Spotipy: How to read more than 100 tracks from a playlist

Question

I'm trying to pull all tracks in a certain playlist using the Spotipy library for python.

The user_playlist_tracks function is limited to 100 tracks, regardless of the parameter limit. The Spotipy documentation describes it as:

user_playlist_tracks(user, playlist_id=None, fields=None, limit=100, offset=0, market=None)

Get full details of the tracks of a playlist owned by a user.

Parameters:

• user
• the id of the user playlist_id
• the id of the playlist fields
• which fields to return limit
• the maximum number of tracks to return offset
• the index of the first track to return market
• an ISO 3166-1 alpha-2 country code.

After authenticating with Spotify, I'm currently using something like this:

username = xxxx
playlist = #fromspotipy
sp_playlist = sp.user_playlist_tracks(username, playlist_id=playlist)
tracks = sp_playlist['items']
print tracks

Is there a way to return more than 100 tracks? I've tried setting the limit=None in the function parameters, but it returns an error.

Answer 1

Many of the spotipy methods return paginated results, so you will have to scroll through them to view more than just the max limit. I've encountered this most often when collecting a playlist's full track listing and consequently created a custom method to handle this:

def get_playlist_tracks(username,playlist_id):
    results = sp.user_playlist_tracks(username,playlist_id)
    tracks = results['items']
    while results['next']:
        results = sp.next(results)
        tracks.extend(results['items'])
    return tracks

Answer 2

Another way around it would be to write a for loop and do:

offset +=100

then you could concatenate the tracks at the end, or put them in a data frame. Function Ref:

playlist_tracks(playlist_id, fields=None, limit=100, offset=0, market=None)

Reference: https://spotipy.readthedocs.io/en/2.7.0/#spotipy.client.Spotify.playlist_tracks

Answer 3

I wrote a function that can output Panda's DataFrame where it pulls all the metadata (not all of it because I didn't want to, but you can make some space for that) for playlists over 100 songs. I do it by iterating over every song, finding the metadata for each, saving the metadata to a dictionary, and then concatenating the dictionary to the DataFrame. It takes your username and the Playlist ID as input.

# Function to extract MetaData from a playlist thats longer than 100 songs
def get_playlist_tracks_more_than_100_songs(username, playlist_id):
    results = sp.user_playlist_tracks(username,playlist_id)
    tracks = results['items']
    while results['next']:
        results = sp.next(results)
        tracks.extend(results['items'])
    results = tracks    

    playlist_tracks_id = []
    playlist_tracks_titles = []
    playlist_tracks_artists = []
    playlist_tracks_first_artists = []
    playlist_tracks_first_release_date = []
    playlist_tracks_popularity = []

    for i in range(len(results)):
        print(i) # Counter
        if i == 0:
            playlist_tracks_id = results[i]['track']['id']
            playlist_tracks_titles = results[i]['track']['name']
            playlist_tracks_first_release_date = results[i]['track']['album']['release_date']
            playlist_tracks_popularity = results[i]['track']['popularity']

            artist_list = []
            for artist in results[i]['track']['artists']:
                artist_list= artist['name']
            playlist_tracks_artists = artist_list

            features = sp.audio_features(playlist_tracks_id)
            features_df = pd.DataFrame(data=features, columns=features[0].keys())
            features_df['title'] = playlist_tracks_titles
            features_df['all_artists'] = playlist_tracks_artists
            features_df['popularity'] = playlist_tracks_popularity
            features_df['release_date'] = playlist_tracks_first_release_date
            features_df = features_df[['id', 'title', 'all_artists', 'popularity', 'release_date',
                                       'danceability', 'energy', 'key', 'loudness',
                                       'mode', 'acousticness', 'instrumentalness',
                                       'liveness', 'valence', 'tempo',
                                       'duration_ms', 'time_signature']]
            continue
        else:
            try:
                playlist_tracks_id = results[i]['track']['id']
                playlist_tracks_titles = results[i]['track']['name']
                playlist_tracks_first_release_date = results[i]['track']['album']['release_date']
                playlist_tracks_popularity = results[i]['track']['popularity']
                artist_list = []
                for artist in results[i]['track']['artists']:
                    artist_list= artist['name']
                playlist_tracks_artists = artist_list
                features = sp.audio_features(playlist_tracks_id)
                new_row = {'id':[playlist_tracks_id],
               'title':[playlist_tracks_titles],
               'all_artists':[playlist_tracks_artists],
               'popularity':[playlist_tracks_popularity],
               'release_date':[playlist_tracks_first_release_date],
               'danceability':[features[0]['danceability']],
               'energy':[features[0]['energy']],
               'key':[features[0]['key']],
               'loudness':[features[0]['loudness']],
               'mode':[features[0]['mode']],
               'acousticness':[features[0]['acousticness']],
               'instrumentalness':[features[0]['instrumentalness']],
               'liveness':[features[0]['liveness']],
               'valence':[features[0]['valence']],
               'tempo':[features[0]['tempo']],
               'duration_ms':[features[0]['duration_ms']],
               'time_signature':[features[0]['time_signature']]
               }

                dfs = [features_df, pd.DataFrame(new_row)]
                features_df = pd.concat(dfs, ignore_index = True)
            except:
                continue
                
    return features_df