Though not essential, I’ve also included Pandas because it makes it very easy to save and load tabular data. We will also use the Requests module for addressing the API. The main requirement for this task is the Spotipy library. Finally, we export the data to our favorite format. Since the features are not included, we request them from the Spotify API. What we need to doįirst, we get our streaming data from Spotify. You can read more about features in Spotify’s documentation. Spotify also measures the duration, key, mode and time signature of each track. Here is a brief description of each:Īcousticness - how acoustic danceability - self-explanatory energy - how 'fast, loud an noisy' instrumentalness - the less vocals, the higher liveness - whether there is audience in the recording loudness - self-explanatory speechiness - the more spoken words, the higher valence - whether the track sounds happy or sad tempo - the bpm Spotify’s audio features are complex metrics that aim to describe a track’s personality and general impression on the listener. Read on!īut if you’re in a hurry, the full code is available at my GitHub.

By the time we get there, you will also gain a basic understanding of how the Spotify API works, how to complete the Authorization Code Flow, and how you can build your own Spotify app. In this tutorial, I will show you how to extract this data, flavor it with delicious song features, and organize it into a handy CSV file that you can analyse with your favorite tools.īut there’s more. Luckily, Spotify allows you to request a download of all your streaming history. Do you tend to listen to sad songs in winter? What happened to your music preferences when you fell in love? If you’re a dedicated Spotifyer like myself, there are lots of things you could learn by looking at your streaming data. This is my first Medium story! Any feedback is appreciated. It ain’t the same without those features.

Stats for spotify hours