valify 0.8.0

A composable, expressive data validation library for python

valify

A composable, expressive data validation library for Python.

Installation

pip install valify

Quick Start

from valify import Schema, StringValidator, IntValidator, EmailValidator

schema = Schema({
    "name":  StringValidator(min_length=2, max_length=50),
    "age":   IntValidator(min_value=0, max_value=120),
    "email": EmailValidator(),
})

# Valid data — returns cleaned, validated dictionary
result = schema.validate({
    "name":  "Alice",
    "age":   30,
    "email": "[e-mail]",
})
print(result)
# {'name': 'Alice', 'age': 30, 'email': '[e-mail]'}

# Invalid data — raises ValidationError with ALL errors at once
schema.validate({
    "name":  "A",
    "age":   -5,
    "email": "not-an-email",
})
# ValidationError: Validation failed:
#   name: Must be at least 2 characters long.
#   age: Must be at least 0.
#   email: 'not-an-email' is not a valid email address.

Validators

Validator	What it checks
StringValidator	Strings, with optional min/max length
IntValidator	Integers, with optional min/max value
FloatValidator	Floats, with optional min/max value
BoolValidator	Booleans, with optional string coercion
EmailValidator	Email address format
OptionalValidator	Wraps any validator and makes it optional
ListValidator	Validates every item in a list
EnumValidator	Value must be one of a fixed set of choices
RegexValidator	Value must match against a given regex

Validators in Detail

StringValidator

from valify import StringValidator

v = StringValidator(
    min_length=2,   # minimum character length
    max_length=50,  # maximum character length
    strip=True,     # strip whitespace before validating (default: True)
)

IntValidator

from valify import IntValidator

v = IntValidator(
    min_value=0,    # minimum allowed value
    max_value=120,  # maximum allowed value
    coerce=False,   # if True, converts "42" -> 42 (default: False)
)

EmailValidator

from valify import EmailValidator

v = EmailValidator()
v.validate("[e-mail]")  # returns "[e-mail]"

Using Validators Standalone

Validators work without a Schema too:

from valify import IntValidator
from valify.exceptions import ValidationError

v = IntValidator(min_value=0)

try:
    v.validate(-1)
except ValidationError as e:
    print(e.message)  # Must be at least 0.
    print(e.value)    # -1

Nested Schemas

Schemas can be nested inside other schemas for validating complex data:

from valify import Schema, StringValidator, IntValidator

address_schema = Schema({
    "street": StringValidator(min_length=2),
    "city":   StringValidator(min_length=2),
    "pin":    StringValidator(min_length=6, max_length=6),
})

user_schema = Schema({
    "name":    StringValidator(min_length=2),
    "age":     IntValidator(min_value=0),
    "address": address_schema,
})

user_schema.validate({
    "name": "Darshan",
    "age":  20,
    "address": {
        "street": "MG Road",
        "city":   "Pune",
        "pin":    "411001",
    }
})

Schema Utilities

is_valid()

Checks whether the provided data is valid without raising exceptions.

Returns True when validation succeeds and False otherwise.

Example

if schema.is_valid(data):
    process(data)

Valid data:

schema.is_valid(
    {
        "name": "Darshan",
        "age": 21,
    }
)
# True

Invalid data:

schema.is_valid(
    {
        "name": "",
        "age": 15,
    }
)
# False

---

errors()

Returns validation errors as a dictionary instead of raising an exception.

This is useful when building APIs, forms, CLIs, or user-facing validation flows.

Example

errors = schema.errors(data)

if errors:
    return {"errors": errors}

Input:

{
    "name": "",
    "age": 15,
}

Output:

{
    "name": "Name cannot be empty",
    "age": "Value must be at least 18",
}

When validation succeeds:

schema.errors(valid_data)
# {}

Invalid Root Data

Schemas expect a dictionary as input.

schema.errors(["invalid"])

Output:

{
    "__root__": "Expected a dictionary, got 'list'"
}

Error Handling

from valify.exceptions import (
    ValifyError,        # base — catches everything
    ValidationError,    # a value failed validation
    RequiredFieldError, # a required field was missing
    SchemaError,        # the schema definition is invalid
)

Version History

• 0.8.0 — Added RegexValidator and security add-ons
• 0.7.0 — Added to_json_schema() on all validators and Schema
• 0.6.0 — Added Schema.from_example()
• 0.5.0 — Added Documentation using sphinx
• 0.4.0 — Added nested schema support
• 0.3.0 — Added OptionalValidator, ListValidator, EnumValidator
• 0.2.0 — Full type hints and mypy compatibility
• 0.1.0 — Initial release

License

MIT