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Interacting with the Spotify API

Spotify can be used as a data source for various data science projects. In this exercise, we will learn how to interact with the API of this social network. Spotipy is an open-source Python library that allows high-level use of the Spotify API.

Step 1: Create a Spotify Developer Account

Before starting to code, you need access to Spotify developer credentials. Visit developer.spotify.com.

  • Log in with your Spotify account (or create one if you don't have one yet).

  • Go to the Dashboard and click on Create an App. Fill in the required fields. In Redirect URI, enter: http://localhost/

Spotify create app

Once the app is created, go to the Settings section to copy your Client ID and Client Secret. You will use them later to authenticate with the API.

Step 2: Initial configuration

  • Create an app.py file inside the ./src/ folder.
  • Install all the requirements from the requirements.txt file or just the required library.
! pip install spotipy
Collecting spotipy
  Downloading spotipy-2.23.0-py3-none-any.whl (29 kB)
Collecting redis>=3.5.3 (from spotipy)
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Requirement already satisfied: requests>=2.25.0 in /usr/local/python/3.10.8/lib/python3.10/site-packages (from spotipy) (2.27.1)
Requirement already satisfied: six>=1.15.0 in /home/codespace/.local/lib/python3.10/site-packages (from spotipy) (1.16.0)
Requirement already satisfied: urllib3>=1.26.0 in /usr/local/python/3.10.8/lib/python3.10/site-packages (from spotipy) (1.26.16)
Collecting async-timeout>=4.0.2 (from redis>=3.5.3->spotipy)
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Requirement already satisfied: certifi>=2017.4.17 in /home/codespace/.local/lib/python3.10/site-packages (from requests>=2.25.0->spotipy) (2023.5.7)
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Requirement already satisfied: idna<4,>=2.5 in /home/codespace/.local/lib/python3.10/site-packages (from requests>=2.25.0->spotipy) (3.4)
Installing collected packages: async-timeout, redis, spotipy
Successfully installed async-timeout-4.0.2 redis-4.6.0 spotipy-2.23.0

Step 3: Environment variables

You already have the .env file in the root of the project. Make sure it contains the following variables with your Spotify credentials (replace the content with your own data):

CLIENT_ID=your_client_id
CLIENT_SECRET=your_client_secret

⚠️ It is important to place your data in environment variables to avoid exposing your credentials if you upload the project to a repository.

Now, in the app.py file, add the following code to read the environment variables:

import os
import pandas as pd
import matplotlib.pyplot as plt
from dotenv import load_dotenv

# load the .env file variables
load_dotenv()

# Get credential values
client_id = os.environ.get("CLIENT_ID")
client_secret = os.environ.get("CLIENT_SECRET")

With this, your credentials will be ready to use for authentication with the Spotify API.

Step 4: Initialize Spotipy library

  • Import Spotipy.

  • Connect to the API. To do this, you can use the spotipy.Spotify() function.

    import spotipy
from spotipy.oauth2 import SpotifyClientCredentials

auth_manager = SpotifyClientCredentials(client_id=client_id, client_secret=client_secret)
spotify = spotipy.Spotify(auth_manager=auth_manager)

    💡 NOTE: Use the following documentation as a guide for the parameters: https://spotipy.readthedocs.io/en/2.22.1

Step 5: Make API requests

In this case, I have chosen Drake. First, I get his ID by searching for his Spotify page and getting it from the URI. Now, I perform the search for his top 10 songs.

artist_id = "3TVXtAsR1Inumwj472S9r4"

# Get the top tracks of an artist
results = spotify.artist_top_tracks(artist_id)

songs = []
for track in results['tracks']:
    songs.append({
        'name': track['name'],
        'popularity': track['popularity'],
        'duration_min': track['duration_ms'] / 60000
    })

Step 6: Transform to Pandas DataFrame

Once we have modified the answer, we create the Pandas DataFrame from it:

tracks_df = pd.DataFrame(songs)

print(tracks_df.head(3))

Step 7: Analyze statistical relationship

A scatter plot is a good alternative to determine visually and graphically whether two variables may or may not be related to each other:

plt.scatter(df['duration_min'], df['popularity'])
plt.xlabel('Duration (minutes)')
plt.ylabel('Popularity')
plt.title('Relationship between duration and popularity')
plt.show()

Scatter plot of popularity and duration of songs

As can be seen, there is no direct relationship between the length of the song and its popularity.