spotify.py

class Spotify_Recommendation():
    def __init__(self, dataset):
        self.dataset = dataset
    def recommend(self, songs, amount=1):
        distance = []
        song = self.dataset[(self.dataset.name.str.lower() == songs.lower())].head(1).values[0]
        rec = self.dataset[self.dataset.name.str.lower() != songs.lower()]
        for songs in tqdm(rec.values):
            d = 0
            for col in np.arange(len(rec.columns)):
                if not col in [1, 6, 12, 14, 18]:
                    d = d + np.absolute(float(song[col]) - float(songs[col]))
            distance.append(d)
        rec['distance'] = distance
        rec = rec.sort_values('distance')
        columns = ['artists', 'name']
        return rec[columns][:amount]

recommendations = Spotify_Recommendation(data)
recommendations.recommend("Lovers Rock", 10)