Metadata-Version: 2.4
Name: dataforge-check
Version: 0.1.0
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Dist: pandas>=1.5
Requires-Dist: pyarrow>=12.0
Requires-Dist: pytest>=7.0 ; extra == 'dev'
Requires-Dist: pandas>=2.0 ; extra == 'dev'
Provides-Extra: dev
Summary: High-performance data validation for pandas/pyarrow DataFrames
Keywords: data,validation,pandas,pyarrow,schema,quality,checks
Author-email: DataForge Team <dataforge@example.com>
License: Apache-2.0
Requires-Python: >=3.9
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
Project-URL: Documentation, https://github.com/shyamsivakumar/dataforge#readme
Project-URL: Homepage, https://github.com/shyamsivakumar/dataforge
Project-URL: Issues, https://github.com/shyamsivakumar/dataforge/issues
Project-URL: Repository, https://github.com/shyamsivakumar/dataforge.git

# DataForge Check

High-performance data validation for Python.

## Installation

```bash
pip install dataforge-check
```

## Usage

```python
from dataforge_check import validate
import pandas as pd

df = pd.DataFrame({"age": [25, 30, -5, 150]})
rules = [
    {"column": "age", "rule": "between", "min": 0, "max": 120}
]
result = validate(df, rules)
print(result["all_passed"])  # False
```

