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.
Python is an incredibly versatile language. In this video, Chris Hawkes highlights his top 10 list of Python projects for 2018.
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.
We all know that engineers don’t think like normal people: it’s what makes us unique.
Watch this video to see how “Engineer Man” responds to a Craigslist scammer and learn a Python trick or two.
Now, I want him to show me how to deal with robocalls.
In this tutorial, recent guest on Data Driven, Micheleen Harris demonstrates common Python libraries for image manipulation are introduced and used in a Jupyter notebook for manipulating a sample image.
Code from the tutorial is on GitHub.
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.
Brandon Rohrer explains autocorrelation and partial autocorrelation, complete with pictures and Python code.
Daniel Chen presents an introduction to Pandas at the PyData Carolinas conference last year.
Comments
Deep Fakes and the Technology Behind It
The Code Behind Deep Learning for Music Generation
Explaining Quantum Computing with a Deck of Cards
Android nougat begins rolling out to nexus
Android nougat begins rolling out to nexus