Python has quickly risen to be the top language for AI and Data Science. A few years ago, the question was “should I learn Python?” now it’s “How can I learn Python?”

Thankfully, here’s a great tutorial for beginners that’s thorough and free on YouTube.

Engineer Man has a great video demonstrating the power of Python and Beautiful Soup when applied to web scraping. Long time readers will know that I have long been a fan of web scraping, often employing in apps. I’ve even written a C# library and a “Screen Scrapers Manifesto” that implored content creators to create APIs and if they didn’t, we’d just grab the data anyways. 😉

Unfortunately, said manifesto was wiped out last year.

In any case, enjoy the video and browse the source code to learn how to be a lean, mean screen scraping machine.

Gaurav Malhotra discusses how you can operationalize Jars and Python scripts running on Azure Databricks as an activity step in a Data Factory pipeline.

For more information:

Spend some time in Python and you’ll likely encounter its bytecode files — those ‘.pyc’ files Python likes to leave behind after it runs.

Have you ever wondered what’s really going on in those files? Watch this video from PyCon 2018 to learn more about these files and what’s in them.

The pandas library is a powerful tool for multiple phases of the data science workflow, including data cleaning, visualization, and exploratory data analysis. However, proper data science requires careful coding, and pandas will not stop you from creating misleading plots, drawing incorrect conclusions, ignoring relevant data, including misleading data, or executing incorrect calculations.

In this tutorial session from PyCon Cleveland 2018, you’ll perform a variety of data science tasks on a handful of real-world datasets using pandas.