get.py

class Get:
    def __init__(self, user, spotipy):
        self.user = user
        self.sp = spotipy

    def liked_tracks(self):
        liked_tracks = self.sp.current_user_saved_tracks(limit=20, offset=0)
        return liked_tracks

    def user_playlists(self):
        user_playlists = self.sp.user_playlists(self.user)
        return user_playlists

    def genre_playlists(self, category):
        category_playlists = self.sp.category_playlists(category_id=category)
        playlist_items = category_playlists['playlists']['items']
        playlists_frame = pandas.DataFrame(playlist_items)
        return playlists_frame

    def playlist_tracks(self, playlist_id):
        results = self.sp.playlist_tracks(playlist_id=playlist_id)
        playlist_tracks = results['items']
        while results['next']:
            results = self.sp.next(results)
            playlist_tracks.extend(results['items'])
        return playlist_tracks

    def recommendations(self, seed_tracks, **kwargs):
        recs = self.sp.recommendations(seed_tracks=seed_tracks[-5:], **kwargs)
        return recs

    def features_ideal(self,track_ids, name):
        import pandas
        print(track_ids)

        ideal_dict = {}
        features_list = ['acousticness', 'danceability', 'energy', 'instrumentalness', 'liveness', 'loudness', 'speechiness', 'tempo']
        a = self.sp.audio_features(track_ids)
        features_frame = pandas.DataFrame(a)
        print(features_frame)
        for feature in features_list:
            ideal_dict['min_' + feature] = features_frame[feature].min()
            ideal_dict['max_' + feature] = features_frame[feature].max()
            ideal_dict['target_' + feature] = features_frame[feature].median()
        return ideal_dict