enforce-rules 2.0.2

Project Enforce Rules Project Enforce Rules (or PER for short) expands the Python type system to allow more constraints.

Version #

MAJOR: 2

MINOR: 0

PATCH: 2

If you need to catch up, you can see the full version history in the CHANGELOG.

It exists to let you use features Python doesn't already provide in the typing system — things you probably want, like min, max, length, all_same, and many more.

PER now supports runtime validation anywhere using:

validate(value: object, rules: Dict[str, Any])

This enforces rule dictionaries and returns the original value if valid. If invalid, it raises a descriptive error.

Why I Made It I wanted type‑hint features Python didn’t give me. I thought dictionaries would work until I learned... well... Python didn’t enforce them.

So with the help of Microsoft Copilot and my dad, we designed a module that enforces this type of stuff. Then I realized everyone in the Python community could use this, so it became a project. Hence, creating PER.

PER uses rule dictionaries and validate() to enforce constraints at runtime.

Install it with pip:

pip install enforce-rules

Then use it like:

from enforce_rules import validate

Features

1. Runtime enforcement of rule dictionaries
2. Dictionary‑based rule definitions
3. No extra objects required
4. Works anywhere in your code
5. Extensible via must_be_true

How It Works PER validates values using:

validate(value, rules)

If the value violates a rule, PER raises an error. If the value passes, PER returns the original value unchanged.

This means validated values behave exactly like normal Python values.

Keywords and Usage Below are all supported keywords.

length

The length of the object must be exactly this.

lst = validate([1, 2, 3, 4, 5], {"length": 5})

min_length

Minimum length (inclusive).

lst = validate(['a', 'b', 'c', 'd', 'e'], {"min_length": 3})

max_length

Maximum length (inclusive).

lst = validate([1, 2, 3, 4, 5, 6], {"max_length": 7})

min

Minimum numeric value (inclusive).

number = validate(10, {"min": 0})

max

Maximum numeric value (inclusive).

number = validate(10, {"max": 20})

allowed_values

Similar to Literal; value must be one of the allowed values.

val = validate("a", {"allowed_values": ("a", "b", "c", "d")})

invariant

Value must be truthy.

val = validate((0 == 0), {"invariant": True})

all_same

All values in the collection must be the same.

numbers = validate([1, 1, 1], {"all_same": True})

all_unique

All values in the collection must be unique.

numbers = validate([1, 2, 3], {"all_unique": True})

non_empty

Collection must not be empty.

my_strings = validate(['a', 'b', 'c'], {"non_empty": True})

no_nulls

Collection must not contain None.

my_things = validate([1, 2, 3, "a", "b", "c"], {"no_nulls": True})

sorted

List must be sorted (increasing or decreasing).

numbers = validate([1, 5, 9], {"sorted": True})

increasing

List must be strictly increasing.

numbers = validate([1, 5, 9], {"increasing": True})

decreasing

List must be strictly decreasing.

numbers = validate([9, 5, 1], {"decreasing": True})

sum_min

Minimum sum of the collection (inclusive).

numbers = validate([10, 20, 30], {"sum_min": 50})

sum_max

Maximum sum of the collection (inclusive).

numbers = validate([10, 20, 30], {"sum_max": 70})

element_min

Minimum value for any element (inclusive).

numbers = validate([10, 20, 30], {"element_min": 5})

element_max

Maximum value for any element (inclusive).

numbers = validate([10, 20, 30], {"element_max": 40})

regex

String must match the regex.

cat_or_dog = validate("cat", {"regex": "cat|dog"})

regex_flags

Turns out I didn't notice this in my code, until 1.1.0. This is the regex flags

from re import RegexFlag
cat_or_dog = validate("cat", {"regex": "cat|dog", {"regex_flags": RegexFlag.I | RegexFlag.M | RegexFlag.X

before_date

Value must be strictly before the given datetime.

validate(datetime(1999, 8, 29), {"before_date": datetime(2000, 1, 1)})

after_date

Value must be strictly after the given datetime.

validate(datetime(2026, 8, 29), {"after_date": datetime(2000, 1, 1)})

must_be_true

Custom rule: a function that returns True for allowed values.

def is_even(x: int) -> bool:
    return x % 2 == 0
even_number = validate(8, {"must_be_true": is_even})

This calls:

is_even(8)

If enough people use a must_be_true lambda, it may become an added keyword in a later version

Contributing Contributions are welcome. Please open an issue or pull request. When contributing, ensure backwards compatibility (you cannot remove keywords and/or features).

Please note, when using my module, that you will manually have to validate each time should you choose to mutate a variable.

Versioning Policy

This project guarantees full backwards compatibility. Existing rule files, keyword meanings, validator behaviors, and metadata formats will continue to work exactly as before. No update will ever break existing configurations.

Version Numbering

This project uses a non-breaking semantic versioning model:

MAJOR.MINOR.PATCH

MAJOR = large new feature families MINOR = small additive keywords or enhancements PATCH = bug fixes or internal improvements

Major bumps do not imply breaking changes. They only indicate that a significant new capability has been added.

Major bumps always reset MINOR and PATCH to 0. For example: 1.7.3 -> 2.0.0

1.12.0 -> 2.0.0

1.0.0 -> 2.0.0

Minor bumps always reset PATCH to 0. For example: 1.7.3 -> 1.8.0

1.12.9 -> 1.13.0

1.0.4 -> 1.1.0

Minor Version Bumps

Minor bumps occur when adding small keywords. Examples include:

min_inclusive

max_inclusive

trim_whitespace

pattern_flags

These additions do not change the meaning of existing keywords, do not require users to modify rule files, and do not alter validator behavior. They are classified as minor updates.

Minor bumps always reset PATCH to 0

Project Enforce Rules

Project Enforce Rules (or PER for short) expands the Python type system to allow more constraints.

Version #

MAJOR: 1

MINOR: 1

PATCH: 4

If you need to catch up, you can see the full version history in the CHANGELOG.

It exists to let you use features Python doesn't already provide in the typing system — things you probably want, like min, max, length, all_same, and many more.

PER now supports runtime validation anywhere using:

validate(value: object, rules: Dict[str, Any])

This enforces rule dictionaries and returns the original value if valid. If invalid, it raises a descriptive error.

Why I Made It I wanted type‑hint features Python didn’t give me. I thought dictionaries would work until I learned... well... Python didn’t enforce them.

So with the help of Microsoft Copilot and my dad, we designed a module that enforces this type of stuff. Then I realized everyone in the Python community could use this, so it became a project. Hence, creating PER.

PER uses rule dictionaries and validate() to enforce constraints at runtime.

Install it with pip:

pip install enforce-rules

Then use it like:

from enforce_rules import validate

Features

1. Runtime enforcement of rule dictionaries
2. Dictionary‑based rule definitions
3. No extra objects required
4. Works anywhere in your code
5. Extensible via must_be_true

How It Works PER validates values using:

validate(value, rules)

If the value violates a rule, PER raises an error. If the value passes, PER returns the original value unchanged.

This means validated values behave exactly like normal Python values.

Keywords and Usage Below are all supported keywords.

length

The length of the object must be exactly this.

lst = validate([1, 2, 3, 4, 5], {"length": 5})

min_length

Minimum length (inclusive).

lst = validate(['a', 'b', 'c', 'd', 'e'], {"min_length": 3})

max_length

Maximum length (inclusive).

lst = validate([1, 2, 3, 4, 5, 6], {"max_length": 7})

min

Minimum numeric value (inclusive).

number = validate(10, {"min": 0})

max

Maximum numeric value (inclusive).

number = validate(10, {"max": 20})

allowed_values

Similar to Literal; value must be one of the allowed values.

val = validate("a", {"allowed_values": ("a", "b", "c", "d")})

invariant

Value must be truthy.

val = validate((0 == 0), {"invariant": True})

all_same

All values in the collection must be the same.

numbers = validate([1, 1, 1], {"all_same": True})

all_unique

All values in the collection must be unique.

numbers = validate([1, 2, 3], {"all_unique": True})

non_empty

Collection must not be empty.

my_strings = validate(['a', 'b', 'c'], {"non_empty": True})

no_nulls

Collection must not contain None.

my_things = validate([1, 2, 3, "a", "b", "c"], {"no_nulls": True})

sorted

List must be sorted (increasing or decreasing).

numbers = validate([1, 5, 9], {"sorted": True})

increasing

List must be strictly increasing.

numbers = validate([1, 5, 9], {"increasing": True})

decreasing

List must be strictly decreasing.

numbers = validate([9, 5, 1], {"decreasing": True})

sum_min

Minimum sum of the collection (inclusive).

numbers = validate([10, 20, 30], {"sum_min": 50})

sum_max

Maximum sum of the collection (inclusive).

numbers = validate([10, 20, 30], {"sum_max": 70})

element_min

Minimum value for any element (inclusive).

numbers = validate([10, 20, 30], {"element_min": 5})

element_max

Maximum value for any element (inclusive).

numbers = validate([10, 20, 30], {"element_max": 40})

regex

String must match the regex.

cat_or_dog = validate("cat", {"regex": "cat|dog"})

regex_flags

Turns out I didn't notice this in my code, until 1.1.0. This is the regex flags

from re import RegexFlag
cat_or_dog = validate("cat", {"regex": "cat|dog", {"regex_flags": RegexFlag.I | RegexFlag.M | RegexFl

must_be_true

Custom rule: a function that returns True for allowed values.

def is_even(x: int) -> bool:
    return x % 2 == 0
even_number = validate(8, {"must_be_true": is_even})

This calls:

is_even(8)

If enough people use a must_be_true lambda, it may become an added keyword in a later version

before_date

Value must be strictly before the given datetime. Example: validate(datetime(1999, 8, 29), {"before_date": datetime(2000, 1, 1)})

after_date

Value must be strictly after the given datetime. Example: validate(datetime(2026, 8, 29), {"after_date": datetime(2000, 1, 1)})

Contributing Contributions are welcome. Please open an issue or pull request. When contributing, ensure backwards compatibility (you cannot remove keywords and/or features).

Please note, when using my module, that you will manually have to validate each time should you choose to mutate a variable.

Versioning Policy

This project guarantees full backwards compatibility. Existing rule files, keyword meanings, validator behaviors, and metadata formats will continue to work exactly as before. No update will ever break existing configurations.

Version Numbering

This project uses a non-breaking semantic versioning model:

MAJOR.MINOR.PATCH

MAJOR = large new feature families

MINOR = small additive keywords or enhancements

PATCH = bug fixes or internal improvements

Major bumps do not imply breaking changes. They only indicate that a significant new capability has been added.

Major bumps always reset MINOR and PATCH to 0. For example: 1.7.3 -> 2.0.0

1.12.0 -> 2.0.0

1.0.0 -> 2.0.0

Minor bumps always reset PATCH to 0. For example: 1.7.3 -> 1.8.0

1.12.9 -> 1.13.0

1.0.4 -> 1.1.0

Minor Version Bumps

Minor bumps occur when adding small keywords. Examples include:

min_inclusive

max_inclusive

trim_whitespace

pattern_flags

These additions do not change the meaning of existing keywords, do not require users to modify rule files, and do not alter validator behavior. They are classified as minor updates.

Minor bumps always reset PATCH to 0

Major Version Bumps

Major bumps occur only when adding large new keyword families or entire new capability domains. Examples include:

a full date-validation system

a full numeric-range system

a full conditional-rules system

a full schema-level rule system

These are major features, even though they remain fully backwards-compatible. Major bumps communicate that the release adds a significant new capability.

Major bumps always reset MINOR and PATCH to 0.

Patch Version Bumps

Patch bumps occur when fixing bugs, improving internal logic, optimizing performance, correcting documentation, or adjusting error messages without changing meaning. Patch updates never add new keywords.

Summary

MAJOR: large new feature families, backwards-compatible, resets MINOR and PATCH to 0

MINOR: small additive keywords, backwards-compatible

PATCH: fixes only, backwards-compatible

Credits Microsoft Copilot — for helping me code it. It did a TON of the coding, and to be honest, I couldn't have made this project without it. It did some a lot of readme, in addition to it writing most of CONTRIBUTING.md Dad — for helping me along the journey.

License Type: BSD 3-clause