Metadata-Version: 2.4
Name: dataforge-mask
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 masking and pseudonymization for pandas/pyarrow DataFrames
Keywords: data,mask,privacy,pseudonymization,pandas,pyarrow,pii,gdpr
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 Mask

High-performance data masking for Python.

## Installation

```bash
pip install dataforge-mask
```

## Usage

```python
from dataforge_mask import mask
import pandas as pd

df = pd.DataFrame({
    "email": ["a@test.com", "b@test.com", "c@test.com"],
    "ssn": ["123-45-6789", "987-65-4321", "111-22-3333"]
})
transforms = [
    {"column": "email", "type": "hash"},
    {"column": "ssn", "type": "redact"}
]
result = mask(df, transforms, key="secret_key")
```

