structured-data-generator 1.0.0

A powerful CLI tool to generate realistic synthetic data for testing and development

📊 Synthetic Data Generator - Complete Documentation

A simple, powerful CLI tool to generate realistic fake data for testing, development, and demonstrations.

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📋 Quick Navigation

1. What is This?
2. Installation
3. Getting Started
4. How It Works
5. Available Templates
6. Step-by-Step Guide
7. Export Options
8. Troubleshooting
9. FAQ

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What is This?

The Synthetic Data Generator is a command-line tool that creates realistic fake data for different scenarios:

🎯 What Can You Do?

✅ Generate fake user profiles (names, emails, addresses)
✅ Create e-commerce transaction records
✅ Generate banking & financial data
✅ Create healthcare patient records
✅ Generate IoT sensor readings
✅ Create product catalogs
✅ Generate NLP text data
✅ Create web analytics data
✅ Generate image metadata
✅ Create student academic records

🤔 Why Use This?

• Test your application with realistic data without using real customer data
• Database training - populate your database for demos
• Development work - test features with varied data
• Privacy safe - all data is fake and synthetic
• Customize data - select only the fields you need

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Installation

📦 What You Need

• Python 3.7+ installed on your computer
• pip (package manager for Python)

🔧 Step 1: Install Required Libraries

Open your terminal/command prompt and run:

pip install pandas faker

This installs:

• pandas - for working with data tables
• faker - for generating realistic fake data

🔧 Step 2: Download the Files

Make sure you have these files in the same folder:

• CLI.py (the main program)
• DATA_ENGINE.py (all data templates)

✅ Step 3: Verify Installation

Run this command to check if everything is set up:

python CLI.py

You should see the menu appear. If yes, you're ready to go! 🎉

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Getting Started

⚡ Quick Start (30 seconds)

# 1. Open terminal/command prompt
# 2. Go to your project folder
cd /path/to/your/project

# 3. Run the program
python CLI.py

# 4. Follow the menu

That's it! The program will guide you through everything.

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How It Works

🔄 The Process (Simple Explanation)

Step 1: You choose a TEMPLATE (what kind of data you want)
           ↓
Step 2: You choose SUBCATEGORIES (which fields from that template)
           ↓
Step 3: You enter COUNT (how many rows of data)
           ↓
Step 4: Program generates the fake data
           ↓
Step 5: You save it as CSV or JSON

📸 Example Workflow

[INSERT SCREENSHOT: Full CLI menu here]

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Available Templates

1️⃣ USER TEMPLATE

Generate fake user/customer profiles.

What data you get:

• Full name, username, email, phone
• Address (street, city, state, country)
• Account info (creation date, status)
• Preferences (language, currency)
• Device info (mobile/desktop/tablet)

Use cases: Customer databases, user profiles, testing auth systems

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2️⃣ E-COMMERCE TEMPLATE

Generate fake online shopping transactions.

What data you get:

• Order ID, transaction date, order status
• Customer info (name, email, phone)
• Product info (name, category, quantity, price)
• Payment details (method, status, transaction ID)
• Shipping info (address, partner, status)
• Device info

Use cases: E-commerce websites, order management systems, payment testing

[INSERT SCREENSHOT: Sample generated e-commerce data]

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3️⃣ FINANCIAL BANKING TEMPLATE

Generate fake banking transactions.

What data you get:

• Account info (ID, type, bank name, IFSC)
• Transaction details (ID, date, type, amount, status)
• Customer info (name, email, PAN number, Aadhaar)
• Card details (type, network, last 4 digits)
• Loan info (type, amount, interest rate, EMI)
• Device info

Use cases: Banking apps, financial dashboards, transaction analytics

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4️⃣ HEALTHCARE TEMPLATE

Generate fake medical records.

What data you get:

• Patient info (ID, name, age, gender, blood group)
• Medical records (diagnosis, symptoms, severity)
• Doctor info (name, specialization, hospital)
• Appointment details (date, status, type)
• Prescription info (medicine, dosage, duration)
• Billing info (consultation fee, medicine charges)
• Device info

Use cases: Hospital systems, telemedicine apps, medical records

[INSERT SCREENSHOT: Sample healthcare data]

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5️⃣ IOT SENSOR TEMPLATE

Generate fake IoT sensor readings.

What data you get:

• Device info (ID, type, firmware version)
• Location data (latitude, longitude, altitude)
• Sensor readings (temperature, humidity, air quality)
• Network data (signal strength, connection type, IP)
• Battery info (level, health, charging status)
• Timestamp & maintenance info

Use cases: IoT dashboards, sensor data analysis, smart home testing

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6️⃣ NLP TEXT TEMPLATE

Generate fake text documents and NLP data.

What data you get:

• Text data (sentences, paragraphs, words, keywords)
• Document metadata (title, author, published year)
• NLP annotations (language, sentiment, emotion, toxicity)
• Named Entity Recognition (person, location, organization)
• Text statistics (word count, character count, avg word length)
• Timestamps

Use cases: NLP model testing, text analysis, chatbot training

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7️⃣ WEB ANALYTICS TEMPLATE

Generate fake website analytics data.

What data you get:

• Session info (session ID, user ID, duration, engagement score)
• Page metrics (URL, time on page, scroll depth, interactions)
• Traffic source (source, medium, campaign, keyword)
• Device & browser info
• Geo data (IP, country, city, timezone)
• Performance data (page load time, DNS lookup, resource count)

Use cases: Analytics dashboards, website performance testing, traffic analysis

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8️⃣ IMAGE METADATA TEMPLATE

Generate fake image metadata.

What data you get:

• Basic info (filename, format, file size, color mode)
• Dimensions (width, height, aspect ratio, DPI)
• Camera EXIF (camera make, lens, focal length, aperture, ISO)
• Geolocation (latitude, longitude, city, country)
• Tags & labels (primary label, confidence score)
• Color stats (dominant color, brightness, contrast)

Use cases: Image database testing, photo library apps, image analysis

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9️⃣ EDUCATION STUDENT TEMPLATE

Generate fake student academic records.

What data you get:

• Student profile (ID, name, age, grade level)
• Academic scores (math, science, English, social science, computer)
• Attendance (total classes, classes attended, percentage)
• Behavior & activity (disciplinary actions, sports score, creativity)
• Performance metrics (study hours, homework completion, participation)

Use cases: School management systems, student performance dashboards

[INSERT SCREENSHOT: Sample student data]

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🔟 PRODUCT CATALOG TEMPLATE

Generate fake product listings.

What data you get:

• Basic info (product ID, name, category, brand, description)
• Variants (color, size, material, model number)
• Pricing (price, discount, final price, currency)
• Inventory (stock status, quantity, warehouse location)
• Ratings & reviews (average rating, total reviews, review text)
• Timestamps (release date, last updated)

Use cases: E-commerce catalogs, product databases, inventory management

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Step-by-Step Guide

🚀 Complete Tutorial

Step 1: Start the Program

python CLI.py

You'll see:

-----------------------------
🔥 SYNTHETIC DATA GENERATOR 🔥
-----------------------------

Available Templates:

1. USER TEMPLATE

2. ECOMMERCE TRANSACTION TEMPLATE

3. FINANCIAL BANKING TEMPLATE

... (and more)

[INSERT SCREENSHOT: Initial menu]

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Step 2: Select a Template

When asked:

Select a Template (number): 

Type the number of the template you want. For example:

• Type 1 for User Template
• Type 2 for E-commerce Template

Example:

Select a Template (number): 2

[INSERT SCREENSHOT: After selecting template]

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Step 3: Choose Subcategories

After selecting, you'll see available subcategories:

Available Subcategories:

1. Order Info
2. Customer Info
3. Product Info
4. Payment Info
5. Shipping Info
6. Device Info

Enter Subcategories (comma-separated numbers):

Simple explanation: Each template has groups of related data fields.

Example:

Enter Subcategories (comma-separated numbers): 1,2,3

This means: "I want Order Info, Customer Info, and Product Info"

💡 Pro Tip:

• Type 1,2,3,4,5,6 for ALL fields
• Type 1,3,5 for only some fields
• Pick only what you need!

[INSERT SCREENSHOT: Subcategory selection]

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Step 4: Enter Number of Rows

Enter number of rows to generate: 

Simple: How many fake records do you want?

Examples:

Enter number of rows to generate: 100

Creates 100 rows of fake data.

Timing guide:

• 100 rows = ~1-2 seconds
• 1,000 rows = ~5-10 seconds
• 10,000 rows = ~30-60 seconds

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Step 5: Optional Seed (Skip if Unsure)

Enter Seed (press Enter to skip):

What is a seed? It makes the data reproducible (same fake data every time).

Simple usage:

• Skip it (press Enter) = Different data each time
• Enter a number (like 42) = Same data every time with that number

Example:

Enter Seed (press Enter to skip): 42

[INSERT SCREENSHOT: Seed option]

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Step 6: Data Generated!

You'll see:

✅ DATA GENERATED SUCCESSFULLY!
==============================
 SUMMARY 

* Template: ECOMMERCE TRANSACTION TEMPLATE
⏱ Time Taken: 2.45 seconds
* Rows Generated: 100
* Columns Generated: 18
* Seed: 42
==============================

Great! Your data is ready.

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Step 7: Choose Export Option

Select what you want to do next:

1. Preview data (first 5 rows)
2. Save as CSV
3. Save as JSON
4. Exit

Enter options (comma-separated):

You can pick multiple options!

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Export Options

📥 Option 1: Preview Data

What it does: Shows you the first 5 rows in your terminal.

Use case: Quick check before saving.

Example output:

S.no. Order ID    Customer Name Customer Email        Total Amount
1     a1b2c3d4   John Doe      [e-mail]      15,500
2     e5f6g7h8   Jane Smith    [e-mail]      8,200
3     i9j0k1l2   Bob Johnson   [e-mail]       25,000
...

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💾 Option 2: Save as CSV

What it does: Saves data as an Excel-compatible CSV file.

How to use:

Enter CSV file name: my_orders

This creates: my_orders.csv

Open it:

• Double-click to open in Excel/Sheets
• Use in Python with: pd.read_csv('my_orders.csv')

When to use:

• ✅ For Excel/spreadsheet work
• ✅ For uploading to most tools
• ✅ For data analysis in Excel

[INSERT SCREENSHOT: CSV file in Excel]

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🔗 Option 3: Save as JSON

What it does: Saves data as JSON format (web-friendly).

How to use:

Enter JSON file name: my_orders

This creates: my_orders.json

Example JSON output:

[
  {
    "S.no.": 1,
    "Order ID": "a1b2c3d4",
    "Customer Name": "John Doe",
    "Customer Email": "[e-mail]",
    "Total Amount": 15500
  },
  {
    "S.no.": 2,
    "Order ID": "e5f6g7h8",
    ...
  }
]

When to use:

• ✅ For APIs and web services
• ✅ For Python/JavaScript applications
• ✅ For database imports

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🚪 Option 4: Exit

Just closes the program. Simple!

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Complete Examples

🎓 Example 1: Generate User Profiles

Goal: Create 50 fake user profiles for testing

Steps:

1. Run: python CLI.py
2. Select Template: 1 (USER TEMPLATE)
3. Select Subcategories: 1,2,3,4 (Personal, Address, Account, Preferences)
4. Count: 50
5. Seed: (skip)
6. Export: 2 (Save as CSV)
7. Filename: user_profiles

Result: user_profiles.csv with 50 fake users ✅

[INSERT SCREENSHOT: User data sample]

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🛒 Example 2: E-commerce Testing

Goal: Create 100 fake orders for testing checkout system

Steps:

1. Run: python CLI.py
2. Select Template: 2 (ECOMMERCE TRANSACTION TEMPLATE)
3. Select Subcategories: 1,2,3,4,5 (Order, Customer, Product, Payment, Shipping)
4. Count: 100
5. Seed: 123 (reproducible)
6. Export: 1,2 (Preview + Save as CSV)
7. Filename: test_orders

Result: Preview in terminal + test_orders.csv ✅

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🏥 Example 3: Healthcare Testing

Goal: Generate 200 patient records for testing medical software

Steps:

1. Run: python CLI.py
2. Select Template: 4 (HEALTHCARE TEMPLATE)
3. Select Subcategories: 1,2,3,4 (Patient, Medical, Doctor, Appointment)
4. Count: 200
5. Seed: (skip)
6. Export: 3 (Save as JSON)
7. Filename: patient_records

Result: patient_records.json with 200 patients ✅

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Troubleshooting

❌ Problem: "ModuleNotFoundError: No module named 'pandas'"

Solution:

pip install pandas faker

Then try again.

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❌ Problem: Command "python" not recognized

Solution:

• You might need to use python3 instead:

python3 CLI.py

Or on Windows, use full path to Python.

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❌ Problem: "Invalid template selection"

Reason: You entered a wrong number or non-existent template.

Solution:

• Look at the menu carefully
• Enter a number that's listed (usually 1-10)
• Don't enter letters or special characters

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❌ Problem: Data generation is very slow

Why: You're generating too many rows at once.

Solution:

• Try smaller numbers first: 10, 50, 100
• Large datasets (100,000+) will take time
• Be patient! It's still much faster than manual entry

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❌ Problem: CSV file won't open in Excel

Reason: File might be corrupted or encoding issue.

Solution:

# Regenerate the data
python CLI.py

# Or check if file was saved correctly
# Try opening with a text editor first

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❌ Problem: Subcategory selection fails

Reason: You entered numbers that don't exist or wrong format.

Solution:

• Separate numbers with commas: 1,2,3 ✅
• Don't use spaces: 1, 2, 3 ❌
• Check max number shown in menu

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FAQ

❓ Is this data real?

No! All data is 100% fake and synthetic. It's safe to use anywhere.

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❓ Can I use this for production?

No. This is for testing and development only. Never use fake data in real production systems for customers.

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❓ Can I modify the templates?

Yes! The templates are in DATA_ENGINE.py. You can edit them to add custom fields.

(Advanced topic - ask me if interested!)

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❓ How is fake data generated?

Using a library called Faker which creates realistic-looking but fake data using algorithms.

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❓ Why choose JSON over CSV?

CSV: Better for Excel, spreadsheets, simple analysis JSON: Better for web APIs, web applications, databases

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❓ Can I generate millions of rows?

Technically yes, but:

• Will take time (depends on your computer)
• Might use lots of memory
• Better to generate in batches (100K at a time)

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❓ What if I want different data fields?

You have two options:

Option 1: Use only the subcategories you need (select specific fields)

Option 2: Modify DATA_ENGINE.py to customize templates

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❓ Can I use this offline?

Yes! No internet needed once installed. Everything runs on your computer.

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Need Help?

📞 Common Issues Checklist

• Did I install pandas and faker?
• Are CLI.py and DATA_ENGINE.py in same folder?
• Did I enter valid template number?
• Did I enter subcategories correctly (comma-separated)?
• Did I enter a valid count (number)?
• Did I check the error message for clues?

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Tips & Tricks

💡 Pro Tips

1. Test with small data first

   # Generate 10 rows first to test
   # If it works, generate 1000
   

2. Use seeds for consistent testing

   # Same seed = same data every time
   # Good for reproducible tests
   

3. Preview before saving large files

   # Select option 1 first
   # Check if data looks correct
   # Then save as CSV/JSON
   

4. Combine templates

   # Generate users (Template 1)
   # Generate orders (Template 2)
   # Connect them manually for realistic data
   

5. Clean up old files

   # Delete old CSV/JSON files before regenerating
   # Keeps your folder organized
   

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Advanced: Customize Templates

(For experienced Python users)

To add custom fields to a template, edit DATA_ENGINE.py:

"New Field": lambda: fake.company(),  # Uses Faker
"Age": lambda: random.randint(18, 65),  # Random number
"ID": lambda: str(uuid.uuid4()),  # Unique ID

Then select it in CLI!

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Architecture Overview

┌─────────────────────────────────────────┐
│         CLI.py (Main Interface)         │
│  - Menu display                         │
│  - User input                           │
│  - Export options                       │
└──────────────────┬──────────────────────┘
                   │
                   ▼
┌─────────────────────────────────────────┐
│    DATA_ENGINE.py (All Templates)       │
│  - USER_TEMPLATE                        │
│  - ECOM_TEMPLATE                        │
│  - FINANCIAL_TEMPLATE                   │
│  - HEALTHCARE_TEMPLATE                  │
│  - IOT_SENSOR_TEMPLATE                  │
│  - ... (7 more templates)               │
│  - generate_from_template() function    │
└──────────────────┬──────────────────────┘
                   │
                   ▼
┌─────────────────────────────────────────┐
│    External Libraries                   │
│  - Faker (generates fake data)          │
│  - Pandas (data processing)             │
│  - Random (random selections)           │
│  - UUID (unique IDs)                    │
└─────────────────────────────────────────┘

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File Structure

your_project_folder/
├── CLI.py                 ← Run this file
├── DATA_ENGINE.py         ← All templates here
├── user_profiles.csv      ← Generated file (example)
├── test_orders.json       ← Generated file (example)
└── README.md             ← This documentation

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Version Info

Current Version: 1.0
Last Updated: December 2025
Python Required: 3.7+

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Support & Feedback

Having issues?

1. Check troubleshooting section
2. Check FAQ
3. Review examples
4. Check file structure

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License

This tool is free to use for personal, educational, and testing purposes.

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Happy data generating! 🚀

Made with ❤️ for developers, testers, and data enthusiasts.