Episode Details

Are you ready to practice your Python skills some more? There is a new set of practice problems prepared for you to tackle, and this time they’re based on working with CSV files. This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

David shares an article about functional programming with a focus on the “filter” function. The tutorial covers how to process an iterable and extract the items that satisfy a given condition. It also covers combining filter with other functional tools, and compares it to coding with Pythonic tools like list comprehensions and generator expressions.

We cover several other articles and projects from the Python community including, Excel, Python, and the future of data science, a Bayesian analysis of Lego prices in Python, why can’t comments appear after a line continuation character, teaching Python on the Raspberry Pi400 at the public library, a cross-platform editor designed for writing novels built with Python and Qt, and a text user interface with rich as the renderer.

Topics:

• 00:00:00 – Introduction
• 00:02:29 – Excel, Python, and the Future of Data Science
• 00:07:50 – Python Practice Problems: Parsing CSV Files
• 00:17:09 – Sponsor: Digital Ocean’s App Platform
• 00:17:45 – A Bayesian Analysis of Lego Prices in Python With PyMC3
• 00:23:02 – Why Can’t Comments Appear After a Line Continuation Character?
• 00:28:40 – Python’s filter(): Extract Values From Iterables
• 00:34:57 – Video Course Spotlight
• 00:36:24 – How I Teach Python on the Raspberry Pi 400 at the Public Library
• 00:46:23 – novelWriter: Cross-Platform Editor Designed for Writing Novels Built With Python and Qt
• 00:48:02 – textual: A Text User Interface With Rich as the Renderer
• 00:54:58 – Thanks and goodbye

Show Links:

Excel, Python, and the Future of Data Science – What’s the most widely used tool in data science? Is it pandas or NumPy? Is it the Python language itself? Not really. It’s Excel. You might argue that data scientists aren’t using Excel as their primary tool, and you might be right. But Excel enables non-technical users, like small business owners, to gain insights into their data. In this article, Anaconda CEO Peter Wang discusses his goal of making Python and PyData the “conceptual successor” to Excel.

Python Practice Problems: Parsing CSV Files – In this tutorial, you’ll prepare for future interviews by working through a set of Python practice problems that involve CSV files. You’ll work through the problems yourself and then compare your results with solutions developed by the Real Python team.

A Bayesian Analysis of Lego Prices in Python With PyMC3 – Follow along with this in-depth analysis of LEGO prices to see Bayesian analysis in action. Along the way, you’ll how pooled and unpooled linear models can be used to determine if a LEGO set is fairly priced. The article is quite technical, so experience with Bayesian statistics is recommended.

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