Dataset Reference

TempDataset provides 40 comprehensive datasets across 6 categories for various use cases. Each dataset is carefully designed with realistic data patterns and relationships.

Core Business Datasets (10)

CRM Dataset

Usage: tempdataset.create_dataset('crm', rows)

Description: Customer relationship management data with lead tracking, sales pipeline, and customer interactions.

Key Features: - Lead and opportunity tracking - Sales pipeline management - Customer interaction history - Revenue forecasting data - Sales team performance metrics

Customers Dataset

Usage: tempdataset.create_dataset('customers', rows)

Columns: 31

Description: Comprehensive customer profiles with personal information, demographics, purchase history, and loyalty data.

Key Features: - Complete personal and contact information - Professional and demographic details - Purchase history and spending patterns - Loyalty program participation - Account status and preferences - Geographic distribution

E-commerce Dataset

Usage: tempdataset.create_dataset('ecommerce', rows)

Columns: 35+

Description: Advanced e-commerce transaction data with customer behavior, product details, reviews, returns, and digital metrics.

Key Features: - Transaction details with timestamps - Customer behavior and device information - Product catalog with reviews and ratings - Return and refund processing - Digital metrics (conversion rates, sessions) - Seller and marketplace data

Employees Dataset

Usage: tempdataset.create_dataset('employees', rows)

Columns: 30+

Description: Complete HR and employee management data with performance metrics, benefits, and organizational structure.

Key Features: - Personal and contact information - Job details and organizational structure - Performance ratings and reviews - Compensation and benefits data - Skills and certifications - Training and development records

Inventory Dataset

Usage: tempdataset.create_dataset('inventory', rows)

Description: Warehouse and inventory management data with stock levels, product information, and supply chain metrics.

Key Features: - Product catalog and SKU management - Stock levels and warehouse locations - Supplier and vendor information - Reorder points and lead times - Cost and pricing data

Marketing Dataset

Usage: tempdataset.create_dataset('marketing', rows)

Columns: 32+

Description: Marketing campaign performance data with channel metrics, ROI analysis, and audience insights.

Key Features: - Campaign identification and metadata - Multi-channel performance metrics - ROI and conversion analysis - Audience demographics and targeting - Budget allocation and spending - Attribution and touch point analysis

Retail Dataset

Usage: tempdataset.create_dataset('retail', rows)

Columns: 28+

Description: In-store retail operations data with point-of-sale transactions, inventory management, and store operations.

Key Features: - Point-of-sale transaction data - Inventory levels and stock management - Store location and staff information - Seasonal trends and patterns - Customer loyalty card integration - Shift and operational data

Reviews Dataset

Usage: tempdataset.create_dataset('reviews', rows)

Description: Product and service reviews with ratings, sentiment analysis, and customer feedback data.

Key Features: - Review ratings and sentiment scores - Product and service categorization - Customer demographics and purchase history - Review helpfulness and verification - Response and moderation data

Sales Dataset

Usage: tempdataset.create_dataset('sales', rows)

Columns: 27

Description: Complete sales transaction data with order information, customer details, product data, financial calculations, and geographic information.

Key Features: - Realistic transaction IDs and order tracking - Customer demographics and segmentation - Product catalog with categories and brands - Financial calculations with discounts and profit - Geographic distribution across regions - Shipping and delivery logistics

Suppliers Dataset

Usage: tempdataset.create_dataset('suppliers', rows)

Columns: 22+

Description: Supplier and vendor management data with performance metrics, contract information, and quality ratings.

Key Features: - Supplier company profiles - Performance and quality metrics - Contract terms and conditions - Delivery performance tracking - Financial and credit information - Geographic coverage areas

Financial Datasets (8)

Stocks Dataset

Usage: tempdataset.create_dataset('stocks', rows)

Description: Stock market trading data with prices, volumes, and market indicators.

Key Features: - Stock symbols and company information - OHLC (Open, High, Low, Close) pricing - Trading volumes and market cap - Technical indicators and ratios - Sector and industry classification

Banking Dataset

Usage: tempdataset.create_dataset('banking', rows)

Columns: 20

Description: Banking transaction data with account details, transaction types, and fraud detection indicators.

Key Features: - Account information and balances - Transaction types and amounts - Merchant and location data - Fraud detection scores - Currency and exchange rates

Cryptocurrency Dataset

Usage: tempdataset.create_dataset('cryptocurrency', rows)

Description: Cryptocurrency trading data with wallet addresses, transaction hashes, and market data.

Key Features: - Cryptocurrency symbols and prices - Wallet addresses and transaction IDs - Trading volumes and market metrics - Mining and staking information - Exchange and platform data

Insurance Dataset

Usage: tempdataset.create_dataset('insurance', rows)

Description: Insurance policies and claims data with coverage details and risk assessment.

Key Features: - Policy information and coverage types - Claims processing and settlements - Risk assessment and underwriting - Premium calculations and payments - Agent and broker information

Loans Dataset

Usage: tempdataset.create_dataset('loans', rows)

Description: Loan applications and management data with credit scores and repayment tracking.

Key Features: - Loan application details - Credit scores and risk assessment - Repayment schedules and history - Interest rates and terms - Collateral and guarantor information

Investments Dataset

Usage: tempdataset.create_dataset('investments', rows)

Description: Investment portfolio data with asset allocation and performance tracking.

Key Features: - Portfolio composition and allocation - Asset performance and returns - Risk metrics and volatility - Investment strategies and goals - Advisor and management fees

Accounting Dataset

Usage: tempdataset.create_dataset('accounting', rows)

Description: General ledger and accounting data with journal entries and financial statements.

Key Features: - Chart of accounts and classifications - Journal entries and transactions - Balance sheet and income statement data - Budget vs actual comparisons - Audit trails and compliance

Payments Dataset

Usage: tempdataset.create_dataset('payments', rows)

Description: Digital payment processing data with transaction details and payment methods.

Key Features: - Payment methods and processors - Transaction amounts and fees - Success rates and failure reasons - Merchant and customer information - Settlement and reconciliation data

IoT Sensors Datasets (6)

Weather Dataset

Usage: tempdataset.create_dataset('weather', rows)

Description: Weather sensor monitoring data with temperature, humidity, pressure, and atmospheric conditions.

Key Features: - Temperature and humidity readings - Atmospheric pressure and wind data - Precipitation and weather conditions - Air quality and visibility metrics - Geographic coordinates and timestamps

Energy Dataset

Usage: tempdataset.create_dataset('energy', rows)

Description: Smart meter energy consumption data with usage patterns and billing information.

Key Features: - Energy consumption readings - Peak and off-peak usage patterns - Billing and rate information - Renewable energy generation - Grid stability and load balancing

Traffic Dataset

Usage: tempdataset.create_dataset('traffic', rows)

Description: Traffic sensor and flow data with vehicle counts and congestion metrics.

Key Features: - Vehicle counts and classifications - Speed and congestion measurements - Traffic light and signal data - Incident and accident reporting - Route optimization metrics

Environmental Dataset

Usage: tempdataset.create_dataset('environmental', rows)

Description: Environmental monitoring data with air quality, pollution levels, and ecological metrics.

Key Features: - Air quality indices and pollutants - Water quality measurements - Noise pollution levels - Radiation and chemical sensors - Ecological impact assessments

Industrial Dataset

Usage: tempdataset.create_dataset('industrial', rows)

Description: Manufacturing and industrial sensor data with equipment monitoring and production metrics.

Key Features: - Equipment performance and maintenance - Production line efficiency - Quality control measurements - Safety and compliance monitoring - Energy consumption and optimization

Smart Home Dataset

Usage: tempdataset.create_dataset('smarthome', rows)

Description: Smart home IoT device data with automation, security, and energy management.

Key Features: - Device status and automation rules - Security system monitoring - Energy usage optimization - Environmental controls - User preferences and schedules

Healthcare Datasets (6)

Patients Dataset

Usage: tempdataset.create_dataset('patients', rows)

Description: Patient medical records with demographics, medical history, and treatment information.

Key Features: - Patient demographics and contact info - Medical history and conditions - Insurance and billing information - Emergency contacts and preferences - Treatment plans and outcomes

Appointments Dataset

Usage: tempdataset.create_dataset('appointments', rows)

Description: Medical appointment scheduling data with provider information and visit details.

Key Features: - Appointment scheduling and status - Healthcare provider information - Visit types and specialties - Insurance verification and copays - Follow-up and referral tracking

Lab Results Dataset

Usage: tempdataset.create_dataset('lab_results', rows)

Description: Laboratory test results with diagnostic information and reference ranges.

Key Features: - Test types and methodologies - Result values and reference ranges - Quality control and validation - Ordering physician information - Turnaround times and priorities

Prescriptions Dataset

Usage: tempdataset.create_dataset('prescriptions', rows)

Description: Medication prescriptions with dosage information and pharmacy data.

Key Features: - Medication names and dosages - Prescribing physician information - Pharmacy and dispensing data - Insurance coverage and copays - Refill history and adherence

Medical History Dataset

Usage: tempdataset.create_dataset('medical_history', rows)

Description: Patient medical history with chronic conditions, surgeries, and family history.

Key Features: - Chronic conditions and diagnoses - Surgical history and procedures - Family medical history - Allergies and adverse reactions - Lifestyle and risk factors

Clinical Trials Dataset

Usage: tempdataset.create_dataset('clinical_trials', rows)

Description: Clinical trial participant data with study protocols and outcome measures.

Key Features: - Study protocols and phases - Participant demographics and eligibility - Treatment arms and randomization - Outcome measures and endpoints - Adverse events and safety monitoring

Technology Datasets (8)

Web Analytics Dataset

Usage: tempdataset.create_dataset('web_analytics', rows)

Description: Website traffic and user behavior data with page views, sessions, and conversion metrics.

Key Features: - Page views and session data - User behavior and navigation paths - Conversion tracking and goals - Traffic sources and campaigns - Device and browser information

App Usage Dataset

Usage: tempdataset.create_dataset('app_usage', rows)

Description: Mobile app usage analytics with user sessions, feature usage, and performance metrics.

Key Features: - User sessions and screen time - Feature usage and interactions - App performance and crashes - User retention and churn - In-app purchases and monetization

System Logs Dataset

Usage: tempdataset.create_dataset('system_logs', rows)

Description: System and application logs with error tracking and performance monitoring.

Key Features: - Log levels and message types - System components and services - Error codes and stack traces - Performance metrics and timing - User actions and system events

API Calls Dataset

Usage: tempdataset.create_dataset('api_calls', rows)

Description: API performance and usage data with request/response metrics and error tracking.

Key Features: - API endpoints and methods - Request/response times and sizes - Status codes and error rates - Authentication and rate limiting - Client information and usage patterns

Server Metrics Dataset

Usage: tempdataset.create_dataset('server_metrics', rows)

Description: Server performance monitoring data with CPU, memory, disk, and network metrics.

Key Features: - CPU and memory utilization - Disk I/O and storage metrics - Network traffic and bandwidth - Load balancing and scaling - Health checks and alerts

User Sessions Dataset

Usage: tempdataset.create_dataset('user_sessions', rows)

Description: User session tracking data with login/logout events and activity monitoring.

Key Features: - Session start/end times and duration - User authentication and authorization - Activity tracking and page views - Device and location information - Security events and anomalies

Error Logs Dataset

Usage: tempdataset.create_dataset('error_logs', rows)

Description: Application error tracking data with exception details and debugging information.

Key Features: - Error types and severity levels - Stack traces and debugging info - User context and session data - Error frequency and patterns - Resolution status and fixes

Performance Dataset

Usage: tempdataset.create_dataset('performance', rows)

Description: Application performance monitoring data with response times, throughput, and resource usage.

Key Features: - Response times and latency metrics - Throughput and transaction rates - Resource utilization and bottlenecks - Performance trends and baselines - SLA compliance and alerts

Getting Help

Use the built-in help functions to explore datasets:

import tempdataset

# Comprehensive help with examples
tempdataset.help()

# Quick dataset overview with categories
tempdataset.list_datasets()

# Explore specific dataset structure
data = tempdataset.create_dataset('sales', 10)
print(data.columns)

Common Patterns

All datasets follow these common patterns:

ID Generation: Sequential IDs with realistic formatting Dates: Proper chronological relationships between related dates Geographic Data: Consistent country, state, and city relationships Financial Data: Realistic pricing with proper calculations Demographics: Age-appropriate and statistically realistic distributions Relationships: Logical correlations between related fields

Data Quality: All datasets include: - Proper data types for each column - Realistic value distributions - Consistent formatting - Logical relationships between fields - No missing values (except where realistic)

Usage Examples

import tempdataset

# Generate different dataset categories

# Business data
sales = tempdataset.create_dataset('sales', 1000)
customers = tempdataset.create_dataset('customers', 500)

# Financial data
banking = tempdataset.create_dataset('banking', 800)
stocks = tempdataset.create_dataset('stocks', 1200)

# IoT sensor data
weather = tempdataset.create_dataset('weather', 2000)
energy = tempdataset.create_dataset('energy', 1500)

# Healthcare data
patients = tempdataset.create_dataset('patients', 300)
appointments = tempdataset.create_dataset('appointments', 600)

# Technology data
web_analytics = tempdataset.create_dataset('web_analytics', 5000)
api_calls = tempdataset.create_dataset('api_calls', 10000)

# Save to files
tempdataset.create_dataset('financial_data.csv', 1000)  # Uses 'sales' as default
tempdataset.create_dataset('iot_sensors.json', 2000)