Python data validation library
Project description
icon: shield-check description: Data Validation Library for Python
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
, andEmail
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.
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