Spotipy, A Python Library for Spotify Music Lovers

Abstract

Ever wondered about the intricacies of your listening habits on Spotify? Or perhaps you’re a developer looking to build a music-related application? Enter Spotipy, a fantastic Python library that acts as a bridge between your code and the Spotify Web API.

What is Spotipy?

Spotipy is a lightweight, easy-to-use Python library that simplifies interacting with the Spotify API. It handles the nitty-gritty details of authentication, making requests, and parsing responses, letting you focus on the fun part: exploring and manipulating music data.

Why Use Spotipy?

Access a Universe of Music Data

Retrieve information about artists, albums, tracks, playlists, audio features (like danceability and energy), and much more.
Automate Tasks

Create scripts to manage your playlists, discover new music based on your taste, analyze your listening habits, or even build your own music recommendation system.
Integrate with Other Tools

Combine Spotipy with other Python libraries like Pandas for data analysis, Matplotlib for visualization, or Flask for building web applications.
Build Music-Focused Apps

Develop custom applications that leverage Spotify’s vast music catalog and user data. Think of creating personalized radio stations, music visualizations, or tools for music discovery.
Easy to Learn and Use

Spotipy’s well-documented API and intuitive design make it accessible to both beginners and experienced Python developers.

Key Features and Capabilities

Authentication

Handles the OAuth 2.0 flow for authenticating users and obtaining access tokens, allowing you to access both public and private data.
Searching

Search for artists, tracks, albums, and playlists using keywords.
Retrieving Information

Fetch detailed information about specific artists, tracks, albums, and playlists, including metadata, audio features, and related artists.
Playlist Management

Create, modify, and manage playlists, including adding and removing tracks.
User Profile Access

Access user profile information, including listening history, followed artists, and saved tracks.
Audio Features Analysis

Retrieve audio features for tracks, such as danceability, energy, tempo, valence, and more. This data can be used to analyze music and build interesting applications.

Environment settings

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

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# Token gotten from spotify api
filename = '../APIs/credentials.json'
# read json file
with open(filename) as f:
    keys = json.load(f)
# read credentials
clientID = keys['spotipy_client_id']
clientSecret = keys['spotipy_client_secret']

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client_credential_manager = SpotifyClientCredentials(client_id=clientID, client_secret=clientSecret)
sp = spotipy.Spotify(client_credentials_manager=client_credential_manager)

Artist

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results = sp.artist_albums('spotify:artist:06HL4z0CvFAxyc27GXpf02', album_type='album')
albums = results['items']
while results['next']:
    results = sp.next(results)
    albums.extend(results['items'])

for album in albums:
    print(album['name'])

1989 (Taylor's Version) [Deluxe]
1989 (Taylor's Version)
Speak Now (Taylor's Version)
Midnights (The Til Dawn Edition)
Midnights (3am Edition)
Midnights
Red (Taylor's Version)
Fearless (Taylor's Version)
evermore (deluxe version)
evermore
folklore: the long pond studio sessions (from the Disney+ special) [deluxe edition]
folklore (deluxe version)
folklore
Lover
reputation
reputation Stadium Tour Surprise Song Playlist
1989 (Deluxe)
1989
Red (Deluxe Edition)
Speak Now World Tour Live
Speak Now
Speak Now (Deluxe Package)
Fearless (Platinum Edition)
Fearless (International Version)
Live From Clear Channel Stripped 2008
Taylor Swift

Album

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album = sp.album_tracks('spotify:album:6MeLjaERUK6fJ58YZpPlyC')
lista_canciones = album['items']

for cancion in lista_canciones:
    print(cancion['name'])

Y Nos Dieron las Diez
Conductores Suicidas
Yo Quiero Ser una Chica Almodovar
A la Orilla de la Chimenea
Todos Menos Tú
La del Pirata Cojo
La Canción de las Noches Perdidas
Los Cuentos Que Yo Cuento
Peor para el Sol
Amor Se Llama el Juego
Pastillas para No Soñar

Search

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artist_name = []
track_name = []
popularity = []
track_id = []

for i in range(0,1_000,50):
    track_results = sp.search(q='year:2020', type='track', limit=50, offset=i)
    for i, t in enumerate(track_results['tracks']['items']):
        artist_name.append(t['artists'][0]['name'])
        track_name.append(t['name'])
        track_id.append(t['id'])
        popularity.append(t['popularity'])

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track_dataframe = pd.DataFrame(
        {'artist_name':artist_name,
         'track_name':track_name,
         'track_id':track_id,
         'popularity':popularity}
)

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track_dataframe

	artist_name	track_name	track_id	popularity
0	Dream Supplier	Clean Baby Sleep White Noise (Loopable)	0zirWZTcXBBwGsevrsIpvT	94
1	Hotel Ugly	Shut up My Moms Calling	3hxIUxnT27p5WcmjGUXNwx	90
2	Brent Faiyaz	Clouded	2J6OF7CkpdQGSfm1wdclqn	86
3	21 Savage	Glock In My Lap	6pcywuOeGGWeOQzdUyti6k	87
4	Steve Lacy	Infrunami	0f8eRy9A0n6zXpKSHSCAEp	86
...	...	...	...	...
995	Edith Whiskers	Home	18V1UiYRvWYwn01CRDbbuR	73
996	Duke Dumont	Ocean Drive	4b93D55xv3YCH5mT4p6HPn	74
997	Bad Bunny	TE DESEO LO MEJOR	23XjN1s3DZC8Q9ZwuorYY4	73
998	Junior H	No Me Pesa	4YU704KDCv4tyE6qQxliY3	69
999	Destroy Lonely	In The Air	2eJBpNlTTPatjec4SDQvuo	64

1000 rows × 4 columns

Download music songs from Spotify

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import spotdl
import spotify_dl

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%%bash
cd ~/Downloads;
spotify_dl -l 'https://open.spotify.com/track/3D0bXrSv7O73vOaGOG8J9c?si=2eb4c15e2d7c4adf' \
> /dev/null;

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%%bash
cd ~/Downloads;
spotdl 'https://open.spotify.com/track/3D0bXrSv7O73vOaGOG8J9c?si=2eb4c15e2d7c4adf' \
> /dev/null;

Conclusions

Spotipy is a valuable tool for anyone interested in working with Spotify’s music data. It bridges the gap between the Spotify API and the Python programming language, enabling developers to create innovative and data-driven music experiences. While there are considerations related to API limitations and authentication, the benefits of using Spotipy generally outweigh the challenges.

Considerations and Limitations

API Rate Limits: Spotify’s API has rate limits, which can restrict the number of requests you can make within a given time period. This necessitates careful planning and optimization of API calls, especially for large-scale data retrieval.
Authentication Complexity: While Spotipy simplifies authentication, understanding OAuth 2.0 and managing access tokens can still be a hurdle for some users.
Data Structure Awareness: Effective use of Spotipy requires a good understanding of the structure of Spotify’s data, including the various object types (artists, tracks, playlists) and their attributes.
Dependence on Spotify API: Spotipy’s functionality is entirely dependent on the Spotify Web API. Any changes or limitations to the API will directly affect the library’s capabilities.
Maintaining Token Refreshing: Applications that use spotipy and run for long periods of time, need to implement robust token refreshing, or the application will cease to function.

Contact

Jesus LM
Economist & Data Scientist

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