Selenium WebDriver VS Pandas

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

Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.

there is pull request to integrate in both pydantic extra types and into pandas cose [1]

[1]: https://github.com/pandas-dev/pandas/issues/53999

Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.

pandas: A library for data analysis in Python

Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.

Pandas, a powerful data manipulation library in Python, has become an essential tool for data scientists and analysts. One of its key functions is read_csv(), which allows users to read data from CSV (Comma-Separated Values) files into a Pandas DataFrame. In this tutorial, brought to you by CodesWithPankaj.com, we will explore the intricacies of read_csv() with clear examples to help you harness its full potential.

So, what I'd like to do is write a documentation package in Python to recreate what I've lost. I plan to build upon the fantastic python-docx and docxtpl packages, and I'll probably rely on pandas from much of the tabular stuff. Here are the features I intend to include:

Weirdly most of my time spent with data analysis was in the C layers in pandas.

Explore here.