Web Development

Top 10 Most Popular Python Frameworks for US Developers

Struggling to choose the right Python framework for your next project? We explore the Top 10 Most Popular Python Frameworks for US Developers! Discover frameworks for web development, machine learning, and more!

Python’s grip on U.S. development is undeniable. A major reason for this is its vast collection of frameworks – pre-written code libraries that act like developer toolkits. These frameworks save time and effort, empowering engineers to build powerful applications.

We’ll break down each framework’s key features and strengths, helping you choose the ideal tool for your project needs. Whether you’re crafting complex web apps or venturing into the world of machine learning, there’s a Python framework waiting to supercharge your development journey.

Top 10 Most Popular Python Frameworks for US Developers

US developersFrom Django, the web development giant, to Scikit-learn, the machine learning powerhouse, get ready to discover the incredible potential of Python frameworks!

1) Django

Django is a high-level Python web framework that is popular in United States for building secure and maintainable websites efficiently. Known for its “batteries-included” philosophy, Django comes with numerous built-in features such as an ORM (Object-Relational Mapper), authentication, and an admin interface.

It promotes the use of reusable code and adheres to the DRY (Don’t Repeat Yourself) principle. With comprehensive documentation and strong community support, Django is widely adopted for both small and large-scale projects, offering scalability and flexibility that meet the diverse needs of developers in USA.


  • ORM for database operations
  • Built-in authentication system
  • Admin interface for easy management
  • URL routing and templating
  • Support for multiple database backends


2) Flask

Flask is a lightweight and micro web framework that is highly regarded in USA for its simplicity and flexibility. It provides the essentials to get an application up and running quickly without many built-in features, making it ideal for developers who prefer a minimalistic and modular approach.
Flask’s modular design allows developers to choose the components they need, making it suitable for small to medium-sized projects. Its ease of use and extensibility with third-party libraries make Flask a favorite among Python developers in USA.


  • Lightweight and minimalistic
  • Modular design
  • URL routing
  • Templating with Jinja2
  • Support for extensions


3) FastAPI

fast API

FastAPI is a modern, high-performance web framework for building APIs with Python 3.6+ that has gained significant popularity in USA. It leverages standard Python type hints for automatic interactive API documentation and validation. Built on Starlette for web parts and Pydantic for data parts, FastAPI ensures both speed and accuracy.

Its ease of use, combined with asynchronous support, makes it an excellent choice for developing robust, high-performance APIs and web applications quickly and efficiently in USA.


  • High performance
  • Automatic interactive API documentation
  • URL routing
  • Based on standard Python type hints
  • Built on Starlette and Pydantic
  • Asynchronous support


4) TensorFlow

TensorFlow is an open-source library developed by Google for numerical computation and machine learning, widely used in USA. It provides a comprehensive ecosystem of tools, libraries, and community resources that enable researchers and developers to build and deploy machine learning-powered applications easily.

TensorFlow’s flexibility allows it to run on various platforms, including CPUs, GPUs, and TPUs, making it suitable for a range of tasks from research to production. Its popularity in USA is bolstered by its robust performance and extensive support.


  • Comprehensive ecosystem for ML
  • Flexible architecture
  • Runs on multiple platforms
  • Robust community support
  • Pre-trained models and transfer learning


5) NumPy


NumPy is the fundamental package for scientific computing with Python, extensively used in USA for data science, machine learning, and scientific research. It supports large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
NumPy provides the backbone for many other scientific libraries in Python, making it indispensable for numerical computations. Its powerful n-dimensional array object and integration with C/C++ and Fortran code make it a staple in USA for those performing high-performance scientific computing.


  • N-dimensional array object
  • Broadcasting functions
  • Mathematical functions for arrays
  • Integration with C/C++ and Fortran
  • Extensive library of algorithms


6) Requests (HTTP Library)

Requests is a simple and elegant HTTP library for Python that is highly popular in USA for making HTTP requests. It abstracts the complexities of making requests behind a simple API, allowing developers to send HTTP requests with minimal code.

Requests support various methods such as GET, POST, PUT, DELETE, and more, handling many issues like connection pooling, redirects, and cookies automatically. It is widely used for web scraping, API interactions, and integrating third-party services in USA.


  • Simple and elegant API
  • Supports various HTTP methods
  • Automatic handling of redirects and cookies
  • Connection pooling
  • SSL verification


7) Beautiful Soup (Web Scraping)

Beautiful Soup is a library for parsing HTML and XML documents and extracting data from them, widely used in USA for web scraping projects. It provides Pythonic idioms for iterating, searching, and modifying the parse tree, making it easier to navigate and manipulate web content.

Beautiful Soup creates a parse tree from page source code that can be used to extract data easily. It works with your favorite parser to provide ways of navigating, searching, and modifying the parse tree, making web scraping efficient and straightforward.


  • Parses HTML and XML
  • Provides Pythonic idioms for navigation
  • Works with different parsers
  • Handles bad markup
  • Facilitates web scraping

Beautiful Soup

8) Pydantic (Data Validation)

Pydantic is a data validation and settings management library for Python, leveraging Python type annotations. It is widely used in USA for its robust framework for data validation and management, ensuring data correctness and allowing for complex nested data structures.

Pydantic uses Python’s type hints to validate and parse data, providing an easy-to-use solution for data validation. It is especially popular in conjunction with FastAPI, thanks to its performance and ease of integration.


  • Data validation using type hints
  • Parses JSON and other data formats
  • Enforces data correctness
  • Supports complex nested data structures
  • Integrates well with FastAPI


9) Dash (Data Visualization)


Dash is a productive Python framework for building web applications, particularly for data visualization, widely used in USA. Built on top of Flask, Plotly.js, and React.js, Dash abstracts away the technologies and protocols needed to build interactive web-based applications, allowing developers to focus on writing Python code.

It’s widely used for creating dashboards and data visualization applications, providing a simple interface to complex visualizations and the ability to connect these visualizations to data, making it a preferred choice in USA for data-driven applications.


  • Interactive web applications
  • Built on Flask, Plotly.js, and React.js
  • Simple interface for data visualization
  • Supports numerous chart types
  • Extensible with plugins


10) Scikit-learn (Machine Learning)

Scikit-learn is a powerful and user-friendly library for machine learning in Python, extensively used in USA for data mining and data analysis. It features a variety of classification, regression, and clustering algorithms, including support vector machines, random forests, gradient boosting, k-means, and DBSCAN.

Built on NumPy, SciPy, and matplotlib, scikit-learn is designed to interoperate with other Python libraries, providing simple and efficient tools for data modeling. Its accessibility to both beginners and experienced practitioners makes it a popular choice in United States.


  • Wide range of machine learning algorithms
  • Built on NumPy, SciPy, and matplotlib
  • Easy to integrate with other Python libraries
  • Tools for model evaluation and selection
  • User-friendly API



In the ever-evolving field of software development in the United States, Python frameworks are essential for creating robust, efficient, and scalable applications.

Whether you’re developing a complex web application with Django, a high-performance API with FastAPI, or venturing into machine learning with TensorFlow and Scikit-learn, these frameworks offer the tools and libraries needed to simplify and speed up the development process.

With options for web development, data science, and more, Python’s versatility makes it a preferred choice for developers across the U.S.

At Shiv Technolabs, we specialize in using these top frameworks to deliver advanced solutions tailored to your requirements. As a leading Python app development company in USA, our expertise guarantees that your projects are developed to the highest standards of quality and performance. Partner with us to turn your ideas into reality with the power of Python.


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Written by

Dipen Majithiya

I am a proactive chief technology officer (CTO) of Shiv Technolabs. I have 10+ years of experience in eCommerce, mobile apps, and web development in the tech industry. I am Known for my strategic insight and have mastered core technical domains. I have empowered numerous business owners with bespoke solutions, fearlessly taking calculated risks and harnessing the latest technological advancements.