This document provides dataset-level documentation for all datasets currently available in DataForge.
Each dataset follows a predefined schema and returns a clean pandas.DataFrame suitable for analytics, visualization, and testing workflows.
Simulates ecommerce and retail transaction data commonly used in sales analysis, pricing strategy, and customer segmentation.
- Revenue and profit analysis
- Product performance tracking
- Time-based sales trends
- Store and region comparison
- Order & Customer:
order_date,first_name,last_name,gender,email - Product:
product_category,product,brand,unit_price,quantity - Financials:
sales_amount,cost_amount - Store & Location:
store_name,city,state,country
Represents workforce and HR data for organizational analytics and planning.
- Headcount analysis
- Attrition tracking
- Salary benchmarking
- Workforce diversity metrics
- Employee Info:
employee_id,first_name,last_name,gender - Employment:
department,job_title,employment_type - Dates:
start_date,exit_date,date_of_birth - Compensation:
salary - Location:
city,state,country
Synthetic job postings dataset designed for talent analytics and job market insights.
- Skill demand analysis
- Salary range comparison
- Job recommendation systems
- Market trend analysis
- Job Details:
job_title,role,experience,work_type - Compensation:
salary_range - Skills & Requirements:
skills,qualifications - Location:
location,country,region - Company:
company_name,company_profile
Simulates shipment and delivery lifecycle data used in logistics and supply chain analytics.
- SLA monitoring
- Route and delivery analysis
- Cost and tariff evaluation
- Operational performance tracking
- Shipment:
consignment_no,booking_code,mode - Sender & Receiver:
origin,destination,sender_city,receiver_city - Weights:
actual_wt,volumetric_wt,chargeable_wt - Charges:
tariff,vas_charges,total_amount - Dates:
booking_date,receive_date
Represents patient visit records used for healthcare operations and analytics simulations.
- Patient flow analysis
- Department workload tracking
- Billing and insurance analysis
- Hospital performance metrics
- Patient & Visit:
patient_id,visit_id,visit_date - Clinical:
department,diagnosis,treatment - Financial:
billing_amount,insurance_provider - Status:
visit_status
ℹ️ All datasets return a
pandas.DataFrameand maintain consistent schemas across generations.