Python frameworks help project owners quickly track the time to market for their applications. For fast track development, all frameworks from different programming languages have the same functions. Python frameworks are no different; they are a module and a package collection. These frameworks automate and deploy common processes. For example, developers can focus on application logic, rather than on routine processes. Complexity is the principal difference between frameworks and libraries. Libraries offer fewer complexities and the opposite is frameworks. A library is a collection of packages where specific operations are performed. In contrast, the frameworks contain the application’s basic flow and architecture. But developers need to construct the remaining functionalities. Control also represents a key difference between these two. When a developer calls a library method the developer is in control of the process of development. He’s the one calling with frameworks. It means the processes are called by the frameworks, and the control lies within the framework. Frameworks are often used due to its key flexibility advantage. They are expandable and provide the tools necessary for extending its features. Unlike libraries, developers need to learn to perform certain operations with functionality. Python Frameworks: Micro-framework: These frameworks are simple and easy to use; a good choice for applications small to medium in size. Full-stack framework: Used to make different applications; complex in nature and offering extensive libraries, data management, etc. Here are some frameworks: Django Flask CherryPy Bottle Web2Py Python Libraries: TensorFlow Numpy Scikit-Learn PyTorch Keras