# DataGen CLI — Synthetic Dataset Generator
**Created by:** Rishabh Kumar
**Version:** 0.1.3
**License:** Rishabh License --MIT(Non-Commercial) (For commercial use need to contact the developer)
---
## Command to Run
Once installed, simply launch the generator using: datagen
```bash
datagen
## Overview
DataGen CLI is a command-line tool that allows you to generate synthetic datasets with customizable columns, formats, and sizes.
It is designed for developers, data analysts, and machine learning practitioners who need realistic data for testing, demonstrations, or prototyping.
---
## Features
- 50+ predefined column types (customer, sales, weather, system logs, etc.)
- Supports multiple output formats: CSV, Excel (XLSX), and JSON
- Interactive CLI prompts for easy configuration
- Color-coded terminal interface using Rich
- Custom date range support for time-based data
- Lightweight and fast (powered by Typer, Faker, and Pandas)
---
## Installation
Install directly from PyPI (once published):
```bash
pip install datagen-cli
Verify installation:
datagen --help
Quick Start
----- To generate your first dataset, run: datagen
Example Output in Terminal
────────────────────────────────────────────────────────────
DataGen CLI - Synthetic Dataset Generator
────────────────────────────────────────────────────────────
Created by: Rishabh Kumar
Version: 0.1.3
Description: A CLI tool to generate customizable synthetic datasets
────────────────────────────────────────────────────────────
Use 'datagen --help' to see available commands
Tip: Run 'datagen generate' to create your first dataset.
────────────────────────────────────────────────────────────
Example Usage
Step 1: Start the Generator
datagen generate
Step 2: Follow Prompts
vbnet
Enter number of rows to generate: 10000
Enter start date (YYYY-MM-DD): 2022-01-01
Enter end date (YYYY-MM-DD): 2024-12-31
Available Columns:
1. OrderID 2. CustomerName 3. Country 4. Sales 5. Profit ... up to 50
Enter column numbers to include (comma-separated): 1,2,4,5
Enter folder path to save file: ./data/
Enter file name: sample_sales
Choose file type (csv/xlsx/json): csv
Data is generated and saved automatically:
Data generated successfully and saved to: ./data/sample_sales.csv
Supported Column Types
Category Example Columns
Customer Info Name, Email, Gender, Age, Country, City
Sales Data OrderID, Quantity, Sales, Profit, Discount
Employment EmployeeName, Department, JobTitle, Salary
System Logs IP_Address, Browser, DeviceType, LoginTime
Weather Temperature, Humidity, WeatherCondition
Finance AccountBalance, CreditScore, TransactionID
Miscellaneous RandomText, BooleanFlag, Region, State
--- Total Columns: 50 predefined and ready to use.
Technical Details
Component Library
CLI Framework Typer
Terminal UI Rich
Fake Data Faker
Data Handling Pandas
Excel Support OpenPyXL
Developer Information
Author: Rishabh Kumar
Email: rishabh@example.com
GitHub: github.com/rishabhkumar
License
This project is licensed under the MIT License (if not for commercial use).
You are free to use, modify, and distribute this software(non-commercial).
---- For commercial use contact rishabh.contact.info@gmail.com
Contributing
Fork the repository
Create your feature branch (git checkout -b feature/new-feature)
Commit your changes (git commit -m 'Add new feature')
Push to the branch (git push origin feature/new-feature)
Notes--
Good datasets lead to better models.
DataGen CLI helps you build, test, and experiment faster with clean and realistic synthetic data.
Raw data
{
"_id": null,
"home_page": null,
"name": "datagen-cli",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": null,
"keywords": "dataset, data-generator, cli, faker, synthetic-data",
"author": null,
"author_email": "Rishabh Kumar <rishabh.contact.info@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/9e/fe/15d9b1591aad53cd2c0719cda13c57946d04965612b8358ef7ded95c7864/datagen_cli-0.1.3.tar.gz",
"platform": null,
"description": "# DataGen CLI \u2014 Synthetic Dataset Generator\r\n\r\n**Created by:** Rishabh Kumar \r\n**Version:** 0.1.3 \r\n**License:** Rishabh License --MIT(Non-Commercial) (For commercial use need to contact the developer)\r\n\r\n---\r\n## Command to Run\r\n\r\nOnce installed, simply launch the generator using: datagen\r\n\r\n```bash\r\ndatagen\r\n## Overview\r\n\r\nDataGen CLI is a command-line tool that allows you to generate synthetic datasets with customizable columns, formats, and sizes. \r\nIt is designed for developers, data analysts, and machine learning practitioners who need realistic data for testing, demonstrations, or prototyping.\r\n\r\n---\r\n\r\n## Features\r\n\r\n- 50+ predefined column types (customer, sales, weather, system logs, etc.)\r\n- Supports multiple output formats: CSV, Excel (XLSX), and JSON\r\n- Interactive CLI prompts for easy configuration\r\n- Color-coded terminal interface using Rich\r\n- Custom date range support for time-based data\r\n- Lightweight and fast (powered by Typer, Faker, and Pandas)\r\n\r\n---\r\n\r\n## Installation\r\n\r\nInstall directly from PyPI (once published):\r\n\r\n```bash\r\npip install datagen-cli\r\nVerify installation:\r\n\r\n\r\ndatagen --help\r\nQuick Start\r\n\r\n\r\n----- To generate your first dataset, run: datagen\r\n\r\n\r\nExample Output in Terminal\r\n\r\n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\r\n DataGen CLI - Synthetic Dataset Generator\r\n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\r\nCreated by: Rishabh Kumar\r\nVersion: 0.1.3\r\nDescription: A CLI tool to generate customizable synthetic datasets\r\n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\r\nUse 'datagen --help' to see available commands\r\n\r\nTip: Run 'datagen generate' to create your first dataset.\r\n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\r\nExample Usage\r\n\r\nStep 1: Start the Generator\r\n\r\ndatagen generate\r\nStep 2: Follow Prompts\r\nvbnet\r\nEnter number of rows to generate: 10000\r\nEnter start date (YYYY-MM-DD): 2022-01-01\r\nEnter end date (YYYY-MM-DD): 2024-12-31\r\n\r\nAvailable Columns:\r\n1. OrderID 2. CustomerName 3. Country 4. Sales 5. Profit ... up to 50\r\nEnter column numbers to include (comma-separated): 1,2,4,5\r\n\r\nEnter folder path to save file: ./data/\r\nEnter file name: sample_sales\r\nChoose file type (csv/xlsx/json): csv\r\nData is generated and saved automatically:\r\n\r\n\r\nData generated successfully and saved to: ./data/sample_sales.csv\r\nSupported Column Types\r\nCategory\tExample Columns\r\nCustomer Info\tName, Email, Gender, Age, Country, City\r\nSales Data\tOrderID, Quantity, Sales, Profit, Discount\r\nEmployment\tEmployeeName, Department, JobTitle, Salary\r\nSystem Logs\tIP_Address, Browser, DeviceType, LoginTime\r\nWeather\tTemperature, Humidity, WeatherCondition\r\nFinance\tAccountBalance, CreditScore, TransactionID\r\nMiscellaneous\tRandomText, BooleanFlag, Region, State\r\n\r\n--- Total Columns: 50 predefined and ready to use.\r\n\r\nTechnical Details\r\nComponent\tLibrary\r\nCLI Framework\tTyper\r\nTerminal UI\tRich\r\nFake Data\tFaker\r\nData Handling\tPandas\r\nExcel Support\tOpenPyXL\r\n\r\nDeveloper Information\r\nAuthor: Rishabh Kumar\r\nEmail: rishabh@example.com\r\nGitHub: github.com/rishabhkumar\r\n\r\nLicense\r\nThis project is licensed under the MIT License (if not for commercial use).\r\nYou are free to use, modify, and distribute this software(non-commercial).\r\n\r\n---- For commercial use contact rishabh.contact.info@gmail.com\r\n\r\nContributing\r\nFork the repository\r\n\r\nCreate your feature branch (git checkout -b feature/new-feature)\r\n\r\nCommit your changes (git commit -m 'Add new feature')\r\n\r\nPush to the branch (git push origin feature/new-feature)\r\n\r\n\r\nNotes--\r\nGood datasets lead to better models.\r\nDataGen CLI helps you build, test, and experiment faster with clean and realistic synthetic data.\r\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "A colorful and interactive CLI tool to generate customizable synthetic datasets.",
"version": "0.1.3",
"project_urls": {
"Homepage": "https://github.com/Rishabh2728/datagen-cli"
},
"split_keywords": [
"dataset",
" data-generator",
" cli",
" faker",
" synthetic-data"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "b0d84859a884cca5c1a136f049d4f1de38da9ce539a2354dcf3312e1ff384448",
"md5": "4c43e8061f3ff88d15fe059a0436b42e",
"sha256": "63677a2da7cac9c3fe081a2271ddb7a3793d5e4de2f854aacec6db0ec42d4318"
},
"downloads": -1,
"filename": "datagen_cli-0.1.3-py3-none-any.whl",
"has_sig": false,
"md5_digest": "4c43e8061f3ff88d15fe059a0436b42e",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8",
"size": 7036,
"upload_time": "2025-10-30T21:11:06",
"upload_time_iso_8601": "2025-10-30T21:11:06.707972Z",
"url": "https://files.pythonhosted.org/packages/b0/d8/4859a884cca5c1a136f049d4f1de38da9ce539a2354dcf3312e1ff384448/datagen_cli-0.1.3-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "9efe15d9b1591aad53cd2c0719cda13c57946d04965612b8358ef7ded95c7864",
"md5": "ed7e05e1f2e4312cb32b33971516c7d4",
"sha256": "22ad3b613c751a123ba7498e3b5fd0b272c97f5fecf39f9731de445a30dbd74c"
},
"downloads": -1,
"filename": "datagen_cli-0.1.3.tar.gz",
"has_sig": false,
"md5_digest": "ed7e05e1f2e4312cb32b33971516c7d4",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 6314,
"upload_time": "2025-10-30T21:11:08",
"upload_time_iso_8601": "2025-10-30T21:11:08.017020Z",
"url": "https://files.pythonhosted.org/packages/9e/fe/15d9b1591aad53cd2c0719cda13c57946d04965612b8358ef7ded95c7864/datagen_cli-0.1.3.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-10-30 21:11:08",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "Rishabh2728",
"github_project": "datagen-cli",
"github_not_found": true,
"lcname": "datagen-cli"
}
JSON
