y: ..., ('x', 'y'))`, `x` and `y` must match.

Upgrade

Version history

1.4.0latest on PyPI · released Nov 5, 2018

Audit

Dependencies

No dependency data recorded yet.

Agent activity

7 hits · last 30 days

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Resources

githubhttps://github.com/python-constraint/python-constraint ↗

packagehttps://pypi.org/project/python-constraint/ ↗