| Name | tempdataset JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | None |
| Summary | A lightweight Python library for generating realistic temporary datasets |
| upload_time | 2025-08-12 11:37:49 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | MIT License
Copyright (c) 2025 TempDataset Contributors
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
| keywords |
dataset
testing
development
sample-data
mock-data
csv
json
sales-data
temporary
lightweight
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# TempDataset
[](https://badge.fury.io/py/tempdataset)
[](https://pypi.org/project/tempdataset/)
[](https://opensource.org/licenses/MIT)
A lightweight Python library for generating realistic temporary datasets for testing and development. Generate **40+ different dataset types** including business, financial, IoT, healthcare, and technology data!
## ✨ Features
- **40+ Dataset Types**: Business, financial, IoT sensors, healthcare, social media, and technology datasets
- **Zero Dependencies**: Works with just Python standard library
- **Multiple Formats**: Generate CSV, JSON, or in-memory datasets
- **Realistic Data**: Built-in faker integration with realistic patterns
- **Technology Focus**: New datasets for DevOps, monitoring, web analytics, and system performance
- **Memory Efficient**: Optimized for large dataset generation
- **Python 3.7+**: Compatible with modern Python versions
## 🚀 Quick Start
### Installation
```bash
pip install tempdataset
```
### Basic Usage
```python
import tempdataset
# Generate datasets
data = tempdataset.create_dataset('sales', 1000)
tech_data = tempdataset.create_dataset('web_analytics', 500)
server_metrics = tempdataset.create_dataset('server_metrics', 200)
# Save directly to files
tempdataset.create_dataset('sales_data.csv', 1000)
tempdataset.create_dataset('api_logs.json', 500)
# Get help and see all available datasets
tempdataset.list_datasets() # Show all 40+ datasets
tempdataset.help() # Quick help guide
```
```
## 📊 Available Datasets (40 Total)
TempDataset provides **40 comprehensive datasets** across 6 major categories:
### 🏢 Core Business Datasets (10)
- **`sales`** - Sales transactions and orders (27 columns)
- **`customers`** - Customer profiles and demographics (31 columns)
- **`ecommerce`** - E-commerce transactions and reviews (35+ columns)
- **`employees`** - Employee records and HR data (30+ columns)
- **`marketing`** - Marketing campaigns and performance (32+ columns)
- **`retail`** - Retail store operations (28+ columns)
- **`suppliers`** - Supplier management data (22+ columns)
- **`crm`** - Customer relationship management (30+ columns)
- **`inventory`** - Inventory and warehouse data (25+ columns)
- **`reviews`** - Product and service reviews (15+ columns)
### 💰 Financial Datasets (8)
- **`stocks`** - Stock market trading data (20+ columns)
- **`banking`** - Banking transactions (20+ columns)
- **`cryptocurrency`** - Cryptocurrency trading (20+ columns)
- **`insurance`** - Insurance policies and claims (20+ columns)
- **`loans`** - Loan applications and management (20+ columns)
- **`investments`** - Investment portfolios (20+ columns)
- **`accounting`** - General ledger and accounting (20+ columns)
- **`payments`** - Digital payment processing (25+ columns)
### 🌐 Technology Datasets (8) ⭐ *NEW!*
- **`web_analytics`** - Website analytics and traffic (17 columns)
- **`app_usage`** - Mobile app usage analytics (15 columns)
- **`system_logs`** - System and application logs (11 columns)
- **`api_calls`** - API calls and performance (12 columns)
- **`server_metrics`** - Server performance monitoring (22 columns)
- **`user_sessions`** - User session tracking (20 columns)
- **`error_logs`** - Application error logs (16 columns)
- **`performance`** - Application performance monitoring (21 columns)
### 🏥 Healthcare Datasets (6)
- **`patients`** - Patient medical records (22 columns)
- **`appointments`** - Medical appointments (14 columns)
- **`lab_results`** - Laboratory test results (13 columns)
- **`prescriptions`** - Medication prescriptions (16 columns)
- **`medical_history`** - Patient medical history (11 columns)
- **`clinical_trials`** - Clinical trial data (14 columns)
### �️ IoT Sensor Datasets (6)
- **`weather`** - Weather sensor monitoring (18 columns)
- **`energy`** - Smart meter energy data (14 columns)
- **`traffic`** - Traffic sensor monitoring (15 columns)
- **`environmental`** - Environmental monitoring (17 columns)
- **`industrial`** - Industrial sensor data (16 columns)
- **`smarthome`** - Smart home IoT devices (16 columns)
### 📱 Social Media Datasets (2)
- **`social_media`** - Social media posts and engagement (16 columns)
- **`user_profiles`** - Social media user profiles (17 columns)
### 🚀 Quick Examples
```python
# Generate different types of datasets
sales = tempdataset.create_dataset('sales', 1000)
tech_logs = tempdataset.create_dataset('system_logs', 500)
health_data = tempdataset.create_dataset('patients', 200)
crypto = tempdataset.create_dataset('cryptocurrency', 300)
# Get help and list all datasets
tempdataset.list_datasets() # Show all 40 datasets
tempdataset.help() # Quick reference guide
```
## Advanced Usage
### Working with TempDataFrame
```python
data = tempdataset.create_dataset('sales', 1000)
# Basic operations
data.head(10) # First 10 rows
data.tail(5) # Last 5 rows
data.describe() # Statistical summary
data.info() # Data info
# Filtering and selection
filtered = data.filter(lambda row: row['amount'] > 100)
selected = data.select(['customer_name', 'amount', 'date'])
# Export options
data.to_csv('output.csv')
data.to_json('output.json')
data.to_dict() # Convert to dictionary
```
### Performance Monitoring
```python
import tempdataset
# Generate data
data = tempdataset.create_dataset('sales', 10000)
# 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()
```
## Development
### Setting up Development Environment
```bash
# Clone the repository
git clone https://github.com/dot-css/TempDataset.git
cd TempDataset
# Install development dependencies
pip install -e .[dev]
# Run tests
pytest
# Run tests with coverage
pytest --cov=tempdataset
# Run performance benchmarks
pytest .benchmarks/
```
### Running Tests
```bash
# Run all tests
pytest
# Run specific test categories
pytest -m "not slow" # Skip slow tests
pytest -m integration # Only integration tests
pytest -m performance # Only performance tests
# Run with coverage report
pytest --cov=tempdataset --cov-report=html
```
### Code Quality
```bash
# Format code
black tempdataset tests
# Lint code
flake8 tempdataset tests
# Type checking
mypy tempdataset
```
## API Reference
### Core Functions
#### `create_dataset(dataset_type, rows=500)`
Generate temporary datasets or save to files.
**Parameters:**
- `dataset_type` (str): Dataset type or filename
- **Available types:** `'sales'`, `'customers'`, `'ecommerce'`, `'employees'`, `'marketing'`, `'retail'`, `'suppliers'`
- **File formats:** `'sales.csv'`, `'customers.json'`, etc.
- `rows` (int): Number of rows to generate (default: 500)
**Returns:**
- `TempDataFrame` containing the generated data (also saves to file if filename provided)
#### `help()`
Display comprehensive help information about all available datasets, including column descriptions, usage examples, and feature details.
#### `list_datasets()`
Get a quick overview of all available datasets with their key features and column counts.
#### `read_csv(filename)`
Read CSV file into TempDataFrame.
#### `read_json(filename)`
Read JSON file into TempDataFrame.
### TempDataFrame Methods
- `head(n=5)`: Get first n rows
- `tail(n=5)`: Get last n rows
- `describe()`: Statistical summary
- `info()`: Dataset information
- `filter(func)`: Filter rows by function
- `select(columns)`: Select specific columns
- `to_csv(filename)`: Export to CSV
- `to_json(filename)`: Export to JSON
- `to_dict()`: Convert to dictionary
## Contributing
We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.
### Development Workflow
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Run the test suite
6. Submit a pull request
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Changelog
See [CHANGELOG.md](CHANGELOG.md) for a detailed history of changes.
## Support
- Documentation: https://tempdataset.readthedocs.io/
- Issue Tracker: https://github.com/dot-css/TempDataset/issues
- Discussions: https://github.com/dot-css/TempDataset/discussions
## Acknowledgments
- Built with love for the Python testing community
- Inspired by the need for lightweight, dependency-free test data generation
- Thanks to all contributors who help make this project better!
Raw data
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"description": "# TempDataset\r\n\r\n[](https://badge.fury.io/py/tempdataset)\r\n[](https://pypi.org/project/tempdataset/)\r\n[](https://opensource.org/licenses/MIT)\r\n\r\nA lightweight Python library for generating realistic temporary datasets for testing and development. Generate **40+ different dataset types** including business, financial, IoT, healthcare, and technology data!\r\n\r\n## \u2728 Features\r\n\r\n- **40+ Dataset Types**: Business, financial, IoT sensors, healthcare, social media, and technology datasets\r\n- **Zero Dependencies**: Works with just Python standard library\r\n- **Multiple Formats**: Generate CSV, JSON, or in-memory datasets\r\n- **Realistic Data**: Built-in faker integration with realistic patterns\r\n- **Technology Focus**: New datasets for DevOps, monitoring, web analytics, and system performance\r\n- **Memory Efficient**: Optimized for large dataset generation\r\n- **Python 3.7+**: Compatible with modern Python versions\r\n\r\n## \ud83d\ude80 Quick Start\r\n\r\n### Installation\r\n\r\n```bash\r\npip install tempdataset\r\n```\r\n\r\n### Basic Usage\r\n\r\n```python\r\nimport tempdataset\r\n\r\n# Generate datasets\r\ndata = tempdataset.create_dataset('sales', 1000)\r\ntech_data = tempdataset.create_dataset('web_analytics', 500)\r\nserver_metrics = tempdataset.create_dataset('server_metrics', 200)\r\n\r\n# Save directly to files\r\ntempdataset.create_dataset('sales_data.csv', 1000)\r\ntempdataset.create_dataset('api_logs.json', 500)\r\n\r\n# Get help and see all available datasets \r\ntempdataset.list_datasets() # Show all 40+ datasets\r\ntempdataset.help() # Quick help guide\r\n```\r\n```\r\n\r\n## \ud83d\udcca Available Datasets (40 Total)\r\n\r\nTempDataset provides **40 comprehensive datasets** across 6 major categories:\r\n\r\n### \ud83c\udfe2 Core Business Datasets (10)\r\n- **`sales`** - Sales transactions and orders (27 columns)\r\n- **`customers`** - Customer profiles and demographics (31 columns) \r\n- **`ecommerce`** - E-commerce transactions and reviews (35+ columns)\r\n- **`employees`** - Employee records and HR data (30+ columns)\r\n- **`marketing`** - Marketing campaigns and performance (32+ columns)\r\n- **`retail`** - Retail store operations (28+ columns)\r\n- **`suppliers`** - Supplier management data (22+ columns)\r\n- **`crm`** - Customer relationship management (30+ columns)\r\n- **`inventory`** - Inventory and warehouse data (25+ columns)\r\n- **`reviews`** - Product and service reviews (15+ columns)\r\n\r\n### \ud83d\udcb0 Financial Datasets (8)\r\n- **`stocks`** - Stock market trading data (20+ columns)\r\n- **`banking`** - Banking transactions (20+ columns)\r\n- **`cryptocurrency`** - Cryptocurrency trading (20+ columns)\r\n- **`insurance`** - Insurance policies and claims (20+ columns)\r\n- **`loans`** - Loan applications and management (20+ columns)\r\n- **`investments`** - Investment portfolios (20+ columns)\r\n- **`accounting`** - General ledger and accounting (20+ columns)\r\n- **`payments`** - Digital payment processing (25+ columns)\r\n\r\n### \ud83c\udf10 Technology Datasets (8) \u2b50 *NEW!*\r\n- **`web_analytics`** - Website analytics and traffic (17 columns)\r\n- **`app_usage`** - Mobile app usage analytics (15 columns)\r\n- **`system_logs`** - System and application logs (11 columns)\r\n- **`api_calls`** - API calls and performance (12 columns)\r\n- **`server_metrics`** - Server performance monitoring (22 columns)\r\n- **`user_sessions`** - User session tracking (20 columns)\r\n- **`error_logs`** - Application error logs (16 columns)\r\n- **`performance`** - Application performance monitoring (21 columns)\r\n\r\n### \ud83c\udfe5 Healthcare Datasets (6)\r\n- **`patients`** - Patient medical records (22 columns)\r\n- **`appointments`** - Medical appointments (14 columns)\r\n- **`lab_results`** - Laboratory test results (13 columns)\r\n- **`prescriptions`** - Medication prescriptions (16 columns)\r\n- **`medical_history`** - Patient medical history (11 columns)\r\n- **`clinical_trials`** - Clinical trial data (14 columns)\r\n\r\n### \ufffd\ufe0f IoT Sensor Datasets (6) \r\n- **`weather`** - Weather sensor monitoring (18 columns)\r\n- **`energy`** - Smart meter energy data (14 columns)\r\n- **`traffic`** - Traffic sensor monitoring (15 columns)\r\n- **`environmental`** - Environmental monitoring (17 columns)\r\n- **`industrial`** - Industrial sensor data (16 columns)\r\n- **`smarthome`** - Smart home IoT devices (16 columns)\r\n\r\n### \ud83d\udcf1 Social Media Datasets (2)\r\n- **`social_media`** - Social media posts and engagement (16 columns)\r\n- **`user_profiles`** - Social media user profiles (17 columns)\r\n\r\n### \ud83d\ude80 Quick Examples\r\n\r\n```python\r\n# Generate different types of datasets\r\nsales = tempdataset.create_dataset('sales', 1000)\r\ntech_logs = tempdataset.create_dataset('system_logs', 500) \r\nhealth_data = tempdataset.create_dataset('patients', 200)\r\ncrypto = tempdataset.create_dataset('cryptocurrency', 300)\r\n\r\n# Get help and list all datasets\r\ntempdataset.list_datasets() # Show all 40 datasets\r\ntempdataset.help() # Quick reference guide\r\n```\r\n\r\n## Advanced Usage\r\n\r\n### Working with TempDataFrame\r\n\r\n```python\r\ndata = tempdataset.create_dataset('sales', 1000)\r\n\r\n# Basic operations\r\ndata.head(10) # First 10 rows\r\ndata.tail(5) # Last 5 rows\r\ndata.describe() # Statistical summary\r\ndata.info() # Data info\r\n\r\n# Filtering and selection\r\nfiltered = data.filter(lambda row: row['amount'] > 100)\r\nselected = data.select(['customer_name', 'amount', 'date'])\r\n\r\n# Export options\r\ndata.to_csv('output.csv')\r\ndata.to_json('output.json')\r\ndata.to_dict() # Convert to dictionary\r\n```\r\n\r\n### Performance Monitoring\r\n\r\n```python\r\nimport tempdataset\r\n\r\n# Generate data\r\ndata = tempdataset.create_dataset('sales', 10000)\r\n\r\n# Check performance stats\r\nstats = tempdataset.get_performance_stats()\r\nprint(f\"Generation time: {stats['generation_time']:.2f}s\")\r\nprint(f\"Memory usage: {stats['memory_usage']:.2f}MB\")\r\n\r\n# Reset stats for next operation\r\ntempdataset.reset_performance_stats()\r\n```\r\n\r\n## Development\r\n\r\n### Setting up Development Environment\r\n\r\n```bash\r\n# Clone the repository\r\ngit clone https://github.com/dot-css/TempDataset.git\r\ncd TempDataset\r\n\r\n# Install development dependencies\r\npip install -e .[dev]\r\n\r\n# Run tests\r\npytest\r\n\r\n# Run tests with coverage\r\npytest --cov=tempdataset\r\n\r\n# Run performance benchmarks\r\npytest .benchmarks/\r\n```\r\n\r\n### Running Tests\r\n\r\n```bash\r\n# Run all tests\r\npytest\r\n\r\n# Run specific test categories\r\npytest -m \"not slow\" # Skip slow tests\r\npytest -m integration # Only integration tests\r\npytest -m performance # Only performance tests\r\n\r\n# Run with coverage report\r\npytest --cov=tempdataset --cov-report=html\r\n```\r\n\r\n### Code Quality\r\n\r\n```bash\r\n# Format code\r\nblack tempdataset tests\r\n\r\n# Lint code\r\nflake8 tempdataset tests\r\n\r\n# Type checking\r\nmypy tempdataset\r\n```\r\n\r\n## API Reference\r\n\r\n### Core Functions\r\n\r\n#### `create_dataset(dataset_type, rows=500)`\r\nGenerate temporary datasets or save to files.\r\n\r\n**Parameters:**\r\n- `dataset_type` (str): Dataset type or filename\r\n - **Available types:** `'sales'`, `'customers'`, `'ecommerce'`, `'employees'`, `'marketing'`, `'retail'`, `'suppliers'`\r\n - **File formats:** `'sales.csv'`, `'customers.json'`, etc.\r\n- `rows` (int): Number of rows to generate (default: 500)\r\n\r\n**Returns:**\r\n- `TempDataFrame` containing the generated data (also saves to file if filename provided)\r\n\r\n#### `help()`\r\nDisplay comprehensive help information about all available datasets, including column descriptions, usage examples, and feature details.\r\n\r\n#### `list_datasets()`\r\nGet a quick overview of all available datasets with their key features and column counts.\r\n\r\n#### `read_csv(filename)`\r\nRead CSV file into TempDataFrame.\r\n\r\n#### `read_json(filename)`\r\nRead JSON file into TempDataFrame.\r\n\r\n### TempDataFrame Methods\r\n\r\n- `head(n=5)`: Get first n rows\r\n- `tail(n=5)`: Get last n rows\r\n- `describe()`: Statistical summary\r\n- `info()`: Dataset information\r\n- `filter(func)`: Filter rows by function\r\n- `select(columns)`: Select specific columns\r\n- `to_csv(filename)`: Export to CSV\r\n- `to_json(filename)`: Export to JSON\r\n- `to_dict()`: Convert to dictionary\r\n\r\n## Contributing\r\n\r\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.\r\n\r\n### Development Workflow\r\n\r\n1. Fork the repository\r\n2. Create a feature branch\r\n3. Make your changes\r\n4. Add tests for new functionality\r\n5. Run the test suite\r\n6. Submit a pull request\r\n\r\n## License\r\n\r\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\r\n\r\n## Changelog\r\n\r\nSee [CHANGELOG.md](CHANGELOG.md) for a detailed history of changes.\r\n\r\n## Support\r\n\r\n- Documentation: https://tempdataset.readthedocs.io/\r\n- Issue Tracker: https://github.com/dot-css/TempDataset/issues\r\n- Discussions: https://github.com/dot-css/TempDataset/discussions\r\n\r\n## Acknowledgments\r\n\r\n- Built with love for the Python testing community\r\n- Inspired by the need for lightweight, dependency-free test data generation\r\n- Thanks to all contributors who help make this project better!\r\n",
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