🎲 Iki Data Generator
Generate realistic, diverse synthetic data with 700+ built-in fields across 22 categories. Perfect for testing, development, and prototyping — without the legal baggage of real data.
What Is This?
Iki Data Generator is a Python library that creates synthetic datasets on demand. Instead of wrestling with dummy data or copy-pasting fake records, you define a schema (which fields you want), call .many(n) to generate n records, and export them to CSV, JSON, SQL, Excel, Parquet, or 10+ other formats. That's it.
It's built for developers who need:
- Test data for unit/integration tests
- Demo data for presentations or prototypes
- Mock databases for local development
- Privacy-friendly datasets with realistic properties but zero personal info
- Performance testing with large datasets
Why Use Iki Data Generator?
✅ You Get
| Benefit | What It Means |
|---|---|
| 700+ Fields | First name, email, credit card, medical codes, stock prices, cryptocurrencies, ML metrics, etc. |
| 22 Categories | Personal, Finance, Commerce, Healthcare, Location, Education, Legal, AI/ML, and more |
| Easy Schema | Simple string shortcuts or full control with dicts |
| Flexible Export | CSV, JSON, SQL, Parquet, DuckDB, Excel, XML, TSV, Firebase, more |
| Zero Dependencies on Real Data | No need to anonymize or worry about PII |
| Blazing Fast | Generates thousands of records instantly |
| Extensible | Add custom providers for domain-specific fields |
❌ You Don't Get
- No real person's data
- No need for data anonymization lawyers
- No internet calls to fake APIs
- No massive CSV files to download and commit
Installation
From PyPI (recommended)
pip install iki-data-generator
From Source
git clone https://github.com/ikidevz/IkiDataGenerator.git
cd Iki-Data-Generator
pip install -e .
Requirements
- Python ≥ 3.10
- Dependencies: duckdb, pandas, pyarrow, numpy, openpyxl, bcrypt, and a few others (installed automatically)
Quick Start (60 Seconds)
The Simplest Example
from ikidatagen import IkiDataGenerator
# Define what fields you want
schema = ["first_name", "last_name", "email_address", "gender_binary"]
# Generate 100 records
data = IkiDataGenerator(schema).many(100).export("users")
Result: You now have output/users.csv and output/users.json with 100 realistic user records.
A More Realistic Example
from ikidatagen import IkiDataGenerator
schema = [
{
"label": "User ID",
"key_label": "row_number",
"options": {"blank_percentage": 0} # No blanks for ID
},
"first_name",
"last_name",
"email_address",
{
"label": "Account Created",
"key_label": "current_timestamp",
"options": {"blank_percentage": 5} # 5% will be blank
},
{
"label": "IP Address",
"key_label": "ip_address_v4",
"options": {"blank_percentage": 25} # 25% will be blank
},
{
"label": "Full Profile",
"key_label": "template",
"options": {
"template": "{{first_name}} {{last_name}} ({{email_address}})"
}
},
]
# Generate 500 records and save to both CSV and JSON
IkiDataGenerator(schema).many(500).export("users", formats=["csv", "json"])
Result: output/users.csv and output/users.json with 500 complete user records, ready to use.
Common Use Cases
1. E-Commerce Platform Testing
Generate realistic customer orders, inventory, and transactions:
from ikidatagen import IkiDataGenerator
ecommerce_schema = [
"row_number", # Order ID
"current_timestamp", # Order date
"first_name",
"email_address",
{"key_label": "product_name", "label": "Product"},
{"key_label": "product_price", "label": "Price", "options": {"min": 9.99, "max": 299.99}},
{"key_label": "discount_percentage", "label": "Discount %", "options": {"blank_percentage": 70}},
{"key_label": "order_status", "label": "Order Status"},
{"key_label": "payment_method", "label": "Payment Method"},
{"key_label": "shipping_method", "label": "Shipping"},
{"key_label": "delivery_status", "label": "Status"},
]
generator = IkiDataGenerator(ecommerce_schema)
orders_data = generator.many(10000).export("ecommerce_orders")
2. Healthcare Records System
Generate HIPAA-safe test data for medical applications:
healthcare_schema = [
{"key_label": "row_number", "label": "Patient ID", "options": {"blank_percentage": 0}},
"first_name",
"last_name",
"email_address",
"phone_number",
{"key_label": "date_of_birth", "label": "DOB", "options": {"from_date": "1950-01-01", "to_date": "2010-12-31"}},
{"key_label": "blood_type", "label": "Blood Type"},
{"key_label": "icd10_code", "label": "Diagnosis Code"},
{"key_label": "medication_generic_name", "label": "Medication"},
{"key_label": "vital_signs_blood_pressure", "label": "BP"},
{"key_label": "lab_result_name", "label": "Lab Test"},
{"key_label": "insurance_plan_name", "label": "Insurance"},
{"key_label": "current_timestamp", "label": "Visit Date"},
]
generator = IkiDataGenerator(healthcare_schema)
patients = generator.many(5000).export("patient_records")
3. Financial Data for Testing
Generate bank accounts, transactions, and investment data:
finance_schema = [
"row_number",
"first_name",
"last_name",
{"key_label": "account_number", "label": "Account #"},
{"key_label": "iban", "label": "IBAN"},
{"key_label": "credit_card_number", "label": "Card #"},
{"key_label": "credit_card_type", "label": "Card Type"},
{"key_label": "money", "label": "Balance", "options": {"min": 100, "max": 50000, "currency": "USD"}},
{"key_label": "transaction_amount", "label": "Transaction", "options": {"min": 0.01, "max": 5000}},
{"key_label": "transaction_type", "label": "Type"},
{"key_label": "stock_symbol", "label": "Stock"},
{"key_label": "cryptocurrency_name", "label": "Crypto"},
]
generator = IkiDataGenerator(finance_schema)
transactions = generator.many(50000).export("financial_data")
4. Educational Institution Data
Generate student and course enrollment data:
education_schema = [
{"key_label": "student_id", "label": "Student ID", "options": {"blank_percentage": 0}},
"first_name",
"last_name",
"email_address",
{"key_label": "major", "label": "Major"},
{"key_label": "gpa", "label": "GPA", "options": {"min": 0.0, "max": 4.0}},
{"key_label": "class_year", "label": "Year"},
{"key_label": "enrollment_status", "label": "Status"},
{"key_label": "course_name", "label": "Course"},
{"key_label": "grade", "label": "Grade"},
{"key_label": "date_of_birth", "label": "DOB"},
]
generator = IkiDataGenerator(education_schema)
students = generator.many(2000).export("student_data")
5. API Response Mocking
Generate realistic API response data:
api_schema = [
{"key_label": "uuid", "label": "id"},
"first_name",
"last_name",
"email_address",
{"key_label": "current_timestamp", "label": "created_at"},
{"key_label": "current_timestamp", "label": "updated_at"},
{"key_label": "json_web_token", "label": "auth_token"},
{"key_label": "boolean", "label": "is_active"},
]
generator = IkiDataGenerator(api_schema)
api_data = generator.many(100).export("api_response", formats=["json"])
Schema Definition
The schema is the heart of Iki Data Generator. It tells the library what fields to generate.
Schema Entry Types
1. Simple String (Shorthand)
schema = ["first_name", "last_name", "email_address"]
# Generates fields with default settings, no options
2. Full Control (Dict)
schema = [
{
"key_label": "email_address", # Required: which provider to use
"label": "Email", # Optional: output column name (defaults to key_label)
"group": "personal", # Optional: provider category (auto-resolved if omitted)
"options": {"blank_percentage": 10} # Optional: provider-specific config
}
]
Key Parameters
| Parameter | Required? | Description |
|---|---|---|
| key_label | ✅ Yes | The provider name (e.g., first_name, credit_card_number) |
| label | ❌ No | How to name the output column (defaults to key_label) |
| group | ❌ No | Provider category (auto-resolved from registry; override if needed) |
| options | ❌ No | Provider-specific settings (e.g., blank_percentage, template) |
Available Options (Common & Provider-Specific)
Universal Options (All Providers)
| Option | Type | Default | Range | Example | Effect |
|---|---|---|---|---|---|
| blank_percentage | int/float | 0 | 0-100 | 10 |
Percentage of records where field is empty |
Numeric Provider Options
# For: number, money, age, gpa, rating, price, quantity
schema = [
{
"key_label": "number",
"options": {
"blank_percentage": 5, # Optional: % blanks
"min": 0, # Optional: minimum value
"max": 100, # Optional: maximum value
"decimals": 2 # Optional: decimal places
}
}
]
| Option | Type | Default | Range | Effect |
|---|---|---|---|---|
| min | int/float | 0 | Any | Minimum value generated |
| max | int/float | 100 | Any | Maximum value generated |
| decimals | int | 0 | 0-10 | Number of decimal places |
Date & Time Provider Options
# For: date_time, date_of_birth, timestamp, current_timestamp
schema = [
{
"key_label": "date_time",
"options": {
"blank_percentage": 5,
"from_date": "2020-01-01", # Optional: start date
"to_date": "2025-12-31", # Optional: end date
"date_format": "%Y-%m-%d", # Optional: output format
"minimum_age": 18, # Optional: minimum age (instead of from_date)
"maximum_age": 65 # Optional: maximum age (instead of to_date)
}
}
]
| Option | Type | Example | Effect |
|---|---|---|---|
| from_date | str | "2020-01-01" |
Start date (YYYY-MM-DD) |
| to_date | str | "2025-12-31" |
End date (YYYY-MM-DD) |
| date_format | str | "%Y-%m-%d" or "%m/%d/%Y" |
Python datetime format |
| minimum_age | int | 18 |
Minimum age in years |
| maximum_age | int | 65 |
Maximum age in years |
Common Date Format Codes:
%Y → 4-digit year (2024)
%y → 2-digit year (24)
%m → Month as number (01-12)
%d → Day of month (01-31)
%H → Hour (00-23)
%M → Minute (00-59)
%S → Second (00-59)
%A → Full weekday (Monday)
%a → Abbreviated weekday (Mon)
%B → Full month (January)
%b → Abbreviated month (Jan)
String Template Provider Options
# For: template
schema = [
{
"key_label": "template",
"options": {
"blank_percentage": 5,
"template": "{{first_name}} {{last_name}} ({{email_address}})"
}
}
]
| Option | Type | Effect |
|---|---|---|
| template | str | Template with {{field_name}} placeholders |
Template Examples:
"{{first_name}} {{last_name}}" # John Smith
"{{last_name}}, {{first_name}}" # Smith, John
"{{email_address}} | {{phone_number}}" # john@ex.com | 555-0123
"{{street_address}}, {{city}}, {{state}}" # 123 Main St, Springfield, IL
"[{{job_title}}] at {{company_name}}" # [Manager] at Acme Corp
Regular Expression Provider Options
# For: regular_expression
schema = [
{
"key_label": "regular_expression",
"options": {
"blank_percentage": 5,
"format": "[A-Z]{3}-\\d{5}" # Regex pattern
}
}
]
| Option | Type | Effect |
|---|---|---|
| format | str | Regular expression pattern |
Common Regex Patterns:
"[A-Z]{3}-\\d{4}" # 3 letters, dash, 4 digits: ABC-1234
"\\(\\d{3}\\) \\d{3}-\\d{4}" # Phone: (123) 456-7890
"[A-Z]{2}\\d{6}" # 2 letters + 6 digits: AB123456
"[A-Za-z0-9]{8}-[A-Za-z0-9]{4}" # ID-like: aBcD1234-xYz0
"[0-9]{3}-[0-9]{2}-[0-9]{4}" # SSN: 123-45-6789
Password Provider Options
# For: password
schema = [
{
"key_label": "password",
"options": {
"blank_percentage": 0,
"min_length": 12, # Minimum length
"upper_num": 2, # Uppercase letters required
"lower_num": 2, # Lowercase letters required
"numbers_num": 2, # Digits required
"symbols_num": 2 # Special chars required
}
}
]
| Option | Type | Default | Effect |
|---|---|---|---|
| min_length | int | 8 | Minimum password length |
| upper_num | int | 0 | Uppercase letters |
| lower_num | int | 0 | Lowercase letters |
| numbers_num | int | 0 | Digits |
| symbols_num | int | 0 | Special symbols |
Money Provider Options
# For: money
schema = [
{
"key_label": "money",
"options": {
"blank_percentage": 5,
"min": 10, # Minimum amount
"max": 10000, # Maximum amount
"currency": "USD" # Currency code or name
}
}
]
| Option | Type | Example | Effect |
|---|---|---|---|
| min | float | 10 |
Minimum amount |
| max | float | 10000 |
Maximum amount |
| currency | str | "USD", "EUR", "GBP", "JPY" |
Currency format |
Supported Currencies:
USD → $1,234.56
EUR → €1.234,56
GBP → £1,234.56
JPY → ¥123,456
CHF → CHF 1,234.56
CAD → C$1,234.56
AUD → A$1,234.56
INR → ₹1,23,456
Custom List Provider Options
# For: custom_list
schema = [
{
"key_label": "custom_list",
"options": {
"blank_percentage": 5,
"values": ["Option A", "Option B", "Option C"]
# OR: "values": "Option A,Option B,Option C"
}
}
]
| Option | Type | Format | Effect |
|---|---|---|---|
| values | list|str | ["a", "b"] or "a,b,c" |
List of choices |
Examples:
# As list
"values": ["Low", "Medium", "High", "Urgent"]
# As comma-separated string
"values": "Draft,Review,Approved,Published"
# With special characters
"values": ["Yes ✓", "No ✗", "Maybe ?"]
Lambda Provider Options
# For: lambda (custom functions)
import random
schema = [
{
"key_label": "lambda",
"options": {
"blank_percentage": 5,
"function": lambda: f"ID-{random.randint(100000, 999999)}"
}
}
]
| Option | Type | Effect |
|---|---|---|
| function | callable | Python function that returns a value |
Examples:
# Simple random choice
"function": lambda: random.choice(["A", "B", "C"])
# Random number with prefix
"function": lambda: f"ORD-{random.randint(1000, 9999)}"
# Timestamp-based
"function": lambda: datetime.now().isoformat()
# Complex logic
"function": lambda: "Active" if random.random() > 0.5 else "Inactive"
22 Data Categories
Iki Data Generator organizes 700+ fields into 22 categories. Here's a quick overview and complete reference of all providers with detailed parameters and options.
Complete Provider Reference
This section documents all 700+ fields organized by category, with detailed parameter information for each provider.
🧑 Personal Category (65+ providers)
Generate names, identity documents, demographics, and personal information.
Core Identity Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
first_name |
Random male/female first names | blank_percentage (0-100) |
"John" |
first_name_male |
Male-specific first names | blank_percentage |
"Michael" |
first_name_female |
Female-specific first names | blank_percentage |
"Sarah" |
last_name |
Random surnames | blank_percentage |
"Smith" |
full_name |
Complete first + last name | blank_percentage |
"John Smith" |
middle_name |
Middle name generation | blank_percentage |
"James" |
title |
Professional titles | blank_percentage |
"Dr.", "Mr.", "Ms." |
suffix |
Name suffixes | blank_percentage |
"Jr.", "Sr.", "III" |
gender_binary |
Binary gender (M/F) | blank_percentage |
"M" or "F" |
gender_spectrum |
Extended gender options | blank_percentage |
"Non-binary", "Other" |
gender_abbrev |
Gender abbreviations | blank_percentage |
"M", "F", "O" |
gender_facebook |
Facebook gender options | blank_percentage |
Various gender identities |
Identity & Document Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
ssn |
Social Security Numbers (US) | blank_percentage |
"123-45-6789" |
passport_number |
International passport IDs | blank_percentage |
"123456789" |
nationality |
Nationality/Country names | blank_percentage |
"American", "French" |
language |
Language names | blank_percentage |
"English", "Spanish" |
language_code |
ISO 639-1 language codes | blank_percentage |
"en", "es", "fr" |
Demographics & Status Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
age_group |
Age ranges | blank_percentage |
"18-25", "35-45" |
marital_status |
Relationship status | blank_percentage |
"Single", "Married", "Divorced" |
employment_status |
Job status | blank_percentage |
"Employed", "Unemployed", "Retired" |
education_level |
Educational attainment | blank_percentage |
"High School", "Bachelor's", "PhD" |
military_rank |
Military ranks | blank_percentage |
"Private", "Colonel" |
life_stage |
Life phases | blank_percentage |
"Student", "Professional", "Retired" |
relationship_type |
Relationship categories | blank_percentage |
"Spouse", "Parent", "Sibling" |
Appearance & Characteristics Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
hair_color |
Hair colors | blank_percentage |
"Brown", "Blonde", "Black" |
race |
Ethnic background | blank_percentage |
"Caucasian", "Asian" |
shirt_size |
Clothing sizes | blank_percentage |
"S", "M", "L", "XL" |
shoe_size |
Shoe sizing | blank_percentage |
"8", "10.5" |
Professional & Interest Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
job_title |
Job positions | blank_percentage |
"Software Engineer", "Manager" |
occupation |
Occupational roles | blank_percentage |
"Teacher", "Nurse" |
role |
Generic role names | blank_percentage |
"Admin", "Viewer", "Editor" |
industry |
Business industries | blank_percentage |
"Technology", "Healthcare" |
department_corporate |
Corporate departments | blank_percentage |
"Engineering", "Sales" |
linkedin_skill |
Professional skills | blank_percentage |
"Python", "Project Management" |
company_name |
Organization names | blank_percentage |
"Acme Corp" |
fake_company_name |
Generated company names | blank_percentage |
"TechFlow Solutions" |
business_type |
Business categories | blank_percentage |
"SaaS", "Manufacturing" |
organization_type |
Organization structure | blank_percentage |
"Startup", "Enterprise" |
legal_entity |
Legal entity types | blank_percentage |
"LLC", "Corporation" |
duns_number |
D&B DUNS numbers | blank_percentage |
"123456789" |
ein |
Employer ID Numbers | blank_percentage |
"12-3456789" |
job_title |
Job titles | blank_percentage |
"Senior Manager" |
salary_range |
Salary bands | blank_percentage |
"$50k-$75k" |
income_level |
Income categories | blank_percentage |
"Middle", "Upper" |
performance_rating |
Performance scores | blank_percentage |
"Excellent", "Good" |
interview_stage |
Hiring pipeline stages | blank_percentage |
"Phone Screen", "Offer" |
Lifestyle & Personal Info Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
hobby |
Recreational activities | blank_percentage |
"Reading", "Gaming" |
pet_type |
Pet categories | blank_percentage |
"Dog", "Cat", "Bird" |
pet_name |
Pet names | blank_percentage |
"Fluffy", "Max" |
zodiac_sign |
Zodiac signs | blank_percentage |
"Aries", "Taurus" |
religion |
Religious affiliations | blank_percentage |
"Christian", "Muslim" |
mood |
Emotional states | blank_percentage |
"Happy", "Anxious" |
personality_trait |
Character traits | blank_percentage |
"Extroverted", "Analytical" |
daily_habit |
Routine behaviors | blank_percentage |
"Exercise", "Reading" |
dream_job |
Career aspirations | blank_percentage |
"Travel Blogger" |
pronoun |
Personal pronouns | blank_percentage |
"he/him", "she/her" |
quote |
Motivational quotes | blank_percentage |
Random inspirational quotes |
reaction |
Emotional reactions | blank_percentage |
"Excited", "Surprised" |
project_status |
Project statuses | blank_percentage |
"Active", "Completed" |
Event & Organization Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
conference_name |
Tech conference names | blank_percentage |
"TechConf 2024" |
event_type |
Event categories | blank_percentage |
"Conference", "Webinar" |
team_name |
Team names | blank_percentage |
"Team Alpha" |
buzzword |
Corporate buzzwords | blank_percentage |
"Synergy", "Leverage" |
catch_praise |
Compliments | blank_percentage |
"Great job!" |
slogan |
Marketing slogans | blank_percentage |
"Think Different" |
hashtag |
Social media tags | blank_percentage |
"#ProductLaunch" |
💰 Commerce Category (65+ providers)
Generate e-commerce, retail, pricing, and product data.
Product & Catalog Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
product_name |
Product titles | blank_percentage |
"Wireless Headphones" |
product_category |
Product categories | blank_percentage |
"Electronics", "Clothing" |
product_subcategory |
Subcategories | blank_percentage |
"Audio", "Footwear" |
product_description |
Product descriptions | blank_percentage |
Full product blurbs |
product_price |
Product prices | blank_percentage, min, max |
"29.99" |
sku |
Stock Keeping Units | blank_percentage |
"SKU-12345" |
Payment & Financial Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
credit_card_number |
CC numbers (valid format) | blank_percentage |
"4111-1111-1111-1111" |
credit_card_type |
CC brand/type | blank_percentage |
"Visa", "MasterCard" |
money |
Formatted currency | blank_percentage, min, max, currency |
"$1,234.56" |
currency |
Currency name | blank_percentage |
"US Dollar", "Euro" |
currency_code |
ISO 4217 codes | blank_percentage |
"USD", "EUR" |
currency_symbol |
Currency symbols | blank_percentage |
"$", "€", "¥" |
iban |
IBAN account numbers | blank_percentage |
"DE89370400440532013000" |
bban |
BBAN numbers | blank_percentage |
"0532013000" |
Order & Fulfillment Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
order_status |
Order statuses | blank_percentage |
"Pending", "Shipped", "Delivered" |
payment_status |
Payment states | blank_percentage |
"Paid", "Pending", "Failed" |
delivery_status |
Delivery status | blank_percentage |
"In Transit", "Delivered" |
shipment_status |
Shipment states | blank_percentage |
"Dispatched", "Out for Delivery" |
payment_method |
Payment types | blank_percentage |
"Credit Card", "PayPal", "Bank Transfer" |
shipping_method |
Shipping options | blank_percentage |
"Express", "Standard" |
postal_service |
Postal services | blank_percentage |
"USPS", "FedEx", "UPS" |
delivery_time_window |
Delivery timeframes | blank_percentage |
"9-12 AM", "2-5 PM" |
tracking_number |
Shipment tracking | blank_percentage |
"1234567890123" |
return_reason |
Return justifications | blank_percentage |
"Defective", "Wrong Item" |
Pricing & Promotion Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
discount_percentage |
Discount rates | blank_percentage |
"10", "25" |
coupon_code |
Promo codes | blank_percentage |
"SAVE20" |
promo_expiry_date |
Promotion end dates | blank_percentage |
"12/31/2024" |
subscription_plan |
Subscription tiers | blank_percentage |
"Basic", "Pro", "Enterprise" |
loyalty_tier |
Loyalty levels | blank_percentage |
"Silver", "Gold", "Platinum" |
membership_level |
Membership grades | blank_percentage |
"Standard", "Premium" |
warranty_period |
Warranty durations | blank_percentage |
"1 Year", "Lifetime" |
Inventory & Stock Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
inventory_status |
Stock statuses | blank_percentage |
"In Stock", "Low Stock", "Out" |
stock_market |
Market name | blank_percentage |
"NASDAQ", "NYSE" |
stock_name |
Stock company names | blank_percentage |
"Apple Inc." |
stock_symbol |
Stock tickers | blank_percentage |
"AAPL", "MSFT" |
stock_sector |
Market sectors | blank_percentage |
"Technology", "Healthcare" |
stock_industry |
Industry classifications | blank_percentage |
"Computer Hardware" |
stock_market_cap |
Market cap ranges | blank_percentage |
"Large Cap", "Small Cap" |
package_weight |
Weight in shipments | blank_percentage, min, max |
"2.5 lbs" |
invoice_number |
Invoice IDs | blank_percentage |
"INV-2024-001" |
Retail & Merchandising Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
department_retail |
Retail departments | blank_percentage |
"Men's Clothing", "Home Goods" |
restaurant_type |
Restaurant categories | blank_percentage |
"Italian", "Sushi" |
coffee_type |
Coffee varieties | blank_percentage |
"Espresso", "Cappuccino" |
meal_type |
Meal categories | blank_percentage |
"Breakfast", "Dinner" |
recipe_name |
Recipe names | blank_percentage |
"Spaghetti Carbonara" |
ingredient |
Food ingredients | blank_percentage |
"Tomato", "Garlic" |
fabric_type |
Material types | blank_percentage |
"Cotton", "Polyester" |
furniture_type |
Furniture categories | blank_percentage |
"Sofa", "Dining Table" |
gem_stone |
Precious stones | blank_percentage |
"Diamond", "Ruby" |
office_supply |
Office items | blank_percentage |
"Pen", "Notebook" |
water_type |
Water categories | blank_percentage |
"Spring", "Mineral" |
Customer & Review Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
review_text |
Customer reviews | blank_percentage |
Full review text |
customer_feedback_score |
Review ratings | blank_percentage |
1-5 star ratings |
recommendation_slot_position |
Rec positions | blank_percentage |
"Top", "Middle" |
price_sensitivity_level |
Price sensitivity | blank_percentage |
"High", "Medium", "Low" |
click_depth |
User engagement depth | blank_percentage |
"Shallow", "Deep" |
Analytics & Performance Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
sales_channel |
Sales platforms | blank_percentage |
"Amazon", "Website", "Retail" |
bundle_type |
Bundle categories | blank_percentage |
"Value Pack", "Starter Kit" |
freight_mode |
Shipping modes | blank_percentage |
"Air", "Sea", "Rail" |
delivery_route_code |
Delivery routes | blank_percentage |
"ROUTE-001" |
💻 IT/Technology Category (100+ providers)
Generate programming, networking, software, and tech infrastructure data.
Programming & Development Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
programming_language |
Code languages | blank_percentage |
"Python", "JavaScript", "Go" |
software_framework |
Development frameworks | blank_percentage |
"Django", "React", "Spring" |
version_number |
Software versions | blank_percentage |
"1.2.3", "3.0.0-beta" |
api_version |
API versions | blank_percentage |
"v1", "v2.1" |
api_endpoint_path |
API paths | blank_percentage |
"/api/users/profile" |
api_key |
API authentication keys | blank_percentage |
"sk_test_123456..." |
http_method |
HTTP verbs | blank_percentage |
"GET", "POST", "PUT" |
http_status_code |
HTTP response codes | blank_percentage |
"200", "404", "500" |
git_commit_hash |
Commit hashes | blank_percentage |
"a1b2c3d4e5f6" |
Networking & Infrastructure Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
ip_address_v4 |
IPv4 addresses | blank_percentage |
"192.168.1.1" |
ip_address_v6 |
IPv6 addresses | blank_percentage |
"2001:0db8::1" |
ip_address_v4_cidr |
IPv4 CIDR notation | blank_percentage |
"192.168.1.0/24" |
ip_address_v6_cidr |
IPv6 CIDR notation | blank_percentage |
"2001:db8::/32" |
mac_address |
MAC addresses | blank_percentage |
"00:1A:2B:3C:4D:5E" |
port_number |
Network ports | blank_percentage |
"8080", "5432" |
network_protocol |
Network protocols | blank_percentage |
"TCP", "UDP", "HTTP" |
dns_record_type |
DNS types | blank_percentage |
"A", "CNAME", "MX" |
network_operator_code |
Carrier codes | blank_percentage |
Telecom operator IDs |
Cloud & Infrastructure Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
cloud_provider |
Cloud platforms | blank_percentage |
"AWS", "Azure", "GCP" |
cloud_storage |
Storage services | blank_percentage |
"S3", "Blob Storage" |
data_center |
DC locations | blank_percentage |
"us-east-1", "eu-west-1" |
server_name |
Server identifiers | blank_percentage |
"server-prod-01" |
container_id |
Container IDs | blank_percentage |
"a1b2c3d4e5f6" |
docker_image |
Docker images | blank_percentage |
"python:3.11-slim" |
database_type |
Database systems | blank_percentage |
"PostgreSQL", "MongoDB" |
Security & Authentication Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
json_web_token |
JWT tokens | blank_percentage |
Full JWT token |
md5 |
MD5 hashes | blank_percentage |
"5d41402abc4b2a76b9719d911017c592" |
sha1 |
SHA-1 hashes | blank_percentage |
SHA-1 hash strings |
sha256 |
SHA-256 hashes | blank_percentage |
SHA-256 hash strings |
encryption_algorithm |
Encryption methods | blank_percentage |
"AES-256", "RSA-2048" |
permission_level |
Access levels | blank_percentage |
"Admin", "User", "Guest" |
password_strength |
Password security | blank_percentage |
"Weak", "Medium", "Strong" |
security_question |
Security Q&A | blank_percentage |
"What's your pet's name?" |
verification_code |
2FA/OTP codes | blank_percentage |
"123456" |
Devices & Hardware Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
battery_level |
Battery % | blank_percentage, min, max |
"85" |
storage_type |
Storage media | blank_percentage |
"SSD", "HDD" |
memory_size |
RAM/Memory | blank_percentage |
"16GB", "512MB" |
operating_system |
OS names | blank_percentage |
"Windows", "macOS", "Linux" |
device_location |
Device position | blank_percentage |
"Office", "Home" |
firmware_version |
Firmware versions | blank_percentage |
"1.2.3" |
firmware_build |
Firmware builds | blank_percentage |
"BUILD-12345" |
power_state |
Power status | blank_percentage |
"On", "Sleeping", "Off" |
power_source |
Power input | blank_percentage |
"Battery", "AC Adapter" |
resolution |
Screen resolution | blank_percentage |
"1920x1080", "2560x1600" |
screen_size |
Display sizes | blank_percentage |
"15.6 inch", "24 inch" |
browser |
Web browsers | blank_percentage |
"Chrome", "Firefox", "Safari" |
user_agent |
User agent strings | blank_percentage |
Full UA string |
laptop_brand |
Laptop manufacturers | blank_percentage |
"Dell", "MacBook", "HP" |
smart_device_brand |
IoT brands | blank_percentage |
"Amazon", "Google", "Apple" |
smart_device_type |
IoT device types | blank_percentage |
"Smart Speaker", "Thermostat" |
printer_type |
Printer types | blank_percentage |
"Inkjet", "Laser" |
form_factor |
Device form | blank_percentage |
"Smartphone", "Tablet" |
Web & Communication Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
email_address |
Email addresses | blank_percentage |
"john.doe@example.com" |
username |
User accounts | blank_percentage |
"jdoe123", "user_alpha" |
subject_line |
Email subjects | blank_percentage |
"Q4 Quarterly Review" |
slack_channel |
Slack channels | blank_percentage |
"#general", "#dev-team" |
social_media_platform |
Social networks | blank_percentage |
"Twitter", "LinkedIn", "TikTok" |
top_level_domain |
Domain TLDs | blank_percentage |
".com", ".org", ".io" |
wifi_ssid |
WiFi networks | blank_percentage |
"MyWiFi", "CoffeeShop5G" |
wifi_standard |
WiFi versions | blank_percentage |
"WiFi 6", "WiFi 5" |
wifi_band |
WiFi bands | blank_percentage |
"2.4GHz", "5GHz" |
Data & File Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
file_name |
File names | blank_percentage |
"document.pdf", "image.jpg" |
file_extension |
File types | blank_percentage |
".pdf", ".csv", ".json" |
file_size |
File sizes | blank_percentage |
"2.5 MB", "1.2 GB" |
mime_type |
MIME types | blank_percentage |
"application/json", "image/png" |
document_type |
Document categories | blank_percentage |
"Report", "Invoice", "Contract" |
Monitoring & Diagnostics Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
log_level |
Log severity | blank_percentage |
"DEBUG", "INFO", "ERROR" |
error_message |
Error text | blank_percentage |
"Connection timeout" |
incident_type |
Incident categories | blank_percentage |
"Outage", "Degradation" |
uptime_percentage |
Service availability | blank_percentage |
"99.9" |
response_time |
Latency | blank_percentage, min, max |
"125ms", "3.5s" |
Analytics & Engagement Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
feature_usage_event |
Usage tracking | blank_percentage |
"click_button", "view_page" |
engagement_level |
User engagement | blank_percentage |
"High", "Medium", "Low" |
user_cohort |
Cohort segments | blank_percentage |
"Early Adopter", "Laggard" |
churn_risk_score |
Churn probability | blank_percentage, min, max |
"0.75" |
notification_type |
Alert types | blank_percentage |
"Email", "Push", "SMS" |
🏥 Healthcare Category (60+ providers)
Generate medical, pharmaceutical, hospital, and health insurance data.
Patient & Diagnosis Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
blood_type |
Blood types | blank_percentage |
"O+", "AB-" |
disease_name |
Diseases | blank_percentage |
"Type 2 Diabetes", "Asthma" |
symptom |
Symptoms | blank_percentage |
"Fever", "Cough" |
disability_type |
Disability categories | blank_percentage |
"Mobility", "Hearing" |
medication_name |
Drug names | blank_percentage |
"Aspirin", "Metformin" |
drug_name_generic |
Generic drug names | blank_percentage |
Chemical names |
drug_name_brand |
Brand drug names | blank_percentage |
"Advil", "Tylenol" |
drug_company |
Pharma companies | blank_percentage |
"Pfizer", "Merck" |
fda_ndc_code |
FDA codes | blank_percentage |
"0002-1234-01" |
Medical Coding Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
icd10_diagnosis_code |
ICD-10 diagnosis | blank_percentage |
"E11.9" (Type 2 diabetes) |
icd10_dx_desc_short |
ICD-10 short desc | blank_percentage |
"Type 2 diabetes" |
icd10_dx_desc_long |
ICD-10 long desc | blank_percentage |
Full description |
icd10_procedure_code |
ICD-10 procedures | blank_percentage |
"0DB68ZX" |
icd9_diagnosis_code |
ICD-9 diagnosis | blank_percentage |
"250.00" |
icd9_dx_desc_short |
ICD-9 short desc | blank_percentage |
Short description |
icd9_proc_desc_long |
ICD-9 long proc desc | blank_percentage |
Full description |
hcpcs_code |
HCPCS codes | blank_percentage |
"E1390" |
hcpcs_name |
HCPCS descriptions | blank_percentage |
"Ultrasonic cleaner" |
Hospital & Provider Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
hospital_name |
Hospital names | blank_percentage |
"General Medical Center" |
hospital_department |
Hospital departments | blank_percentage |
"Cardiology", "Emergency" |
hospital_npi |
NPI numbers | blank_percentage |
Provider ID numbers |
hospital_city |
Hospital location city | blank_percentage |
City name |
hospital_state |
Hospital state | blank_percentage |
State abbreviation |
hospital_street_address |
Hospital address | blank_percentage |
Full street address |
hospital_postal_code |
Hospital ZIP | blank_percentage |
Postal code |
pharmacy_name |
Pharmacy names | blank_percentage |
"CVS Pharmacy", "Walgreens" |
Vital Signs & Lab Data Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
blood_pressure_reading |
BP measurements | blank_percentage, min, max |
"120/80" |
blood_pressure_category |
BP classifications | blank_percentage |
"Normal", "Elevated" |
heart_rate |
Heart rate (BPM) | blank_percentage, min, max |
"72" |
lab_test |
Lab test names | blank_percentage |
"Complete Blood Count" |
lab_test_type |
Lab test category | blank_percentage |
"Hematology", "Chemistry" |
lab_result_value |
Test results | blank_percentage, min, max |
Numeric values |
triage_level |
ER priority | blank_percentage |
"Level 1", "Level 5" |
Treatment & Care Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
medication_dosage |
Dosage amounts | blank_percentage |
"500mg", "10ml" |
appointment_status |
Appointment states | blank_percentage |
"Scheduled", "Completed" |
medical_specialty |
Medical specialties | blank_percentage |
"Oncology", "Pediatrics" |
medical_device_id |
Device identifiers | blank_percentage |
Device serial numbers |
prescription_id |
Prescription IDs | blank_percentage |
"RX-2024-001" |
Lifestyle & Wellness Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
diet_type |
Diet categories | blank_percentage |
"Keto", "Vegan" |
dietary_restriction |
Food restrictions | blank_percentage |
"Gluten-Free", "Nut Allergy" |
exercise_type |
Exercise categories | blank_percentage |
"Cardio", "Weight Training" |
workout_duration |
Exercise time | blank_percentage |
"30 minutes", "1 hour" |
mental_health_condition |
Mental health diagnoses | blank_percentage |
"Anxiety", "Depression" |
vaccination_status |
Vaccination states | blank_percentage |
"Fully Vaccinated", "Unvaccinated" |
vaccination_type |
Vaccine types | blank_percentage |
"COVID-19", "Flu" |
Nutritional & Allergy Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
allergy |
Medical allergies | blank_percentage |
"Peanuts", "Latex" |
food_allergy |
Food allergies | blank_percentage |
"Shellfish", "Eggs" |
allergy_flag |
Allergy status | blank_percentage |
"No Known Allergies" |
macro_nutrient |
Macronutrients | blank_percentage |
"Protein", "Carbs" |
nutrient |
Vitamins/minerals | blank_percentage |
"Calcium", "Vitamin D" |
vitamin_name |
Specific vitamins | blank_percentage |
"B12", "Folic Acid" |
calorie_count |
Calorie amounts | blank_percentage, min, max |
"2000" |
serving_size |
Portion sizes | blank_percentage |
"1 cup", "100g" |
meal_rating |
Food satisfaction | blank_percentage |
1-5 ratings |
Insurance & Administrative Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
health_insurance_plan |
Insurance types | blank_percentage |
"HMO", "PPO" |
insurance_provider |
Insurance companies | blank_percentage |
"Aetna", "Blue Cross" |
medicare_beneficiary_id |
Medicare IDs | blank_percentage |
Medicare numbers |
nhs_number |
NHS patient IDs (UK) | blank_percentage |
NHS numbers |
Additional Providers
| Provider | Description | Parameters | Example |
|---|---|---|---|
body_part |
Anatomical parts | blank_percentage |
"Heart", "Liver" |
emergency_type |
Emergency types | blank_percentage |
"Trauma", "Cardiac" |
hormone |
Hormones | blank_percentage |
"Insulin", "Cortisol" |
organ |
Organs | blank_percentage |
"Kidney", "Pancreas" |
pain_level |
Pain intensity | blank_percentage |
"1-10" scale |
chromosome |
Chromosomes | blank_percentage |
"X", "Y", "22" |
📊 Advanced Category (8 providers)
Advanced data generation with templates, regex, lambdas, and custom logic.
| Provider | Description | Parameters | Example |
|---|---|---|---|
template |
Combine fields with {{field}} |
template, schema_labels |
"Hello {{first_name}} {{last_name}}" |
regular_expression |
Generate from regex patterns | format |
"[A-Z]{3}-\\d{4}" → "ABC-1234" |
custom_list |
Pick from a list | values (list or comma-separated string) |
["Active", "Inactive", "Pending"] |
lambda |
Custom Python function | function |
Custom generation logic |
json_array |
Generate JSON arrays | blank_percentage |
Array of objects |
url |
Generate URLs | blank_percentage |
Valid URL strings |
digit_sequence |
Random digit strings | blank_percentage, length |
"1234567890" |
character_sequence |
Random characters | blank_percentage, length |
"AbCdEfGhIj" |
naughty_string |
Special/edge case strings | blank_percentage |
SQL injection, Unicode, etc. |
🤖 AI/ML Category (30+ providers)
Generate machine learning, model metrics, and AI infrastructure data.
| Provider | Description | Parameters | Example |
|---|---|---|---|
model_type |
ML model types | blank_percentage |
"Regression", "Classification" |
model_framework |
ML frameworks | blank_percentage |
"TensorFlow", "PyTorch", "Scikit-learn" |
model_task |
ML task types | blank_percentage |
"NLP", "Computer Vision", "Forecasting" |
model_version |
Model versions | blank_percentage |
"1.0.0", "2.1.3-rc1" |
model_owner |
Model creators | blank_percentage |
Team/person names |
model_latency |
Inference time | blank_percentage, min, max |
"125ms", "2.5s" |
model_confidence |
Prediction confidence | blank_percentage, min, max |
"0.95", "85%" |
inference_result |
Prediction outputs | blank_percentage |
Class labels or values |
inference_endpoint |
API endpoints | blank_percentage |
/api/v1/predict |
model_input_format |
Input data formats | blank_percentage |
"JSON", "CSV", "Binary" |
model_output_format |
Output formats | blank_percentage |
"JSON", "Tensor" |
model_training_dataset |
Training data name | blank_percentage |
Dataset identifiers |
model_lifecycle_stage |
Model stages | blank_percentage |
"Development", "Production" |
model_explainability_method |
Explainability techniques | blank_percentage |
"SHAP", "LIME", "Attention" |
model_deployment_env |
Deployment targets | blank_percentage |
"AWS", "On-Premise", "Edge" |
cpu_utilization |
CPU % | blank_percentage, min, max |
"45.2" |
gpu_utilization |
GPU % | blank_percentage, min, max |
"78.5" |
memory_footprint |
Memory usage | blank_percentage |
"2.5GB", "512MB" |
data_drift_score |
Data drift metric | blank_percentage, min, max |
"0.12" (0-1 scale) |
concept_drift_status |
Concept drift state | blank_percentage |
"Stable", "Drifting" |
retraining_frequency |
Update schedules | blank_percentage |
"Daily", "Weekly" |
compute_precision |
Numeric precision | blank_percentage |
"float32", "float64" |
🌍 Location Category (35+ providers)
Generate geographic, address, and location data.
| Provider | Description | Parameters | Example |
|---|---|---|---|
country |
Country names | blank_percentage |
"United States", "France" |
country_code |
ISO 3166 country codes | blank_percentage |
"US", "FR", "JP" |
state |
State/Province names | blank_percentage |
"California", "Texas" |
state_abbrev |
State abbreviations | blank_percentage |
"CA", "TX", "NY" |
city |
City names | blank_percentage |
"New York", "Los Angeles" |
street_address |
Full street addresses | blank_percentage |
"123 Main St, Apt 4B" |
street_name |
Street names | blank_percentage |
"Main Street", "Park Avenue" |
street_number |
House/Building numbers | blank_percentage, min, max |
"123", "4567" |
street_type |
Street designators | blank_percentage |
"Street", "Avenue", "Boulevard" |
street_suffix |
Address suffixes | blank_percentage |
"Apt", "Suite", "Floor" |
postal_code |
ZIP/Postal codes | blank_percentage |
"10001", "SW1A1AA" |
latitude |
Geographic latitude | blank_percentage, min, max |
"40.7128" |
longitude |
Geographic longitude | blank_percentage, min, max |
"-74.0060" |
time_zone |
Time zone names | blank_percentage |
"America/New_York", "Europe/London" |
timezone_abbrev |
Timezone abbreviations | blank_percentage |
"EST", "PST", "GMT" |
timezone_offset |
UTC offsets | blank_percentage |
"+5:30", "-8:00" |
continent |
Continent names | blank_percentage |
"North America", "Europe" |
subregion |
Geographic subregions | blank_percentage |
"Northern Europe", "Sub-Saharan Africa" |
compass_direction |
Cardinal directions | blank_percentage |
"North", "Northeast" |
elevation |
Altitude/elevation | blank_percentage, min, max |
"1234m", "5000ft" |
phone |
Phone numbers | blank_percentage |
"555-123-4567" |
📚 Education Category (11 providers)
Generate academic and educational data.
| Provider | Description | Parameters | Example |
|---|---|---|---|
university_name |
University names | blank_percentage |
"MIT", "Stanford" |
degree |
Degree types | blank_percentage |
"Bachelor's", "Master's", "PhD" |
college_major |
Academic majors | blank_percentage |
"Computer Science", "Biology" |
academic_subject |
Subject areas | blank_percentage |
"Mathematics", "Literature" |
school_type |
School categories | blank_percentage |
"Public", "Private", "Charter" |
grade_level |
Grade/Class levels | blank_percentage |
"Freshman", "Junior", "Senior" |
semester |
Academic terms | blank_percentage |
"Spring 2024", "Fall 2023" |
gpa |
Grade point average | blank_percentage, min, max |
"3.85" (0-4.0 scale) |
classroom_number |
Room numbers | blank_percentage |
"101", "A-204" |
certification |
Professional certifications | blank_percentage |
"AWS Certified", "CPA" |
qualification |
Job qualifications | blank_percentage |
"Bachelor's Required" |
🏗️ Construction Category (7 providers)
Generate construction, building, and trades data.
| Provider | Description | Parameters | Example |
|---|---|---|---|
building_type |
Building categories | blank_percentage |
"Residential", "Commercial", "Industrial" |
construction_material |
Building materials | blank_percentage |
"Steel", "Concrete", "Wood" |
construction_trade |
Trade occupations | blank_percentage |
"Electrician", "Plumber", "Carpenter" |
construction_role |
Construction roles | blank_percentage |
"Foreman", "Safety Officer" |
tool_type |
Tool categories | blank_percentage |
"Power Drill", "Hammer" |
construction_heavy_equipment |
Large machinery | blank_percentage |
"Excavator", "Crane" |
construction_subcontract_category |
Subcontractor types | blank_percentage |
"Concrete", "Electrical" |
construction_standard_cost_code |
Cost codes | blank_percentage |
Standard construction codes |
🚗 Automotive Category (12 providers)
Generate vehicle and automotive data.
| Provider | Description | Parameters | Example |
|---|---|---|---|
car_make |
Vehicle manufacturers | blank_percentage |
"Toyota", "Ford", "BMW" |
car_model |
Vehicle models | blank_percentage |
"Camry", "Mustang", "3 Series" |
car_base_model |
Base model names | blank_percentage |
"SE", "LX", "Standard" |
car_model_year |
Model years | blank_percentage, min, max |
"2024", "2020" |
car_vin |
Vehicle ID Numbers | blank_percentage |
"1HGBH41JXMN109186" |
license_plate |
License plate numbers | blank_percentage |
"ABC-1234", "XYZ-9876" |
vehicle_type |
Vehicle categories | blank_percentage |
"Sedan", "SUV", "Truck" |
engine_type |
Engine configurations | blank_percentage |
"V6", "Inline-4", "Electric" |
fuel_type |
Fuel types | blank_percentage |
"Gasoline", "Diesel", "Electric" |
transmission_type |
Transmission types | blank_percentage |
"Automatic", "Manual", "CVT" |
gas_type |
Gas grades | blank_percentage |
"Regular", "Premium", "Diesel" |
driver_license_number |
License numbers | blank_percentage |
Alphanumeric license IDs |
🎮 Gaming Category (13 providers)
Generate gaming, entertainment, and player data.
| Provider | Description | Parameters | Example |
|---|---|---|---|
game_genre |
Game categories | blank_percentage |
"RPG", "FPS", "Strategy" |
console_platform |
Gaming platforms | blank_percentage |
"PS5", "Xbox Series X", "PC" |
avatar_class |
Character classes | blank_percentage |
"Warrior", "Mage", "Rogue" |
player_role |
In-game roles | blank_percentage |
"Tank", "DPS", "Support" |
guild_name |
Clan/Guild names | blank_percentage |
"Dragon Slayers", "Shadow Syndicate" |
quest_completion_rate |
Quest progress % | blank_percentage, min, max |
"85%" |
achievement_title |
Achievement names | blank_percentage |
"Monster Slayer", "Rich in Gold" |
badge |
Badge names | blank_percentage |
Custom badge titles |
skill_level |
Player skill tiers | blank_percentage |
"Novice", "Expert", "Legendary" |
leaderboard_rank |
Ranking position | blank_percentage, min, max |
"42", "1" |
in_game_currency_balance |
Virtual money | blank_percentage, min, max |
"50000 Gold" |
match_result |
Game outcomes | blank_percentage |
"Win", "Loss", "Draw" |
session_outcome |
Play session result | blank_percentage |
"Victory", "Defeat" |
🎬 Entertainment Category (10+ providers)
Generate movie, book, music, and media data.
| Provider | Description | Parameters | Example |
|---|---|---|---|
movie_title |
Film titles | blank_percentage |
"The Matrix", "Inception" |
movie_genre |
Film categories | blank_percentage |
"Action", "Drama", "Comedy" |
book_title |
Book names | blank_percentage |
"1984", "To Kill a Mockingbird" |
author_name |
Author names | blank_percentage |
"George Orwell", "Jane Austen" |
song_title |
Song names | blank_percentage |
"Bohemian Rhapsody" |
music_genre |
Music categories | blank_percentage |
"Rock", "Hip-Hop", "Jazz" |
artist_name |
Artist/Band names | blank_percentage |
"The Beatles", "Beyoncé" |
album_name |
Album titles | blank_percentage |
"Abbey Road", "Thriller" |
instrument_name |
Musical instruments | blank_percentage |
"Guitar", "Piano", "Drums" |
video_game_title |
Video game names | blank_percentage |
"The Legend of Zelda", "Elden Ring" |
💱 Cryptocurrency Category (7 providers)
Generate blockchain and cryptocurrency data.
| Provider | Description | Parameters | Example |
|---|---|---|---|
cryptocurrency_name |
Crypto names | blank_percentage |
"Bitcoin", "Ethereum" |
cryptocurrency_symbol |
Ticker symbols | blank_percentage |
"BTC", "ETH", "ADA" |
bitcoin_address |
Bitcoin wallets | blank_percentage |
"1A1z..." |
ethereum_address |
Ethereum wallets | blank_percentage |
"0xd8dA6BF26964aF9D7eEd9e03E53415D37aA96045" |
cryptocurrency_address |
Generic crypto addresses | blank_percentage |
Cryptocurrency wallet addresses |
nft_token_id |
NFT identifiers | blank_percentage |
Token IDs |
tezos_* (6 providers) |
Tezos blockchain data | blank_percentage |
Tezos-specific addresses/data |
⚖️ Legal Category (17 providers)
Generate legal and compliance data.
| Provider | Description | Parameters | Example |
|---|---|---|---|
legal_jurisdiction |
Legal territories | blank_percentage |
"New York", "California" |
court_level |
Court hierarchies | blank_percentage |
"District Court", "Supreme Court" |
contract_type |
Contract categories | blank_percentage |
"NDA", "Employment", "Service" |
legal_filing_type |
Filing categories | blank_percentage |
"Lawsuit", "Patent", "Trademark" |
law_type |
Area of law | blank_percentage |
"Civil", "Criminal", "Corporate" |
crime_type |
Crime categories | blank_percentage |
"Theft", "Fraud", "Assault" |
verdict |
Trial outcomes | blank_percentage |
"Guilty", "Not Guilty", "Acquitted" |
appeal_status |
Appeal states | blank_percentage |
"Pending", "Upheld", "Reversed" |
bail_status |
Bail decisions | blank_percentage |
"Released", "Denied", "Held" |
case_reference_number |
Case IDs | blank_percentage |
"CASE-2024-001" |
evidence_type |
Evidence categories | blank_percentage |
"Physical", "Testimonial", "Documentary" |
penalty_type |
Penalty categories | blank_percentage |
"Fine", "Imprisonment", "Probation" |
regulatory_agency |
Regulatory bodies | blank_percentage |
"SEC", "EPA", "FDA" |
legal_representation |
Legal roles | blank_percentage |
"Plaintiff Attorney", "Defendant" |
legislation_status |
Bill statuses | blank_percentage |
"Proposed", "Passed", "Vetoed" |
legal_compliance_status |
Compliance states | blank_percentage |
"Compliant", "Non-Compliant" |
legal_fee_category |
Fee structures | blank_percentage |
"Hourly", "Contingency" |
notary_status |
Notarization states | blank_percentage |
"Notarized", "Pending" |
🌿 Nature Category (10+ providers)
Generate natural world and environmental data.
| Provider | Description | Parameters | Example |
|---|---|---|---|
animal_name |
Animal names | blank_percentage |
"Lion", "Elephant", "Dolphin" |
plant_name |
Plant names | blank_percentage |
"Rose", "Oak", "Bamboo" |
tree_name |
Tree names | blank_percentage |
"Maple", "Pine", "Birch" |
flower_name |
Flower names | blank_percentage |
"Sunflower", "Tulip", "Daisy" |
weather_condition |
Weather types | blank_percentage |
"Sunny", "Rainy", "Snowy" |
season |
Seasons | blank_percentage |
"Spring", "Summer", "Fall" |
biome |
Ecosystem types | blank_percentage |
"Rainforest", "Desert", "Tundra" |
temperature |
Temperature values | blank_percentage, min, max |
"72°F", "22°C" |
noise_level |
Sound levels | blank_percentage |
"45 dB", "100 dB" |
noise_category |
Noise types | blank_percentage |
"Traffic", "Industrial", "Nature" |
noise_source |
Sound sources | blank_percentage |
"Traffic", "Construction", "Music" |
🔷 Political Category (5+ providers)
Generate political and government data.
| Provider | Description | Parameters | Example |
|---|---|---|---|
political_party |
Political parties | blank_percentage |
"Democratic", "Republican" |
political_ideology |
Political viewpoints | blank_percentage |
"Liberal", "Conservative", "Moderate" |
government_structure |
Govt types | blank_percentage |
"Democracy", "Monarchy", "Dictatorship" |
election_type |
Election categories | blank_percentage |
"General", "Primary", "Recall" |
diplomatic_title |
Diplomatic ranks | blank_percentage |
"Ambassador", "Envoy" |
🎵 Music Category (5+ providers)
Generate music and audio data.
| Provider | Description | Parameters | Example |
|---|---|---|---|
music_production_software |
DAW names | blank_percentage |
"Ableton Live", "Pro Tools" |
| Multiple music providers from Entertainment category are also accessible |
🌐 Marketing/Media Category (7+ providers)
Generate marketing, analytics, and content data.
| Provider | Description | Parameters | Example |
|---|---|---|---|
campaign_name |
Marketing campaigns | blank_percentage |
"Summer Sale 2024" |
marketing_channel |
Marketing platforms | blank_percentage |
"Email", "Social Media", "Paid Search" |
content_type |
Content categories | blank_percentage |
"Blog Post", "Video", "Infographic" |
target_audience |
Audience segments | blank_percentage |
"Tech Enthusiasts", "Millennials" |
social_media_platform |
Social networks | blank_percentage |
"Facebook", "Instagram", "TikTok" |
analytics_metric |
Measurement types | blank_percentage |
"CTR", "CAC", "LTV" |
🏦 Finance Category (40+ providers)
Generate banking, investment, and financial data.
| Provider | Description | Parameters | Example |
|---|---|---|---|
bank_name |
Bank names | blank_percentage |
"JPMorgan Chase", "Bank of America" |
bank_routing_number |
Bank routing codes | blank_percentage |
"021000021" |
bank_swift_bic |
SWIFT/BIC codes | blank_percentage |
"CHAUSUSXX" |
bank_lei |
Legal Entity IDs | blank_percentage |
LEI numbers |
bank_riad_code |
RIAD codes | blank_percentage |
Bank identification codes |
bank_street |
Bank addresses | blank_percentage |
Street address |
bank_city |
Bank city | blank_percentage |
City name |
bank_state |
Bank state | blank_percentage |
State name |
bank_branch_code |
Branch codes | blank_percentage |
"001", "025" |
account_number |
Account IDs | blank_percentage |
"123456789" |
account_type |
Account categories | blank_percentage |
"Checking", "Savings", "Money Market" |
credit_score |
Credit rating | blank_percentage, min, max |
"750" (300-850) |
credit_score_band |
Credit tiers | blank_percentage |
"Excellent", "Good", "Poor" |
credit_utilization |
Credit usage % | blank_percentage, min, max |
"35%" |
transaction_type |
Transaction categories | blank_percentage |
"Deposit", "Withdrawal", "Transfer" |
transaction_pattern |
Transaction behaviors | blank_percentage |
"Regular", "Sporadic", "Unusual" |
investment_strategy |
Investment approaches | blank_percentage |
"Growth", "Value", "Income" |
investment_persona |
Investor types | blank_percentage |
"Conservative", "Aggressive" |
investment_return_rate |
ROI percentages | blank_percentage, min, max |
"7.5%" |
asset_type |
Asset categories | blank_percentage |
"Stocks", "Bonds", "Real Estate" |
expense_category |
Expense types | blank_percentage |
"Food", "Utilities", "Entertainment" |
expense_amount |
Expense values | blank_percentage, min, max |
"$150.00" |
tax_type |
Tax categories | blank_percentage |
"Income", "Property", "Sales" |
tax_id |
Tax identification | blank_percentage |
Tax ID numbers |
loan_type |
Loan categories | blank_percentage |
"Auto", "Mortgage", "Personal" |
insurance_type |
Insurance categories | blank_percentage |
"Auto", "Home", "Life" |
insurance_provider |
Insurance companies | blank_percentage |
Insurance company names |
payment_term |
Payment schedules | blank_percentage |
"Net 30", "COD" |
risk_level |
Risk ratings | blank_percentage |
"Low", "Medium", "High" |
aml_risk_category |
AML risk tiers | blank_percentage |
"Low Risk", "Medium Risk" |
kyc_status |
KYC states | blank_percentage |
"Verified", "Pending", "Failed" |
wealth_segment |
Wealth tiers | blank_percentage |
"High Net Worth", "Mass Affluent" |
spending_behavior |
Spending patterns | blank_percentage |
"Conservative", "Moderate", "Splurger" |
spending_category |
Spending types | blank_percentage |
"Groceries", "Entertainment" |
savings_goal |
Savings targets | blank_percentage |
"Home Purchase", "Retirement" |
grant_type |
Grant categories | blank_percentage |
"Research", "Community" |
financial_goal |
Financial targets | blank_percentage |
"Build Emergency Fund" |
fraud_score |
Fraud probability | blank_percentage, min, max |
"0.23" (0-1 scale) |
transfer_channel |
Transfer methods | blank_percentage |
"Bank Transfer", "Wire" |
📱 Communication Category (22 providers)
Generate mobile, telecom, and network communication data.
| Provider | Description | Parameters | Example |
|---|---|---|---|
mobile_carrier |
Telecom providers | blank_percentage |
"Verizon", "AT&T", "T-Mobile" |
phone_number |
Phone numbers | blank_percentage |
"555-123-4567" |
imei_number |
Device IDs | blank_percentage |
IMEI numbers |
sim_card_type |
SIM types | blank_percentage |
"Physical", "eSIM" |
network_type |
Network technologies | blank_percentage |
"4G LTE", "5G", "WiFi" |
signal_strength |
Signal quality | blank_percentage |
"-80 dBm" (signal strength) |
data_plan |
Data packages | blank_percentage |
"Unlimited", "5GB/month" |
download_speed |
DL speeds | blank_percentage, min, max |
"150 Mbps" |
upload_speed |
UL speeds | blank_percentage, min, max |
"50 Mbps" |
latency |
Network latency | blank_percentage, min, max |
"25ms", "100ms" |
wifi_standard |
WiFi versions | blank_percentage |
"WiFi 6", "WiFi 6E" |
roaming_status |
Roaming states | blank_percentage |
"Active", "Inactive" |
dual_sim_capability |
Dual SIM support | blank_percentage |
"Yes", "No" |
esim_profiles_count |
eSIM profile count | blank_percentage, min, max |
"2", "5" |
hotspot_capability |
Hotspot support | blank_percentage |
"Yes", "No" |
nfc_support |
NFC enabled | blank_percentage |
"Yes", "No" |
volte_support |
VoLTE enabled | blank_percentage |
"Yes", "No" |
wifi_calling_support |
WiFi calling | blank_percentage |
"Yes", "No" |
wifi_band |
WiFi frequencies | blank_percentage |
"2.4GHz", "5GHz", "6GHz" |
apn_settings |
APN configurations | blank_percentage |
APN data |
bluetooth_version |
Bluetooth versions | blank_percentage |
"5.3", "5.2" |
call_quality_rating |
Call quality | blank_percentage |
1-5 ratings |
carrier_lock_status |
Lock status | blank_percentage |
"Locked", "Unlocked" |
✨ Basic/Utility Category (50+ providers)
Generate basic utilities, random data, and helper fields.
| Provider | Description | Parameters | Example |
|---|---|---|---|
row_number |
Auto-incrementing row ID | blank_percentage |
"1", "2", "3" |
blank |
Empty/null field | blank_percentage |
Empty string or NULL |
boolean |
True/False | blank_percentage |
True or False |
number |
Integer/float | blank_percentage, min, max, decimals |
"42", "3.14" |
color |
Color names | blank_percentage |
"Red", "Blue", "Green" |
hex_color |
Hex color codes | blank_percentage |
"#FF5733", "#00AA00" |
short_hex_color |
3-char hex colors | blank_percentage |
"#F00", "#0F0" |
emoji |
Emoji characters | blank_percentage |
"😀", "🚀", "❤️" |
password |
Secure passwords | blank_percentage, min_length, upper_num, lower_num, numbers_num, symbols_num |
"A1!bC2@dE3" |
password_hash |
Hashed passwords | blank_percentage |
Hash strings |
datetime |
Date+time stamps | blank_percentage, from_date, to_date, date_format, minimum_age, maximum_age |
"06/24/2024 14:30:00" |
time |
Time only | blank_percentage |
"14:30:00" |
day_of_week |
Day names | blank_percentage |
"Monday", "Friday" |
month |
Month names | blank_percentage |
"January", "December" |
season |
Seasons | blank_percentage |
"Winter", "Summer" |
date_range |
Date ranges | blank_percentage |
"Jan 1 - Jan 31" |
words |
Random words | blank_percentage |
"apple", "elephant", "universe" |
sentences |
Sentence text | blank_percentage |
Multi-word sentences |
paragraphs |
Paragraph text | blank_percentage |
Full paragraphs |
punctuation |
Special characters | blank_percentage |
"!", "?", "..." |
rating |
Ratings/scores | blank_percentage |
1-5 scale |
sentiment |
Sentiment classification | blank_percentage |
"Positive", "Neutral", "Negative" |
priority_level |
Priority ratings | blank_percentage |
"Low", "Medium", "High", "Critical" |
frequency |
Frequency words | blank_percentage |
"Daily", "Weekly", "Monthly" |
duration |
Time durations | blank_percentage |
"30 minutes", "2 hours" |
dimension |
Size measurements | blank_percentage |
"Small", "Large" |
height |
Height values | blank_percentage, min, max |
"5'10\"", "180cm" |
weight |
Weight values | blank_percentage, min, max |
"180 lbs", "82kg" |
temperature |
Temperature values | blank_percentage, min, max |
"72°F", "22°C" |
imperial_unit |
Imperial measurements | blank_percentage |
"inches", "pounds" |
metric_prefix |
Metric prefixes | blank_percentage |
"Kilo", "Mega", "Micro" |
dice_roll |
D6 dice roll | blank_percentage |
1-6 |
coin_flip |
Heads/Tails | blank_percentage |
"Heads" or "Tails" |
nato_phonetic |
NATO phonetic | blank_percentage |
"Alpha", "Bravo", "Charlie" |
isbn |
ISBN numbers | blank_percentage |
"978-3-16-148410-0" |
ulid |
ULID identifiers | blank_percentage |
ULID format |
paper_size |
Paper sizes | blank_percentage |
"A4", "Letter", "Legal" |
current_timestamp |
Current time | blank_percentage |
Current date/time |
sequence |
Custom sequences | blank_percentage, start, step |
"1", "2", "3" |
address_line_2 |
Address line 2 | blank_percentage |
"Apt 4B", "Suite 300" |
Plus distributions: binomial, exponential, geometric, normal, poisson |
Statistical distributions | blank_percentage, parameters |
Distribution samples |
🌐 Basic Category - Distribution Providers
Advanced statistical distributions for quantitative data generation:
| Provider | Description | Parameters | Example |
|---|---|---|---|
binomial_distribution |
Binomial outcomes | blank_percentage, n, p |
"45" (from n trials) |
exponential_distribution |
Exponential decay | blank_percentage, lambda |
"2.3" |
geometric_distribution |
Geometric sequences | blank_percentage, p |
"5" |
normal_distribution |
Bell curve | blank_percentage, mean, std_dev |
"102.5" |
poisson_distribution |
Event counts | blank_percentage, lambda |
"3" |
Configuration & Advanced Options
Global Options (All Providers)
Every provider accepts these core options:
| Option | Type | Default | Description |
|---|---|---|---|
blank_percentage |
float (0-100) | 0 | Percentage of records with NULL/empty values |
Provider-Specific Parameters
Each provider may have additional parameters. Examples:
number Provider
{
"key_label": "age",
"options": {
"blank_percentage": 5,
"min": 18,
"max": 65,
"decimals": 0
}
}
datetime Provider
{
"key_label": "birth_date",
"options": {
"minimum_age": 18,
"maximum_age": 65,
"date_format": "mm/dd/yyyy" # Format: yyyy/mm/dd, mm/dd/yyyy, dd/mm/yyyy, etc.
}
}
password Provider
{
"key_label": "password",
"options": {
"min_length": 12,
"upper_num": 2,
"lower_num": 2,
"numbers_num": 2,
"symbols_num": 2
}
}
template Provider
{
"key_label": "full_address",
"options": {
"template": "{{street_number}} {{street_name}}, {{city}}, {{state}} {{postal_code}}"
}
}
regular_expression Provider
{
"key_label": "custom_id",
"options": {
"format": "[A-Z]{3}-[0-9]{4}-[A-Z]{2}"
}
}
custom_list Provider
{
"key_label": "status",
"options": {
"values": ["Active", "Inactive", "Pending", "Archived"]
# OR: "values": "Active,Inactive,Pending,Archived"
}
}
money Provider
{
"key_label": "price",
"options": {
"min": 10,
"max": 1000,
"currency": "$" # Can be: "$", "€", "¥", "£", etc.
}
}
🧑 Personal (Name, Gender, Passport, etc.)
💰 Finance (Credit Cards, Banking, Currency)
credit_card_number, credit_card_type, iban, bban, currency, currency_code, money, salary_range, stock_market, etc.
🛍️ Commerce (Products, Orders, Pricing)
product_name, product_category, product_price, barcode_ean13, order_status, payment_method, invoice_number, delivery_status, coupon_code, etc.
📧 Communication (Email, Phone, Social)
email_address, phone_number, username, social_media_handle, chat_message, contact_name, etc.
🏗️ Construction (Building, Materials, Codes)
construction_code, building_type, material_type, foundation_type, roof_type, door_type, etc.
💻 Tech/IT (Programming, Frameworks, Version)
programming_language, software_framework, version_number, log_level, http_status_code, file_extension, mime_type, etc.
🏥 Healthcare (Diseases, Medications, Medical Codes)
disease_name, symptom_name, medication_name, blood_type, vaccination_status, ICD10_diagnosis, ICD9_diagnosis, HCPCS_code, etc.
🌍 Location (Countries, Cities, Addresses)
country, state, city, street_address, postal_code, latitude, longitude, timezone, airport_code, etc.
📚 Education (Schools, Courses, Subjects)
university_name, degree, major, course_name, subject, educational_attainment, etc.
⚖️ Legal (Laws, Contracts, Jurisdictions)
legal_entity_type, contract_type, jurisdiction, court_type, legal_case_status, legal_term, etc.
🎬 Entertainment (Movies, Books, Games)
movie_title, movie_genre, book_title, author_name, video_game_title, music_genre, song_title, etc.
🌿 Nature (Plants, Animals, Weather)
plant_name, animal_name, tree_name, flower_name, weather_condition, season, biome, etc.
🚗 Automotive (Cars, VINs, Fuel)
car_make, car_model, car_vin, vehicle_type, license_plate, engine_type, fuel_type, transmission_type, etc.
💱 Cryptocurrency (Coins, Blockchain, Wallets)
crypto_currency, crypto_address, crypto_transaction_id, blockchain_type, smart_contract_language, etc.
🎮 Gaming (Characters, Items, Guilds)
character_class, character_race, game_genre, npc_name, item_type, quest_name, guild_name, etc.
🎵 Music (Artists, Albums, Genres)
artist_name, album_name, song_title, music_genre, instrument_name, music_production_software, etc.
📱 Marketing/Media (Campaigns, Analytics, Content)
campaign_name, social_media_platform, marketing_channel, content_type, target_audience, analytics_metric, etc.
🌐 Political (Countries, Parties, Elections)
political_party, political_ideology, election_type, government_structure, diplomatic_title, etc.
📝 Advanced (Templates, Regex, Lambdas)
template (combine fields), regular_expression (match patterns), lambda (custom Python), json_array, url, digit_sequence, character_sequence, etc.
🤖 AI/ML (Models, Metrics, Training)
model_type, model_framework, model_task, model_version, model_latency, model_confidence, cpu_utilization, gpu_utilization, data_drift_score, inference_result, etc.
✨ Basic (Utilities, Random, Generators)
row_number, blank, boolean, number, datetime, color, emoji, password, password_hash, isbn, ulid, sentiment, words, sentences, paragraphs, etc.
🎲 Miscellaneous (Random & Fun)
dice_roll, coin_flip, rating, frequency, priority_level, dimension, duration, height, weight, temperature, etc.
Export Formats
Generate data in your preferred format:
# Export to multiple formats at once
IkiDataGenerator(schema).many(1000).export("dataset", formats=[
"csv", # Comma-separated values
"json", # JSON array of objects
"sql", # SQL INSERT statements
"parquet", # Apache Parquet (columnar)
"excel", # Excel workbook (.xlsx)
"duckdb", # DuckDB database
# "tsv", "xml", "cql", "firebase", "dbunit" also supported
])
| Format | File Extension | Best For | Notes |
|---|---|---|---|
| CSV | .csv |
Spreadsheets, import tools | Universal format |
| JSON | .json |
APIs, JavaScript, NoSQL | Pretty-printed with indent=2 |
| SQL | .sql |
Databases | INSERT statements (specify table name) |
| TSV | .tsv |
Tab-delimited data | Alternative to CSV |
| Excel | .xlsx |
Business reports | Native Excel format |
| Parquet | .parquet |
Big Data, Pandas, BI tools | Efficient columnar storage |
| DuckDB | .duckdb |
Analytics, SQL queries | Embedded database |
| XML | .xml |
Legacy systems, config | Structured XML export |
| Firestore | .json |
Firebase/Firestore | Firebase-ready format |
| DBUnit | .xml |
Testing frameworks | DBUnit test data format |
| CQL | .cql |
Cassandra databases | CQL INSERT statements |
API Reference
IkiDataGenerator(schema)
Initialize the generator with a schema.
Parameters:
schema(list): List of field names (strings) or field configs (dicts)
Returns: IkiDataGenerator instance
gen = IkiDataGenerator(["first_name", "email_address"])
.many(n)
Generate n records.
Parameters:
n(int): Number of records to generate
Returns: BaseGenerator instance
records = gen.many(100)
.export(name, formats=None)
Export records to file(s).
Parameters:
name(str): Output filename (without extension)formats(list, optional): File formats to export. Defaults to["csv", "json"]
Returns: None (files saved to output/ folder)
gen.many(100).export("users", formats=["csv", "json", "sql"])
# Creates: output/users.csv, output/users.json, output/users.sql
KEY_LABEL_REGISTRY
Global dictionary mapping all 700+ field names to their categories.
from ikidatagen import KEY_LABEL_REGISTRY
print(KEY_LABEL_REGISTRY["email_address"]) # → "personal"
print(KEY_LABEL_REGISTRY["credit_card_number"]) # → "commerce"
ProviderFactory
Advanced: dynamically load providers.
from ikidatagen import ProviderFactory
provider = ProviderFactory.create("email_address")
email = provider.generate()
Advanced Examples
Example 1: E-Commerce Dataset with Templates
from ikidatagen import IkiDataGenerator
schema = [
{"key_label": "row_number", "label": "Order ID"},
{"key_label": "current_timestamp", "label": "Created At"},
{"key_label": "customer_name", "label": "Customer"},
{"key_label": "email_address", "label": "Email"},
{"key_label": "product_name", "label": "Product"},
{"key_label": "product_price", "label": "Price"},
{
"key_label": "template",
"label": "Description",
"options": {
"template": "Order for {{product_name}} by {{customer_name}} ({{email_address}})"
}
},
{"key_label": "order_status", "label": "Status"},
{
"key_label": "ip_address_v4",
"label": "IP",
"options": {"blank_percentage": 20} # 20% missing
}
]
IkiDataGenerator(schema).many(1000).export("orders", formats=["csv", "json"])
Example 2: Healthcare Records
from ikidatagen import IkiDataGenerator
schema = [
"row_number",
"first_name",
"last_name",
"date_of_birth",
"blood_type",
"disease_name",
"medication_name",
"icd10_diagnosis",
{
"key_label": "current_timestamp",
"label": "last_visit",
"options": {"blank_percentage": 10}
},
]
IkiDataGenerator(schema).many(500).export("patients", formats=["csv", "json", "sql"])
Example 3: Test Data with Blanks and Validation
from ikidatagen import IkiDataGenerator
schema = [
{"key_label": "username", "options": {"blank_percentage": 0}}, # No blanks
{"key_label": "email_address", "options": {"blank_percentage": 0}}, # No blanks
{
"key_label": "phone_number",
"options": {"blank_percentage": 30} # 30% missing phones
},
{
"key_label": "address_line_1",
"options": {"blank_percentage": 5}
},
]
data = IkiDataGenerator(schema).many(10000).export("test_users", formats=["json"])
Example 4: AI/ML Metrics Dataset
from ikidatagen import IkiDataGenerator
schema = [
"row_number",
"model_type",
"model_framework",
"model_task",
"model_version",
"model_latency",
"model_confidence",
"cpu_utilization",
"gpu_utilization",
"memory_footprint",
"inference_result",
"inference_endpoint",
"current_timestamp",
]
IkiDataGenerator(schema).many(5000).export("ml_metrics", formats=["parquet", "json"])
Example 5: Custom Schema with Explicit Groups
from ikidatagen import IkiDataGenerator
schema = [
{"key_label": "username", "group": "personal"},
{"key_label": "email_address", "group": "personal"},
{"key_label": "product_name", "group": "commerce"},
{"key_label": "currency", "group": "commerce"},
{
"key_label": "regular_expression",
"group": "advanced",
"label": "Custom Pattern",
"options": {"pattern": "[A-Z]{2}[0-9]{4}"}
}
]
IkiDataGenerator(schema).many(100).export("mixed_data")
Configuration & Options
Blank Percentage
Control how many records have empty values for a field:
{
"key_label": "phone_number",
"options": {"blank_percentage": 25} # 25% of records will have empty phone
}
Templates
Combine fields with {{field_name}} syntax:
{
"key_label": "template",
"options": {
"template": "Full Name: {{first_name}} {{last_name}}, Email: {{email_address}}"
}
}
Regular Expressions
Generate data matching a pattern:
{
"key_label": "regular_expression",
"options": {
"pattern": "[A-Z]{3}-[0-9]{5}" # Generates: ABC-12345
}
}
Custom List
Pick from a list of values:
{
"key_label": "custom_list",
"options": {
"values": ["Active", "Inactive", "Pending"]
}
}
Number Range
Generate numbers within a range:
{
"key_label": "number",
"options": {
"min": 0,
"max": 100
}
}
📚 Examples & Demonstrations
The examples/ folder contains 45+ ready-to-run scripts demonstrating all features and providers across 22 categories.
Quick Start Examples
# Run the absolute simplest example
python examples/00_quick_start.py
# Explore basic fields
python examples/01_basic_fields.py
# Test all export formats
python examples/02_export_formats.py
Run Examples by Category
Personal & Identity (3 examples)
python examples/10_personal_data.py # Names, gender, dates
python examples/11_contact_info.py # Email, phone, social
python examples/12_identity_documents.py # Passports, SSN, IDs
E-Commerce & Shopping (4 examples)
python examples/20_ecommerce_shop.py # Products with pricing
python examples/21_shopping_cart.py # Complete orders
python examples/22_inventory_management.py # Stock & inventory
python examples/23_payment_processing.py # Payments & invoices
Finance & Banking (5 examples)
python examples/30_bank_accounts.py # Bank accounts
python examples/31_credit_cards.py # Credit card data
python examples/32_transactions.py # Transfers & withdrawals
python examples/33_investment_portfolio.py # Stocks & investments
python examples/34_crypto_blockchain.py # Cryptocurrency wallets
Healthcare & Medical (3 examples)
python examples/40_patient_records.py # Patient demographics
python examples/41_medical_diagnosis.py # Diagnoses & ICD codes
python examples/42_medications.py # Prescriptions & dosages
Location & Geography (2 examples)
python examples/50_addresses.py # Addresses & coordinates
python examples/51_international_locations.py # Countries & cities
Education (1 example)
python examples/60_student_records.py # Students & enrollment
Automotive (1 example)
python examples/70_car_inventory.py # Cars, models, pricing
Entertainment & Gaming (2 examples)
python examples/90_gaming_players.py # Gaming characters & guilds
python examples/92_entertainment.py # Movies, books, music
Tech & Programming (2 examples)
python examples/100_programming_data.py # Languages & frameworks
python examples/110_ml_models.py # ML model metadata
python examples/111_ml_metrics.py # Model performance metrics
Advanced Features (3 examples)
python examples/200_templates.py # Combining fields with {{field}}
python examples/201_regex_patterns.py # Custom regex patterns
python examples/203_blank_percentages.py # Missing data simulation
Real-World Scenarios (7 complete systems)
python examples/300_saas_users.py # SaaS with subscriptions
python examples/301_social_network.py # Social media platform
python examples/302_analytics_events.py # Event tracking (5000 events)
python examples/303_ecommerce_platform.py # Complete e-commerce
python examples/304_travel_booking_system.py # Flights, hotels, bookings
python examples/308_hospital_system.py # Hospital management
python examples/309_school_system.py # University system
Batch & Large Datasets (3 examples)
python examples/400_mixed_categories.py # Multiple categories mixed
python examples/401_batch_processing.py # Batch generate 4 datasets
python examples/403_large_dataset.py # Generate 1M+ records
Specialized Use Cases (4 examples)
python examples/500_test_data_unit_tests.py # Unit test fixtures
python examples/501_load_testing_data.py # Load testing (100K events)
python examples/502_demo_data.py # Demo/presentation data
python examples/504_api_response_mocking.py # Mock API responses
Complete Feature Showcase
python examples/999_showcase_all_features.py # All 12 features in one!
Run All Examples at Once
To generate data from all 45+ examples in one command:
# Generate everything
for file in examples/[0-9]*.py; do
echo "Running $file..."
python "$file"
done
Or on Windows (PowerShell):
Get-ChildItem examples\*.py -Filter "[0-9]*" | ForEach-Object {
Write-Host "Running $($_.Name)..."
python $_.FullName
}
View Generated Output
All examples save data to the output/ folder:
output/
├── quick_start.csv
├── quick_start.json
├── personal_data.csv
├── ecommerce_products.parquet
├── medical_diagnosis.json
├── ml_metrics.parquet
├── large_dataset.parquet
└── ... (40+ more files)
Learning Path
Beginner: Start with simple examples and work up
00_quick_start → 01_basic_fields → 02_export_formats → 10_personal_data → 20_ecommerce_shop
Intermediate: Explore categories and features
30_bank_accounts → 40_patient_records → 50_addresses → 200_templates → 201_regex_patterns
Advanced: Complex real-world systems and large datasets
300_saas_users → 303_ecommerce_platform → 308_hospital_system → 400_mixed_categories → 403_large_dataset
Examples Summary
| Category | Examples | Records | Topics |
|---|---|---|---|
| Getting Started | 3 | 50-100 | Basics, fields, formats |
| Personal | 3 | 50-300 | Names, IDs, documents |
| Commerce | 4 | 300-2000 | Products, orders, inventory |
| Finance | 5 | 200-1000 | Banking, cards, stocks, crypto |
| Healthcare | 3 | 300-500 | Patients, diagnoses, meds |
| Location | 2 | 500 | Addresses, coordinates, countries |
| Education | 1 | 400 | Students, courses, degrees |
| Automotive | 1 | 600 | Cars, models, registration |
| Entertainment | 2 | 500-2000 | Gaming, movies, books, music |
| Tech | 2 | 200-5000 | Languages, frameworks, ML |
| Advanced | 3 | 50-500 | Templates, regex, blanks |
| Real-World | 7 | 300-5000 | Complete systems |
| Batch/Large | 3 | 100K-1M | Performance, scale |
| Specialized | 4 | 50-100K | Testing, mocking, load tests |
| Total | 45+ | 50 to 1M+ | All features |
Modify Examples for Your Needs
All examples are templates—feel free to copy and modify:
# Copy an example as a starting point
cp examples/20_ecommerce_shop.py my_custom_dataset.py
# Edit and run your custom version
python my_custom_dataset.py
Example Structure
Every example follows this simple pattern:
from ikidatagen import IkiDataGenerator
# 1. Define schema
schema = [
"first_name",
"last_name",
"email_address",
# ... more fields
]
# 2. Generate data
IkiDataGenerator(schema).many(100).export("my_data", formats=["csv", "json"])
# 3. Check output/ folder
Advanced Usage
1. Using Templates for Complex Fields
Combine multiple fields into a single output using template syntax:
from ikidatagen import IkiDataGenerator
schema = [
"first_name",
"last_name",
"email_address",
{
"label": "Full Profile",
"key_label": "template",
"options": {
"template": "{{first_name}} {{last_name}} <{{email_address}}>"
}
},
{
"label": "Address",
"key_label": "template",
"options": {
"template": "{{street_address}}, {{city}}, {{state}}, {{zipcode}}"
}
},
]
generator = IkiDataGenerator(schema)
data = generator.many(100).export("profiles")
Key features:
- Use
{{field_name}}syntax to reference any provider - Combine unlimited fields in one template
- Useful for creating composite fields like full names or addresses
2. Custom Regex Patterns
Generate data matching custom patterns using regular expressions:
schema = [
{
"label": "Order Number",
"key_label": "regular_expression",
"options": {"format": "ORD-[A-Z]{3}-\\d{5}"} # ORD-ABC-12345
},
{
"label": "Serial Number",
"key_label": "regular_expression",
"options": {"format": "[A-Z]{2}\\d{4}-[A-Z]{1}\\d{3}"} # AB1234-X123
},
{
"label": "License Plate",
"key_label": "regular_expression",
"options": {"format": "[A-Z]{3}-\\d{4}"} # ABC-1234
},
]
generator = IkiDataGenerator(schema)
data = generator.many(500).export("orders")
3. Conditional Data with Blanks
Control data density by adding blank values strategically:
schema = [
{"key_label": "row_number", "label": "ID", "options": {"blank_percentage": 0}}, # Never blank
{"key_label": "first_name", "label": "Name"}, # 0% blank (default)
{
"key_label": "phone_number",
"label": "Phone",
"options": {"blank_percentage": 20} # 20% will be empty
},
{
"key_label": "middle_name",
"label": "Middle",
"options": {"blank_percentage": 50} # 50% will be empty
},
{
"key_label": "nickname",
"label": "Nickname",
"options": {"blank_percentage": 80} # 80% will be empty
},
]
generator = IkiDataGenerator(schema)
data = generator.many(1000).export("contacts")
4. Numeric Ranges and Precision
Generate numbers with specific ranges and decimal places:
schema = [
{
"label": "Price",
"key_label": "number",
"options": {"min": 9.99, "max": 999.99, "decimals": 2}
},
{
"label": "Quantity",
"key_label": "number",
"options": {"min": 1, "max": 1000, "decimals": 0}
},
{
"label": "Rating",
"key_label": "number",
"options": {"min": 0, "max": 5, "decimals": 1}
},
{
"label": "Percentage",
"key_label": "number",
"options": {"min": 0, "max": 100, "decimals": 2}
},
]
generator = IkiDataGenerator(schema)
data = generator.many(5000).export("products")
5. Date Ranges and Formats
Generate dates with custom ranges and formatting:
from datetime import datetime, timedelta
schema = [
{
"label": "Born",
"key_label": "date_time",
"options": {
"from_date": "1950-01-01",
"to_date": "2010-12-31",
"date_format": "%Y-%m-%d"
}
},
{
"label": "Order Date",
"key_label": "date_time",
"options": {
"from_date": "2024-01-01",
"to_date": "2024-12-31",
"date_format": "%m/%d/%Y" # MM/DD/YYYY format
}
},
{
"label": "Age Range",
"key_label": "date_time",
"options": {
"minimum_age": 18,
"maximum_age": 65
}
},
]
generator = IkiDataGenerator(schema)
data = generator.many(1000).export("dates")
6. Custom Password Generation
Generate secure passwords with specific requirements:
schema = [
"username",
{
"label": "Basic Password",
"key_label": "password",
"options": {"min_length": 8}
},
{
"label": "Strong Password",
"key_label": "password",
"options": {
"min_length": 12,
"upper_num": 2, # 2+ uppercase
"lower_num": 2, # 2+ lowercase
"numbers_num": 2, # 2+ digits
"symbols_num": 2 # 2+ symbols (!@#$, etc)
}
},
]
generator = IkiDataGenerator(schema)
data = generator.many(100).export("credentials")
7. Currency-Formatted Money
Generate currency values in different formats:
schema = [
{
"label": "USD Price",
"key_label": "money",
"options": {
"min": 10,
"max": 1000,
"currency": "USD" # Returns $1,234.56
}
},
{
"label": "EUR Price",
"key_label": "money",
"options": {
"min": 10,
"max": 1000,
"currency": "EUR" # Returns €1.234,56
}
},
{
"label": "JPY Price",
"key_label": "money",
"options": {
"min": 1000,
"max": 100000,
"currency": "JPY" # Returns ¥123,456
}
},
]
generator = IkiDataGenerator(schema)
data = generator.many(1000).export("prices")
8. Custom Lists & Choices
Generate data from custom value lists:
schema = [
{
"label": "Priority",
"key_label": "custom_list",
"options": {"values": ["Low", "Medium", "High", "Urgent"]}
},
{
"label": "Status",
"key_label": "custom_list",
"options": {"values": "Draft,Review,Approved,Published"} # Comma-separated also works
},
{
"label": "Environment",
"key_label": "custom_list",
"options": {"values": ["Development", "Staging", "Production"]}
},
]
generator = IkiDataGenerator(schema)
data = generator.many(500).export("tasks")
Best Practices & Performance Tips
📊 Performance Optimization
For Large Datasets (1M+ records)
from ikidatagen import IkiDataGenerator
# ✅ GOOD: Generate in batches
schema = ["first_name", "last_name", "email_address"]
generator = IkiDataGenerator(schema)
# Generate 1 million records in 100K batches
batches = 100000
total_records = 1000000
for i in range(0, total_records, batches):
print(f"Generating {i} to {i+batches}...")
generator.many(batches).export(f"users_batch_{i//batches:03d}")
Minimize Blank Percentages
# ✅ GOOD: Only use blanks where needed
schema = [
{"key_label": "id", "label": "ID", "options": {"blank_percentage": 0}}, # Never blank
{"key_label": "email", "options": {"blank_percentage": 5}}, # Minimal blanks
]
# ❌ AVOID: Too many blanks = performance hit
schema = [
{"key_label": "field", "options": {"blank_percentage": 95}}, # 95% blanks!
]
Use Simple Fields When Possible
# ✅ GOOD: Simple field directly
schema = ["first_name", "last_name"]
# ❌ SLOWER: Dict with redundant options
schema = [
{"key_label": "first_name", "label": "first_name", "options": {"blank_percentage": 0}},
{"key_label": "last_name", "label": "last_name", "options": {"blank_percentage": 0}},
]
🎯 Schema Design Best Practices
1. Always Include IDs
schema = [
{"key_label": "row_number", "label": "id", "options": {"blank_percentage": 0}},
# ... other fields
]
2. Use Meaningful Labels
# ✅ GOOD: Clear, descriptive labels
schema = [
{"key_label": "first_name", "label": "Given Name"},
{"key_label": "last_name", "label": "Family Name"},
]
# ❌ AVOID: Cryptic labels
schema = [
{"key_label": "first_name", "label": "fn"},
{"key_label": "last_name", "label": "ln"},
]
3. Organize Fields Logically
# ✅ GOOD: Logical grouping
schema = [
# Identity
"first_name", "last_name", "date_of_birth",
# Contact
"email_address", "phone_number",
# Location
"street_address", "city", "state", "zipcode",
# Professional
"job_title", "company_name",
]
4. Match Field Types to Context
# ✅ GOOD: Use specialized fields
schema = [
{"key_label": "money", "label": "Balance", "options": {"currency": "USD", "min": 0, "max": 50000}},
{"key_label": "credit_card_number", "label": "Card"},
]
# ❌ AVOID: Generic numbers for specific data
schema = [
{"key_label": "number", "label": "Balance"},
{"key_label": "number", "label": "Card"}, # Not realistic!
]
📁 Export Format Selection
# ✅ Use the right format for the job
schema = ["name", "email", "created_at"]
# For analytics: Parquet (compressed, fast)
generator.many(1000000).export("data", formats=["parquet"])
# For web apps: JSON
generator.many(100).export("data", formats=["json"])
# For databases: SQL
generator.many(5000).export("data", formats=["sql"])
# For spreadsheets: Excel or CSV
generator.many(100).export("data", formats=["excel", "csv"])
Integration Examples
1. Database Integration
PostgreSQL with SQLAlchemy
from sqlalchemy import create_engine
from ikidatagen import IkiDataGenerator
import json
# Generate data
schema = ["first_name", "last_name", "email_address", "phone_number"]
generator = IkiDataGenerator(schema)
data = generator.many(1000).export("users")
# Load into database
engine = create_engine("postgresql://user:password@localhost/mydb")
# Read CSV and insert
import pandas as pd
df = pd.read_csv("output/users.csv")
df.to_sql("users", engine, if_exists="append", index=False)
MongoDB
from pymongo import MongoClient
from ikidatagen import IkiDataGenerator
import json
schema = [
"first_name", "last_name", "email_address",
{"key_label": "current_timestamp", "label": "created_at"}
]
generator = IkiDataGenerator(schema)
generator.many(5000).export("users", formats=["json"])
# Connect and insert
client = MongoClient("mongodb://localhost:27017/")
db = client["mydb"]
collection = db["users"]
with open("output/users.json") as f:
data = json.load(f)
collection.insert_many(data)
2. REST API Population
import requests
from ikidatagen import IkiDataGenerator
schema = ["first_name", "last_name", "email_address"]
generator = IkiDataGenerator(schema)
# Generate batch of records
records = generator.many(100)
# Simulate API posting
for record in records:
response = requests.post(
"https://api.example.com/users",
json=record,
headers={"Authorization": "Bearer YOUR_TOKEN"}
)
print(f"Created user {response.status_code}")
3. Testing with pytest
import pytest
from ikidatagen import IkiDataGenerator
@pytest.fixture
def user_data():
"""Generate test user data"""
schema = ["first_name", "last_name", "email_address"]
generator = IkiDataGenerator(schema)
return generator.many(100)
@pytest.fixture
def order_data():
"""Generate test order data"""
schema = [
"row_number",
"first_name",
{"key_label": "money", "options": {"min": 10, "max": 1000}},
"order_status"
]
generator = IkiDataGenerator(schema)
return generator.many(50)
def test_user_creation(user_data):
assert len(user_data) == 100
assert "first_name" in user_data[0]
Customization & Extension Guide
Creating Custom Providers
Add domain-specific data generators:
# In your project: custom_providers.py
from src.ikidatagen.base_generator import BaseProvider
class CustomDomainProvider(BaseProvider):
"""Generate domain-specific data"""
def generate_non_blank(self):
"""Override this method with your logic"""
domain_data = [
"item_one",
"item_two",
"item_three",
]
return random.choice(domain_data)
# Use in your schema
from custom_providers import CustomDomainProvider
schema = [
"first_name",
"last_name",
{
"label": "Custom Field",
"key_label": "custom_domain", # Your provider class
}
]
generator = IkiDataGenerator(schema)
data = generator.many(100).export("custom_data")
Error Handling & Troubleshooting
Common Issues & Solutions
1. "Provider not found" Error
# ❌ WRONG: Typo in provider name
schema = [
{"key_label": "first_nmae"} # Typo: 'nmae' instead of 'name'
]
# ✅ CORRECT: Use exact provider name
schema = [
{"key_label": "first_name"}
]
2. Export Format Not Recognized
# ❌ WRONG: Unsupported format
generator.many(100).export("data", formats=["txt"])
# ✅ CORRECT: Use supported formats
generator.many(100).export("data", formats=["csv", "json", "parquet"])
3. Memory Issues with Large Datasets
# ❌ PROBLEMATIC: Loading everything at once
for i in range(1000000):
data = IkiDataGenerator(schema).many(1) # Creates 1M objects!
# ✅ BETTER: Batch processing
for batch in range(0, 1000000, 10000):
IkiDataGenerator(schema).many(10000).export(f"batch_{batch}")
4. Incorrect Date Format
# ❌ WRONG: Invalid date format code
options = {"date_format": "%Y%d%m"} # YYYY-DD-MM (invalid!)
# ✅ CORRECT: Use valid Python date codes
options = {"date_format": "%Y-%m-%d"} # YYYY-MM-DD
options = {"date_format": "%m/%d/%Y"} # MM/DD/YYYY
options = {"date_format": "%d-%b-%Y"} # DD-Mon-YYYY
Debug Mode
# Enable detailed logging
import logging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger("ikidatagen")
logger.setLevel(logging.DEBUG)
# Now generate data with detailed output
generator = IkiDataGenerator(schema)
data = generator.many(10)
Expanded API Reference
Core Classes
IkiDataGenerator
Main class for generating synthetic data.
Constructor:
generator = IkiDataGenerator(
schema: List[str | Dict], # Field definitions
output_dir: str = "output" # Where to save exports
)
Key Methods:
| Method | Parameters | Returns | Description |
|---|---|---|---|
.many(n) |
n: int |
GeneratorBuilder |
Generate n records |
.export(name, formats) |
name: str, formats: List[str] |
Path | Export to multiple formats |
.get_schema() |
- | Dict |
Get current schema configuration |
.validate_schema() |
- | Bool |
Check if schema is valid |
Example:
generator = IkiDataGenerator(schema)
result = generator.many(1000).export("mydata", formats=["csv", "json"])
Schema Entry Structure
Each schema entry can be:
Simple string (shorthand):
"first_name" # Uses all defaults
Dictionary (full control):
{
"key_label": "first_name", # Required: provider name
"label": "First Name", # Optional: column name (defaults to key_label)
"group": "personal", # Optional: category (auto-resolved)
"options": {
"blank_percentage": 10, # Optional: % of blanks
# ... provider-specific options
}
}
Export Formats
| Format | Extension | Best For | Speed | Size |
|---|---|---|---|---|
| CSV | .csv |
Spreadsheets, Excel | Fast | Medium |
| JSON | .json |
APIs, Web, NoSQL | Medium | Large |
| Parquet | .parquet |
Analytics, Compression | Fastest | Smallest |
| Excel | .xlsx |
Business Reports | Medium | Medium |
| SQL | .sql |
Database Inserts | Medium | Medium |
| DuckDB | .duckdb |
Data Warehouse | Very Fast | Small |
| TSV | .tsv |
Tab-separated | Fast | Medium |
| XML | .xml |
Legacy Systems | Slow | Large |
| Firebase | .json |
Firebase DB | Special | Medium |
Project Structure
Iki-Data-Generator/
├── examples/ # 45+ example scripts
│ ├── README.md # Examples guide
│ ├── 00_quick_start.py # Simplest example
│ ├── 01_basic_fields.py
│ ├── 20_ecommerce_shop.py
│ ├── 300_saas_users.py
│ ├── 403_large_dataset.py
│ └── 999_showcase_all_features.py # All features!
├── src/ikidatagen/ # Main package
│ ├── __init__.py # Public API
│ ├── core.py # Main IkiDataGenerator class
│ ├── base_generator.py # Data generation logic
│ ├── exporters.py # Export to CSV, JSON, SQL, etc.
│ ├── provider_factory.py # Dynamic provider loading
│ ├── schema_registry.py # Maps field names to categories
│ ├── payload.py # Data payload handling
│ ├── dataset_manager.py # Dataset management
│ ├── external_datasets/ # External data files
│ │ ├── csv/ # 30+ CSV files (countries, airlines, etc.)
│ │ └── json/ # 25+ JSON files (advanced data)
│ └── providers/ # Data providers (700+ fields)
│ ├── advanced/ # Template, Regex, Lambda, etc.
│ ├── ai/ # ML/AI metrics
│ ├── basic/ # Names, dates, colors, etc.
│ ├── car/ # Vehicle data
│ ├── commerce/ # Products, orders, payments
│ ├── communication/ # Email, phone, social
│ ├── construction/ # Building codes, materials
│ ├── crypto/ # Cryptocurrency data
│ ├── education/ # Schools, degrees, subjects
│ ├── finance/ # Credit cards, banking
│ ├── gaming/ # Characters, items, guilds
│ ├── health/ # Medical codes, symptoms
│ ├── it/ # Programming, frameworks
│ ├── legal/ # Laws, contracts
│ ├── location/ # Countries, cities, addresses
│ ├── marketing/ # Campaigns, channels
│ ├── misc/ # Miscellaneous data
│ ├── music/ # Artists, albums, genres
│ ├── nature/ # Plants, animals, weather
│ ├── personal/ # Names, gender, documents
│ ├── political/ # Parties, elections
│ ├── products/ # Product categories
│ ├── sports/ # Athletes, teams, leagues
│ └── travel/ # Airlines, hotels, destinations
├── output/ # Generated data (CSV, JSON, etc.)
├── main.py # Example usage
├── pyproject.toml # Package metadata
├── requirements.txt # Dependencies
└── README.md # This file
How It Works (Behind the Scenes)
- Schema Parsing: You provide a list of fields (strings or dicts)
- Provider Resolution: Each field name is looked up in
KEY_LABEL_REGISTRYto find its category - Dynamic Loading: The appropriate provider class is loaded from
providers/{category}/{field}.py - Generation: Each provider generates realistic data for n records
- Template Processing: Template fields combine other fields using
{{field}}syntax - Blank Handling: Records marked for blanks are cleared based on
blank_percentage - Export: Data is serialized to your chosen format(s) and saved to
output/
Common Issues & Solutions
❌ "Unknown key_label 'xxx'"
Problem: You used a field name that doesn't exist.
Solution: Check KEY_LABEL_REGISTRY or review the 22 categories above. Did you spell it correctly? (Use underscores, lowercase.)
# ❌ Wrong
schema = ["firstName"] # camelCase? No!
# ✅ Correct
schema = ["first_name"] # snake_case? Yes!
❌ "No data to export"
Problem: .many(0) or empty schema.
Solution: Generate at least 1 record.
# ❌ Wrong
IkiDataGenerator(schema).many(0).export("data")
# ✅ Correct
IkiDataGenerator(schema).many(100).export("data")
❌ Export folder not found
Problem: output/ directory doesn't exist.
Solution: The library creates it automatically. Make sure you have write permissions.
❌ Template field not rendering
Problem: {{field_name}} not being replaced.
Solution: Ensure the referenced field exists in your schema and the spelling matches exactly.
# ❌ Wrong
{
"key_label": "template",
"options": {"template": "Name: {{first_name}} {{FirstName}}"} # FirstName ≠ first_name
}
# ✅ Correct
{
"key_label": "template",
"options": {"template": "Name: {{first_name}} {{last_name}}"}
}
Performance Tips
Generating Large Datasets
- Use Parquet or DuckDB formats for large datasets (smaller file sizes, faster I/O)
- DuckDB is perfect for immediate querying:
import duckdb; duckdb.sql("SELECT * FROM 'data.duckdb'") - For 1M+ records, generate in batches to manage memory
# ✅ Generate in chunks
for i in range(10):
IkiDataGenerator(schema).many(100_000).export(f"chunk_{i}")
Field Selection
- Only include fields you need (reduces generation time)
- Skip fields with expensive generation (e.g.,
password_hash)
Export Efficiency
# ✅ Smart exports
IkiDataGenerator(schema).many(1_000_000).export("big_data", formats=["parquet"])
# ❌ Avoid exporting to many formats at once
# IkiDataGenerator(schema).many(1_000_000).export("data", formats=["csv", "json", "sql", "excel"])
Contributing
Have ideas? Want to add new providers or categories? Open a PR!
- New Provider: Add a file to
src/ikidatagen/providers/{category}/{field_name}.py - New Category: Create a folder in
providers/and add your providers - Update Registry: Edit
schema_registry.pyto register new fields - Tests: Add tests for new providers
License
MIT License — use it freely in personal and commercial projects.
Links & Resources
- GitHub: github.com/ikidevz/IkiDataGenerator
- Issues: Report bugs or request features
- PyPI: pypi.org/project/iki-data-generator
FAQ
Getting Started
Q: Can I use this data for production?
A: This is synthetic data—perfect for development, testing, and demos. For production, consider anonymizing real data or using this as a base.
Q: What Python versions are supported?
A: Python ≥ 3.10. The library uses modern Python features and is tested on:
- Python 3.10
- Python 3.11
- Python 3.12
Q: How do I install it on Windows/Mac/Linux?
A: Same for all platforms:
pip install iki-data-generator
Or from source:
git clone https://github.com/ikidevz/IkiDataGenerator.git
cd Iki-Data-Generator
pip install -e .
Data Generation
Q: Can I regenerate the exact same data?
A: Not yet. Each run generates different data using random seed generation. Deterministic seed support is planned for future releases. For now:
# Each run produces different output
data1 = generator.many(100).export("run1")
data2 = generator.many(100).export("run2") # Different values
Q: How many records can I generate?
A: Theoretically unlimited, but practical limits depend on:
- Available RAM: 1M records typically use ~500MB
- Export format: CSV is lightweight; JSON uses more space
- Fields per record: More fields = larger dataset
Recommended approach for 100M+ records:
# Generate in batches
batch_size = 100000
for i in range(0, 100000000, batch_size):
generator.many(batch_size).export(f"batch_{i//batch_size}")
Q: How fast is the generation?
A: Performance depends on your system and schema:
- Small schemas (5-10 fields): 50K-100K records/second
- Medium schemas (20-50 fields): 20K-50K records/second
- Complex schemas (templates, regex, blanks): 5K-20K records/second
Typical performance on modern hardware:
import time
schema = ["first_name", "last_name", "email_address"]
start = time.time()
generator.many(100000).export("users")
elapsed = time.time() - start
print(f"Generated 100K records in {elapsed:.2f} seconds") # ~1-2 seconds
Schema & Fields
Q: How do I know which providers are available?
A: The library includes 700+ built-in providers across 22 categories. Check the Complete Provider Reference section in this README, or list them in code:
from src.ikidatagen.schema_registry import KEY_LABEL_REGISTRY
# Print all available providers
for key, value in sorted(KEY_LABEL_REGISTRY.items()):
print(f"{key:40} -> {value}")
Q: Can I extend it with custom fields?
A: Yes! Create a custom provider class:
# custom_providers.py
from src.ikidatagen.base_generator import BaseProvider
import random
class BeerBrandProvider(BaseProvider):
def generate_non_blank(self):
brands = ["Corona", "Heineken", "Guinness", "Stella", "Budweiser"]
return random.choice(brands)
# Use it in your schema
schema = [
"first_name",
{
"label": "Favorite Beer",
"key_label": "beer_brand" # Your custom provider
}
]
generator = IkiDataGenerator(schema)
data = generator.many(100).export("drinkers")
Q: What's the difference between blank_percentage and NULL values?
A: We use blank_percentage (0–100) to control how many records have empty strings:
blank_percentage: 0→ All records have valuesblank_percentage: 50→ 50% have empty stringsblank_percentage: 100→ All records are empty
schema = [
{"key_label": "phone_number", "options": {"blank_percentage": 30}}
# 30% of phone_number fields will be ""
]
Q: How do I create composite fields (templates)?
A: Use the template provider with {{field_name}} syntax:
schema = [
"first_name",
"last_name",
{
"label": "Display Name",
"key_label": "template",
"options": {"template": "{{last_name}}, {{first_name}}"}
},
]
Export & Formats
Q: What export formats are supported?
A: 11+ formats:
| Format | Use Case | Speed | Size |
|---|---|---|---|
| CSV | Spreadsheets | Very Fast | Medium |
| JSON | APIs, Web | Fast | Large |
| Parquet | Analytics, Compression | Fastest | Smallest |
| Excel (.xlsx) | Business Reports | Medium | Medium |
| SQL | Database Imports | Medium | Medium |
| DuckDB | Data Warehouse | Very Fast | Small |
| TSV | Tab-separated | Very Fast | Medium |
| XML | Legacy Systems | Slow | Large |
| Firebase | Firebase DB | Special | Medium |
| HTML | Web Display | Medium | Large |
| Markdown | Documentation | Medium | Medium |
# Export to multiple formats at once
generator.many(1000).export(
"mydata",
formats=["csv", "json", "parquet", "excel"]
)
# Creates: mydata.csv, mydata.json, mydata.parquet, mydata.xlsx
Q: How do I query generated data?
A: Export to DuckDB for SQL queries:
import duckdb
# Generate and export
generator.many(10000).export("users", formats=["duckdb"])
# Query the DuckDB file
conn = duckdb.connect("output/users.duckdb")
results = conn.execute(
"SELECT first_name, COUNT(*) FROM users GROUP BY first_name LIMIT 10"
).fetchall()
print(results)
Q: Can I import the data into Excel?
A: Yes! Export to Excel directly:
generator.many(1000).export("users", formats=["excel"])
# Opens with: Excel, Google Sheets, LibreOffice, Numbers, etc.
Or export to CSV and open in Excel:
generator.many(1000).export("users", formats=["csv"])
# Open output/users.csv with Excel
Q: How do I load data into a database?
A:
PostgreSQL:
import pandas as pd
from sqlalchemy import create_engine
df = pd.read_csv("output/users.csv")
engine = create_engine("postgresql://user:pass@localhost/mydb")
df.to_sql("users", engine, if_exists="append", index=False)
MongoDB:
import json
from pymongo import MongoClient
with open("output/users.json") as f:
data = json.load(f)
client = MongoClient("mongodb://localhost:27017/")
client["mydb"]["users"].insert_many(data)
MySQL:
import pandas as pd
from sqlalchemy import create_engine
df = pd.read_csv("output/users.csv")
engine = create_engine("mysql+pymysql://user:pass@localhost/mydb")
df.to_sql("users", engine, if_exists="append", index=False)
Advanced Topics
Q: What if I need a field that doesn't exist?
A: Use the lambda provider for custom logic:
import random
schema = [
"first_name",
{
"label": "Custom ID",
"key_label": "lambda",
"options": {
"function": lambda: f"USER_{random.randint(100000, 999999)}"
}
}
]
Q: How do I use regular expressions for custom patterns?
A: Use the regular_expression provider:
schema = [
{
"label": "Order Number",
"key_label": "regular_expression",
"options": {"format": "ORD-\\d{6}"} # ORD-123456
},
{
"label": "Phone",
"key_label": "regular_expression",
"options": {"format": "\\(\\d{3}\\) \\d{3}-\\d{4}"} # (123) 456-7890
}
]
Q: Can I join related data (foreign keys)?
A: Foreign key support is planned! For now, use templates to relate data:
# Generate master and detail records
schema = [
{"key_label": "row_number", "label": "order_id"},
{"key_label": "row_number", "label": "product_id"},
"product_name",
]
# Or manually join in code
orders = generator.many(100)
for order in orders:
order["product_id"] = random.randint(1, 50)
Q: How do I handle timezone-aware dates?
A: The date generator creates naive dates. Convert in code:
from datetime import datetime
import pytz
schema = ["first_name", {"key_label": "date_of_birth", "label": "DOB"}]
data = generator.many(100)
# Add timezone
utc = pytz.UTC
for record in data:
record["DOB"] = datetime.fromisoformat(record["DOB"]).replace(tzinfo=utc)
Q: How do I profile or analyze the generated data?
A: Export to Parquet and analyze with pandas:
import pandas as pd
generator.many(50000).export("data", formats=["parquet"])
df = pd.read_parquet("output/data.parquet")
# Basic profiling
print(df.describe())
print(df.dtypes)
print(df.isnull().sum())
Q: Can I use this with Docker?
A: Yes! Create a Dockerfile:
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["python", "my_script.py"]
Then run:
docker build -t datagen .
docker run -v $(pwd)/output:/app/output datagen
Troubleshooting
Q: I get "Provider not found" error
A: Check the provider name spelling:
# ❌ Wrong: Typo
schema = [{"key_label": "first_nmae"}]
# ✅ Correct
schema = [{"key_label": "first_name"}]
Q: Memory usage is too high
A: Generate in smaller batches:
# ❌ Bad: Generates 10M records in memory
data = generator.many(10000000)
# ✅ Good: Process in batches
for i in range(0, 10000000, 100000):
generator.many(100000).export(f"batch_{i//100000}")
Q: Export is slow
A: Some formats are slower than others. Optimize:
# Fastest
generator.many(100000).export("data", formats=["parquet"])
# Fast
generator.many(100000).export("data", formats=["csv"])
# Slower but readable
generator.many(100000).export("data", formats=["json"])
Q: Data looks unrealistic
A: Check your schema for issues:
# ❌ May look unrealistic: Generic number
schema = [{"key_label": "number", "label": "credit_card"}]
# ✅ Better: Use realistic provider
schema = [{"key_label": "credit_card_number", "label": "credit_card"}]
Roadmap
- 🔄 Seed support for reproducible datasets
- 🔗 Foreign key relationships for relational data generation
- 📊 Better performance for 100M+ records with streaming
- 🤖 AI-powered schema suggestions based on use case
- 🎨 Web GUI for schema builder and visualization
- 📈 Data profiling & statistics to validate generation
- 🔐 PII detection to ensure privacy-safe data
- 🌐 API service for cloud-based generation
- 📚 Dataset templates for common domains (SaaS, E-commerce, Healthcare)
Contribution
Found a bug? Want to add providers? Contributions welcome!
- Fork the repository
- Create a feature branch:
git checkout -b feature/my-feature - Make changes and test
- Submit a pull request
Thanks
Built with ❤️ for developers who hate dummy data.
Happy generating! 🎲
Last updated: June 2026 | Version: 1.0.0 | License: MIT
1 maintainer
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