Selenium WebDriver VS NumPy

Selenium is an extensively used open-source automation framework for web applications. It allows for cross-browser testing by automating browser actions, making it a staple tool for end-to-end testing in diverse web development environments.

While similar to Puppeteer, Cypress, and Selenium, there are some differences. Let’s find out what they are.

browser.get('http://selenium.dev/')

Cypress is a next-generation front-end Automation testing tool built for modern web applications. It is a JavaScript-based end-to-end testing framework and is known for faster test execution as compared with other testing tools (like Selenium or Protractor).

Removing ExpectedConditions from documentation makes sense since it was removed from the codebase around 2019 already.

https://github.com/SeleniumHQ/selenium --> trunk

You won't be able to test the javascript function itself from within python, but you can exercise the front-end code using something like cypress (https://cypress.io) or the older but still respectable selenium (https://selenium.dev).

once you solve this you may run into: https://github.com/SeleniumHQ/selenium/issues/11750, so add that workaround.

Other libraries with similar functionality are Selenium, which is very popular outside the JavaScript world, and Playwright, a younger step-brother of Puppeteer.

🔗 https://selenium.dev

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]