Top Data science with Python Frameworks For Test Automation In 2023

After being voted as the best programming language in the year 2018, Python still continues rising up the charts and currently ranks as the third-best programming language just after Java and C, as per the index published by Tiobe. With the increasing use of this language, the popularity of test automation frameworks based on Python is increasing as well. Obviously, developers and testers will get a little bit confused when it comes to choosing the best framework for their project. While choosing one, you should judge a lot of things, the script quality of the framework, here Data science online training test case simplicity, and the technique to run the modules and find out their weaknesses. This is my attempt to help you compare the top five Python frameworks for test automation in 2019, and their advantages over the other as well as disadvantages, so you can choose the ideal Python framework for test automation according to your needs.

Robot Framework

Used Machine learning online course mostly for development that is acceptance test-driven as well as for acceptance testing, the Robot Framework is one of the top Python test frameworks. Although it is developed using Python, it can also run on IronPython, which is . Net-based and on Java-based Jython. Robot as a Python framework is compatible across all platforms — Windows, macOS or Linux.

What Are The Prerequisites?

First of all, you will be able to use Robot Framework (RF) only when you have Python 2.7.14 or any version above it installed. Although Python 3.6.4 is frequently used, code snippets provided in the official blog of RF will make sure that appropriate notes are added consisting of all the changes required.