Top Python Libraries and Frameworks for 2022

Python is undoubtedly one of the most famous languages existing in the world of technology. In addition, it has many applications in different technological industries. However, there are many reasons this is preferred by many. For instance, it is a high-level language and it is simpler to learn. Therefore, depending on your niche as a python developer, you have to be conversant with working with these libraries and frameworks.

Introduction to Python Libraries:

To start with, a  library in a layman’s explanation is a place or room where books are stored to be accessed or used later. However, in the programming world, a library is a collection of already complied codes that are used to optimize tasks. Therefore, a library needs to contain documentation, configuration data, message templates, classes, values, and so on.

Furthermore, python has different libraries and frameworks. These are a collection of related modules. As a result, they contain bundles of code that can be used repeatedly in different programs. Therefore, this makes python simpler and easy to use for programmers.

In addition, the simplicity of python has attracted many developers to contribute to different libraries. Hence, the following examples show the fields in which these libraries and frameworks can be used:

• Artificial Intelligence and Machine Learning
• Data Science
• Web development
• Automation Testing
• Game development
• Image Processing
• Web scraping
• Finance
• Programming applications
• SEOs

Furthermore, most of these libraries and frameworks are open-sourced. Hence, this makes python stand out amongst other programming languages.

Application and Uses of Python Libraries and Frameworks

The following below is the list of applications where these libraries and frameworks can be applied.

1. Data Science, Artificial Intelligence, and Machine Learning:

• Pandas
• Numpy
• Tensor Flow
• PyTorch
• Seaborn
• Scipy
• Matplotlib
• Keras
• Scikit-Learn

2. Game Development

• PyGame
• PyGlet
• Arcade
• Panda3D
• PyKyra
• PyOpenGL
• Kivy
• Python-Ogre
• Ren’Py

3. Automation Testing

• Splinter
• Robot
• Behave
• PyUnit
• PyTest

4. Web Development

• Django
• Flask
• Web2py
• Bottle
• Pyramid
• Falcon
• CherryPy
• CubicWeb
• TurboGears

5. Web Scraping

• Selenium
• Requests
• Beautiful Soup
• Scrapy
• Lxml

6. Image Processing

• OpenCV
• Mahotas
• Scikit-image
• Pgmagick
• SimpleITK

7. Finance

• PyAlgoTrade
• Pyfolio
• Zipline
• Finmarketpy

Conclusion:

Finally, as an aspiring data analyst or scientist, you should get conversant with using and working with these libraries and frameworks. You can start building up projects. However, you can check out here for a project you can work on using these libraries. This will help your portfolio stand out. As a result, you can land that dream job. Therefore, why not start now!