Top 10 Must-Watch PyCon Talks


Primer on Python Decorators

In this introductory tutorial, we’ll look at what decorators are and how to create and use them. Decorators provide a simple syntax for calling higher-order functions.

By definition, a decorator is a function that takes another function and extends the behavior of the latter function without explicitly modifying it.

Sounds confusing—but it’s really not, especially after we go over a number of examples. You can find all the examples from this article here.

4 Techniques for Testing Python Command-Line (CLI) Apps

You’ve just finished building your first Python command-line app. Or maybe your second or third. You’ve been learning Python for a while, and now you’re ready to build something bigger and more complex, but still runnable on a command-line. Or you are used to building and testing web applications or desktop apps with a GUI, but now are starting to build CLI applications.

In all these situations and more, you will need to learn and get comfortable with the various methods for testing a Python CLI application.

While the tooling choices can be intimidating, the main thing to keep in mind is that you’re just comparing the outputs your code generates to the outputs you expect. Everything follows from that.

In this tutorial you’ll learn four hands-on techniques for testing Python command-line apps:

  • “Lo-Fi” debugging with print()
  • Using a visual Python debugger
  • Unit testing with pytest and mocks
  • Integration testing

Face recognition with OpenCV, Python, and deep learning

In today’s blog post you are going to learn how to perform face recognition in both images and video streams using:

  • OpenCV
  • Python
  • Deep learning

As we’ll see, the deep learning-based facial embeddings we’ll be using here today are both (1) highly accurate and (2) capable of being executed in real-time.

To learn more about face recognition with OpenCV, Python, and deep learning, just keep reading!

AWS DeepLens: first impressions + tutorial

Getting up-and-running with Amazon’s new machine learning-enabled camera

tl;dr It’s awesome. Get one.

At the end of 2017, Amazon announced DeepLens, a camera with specialized hardware that allows developers to deploy machine learning and computer vision models to “the edge,” and integrate the data it collects with other AWS services.

On a whim, I put in a one-click order on Prime (devices started shipping just last week); it arrived a couple days later and just hours from unboxing — with one or two minor hiccups — I got it up-and-running and integrated with other AWS services. I’ve been pleasantly surprised, to say the least.

How To Scrape Web Pages with Beautiful Soup and Python 3

Many data analysis, big data, and machine learning projects require scraping websites to gather the data that you’ll be working with. The Python programming language is widely used in the data science community, and therefore has an ecosystem of modules and tools that you can use in your own projects. In this tutorial we will be focusing on the Beautiful Soup module.

Beautiful Soup, an allusion to the Mock Turtle’s song found in Chapter 10 of Lewis Carroll’s Alice’s Adventures in Wonderland, is a Python library that allows for quick turnaround on web scraping projects. Currently available as Beautiful Soup 4 and compatible with both Python 2.7 and Python 3, Beautiful Soup creates a parse tree from parsed HTML and XML documents (including documents with non-closed tags or tag soup and other malformed markup).

In this tutorial, we will collect and parse a web page in order to grab textual data and write the information we have gathered to a CSV file.