edapipeline 0.1.0

Comprehensive Exploratory Data Analysis Pipeline

Great progress on setting up your package structure! Let's continue with the next steps to properly publish your EDAPipeline package.

Next Steps for Publishing Your EDAPipeline Package

1. Complete Your README.md

First, let's improve your README.md with proper documentation:

# EDAPipeline

A comprehensive Exploratory Data Analysis (EDA) toolkit that streamlines the process of analyzing datasets through visualization and statistical methods.

## Features

- Automated data type detection (numerical, categorical, datetime)
- Comprehensive univariate analysis for all data types
- Correlation analysis with heatmaps
- Bivariate analysis between different feature types
- Datetime feature decomposition and analysis
- Outlier detection using multiple methods
- Customizable visualization options

## Installation

```bash
pip install edapipeline

Quick Start

from edapipeline import EDAPipeline
import pandas as pd

# Load your dataset
df = pd.read_csv('your_data.csv')

# Initialize the pipeline
eda = EDAPipeline(df, target_col='your_target_column')

# Run the complete analysis
eda.run_complete_analysis()

# Or run specific analyses
eda.data_overview()
eda.analyze_numerical_features()
eda.correlation_analysis()

Dependencies

• numpy
• pandas
• matplotlib
• seaborn
• scipy

Contributing

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

License

This project is licensed under the MIT License - see the LICENSE file for details.

### 2. Create a Basic __init__.py File

Edit your `src/edapipeline/__init__.py` file:

```python
"""EDAPipeline - Comprehensive EDA toolkit for data analysis."""

from .core import EDAPipeline
from .__version__ import __version__

__all__ = ['EDAPipeline']