datrix 0.1.0

A library to generate realistic dummy datasets.

📊 datrix

datrix is a comprehensive Python library designed to generate realistic dummy datasets across 100+ domains. Whether you need financial data, healthcare records, marketing insights, or geospatial information, Data Pulse provides an easy-to-use interface for creating high-quality sample data.

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🚀 Features

✅ 100+ Dataset Types: Generate a wide variety of datasets, including Financial, Medical, E-commerce, and more.
✅ Realistic Data: Each dataset is populated with realistic values for testing and development.
✅ Fast & Scalable: Generate up to 500,000 records in seconds.
✅ Export Options: Download datasets in CSV, JSON, or other formats.
✅ Customizable: Tailor attributes to your specific needs with ease.

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📦 Installation

You can install datrix directly from PyPI using:

pip install datrix

Ensure Python 3.8 or newer is installed on your system.

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📊 Available Datasets

• Financial Data
• Sales Data
• Medical/Healthcare Data
• Customer Data
• Marketing Data
• E-commerce Data
• Weather Data
• Geospatial Data
• Demographic Data
• Social Media Data
• IoT (Internet of Things) Data
• Supply Chain Data
• Government/Public Sector Data
• Transportation Data
• Educational Data
• Manufacturing Data
• Energy Consumption Data
• Telecommunication Data
• Environmental Data
• Real Estate Data
• Cybersecurity Data
• Human Resources (HR) Data
• Insurance Data
• Logistics Data
• Sports Data
• Textual/NLP Data
• Image Data
• Video Data
• Audio Data
• Retail Data
• Survey Data
• Sentiment Analysis Data
• Biometric Data
• Genomic Data
• Banking Transaction Data
• Agricultural Data
• Tourism and Hospitality Data
• Consumer Behavior Data
• Cryptocurrency Data
• Air Quality Data
• Automotive Data
• Construction Data
• Event Data
• Satellite Data
• Climate Data
• Patent Data
• Credit Scoring Data
• Fraud Detection Data
• Pharmaceutical Data
• Political Data
• Wildlife Data
• Linguistic Data
• Census Data
• Electronic Health Record (EHR) Data
• Emergency Services Data
• Fisheries Data
• Disaster Response Data
• Food and Nutrition Data
• Space Science Data
• Cultural Heritage Data
• Media Consumption Data
• Voting and Election Data
• Academic Research Data
• Financial Market Data
• Water Resource Data
• Traffic and Mobility Data
• Historical Data
• Clinical Trial Data
• Renewable Energy Data
• Drug Development Data
• Software Usage Data
• Smart City Data
• Behavioral Data
• Airline Data
• Social Services Data
• Import/Export Data
• Digital Marketing Data
• Public Health Data
• Cyber Threat Intelligence Data
• Pension and Retirement Data
• Investment Data
• Consumer Product Data
• Wildlife Conservation Data
• IT Infrastructure Data
• Biomedical Imaging Data
• Livestock Data
• Food Supply Chain Data
• Online Learning Data
• Maritime Data
• Public Transportation Data
• Traffic Accident Data
• Copyright and Intellectual Property Data
• Urban Development Data
• Financial Crime Data
• Population Health Data
• Asset Management Data
• Gaming Data
• Call Center Data
• Legal Compliance Data

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🧑‍💻 Usage

Here's how to generate and download a dataset:

from datrix import generate_sales_data

data = generate_sales_data(num_records=100)
print(data.head())

data.to_csv("sales_data.csv", index=False)

You can switch datasets by importing the corresponding generator function (e.g., generate_financial_data, generate_medical_data).

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📂 Example API Endpoint (Using Flask)

from flask import Flask, send_file
import io
from genflow import generate_financial_data

app = Flask(__name__)

@app.route('/download_financial_data')
def download_financial_data():
    df = generate_financial_data(10000)
    output = io.BytesIO()
    df.to_csv(output, index=False)
    output.seek(0)
    return send_file(output, mimetype='text/csv', as_attachment=True, download_name='financial_data.csv')

if __name__ == '__main__':
    app.run(debug=True)

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📘 Documentation

For detailed usage instructions and examples, check the official documentation:
👉 genflow Documentation

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🤝 Contributing

Contributions are welcome! If you want to add new datasets or improve existing ones:

1. Fork the repository
2. Create a new branch
3. Submit a pull request

Feel free to open issues for bug reports and feature requests.

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📜 License

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

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📧 Contact

If you have questions or feedback, feel free to reach out:

• Email: [e-mail]
• GitHub: yourusername