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title	description	last_updated	python_compatibility
Modern Python Modules Reference	Comprehensive guide to high-quality, Python 3.11+ compatible modules organized by use case	2025-10-29	3.11+

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.

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/.

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Modern Python Modules Reference

Quick Navigation

Package & Project Management

uv

hatch

CLI Development

Click

Typer

Rich

Fabric

Web Frameworks & APIs

FastAPI

Starlette

Pydantic

HTTP & Network

httpx

requests

aiohttp

paho-mqtt

Data Processing & Analysis

Pandas

NumPy

Polars

DuckDB

Testing & Quality

pytest

pytest-cov

Coverage

mypy

Ruff

Black

Hypothesis

Async & Concurrency

asyncio

Trio

uvloop

APScheduler

Type Checking & Validation

Pydantic

attrs

dataclasses

marshmallow

Configuration Management

python-dotenv

python-decouple

dynaconf

Logging & Monitoring

structlog

loguru

OpenTelemetry

Database & ORM

SQLAlchemy

Tortoise ORM

Peewee

asyncpg

Data Structures & Utilities

attrs

bidict

boltons

python-diskcache

Box

blinker

Serialization

orjson

msgpack

cattrs

Templates & Code Generation

Copier

Jinja2

Automation & Deployment

GitPython

Fabric

Prefect

Testing & Automation Frameworks

Robot Framework

Shiv

arrow

Guide Structure

How to Navigate This Reference

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