Django This is like the Beyoncé of Python frameworks, it’s got everything you need to build complex apps quickly without breaking a sweat (or your bank account). It comes with its own ORM, URL routing system, template engine, and database schema migrations. Plus, it doesn’t rely on other Python libraries or dependencies like some other frameworks do.
2. Pyramid This is the perfect choice for small web apps that you want to scale up into big ones later on. It has a lightweight design philosophy with minimal code and redundancy, but still allows for configuration, extension, and add-ons as your app grows in size and complexity.
3. TurboGears This is the ultimate solution for those who are tired of dealing with limitations in other web frameworks like Django or Rails. It can start as a single file app and scale to full stack solutions for more complex apps, all while using an ORM with multi-database support, sharding capabilities, widget system for AJAX apps, and designer-friendly template system.
4. Web2py This is the go-to framework for database-driven web applications that come with their own built-in development server and debugger, as well as internationalization features and a ticketing system to handle errors. It’s also cross-platform compatible (Windows, Mac, Linux/Unix) and can be deployed on Amazon EC2 or Google App Engine.
5. Bottle This is the fastest and lightest Python micro-framework out there, with no dependencies other than the standard library. Its default features include a basic abstraction over WSGI, utilities, templating, routing, and support for Netflix’s web interfaces.
6. Flask Similar to Bottle but with built-in development server and debugger, integrated unit testing support, RESTful request dispatching, and more. It’s 100% WSGI compliant and Unicode-based, making it a popular choice for big companies like LinkedIn and Pinterest.
7. CherryPy This is an object-oriented web framework that allows you to build web apps as if they were any other Python program. It’s been around since 2002 and has remained fast and stable, with standalone multi-threaded web servers for each app on any OS that supports Python.
8. NumPy This is a fundamental package for scientific computing in Python, supporting large, multi-dimensional arrays and high-level math functions to operate on them. It also offers tools for integrating C/C++ code and Fortran code, as well as linear algebra, Fourier transforms, and random number capabilities.
9. Pattern This is a web mining module that provides tools for scraping, natural language processing (NLP), machine learning, network analysis, and visualization. It’s free, comes with extensive documentation, and includes 50+ examples and 350+ unit tests.
10. Scikit-Learn This is a very fast machine learning library for Python that offers simple and efficient data mining and analysis tools. Its main features include regression, clustering, model selection, preprocessing, and classification.
11. Keras This is a neural network library that can run on top of TensorFlow or Microsoft Cognitive Toolkit (CNTK) for those who prefer to use Python instead of other languages like R or MATLAB. It’s considered one of the best tools for beginners in machine learning because it’s much more understandable than other libraries.
12. Shogun This is an ML software library written in C++ that offers interfaces for various programming languages, including Python. Its main focus is on Support Vector Machines (SVM), but it also provides a wide range of unified methods of machine learning based on understandable algorithms.
13. Cython This is a superset of Python designed to give C-like performance for code mostly written in Python, thanks to its Pyrex-based translators that allow developers to write C extensions for Python. It’s particularly useful for mathematical code and offers integration support with IPython and Jupyter.
14. SciPy This is a Python-based ecosystem of open-source software for math, science, and engineering that contains modules for optimization, linear algebra, integration, interpolation, ODE solvers, and more. It uses packages like NumPy, IPython, and Pandas to make it easier to manipulate numbers on a computer and display or publish those results.
15. Dask This provides advanced parallelism for analytics by enabling performance at scale and integrating with projects like NumPy, Pandas, and Scikit-Learn. Its schedulers allow you to scale to thousand-node clusters, making it a great choice for big data processing tasks.
16. Numba This is an optimizing compiler for Python that uses LLVM compiler infrastructure to compile Python code into CPU or GPU machine code. It’s particularly useful for science research that involves NumPy arrays and works well on hardware specifically built for ML or data science applications.