Metadata-Version: 2.4
Name: DatacleanAir
Version: 0.2.0
Summary: Data cleaning tools for air quality analysis
Author-email: Luis Fernando Muñiz Torres <lmunizt2400@alumno.ipn.mx>, Erika Alarcón Ruiz <erika.ar@cdmadero.tecnm.mx>, Francisco López Huerta <flo012579@gmail.com>, Felipe Caballero Briones <fcaballero@ipn.mx>
License: MIT
Project-URL: Homepage, https://github.com/luisfmuniztorres/DatacleanAir
Project-URL: Documentation, https://github.com/luisfmuniztorres/DatacleanAir
Project-URL: Source, https://github.com/luisfmuniztorres/DatacleanAir
Project-URL: Issues, https://github.com/luisfmuniztorres/DatacleanAir/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Atmospheric Science
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENCE.txt
Requires-Dist: numpy>=1.22
Requires-Dist: pandas>=1.4
Requires-Dist: scipy>=1.8
Dynamic: license-file

# DatacleanAir

A Python library for preprocessing, cleaning, and filtering air quality datasets.

## Citation

Muñiz-Torres, L. F., et al. (2025). *DatacleanAir: A Python library for air quality data cleaning*. GitHub Repository.

## Installation

```bash
pip install dataclenair
```

## Quick Start

```python
import pandas as pd
from DatacleanAir import kalman_filter, hampel_filter, z_score_normalize

# Load your dataset
df = pd.read_csv("pm25_data.csv")

pm = df["PM2.5"].values

# Apply normalization
pm_norm = z_score_normalize(pm)

# Apply Kalman filter
pm_kalman = kalman_filter(pm_norm)

# Remove outliers with Hampel
pm_clean = hampel_filter(pm_kalman, window_size=15, n_sigmas=3)
```

## Features

* Z-score normalization
* Kalman filtering
* Hampel outlier detection
* Tools for common air quality preprocessing pipelines

## License

MIT License
