dataforgeplus 1.0.1

Generate realistic synthetic datasets with a simple and powerful API.

🚀 DataForge

Generate realistic synthetic datasets with a simple and powerful API.

DataForge is a modern Python library for generating realistic synthetic datasets for testing, software development, machine learning, education, demonstrations, and AI projects.

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✨ Features

• 🚀 Generate thousands of realistic records in seconds
• 📦 15 built-in datasets
• 🛠 Create custom datasets using schemas
• 📊 Returns Pandas DataFrame
• 💾 Export to CSV
• 📄 Export to JSON
• 📗 Export to Excel (.xlsx)
• 🗄 Export to SQLite
• ⚡ Fast and lightweight
• 🌍 Cross-platform
• 🐍 Python 3.10+

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📦 Installation

Install from PyPI:

pip install dataforgeplus

Or install from source:

git clone https://github.com/Aatif05-it/DataForge.git

cd DataForge

pip install -e .

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🚀 Quick Start

Generate an Employee Dataset

from dataforge import forge

df = forge(
    dataset="employee",
    rows=10
)

print(df.head())

Example Output

Name	Email	Department	Salary
John Smith	[e-mail]	IT	72000
Emma Wilson	[e-mail]	HR	61000

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🛠 Custom Dataset

from dataforge import forge

df = forge(
    rows=100,
    schema={
        "name": "name",
        "email": "email",
        "phone": "phone",
        "salary": "salary"
    }
)

print(df.head())

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📚 Available Datasets

from dataforge import available_datasets

print(available_datasets())

Output

[
'airline',
'banking',
'countries',
'creditcard',
'ecommerce',
'employee',
'hospital',
'hr',
'library',
'mobile',
'realestate',
'restaurant',
'student',
'vehicle',
'weather'
]

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💾 Export Examples

CSV

df.to_csv("employees.csv", index=False)

Excel

df.to_excel("employees.xlsx", index=False)

JSON

df.to_json(
    "employees.json",
    orient="records",
    indent=4
)

SQLite

import sqlite3

conn = sqlite3.connect("employees.db")

df.to_sql(
    "employees",
    conn,
    if_exists="replace",
    index=False
)

conn.close()

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📚 Complete Example

from dataforge import forge

employees = forge(
    dataset="employee",
    rows=100
)

employees.to_csv("employees.csv", index=False)

print(employees.head())

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🏷 Built-in Datasets

• Airline
• Banking
• Countries
• Credit Card
• Ecommerce
• Employee
• Hospital
• HR
• Library
• Mobile
• Real Estate
• Restaurant
• Student
• Vehicle
• Weather

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🔧 Supported Providers

Some commonly used providers include:

name
first_name
last_name
email
phone
city
state
country
company
department
salary
gender
uuid
date
price
product
invoice_number
order_id
doctor
student_id
employee_id
vehicle
credit_card
passport_number
pan
aadhaar

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📁 Project Structure

DataForge/
│
├── dataforge/
│   ├── __init__.py
│   ├── forge.py
│   ├── cli.py
│   ├── datasets/
│   ├── providers/
│   └── utils/
│
├── docs/
├── examples/
├── tests/
├── LICENSE
├── MANIFEST.in
├── pyproject.toml
├── setup.py
└── README.md

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🗺 Roadmap

• ✅ CSV Export
• ✅ JSON Export
• ✅ Excel Export
• ✅ SQLite Export
• 🔜 YAML Schema Support
• 🔜 AI Dataset Generator
• 🔜 PostgreSQL Export
• 🔜 MongoDB Export
• 🔜 Parquet Export
• 🔜 REST API

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📋 Requirements

• Python 3.10+
• pandas
• Faker
• openpyxl

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📜 License

This project is licensed under the MIT License.

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👨‍💻 Author

Khan Aatif

GitHub

https://github.com/Aatif05-it

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🤝 Contributing

Contributions are welcome.

1. Fork the repository.
2. Create a feature branch.
3. Commit your changes.
4. Push to your fork.
5. Open a Pull Request.

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⭐ Support

If you find DataForge useful, please consider giving the project a ⭐ on GitHub.

Repository:

https://github.com/Aatif05-it/DataForge

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📧 Contact

Email:

[e-mail]