Spotify Web API: How to Pull and Clean Top Song Data using Python

I used the Spotify Web API to pull the top songs from my personal account. I’ll go over how to get the fifty most popular songs from a user’s Spotify account using spotipy, clean the data, and produce visualizations in Python.

Top 50 Spotify Songs

Using Spotipy and the Spotify Web API

First, I created an account with Spotify for Developers and created a client ID from the dashboard. This provides both a client ID and client secret for your application to be used when making requests to the API.

Next, from the application page, in ‘Edit Settings’, in Redirect URIs, I add http://localhost:8888/callback . This will come in handy later when logging into a specific Spotify account to pull data.

Then, I write the code to make the request to the API. This will pull the data and put it in a JSON file format.

I import the following libraries:

• Python’s OS library to facilitate the client ID, client secret, and redirect API for the code using the computer’s operating system. This will temporarily set the credentials in the environmental variables.
• Python’s json library to encode the data.
• Spotipy to provide an authorization flow for logging in to a Spotify account and obtain current top tracks for export.

import os
import json
import spotipy
from spotipy.oauth2 import SpotifyClientCredentials
import spotipy.util as util

Next, I define the client ID and secret to what has been assigned to my application from the Spotify API. Then, I set the environmental variables to include the the client ID, client secret, and the redirect URI.

cid ="XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX" 
secret = "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"

os.environ['SPOTIPY_CLIENT_ID']= cid
os.environ['SPOTIPY_CLIENT_SECRET']= secret
os.environ['SPOTIPY_REDIRECT_URI']='http://localhost:8888/callback'

Then, I work through the authorization flow from the Spotipy documentation. The first time this code is run, the user will have to provide their Sptofy username and password when prompted in the web browser.

username = ""
client_credentials_manager = SpotifyClientCredentials(client_id=cid, client_secret=secret) 
sp = spotipy.Spotify(client_credentials_manager=client_credentials_manager)
scope = 'user-top-read'
token = util.prompt_for_user_token(username, scope)

if token:
    sp = spotipy.Spotify(auth=token)
else:
    print("Can't get token for", username)

In the results section, I specify the information to pull. The arguments I provide indicate 50 songs as the limit, the index of the first item to return, and the time range. The time range options, as specified in Spotify’s documentation, are:

• short_term : approximately last 4 weeks of listening
• medium_term : approximately last 6 months of listening
• long_term : last several years of listening

For my query, I decided to use the medium term argument because I thought that would give the best picture of my listening habits for the past half year. Lastly, I create a list to append the results to and then write them to a JSON file.

if token:
    sp = spotipy.Spotify(auth=token)
    results = sp.current_user_top_tracks(limit=50,offset=0,time_range='medium_term')
    for song in range(50):
        list = []
        list.append(results)
        with open('top50_data.json', 'w', encoding='utf-8') as f:
            json.dump(list, f, ensure_ascii=False, indent=4)
else:
    print("Can't get token for", username)

After compiling this code into a Python file, I run it from the command line. The output is top50_data.JSON which will need to be cleaned before using it to create visualizations.

Cleaning JSON Data for Visualizations

The top song data JSON file output is nested according to different categories, as seen in the sample below.

 "artists": [
                    {
                        "external_urls": {
                            "spotify": "https://open.spotify.com/artist/5PbpKlxQE0Ktl5lcNABoFf"
                        },
                        "href": "https://api.spotify.com/v1/artists/5PbpKlxQE0Ktl5lcNABoFf",
                        "id": "5PbpKlxQE0Ktl5lcNABoFf",
                        "name": "Car Seat Headrest",
                        "type": "artist",
                        "uri": "spotify:artist:5PbpKlxQE0Ktl5lcNABoFf"
                    }
                ],
                "disc_number": 1,
                "duration_ms": 303573,
                "explicit": true,
                "href": "https://api.spotify.com/v1/tracks/5xy3350chgFfFcdTET4xz3",
                "id": "5xy3350chgFfFcdTET4xz3",
                "is_local": false,
                "name": "Destroyed By Hippie Powers",
                "popularity": 51,
                "preview_url": "https://p.scdn.co/mp3-preview/cd1a18f3f7c8ada17bb54c55524ef42e80719d1f?cid=39e9cdce36dc45e589ce5b564c0594a2",
                "track_number": 3,
                "type": "track",
                "uri": "spotify:track:5xy3350chgFfFcdTET4xz3"
            },

Before cleaning the JSON data and creating visualizations in a new file, I import json, pandas, matplotlib, and seaborn. Next, I load the JSON file with the top 50 song data.

import json
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb

with open('top50_data.json') as f:
  data = json.load(f)

I create a full list of all the data to start. Next, I create lists where I will append the specific JSON data. Using a loop, I access each of the items of interest for analysis and append them to the lists.

list_of_results = data[0]["items"]
list_of_artist_names = []
list_of_artist_uri = []
list_of_song_names = []
list_of_song_uri = []
list_of_durations_ms = []
list_of_explicit = []
list_of_albums = []
list_of_popularity = []

for result in list_of_results:
    result["album"]
    this_artists_name = result["artists"][0]["name"]
    list_of_artist_names.append(this_artists_name)
    this_artists_uri = result["artists"][0]["uri"]
    list_of_artist_uri.append(this_artists_uri)
    list_of_songs = result["name"]
    list_of_song_names.append(list_of_songs)
    song_uri = result["uri"]
    list_of_song_uri.append(song_uri)
    list_of_duration = result["duration_ms"]
    list_of_durations_ms.append(list_of_duration)
    song_explicit = result["explicit"]
    list_of_explicit.append(song_explicit)
    this_album = result["album"]["name"]
    list_of_albums.append(this_album)
    song_popularity = result["popularity"]
    list_of_popularity.append(song_popularity)

Then, I create a pandas DataFrame, name each column and populate it with the above lists, and export it as a CSV for a backup copy.

all_songs = pd.DataFrame(
    {'artist': list_of_artist_names,
     'artist_uri': list_of_artist_uri,
     'song': list_of_song_names,
     'song_uri': list_of_song_uri,
     'duration_ms': list_of_durations_ms,
     'explicit': list_of_explicit,
     'album': list_of_albums,
     'popularity': list_of_popularity
     
    })

all_songs_saved = all_songs.to_csv('top50_songs.csv')

Using the DataFrame, I create two visualizations. The first is a count plot using seaborn to show how many top songs came from each artist represented in the top 50 tracks.

descending_order = top50['artist'].value_counts().sort_values(ascending=False).index
ax = sb.countplot(y = top50['artist'], order=descending_order)

sb.despine(fig=None, ax=None, top=True, right=True, left=False, trim=False)
sb.set(rc={'figure.figsize':(6,7.2)})

ax.set_ylabel('')    
ax.set_xlabel('')
ax.set_title('Songs per Artist in Top 50', fontsize=16, fontweight='heavy')
sb.set(font_scale = 1.4)
ax.axes.get_xaxis().set_visible(False)
ax.set_frame_on(False)

y = top50['artist'].value_counts()
for i, v in enumerate(y):
    ax.text(v + 0.2, i + .16, str(v), color='black', fontweight='light', fontsize=14)
    
plt.savefig('top50_songs_per_artist.jpg', bbox_inches="tight")

The second graph is a seaborn box plot to show the popularity of songs within individual artists represented.

popularity = top50['popularity']
artists = top50['artist']

plt.figure(figsize=(10,6))

ax = sb.boxplot(x=popularity, y=artists, data=top50)
plt.xlim(20,90)
plt.xlabel('Popularity (0-100)')
plt.ylabel('')
plt.title('Song Popularity by Artist', fontweight='bold', fontsize=18)
plt.savefig('top50_artist_popularity.jpg', bbox_inches="tight")

Further Considerations

For future interactions with the Spotify Web API, I would like to complete requests that pull top song data for each of the three term options and compare them. This would give a comprehensive view of listening habits and could lead to pulling further information from each artist.