Paul McCartney Song Evolution Analysis

Paul McCartney, happy 82nd birthday! We're going to do a data analysis of your career. Here we are on Kaggle with a massive Spotify dataset for many artists, but we're just going to focus on you, Paul McCartney. The dataset has many different dimensions like energy, beats per minute, and danceability. We're going to look at how those have changed over time for you, Paul McCartney.

I've already downloaded the dataset, so let's open up Devra in our Kaggle project. We're going to add a task, look in the Spotify Paul McCartney directory, and create an analysis that filters for the artist being Paul McCartney. Then, we'll plot over time each of the following: beats per minute, energy, danceability, loudness, valence, length, acousticness, speechiness, and popularity. Let's hit create and start and see what it does.

Okay, so it's gone to the right directory now. Here it is looking at the CSV file and getting an idea of the data that is in there. It's come up with this proposal. Let's just say that plan looks good. Okay, Paul, so it's come up with this Python notebook. Let's create it. We see it pop up here, and let's run it and see what happens.

Alright, Paul, here's the notebook. It looks like it's looking at all of these different dimensions that we asked about. Let's just see the results. Over time, it looks like the tempo has gotten slower, energy went up and is now really down, danceability is making a bit of a return, loudness is way down, and acousticness—oh, you must have had an acoustic album, Paul. Popularity, well, Paul, I'm sorry if this is a bummer, but happy 82nd birthday!

If you want to do this analysis yourself to set the record straight, go to Devra, and you can get the software for free and do this analysis to make data science a whole lot easier. Alright, take care and happy birthday!