Python Frameworks: Top 10 Python Frameworks To Learn in 2025
By Rohit Sharma
Updated on Jun 23, 2025 | 21 min read | 17.03K+ views
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By Rohit Sharma
Updated on Jun 23, 2025 | 21 min read | 17.03K+ views
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Latest update: In 2025, Python frameworks continue to evolve, offering enhanced features and capabilities to developers. Notably, Django has introduced support for Python 3.13, enabling developers to leverage the latest language features and application improvements. |
Python frameworks are pre-written code libraries that offer a structured foundation for building applications, letting developers focus on logic over repetitive tasks. They simplify complex processes like routing, database handling, and UI creation. Popular Python frameworks like Django, Flask, FastAPI, and Pyramid provide built-in tools, scalability, and security, making them ideal for web apps, RESTful APIs, and microservices. Python frameworks are essential for faster development and maintaining clean and efficient code.
In this blog, we’ll explore the top 10 Python frameworks to learn in 2025, covering their core features, advantages, and real-world use cases where they excel.
Python frameworks are development tools that provide a pre-built structure and set of libraries for building software applications efficiently. These frameworks eliminate the need to write repetitive, boilerplate code and offer built-in support for key functionalities like URL routing, database interaction, user authentication, and more. They help developers focus on the core logic and features of the application rather than foundational infrastructure.
Based on speed, scalability, community backing, and adaptability, here are 10 Python frameworks you should prioritize.
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Before diving into specific frameworks, it’s important to understand the different types available in Python. Let’s explore the main categories of Python frameworks and how each serves different development needs.
Python frameworks are generally classified into full-stack frameworks and microframeworks. Full-stack frameworks like Django and Web2Py provide everything needed to build a complete application, including ORM (Object Relational Mapping), templating engines, and built-in security features. Microframeworks like Flask and FastAPI, on the other hand, offer more flexibility and minimalism, giving developers control over which components to use.
The following table addresses some of the prominent types of Python frameworks, such as full-stack frameworks, along with their benefits, descriptions, and use cases.
Framework Type |
Description |
Key Benefits |
Ideal Use Cases |
Full-Stack Frameworks | Comprehensive frameworks like Django and Web2py that cover front-end and back-end development in a single package. | Complete development environment- Built-in features (authentication, ORM, form handling)- Enforces consistent project structure- Easily scalable- Security features included by default | Large-scale applications, enterprise projects, content management systems |
Micro-Frameworks | Minimalist frameworks such as Flask and Bottle that provide core functionality with high flexibility for customization. | Fast setup with minimal configuration- Freedom to choose components like databases and templates- Lightweight codebase for easy maintenance- Simple debugging process | Small applications, APIs, prototypes, microservices |
Asynchronous Frameworks | Modern frameworks like FastAPI, Sanic, and Tornado built to support concurrency and non-blocking operations. | - Handles multiple simultaneous requests efficiently- Low latency and fast response times- Optimizes CPU and memory usage- Suitable for real-time and data-heavy apps | Real-time services, streaming platforms, chat apps, and high-performance APIs |
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Now that you understand the importance of selecting the right framework, let's explore the top 10 Python frameworks you should consider learning in 2025.
When choosing a Python framework in 2025, it’s important to consider your project’s purpose and the specific benefits and limitations of each option. You should also stay updated on current trends, including the rise of async-first designs, growing enterprise AI APIs, and evolving standards like compatibility with Python 3.13.
Depending on your project's needs, frameworks can simplify development by offering built-in tools and features, or they can provide flexibility for more custom solutions.
Here’s a quick comparison of the top 10 Python frameworks in 2025, highlighting their core features and the types of projects they’re best suited for.
Framework |
Key Features |
Django | Authentication, ORM, URL routing, template engine |
Pyramid | Routing, HTML form validation, URL dispatch |
Turbo Gears | Genshi, SQLAlchemy, Repoze, WebOb |
Web2py | Built-in IDE, debugger, one-click deployment |
CherryPy | Minimalistic, extensible design |
Flask | Jinja2 template, Werkzeug WSGI toolkit |
Sanic | Asynchronous support, Async/Await |
FastAPI | High performance, automatic validation, and async capabilities |
Bottle | Lightweight, single-file, minimal dependencies |
Dash | Data apps, Plotly integration, interactive dashboards |
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Let's discuss each one of the above in detail.
Django is a high-level Python web framework for fast development and clean, pragmatic design. It's known for its scalability and robustness, making it one of the most widely used frameworks in the industry.
Django’s built-in components like its ORM, URL routing, and database schema migration provide reliability and scalability, though it is not optimized for lightweight or async-first applications
Django powers high-traffic platforms such as Instagram and Pinterest, demonstrating its ability to support scalable and high-performance web applications.
Features of Django:
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Advantages of Django:
Companies Using Django:
Instagram, Mozilla, Pinterest, National Geographic, and more.
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Pyramid is a flexible and minimalistic Python framework known for its simplicity and scalability. It's designed for both small applications and large, complex systems, making it a popular choice for developers who require flexibility.
Pyramid's routing system and HTML form validation allow for quick and efficient web application development. With the option to extend or replace its core components, Pyramid is perfect for building high-performance applications.
Features of Pyramid:
Advantages of Pyramid:
Companies Using Pyramid:
Reddit, Mozilla, and Yelp.
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Turbo Gears is a Python framework that integrates various components, such as Genshi, SQLAlchemy, and WebOb, to streamline the development process. It's well-suited for building scalable, database-driven applications.
With Turbo Gears, developers can easily manage complex data models and enhance their application's performance. It's a great choice if you're looking to work on projects with demanding database interaction and need flexibility.
Features of Turbo Gears:
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Advantages of Turbo Gears:
Companies Using Turbo Gears:
The Washington Post, CNET.
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Web2py is a full-stack Python framework with an integrated web-based IDE that offers code editing, debugging, and one-click deployment. It's designed for rapid application development, especially when working on projects that require quick turnarounds.
The self-contained environment makes it easy to develop, deploy, and maintain web apps. It's particularly useful for those who want an all-in-one solution without relying on external tools.
Features of Web2py:
Advantages of Web2py:
Companies Using Web2py:
Express Technologies, Betfair, and Computaris.
CherryPy follows a minimalistic approach, offering a lightweight framework for building web applications. It is designed for extensibility, allowing developers to add new features without modifying the core framework. CherryPy's simple yet powerful architecture makes it a great choice for smaller projects or for developers who want fine control over their application's design.
Features of CherryPy:
Advantages of CherryPy:
Companies Using CherryPy:
Netflix, OpenStack, and Red Hat.
Flask is a lightweight micro-framework for Python that's perfect for small applications or APIs. With minimal built-in functionality, Flask gives developers complete flexibility to choose their tools and libraries. It's designed to be simple yet extendable, making it an ideal choice for projects that don't require a heavy framework.
Features of Flask:
Advantages of Flask:
Companies Using Flask:
Netflix, Airbnb, and LinkedIn.
Sanic is an asynchronous Python framework designed for high-performance web applications. It supports asynchronous request handlers and is built to handle large numbers of concurrent requests. With native support for Python 3.5's 'async' and 'await’ syntax, Sanic is ideal for building fast, scalable APIs and services.
Features of Sanic:
Advantages of Sanic:
Companies Using Sanic:
Cigna, Darktrace, and Uber.
FastAPI is an asynchronous Python framework designed for high performance and ease of use. It leverages Python's type hints for automatic validation and documentation, making it incredibly productive for developers. FastAPI is well-suited for building modern, scalable APIs and backend services, particularly when speed and performance are critical.
Features of FastAPI:
Advantages of FastAPI:
Companies Using FastAPI:
Microsoft, Uber, and Netflix.
Bottle is a minimalist micro-framework ideal for small applications and rapid prototyping. With a single-file design, it's extremely lightweight and easy to use. Despite its simplicity, Bottle supports essential features like routing, templating, and request handling, making it perfect for projects with minimal complexity.
Features of the Bottle:
Advantages of Bottle:
Companies Using Bottles:
Hootsuite, GitHub, and Dropbox.
Dash is a specialized framework for creating analytical web applications and dashboards using Python. Its integration with Plotly allows developers to easily create interactive visualizations and data apps. Dash is especially popular in the data science and business intelligence sectors, where data-driven insights are key.
Features of Dash:
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Advantages of Dash:
Companies Using Dash:
NASA, Google, and Deloitte use Dash to create sophisticated dashboards. NASA utilizes it for telemetry visualizations, Google for real-time metrics interfaces, and Deloitte for business intelligence dashboards, integrating SQL and machine learning models.
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It's important to distinguish between libraries and frameworks before selecting a framework. This understanding will shape how you approach your project's architecture and development.
The key difference between libraries and frameworks in Python lies in control and structure. A library offers a set of functions and tools that you call as needed to perform specific tasks, giving you control over the flow. In contrast, a framework defines the overall structure and flow of your application, calling your code at predefined points.
For example, using the Requests library, you decide when to make HTTP calls in your script, while using Django, the framework manages requests, routing, and responses to build a full backend stack. In the next section, we'll explore how to choose between them based on your project needs.
Here's a detailed comparison to help you understand their key differences:
Aspect |
Python Library |
Python Framework |
Control Flow | You control the flow of the program and call library functions when necessary. | The framework controls the application's flow; it calls your code at predefined points (Inversion of Control). |
Purpose | Libraries provide reusable code that handles specific tasks, such as data processing or making HTTP requests. | Frameworks provide a structured foundation for building entire applications, often including design patterns. |
Structure | There is no prescribed structure. You can use libraries freely within your application as needed. | Frameworks enforce a certain structure and dictate how the application is organized. |
Flexibility | Libraries offer high flexibility; you can use them for specific tasks without following a pattern. | Frameworks are less flexible, as they impose specific guidelines, rules, and patterns that must be followed. |
Integration | Libraries can be easily integrated into existing applications and systems without altering the overall design. | Frameworks may require a significant restructuring of your existing codebase to fit within their structure. |
Learning Curve | Libraries generally have a lower learning curve. You simply pick up the functions you need. | Frameworks have a steeper learning curve since you need to understand their design patterns, rules, and conventions. |
Complexity | Libraries are simple and designed to be used for specific tasks or functions. | Frameworks are more complex because they offer a comprehensive set of tools to manage an entire application. |
Customization | You can easily customize libraries to fit your specific needs since they are modular. | Frameworks offer less customization, as they are built to enforce a certain structure and flow in applications. |
Examples | NumPy (for numerical computations), Pandas (for data manipulation), Matplotlib (for data visualization), and Requests (for HTTP requests). | Django (for web development), Flask (for microservices), FastAPI (for fast APIs), Pyramid (for flexible web apps). |
Usage | Libraries are used for specific tasks within a program, such as performing calculations, managing data, or interacting with an API. | Frameworks are used to build the core structure of an application, providing predefined tools for different layers. |
Customization Level | High customization, as you can select only the functions you need. | Limited customization, as frameworks enforce predefined structures and logic flow. |
Development Speed | Libraries can speed up development by offering specific tools, but you must manage the architecture and design yourself. | Frameworks accelerate development by offering a ready-to-use structure and integrated tools, saving you design time. |
Code Ownership | With libraries, you decide when and how to call functions. Frameworks, however, control the overall flow and call your code at designated points. | The framework owns the application's structure and logic flow, and you plug your code into the predefined points. |
Maintenance | You are responsible for maintaining how libraries are integrated into your project. | Frameworks help with maintenance by standardizing how the application is structured, making long-term maintenance easier. |
Testing | Testing libraries often require custom setups to ensure everything is integrated correctly. | Frameworks generally come with built-in testing utilities and guidelines to make testing easier. |
Common Usage | Libraries are often used for single tasks or to extend functionality within existing projects. | Frameworks are often used to build large, scalable applications from the ground up, such as web apps or APIs. |
Key Insights:
Now that we've clarified the distinction between libraries and frameworks, let's explore how to choose the right Python framework for your project.
Choosing the right Python framework is a critical decision that can significantly impact your project's success, scalability, and maintainability. While understanding what a framework is in Python is important, the bigger question lies in how to select the most suitable one for your specific needs.
The choice depends on several key factors such as project type, development speed, scalability requirements, community support, security features, and learning curve.
Example: A high-level framework like Django offers built-in tools and rapid development capabilities if you're building a full-featured web application with tight deadlines.
A minimal framework like Flask or FastAPI may provide more flexibility and performance for lightweight APIs or microservices. Additionally, considering aspects like ORM support, asynchronous capabilities, and integration with third-party tools can influence long-term efficiency and ease of maintenance.
Here's a clearer way to compare popular frameworks based on key criteria that affect real-world development:
Example Scenario:
A startup building a social media MVP might start with Flask for speed, while a company developing an enterprise CRM would benefit from Django's structure.
Example Scenario:
A real-time analytics dashboard is a good example where FastAPI’s speed can be critical.
Example Scenario:
For projects where compliance and data protection are paramount, Django's framework helps ensure safety by default.
Example Scenario:
Teams new to Python web development might start with Flask before scaling up.
Example Scenario:
FastAPI supports modern DevOps workflows with its lightweight footprint and automatic documentation.
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Understanding how to choose the right Python framework sets the foundation. Now, upGrad can help you build advanced skills to put that knowledge into practice.
Choosing the right Python framework depends on your project needs, such as scalability, ease of use, and community support. Frameworks like Django offer full-featured solutions, while Flask and FastAPI provide lightweight, flexible options. Understanding these differences helps you pick the best fit for your development goals.
If you're struggling to choose the right Python framework or gain hands-on experience, upGrad’s structured courses can help. Learn Django, Flask, and FastAPI through real-world projects and expert mentorship to fast-track your development career.
While the courses covered in the tutorial can significantly improve your knowledge, here are some free courses to facilitate your continued learning:
Curious which courses can help you gain expertise in Python frameworks? Contact upGrad for personalized counseling and valuable insights. For more details, you can also visit your nearest upGrad offline center.
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References:
https://docs.python.org/3/whatsnew/3.13.html
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