Metadata-Version: 2.4
Name: dataforge-diff
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 diffing for pandas/pyarrow DataFrames
Keywords: data,diff,compare,pandas,pyarrow,deduplication,changes
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 Diff

High-performance data diffing for Python.

## Installation

```bash
pip install dataforge-diff
```

## Usage

```python
from dataforge_diff import diff
import pandas as pd

df_a = pd.DataFrame({"id": [1, 2, 3], "name": ["a", "b", "c"]})
df_b = pd.DataFrame({"id": [1, 2, 4], "name": ["a", "b", "changed"]})

result = diff(df_a, df_b, "id")
print(f"Modified: {result['modified_count']}")
```

