Python Learning Guide: Web Frameworks & Application Development

Who this guide is for

Learners ready to build real Python web applications
Developers choosing among Flask, Django, FastAPI, and other frameworks
Teams evaluating sync vs async approaches for backend services

What you’ll learn

How to choose a framework based on project requirements
Differences between synchronous and asynchronous Python web frameworks
Core trade-offs of Flask, Django, FastAPI, and Starlette
Where Pyramid, aiohttp, Sanic, Tornado, and gevent fit
When to use Streamlit or Dash for data-focused applications

Why this topic matters

Framework choice affects speed of development, architecture flexibility, deployment complexity, and long-term maintenance. Picking the right framework early can save major refactors later.

There is no one-size-fits-all option. Your ideal framework depends on team experience, performance needs, ecosystem requirements, and product scope.

Core concepts

Framework selection criteria

Use these criteria first:

Team familiarity and learning curve
Built-in features needed (auth, admin, ORM, routing)
Performance profile and concurrency needs
Community maturity and documentation quality

This keeps decisions objective and project-oriented.

Synchronous vs asynchronous frameworks

Synchronous frameworks are simpler for many CRUD apps. Asynchronous frameworks help with high-concurrency I/O workloads.

Examples:

Sync-first: Flask, Django, Pyramid
Async-capable or async-first: FastAPI, Starlette, aiohttp, Sanic, Tornado, gevent

Async is not automatically better; match it to workload.

Typical framework roles

Flask: lightweight microframework, flexible composition
Django: batteries-included full-stack framework
FastAPI: modern API-first, async-friendly, automatic docs
Streamlit/Plotly Dash: rapid data app UI and dashboards

Choose based on product type and delivery speed goals.

Step-by-step walkthrough

Step 1 — Build a tiny app in one framework

Start with one framework and one endpoint.

FastAPI minimal example:

from fastapi import FastAPI

app = FastAPI()


@app.get("/")
def root():
	return {"message": "Hello from FastAPI"}

Run:

uvicorn main:app --reload

Step 2 — Compare with a second framework

Create equivalent endpoint in Flask:

from flask import Flask

app = Flask(__name__)


@app.get("/")
def root():
	return {"message": "Hello from Flask"}


if __name__ == "__main__":
	app.run(debug=True)

Compare ergonomics, defaults, and extension needs.

Step 3 — Evaluate against real project constraints

Score candidate frameworks on:

Required features
Performance target
Team experience
Hosting/deployment fit

Pick one and avoid multi-framework drift unless there is a strong reason.

Practical examples

Example 1 — API endpoint with validation (FastAPI)

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
	name: str
	price: float


@app.post("/items")
def create_item(item: Item):
	return {"name": item.name, "price_with_tax": round(item.price * 1.1, 2)}

Expected behavior:

Valid JSON payload returns computed response.
Invalid payload returns validation error automatically.

Example 2 — Data app prototype (Streamlit)

import streamlit as st

st.title("Sales Dashboard")
sales = [120, 150, 170, 160]
st.line_chart(sales)

Run:

streamlit run app.py

Expected result:

Browser UI with chart rendered instantly.

Simple “Hello World” comparison table:

Minimal Plotly Dash hello world:

from dash import Dash, html

app = Dash(__name__)
app.layout = html.Div("Hello from Plotly Dash")

if __name__ == "__main__":
	app.run(debug=True)

Minimal gevent-style server concept (green threads):

from gevent import monkey
monkey.patch_all()

from gevent.pywsgi import WSGIServer
from flask import Flask

app = Flask(__name__)

@app.get("/")
def home():
	return {"message": "Hello from gevent"}


if __name__ == "__main__":
	WSGIServer(("0.0.0.0", 8000), app).serve_forever()

Common mistakes and how to avoid them

Choosing framework by popularity only -> Decide from project requirements and constraints.
Starting async architecture without async workload -> Use sync-first for simpler systems.
Underestimating built-in features value -> Full-stack frameworks can reduce custom boilerplate.
Mixing multiple framework paradigms in one early project -> Keep architecture focused.

Quick practice

Implement the same /health endpoint in Flask and FastAPI.
List three project scenarios and map each to one framework choice with reasons.
Build one mini data app in Streamlit showing a simple chart and filter.

Key takeaways

Framework selection should be requirement-driven and explicit.
Sync and async models both have valid use cases.
FastAPI, Flask, and Django cover most common backend needs with different trade-offs.
Data apps often benefit from Streamlit or Dash for rapid delivery.

Next step

Continue to Deployment & Production. In the next guide, you will package and deploy Python apps with environment variables, containers, and platform options.