Metadata-Version: 2.4
Name: damadara_xyz
Version: 0.1.1
Summary: 3-Layer Data Cleaning Pipeline: L0 Basic, L1 Semantic, L2 Presentation
Author-email: Your Name <your.email@example.com>
License: MIT
Project-URL: Homepage, https://github.com/yourusername/damadara_xyz
Project-URL: Repository, https://github.com/yourusername/damadara_xyz
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: pandas>=1.5.0
Requires-Dist: pyyaml>=6.0
Requires-Dist: numpy<2.0,>=1.21.0
Provides-Extra: dev
Requires-Dist: pytest; extra == "dev"
Requires-Dist: black; extra == "dev"

# DaMadara_xyz

A 3-layer data cleaning library: L0 (basic hygiene), L1 (semantic logic), L2 (presentation polish).

## Quick Start

1. Install: `pip install -e .`

2. Run full pipeline: `damadara --layers all --input dirty.csv --output clean.csv`

3. Or via API:
   ```python
   from damadara_xyz import run_pipeline
   df_clean = run_pipeline('dirty.csv', layers='all')
   df_clean.to_csv('clean.csv') 

# DaMadara_xyz

A powerful 3-layer data cleaning library.

## Installation
```bash
pip install damadara_xyz
from damadara_xyz import run_pipeline

df_clean = run_pipeline("dirty.csv", layers="all", output_path="clean.csv")
