Examples

Getting Started

Help and Dataset Discovery

import tempdataset

# Get comprehensive help
tempdataset.help()

# List all 40 available datasets by category
tempdataset.list_datasets()

Core Business Dataset Examples

Sales Dataset Examples

import tempdataset

# Generate sales transaction data (27 columns)
sales = tempdataset.create_dataset('sales', 1000)

# View structure
print(f"Shape: {sales.shape}")
print(f"Columns: {sales.columns}")

# View sample data
print(sales.head())

# Filter high-value transactions
high_value = sales.filter(lambda row: row['final_price'] > 500)

Customers Dataset Examples

import tempdataset

# Generate customer profiles (31 columns)
customers = tempdataset.create_dataset('customers', 500)

# Analyze customer segments
vip_customers = customers.filter(lambda row: row['loyalty_member'] and row['total_spent'] > 5000)

# Export customer data
customers.to_csv('customer_profiles.csv')

CRM Dataset Examples

import tempdataset

# Generate CRM data with lead tracking
crm = tempdataset.create_dataset('crm', 800)

# Analyze sales pipeline
hot_leads = crm.filter(lambda row: row['lead_score'] > 80)
closed_deals = crm.filter(lambda row: row['deal_status'] == 'Closed Won')

Financial Dataset Examples

Banking Dataset Examples

import tempdataset

# Generate banking transaction data
banking = tempdataset.create_dataset('banking', 2000)

# Fraud detection analysis
suspicious = banking.filter(lambda row: row['fraud_score'] > 0.7)
high_value = banking.filter(lambda row: row['transaction_amount'] > 10000)

Stocks Dataset Examples

import tempdataset

# Generate stock market data
stocks = tempdataset.create_dataset('stocks', 1500)

# Market analysis
high_volume = stocks.filter(lambda row: row['volume'] > 1000000)
tech_stocks = stocks.filter(lambda row: row['sector'] == 'Technology')

Cryptocurrency Dataset Examples

import tempdataset

# Generate crypto trading data
crypto = tempdataset.create_dataset('cryptocurrency', 1000)

# Trading analysis
bitcoin_trades = crypto.filter(lambda row: row['symbol'] == 'BTC')
large_trades = crypto.filter(lambda row: row['trade_value'] > 50000)

IoT Sensors Dataset Examples

Weather Dataset Examples

import tempdataset

# Generate weather sensor data
weather = tempdataset.create_dataset('weather', 2000)

# Climate analysis
extreme_temps = weather.filter(lambda row: row['temperature'] > 35 or row['temperature'] < -10)
high_humidity = weather.filter(lambda row: row['humidity'] > 80)

Energy Dataset Examples

import tempdataset

# Generate smart meter energy data
energy = tempdataset.create_dataset('energy', 1500)

# Energy consumption analysis
peak_usage = energy.filter(lambda row: row['consumption_kwh'] > 50)
renewable_gen = energy.filter(lambda row: row['solar_generation'] > 0)

Healthcare Dataset Examples

Patients Dataset Examples

import tempdataset

# Generate patient medical records
patients = tempdataset.create_dataset('patients', 500)

# Medical analysis
high_risk = patients.filter(lambda row: row['risk_score'] > 7)
chronic_conditions = patients.filter(lambda row: len(row['conditions']) > 2)

Lab Results Dataset Examples

import tempdataset

# Generate laboratory test results
lab_results = tempdataset.create_dataset('lab_results', 1000)

# Clinical analysis
abnormal_results = lab_results.filter(lambda row: row['result_flag'] == 'Abnormal')
urgent_tests = lab_results.filter(lambda row: row['priority'] == 'STAT')

Technology Dataset Examples

Web Analytics Dataset Examples

import tempdataset

# Generate web analytics data
web_analytics = tempdataset.create_dataset('web_analytics', 5000)

# Traffic analysis
mobile_users = web_analytics.filter(lambda row: row['device_type'] == 'Mobile')
high_engagement = web_analytics.filter(lambda row: row['session_duration'] > 300)

API Calls Dataset Examples

import tempdataset

# Generate API performance data
api_calls = tempdataset.create_dataset('api_calls', 10000)

# Performance analysis
slow_requests = api_calls.filter(lambda row: row['response_time'] > 1000)
error_requests = api_calls.filter(lambda row: row['status_code'] >= 400)

Social Media Dataset Examples

Social Media Dataset Examples

import tempdataset

# Generate social media posts data
social_media = tempdataset.create_dataset('social_media', 3000)

# Engagement analysis
viral_posts = social_media.filter(lambda row: row['likes'] > 1000)
trending_hashtags = social_media.filter(lambda row: '#trending' in row['hashtags'])

File Operations

Direct File Generation

import tempdataset

# Generate and save to CSV
tempdataset.create_dataset('sales_data.csv', 1000)
tempdataset.create_dataset('customer_data.csv', 500)

# Generate and save to JSON
tempdataset.create_dataset('ecommerce_data.json', 800)
tempdataset.create_dataset('marketing_data.json', 600)

# Read data back
sales_data = tempdataset.read_csv('sales_data.csv')
marketing_data = tempdataset.read_json('marketing_data.json')

Performance Monitoring

import tempdataset

# Generate large dataset
data = tempdataset.create_dataset('ecommerce', 50000)

# Check performance stats
stats = tempdataset.get_performance_stats()
print(f"Generation time: {stats['generation_time']:.2f}s")
print(f"Memory usage: {stats['memory_usage']:.2f}MB")

# Reset stats for next operation
tempdataset.reset_performance_stats()

Advanced Data Analysis

Multi-Dataset Analysis

import tempdataset

# Generate related datasets
customers = tempdataset.create_dataset('customers', 1000)
sales = tempdataset.create_dataset('sales', 5000)
marketing = tempdataset.create_dataset('marketing', 500)

# Cross-dataset analysis
vip_customers = customers.filter(lambda row: row['loyalty_member'])
high_value_sales = sales.filter(lambda row: row['final_price'] > 1000)
successful_campaigns = marketing.filter(lambda row: row['conversion_rate'] > 0.05)

Data Export and Integration

import tempdataset

# Generate and export multiple formats
data = tempdataset.create_dataset('retail', 2000)

# Export options
data.to_csv('retail_analysis.csv')
data.to_json('retail_data.json')

# Convert to dictionary for further processing
dict_data = data.to_dict()

# Select specific columns for reports
summary = data.select(['store_id', 'total_sales', 'date', 'staff_id'])
summary.to_csv('daily_summary.csv')

Dataset Schema Exploration

import tempdataset

# Explore dataset structure
for dataset_name in ['sales', 'customers', 'ecommerce', 'employees']:
    data = tempdataset.create_dataset(dataset_name, 10)  # Small sample
    print(f"\n{dataset_name.upper()} Dataset:")
    print(f"Columns ({len(data.columns)}): {list(data.columns)}")
    print(f"Sample data:\n{data.head(3)}")

Co mplete Dataset Category Examples ———————————-

All Core Business Datasets

import tempdataset

# Generate all core business datasets
crm = tempdataset.create_dataset('crm', 500)
customers = tempdataset.create_dataset('customers', 1000)
ecommerce = tempdataset.create_dataset('ecommerce', 2000)
employees = tempdataset.create_dataset('employees', 300)
inventory = tempdataset.create_dataset('inventory', 800)
marketing = tempdataset.create_dataset('marketing', 600)
retail = tempdataset.create_dataset('retail', 1500)
reviews = tempdataset.create_dataset('reviews', 1200)
sales = tempdataset.create_dataset('sales', 2500)
suppliers = tempdataset.create_dataset('suppliers', 150)

All Financial Datasets

import tempdataset

# Generate all financial datasets
stocks = tempdataset.create_dataset('stocks', 2000)
banking = tempdataset.create_dataset('banking', 3000)
cryptocurrency = tempdataset.create_dataset('cryptocurrency', 1500)
insurance = tempdataset.create_dataset('insurance', 800)
loans = tempdataset.create_dataset('loans', 1000)
investments = tempdataset.create_dataset('investments', 600)
accounting = tempdataset.create_dataset('accounting', 2000)
payments = tempdataset.create_dataset('payments', 5000)

All IoT Sensors Datasets

import tempdataset

# Generate all IoT sensor datasets
weather = tempdataset.create_dataset('weather', 10000)
energy = tempdataset.create_dataset('energy', 8000)
traffic = tempdataset.create_dataset('traffic', 15000)
environmental = tempdataset.create_dataset('environmental', 5000)
industrial = tempdataset.create_dataset('industrial', 12000)
smarthome = tempdataset.create_dataset('smarthome', 6000)

All Healthcare Datasets

import tempdataset

# Generate all healthcare datasets
patients = tempdataset.create_dataset('patients', 1000)
appointments = tempdataset.create_dataset('appointments', 2000)
lab_results = tempdataset.create_dataset('lab_results', 3000)
prescriptions = tempdataset.create_dataset('prescriptions', 2500)
medical_history = tempdataset.create_dataset('medical_history', 1500)
clinical_trials = tempdataset.create_dataset('clinical_trials', 500)

All Technology Datasets

import tempdataset

# Generate all technology datasets
web_analytics = tempdataset.create_dataset('web_analytics', 20000)
app_usage = tempdataset.create_dataset('app_usage', 15000)
system_logs = tempdataset.create_dataset('system_logs', 50000)
api_calls = tempdataset.create_dataset('api_calls', 100000)
server_metrics = tempdataset.create_dataset('server_metrics', 25000)
user_sessions = tempdataset.create_dataset('user_sessions', 30000)
error_logs = tempdataset.create_dataset('error_logs', 10000)
performance = tempdataset.create_dataset('performance', 40000)

Real-World Use Cases

E-commerce Analytics Pipeline

import tempdataset

# Generate comprehensive e-commerce data
customers = tempdataset.create_dataset('customers', 5000)
sales = tempdataset.create_dataset('sales', 25000)
reviews = tempdataset.create_dataset('reviews', 8000)
web_analytics = tempdataset.create_dataset('web_analytics', 100000)

# Customer segmentation analysis
premium_customers = customers.filter(lambda row: row['total_spent'] > 2000)

# Sales performance analysis
high_value_orders = sales.filter(lambda row: row['final_price'] > 500)

# Review sentiment analysis
positive_reviews = reviews.filter(lambda row: row['rating'] >= 4)

# Web traffic analysis
converting_sessions = web_analytics.filter(lambda row: row['conversion'] == True)

Financial Risk Assessment

import tempdataset

# Generate financial risk data
banking = tempdataset.create_dataset('banking', 10000)
loans = tempdataset.create_dataset('loans', 3000)
insurance = tempdataset.create_dataset('insurance', 5000)

# Risk analysis
high_risk_transactions = banking.filter(lambda row: row['fraud_score'] > 0.8)
defaulted_loans = loans.filter(lambda row: row['loan_status'] == 'Default')
high_claims = insurance.filter(lambda row: row['claim_amount'] > 50000)

Healthcare Data Analysis

import tempdataset

# Generate healthcare analytics data
patients = tempdataset.create_dataset('patients', 2000)
appointments = tempdataset.create_dataset('appointments', 8000)
lab_results = tempdataset.create_dataset('lab_results', 15000)

# Clinical analysis
high_risk_patients = patients.filter(lambda row: row['risk_score'] > 8)
missed_appointments = appointments.filter(lambda row: row['status'] == 'No Show')
critical_results = lab_results.filter(lambda row: row['result_flag'] == 'Critical')

IoT Monitoring Dashboard

import tempdataset

# Generate IoT sensor data
weather = tempdataset.create_dataset('weather', 50000)
energy = tempdataset.create_dataset('energy', 30000)
traffic = tempdataset.create_dataset('traffic', 100000)

# Environmental monitoring
extreme_weather = weather.filter(lambda row:
    row['temperature'] > 40 or row['temperature'] < -20)

# Energy efficiency analysis
peak_consumption = energy.filter(lambda row: row['consumption_kwh'] > 100)

# Traffic optimization
congested_areas = traffic.filter(lambda row: row['avg_speed'] < 20)

Performance Testing Datasets

import tempdataset

# Generate large datasets for performance testing
large_sales = tempdataset.create_dataset('sales', 100000)
large_logs = tempdataset.create_dataset('system_logs', 500000)
large_api = tempdataset.create_dataset('api_calls', 1000000)

# Monitor performance
stats = tempdataset.get_performance_stats()
print(f"Total generation time: {stats['generation_time']:.2f}s")
print(f"Peak memory usage: {stats['memory_usage']:.2f}MB")

Batch File Generation

import tempdataset

# Generate multiple datasets and save to files
datasets_config = [
    ('sales_q1.csv', 'sales', 10000),
    ('customers_active.csv', 'customers', 5000),
    ('web_traffic.json', 'web_analytics', 50000),
    ('financial_data.csv', 'banking', 20000),
    ('iot_sensors.json', 'weather', 25000)
]

for filename, dataset_type, rows in datasets_config:
    print(f"Generating {filename}...")
    tempdataset.create_dataset(filename, rows)
    print(f"✓ Generated {filename} with {rows} rows")

Dataset Comparison and Validation

import tempdataset

# Generate datasets for comparison
datasets_to_compare = ['sales', 'ecommerce', 'retail']

for dataset_name in datasets_to_compare:
    data = tempdataset.create_dataset(dataset_name, 100)
    print(f"\n{dataset_name.upper()} Dataset Analysis:")
    print(f"Columns: {len(data.columns)}")
    print(f"Sample columns: {list(data.columns)[:5]}")
    print(f"Data types: {[type(data.data[0][col]).__name__ for col in list(data.columns)[:3]]}")

Custom Analysis Workflows

import tempdataset

# Multi-step analysis workflow
def analyze_business_performance():
    # Step 1: Generate core business data
    sales = tempdataset.create_dataset('sales', 5000)
    customers = tempdataset.create_dataset('customers', 2000)
    marketing = tempdataset.create_dataset('marketing', 500)

    # Step 2: Performance metrics
    total_revenue = sum(row['final_price'] for row in sales.data)
    avg_order_value = total_revenue / len(sales.data)

    # Step 3: Customer insights
    loyal_customers = customers.filter(lambda row: row['loyalty_member'])
    high_value_customers = customers.filter(lambda row: row['total_spent'] > 1000)

    # Step 4: Marketing effectiveness
    successful_campaigns = marketing.filter(lambda row: row['roi'] > 2.0)

    # Step 5: Export results
    sales.to_csv('business_analysis_sales.csv')
    loyal_customers.to_csv('loyal_customers.csv')
    successful_campaigns.to_csv('top_campaigns.csv')

    return {
        'total_revenue': total_revenue,
        'avg_order_value': avg_order_value,
        'loyal_customer_count': len(loyal_customers.data),
        'successful_campaign_count': len(successful_campaigns.data)
    }

# Run the analysis
results = analyze_business_performance()
print("Business Performance Analysis Results:")
for key, value in results.items():
    print(f"{key}: {value}")