| Name | py-data-validator JSON |
| Version |
0.1.4
JSON |
| download |
| home_page | None |
| Summary | Python data validation library |
| upload_time | 2024-09-09 04:01:01 |
| maintainer | None |
| docs_url | None |
| author | Krunal Dodiya |
| requires_python | <4.0,>=3.10 |
| license | MIT |
| keywords |
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# PyDataValidator
[](https://krunals-organization-2.gitbook.io/pydatavalidator) [](https://pypi.org/project/py-data-validator/)
# 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
```bash
pip install py_data_validator
```
```python
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](https://github.com/krunaldodiya/py-data-validator).
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## More Information
For more information, visit the [documentation](https://krunals-organization-2.gitbook.io/pydatavalidator) or view the package on [PyPI](https://pypi.org/project/py-data-validator).
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"description": "# PyDataValidator\n\n[](https://krunals-organization-2.gitbook.io/pydatavalidator) [](https://pypi.org/project/py-data-validator/)\n\n# Introduction\n\n**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.\n\n#### Key Features\n\n- **Comprehensive Rule Set**:\n - Validate data with a wide range of built-in rules, including checks for required fields, conditional presence, format validation, and more.\n - Examples include rules for ensuring fields are present, validating email formats, checking numeric ranges, and enforcing unique constraints.\n- **Custom Validators**:\n - Easily create and integrate custom validation rules tailored to your specific needs.\n - Extend the library with your own validation logic to handle any specific data requirements.\n- **Chainable Validation**:\n - Build complex validation logic by chaining multiple rules together for more nuanced data integrity checks.\n - Combine rules like `Required`, `Min`, and `Email` in a single, readable chain to enforce multiple conditions on a single field.\n- **Detailed Error Reporting**:\n - Generate clear, actionable error messages that help you quickly identify and resolve data issues.\n - Each validation failure is accompanied by descriptive messages indicating the nature of the error and the affected data fields.\n- **Ease of Use**:\n - Designed with simplicity in mind, PyDataValidator's intuitive API allows you to validate data with minimal code.\n - Quickly set up validations using a declarative syntax that integrates seamlessly into your Python projects.\n- **Highly Extensible**:\n - Flexible architecture that integrates seamlessly with other libraries and frameworks, making it ideal for use in a variety of projects.\n - Whether you're working with Flask, Django, or standalone scripts, PyDataValidator adapts to your environment.\n\n# Installation\n\n```bash\npip install py_data_validator\n```\n\n```python\nfrom py_data_validator.validator import Validator\n\n# Define the data to be validated\ndata = {\"name\": \"John Doe\", \"email\": \"johndoe@example.com\"}\n\n# Define the validation rules\nrules = {\n \"name\": [\"required\"],\n \"email\": [\"required\", \"email\"],\n}\n\n# Create a Validator instance\nvalidator = Validator(data,rules)\n\n# Perform the validation\nresponse = validator.validate()\n\n# Check if validation failed and print the errors if any\nif response.validated:\n print(\"Validation passed!\", response.data)\nelse:\n print(\"Validation failed with errors:\", response.errors)\n```\n\nAfter 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..\n\n## Contributing\n\nContributions are welcome! If you find a bug or have a feature request, please open an issue or submit a pull request on [GitHub](https://github.com/krunaldodiya/py-data-validator).\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## More Information\n\nFor more information, visit the [documentation](https://krunals-organization-2.gitbook.io/pydatavalidator) or view the package on [PyPI](https://pypi.org/project/py-data-validator).\n",
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