PyDataValidator

Introduction

PyDataValidator is a powerful and flexible Python library designed to streamline data validation processes. Whether you're building data pipelines, developing web applications, or handling complex datasets, PyDataValidator offers a comprehensive suite of tools to ensure your data is clean, consistent, and reliable.

Key Features

• Comprehensive Rule Set:
  • Validate data with a wide range of built-in rules, including checks for required fields, conditional presence, format validation, and more.
  • Examples include rules for ensuring fields are present, validating email formats, checking numeric ranges, and enforcing unique constraints.
• Custom Validators:
  • Easily create and integrate custom validation rules tailored to your specific needs.
  • Extend the library with your own validation logic to handle any specific data requirements.
• Chainable Validation:
  • Build complex validation logic by chaining multiple rules together for more nuanced data integrity checks.
  • Combine rules like Required, Min, and Email in a single, readable chain to enforce multiple conditions on a single field.
• Detailed Error Reporting:
  • Generate clear, actionable error messages that help you quickly identify and resolve data issues.
  • Each validation failure is accompanied by descriptive messages indicating the nature of the error and the affected data fields.
• Ease of Use:
  • Designed with simplicity in mind, PyDataValidator's intuitive API allows you to validate data with minimal code.
  • Quickly set up validations using a declarative syntax that integrates seamlessly into your Python projects.
• Highly Extensible:
  • Flexible architecture that integrates seamlessly with other libraries and frameworks, making it ideal for use in a variety of projects.
  • Whether you're working with Flask, Django, or standalone scripts, PyDataValidator adapts to your environment.

Installation

pip install py_data_validator

from py_data_validator.validator import Validator

# Define the data to be validated
data = {"name": "John Doe", "email": "johndoe@example.com"}

# Define the validation rules
rules = {
    "name": ["required"],
    "email": ["required", "email"],
}

# Create a Validator instance
validator = Validator(data,rules)

# Perform the validation
response = validator.validate()

# Check if validation failed and print the errors if any
if response.validated:
    print("Validation passed!", response.data)
else:
    print("Validation failed with errors:", response.errors)

After installing the PyDataValidator package, providing a list of available validation rules is a great way to help users quickly understand the capabilities of the library..

Contributing

Contributions are welcome! If you find a bug or have a feature request, please open an issue or submit a pull request on GitHub.

License

This project is licensed under the MIT License - see the LICENSE file for details.

More Information

For more information, visit the documentation or view the package on PyPI.