Metadata-Version: 2.4
Name: explore-df
Version: 0.1.13
Summary: A one-line interactive EDA tool using Streamlit
Author: Mandla Sibanda
Author-email: Mandla Sibanda <mandla.sibanda@gmail.com>
Project-URL: Homepage, https://github.com/MandlaSibanda196/explore-df
Project-URL: Bug Tracker, https://github.com/MandlaSibanda196/explore-df/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: streamlit>=1.44.0
Requires-Dist: pandas>=2.0.0
Requires-Dist: plotly>=5.14.0
Requires-Dist: numpy>=1.24.0
Requires-Dist: scipy>=1.10.0
Requires-Dist: statsmodels>=0.14.0
Requires-Dist: scikit-learn>=1.3.0
Requires-Dist: ruptures>=1.1.7
Requires-Dist: wordcloud>=1.9.2
Requires-Dist: requests>=2.31.0
Requires-Dist: matplotlib>=3.7.0
Requires-Dist: plotly-express>=0.4.0
Dynamic: author
Dynamic: license-file
Dynamic: requires-python

# Explore-DF

📊 A powerful interactive Exploratory Data Analysis tool that launches a Streamlit interface for your pandas DataFrames with just one line of code.

## Installation

```bash
pip install explore-df
```

## Quick Start

```python
import pandas as pd
from explore_df import analyze

# Load your data
df = pd.read_csv('your_data.csv')  # or any pandas DataFrame

# Launch the EDA interface
analyze(df)
```

That's it! The tool will automatically:
1. Launch a Streamlit server
2. Open your default web browser to the EDA interface
3. Display interactive visualizations and analysis of your data

## Features

- 🚀 **One-Line Setup**: Just `analyze(df)` to start exploring
- 📊 **Interactive Visualizations**: Dynamic charts and plots
- 🔍 **Data Quality Analysis**:
  - Missing values detection
  - Outlier analysis
  - Data type validation
  - Consistency checks
- 📈 **Automated Insights**:
  - Distribution analysis
  - Correlation detection
  - Time series patterns
  - Text analysis
- 💡 **Smart Suggestions**: Get recommendations for data cleaning and transformation

## Advanced Usage

```python
from explore_df import analyze

# Specify a custom port
analyze(df, port=8502)
```

## Requirements

- Python 3.9+

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

[MIT License](LICENSE) 
