dataclass & validation simple as well as hell.
Project description
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.
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