Are you interested in learning more about Natural Language Processing? Have you heard of sentiment analysis? This week on the show, Kyle Stratis returns to talk about his new article titled, Use Sentiment Analysis With Python to Classify Movie Reviews. David Amos is also here, and all of us cover another batch of PyCoder’s Weekly articles and projects.

Kyle discusses an article about distance metrics for machine learning. David shares a Real Python article about Python signal processing and Fourier transforms with scipy.fft. We also cover several other articles and projects from the Python community including, simulating real-world processes in Python with SimPy, working with Microsoft Excel using Python and OpenPyXL, why running code during import is a bad idea, what I wish I knew as a junior dev, the Raspberry Pi 400 personal computer, dynamic sky replacement and harmonization in videos with SkyAR.

Course Spotlight: Simulating Real-World Processes in Python With SimPy

In this step-by-step course, you’ll see how you can use the SimPy package to model real-world processes with a high potential for congestion. You’ll create an algorithm to approximate a complex system, and then you’ll design and run a simulation of that system in Python.

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Use Sentiment Analysis With Python to Classify Movie Reviews – In this tutorial, you’ll learn about sentiment analysis and how it works in Python. You’ll then build your own sentiment analysis classifier with spaCy that can predict whether a movie review is positive or negative.

OpenPyXL: Working with Microsoft Excel Using Python – Ah, Excel. Everyone loves to hate it. But let’s face it. Excel is one of the most popular pieces of software ever written. But you love Python, not Excel, which is why you might want to learn OpenPyXL.

An Illustration of Why Running Code During Import Is a Bad Idea (And How It Happens Anyway) – Code that runs when a module is imported is usually a code smell. But sometimes there’s no way around it.

Distance Metrics for Machine Learning – Many machine learning algorithms can be summarized as transforming data to n-dimensional vectors and computing similarity between points by means of some distance metric. This article explores four of these metrics—the Euclidean, Manhattan, Minkowski, and Hamming distances—and how to compute them with Python.

What I Wish I Knew as a Junior Dev – Some of these are things even senior devs need to be reminded of sometimes!

Fourier Transforms With scipy.fft: Python Signal Processing – In this tutorial, you’ll learn how to use the Fourier transform, a powerful tool for analyzing signals with applications ranging from audio processing to image compression. You’ll explore several different transforms provided by Python’s scipy.fft module.

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