Python FastAPI Development Best Practices Comprehensive guide for building production-ready APIs with FastAPI, async Python, and modern backend development patterns. Project Structure Modular Application Architecture Organize code into logical modules (routers, models, schemas, services) Separate business logic from API logic Example structure: Configuration Management Use Pydantic BaseSettings for environment-based configuration Keep sensitive data in environment variables Implement different configs for development, testing, and production Example: Database Integration SQLAlchemy Async Patterns Use asyncio-compatible database drivers (asyncpg for PostgreSQL) Implement async session management Use select() for queries instead of legacy query() methods Example: Database Models and Relationships Define clear SQLAlchemy models with proper relationships Use appropriate column types and constraints Implement proper indexing for performance Example: Database Migrations Use Alembic for database schema migrations Create meaningful migration messages and version control Test migrations in development before applying to production Example migration workflow: Error Handling and Validation Custom Exception Handling Create custom exception classes for different error types Implement global exception handlers Return consistent error response formats Example: Input Validation Use Pydantic validators for complex validation logic Implement custom validators for business rules Provide clear error messages for validation failures Example: Testing Test Structure Write comprehensive unit tests for all business logic Implement integration tests for API endpoints Use pytest with async support for testing Example: Test Database Management Use separate test databases for isolation Implement database fixtures for test data Clean up test data after each test run Summary Checklist [ ] Use type hints and Pydantic models throughout [ ] Implement async/await for all I/O operations [ ] Structure project with clear separation of concerns [ ] Use dependency injection for shared logic [ ] Implement proper authentication and authorization [ ] Handle errors consistently with custom exceptions [ ] Optimize database queries and implement caching [ ] Write comprehensive tests for all functionality [ ] Use proper security headers and HTTPS [ ] Configure production deployment with Docker [ ] Implement monitoring and logging Follow these practices to build robust, scalable, and maintainable FastAPI applications.

Machine Learning Pytorch This document provides comprehensive guidelines for machine learning pytorch development and best practices. Tensor Operations Tensor Tensor creation and manipulation Implement proper tensor creation and manipulation Follow best practices for optimal results Broadcasting Broadcasting and reshaping Implement proper broadcasting and reshaping Follow best practices for optimal results Mathematical Mathematical operations Implement proper mathematical operations Follow best practices for optimal results Indexing Indexing and slicing Implement proper indexing and slicing Follow best practices for optimal results Memory Memory management and efficiency Implement proper memory management and efficiency Follow best practices for optimal results Training Loop Implementation Forward Forward and backward propagation Implement proper forward and backward propagation Follow best practices for optimal results Loss Loss function selection Implement proper loss function selection Follow best practices for optimal results Optimizer Optimizer configuration Implement proper optimizer configuration Follow best practices for optimal results Learning Learning rate scheduling Implement proper learning rate scheduling Follow best practices for optimal results Gradient Gradient clipping and regularization Implement proper gradient clipping and regularization Follow best practices for optimal results Computer Vision Convolutional Convolutional Neural Networks (CNNs) Implement proper convolutional neural networks (cnns) Follow best practices for optimal results Transfer Transfer learning with pre-trained models Implement proper transfer learning with pre-trained models Follow best practices for optimal results Image Image classification and detection Implement proper image classification and detection Follow best practices for optimal results Semantic Semantic segmentation Implement proper semantic segmentation Follow best practices for optimal results Object Object detection (YOLO, R-CNN) Implement proper object detection (yolo, r-cnn) Follow best practices for optimal results Model Optimization Mixed Mixed precision training Implement proper mixed precision training Follow best practices for optimal results Model Model quantization Implement proper model quantization Follow best practices for optimal results Pruning Pruning and compression Implement proper pruning and compression Follow best practices for optimal results Knowledge Knowledge distillation Implement proper knowledge distillation Follow best practices for optimal results Hardware-specific Hardware-specific optimization Implement proper hardware-specific optimization Follow best practices for optimal results Model Deployment TorchScript TorchScript for production Implement proper torchscript for production Follow best practices for optimal results ONNX ONNX export for interoperability Implement proper onnx export for interoperability Follow best practices for optimal results Model Model serving with TorchServe Implement proper model serving with torchserve Follow best practices for optimal results Mobile Mobile deployment with PyTorch Mobile Implement proper mobile deployment with pytorch mobile Follow best practices for optimal results Edge Edge deployment optimization Implement proper edge deployment optimization Follow best practices for optimal results Integration with Ecosystem Hugging Hugging Face Transformers Implement proper hugging face transformers Follow best practices for optimal results PyTorch PyTorch Lightning for organization Implement proper pytorch lightning for organization Follow best practices for optimal results TensorBoard TensorBoard for visualization Implement proper tensorboard for visualization Follow best practices for optimal results Weights Weights & Biases for experiment tracking Implement proper weights & biases for experiment tracking Follow best practices for optimal results Ray Ray for distributed computing Implement proper ray for distributed computing Follow best practices for optimal results Testing and Validation Unit Unit testing for models Implement proper unit testing for models Follow best practices for optimal results Integration Integration testing pipelines Implement proper integration testing pipelines Follow best practices for optimal results Model Model validation strategies Implement proper model validation strategies Follow best practices for optimal results A/B A/B testing frameworks Implement proper a/b testing frameworks Follow best practices for optimal results Continuous Continuous integration for ML Implement proper continuous integration for ml Follow best practices for optimal results Summary Checklist [ ] Core principles implemented [ ] Best practices followed [ ] Performance optimized [ ] Security measures in place [ ] Testing strategy implemented [ ] Documentation completed [ ] Monitoring configured [ ] Production deployment ready Follow these comprehensive guidelines for successful machine learning pytorch implementation.

Python Asyncio This document provides comprehensive guidelines for python asyncio development and best practices. Coroutines & Tasks async async def function definitions Implement proper async def function definitions Follow best practices for optimal results await await expressions for suspension Implement proper await expressions for suspension Follow best practices for optimal results Task Task creation with create_task() Implement proper task creation with create_task() Follow best practices for optimal results Task Task cancellation and timeout handling Implement proper task cancellation and timeout handling Follow best practices for optimal results Exception Exception handling in coroutines Implement proper exception handling in coroutines Follow best practices for optimal results Concurrent Programming asyncio.gather() asyncio.gather() for parallel execution Implement proper asyncio.gather() for parallel execution Follow best practices for optimal results asyncio.wait() asyncio.wait() for completion handling Implement proper asyncio.wait() for completion handling Follow best practices for optimal results Semaphores Semaphores for resource limiting Implement proper semaphores for resource limiting Follow best practices for optimal results Locks Locks and synchronization primitives Implement proper locks and synchronization primitives Follow best practices for optimal results Queue Queue patterns for producer-consumer Implement proper queue patterns for producer-consumer Follow best practices for optimal results File I/O Operations aiofiles aiofiles for file operations Implement proper aiofiles for file operations Follow best practices for optimal results Asynchronous Asynchronous file reading/writing Implement proper asynchronous file reading/writing Follow best practices for optimal results Directory Directory operations Implement proper directory operations Follow best practices for optimal results Subprocess Subprocess management Implement proper subprocess management Follow best practices for optimal results Stream Stream processing Implement proper stream processing Follow best practices for optimal results Web Development FastAPI FastAPI for async web APIs Implement proper fastapi for async web apis Follow best practices for optimal results aiohttp aiohttp for web applications Implement proper aiohttp for web applications Follow best practices for optimal results WebSocket WebSocket handling Implement proper websocket handling Follow best practices for optimal results Middleware Middleware implementation Implement proper middleware implementation Follow best practices for optimal results Request/response Request/response streaming Implement proper request/response streaming Follow best practices for optimal results Performance Optimization Profiling Profiling async applications Implement proper profiling async applications Follow best practices for optimal results Memory Memory usage optimization Implement proper memory usage optimization Follow best practices for optimal results CPU-bound CPU-bound task handling Implement proper cpu-bound task handling Follow best practices for optimal results Backpressure Backpressure management Implement proper backpressure management Follow best practices for optimal results Resource Resource cleanup strategies Implement proper resource cleanup strategies Follow best practices for optimal results Advanced Patterns Context Context managers with async with Implement proper context managers with async with Follow best practices for optimal results Async Async generators and iterators Implement proper async generators and iterators Follow best practices for optimal results AsyncIterator AsyncIterator protocol implementation Implement proper asynciterator protocol implementation Follow best practices for optimal results Custom Custom awaitable objects Implement proper custom awaitable objects Follow best practices for optimal results Protocol-based Protocol-based programming Implement proper protocol-based programming Follow best practices for optimal results Production Considerations Event Event loop monitoring Implement proper event loop monitoring Follow best practices for optimal results Resource Resource leak detection Implement proper resource leak detection Follow best practices for optimal results Graceful Graceful shutdown handling Implement proper graceful shutdown handling Follow best practices for optimal results Process Process management Implement proper process management Follow best practices for optimal results Container Container deployment strategies Implement proper container deployment strategies Follow best practices for optimal results Summary Checklist [ ] Core principles implemented [ ] Best practices followed [ ] Performance optimized [ ] Security measures in place [ ] Testing strategy implemented [ ] Documentation completed [ ] Monitoring configured [ ] Production deployment ready Follow these comprehensive guidelines for successful python asyncio implementation.

Python Data Science This document provides comprehensive guidelines for python data science development and best practices. Pandas Data Manipulation DataFrame DataFrame and Series operations Implement proper dataframe and series operations Follow best practices for optimal results Data Data loading from various sources (CSV, JSON, SQL) Implement proper data loading from various sources (csv, json, sql) Follow best practices for optimal results Data Data cleaning and preprocessing Implement proper data cleaning and preprocessing Follow best practices for optimal results Missing Missing data handling strategies Implement proper missing data handling strategies Follow best practices for optimal results Data Data type optimization Implement proper data type optimization Follow best practices for optimal results Data Transformation Filtering Filtering and querying data Implement proper filtering and querying data Follow best practices for optimal results Groupby Groupby operations and aggregations Implement proper groupby operations and aggregations Follow best practices for optimal results Pivot Pivot tables and crosstabs Implement proper pivot tables and crosstabs Follow best practices for optimal results Merging Merging and joining datasets Implement proper merging and joining datasets Follow best practices for optimal results Reshaping Reshaping data (melt, stack, unstack) Implement proper reshaping data (melt, stack, unstack) Follow best practices for optimal results Data Visualization Matplotlib Matplotlib for basic plotting Implement proper matplotlib for basic plotting Follow best practices for optimal results Seaborn Seaborn for statistical visualizations Implement proper seaborn for statistical visualizations Follow best practices for optimal results Plotly Plotly for interactive charts Implement proper plotly for interactive charts Follow best practices for optimal results Best Best practices for effective visualization Implement proper best practices for effective visualization Follow best practices for optimal results Dashboard Dashboard creation with Streamlit Implement proper dashboard creation with streamlit Follow best practices for optimal results Machine Learning Integration Scikit-learn Scikit-learn for traditional ML Implement proper scikit-learn for traditional ml Follow best practices for optimal results Feature Feature engineering and selection Implement proper feature engineering and selection Follow best practices for optimal results Model Model evaluation and cross-validation Implement proper model evaluation and cross-validation Follow best practices for optimal results Pipeline Pipeline creation for reproducibility Implement proper pipeline creation for reproducibility Follow best practices for optimal results Hyperparameter Hyperparameter tuning Implement proper hyperparameter tuning Follow best practices for optimal results Jupyter Notebook Best Practices Notebook Notebook organization and structure Implement proper notebook organization and structure Follow best practices for optimal results Code Code cell optimization Implement proper code cell optimization Follow best practices for optimal results Markdown Markdown documentation Implement proper markdown documentation Follow best practices for optimal results Version Version control for notebooks Implement proper version control for notebooks Follow best practices for optimal results Reproducible Reproducible research practices Implement proper reproducible research practices Follow best practices for optimal results Performance Optimization Vectorization Vectorization over loops Implement proper vectorization over loops Follow best practices for optimal results Memory Memory usage optimization Implement proper memory usage optimization Follow best practices for optimal results Parallel Parallel processing with multiprocessing Implement proper parallel processing with multiprocessing Follow best practices for optimal results Cython Cython for performance-critical code Implement proper cython for performance-critical code Follow best practices for optimal results Profiling Profiling and bottleneck identification Implement proper profiling and bottleneck identification Follow best practices for optimal results Deployment & Production API API development with FastAPI Implement proper api development with fastapi Follow best practices for optimal results Containerization Containerization for reproducibility Implement proper containerization for reproducibility Follow best practices for optimal results Cloud Cloud deployment strategies Implement proper cloud deployment strategies Follow best practices for optimal results Monitoring Monitoring data pipelines Implement proper monitoring data pipelines Follow best practices for optimal results A/B A/B testing frameworks Implement proper a/b testing frameworks Follow best practices for optimal results Follow these comprehensive guidelines for successful python data science implementation.

Python Ml Tensorflow This document provides comprehensive guidelines for python ml tensorflow development and best practices. Keras API Sequential Sequential and Functional API models Implement proper sequential and functional api models Follow best practices for optimal results Model Model subclassing for custom architectures Implement proper model subclassing for custom architectures Follow best practices for optimal results Layer Layer creation and customization Implement proper layer creation and customization Follow best practices for optimal results Activation Activation functions and optimizers Implement proper activation functions and optimizers Follow best practices for optimal results Loss Loss functions and metrics Implement proper loss functions and metrics Follow best practices for optimal results Neural Network Architectures Dense Dense (fully connected) networks Implement proper dense (fully connected) networks Follow best practices for optimal results Convolutional Convolutional Neural Networks (CNNs) Implement proper convolutional neural networks (cnns) Follow best practices for optimal results Recurrent Recurrent Neural Networks (RNNs/LSTMs) Implement proper recurrent neural networks (rnns/lstms) Follow best practices for optimal results Transformer Transformer architectures Implement proper transformer architectures Follow best practices for optimal results Transfer Transfer learning with pre-trained models Implement proper transfer learning with pre-trained models Follow best practices for optimal results Model Evaluation Train/validation/test Train/validation/test splits Implement proper train/validation/test splits Follow best practices for optimal results Cross-validation Cross-validation strategies Implement proper cross-validation strategies Follow best practices for optimal results Performance Performance metrics (accuracy, precision, recall) Implement proper performance metrics (accuracy, precision, recall) Follow best practices for optimal results Confusion Confusion matrices and classification reports Implement proper confusion matrices and classification reports Follow best practices for optimal results Model Model interpretability techniques Implement proper model interpretability techniques Follow best practices for optimal results Model Optimization Hyperparameter Hyperparameter tuning with Keras Tuner Implement proper hyperparameter tuning with keras tuner Follow best practices for optimal results Model Model pruning and quantization Implement proper model pruning and quantization Follow best practices for optimal results TensorFlow TensorFlow Lite for mobile deployment Implement proper tensorflow lite for mobile deployment Follow best practices for optimal results TensorFlow.js TensorFlow.js for web deployment Implement proper tensorflow.js for web deployment Follow best practices for optimal results TensorFlow TensorFlow Serving for production Implement proper tensorflow serving for production Follow best practices for optimal results Advanced Features Custom Custom training loops Implement proper custom training loops Follow best practices for optimal results Mixed Mixed precision training Implement proper mixed precision training Follow best practices for optimal results Distributed Distributed training strategies Implement proper distributed training strategies Follow best practices for optimal results TensorBoard TensorBoard for visualization Implement proper tensorboard for visualization Follow best practices for optimal results Profiling Profiling and performance optimization Implement proper profiling and performance optimization Follow best practices for optimal results Integration & Deployment REST REST API deployment with Flask/FastAPI Implement proper rest api deployment with flask/fastapi Follow best practices for optimal results Containerization Containerization with Docker Implement proper containerization with docker Follow best practices for optimal results Cloud Cloud deployment (AWS, GCP, Azure) Implement proper cloud deployment (aws, gcp, azure) Follow best practices for optimal results Edge Edge deployment considerations Implement proper edge deployment considerations Follow best practices for optimal results Real-time Real-time inference optimization Implement proper real-time inference optimization Follow best practices for optimal results Follow these comprehensive guidelines for successful python ml tensorflow implementation.

Django REST Framework Best Practices Comprehensive guide for building robust, scalable REST APIs with Django REST Framework (DRF) and advanced Django patterns. Authentication and Permissions Authentication Strategy Implementation Configure multiple authentication backends Implement JWT authentication with refresh tokens Handle authentication errors gracefully Example authentication setup: Custom Permission Classes Create reusable permission classes Implement object-level permissions Handle complex permission logic Example permission implementations: Rate Limiting and Throttling Implement different throttle rates for different user types Create custom throttle classes Handle throttle exceeded scenarios Example throttling configuration: API Design and Documentation Versioning Strategy Implement API versioning Handle backward compatibility Provide clear migration paths Example versioning implementation: API Documentation with Swagger/OpenAPI Auto-generate API documentation Add detailed endpoint descriptions Include example requests and responses Example documentation setup: Production Deployment Security and Production Settings Configure proper security settings Implement CORS policies Set up proper logging and monitoring Example production configuration: Follow these practices to build robust, scalable, and maintainable REST APIs with Django REST Framework.