Validata

Dataclass and validation made simple as well as powerful.

Installation

You can install Validata using pip:

pip install validata

To install from GitHub:

git clone https://github.com/MirS0bhan/validata && cd validata
pip install .

Usage

Basic Example

Here’s how to define a dataclass and use the validation features:

from vlidt import BaseModel

class A(BaseModel):
    x: int
    y: int

class B(A):
    z: list[str]

c = B(5, 6, ["abc", "xyz"])

Validation

You can validate the fields of your dataclass instances easily:

from vlidt import validate

# Validate the instance
is_valid = validate(c)
print(is_valid)  # Returns True if valid, otherwise False

Dumping and Loading Data

You can convert your dataclass instances to and from dictionaries:

# Dumping the instance to a dictionary
data_dict = dump(c)
print(data_dict)  # Output: {'x': 5, 'y': 6, 'z': ['abc', 'xyz']}

# Loading from a dictionary
new_instance = load(B, data_dict)
print(new_instance)  # Output: B(x=5, y=6, z=['abc', 'xyz'])

Nested Dataclasses

Validata supports nested dataclasses, allowing you to create more complex data structures:

class C(BaseModel):
    a: A
    additional_info: str

instance_c = C(a=c, additional_info="Some info")
print(instance_c)

Features

• Automatic Validation: Validate dataclass fields with minimal effort.
• Dump and Load: Easily convert dataclass instances to and from dictionaries.
• Nested Support: Handle nested dataclass instances seamlessly.
• Type Annotations: Leverage Python’s type hints for better code clarity and error checking.

Contributing

Contributions are welcome! If you would like to contribute to Validata, please follow these steps:

• Fork the repository.
• Create a new branch for your feature or bugfix.
• Make your changes and commit them.
• Push to your branch and submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for more details. Contact

For any inquiries or suggestions, feel free to reach out via GitHub Issues.