TempDataset Documentation

A lightweight Python library for generating realistic temporary datasets for testing and development.

Contents:

• Installation
  • Requirements
  • Install from PyPI
  • Install from Source
  • Development Installation
  • Verify Installation
• Quick Start Guide
  • Getting Help
  • Basic Usage
  • Explore Dataset Structure
  • Working with Data
  • Dataset-Specific Examples
  • Performance Monitoring
  • All Available Datasets
• Dataset Reference
  • Core Business Datasets (10)
  • Financial Datasets (8)
  • IoT Sensors Datasets (6)
  • Healthcare Datasets (6)
  • Social Media Datasets (2)
  • Technology Datasets (8)
  • Getting Help
  • Common Patterns
  • Usage Examples
• API Reference
  • Core Functions
  • TempDataFrame Class
  • Core Business Dataset Classes
  • Financial Dataset Classes
  • IoT Sensors Dataset Classes
  • Healthcare Dataset Classes
  • Social Media Dataset Classes
  • Technology Dataset Classes
• Examples
  • Getting Started
  • Core Business Dataset Examples
  • Financial Dataset Examples
  • IoT Sensors Dataset Examples
  • Healthcare Dataset Examples
  • Technology Dataset Examples
  • Social Media Dataset Examples
  • File Operations
  • Advanced Data Analysis
  • Real-World Use Cases

Features

• 40 Comprehensive Datasets: Business, Financial, Technology, Healthcare, IoT Sensors, and Social Media

• Technology Focus: New datasets for DevOps monitoring, web analytics, and system performance

• Lightweight: Zero dependencies for core functionality

• Multiple Formats: Generate CSV, JSON, or in-memory datasets

• Realistic Data: Built-in datasets with realistic patterns and relationships

• Extensible: Easy to add custom dataset types

• Memory Efficient: Optimized for large dataset generation

• Built-in Help: Interactive help system with tempdataset.help() and tempdataset.list_datasets()

• Python 3.7+: Compatible with modern Python versions

Quick Example

import tempdataset

# Get comprehensive help
tempdataset.help()

# Generate any of the 40 available datasets
sales_data = tempdataset.create_dataset('sales', 1000)
customers = tempdataset.create_dataset('customers', 500)
banking = tempdataset.create_dataset('banking', 800)
weather = tempdataset.create_dataset('weather', 1200)

# Save directly to files
tempdataset.create_dataset('employees.csv', 300)
tempdataset.create_dataset('web_analytics.json', 600)

Dataset Categories

Core Business (10 datasets) * CRM: Customer relationship management data * Customers: Customer profiles and demographics * E-commerce: E-commerce transactions and reviews * Employees: HR data with performance metrics * Inventory: Warehouse and inventory management * Marketing: Campaign data with ROI analysis * Retail: In-store operations and POS data * Reviews: Product and service reviews * Sales: Transaction data with order details * Suppliers: Vendor management and contracts

Financial (8 datasets) * Stocks: Stock market trading data * Banking: Banking transactions and accounts * Cryptocurrency: Crypto trading and wallets * Insurance: Policies and claims processing * Loans: Loan applications and management * Investments: Investment portfolios and performance * Accounting: General ledger and financial records * Payments: Digital payment processing

IoT Sensors (6 datasets) * Weather: Weather sensor monitoring * Energy: Smart meter energy consumption * Traffic: Traffic sensor and flow data * Environmental: Air quality and pollution monitoring * Industrial: Manufacturing sensor data * Smart Home: IoT device monitoring

Healthcare (6 datasets) * Patients: Patient medical records * Appointments: Medical appointment scheduling * Lab Results: Laboratory test results * Prescriptions: Medication prescriptions * Medical History: Patient medical history * Clinical Trials: Clinical trial participant data

Social Media (2 datasets) * Social Media: Posts, engagement, and metrics * User Profiles: Social media user profiles

Technology (8 datasets) * Web Analytics: Website traffic and user behavior * App Usage: Mobile app usage analytics * System Logs: System and application logs * API Calls: API performance and usage * Server Metrics: Server performance monitoring * User Sessions: User session tracking * Error Logs: Application error tracking * Performance: Application performance monitoring

Indices and tables

• Index

• Module Index

• Search Page