Extracting Spotify Data

Getting started, we want to extract data for a set of tracks within one of Spotify’s top-featured playlists. Leveraging the Spotify Web API, we can seamlessly obtain detailed data for a song, such as the performing artist, the album it belongs to, its release date, popularity, and audio features like danceability, energy, and tempo.

Python libraries like spotipy offer a user-friendly way to interact with the Spotify API, offering a range of functions that streamline tasks like API authentication, retrieving playlist data, and obtaining information about any given song.

Accessing the Spotify Web API

To access data from Spotify, we import the spotipy library and the SpotifyClientCredentials module. Additionally, we utilize the pandas package for data manipulation and display. In order to authenticate our access to the Spotify API, we must provide our client ID and client secret to a client credentials manager. Once authenticated, we can use the spotipy module to interact with the Spotify API and retrieve data.

import spotipy
from spotipy.oauth2 import SpotifyClientCredentials
import pandas as pd

client_id = "xxx"
client_secret = "xxx"
my_auth = SpotifyClientCredentials(client_id, client_secret)
sp = spotipy.Spotify(auth_manager=my_auth)

Spotify’s Featured Playlists

Let’s take a look at the popular Spotify playlists. Below, the code retrieves a range of Spotify playlists and generates a dataframe containing details for each playlist, including its name, ID, description, thumbnail, total number of tracks, and follower count. The resulting dataframe is displayed as an HTML table.

username = "spotify"
spotify_playlists = sp.user_playlists(username)
top_playlists = get_top_playlists(username, 6)

The function get_top_playlists retrieves all playlists for a given user and returns the playlists with the most followers. Specifically, the function gets all playlists from a given user, iterating over each playlist item to extract the thumbnail image URL, the playlist name, ID, description, total number of tracks, and follower count for the playlist. It then creates a DataFrame from the playlist data, sorts the DataFrame by the number of followers in descending order, and returns the top playlists with the most followers.

	thumbnail	name	id	description	tracks	followers
0		Today’s Top Hits	37i9dQZF1DXcBWIGoYBM5M	Karol G is on top of the Hottest 50!	50	34735922
1		RapCaviar	37i9dQZF1DX0XUsuxWHRQd	New music from Eminem, Ice Spice and BossMan DLow.	50	15979449
3		Viva Latino	37i9dQZF1DX10zKzsJ2jva	Today's top Latin hits, elevando nuestra música. Cover: Natanael Cano, Oscar Maydon	50	15144445
12		All Out 2000s	37i9dQZF1DX4o1oenSJRJd	The biggest songs of the 2000s. Cover: The Killers	150	12331870
7		Rock Classics	37i9dQZF1DWXRqgorJj26U	Rock legends & epic songs that continue to inspire generations. Cover: The Rolling Stones	200	12205175
14		All Out 80s	37i9dQZF1DX4UtSsGT1Sbe	The biggest songs of the 1980s. Cover: Bruce Springsteen	150	11312274

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Extracting Tracks From a Playlist

The following script enables the compilation of song and artist data from any Spotify playlist through its URI. To analyze a particular playlist, simply copy the URI from the Spotify Player interface and input it into the function defined below. The get_playlist_tracks method returns a complete list of track IDs and corresponding artists from the selected playlist.

def get_playlist_tracks(playlist_URI):
    tracks = []
    results = sp.playlist_tracks(playlist_URI)
    tracks = results["items"]
    while results["next"]:
        results = sp.next(results)
        tracks.extend(results["items"])
    return tracks

Extracting Features from Tracks

The following script utilizes Spotify’s API to extract further details about each song within the playlist. It obtains metadata such as the track name, the artist it’s sung by, the album it belongs to, the release date, and track features such as danceability, tempo, and popularity.

def playlist_features(id, artist_id, playlist_id):
    meta = sp.track(id)
    audio_features = sp.audio_features(id)
    artist_info = sp.artist(artist_id)
    playlist_info = sp.playlist(playlist_id)

    # print(audio_features)

    if audio_features[0] is None:
        return None
    
    

    name = meta['name']
    track_id = meta['id']
    album = meta['album']['name']
    artist = meta['album']['artists'][0]['name']
    artist_id = meta['album']['artists'][0]['id']
    release_date = meta['album']['release_date']
    length = meta['duration_ms']
    popularity = meta['popularity']

    artist_pop = artist_info["popularity"]
    artist_genres = artist_info["genres"]
    artist_followers = artist_info["followers"]['total']

    acousticness = audio_features[0]['acousticness']
    danceability = audio_features[0]['danceability']
    energy = audio_features[0]['energy']
    instrumentalness = audio_features[0]['instrumentalness']
    liveness = audio_features[0]['liveness']
    loudness = audio_features[0]['loudness']
    speechiness = audio_features[0]['speechiness']
    tempo = audio_features[0]['tempo']
    valence = audio_features[0]['valence']
    key = audio_features[0]['key']
    mode = audio_features[0]['mode']
    time_signature = audio_features[0]['time_signature']
    
    playlist_name = playlist_info['name']

    return [name, track_id, album, artist, artist_id, release_date, length, popularity, 
            artist_pop, artist_genres, artist_followers, acousticness, danceability, 
            energy, instrumentalness, liveness, loudness, speechiness, 
            tempo, valence, key, mode, time_signature, playlist_name]

Choose a specific playlist to analyze by copying the URL from the Spotify Player interface. Using that link, the playlist_tracks method retrieves a list of IDs and corresponding artists for each track from the playlist. Specifically, we analyze Spotify’s Today’s Top Hits playlist.

playlist_links = [top_playlists['id'][0]]

for playlist_URI in playlist_links:
    # playlist_URI = link.split("/")[-1].split("?")[0]
    
    all_tracks = [  # Loop over track ids
    playlist_features(i["track"]["id"], i["track"]["artists"][0]["uri"], playlist_URI)
    for i in get_playlist_tracks(playlist_URI)
]

Putting it all together, the get_playlist_tracks function retrieves basic details for each song in a specified Spotify playlist using its URI. The playlist_features function then iterates through these tracks using their IDs to extract additional information, such as danceability, energy, loudness, speechiness, acousticness, instrumentalness, liveness, valence, tempo, and more. From there, we create a Pandas dataframe by passing in the extracted information.

Loudness Scaled

# Loudness Scaled
from sklearn import preprocessing 

scaler = preprocessing.MinMaxScaler()
# scale loudness to fit the same range [0, 1]
loudness2 = df["loudness"].values
loudness_scaled=scaler.fit_transform(loudness2.reshape(-1, 1))
df['loudness_scaled'] = loudness_scaled

	name	track_id	album	artist	artist_id	release_date	length	popularity	artist_pop	artist_genres	...	liveness	loudness	speechiness	tempo	valence	key	mode	time_signature	playlist	loudness_scaled
0	Please Please Please	5N3hjp1WNayUPZrA8kJmJP	Please Please Please	Sabrina Carpenter	74KM79TiuVKeVCqs8QtB0B	2024-06-06	186365	98	91	[pop]	...	0.1040	-6.073	0.0540	107.071	0.579	9	1	4	Today’s Top Hits	0.575663
1	Si Antes Te Hubiera Conocido	6WatFBLVB0x077xWeoVc2k	Si Antes Te Hubiera Conocido	KAROL G	790FomKkXshlbRYZFtlgla	2024-06-21	195824	91	89	[reggaeton, reggaeton colombiano, trap latino,...	...	0.0678	-6.795	0.0469	128.027	0.787	11	1	4	Today’s Top Hits	0.495503
2	BIRDS OF A FEATHER	6dOtVTDdiauQNBQEDOtlAB	HIT ME HARD AND SOFT	Billie Eilish	6qqNVTkY8uBg9cP3Jd7DAH	2024-05-17	210373	98	94	[art pop, pop]	...	0.1170	-10.171	0.0358	104.978	0.438	2	1	4	Today’s Top Hits	0.120684
3	Good Luck, Babe!	0WbMK4wrZ1wFSty9F7FCgu	Good Luck, Babe!	Chappell Roan	7GlBOeep6PqTfFi59PTUUN	2024-04-05	218423	94	86	[indie pop, pov: indie]	...	0.0881	-5.960	0.0356	116.712	0.785	11	0	4	Today’s Top Hits	0.588209
4	A Bar Song (Tipsy)	2FQrifJ1N335Ljm3TjTVVf	A Bar Song (Tipsy)	Shaboozey	3y2cIKLjiOlp1Np37WiUdH	2024-04-12	171291	93	81	[pop rap]	...	0.0804	-4.950	0.0273	81.012	0.604	9	1	4	Today’s Top Hits	0.700344

5 rows × 25 columns

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