Metadata-Version: 2.4
Name: smarteda-shlok
Version: 1.0.1
Summary: A lightweight Python library for automated exploratory data analysis (EDA).
Author: Shlok Apte
Author-email: Shlok Apte <shlokapte271@gmail.com>
License: MIT
Keywords: eda,exploratory-data-analysis,data-analysis,data-science,pandas,numpy,statistics,analytics,machine-learning
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries
Classifier: License :: OSI Approved :: MIT 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: Programming Language :: Python :: 3.13
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas>=2.0
Requires-Dist: numpy>=1.24
Dynamic: author
Dynamic: license-file
Dynamic: requires-python

# SmartEDA

SmartEDA is a lightweight Python library for **Automated Exploratory Data Analysis (EDA)**. It helps data analysts, data scientists, students, and researchers quickly understand their datasets by generating essential statistical insights with a single function call.

---

## ✨ Features

- 📊 Dataset Summary
- 🧹 Missing Value Analysis
- 🔁 Duplicate Analysis
- 📈 Numerical Feature Analysis
- 📝 Categorical Feature Analysis
- 🔗 Correlation Analysis
- 📦 Outlier Detection
- ⚡ Fast and Lightweight
- 🐍 One-Line Dataset Analysis

---

## 📦 Installation

```bash
pip install smarteda-shlok
```

---

## 🚀 Quick Start

```python
import pandas as pd
from smarteda import analyzer

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

# Generate EDA Report
analyzer(df)
```

---

## 📋 Example Output

```
======================================================================
                        SMARTEDA REPORT
======================================================================

📊 DATASET SUMMARY

Rows                           : 891
Columns                        : 12
Memory Usage (MB)              : 0.28
Duplicate Rows                 : 0
Missing Cells                  : 866
Missing Percentage             : 8.10

🧹 MISSING VALUE ANALYSIS

Total Missing Cells            : 866
Columns with Missing Values    : 3
Missing Percentage             : 8.10

🔁 DUPLICATE ANALYSIS

Duplicate Rows                 : 0

📈 NUMERICAL ANALYSIS

...

📝 CATEGORICAL ANALYSIS

...

🔗 CORRELATION ANALYSIS

...

📦 OUTLIER ANALYSIS

...
```

---

## 📚 Modules

| Module | Description |
|---------|-------------|
| `analyzer.py` | Generates the complete EDA report |
| `summary.py` | Dataset summary |
| `missing.py` | Missing value analysis |
| `duplicates.py` | Duplicate row analysis |
| `numerical.py` | Numerical statistics |
| `categorical.py` | Categorical feature analysis |
| `correlation.py` | Correlation analysis |
| `outliers.py` | Outlier detection |

---

## 💻 Requirements

- Python 3.9 or higher
- pandas
- numpy

---

## 📂 Project Structure

```
smarteda/
│
├── src/
│   └── smarteda/
│       ├── __init__.py
│       ├── analyzer.py
│       ├── summary.py
│       ├── missing.py
│       ├── duplicates.py
│       ├── numerical.py
│       ├── categorical.py
│       ├── correlation.py
│       └── outliers.py
│
├── pyproject.toml
├── README.md
├── LICENSE
└── MANIFEST.in
```

---

## 🎯 Why SmartEDA?

- Beginner-friendly
- Lightweight and fast
- Easy to understand
- Modular architecture
- Works seamlessly with Pandas DataFrames
- Designed for quick exploratory data analysis

---

## 📄 License

This project is licensed under the **MIT License**.

---

## 👨‍💻 Author

**Shlok Apte**
