Spotify and Pandas Incompatability

[PaulGeorge1993]

I am currently working on a code to read each song in a Spotify playlist and save the artist's name, song name, and duration into a CSV file. However, when running the code, I encounter the following error:

ImportError: Unable to import required dependencies: numpy: cannot import name randbits

For reference, I have tested it and my code works well without the pandas import and I have created a virtual environment where the only modules present are spotipy and panda, yet, the error continues to persist. Is the issue that I need to use a different version of pandas (2.03), numpy (1.25.1), or spotipy ( 2.23.0), or is it simply a bug?

[dieser-niko]

Does this error have to do something with the spotipy library? It seems to be an issue in the pandas library.

[CezarGab]

Can you provide the line where this error appears? Also I thinks this is an import error, from pandas, and not Spotipy.

[stephanebruckert]

I have tested it and my code works well without the pandas import

Does it work with only pandas import?

[dpnem]

I use spotipy and pandas all the time together without any problems other than self-inflicted ones. Here's a function that builds a dataframe from a playlist_id. The way my processes work it goes from spotipty -> pandas -> lists -> spotipy.

def build_track_info_df_from_playlist(playlist_id=None):
    '''Given a playlist_id, this function returns a Pandas dataframe
    which contains the following track information:
    playlist_id, playlist_name, playlist_owner,
    track_id, track, artist_id, artist, album_id, album,
    release_date, release_decade, added_date'''

    if playlist_id is None:
        results = sp.current_user_saved_tracks()

        playlist_id = -10
        playlist_name = 'Liked Tracks'
        playlist_owner = 'You'

    else:
        header = sp.playlist(playlist_id)
        playlist_id = playlist_id
        playlist_name = header['name']
        playlist_owner = header['owner']['display_name']

        results = sp.playlist_tracks(playlist_id)

    complete_results = results['items']

    while results['next']:
        results = sp.next(results)
        complete_results.extend(results['items'])

    track_info = []

    for i, track in enumerate(complete_results):
        if track['track']['type'] == 'track':  # Exclude podcast episodes
            track_data = {
                'playlist_id': playlist_id, 
                'playlist_name': playlist_name,
                'playlist_owner': playlist_owner,
                'playlist_track_number': i+1,
                'track_id': track['track']['id'],
                'track': track['track']['name'],
                'artist_id': track['track']['artists'][0]['id'],
                'artist': track['track']['artists'][0]['name'],
                'album_id': track['track']['album']['id'],
                'album': track['track']['album']['name'],
                'popularity': track['track']['popularity'],
                'album_type': track['track']['album']['album_type'],
            }   

            # cleanup release date
            release_date = pd.to_datetime(track['track']['album']['release_date'])
            if pd.notna(release_date):
                track_data['release_date'] = release_date.strftime('%Y-%m-%d')
                track_data['release_decade'] = release_date.year // 10 * 10
                today = datetime.now()
                days_since_release = (today - release_date).days
                track_data['days_since_release'] = days_since_release
            else:
                track_data['release_date'] = today.strftime('%Y-%m-%d')
                track_data['release_decade'] = today.year // 10 * 10
                track_data['days_since_release'] = 0

            # clean up added_at
            added_date = pd.to_datetime(track['added_at'])
            track_data['added_date'] = added_date.strftime('%Y-%m-%d')

            track_info.append(track_data)

    df = pd.DataFrame(track_info)

    df['days_since_group'] = df['days_since_release'].apply(label_days)
    df['days_since_weight'] = 1 / df['days_since_release']

    return df