tools-only Modern Python Modules Reference
This reference guide covers high-quality, actively maintained Python modules that are compatible with Python 3.11+ and represent modern best practices. Each module is vetted for production use and includes guidance on when to use it.
Quick Navigation
- Package & Project Management - Dependency management, packaging, and project tooling
- CLI Development - Building command-line applications
- Web Frameworks & APIs - Web development and API frameworks
- HTTP & Network - HTTP clients, protocols, and networking
- Data Processing & Analysis - Data manipulation and analysis
- Testing & Quality - Testing frameworks and code quality tools
- Async & Concurrency - Async programming and concurrent execution
- Type Checking & Validation - Data validation and type safety
- Configuration Management - Config handling and secrets
- Logging & Monitoring - Logging, tracing, and observability
- Database & ORM - Database drivers and ORM solutions
- Data Structures & Utilities - Specialized data structures and utility functions
- Serialization - Data serialization and encoding
Package & Project Management
uv
PyPI: uv | Status: Active | Python: 3.11+
Modern package manager and project tool written in Rust. Replaces pip, pip-tools, pipx, poetry, pyenv, and virtualenv with 10-100x performance improvements.
Key Features:
- Blazingly fast dependency resolution and installation
- PEP 723 inline script metadata support
- Single lockfile for reproducible environments
- Python version management built-in
- Virtual environment creation and management
When to Use:
- Managing Python projects with modern tooling
- Creating portable scripts with dependencies
- Building CI/CD pipelines
- Replacing Poetry or pip-tools workflows
See Also: For comprehensive uv documentation, activate the uv skill: Skill(command: "uv")
hatch
PyPI: hatch | Status: Active | Python: 3.11+
Modern build backend and project manager. Standardizes Python packaging without configuration complexity.
Key Features:
- Reproducible builds via hatch.build
- Built-in test runner and environment management
- Version bumping automation
- Dynamic field support in pyproject.toml
When to Use:
- Building and packaging libraries
- Automating version management
- Standardizing project structure
CLI Development
Click
PyPI: click | Status: Active | Python: 3.11+
Elegant and intuitive command-line interface creation framework. Emphasizes composability and convention over configuration.
Key Features:
- Automatic help text generation
- Type hints support
- Command composition and nesting
- Custom parameter types
- Shell completion support
When to Use:
- Building user-friendly CLI applications
- Creating command-line tools with subcommands
- Rapid prototyping of command-line interfaces
Related: Typer (Type-based alternative using Pydantic)
Typer
PyPI: typer | Status: Active | Python: 3.11+
Modern CLI framework based on Click but using Python type hints and Pydantic for automatic parsing.
Key Features:
- Pure Python type hints (no decorators needed)
- Automatic shell completion
- Built-in help generation
- Pydantic integration for parameter validation
- Intuitive and explicit API
When to Use:
- Building type-safe CLIs
- Rapid CLI development with type validation
- Creating Python scripts with structured arguments
Comparison with Click:
- Typer emphasizes types and simplicity
- Click is more explicit and composable
- Choose Typer for rapid development, Click for complex nested commands
Rich
PyPI: rich | Status: Active | Python: 3.11+
Terminal rendering library for rich text and beautiful formatting in the terminal.
Key Features:
- Syntax highlighting for code
- Progress bars and spinners
- Formatted tables and tree displays
- Markup-based text styling
- Console control and automation
When to Use:
- Adding attractive output to CLI applications
- Displaying progress in long-running operations
- Creating formatted reports in the terminal
- Building interactive terminal applications
Fabric
PyPI: fabric | Status: Active | Python: 3.11+
High-level API for executing shell commands remotely or locally. Built on top of Paramiko.
Key Features:
- Execute commands over SSH
- Local command execution
- File transfer (PUT/GET)
- Configurable host lists and task runners
- Context managers for clean command management
When to Use:
- Deployment automation
- Remote system administration
- Building deployment scripts
- Task automation across multiple hosts
See Also: Fabric Documentation
Web Frameworks & APIs
FastAPI
PyPI: fastapi | Status: Active | Python: 3.11+
Modern, fast web framework for building APIs with Python type hints. Built on Starlette and Pydantic.
Key Features:
- Automatic OpenAPI/Swagger documentation
- Built-in dependency injection
- Request validation via Pydantic models
- Excellent async/await support
- WebSocket support
When to Use:
- Building REST APIs
- Creating high-performance web services
- Building microservices with documentation
- Real-time applications with WebSockets
Starlette
PyPI: starlette | Status: Active | Python: 3.11+
Lightweight ASGI framework for building async web applications. Foundation for FastAPI.
Key Features:
- ASGI-based async request handling
- Middleware support
- WebSocket support
- Background tasks
- Excellent testing utilities
When to Use:
- Lightweight web applications
- ASGI server applications
- Middleware development
- When you need lower-level control than FastAPI
Pydantic
PyPI: pydantic | Status: Active | Python: 3.11+
Data validation library using Python type hints. Provides runtime type checking and data parsing.
Key Features:
- Runtime type validation
- Automatic type coercion
- JSON Schema generation
- Custom validators
- Serialization support
When to Use:
- API request/response validation
- Configuration validation
- Data pipeline validation
- Creating self-documenting data models
HTTP & Network
httpx
PyPI: httpx | Status: Active | Python: 3.11+
Modern HTTP client library with both sync and async support. Designed as next-generation requests replacement.
Key Features:
- Synchronous and asynchronous APIs
- HTTP/1.1 and HTTP/2 support
- Type annotations throughout
- Default timeout behavior
- ASGI/WSGI testing support
When to Use:
- Building async HTTP clients
- Making HTTP requests with modern Python
- Need both sync and async in one library
- Testing ASGI/WSGI applications
See Also: httpx Deep Dive
requests
PyPI: requests | Status: Maintained | Python: 3.11+
Ubiquitous HTTP library for simple, synchronous HTTP requests.
Key Features:
- Simple, Pythonic API
- Automatic redirects
- Session management
- Cookie handling
- SSL verification
When to Use:
- Simple synchronous HTTP requests
- Legacy project compatibility
- When async support is not needed
- Broad ecosystem compatibility
Note: httpx is recommended for new projects requiring async support
aiohttp
PyPI: aiohttp | Status: Active | Python: 3.11+
Async HTTP client/server framework. Built for both client and server async HTTP operations.
Key Features:
- HTTP server and client
- WebSocket support
- Built-in connection pooling
- Middleware support
- Streaming support
When to Use:
- Async HTTP clients with server component
- Building full async web services
- Need built-in WebSocket server support
paho-mqtt
PyPI: paho-mqtt | Status: Active | Python: 3.11+
MQTT client library for IoT and message-based applications.
Key Features:
- MQTT protocol support
- Synchronous and asynchronous APIs
- TLS/SSL encryption
- Will messages and persistence
- Callbacks for event handling
When to Use:
- Building IoT applications
- MQTT message publishing and subscribing
- Integrating with MQTT brokers
- Message-based system communication
See Also: paho-mqtt Documentation
Data Processing & Analysis
Pandas
PyPI: pandas | Status: Active | Python: 3.11+
Powerful data manipulation and analysis library. Standard for data science and analytics.
Key Features:
- DataFrames for tabular data
- Series for labeled 1D data
- Built-in plotting and visualization integration
- SQL-like operations
- Time series functionality
When to Use:
- Data manipulation and cleaning
- Exploratory data analysis
- Building data pipelines
- Working with tabular data
NumPy
PyPI: numpy | Status: Active | Python: 3.11+
Fundamental library for numerical computing. Foundation for scientific Python stack.
Key Features:
- N-dimensional arrays (ndarray)
- Vectorized operations
- Linear algebra operations
- Random number generation
- FFT capabilities
When to Use:
- Numerical computations
- Scientific and mathematical operations
- Foundation for other data science libraries
- Array-based data processing
Polars
PyPI: polars | Status: Active | Python: 3.11+
Fast DataFrame library written in Rust with Python bindings. Modern alternative to Pandas for large datasets.
Key Features:
- High-performance execution
- Lazy evaluation support
- Memory efficient
- Comprehensive expression API
- Out-of-core processing
When to Use:
- Large dataset processing
- Performance-critical data pipelines
- New projects prioritizing speed
- Memory-constrained environments
DuckDB
PyPI: duckdb | Status: Active | Python: 3.11+
In-process SQL database engine optimized for analytics workloads.
Key Features:
- SQL queries on data files
- Parquet, CSV, and other format support
- Excellent query performance
- Easy Python integration
- No server required
When to Use:
- Analytical SQL queries on files
- Data exploration with SQL
- In-process data warehousing
- Replacing complex pandas operations
Testing & Quality
pytest
PyPI: pytest | Status: Active | Python: 3.11+
Mature testing framework with powerful fixtures and plugin system.
Key Features:
- Simple test function syntax
- Powerful fixtures for test setup
- Parametrization for multiple test cases
- Excellent assertion introspection
- Rich plugin ecosystem
When to Use:
- Writing unit and integration tests
- Any Python project testing
- Standard test framework for projects
pytest-cov
PyPI: pytest-cov | Status: Active | Python: 3.11+
Code coverage measurement plugin for pytest.
Key Features:
- Coverage reporting with pytest
- HTML coverage reports
- Coverage thresholds
- Multiple report formats
When to Use:
- Measuring test coverage
- Enforcing minimum coverage requirements
- Identifying untested code
Coverage
PyPI: coverage | Status: Active | Python: 3.11+
Code coverage measurement and reporting tool.
Key Features:
- Statement and branch coverage
- HTML and XML reports
- Coverage API for custom reporting
- Configuration file support
When to Use:
- Understanding code coverage
- CI/CD coverage validation
- Code quality metrics
mypy
PyPI: mypy | Status: Active | Python: 3.11+
Static type checker for Python. Verifies type hints without running code.
Key Features:
- Type hint verification
- Plugin system
- Incremental checking
- Multiple strictness levels
- Good error messages
When to Use:
- Type-checking Python code
- Catching type errors before runtime
- Enforcing type safety in projects
- Large codebase maintenance
Ruff
PyPI: ruff | Status: Active | Python: 3.11+
Fast Python linter written in Rust. Combines flake8, isort, and other tools.
Key Features:
- Extreme speed (50-100x faster than flake8)
- Multiple rule sets
- Automatic fixing
- Isort-compatible import sorting
- Minimal configuration
When to Use:
- Linting Python code
- Replacing flake8, pylint, or isort
- CI/CD pipelines
- Code quality gates
Black
PyPI: black | Status: Active | Python: 3.11+
Uncompromising code formatter. Enforces consistent style without configuration.
Key Features:
- Deterministic formatting
- Minimal configuration (intentional)
- AST-based (preserves semantics)
- Fast formatting
- Stable formatting output
When to Use:
- Enforcing code style
- Automatic code formatting
- Team collaboration (standardized style)
- CI/CD integration
Hypothesis
PyPI: hypothesis | Status: Active | Python: 3.11+
Property-based testing framework. Generates test cases automatically.
Key Features:
- Property-based testing
- Automatic example generation
- Database of failing cases
- Integrated with pytest
- Profile systems for custom generation
When to Use:
- Property-based testing
- Testing invariants and properties
- Finding edge cases
- Fuzzing and robustness testing
Async & Concurrency
asyncio
PyPI: Built-in stdlib | Status: Maintained | Python: 3.11+
Standard library for asynchronous I/O and concurrent programming.
Key Features:
- Coroutines and tasks
- Event loop
- Futures for deferred results
- Synchronization primitives
- Subprocess support
When to Use:
- Any asynchronous Python code
- Built-in, no installation needed
- Building async applications
- Concurrent I/O operations
Trio
PyPI: trio | Status: Active | Python: 3.11+
Friendly async library with better structured concurrency patterns.
Key Features:
- Structured concurrency (async with blocks)
- Better cancellation semantics
- Excellent debugging support
- Built-in testing utilities
- Simpler mental model than asyncio
When to Use:
- Complex async programs
- Structured concurrency patterns
- Better error handling in concurrent code
- Async testing
uvloop
PyPI: uvloop | Status: Active | Python: 3.11+
Drop-in replacement for asyncio event loop, written in Cython for performance.
Key Features:
- 2-4x faster than default asyncio
- Drop-in replacement (single import)
- Works with all asyncio code
- libuv-based implementation
- Minimal overhead
When to Use:
- Performance-critical async applications
- Deploying async applications
- Speeding up existing asyncio code
See Also: uvloop Documentation
APScheduler
PyPI: apscheduler | Status: Active | Python: 3.11+
Advanced Python Scheduler for task scheduling and automation.
Key Features:
- Cron-like scheduling
- Fixed interval scheduling
- One-off job scheduling
- Persistent job storage
- Multiple scheduler backends
When to Use:
- Scheduling recurring tasks
- Background job execution
- Cron-like task automation
- Building task queues
Type Checking & Validation
Pydantic
PyPI: pydantic | Status: Active | Python: 3.11+
See Web Frameworks & APIs section above.
attrs
PyPI: attrs | Status: Active | Python: 3.11+
Class definition library with minimal boilerplate, validators, and converters.
Key Features:
- Automatic dunder methods (
__init__,__repr__,__eq__) - Built-in validators and converters
- Slot-based classes for performance
- Frozen (immutable) classes
- Field transformers for extensibility
When to Use:
- Defining data classes with validation
- Creating immutable data structures
- Building domain models
- Performance-critical class definitions
See Also: attrs Documentation
dataclasses
PyPI: Built-in stdlib | Status: Maintained | Python: 3.11+
Standard library for data classes with automatic dunder methods.
Key Features:
- Decorator-based class definition
- Automatic special methods
- Field configuration
- Slots support (Python 3.10+)
- Frozen classes
When to Use:
- Simple data container classes
- Zero external dependencies
- Built-in Python solution
- Python 3.10+ projects
marshmallow
PyPI: marshmallow | Status: Active | Python: 3.11+
Object serialization/deserialization and data validation library.
Key Features:
- Field-based schema definition
- Serialization and deserialization
- Data validation
- Nested object support
- Extensive customization
When to Use:
- Data serialization and validation
- API request/response handling
- Legacy codebases
- Complex object mapping
Configuration Management
python-dotenv
PyPI: python-dotenv | Status: Active | Python: 3.11+
Load environment variables from .env files.
Key Features:
- Simple .env file parsing
- Environment variable injection
- Override control
- Interpolation support
- Path helpers
When to Use:
- Development environment setup
- Managing secrets and configuration
- Separating config from code
- Local development workflows
See Also: python-dotenv Documentation
python-decouple
PyPI: python-decouple | Status: Active | Python: 3.11+
Simple library to separate configuration from code.
Key Features:
- Environment variable parsing
- Type casting (int, bool, list)
- Default value support
- Search order: env file, system env, defaults
- Minimal configuration
When to Use:
- Configuration management
- 12-factor app principles
- Simple environment variable handling
dynaconf
PyPI: dynaconf | Status: Active | Python: 3.11+
Configuration management system supporting multiple formats and environments.
Key Features:
- YAML, TOML, JSON configuration
- Environment variable override
- Settings object with dot notation
- Multiple environments
- Validation support
When to Use:
- Complex configuration systems
- Multi-environment projects
- Configuration file management
- Settings management across environments
Logging & Monitoring
structlog
PyPI: structlog | Status: Active | Python: 3.11+
Structured logging library for adding context to log entries.
Key Features:
- Structured (JSON) logging
- Context preservation
- Processor pipelines
- Multiple output formats
- Integration with standard logging
When to Use:
- Building production systems
- Machine-readable log analysis
- Context propagation across function calls
- Structured logging infrastructure
loguru
PyPI: loguru | Status: Active | Python: 3.11+
Simpler logging library with modern features and convenient API.
Key Features:
- Single logger instance
- Automatic file rotation
- Formatting with braces syntax
- Color output by default
- Exception formatting
When to Use:
- Simple logging setup
- Single-file modules
- Automatic formatting and rotation
- Convenient logging API
OpenTelemetry
PyPI: opentelemetry-api | Status: Active | Python: 3.11+
Open standard for observability (metrics, traces, logs).
Key Features:
- Distributed tracing
- Metrics collection
- Log correlation
- Multiple exporter support
- Vendor-agnostic
When to Use:
- Distributed systems tracing
- Observability infrastructure
- Multi-service applications
- Monitoring and debugging
Database & ORM
SQLAlchemy
PyPI: sqlalchemy | Status: Active | Python: 3.11+
Most mature and feature-rich Python ORM and SQL toolkit.
Key Features:
- ORM for object-relational mapping
- Core expression language for queries
- Multiple database support
- Async support (SQLAlchemy 2.0+)
- Extensive customization
When to Use:
- Complex database applications
- Need ORM with full features
- Multiple database backend support
- Well-established projects
Tortoise ORM
PyPI: tortoise-orm | Status: Active | Python: 3.11+
Async-first ORM inspired by Django ORM.
Key Features:
- Async/await native
- Django ORM-like API
- Multiple database support
- Migrations
- Validation
When to Use:
- Async applications
- FastAPI projects
- Django ORM-like experience with async
- Modern async web applications
Peewee
PyPI: peewee | Status: Active | Python: 3.11+
Simple and small ORM for lightweight database interactions.
Key Features:
- Lightweight and simple API
- SQLite, PostgreSQL, MySQL support
- Query builder
- Migrations
- Expression-based querying
When to Use:
- Simple database applications
- Lightweight projects
- SQLite applications
- Learning ORM concepts
asyncpg
PyPI: asyncpg | Status: Active | Python: 3.11+
Fast PostgreSQL database driver for asyncio.
Key Features:
- High performance (fastest Python PostgreSQL driver)
- Async/await support
- Native JSON support
- Connection pooling
- Streaming support
When to Use:
- PostgreSQL with async code
- High-performance database access
- FastAPI/Starlette applications
- Large-scale async applications
Data Structures & Utilities
attrs
PyPI: attrs | Status: Active | Python: 3.11+
See Type Checking & Validation section above.
bidict
PyPI: bidict | Status: Active | Python: 3.11+
Bidirectional dictionary supporting fast forward and reverse lookups.
Key Features:
- Bidirectional mapping
- Inverse access
- One-to-one mapping enforcement
- Immutable variants
When to Use:
- Bidirectional mappings
- ID-to-name relationships
- Reverse lookups required
- Enum-like behavior
See Also: bidict Documentation
boltons
PyPI: boltons | Status: Active | Python: 3.11+
Set of utility functions for common programming tasks.
Key Features:
- Iteration utilities
- Dictionary utilities
- List utilities
- Table-like data structures
- Caching decorators
When to Use:
- Common utility operations
- Functional programming tools
- Extending standard library
- Utility collections
See Also: boltons Documentation
python-diskcache
PyPI: diskcache | Status: Active | Python: 3.11+
Persistent disk-based dictionary-like cache for large datasets.
Key Features:
- Disk-based caching
- Dictionary interface
- LRU eviction
- Transactional semantics
- Compression support
When to Use:
- Caching large datasets
- Disk-persistent cache
- Building caches that survive restarts
- Replacing Redis for simple cases
See Also: python-diskcache Documentation
Box
PyPI: python-box | Status: Active | Python: 3.11+
Dictionary with attribute-style access (dot notation).
Key Features:
- Attribute access to dictionary items
- Nested access support
- Configuration object pattern
- JSON export
- Validation integration
When to Use:
- Configuration objects
- Cleaner dictionary access syntax
- Nested data access
- Configuration management
See Also: Box Documentation
blinker
PyPI: blinker | Status: Active | Python: 3.11+
Signal (event) dispatching library for loose coupling.
Key Features:
- Signal/event dispatching
- Multiple listener support
- Weak references for cleanup
- Sender-based filtering
- Minimal dependencies
When to Use:
- Event-driven architecture
- Plugin systems
- Loose coupling between components
- Application signaling
See Also: blinker Documentation
Serialization
orjson
PyPI: orjson | Status: Active | Python: 3.11+
Fast JSON serialization library written in Rust.
Key Features:
- 10x faster than standard json
- Drop-in json replacement
- Native datetime serialization
- Supports numpy arrays
- Minimal dependencies
When to Use:
- High-performance JSON encoding
- Serializing numpy/pandas data
- Drop-in replacement for json
- Performance-critical serialization
msgpack
PyPI: msgpack | Status: Active | Python: 3.11+
Binary serialization format for fast data interchange.
Key Features:
- Compact binary format
- Fast serialization/deserialization
- Support for various types
- Timestamp support
- Streaming support
When to Use:
- Binary message protocols
- High-performance serialization
- Network protocols
- RPC systems
cattrs
PyPI: cattrs | Status: Active | Python: 3.11+
Custom class converters for attrs/dataclasses serialization.
Key Features:
- Serialization/deserialization
- Works with attrs and dataclasses
- Custom converters
- Nested structure support
- Structural polymorphism
When to Use:
- Converting attrs/dataclass to dictionaries
- Serializing complex structures
- attrs ecosystem serialization
Templates & Code Generation
Copier
PyPI: copier | Status: Active | Python: 3.11+
Project templating and scaffolding tool.
Key Features:
- Template-based project generation
- Question prompts during generation
- Relative path handling
- Pre-commit hooks
- Multi-layer templates
When to Use:
- Project scaffolding
- Template-based project generation
- Reproducible project structures
- Creating new projects
See Also: Copier Documentation
Jinja2
PyPI: jinja2 | Status: Active | Python: 3.11+
Powerful templating engine for dynamic text generation.
Key Features:
- Template syntax with variables and filters
- Control flow (if/for/while)
- Custom filters and globals
- Template inheritance
- Auto-escaping
When to Use:
- HTML/template generation
- Code generation
- Dynamic document creation
- Report generation
Automation & Deployment
GitPython
PyPI: GitPython | Status: Active | Python: 3.11+
Python library for interacting with Git repositories.
Key Features:
- Repository operations
- Commit/branch management
- Remote operations
- Blame and history
- Config management
When to Use:
- Git integration in Python
- Automation with Git
- Repository analysis
- Deployment scripts
See Also: GitPython Documentation
Fabric
PyPI: fabric | Status: Active | Python: 3.11+
See CLI Development section above.
Prefect
PyPI: prefect | Status: Active | Python: 3.11+
Workflow orchestration and task scheduling platform.
Key Features:
- Task-based workflow definition
- Built-in retry and error handling
- Flow visualization
- Caching and result persistence
- API for monitoring
When to Use:
- Complex workflow orchestration
- Data pipeline management
- Task scheduling and execution
- Production workflow management
See Also: Prefect Documentation
Testing & Automation Frameworks
Robot Framework
PyPI: robotframework | Status: Active | Python: 3.11+
Automation and testing framework with keyword-driven syntax.
Key Features:
- Keyword-driven testing
- Built-in libraries
- Custom library support
- Tabular data syntax
- HTML reports
When to Use:
- Acceptance testing
- Robotic process automation
- Non-technical test authoring
- End-to-end testing
See Also: Robot Framework Documentation
Shiv
PyPI: shiv | Status: Active | Python: 3.11+
Command line utility to create self-contained zip applications.
Key Features:
- Creates executable Python applications
- Bundles dependencies
- Standalone distribution
- ZIP-based Python packages
- Single file distribution
When to Use:
- Distributing Python applications
- Creating standalone executables
- Shipping without pip
- Simple application distribution
See Also: Shiv Documentation
arrow
PyPI: arrow | Status: Active | Python: 3.11+
Friendlier datetime and timezone handling library.
Key Features:
- Human-friendly datetime API
- Timezone support
- Parsing and formatting
- Timezone conversion
- Relative time operations
When to Use:
- Datetime handling
- Timezone management
- Human-readable time formatting
- Datetime parsing
See Also: Arrow Documentation
Guide Structure
Each module typically includes:
- Overview - What the module does and why it's useful
- Official Information - Links, version, maintenance status
- Python Compatibility - Supported Python versions
- Installation - How to install (with uv recommended)
- Core Concepts - Key ideas and patterns
- Usage Examples - Practical code examples
- When to Use - Decision guidance
- Alternatives - Competing or complementary modules
- Integration Patterns - How to use with other tools
- Common Gotchas - Pitfalls and edge cases
How to Navigate This Reference
By Use Case
Start with the category that matches your need, then read the module descriptions.
By Python Version
All modules listed are Python 3.11+ compatible. Check individual module references for exact version support.
By Integration
Many modules work together (e.g., FastAPI + Pydantic, attrs + cattrs). Look for "See Also" and "Integration Patterns" sections.
By Performance
For performance-critical code:
- Serialization: orjson, msgpack
- Async I/O: uvloop, httpx
- Data processing: Polars, DuckDB
- Linting: Ruff
- Package management: uv
Installation Pattern
Install modules using uv (recommended) or pip:
# With uv
uv add module-name
# With pip (if uv not available)
pip install module-name
Research Methodology
All modules in this reference are verified to be:
- Actively maintained (recent commits)
- Python 3.11+ compatible
- Production-ready
- Widely used in industry
Module information is gathered from:
- Official repositories and documentation
- PyPI package pages
- Community usage patterns
- Real-world project implementations
Last Updated: October 29, 2025 Python Compatibility: 3.11+ Total Modules Covered: 50+
For module-specific deep dives, see individual reference files in modern-modules/.