Python Ecosystem Guide

When to use what, and which libraries to pick for your project.

Web Frameworks Django vs Flask vs FastAPI

Criteria	Django	Flask	FastAPI
Best for	Full-featured web apps	Small APIs, microservices	Modern async APIs
Learning curve	Steep (many concepts)	Gentle (minimal)	Moderate (async + Pydantic)
Performance	Good	Good	Excellent (async)
Built-in features	ORM, admin, auth, forms	Routing, templates	Validation, docs, DI
Documentation	Excellent	Good	Excellent
Async support	Partial (ASGI)	No (use Quart)	Native
Database	Django ORM	SQLAlchemy	SQLAlchemy/Tortoise
API docs	Manual (DRF has browsable API)	Manual (Swagger via extensions)	Auto-generated OpenAPI

Decision Matrix

Choose Django when: - Building a content-heavy site (CMS, e-commerce, blog) - You need an admin panel - Team prefers conventions over configuration - Building REST APIs with Django REST Framework - Project will grow large (> 50 endpoints)

Choose Flask when: - Building a small service (< 20 endpoints) - Prototyping or building an MVP - You want to pick your own tools (ORM, auth, etc.) - Building microservices - Simple server-rendered pages

Choose FastAPI when: - Building a modern REST API - Need automatic request validation - Want auto-generated API documentation - Working with async (many concurrent I/O operations) - Serving ML models - Building WebSocket applications

Data Science Stack

Library	Purpose	When to use
Pandas	Data manipulation	Tabular data, CSV/Excel, SQL results
NumPy	Numerical computing	Math operations, arrays, linear algebra
Matplotlib	Plotting	Publication-quality charts, full control
Seaborn	Statistical plotting	Quick, beautiful statistical charts
Plotly	Interactive plots	Dashboards, web-based visualization
SciPy	Scientific computing	Statistics, optimization, signal processing
Scikit-learn	Machine learning	Classification, regression, clustering
Polars	Fast DataFrames	When Pandas is too slow (large datasets)

DevOps & Automation

Library	Purpose
boto3	AWS SDK (S3, EC2, Lambda, etc.)
docker	Docker SDK (containers, images, compose)
fabric	SSH remote execution
paramiko	Low-level SSH
ansible	Configuration management (via CLI)
terraform	Infrastructure as Code (via subprocess)
requests	HTTP client
httpx	Async HTTP client

Testing

Library	Purpose
pytest	Test framework (use this)
pytest-cov	Code coverage
pytest-mock	Mocking integration
pytest-asyncio	Async test support
hypothesis	Property-based testing
factory_boy	Test data factories
responses / respx	Mock HTTP requests

Databases

Library	Use case
SQLAlchemy	SQL ORM + Core (most popular)
Django ORM	If using Django
Tortoise ORM	Async ORM (for FastAPI)
SQLModel	SQLAlchemy + Pydantic (by FastAPI creator)
Alembic	Database migrations
Redis (redis-py)	Caching, queues
Motor	Async MongoDB

Code Quality

Tool	Purpose	Speed
ruff	Linter + formatter	⚡ Fastest
mypy	Type checking	Fast
black	Code formatter	Good
isort	Import sorting	Good
bandit	Security linter	Good
pre-commit	Git hooks

Recommendation: Use ruff for linting + formatting (replaces flake8, black, isort, pyupgrade in one tool).

Task Queues & Background Jobs

Library	Use case
Celery	Distributed task queue (heavy, reliable)
RQ (Redis Queue)	Simple job queue
Dramatiq	Modern alternative to Celery
APScheduler	In-process scheduling
Huey	Lightweight task queue

CLI Tools

Library	Use case
argparse	Stdlib, good for most CLIs
click	Decorator-based CLI framework
typer	Type-hint based CLI (by FastAPI creator)
rich	Beautiful terminal output
textual	TUI applications

Package Management

Tool	Use case
pip + venv	Standard (always works)
poetry	Dependency management + packaging
uv	Fast pip replacement (Rust-based)
pipx	Install CLI tools globally
conda	Data science environments

Recommendation: Start with pip + venv. Move to poetry or uv for published packages.