What is python?
Python is a high-level, object-oriented programming language. Python is pretty popular these days because of its easy syntax similar to the English language. It is used for various purposes such as backend scripting of any web application, Artificial Intelligence, machine learning, data science, software development, and game development, etc.
What is the difference between python libraries and frameworks?
Python is rich in libraries and frameworks. Both of them make your programming tasks easier. They are fundamentally different in terms of complexities. Frameworks define the workflow and architecture of the software. at the same time, libraries deliver methods and rules that perform certain operations. Hence, in a nutshell, libraries are simpler than frameworks.
Using libraries, you’ve control to either use all or a few methods. On the contrary, you lose some control when using a framework. Whereas, frameworks, being complex, provides you with more functionality than libraries.
Important python libraries/frameworks:
- Tkinter:
Tkinter is a Python library that supports the creation of an eye-catching GUI. Be it your database or notepad application, or simple games (like tic-tac-toe, hangman, etc.), Tkinter is always there to provide you with an easy interface and widgets from the Tk GUI toolkit.
- Pygame:
Pygame is the most famous, free, and open-source python library that helps you in managing the logic and graphics involved in developing a game. It contains a huge collection of graphics and sound. Pygame is capable of running across various platforms and operating systems.
- Panda3D:
This is a free gaming framework for both C++ and python. It combines the speed of C++ and the easy to use approach of Python. It was initially written in C++. Panda3D is used to create 3D simulations and visualization. It incorporates visuals and audio with remarkable abilities such as collision detection.
- Numpy:
Numerical Python(Numpy) library of python allows you to efficiently handle multi-dimensional arrays and scientific computing. Not only it supports arrays, but also provides sufficient tools to work with such arrays. Furthermore, numpy is efficient enough to deal with linear algebra, matrices and other complex mathematical operations.
- Pandas:
This library upholds the domain of data analysis and manipulation. It is built on top of a very similar another package namely Numpy. Pandas makes the work of data scientists less time-consuming and more professional.
- Flask:
Flask is a web framework (that is a third-party library) for the creation of web apps. Web applications might be dynamic or static, but Flask is present to help you with its tools and techniques. Flask supports extensions. These extensions are useful for form validation, rendering, etc. Other web frameworks are bottle, Django pyramid, etc. Famous brands that use Flask are Netflix, Reddit, and Lyft.
- Tensor flow:
Focusing on machine learning in Python is made straightforward by the tensor flow. This open-source library is developed by Google. It particularly focuses on deep understanding (a sub-domain of machine learning). A large-scale neural network can be created with multiple layers using tensor flow. It can be used to train models based on artificial intelligence.
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Blog by urooj fatima
