Top 10 Python Libraries and Frameworks in 2025

Introduction

Python is a versatile and powerful server-side programming language that has gained popularity in recent years and is utilized by industrial leaders such as Google, Netflix, Spotify, Instagram, and many more. These companies have leveraged Python to develop robust and scalable applications tailored to specific use cases.

From the development of websites, custom web app solutions, machine learning models, and data science solutions, you can get comprehensive solutions using Python. It provides you with vast libraries and frameworks that can be used for diverse development projects.

But which one to choose from the available Python tools and frameworks? How to select the best Python libraries? Every expert Python developer considers the available options, their capabilities, and evaluates them based on specific key factors to determine whether it is suitable for their project or not.

In this blog, we will provide the top 10 Python frameworks and libraries with their top features, USPs and where not to consider them. Whether you’re a beginner starting your first web app or an expert developer optimizing an enterprise solution, this list will help you choose wisely.

Ready to find your go-to Python toolkit? Let’s explore each framework and library in detail.

Types of Python Frameworks for Web Development

There are diverse Python development frameworks that help you develop robust and futuristic applications. By using the most suitable frameworks, you can perform development projects in a streamlined manner. However, before that, let’s understand the types of frameworks available that help simplify development activities.

Full Stack

A full-stack framework helps provide robust Python web development. It involves built-in tools and libraries that facilitate the smooth management of front-end and back-end tasks.

Microframework

A microframework is a lightweight and simple framework that provides core capabilities and basic tools for developing web applications. Unlike full-stack frameworks, it lacks advanced features and functionalities, such as authentication, form validation, and database abstraction.

Asynchronous

Asynchronous frameworks are specifically designed to develop real-time apps with high performance. It is built on Python’s ‘asyncio’, which allows developers to manage concurrent connections smoothly.

Python Web Development Frameworks and Libraries

From simple website development, managing complex software, to AI development, and ML model development, Python offers specialized frameworks to fulfill your every need. Whether you are looking for a flexible development approach, require built-in features or easy integration, selecting the right framework and library ensures your project’s success.

1. Flask

Flask is a lightweight WSGI (Web Server Gateway Interface) framework written in Python. Imagine building a simple website with the necessary tools, without any unnecessary functionalities. That’s what Flask offers. It utilizes Jinja2 templates and the Werkzeug WSGI toolkit to simplify the customization in the web app development process.

According to a recent study by Survey Stalk overflow, Flask is used by 6.02% of developers worldwide and is expanding further. This is because Flask is a microframework, which basically means that it doesn’t require any libraries or tools.

Flask doesn’t even come with form validation, an abstraction layer, or other components where third-party libraries enable common functions. Instead, you will get the freedom to choose what is essential for your development project.

Features of Flask

• Being a lightweight framework, Flask provides core functionalities necessary for web development, which help you to integrate the required tools and libraries.
• It is flexible, allowing you to customize apps without being restricted to a predefined structure.
• Flask offers you a built-in development server and debugger for running and testing locally. It also helps you resolve bugs during the development process.
• Flask utilizes the Jinja 2 templating engine, which dynamically generates an HTML page for your project with the necessary data and logic.
• The routing system of Flask helps with URL routes and handling view functions to distribute and process user requests.

When not Use Flask

If you are working on a large-scale, complex project or require built-in tools for user authentication, form development and handling, you will find Flask basic.

Quick Insights

If you want simple and flexible apps without a steep learning curve, Flask is ideal to start your next project.

Popular Apps Developed with Flask

• Pinterest utilized Flask for rapid prototyping and API development.
• Reddit has integrated Flask for backend infrastructure.
• Samsung utilized Flask to provide a streamlined user interface and access to product information.

So, if flexibility is important to you, Flask is the perfect fit. But what if you need something more powerful to build complex apps? Let’s explore Django next.

2. Django

Django is a free, open-source Python web development framework that facilitates the rapid development of complex web applications and APIs. While Flask provides the flexibility to choose your components, Django offers an all-in-one package with built-in tools to boost your development process.

So, what is the unique thing about Django? It is a high-level framework that has an extensive toolkit and libraries to develop scalable web apps quickly. In fact, popular brands like Pinterest and Mozilla trust Django to transform their platforms using its reusable components to reduce the coding process.

Features of the Django Framework

• It follows the model-template-view(MTV, a variation of MVC) pattern.
• Django has an in-built ORM that helps developers to establish smooth interaction with the database with Python objects (not using SQL queries) to manage operations smoothly.
• Django offers an automatic admin interface based on models that helps you to perform CRUD operations without adding additional code.
• Due to in-built form handling systems, collecting and validating user inputs can be simplified.
• Built-in authentication helps you manage user accounts, permissions, and authorization.
• Django’s template engine helps you to create dynamic HTML templates by utilizing template tags and filters.

When not to Use Django

• You can avoid Django for small projects or simple websites because it is heavy, complex, and created for enterprise-grade apps, which makes your project setup and resource usage excessive for microservices and quick processes.
• If you want high-performance and real-time apps, it is unsuitable due to limitations of speed and concurrency.
• If you require enhanced flexibility in architecture, it will not be possible with Django due to its rigid structure.

Real World Use Cases

• Pinterest has used Django in its early time to handle media data and user interactions
• Mozilla Foundation utilizes Django to build Firefox and addons.mozilla.org
• NASA used Django to manage user access, host and provide high-resolution space media.
• Bitbucket utilized Django to build its platform, enabling faster development and ensuring scalability.

Expert Insights

The batteries included approach of Django offers built-in tools like ORM, admin panels, and secure authentication that help you in rapid development. It even follows the DRY principle, which enables you to reduce redundancy and enhance productivity.

2. Fast API

If you are focused on achieving smooth performance and want secure API development, FastAPI is your go-to option, which utilizes Python 3.7 or later. FastAPI is a modern web framework designed for speed, efficiency, and clean code. It is compatible with API standards such as JSON Schema and OpenAPI.

As per Statista, Fast API is used by 9.9% developers worldwide to transform their development projects. Developers use Fast API because it uses Pydantic and standard Python type hints to validate, manage, and monitor data. It also helps in automatically generating documentation for web services and APIs, simplifying the testing process.

But do you know it also helps you to build comprehensive solutions like integrated booking systems, CRM systems, shopping platforms, interactive dashboards, and others, where performance is the core.

At Tuvoc, we have also worked with diverse startups that prefer FastAPI to quickly develop MVPs without any complex setup.

Features

• FastAPI generates an interactive API document using OpenAPI, which makes it easy for you to make code easy to read and test.
• It utilizes Pydantic models, which help you validate and manage data automatically.
• Async capabilities make FastAPI ideal for microservices, where apps are distributed into independent services that interact over a network.
• FastAPI efficiently handles concurrent requests, which facilitates the development of a scalable architecture.
• It helps you develop interactive dashboards and reporting platforms that facilitate the visualization of complex data.
• It provides optimized performance and smooth integration with diverse databases and JavaScript frameworks (e.g., Vue.js, React). This helps you implement dynamic front-end features and real-time data delivery.

When Not to Use Fast API

• Fast API is a modern asynchronous API development framework that is not suitable when you need to develop basic static sites or simple web apps using templating.
• It is less suitable if your development team requires a large ecosystem, or you heavily rely on synchronous third-party libraries.

4. Tornado

FriendFeed developed Tornado and was later open sourced by Facebook. Tornado is a high-performing Python web development framework with an asynchronous networking library. It ensures high performance and can handle a large number of concurrent connections.

Features

• Tornado is capable of managing simultaneous connections efficiently.
• It also supports WebSockets, which facilitate real-time communication between the client and server.
• Request handlers to manage incoming HTTP requests. Classes called handlers specify how to handle various types of requests.
• Tornado provides a built-in user authentication approach that helps you to establish enhanced security.
• Due to its enhanced capabilities for managing diverse connections simultaneously, it helps you build scalable web apps.

When Not to Use Tornado

For your standard CRUD web projects or typical REST APIs, Flask, Django or FastAPI is sufficient, Tornado is not preferable.

Real World Use Case

Tornado was developed for FriendFeed (a real-time social media aggregator, before Facebook acquired it) to manage a large number of concurrent and long-lived connections.

Expert Insights

Tornado is suitable for apps that require long-lived connections (WebSockets), and it becomes complex for simple app development.

5. Pyramid

Have you ever wondered how Python frameworks simplify the development process? It is mainly due to its minimalist nature, just like the Pyramid. It will help you to start simple web app development by adding the required components and features.

Pyramid is a simple and open-source web application development framework that assists you in a smooth development process. It follows the WSGI (Web Server Gateway Interface) toolkit and Jinja2 templates.

However, it lacks pre-built tools and architecture, but it includes robust security and quick configuration that help you establish high-end protection.

Features

• It is a scalable framework that helps you provide simple to complex enterprise-grade software development.
• Its modular architecture allows you to independently add components and implement needed functionalities without compiling dependencies.
• Known for high performance, it helps you to build apps with low latency and quick response times.
• The Pyramid has inbuilt security features (e.g., Cross-Site Request Forgery (CSRF)) that will help in protecting your app data from common threats.
• Apps you built using Pyramid work smoothly across Linux, Windows, and macOS ecosystems.
• Pyramid provides a secure configuration that helps you manage routing, settings, views, and security protocols using configuration files and objects.
• You can select pattern-based URL dispatch or resource tree-based routing (traversal) for mapping URLs to your app’s logic.
• Pyramid provides conditional execution of views depending upon request attributes, giving you extended control over request handling.
• With tweens, you can implement custom logic into the request-response cycle, like authentication, handling logging or error handling.
• Pyramid allows you to integrate any database, including NoSQL, ZODB, SQL (using SQLAlchemy), and templating engines like Mako, Jinja2, or Chameleon.
• Pyramid assists expert developers in managing test coverage and documentation, which helps build maintainable apps.

When Not to Use Pyramid

• You can avoid Pyramid for small, simple, or MVP projects as it incurs cost to perform setups and make technical decisions.
• It is not ideal when you need unique features, as Pyramid is minimal, and you need to compile the required components manually.

Python Libraries Based on Specific Purpose 

Data Visualization

You can simplify the data analysis process by using a specific Python library and frameworks. These libraries provide you with pre-defined tools to visualize a large amount of data efficiently. Widely utilized data visualization libraries are Matplotlib, Pandas, Folium, Plotly, Seaborn, and Bokeh. Of all these, the most popular one is Matplotlib. Let’s understand it.

6. Matplotlib

Do you want to turn your comprehensive data into charts and graphs? Matplotlib is the perfect solution for representing your data in an interactive manner. It is a versatile and open-source library that helps you to develop interactive data visualizations to ensure smooth data analysis and monitoring.

It is a plotting Python library used to create interactive, static, and custom visualizations using Python and Numpy (a numerical extension). You can use it to create graphs, plots, and charts with just a few lines of code.

Features

• Matplotlib helps you to build different visualizations like charts, graphs, lines, and scatter plots.
• It provides easy customization of plots including labels, annotations, colors, and markers.
• Smooth integration with NumPy to enhance data manipulation by plotting data effortlessly.

When Not to Use Matplotlib

If you want to create interactive or real-time visualizations, you might find Matplotlib rigid compared to other libraries like Plotly.

AI/ML Development

Python offers a diverse range of frameworks and libraries that enable you to deliver high-quality AI/ML development services. At Tuvoc, we are a team of expert AI/ML developers dedicated to providing optimized, market-leading solutions.

Here, we will analyze various frameworks and libraries, examining their features and suitability.

7. PyTorch

PyTorch is an open-source deep learning and ML framework based on Python and the Torch library. Torch is an open-source machine learning library used to develop deep neural networks and written in Lua (a scripting language).

It was developed by Meta AI, supporting 200+ mathematical operations. In addition to bult in features, PyTorch has an ecosystem of libraries and tools that help in implementing extended functionalities.

It is an adaptable, flexible, and easy to use framework developed by enterprise knowledge management System (KARL, George Soros project). As like mircorframeworks, PyTorch also helps in developing single-file webpages and involves less coding.

Features

• PyTorch helps you build scalable models and network architecture. This enables you to perform easy debugging without being restricted to a pre-defined graph.
• Tensors are the core building blocks, akin to multi-dimensional arrays like NumPy, but with a more advanced approach. That’s what PyTorch offers. Using PyTorch, you can run the project on a powerful GPU, which ensures optimal speed.
• PyTorch helps you handle tricky gradient calculations. Autograd keeps track of mathematical tasks automatically, which makes optimization seamless.
• You get diverse, ready-to-use tools to create complex deep learning architecture and help you with enhanced customization.
• When your model is ready and trained well, TorchScript helps you with a smooth deployment process.

When Not to Use PyTorch

• You should use PyTorch for production deployment specifically for enterprise-grade systems where cross platform model export is important.
• Although PyTorch supports mobile deployments using PyTorch mobile and TorchScript, it is less optimized for embedded systems and resource-constrained devices.
• If your project requires automated model tuning or pipeline generation, you should avoid Pytorch as it lacks native AutoML tools.

Real-world Use Case

• IBM uses PyTorch and TerraTorch to develop geospatial computer vision for environmental studies.
• Tesla has utilized PyTorch for autonomous driving and vision systems.
• Apple has utilized PyTorch to develop Artificial Intelligence, vision, and NLP applications, providing expanded compatibility.

8. TensorFlow

Ever wondered how to develop AI-powered apps easily? TensorFlow is your the ideal approach. It is an open-source platform that helps you to develop robust AI/ML platforms. It offers a comprehensive set of tools, libraries, and community resources that enable you to build high-performance AI-powered apps. It also helps with training and providing LLM development with the necessary functionalities and smart learning capabilities.

The set of libraries, tools, and APIs helps both beginners and experts to create advanced ML models and AI software.

Key Features

• It provides you with a rich source of tools and libraries that help developers to build, train, and deploy ML models like Keras, TensorFlow Core, TensorFlow.js, TensorFlow Lite, and TensorFlow Extended (TFX).
• TensorFlow offers you a flexible architecture that enables the smooth deployment of solutions on CPUs, GPUs, and TPUs.
• It enables smooth integration with Keras (high-level API) that simplifies the process of developing and prototyping neural networks.
• With TensorFlow, you can automatically compute gradients for trainable variables, thereby simplifying the backpropagation process, which is essential for optimizing models during training.
• It also includes TensorBoard (a visualization tool) that helps you monitor model training, visualize the data, and debug the issues smoothly.
• TensorFlow (primarily known as Python) also provides you with a stable API for C++ and expanded support for Java and JavaScript.
• TensorFlow helps you with pre-trained models and datasets through TensorFlow Hub, enabling quick development and implementation of common ML processes.

When Not to Use TensorFlow

• TensorFlow can be more rigid when you require research-focused and iterative model development.
• As a developer, you should avoid TensorFlow if you need custom architecture and model development. PyTorch can help you fill the gap.

Popular Companies Used TensorFlow for Their Projects

• Airbnb used TensorFlow for image classification to provide exact search results to their guests by categorizing photo listings.
• Google has also used TensorFlow for photo search (Google Photos), translation, and cloud AI.
• SAP utilized TensorFlow to create business analytics and intelligent CRM management solutions.

9. Scikit Learn

Scikit-learn is an open-source Python library for machine learning. It is built on NumPy, SciPy, and Matplotlib, which helps you simplify predictive data analytics processes.

Features

• It helps you to build supervised and unsupervised learning models, including classification, regression, clustering, dimensionality reduction, and more.
• With the built-in tools for data scaling, encoding, missing value imputation, feature selection, and dimensionality reduction, you can streamline the model training processes.
• You can get diverse in-built functions to simplify cross-validation, grid search, random search, and metric evaluation (e.g., accuracy, precision, recall, AUC).
• It helps you to build end-to-end pipelines by combining the preprocessing, modeling, and evaluation processes.
• You can integrate it with NumPy, SciPy, Pandas, and Matplotlib for smooth data handling and interactive visualization.

When Not to Use Scikit-learn

• You should avoid using Scikit-learn for neural networks, image recognition, and natural language processing that involve deep learning processes. (For deep learning tasks, you can use PyTorch, Keras, and TensorFlow.)
• Scikit-learn operates in-memory and is not optimized to handle large datasets that don’t fit in RAM.
• Scikit learn doesn’t support GPU acceleration, and if you need to train models using GPUs, you can use libraries like RAPIDS cuML or other deep learning frameworks.
• Scikit-learn is not optimized for real-time inference in ML systems and large-scale production systems.
• Core Scikit-learn doesn’t provide you with automated ML features, but there are Wrappers (e.g., TPOT, Auto-sklearn) that help fulfill AutoML needs.

Data Parsing and Extraction 

Streamline data scraping and parsing process with secured Python libraries like BeautifulSoup, Pandas, Selenium, Scrapy, and more. You can use any of these based on targeted website structure and data requirements. From all these, BeautifulSoup and Pandas are popular but, in this blog, we will understand BeautifulSoup in detail so that you can make a wise decision before selecting any library to build custom data scrapers.

10. BeautifulSoup

BeautifulSoup is an advanced Python library used to monitor and extract data from websites, XML, and HTML documents. It helps in navigating and manipulating web pages, making it crucial for data scraping tasks.

Features

• BeautifulSoup can easily parse HTML and XML files, even with poorly structured markup. It even fixes the common errors in HTML, making it an ideal approach to scraping a website.
• It also supports advanced search capabilities based on tag names, classes, IDs, and text contents to ensure precise data scraping.
• It smoothly integrates with diverse scrapers (eg. html5lib, html.parser, lxml) to select the most ideal option to fulfill specific needs.
• It effectively manages and manipulates nested HTML data, making it ideal for complex web pages.
• Easy integration with requests and automation tools like Selenium, enabling smooth data scraping workflows.

Real-world Use Case

• It helps real estate brokers in tracking property prices by scraping property listings, details, type of property, sales trends, competitor listing data, and others.
• Aggregating job posting data from various job portals that help job seekers to smoothly filter the jobs.

When Not to Use BeautifulSoup

• When your targeted website loads content dynamically via JavaScript, BeautifulSoup can’t access or parse content directly. For this, you can use Selenium, Playwright, or Puppeteer.
• You should avoid BeautifulSoup when you are dealing with large-scale or high-performance scraping processes. It is because BeautifulSoup is slow for large scale scraping processes, as it is optimized for ease of use rather than performing processes quickly.
• BeautifulSoup is a parser, not a crawler. This implies that you cannot handle link-following, depth management, request throttling, or sitemap parsing by itself.
• When you require Asynchronous requests and parsing, BeautifulSoup can be avoided because it works synchronously and doesn’t support asynchronous programming natively.

Quick Analysis of Python Frameworks and Libraries

Conclusion

Selecting the right Python framework or library depends on your project needs and complexity. If you want MVP development solutions or lightweight web apps, Flask or fastAPI offer flexibility and speed.

For built-in tools and a structured approach for large-scale or complex software development, Django is the ideal choice. However, no single framework or library fits every project. Analyzing the strengths and limitations helps you to make smarter choices.

Whether you are a beginner developing your first app or an experienced developer who wants to scale enterprise software, the wide ecosystem of Python helps you to fulfill project goals and streamline your development process.

Rhutu Talati

Rhutu Talati is a seasoned AI & Python Developer with 8+ years of experience, currently leading the AI initiatives at Tuvoc Technologies. Her expertise in Python, AI/ML, and automation empowers the development of custom APIs and intelligent solutions for modern businesses.