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Jupyter Notebooks are a popular and powerful tool used in data science that allow you to create and share documents that blend descriptive content with executable code. This blending of code and content can also be a powerful tool for empowering testers through automation.

Introducing Jupyter notebooks into your test automation strategy allows you to harness test automation code and extend its uses to benefit the day to day activities of testers, supporting quality and reducing the pain of repetitive or time consuming tasks.

In this session I’ll provide an introduction to Jupyter Notebooks and the variety of programming languages that they support. Then I will discuss and demonstrate some of the practical use cases of how the notebooks can be used with selenium, api clients and even existing automation page models to create:

• Templated scenarios for testing, automating the setup and tear down of test data reducing the time to test
• Semi automated tests that allow human interaction to be mixed with automated actions
• An exploratory testing toolbox, integrating and aggregating external data sources into the flow of testing
• Living executable documentation for training, on-boarding or regression testing

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