# python

Dataclass with Validation

Python dataclass with __post_init__ field validation, type coercion, and descriptive error messages.

Best for: Data transfer objects

#dataclass#validation

#context-manager#resource-management

Custom Context Manager

Context managers for resource management using both class-based and decorator approaches with error handling.

Best for: Database connection management

Pytest Fixtures and Parametrize

Reusable pytest fixtures with scope control, parametrize for data-driven tests, and temporary resources.

Best for: Unit test setup

#pytest#testing

#fastapi#dependency-injection

FastAPI Dependency Injection

Use FastAPI's dependency injection for database sessions, auth checks, and shared service logic.

Best for: Database session management

Dataclasses with Post-Init Processing

Use Python dataclasses with __post_init__ for computed fields, validation, and default factories.

Best for: Domain models

#dataclasses#python

Python Context Manager Patterns

Create custom context managers with __enter__/__exit__ and contextlib for resource management.

Best for: Automatic resource cleanup and lifecycle management

#python#context-manager

Python Dataclass Advanced Patterns

Advanced dataclass usage with validation, post-init processing, slots, and frozen instances.

Best for: Type-safe data models without ORMs

#python#dataclass

Python Concurrent Futures for Parallel Work

Run tasks in parallel using ThreadPoolExecutor and ProcessPoolExecutor with error handling.

Best for: Parallel HTTP requests for web scraping

#python#concurrency

Python Functools and Decorator Patterns

Useful decorator patterns with functools including caching, retry, timing, and rate limiting.

Best for: Adding retry logic to flaky operations

#python#decorators

Python Pathlib File Operations

Modern file system operations using pathlib for cross-platform path handling and file management.

Best for: Cross-platform file path handling

#python#pathlib

Python Logging Configuration

Configure structured logging with handlers, formatters, rotation, and JSON output for production.

Best for: Production-ready structured logging

#python#logging

Python Advanced Typing Patterns

Advanced type hints with Protocol, TypeVar, Generic, overload, and TypeGuard for safer code.

Best for: Building type-safe generic containers

#python#typing

Python Itertools Recipes

Practical itertools patterns for batching, flattening, grouping, and combining iterables.

Best for: Efficient batch processing of large datasets

#python#itertools

Python CLI Tool with Argparse

Build a command-line tool with subcommands, arguments, validation, and help text using argparse.

Best for: Building developer tooling CLIs

#python#cli

Python Retry Decorator with Backoff

Create a retry decorator with exponential backoff and configurable attempts for flaky operations.

Best for: Resilient HTTP requests to external APIs

#python#decorator

Dataclass with __post_init__ Validation

Add custom validation to Python dataclasses using __post_init__.

Best for: Input validation

#python#dataclass

Custom Context Manager with enter / exit

Build reusable resource management with Python context managers.

Best for: Resource cleanup

#python#context-manager

Run Async Tasks Concurrently with gather

Execute multiple async operations concurrently using asyncio.gather.

Best for: Parallel API calls

#python#asyncio

Memoize Functions with lru_cache

Cache expensive function results automatically using functools.lru_cache.

Best for: Recursive algorithms

#python#caching

Implement the Iterator Protocol

Create custom iterators using __iter__ and __next__ for lazy evaluation.

Best for: Batch processing

#python#iterator

Type Hints with Generics

Use Python generics for type-safe reusable data structures and functions.

Best for: Reusable data structures

#python#type-hints

Run Shell Commands with subprocess

Execute external commands safely using subprocess.run in Python.

Best for: Automation scripts

#python#subprocess

Data Validation with Pydantic Models

Define and validate data models with Pydantic for type-safe Python applications.

Best for: API request validation

#python#pydantic

Descriptors and the Property Pattern

Use Python descriptors and @property for controlled attribute access.

Best for: Controlled attribute access

#python#descriptors

Optimize Memory with slots

Reduce memory usage for classes with many instances using __slots__.

Best for: Memory-constrained applications

#python#slots

Abstract Base Classes with abc

Define interfaces and enforce method implementation with Python's abc module.

Best for: Interface contracts

#python#abc

Walrus Operator := Patterns

Use the walrus operator (:=) for concise assignment-in-expression patterns.

Best for: Concise filtering

#python#walrus-operator

Python Enum Advanced Patterns

Use Python enums with custom values, methods, auto(), and Flag for type-safe constants.

Best for: type-safe constants

#python#enum

Frozen Dataclass for Immutable Objects

Create immutable value objects using frozen dataclasses with hash support and custom methods.

Best for: value objects

#python#dataclass

Structural Pattern Matching (match/case)

Use Python 3.10+ match/case for expressive pattern matching with guards and destructuring.

Best for: command parsing

#python#match

contextlib Utilities for Resource Management

Use contextlib for lightweight context managers, suppressing exceptions, and redirect output.

Best for: timing code

#python#contextlib

Collections Module Patterns

Use Counter, defaultdict, deque, namedtuple, and ChainMap from the collections module.

Best for: counting

#python#collections

Asyncio Semaphore and Timeout Patterns

Control concurrency with asyncio semaphores, timeouts, and task groups for robust async code.

Best for: rate limiting API calls

#python#asyncio

Protocol Classes for Structural Typing

Define interfaces with Protocol for duck-typing that works with static type checkers.

Best for: dependency injection

#python#protocol

Advanced Generators — send, yield from, close

Use generator.send(), yield from delegation, and close() for coroutine-style generators.

Best for: streaming pipelines

#python#generator