Python Programming Language

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  • 31st Jan, 2023

Python Programming Language

Python is an interpreted high-level programming language. It was created by Guido van Rossum and released in the year 1991. Python supports multiple programming paradigms including object-oriented, imperative, functional and procedural.

Finally, practice here Most Popular Interview Questions on Python Frameworks like Python Pyramid, Python Flask, CherryPy, Django etc.

1. Django

Django is an open-source web application framework written in Python programming language and was developed with front-end developers in mind. It offers a big collection of modules that the users can use in their projects. It also has its markup language with many tools. It was based on the Model–View–Controller(MVC) framework which allows developers to change the visual part of an app and the business logic part separately, without their affecting one another. The three layers of the MVC model are responsible for different things and can be used independently.

2. Cherrypy

CherryPy allows developers to make web applications in much the similar way they would make any other object-oriented Python program. This results in smaller source code being developed in a lesser amount of time. CherryPy can be defined as an object-oriented web application framework, using Python Programming Language. The design of this is made in a way to supports the fast development of web applications by wrapping the HTTP protocol.

3. Devops

DevOps is a set of practices that automates the processes between IT teams and software development, in command that they can test, make, and release software faster and more dependably. The concept of DevOps is founded on building a culture of partnership between teams that historically functioned in virtual siloes. The promised reimbursement includes faster software releases, increased trust, the ability to solve critical issues quickly, and improved manage unplanned work.

4. Opencv

OpenCV is an Open source Computer Vision library developed, to begin with by Intel. You can grip OpenCV for Linux, Windows, and Mac OSX. There is a lot of but in short, documentation and examples; OpenCV provides hardware-accelerated APIs that very much aid in image analysis, motion detection, image processing, and machine learning. It is used in the demo applications in order to show that even with hardware acceleration; a memory leak can create every application useless.

5. Python Flask

Python Flask is a popular, extensible web micro-framework for building web applications with Python. It is a lightweight WSGI web application framework that was designed to make the starting of the use quick and easy. It can scale up simple applications to complex applications. Starting as a simple wrapper around Werkzeug and Jinja, Python Flask has become one of the most popular Python web application frameworks.

6. Python

Python is a high-level, interpreted programming language that is widely used for a variety of purposes, including web development, data analysis, scientific computing, artificial intelligence, machine learning, and more. It was first released in 1991 by Guido van Rossum and has since grown in popularity due to its simplicity, readability, and versatility.

7. Python Pyramid

Python Pyramid is a general, web application development framework built in python. It allows python developers to create web applications with ease. The Python Pyramid is supported by the enterprise knowledge Management System KARL. In Pyramid, users can generate URLs for routes, resources, and static assets. The ease and flexibility to work with URL generation APIs have given it worldwide popularity.

8. Tensorflow

TensorFlow is an open-source software library for dataflow and differentiable programming across a range of tasks. It is designed to enable developers to build and deploy machine learning models efficiently. TensorFlow was developed by the Google Brain team and was first released in 2015.

9. Python Pandas

Python Pandas is a popular open-source data analysis and manipulation library. It provides a powerful set of tools for working with structured data, including data frames and series. Pandas is built on top of the NumPy library, making it fast and efficient for working with large datasets.