search
HomeBackend DevelopmentPython TutorialTop Python Frameworks for 4

Top Python Frameworks for 4

Python is one of the most versatile programming languages available today. Whether you're building web applications, APIs, or machine learning models, Python has a framework to simplify the process. Below are the top 10 Python frameworks to learn, along with a brief description, example code, and a link to their official documentation or website.


1. Django

Category: Web Development
Description: Django is a high-level Python web framework that promotes rapid development and clean, pragmatic design. It's fully featured and comes with a built-in admin panel, ORM, and many other tools for building scalable web applications.

Why Use It: Fast development, security features, scalability.
Use Cases: Content management systems, e-commerce, social networks.
Example Code:

# Install Django
pip install django

# Create a new Django project
django-admin startproject mysite

# Create a new app
cd mysite
python manage.py startapp myapp

# Example view (in myapp/views.py)
from django.http import HttpResponse

def hello_world(request):
    return HttpResponse("Hello, Django!")

link: Django Documentation


2. Flask

Category: Web Development
Description: Flask is a lightweight and easy-to-use web framework. It’s often called a "micro-framework" because it keeps the core simple but allows you to add plugins and extensions as your project grows.

Why Use It: Simple, highly customizable, lightweight.
Use Cases: APIs, web apps, microservices.
Example Code:

# Install Flask
pip install flask

# Simple Flask app
from flask import Flask
app = Flask(__name__)

@app.route('/')
def hello_world():
    return 'Hello, World!'

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

link: Flask Documentation


3. FastAPI

Category: Web Development / APIs
Description: FastAPI is one of the fastest frameworks for building APIs with Python, using asynchronous programming. It also includes automatic data validation and documentation generation.

Why Use It: High performance, automatic validation, asynchronous programming.
Use Cases: APIs, microservices, web apps.
Example Code:

# Install FastAPI and Uvicorn
pip install fastapi uvicorn

# Simple FastAPI app
from fastapi import FastAPI
app = FastAPI()

@app.get("/")
def read_root():
    return {"Hello": "World"}

# Run the server: uvicorn main:app --reload

link: FastAPI Documentation


4. Pyramid

Category: Web Development
Description: Pyramid is a highly flexible web framework that allows developers to build web apps from simple to complex. It is suitable for both large and small projects.

Why Use It: Flexible, scalable, minimal setup.
Use Cases: Large-scale apps, APIs, customizable systems.
Example Code:

# Install Pyramid
pip install "pyramid==2.0"

# Create a Pyramid project
cookiecutter gh:Pylons/pyramid-cookiecutter-starter

# Example view (in views.py)
from pyramid.view import view_config

@view_config(route_name='home', renderer='templates/mytemplate.jinja2')
def my_view(request):
    return {'project': 'Pyramid'}

link: Pyramid Documentation


5. Tornado

Category: Web Development / Networking
Description: Tornado is a web framework and asynchronous networking library that handles long-lived network connections. It’s perfect for building real-time applications such as chat apps.

Why Use It: Asynchronous programming, real-time support.
Use Cases: Real-time apps, chat applications, streaming.
Example Code:

# Install Django
pip install django

# Create a new Django project
django-admin startproject mysite

# Create a new app
cd mysite
python manage.py startapp myapp

# Example view (in myapp/views.py)
from django.http import HttpResponse

def hello_world(request):
    return HttpResponse("Hello, Django!")

link: Tornado Documentation


6. Bottle

Category: Web Development
Description: Bottle is a simple and lightweight web framework for building small web apps. It’s perfect for small projects or for prototyping quickly.

Why Use It: Simple, lightweight, fast to prototype.
Use Cases: Prototypes, small web applications.
Example Code:

# Install Flask
pip install flask

# Simple Flask app
from flask import Flask
app = Flask(__name__)

@app.route('/')
def hello_world():
    return 'Hello, World!'

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

link: Bottle Documentation


7. CherryPy

Category: Web Development
Description: CherryPy is an object-oriented web framework that allows developers to build web applications in a Pythonic way. It’s a scalable and flexible solution.

Why Use It: Object-oriented, scalable, simple.
Use Cases: Web applications, custom servers.
Example Code:

# Install FastAPI and Uvicorn
pip install fastapi uvicorn

# Simple FastAPI app
from fastapi import FastAPI
app = FastAPI()

@app.get("/")
def read_root():
    return {"Hello": "World"}

# Run the server: uvicorn main:app --reload

link: CherryPy Documentation


8. Web2py

Category: Web Development
Description: Web2py is a full-stack web framework with an integrated IDE, web server, and database abstraction layer. It’s great for rapid application development.

Why Use It: All-in-one solution, easy deployment, integrated IDE.
Use Cases: Full-stack applications, rapid prototyping.
Example Code:

# Install Pyramid
pip install "pyramid==2.0"

# Create a Pyramid project
cookiecutter gh:Pylons/pyramid-cookiecutter-starter

# Example view (in views.py)
from pyramid.view import view_config

@view_config(route_name='home', renderer='templates/mytemplate.jinja2')
def my_view(request):
    return {'project': 'Pyramid'}

link: Web2py Documentation


9. Dash

Category: Data Visualization
Description: Dash is a Python framework for building web-based data visualizations. It integrates with Plotly to create interactive charts and dashboards.

Why Use It: Great for data visualization, easy to use, integrates with Plotly.
Use Cases: Data dashboards, visualizations, analytics.
Example Code:

# Install Tornado
pip install tornado

# Simple Tornado app
import tornado.ioloop
import tornado.web

class MainHandler(tornado.web.RequestHandler):
    def get(self):
        self.write("Hello, Tornado!")

def make_app():
    return tornado.web.Application([
        (r"/", MainHandler),
    ])

if __name__ == "__main__":
    app = make_app()
    app.listen(8888)
    tornado.ioloop.IOLoop.current().start()

link: Dash Documentation


10. PyTorch

Category: Machine Learning
Description: PyTorch is a deep learning framework known for its flexibility and ease of use. It’s widely used for developing neural networks and working with complex data.

Why Use It: Dynamic computation, flexible, great for deep learning.
Use Cases: Deep learning, neural networks, computer vision.
Example Code:

# Install Bottle
pip install bottle

# Simple Bottle app
from bottle import route, run

@route('/hello')
def hello():
    return "Hello, Bottle!"

run(host='localhost', port=8080)

link: PyTorch Documentation


Conclusion

These 10 Python frameworks are an excellent starting point for building web applications, APIs, data visualizations, and machine learning models. Whether you're a beginner or an experienced developer, these frameworks offer a range of tools to accelerate your projects. Happy coding!

The above is the detailed content of Top Python Frameworks for 4. For more information, please follow other related articles on the PHP Chinese website!

Statement
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn
How to Use Python to Find the Zipf Distribution of a Text FileHow to Use Python to Find the Zipf Distribution of a Text FileMar 05, 2025 am 09:58 AM

This tutorial demonstrates how to use Python to process the statistical concept of Zipf's law and demonstrates the efficiency of Python's reading and sorting large text files when processing the law. You may be wondering what the term Zipf distribution means. To understand this term, we first need to define Zipf's law. Don't worry, I'll try to simplify the instructions. Zipf's Law Zipf's law simply means: in a large natural language corpus, the most frequently occurring words appear about twice as frequently as the second frequent words, three times as the third frequent words, four times as the fourth frequent words, and so on. Let's look at an example. If you look at the Brown corpus in American English, you will notice that the most frequent word is "th

How Do I Use Beautiful Soup to Parse HTML?How Do I Use Beautiful Soup to Parse HTML?Mar 10, 2025 pm 06:54 PM

This article explains how to use Beautiful Soup, a Python library, to parse HTML. It details common methods like find(), find_all(), select(), and get_text() for data extraction, handling of diverse HTML structures and errors, and alternatives (Sel

Mathematical Modules in Python: StatisticsMathematical Modules in Python: StatisticsMar 09, 2025 am 11:40 AM

Python's statistics module provides powerful data statistical analysis capabilities to help us quickly understand the overall characteristics of data, such as biostatistics and business analysis. Instead of looking at data points one by one, just look at statistics such as mean or variance to discover trends and features in the original data that may be ignored, and compare large datasets more easily and effectively. This tutorial will explain how to calculate the mean and measure the degree of dispersion of the dataset. Unless otherwise stated, all functions in this module support the calculation of the mean() function instead of simply summing the average. Floating point numbers can also be used. import random import statistics from fracti

How to Perform Deep Learning with TensorFlow or PyTorch?How to Perform Deep Learning with TensorFlow or PyTorch?Mar 10, 2025 pm 06:52 PM

This article compares TensorFlow and PyTorch for deep learning. It details the steps involved: data preparation, model building, training, evaluation, and deployment. Key differences between the frameworks, particularly regarding computational grap

Serialization and Deserialization of Python Objects: Part 1Serialization and Deserialization of Python Objects: Part 1Mar 08, 2025 am 09:39 AM

Serialization and deserialization of Python objects are key aspects of any non-trivial program. If you save something to a Python file, you do object serialization and deserialization if you read the configuration file, or if you respond to an HTTP request. In a sense, serialization and deserialization are the most boring things in the world. Who cares about all these formats and protocols? You want to persist or stream some Python objects and retrieve them in full at a later time. This is a great way to see the world on a conceptual level. However, on a practical level, the serialization scheme, format or protocol you choose may determine the speed, security, freedom of maintenance status, and other aspects of the program

What are some popular Python libraries and their uses?What are some popular Python libraries and their uses?Mar 21, 2025 pm 06:46 PM

The article discusses popular Python libraries like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Django, Flask, and Requests, detailing their uses in scientific computing, data analysis, visualization, machine learning, web development, and H

How to Create Command-Line Interfaces (CLIs) with Python?How to Create Command-Line Interfaces (CLIs) with Python?Mar 10, 2025 pm 06:48 PM

This article guides Python developers on building command-line interfaces (CLIs). It details using libraries like typer, click, and argparse, emphasizing input/output handling, and promoting user-friendly design patterns for improved CLI usability.

Scraping Webpages in Python With Beautiful Soup: Search and DOM ModificationScraping Webpages in Python With Beautiful Soup: Search and DOM ModificationMar 08, 2025 am 10:36 AM

This tutorial builds upon the previous introduction to Beautiful Soup, focusing on DOM manipulation beyond simple tree navigation. We'll explore efficient search methods and techniques for modifying HTML structure. One common DOM search method is ex

See all articles

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

AI Hentai Generator

AI Hentai Generator

Generate AI Hentai for free.

Hot Article

Hot Tools

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Dreamweaver Mac version

Dreamweaver Mac version

Visual web development tools

WebStorm Mac version

WebStorm Mac version

Useful JavaScript development tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SecLists

SecLists

SecLists is the ultimate security tester's companion. It is a collection of various types of lists that are frequently used during security assessments, all in one place. SecLists helps make security testing more efficient and productive by conveniently providing all the lists a security tester might need. List types include usernames, passwords, URLs, fuzzing payloads, sensitive data patterns, web shells, and more. The tester can simply pull this repository onto a new test machine and he will have access to every type of list he needs.