API Reference

Core Functions

tempdataset.create_dataset(dataset_type: str, rows: int = 500)

Generate temporary datasets or save to files.

Parameters:

• dataset_type – Dataset type or filename Available types: ‘crm’, ‘customers’, ‘ecommerce’, ‘employees’, ‘inventory’, ‘marketing’, ‘retail’, ‘reviews’, ‘sales’, ‘suppliers’, ‘stocks’, ‘banking’, ‘cryptocurrency’, ‘insurance’, ‘loans’, ‘investments’, ‘accounting’, ‘payments’, ‘weather’, ‘energy’, ‘traffic’, ‘environmental’, ‘industrial’, ‘smarthome’, ‘patients’, ‘appointments’, ‘lab_results’, ‘prescriptions’, ‘medical_history’, ‘clinical_trials’, ‘social_media’, ‘user_profiles’, ‘web_analytics’, ‘app_usage’, ‘system_logs’, ‘api_calls’, ‘server_metrics’, ‘user_sessions’, ‘error_logs’, ‘performance’ Or filename with extension: ‘data.csv’, ‘data.json’

• rows – Number of rows to generate (default: 500)

Returns:

TempDataFrame containing the generated data (also saves to file if filename provided)

Raises:

• ValidationError – If parameters are invalid

• DatasetNotFoundError – If dataset type is not available

• DataGenerationError – If data generation fails

• TempDatasetMemoryError – If memory limits are exceeded

• CSVWriteError – If CSV file writing fails

• JSONWriteError – If JSON file writing fails

tempdataset.help()

Display help information about TempDataset usage.

tempdataset.list_datasets()

List all available datasets with their names only.

tempdataset.read_csv(filename: str) → TempDataFrame

Read CSV file into TempDataFrame.

Parameters:

filename – Path to CSV file

Returns:

TempDataFrame containing the CSV data

Raises:

• ValidationError – If parameters are invalid

• CSVReadError – If the CSV file is malformed or cannot be read

tempdataset.read_json(filename: str) → TempDataFrame

Read JSON file into TempDataFrame.

Parameters:

filename – Path to JSON file

Returns:

TempDataFrame containing the JSON data

Raises:

• ValidationError – If parameters are invalid

• JSONReadError – If the JSON file is malformed or cannot be read

tempdataset.get_performance_stats()

Get performance statistics from the data generator.

tempdataset.reset_performance_stats()

Reset performance monitoring counters.

TempDataFrame Class

class tempdataset.TempDataFrame(data: List[Dict[str, Any]], columns: List[str])

Bases: object

Lightweight DataFrame-like class for data manipulation and exploration.

Provides essential methods for working with tabular data without pandas dependency.

property columns: List[str]

Return column names.

Returns:

List of column names

describe() → DisplayFormatter

Generate descriptive statistics for numeric columns.

Returns:

DisplayFormatter object with statistical summary

filter(condition_func) → TempDataFrame

Filter rows based on a condition function.

Parameters:

condition_func – Function that takes a row dict and returns True/False

Returns:

New TempDataFrame with filtered rows

Raises:

ValidationError – If condition_func is not callable

head(n: int = 5) → DisplayFormatter

Display first n rows in a readable format.

Parameters:

n – Number of rows to display (default: 5)

Returns:

DisplayFormatter object with formatted representation of the first n rows

Raises:

ValidationError – If n is not a positive integer

info() → DisplayFormatter

Display dataset information including column types and memory usage.

Returns:

DisplayFormatter object with dataset information

memory_usage() → float

Get memory usage in megabytes.

Returns:

Memory usage in MB as a float

select(columns: List[str]) → TempDataFrame

Select specific columns from the DataFrame.

Parameters:

columns – List of column names to select

Returns:

New TempDataFrame with selected columns only

Raises:

ValidationError – If columns parameter is invalid or contains non-existent columns

shape

Custom descriptor that allows shape to work both as property and method.

tail(n: int = 5) → DisplayFormatter

Display last n rows in a readable format.

Parameters:

n – Number of rows to display (default: 5)

Returns:

DisplayFormatter object with formatted representation of the last n rows

Raises:

ValidationError – If n is not a positive integer

to_csv(filename: str) → None

Export to CSV file.

Parameters:

filename – Path to output CSV file

Raises:

• ValidationError – If filename is invalid

• CSVWriteError – If CSV writing fails

to_dict() → List[Dict[str, Any]]

Convert DataFrame to list of dictionaries.

Returns:

List of dictionaries representing the data

to_json(filename: str) → None

Export to JSON file.

Parameters:

filename – Path to output JSON file

Raises:

• ValidationError – If filename is invalid

• JSONWriteError – If JSON writing fails

Core Business Dataset Classes

class tempdataset.CrmDataset(rows: int = 500)

Bases: BaseDataset

CRM dataset generator that creates realistic customer relationship management data.

Generates 30+ columns of CRM data including: - Customer information (customer_id, name, email, company, demographics) - Account management (account_manager, creation_date, status) - Interactions (channel, notes, contact dates) - Sales pipeline (stage, deal value, probability) - Support data (tickets, satisfaction ratings) - Geographic and preference data

generate() → List[Dict[str, Any]]

Generate CRM dataset rows.

Returns:

List of dictionaries representing CRM customer rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.CustomersDataset(rows: int = 500)

Bases: BaseDataset

Customers dataset generator that creates realistic customer profile data.

Generates comprehensive customer data including: - Personal information (names, contact details, demographics) - Geographic data (addresses, regions) - Financial data (income, spending patterns) - Account information (registration, status, preferences) - Loyalty and engagement metrics

generate() → List[Dict[str, Any]]

Generate customers dataset rows.

Returns:

List of dictionaries representing customer profile rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.EcommerceDataset(rows: int = 500)

Bases: BaseDataset

E-commerce dataset generator that creates realistic transaction data.

Generates comprehensive e-commerce data including: - Transaction and customer information - Product details with categories and brands - Pricing with discounts and profit calculations - Shipping and delivery information - Geographic and demographic data - Reviews and returns data

generate() → List[Dict[str, Any]]

Generate e-commerce dataset rows.

Returns:

List of dictionaries representing e-commerce transaction rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.EmployeesDataset(rows: int = 500)

Bases: BaseDataset

Employees dataset generator that creates realistic employee HR data.

Generates comprehensive employee data including: - Personal information (names, demographics, contact details) - Employment details (hire dates, departments, job titles) - Compensation (salary, bonus, total compensation) - Performance metrics (scores, reviews, training) - Work arrangements (location, status, projects) - Management hierarchy (manager relationships)

generate() → List[Dict[str, Any]]

Generate employees dataset rows.

Returns:

List of dictionaries representing employee records

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.InventoryDataset(rows: int = 500)

Bases: BaseDataset

Inventory dataset generator that creates realistic warehouse and stock data.

Generates 25+ columns of inventory data including: - Product information (SKU, name, category, supplier) - Stock levels (on hand, reserved, reorder thresholds) - Pricing and valuation (unit price, total value) - Warehouse location (warehouse, aisle, shelf, bin) - Restock scheduling and lead times - Special handling requirements

generate() → List[Dict[str, Any]]

Generate inventory dataset rows.

Returns:

List of dictionaries representing inventory item rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.MarketingDataset(rows: int = 500)

Bases: BaseDataset

Marketing dataset generator that creates realistic marketing campaign data.

Generates 36 columns of marketing data including: - Campaign information (campaign_id, name, dates, status) - Channel and platform details (channel, platform, creative information) - Audience information (target_audience, audience_size, demographics) - Financial data (budget, spend, revenue, costs) - Performance metrics (impressions, clicks, conversions, rates) - Geographic data (region, country) - Engagement metrics (likes, comments, shares) - Management information (agency, manager)

generate() → List[Dict[str, Any]]

Generate marketing dataset rows.

Returns:

List of dictionaries representing marketing campaign rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.RetailDataset(rows: int = 500)

Bases: BaseDataset

Retail store operations & POS transactions dataset generator.

Generates comprehensive retail data including: - Transaction information (transaction_id, receipt_number, datetime) - Store details (store_id, name, type, location) - POS and cashier information - Product details (product_id, name, category, brand) - Pricing and discount calculations - Payment and loyalty information - Inventory tracking (before/after sale) - Financial metrics (gross margin)

generate() → List[Dict[str, Any]]

Generate retail dataset rows.

Returns:

List of dictionaries representing retail transaction rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.ReviewsDataset(rows: int = 500)

Bases: BaseDataset

Reviews dataset generator that creates realistic product and service review data.

Generates 15+ columns of review data including: - Review identification (review_id, product_id, customer info) - Rating and feedback (rating, title, text, sentiment) - Verification and engagement (verified purchase, helpful votes) - Seller interaction (response from seller, response date) - Geographic and temporal data

generate() → List[Dict[str, Any]]

Generate reviews dataset rows.

Returns:

List of dictionaries representing review rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.SalesDataset(rows: int = 500)

Bases: BaseDataset

Sales dataset generator that creates realistic sales transaction data.

Generates 27 columns of sales data including: - Order information (order_id, dates, priority) - Customer details (customer_id, name, email, demographics) - Product information (product_id, name, category, brand) - Financial data (prices, discounts, profit) - Geographic data (region, country, state, city) - Shipping and payment information

generate() → List[Dict[str, Any]]

Generate sales dataset rows.

Returns:

List of dictionaries representing sales transaction rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.SuppliersDataset(rows: int = 500)

Bases: BaseDataset

Suppliers dataset generator that creates realistic supplier data.

Generates 30 columns of supplier data including: - Supplier identification (supplier_id, name) - Contact information (name, title, email, phone, fax, website) - Address details (address, city, state, country, postal code) - Business classification (type, industry, product categories) - Performance metrics (rating, on-time delivery, lead time) - Contract information (dates, value, payment terms) - Order history and statistics

generate() → List[Dict[str, Any]]

Generate suppliers dataset rows.

Returns:

List of dictionaries representing supplier rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

Financial Dataset Classes

class tempdataset.StocksDataset(rows: int = 500)

Bases: BaseDataset

Stocks dataset generator that creates realistic stock market data.

Generates 20 columns of stock data including: - Basic stock info (ticker, company, sector, industry) - OHLCV data (open, high, low, close, volume) - Financial metrics (market cap, PE ratio, dividend yield) - Market performance indicators

generate() → List[Dict[str, Any]]

Generate stocks dataset rows.

Returns:

List of dictionaries representing stock data rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.BankingDataset(rows: int = 500)

Bases: BaseDataset

Banking dataset generator that creates realistic banking transaction data.

Generates 20 columns of banking data including: - Transaction details (ID, type, amount, currency) - Account information (account ID, type, balances) - Merchant and location data - Fraud detection indicators

generate() → List[Dict[str, Any]]

Generate banking dataset rows.

Returns:

List of dictionaries representing banking transaction rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.CryptocurrencyDataset(rows: int = 500)

Bases: BaseDataset

Cryptocurrency dataset generator that creates realistic crypto trading data.

Generates 20 columns of cryptocurrency data including: - Basic crypto info (symbol, name, blockchain) - OHLCV data with extreme volatility - Market metrics (market cap, supply, volume) - Blockchain statistics (hash rate, difficulty, fees)

generate() → List[Dict[str, Any]]

Generate cryptocurrency dataset rows.

Returns:

List of dictionaries representing cryptocurrency data rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.InsuranceDataset(rows: int = 500)

Bases: BaseDataset

Insurance dataset generator that creates realistic insurance data.

Generates 20 columns of insurance data including: - Policy information (ID, type, coverage, premiums) - Claims data (claim ID, amounts, status) - Risk assessment and agent information - Geographic and temporal data

generate() → List[Dict[str, Any]]

Generate insurance dataset rows.

Returns:

List of dictionaries representing insurance data rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.LoansDataset(rows: int = 500)

Bases: BaseDataset

Loans dataset generator that creates realistic loan data.

Generates 20 columns of loan data including: - Loan application and approval information - Loan terms and payment details - Risk assessment and collateral information - Payment status and branch data

generate() → List[Dict[str, Any]]

Generate dataset rows.

Returns:

List of dictionaries representing dataset rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.InvestmentsDataset(rows: int = 500)

Bases: BaseDataset

Investment portfolio dataset generator.

generate() → List[Dict[str, Any]]

Generate dataset rows.

Returns:

List of dictionaries representing dataset rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.AccountingDataset(rows: int = 500)

Bases: BaseDataset

Accounting dataset generator for general ledger entries.

generate() → List[Dict[str, Any]]

Generate dataset rows.

Returns:

List of dictionaries representing dataset rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.PaymentsDataset(rows: int = 500)

Bases: BaseDataset

Payments dataset generator for digital payment transactions.

generate() → List[Dict[str, Any]]

Generate dataset rows.

Returns:

List of dictionaries representing dataset rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

IoT Sensors Dataset Classes

class tempdataset.WeatherDataset(rows: int = 500)

Bases: BaseDataset

Weather dataset generator that creates realistic weather sensor data.

Generates weather sensor readings including: - Record identification (record_id, timestamp, location_id) - Geographic data (city, country, coordinates) - Temperature and humidity metrics - Atmospheric pressure and wind data - Precipitation and weather conditions - Air quality and visibility metrics

generate() → List[Dict[str, Any]]

Generate weather dataset rows.

Returns:

List of dictionaries representing weather sensor readings

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.EnergyDataset(rows: int = 500)

Bases: BaseDataset

Energy dataset generator that creates realistic energy meter data.

Generates energy consumption and production data including: - Reading identification (reading_id, timestamp, meter_id) - Location and energy source information - Consumption and production metrics - Cost calculations and tariff plans - Peak demand and outage tracking - CO2 emissions calculations

generate() → List[Dict[str, Any]]

Generate energy dataset rows.

Returns:

List of dictionaries representing energy meter readings

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.TrafficDataset(rows: int = 500)

Bases: BaseDataset

Traffic dataset generator that creates realistic traffic sensor data.

Generates traffic monitoring data including: - Record identification (record_id, timestamp, sensor_id) - Road and location information - Vehicle counts and speed measurements - Traffic density and congestion levels - Incident tracking and weather impact - Public transport delays

generate() → List[Dict[str, Any]]

Generate traffic dataset rows.

Returns:

List of dictionaries representing traffic sensor readings

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.EnvironmentalDataset(rows: int = 500)

Bases: BaseDataset

Environmental dataset generator that creates realistic environmental sensor data.

Generates environmental monitoring data including: - Record identification (record_id, timestamp, location_id) - Geographic information (city, country) - Air quality measurements (PM2.5, PM10, gases) - Noise level monitoring - Air Quality Index calculations - Weather correlation data

generate() → List[Dict[str, Any]]

Generate environmental dataset rows.

Returns:

List of dictionaries representing environmental sensor readings

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.IndustrialDataset(rows: int = 500)

Bases: BaseDataset

Industrial dataset generator that creates realistic industrial sensor data.

Generates industrial monitoring data including: - Sensor identification (sensor_reading_id, timestamp, machine_id, factory_id) - Location and operational status - Machine performance metrics (temperature, vibration, pressure, RPM) - Power consumption and oil levels - Fault detection and maintenance scheduling - Predictive failure analysis

generate() → List[Dict[str, Any]]

Generate industrial dataset rows.

Returns:

List of dictionaries representing industrial sensor readings

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.SmartHomeDataset(rows: int = 500)

Bases: BaseDataset

Smart Home dataset generator that creates realistic smart home IoT data.

Generates smart home device data including: - Event identification (event_id, timestamp, home_id) - Device information (room, device_type, device_id) - Device status and environmental readings - Motion detection and security monitoring - Home automation triggers and alerts

generate() → List[Dict[str, Any]]

Generate smart home dataset rows.

Returns:

List of dictionaries representing smart home device events

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

Healthcare Dataset Classes

class tempdataset.PatientsDataset(rows: int = 500)

Bases: BaseDataset

Patients dataset generator for healthcare records.

generate() → List[Dict[str, Any]]

Generate dataset rows.

Returns:

List of dictionaries representing dataset rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.AppointmentsDataset(rows: int = 500)

Bases: BaseDataset

Appointments dataset generator for medical scheduling.

generate() → List[Dict[str, Any]]

Generate dataset rows.

Returns:

List of dictionaries representing dataset rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.LabResultsDataset(rows: int = 500)

Bases: BaseDataset

Lab results dataset generator for medical testing.

generate() → List[Dict[str, Any]]

Generate dataset rows.

Returns:

List of dictionaries representing dataset rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.PrescriptionsDataset(rows: int = 500)

Bases: BaseDataset

Prescriptions dataset generator for medication records.

generate() → List[Dict[str, Any]]

Generate dataset rows.

Returns:

List of dictionaries representing dataset rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.MedicalHistoryDataset(rows: int = 500)

Bases: BaseDataset

Medical history dataset generator for patient conditions.

generate() → List[Dict[str, Any]]

Generate dataset rows.

Returns:

List of dictionaries representing dataset rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.ClinicalTrialsDataset(rows: int = 500)

Bases: BaseDataset

Clinical trials dataset generator for research studies.

generate() → List[Dict[str, Any]]

Generate dataset rows.

Returns:

List of dictionaries representing dataset rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

Social Media Dataset Classes

class tempdataset.SocialMediaDataset(rows: int = 500)

Bases: BaseDataset

Social Media dataset generator that creates realistic social media post data.

Generates social media posts with: - Post information (post_id, dates, type, content) - User details (user_id, platform) - Engagement metrics (likes, comments, shares, views) - Content analysis (hashtags, mentions, sentiment) - Geographic data (location)

generate() → List[Dict[str, Any]]

Generate social media dataset rows.

Returns:

List of dictionaries representing social media post rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.UserProfilesDataset(rows: int = 500)

Bases: BaseDataset

User Profiles dataset generator that creates realistic social media user profiles.

Generates user profiles with: - User information (user_id, username, personal details) - Account details (join_date, platform, status) - Social metrics (followers, following, posts) - Profile content (bio, interests, connections) - Geographic data (location)

generate() → List[Dict[str, Any]]

Generate user profiles dataset rows.

Returns:

List of dictionaries representing user profile rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

Technology Dataset Classes

class tempdataset.WebAnalyticsDataset(rows: int = 500)

Bases: BaseDataset

Web analytics dataset generator for website traffic data.

generate() → List[Dict[str, Any]]

Generate dataset rows.

Returns:

List of dictionaries representing dataset rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.AppUsageDataset(rows: int = 500)

Bases: BaseDataset

App usage dataset generator for application analytics.

generate() → List[Dict[str, Any]]

Generate dataset rows.

Returns:

List of dictionaries representing dataset rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.SystemLogsDataset(rows: int = 500)

Bases: BaseDataset

System logs dataset generator for server and application logs.

generate() → List[Dict[str, Any]]

Generate dataset rows.

Returns:

List of dictionaries representing dataset rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.ApiCallsDataset(rows: int = 500)

Bases: BaseDataset

API calls dataset generator for API usage analytics and monitoring.

generate() → List[Dict[str, Any]]

Generate dataset rows.

Returns:

List of dictionaries representing dataset rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.ServerMetricsDataset(rows: int = 500)

Bases: BaseDataset

Server metrics dataset generator for system monitoring and performance analysis.

generate() → List[Dict[str, Any]]

Generate dataset rows.

Returns:

List of dictionaries representing dataset rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.UserSessionsDataset(rows: int = 500)

Bases: BaseDataset

User sessions dataset generator for user behavior analytics and session tracking.

generate() → List[Dict[str, Any]]

Generate dataset rows.

Returns:

List of dictionaries representing dataset rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.ErrorLogsDataset(rows: int = 500)

Bases: BaseDataset

Error logs dataset generator for application error tracking and debugging.

generate() → List[Dict[str, Any]]

Generate dataset rows.

Returns:

List of dictionaries representing dataset rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types

class tempdataset.PerformanceDataset(rows: int = 500)

Bases: BaseDataset

Performance monitoring dataset generator for application performance tracking.

generate() → List[Dict[str, Any]]

Generate dataset rows.

Returns:

List of dictionaries representing dataset rows

get_schema() → Dict[str, str]

Return column schema with types.

Returns:

Dictionary mapping column names to their data types