Pydator is a lightweight, easy-to-use validation library for Python. It provides a clean and intuitive way to validate data using predefined rules.
# Using pip
pip install pydator
# Using conda
conda install -c conda-forge pydatorfrom pydator.validator import Validator
# Data to validate
data = {
"username": "john_doe123",
"email": "john@example.com",
"age": 30
}
# Define validation rules
rules = {
"username": ["required", "string", "min:3", "max:20"],
"email": ["required", "email"],
"age": ["required", "integer", "min:18"]
}
# Create validator
validator = Validator(data, rules)
# Validate data
if validator.validate():
print("All validations passed!")
else:
print("Validation errors:", validator.errors)- Simple and intuitive API
- Multiple validation rules per field
- Custom error messages
- Extensive built-in validation rules
- Easy to extend with custom rules
| Rule | Description | Example |
|---|---|---|
required |
Field must be present and not empty | "name": ["required"] |
string |
Field must be a string | "name": ["string"] |
number |
Field must be a number (integer or float) | "price": ["number"] |
min:value |
Minimum value for numbers, minimum length for strings | "age": ["min:18"] |
max:value |
Maximum value for numbers, maximum length for strings | "bio": ["max:200"] |
email |
Field must be a valid email address | "email": ["email"] |
boolean |
Field must be a boolean value | "active": ["boolean"] |
in:val1,val2,val3 |
Field value must be in the specified list | "status": ["in:pending,active,completed"] |
regex:pattern |
Field must match the regular expression pattern | "username": ["regex:^[a-zA-Z0-9_]+$"] |
date |
Field must be a valid date string | "birth_date": ["date"] |
float |
Field must be a float number | "weight": ["float"] |
url |
Field must be a valid URL | "website": ["url"] |
integer |
Field must be an integer | "age": ["integer"] |
contains:value |
Field must contain the specified value | "description": ["contains:important"] |
You can customize error messages for specific rules:
data = {
"username": "",
"email": "invalid-email"
}
rules = {
"username": ["required", "string"],
"email": ["required", "email"]
}
messages = {
"username.required": "Username is required!",
"email.email": "Please provide a valid email address."
}
validator = Validator(data, rules, messages)
if not validator.validate():
print(validator.errors)
# Output: {'username': ['Username is required!'], 'email': ['Please provide a valid email address.']}Here's a comprehensive example showing most features:
from pydator.validator import Validator
data = {
"username": "john_doe123",
"email": "john@example.com",
"age": 30,
"is_active": True,
"website": "https://example.com",
"signup_date": "2025-04-24",
"bio": "Software developer with 10 years of experience.",
"password": "SecurePass123!",
"confirm_password": "SecurePass123!",
"referral_code": "ABC123XYZ",
}
rules = {
"username": ["required", "string", "min:3", "max:20", "regex:^[a-zA-Z0-9_]+$"],
"email": ["required", "email"],
"age": ["required", "integer", "min:18", "max:65"],
"is_active": ["required", "boolean"],
"website": ["required", "url"],
"signup_date": ["required", "date"],
"bio": ["string", "max:100"],
"password": ["required", "string", "min:8"],
"confirm_password": ["required", "string", "min:8"],
"referral_code": ["string", "regex:^[A-Z0-9]{9}$"],
"ip_address": ["required", "string"],
}
messages = {
"username.required": "Username is required!",
"username.min": "Username must be at least 3 characters long.",
"username.max": "Username must not exceed 20 characters.",
"username.regex": "Username can only contain letters, numbers, and underscores.",
"email.required": "Email address is required!",
"email.email": "Please provide a valid email address.",
"age.required": "Age is required!",
"age.integer": "Age must be a valid number.",
"age.min": "You must be at least 18 years old.",
"age.max": "Age must not exceed 65.",
"is_active.required": "Please specify if the account is active.",
"is_active.boolean": "Active status must be a boolean value.",
"website.required": "Website URL is required!",
"website.url": "Please provide a valid URL.",
"signup_date.required": "Signup date is required!",
"signup_date.date": "Please provide a valid date.",
"bio.max": "Bio must not exceed 100 characters.",
"password.required": "Password is required!",
"password.min": "Password must be at least 8 characters long.",
"confirm_password.required": "Please confirm your password.",
"confirm_password.min": "Confirmation password must be at least 8 characters long.",
"referral_code.regex": "Referral code must be exactly 9 characters long and alphanumeric.",
"ip_address.required": "IP address is required!",
}
validator = Validator(data, rules, messages)
if validator.validate():
print("All validations passed!")
else:
print("Validation errors:", validator.errors)You can create custom validation rules by extending the BaseRule class:
from pydator.base_rule import BaseRule
from pydator.validator import Validator
# Define a custom rule by extending BaseRule
class EvenNumberRule(BaseRule):
def __call__(self, value, *args):
if not isinstance(value, int):
return False, self.message("even_number")
if value % 2 != 0:
return False, self.message("even_number")
return True, None
def message(self, field, *args):
return f"The {field} must be an even number."
# Register the custom rule
Validator.register_rule("even_number", EvenNumberRule())
# Now you can use your custom rule in validation
data = {
"number": 5
}
rules = {
"number": ["required", "even_number"]
}
validator = Validator(data, rules)
if validator.validate():
print("Validation passed!")
else:
print("Validation errors:", validator.errors)- Simplicity: Easy to learn and use with a clean, intuitive API
- Flexibility: Validate any data structure with customizable rules
- Performance: Lightweight with minimal dependencies
- Extensible: Easy to add custom validation rules
- Readable: Straightforward validation definitions that are easy to understand
| Feature | Pydator | Other Validation Libraries |
|---|---|---|
| Learning Curve | Minimal | Often steeper |
| Configuration | Simple dict-based | Various (class-based, schema-based) |
| Custom Rules | Easy to add | Usually requires more code |
| Dependencies | Minimal | Often more dependencies |
| Performance | Lightweight | Can be heavier |
Contributions are welcome! Please feel free to submit a Pull Request.
- Fork the repository
- Create your feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add some amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
This project is licensed under the terms of the MIT license. See LICENSE for more details.