Statify/spotify_test.py at main · n00b-coder9/Statify · GitHub

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import spotipy
import pandas as pd
from spotipy.oauth2 import SpotifyClientCredentials
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
from math import pi
from credentials import client_id, client_secret

min_max_scaler = MinMaxScaler()

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

# playlist_id = "spotify:playlist:3lRvb9RIb0MyUTU4O0IZAv"
def playlist_stats(playlist_url):
    temp = playlist_url.split("/")[::-1]
    playlist_id = "spotify:" + temp[1] + ":" + temp[0].split("?")[0]
    results = sp.playlist(playlist_id)

    # create a list of song ids
    ids = []

    for item in results["tracks"]["items"]:
        track = item["track"]["id"]
        ids.append(track)

    song_meta = {
        "id": [],
        "album": [],
        "name": [],
        "artist": [],
        "explicit": [],
        "popularity": [],
    }

    for song_id in ids:
        # get song's meta data
        meta = sp.track(song_id)

        # song id
        song_meta["id"].append(song_id)

        # album name
        album = meta["album"]["name"]
        song_meta["album"] += [album]

        # song name
        song = meta["name"]
        song_meta["name"] += [song]

        # artists name
        s = ", "
        artist = s.join([singer_name["name"] for singer_name in meta["artists"]])
        song_meta["artist"] += [artist]

        # explicit: lyrics could be considered offensive or unsuitable for children
        explicit = meta["explicit"]
        song_meta["explicit"].append(explicit)

        # song popularity
        popularity = meta["popularity"]
        song_meta["popularity"].append(popularity)

    song_meta_df = pd.DataFrame.from_dict(song_meta)

    song_meta_df = pd.DataFrame.from_dict(song_meta)

    # check the song feature
    features = sp.audio_features(song_meta["id"])
    # change dictionary to dataframe
    features_df = pd.DataFrame.from_dict(features)

    # convert milliseconds to mins
    # duration_ms: The duration of the track in milliseconds.
    # 1 minute = 60 seconds = 60 × 1000 milliseconds = 60,000 ms
    features_df["duration_ms"] = features_df["duration_ms"] / 60000

    # combine two dataframe
    final_df = song_meta_df.merge(features_df)

    music_feature = features_df[
        [
            "danceability",
            "energy",
            "loudness",
            "speechiness",
            "acousticness",
            "instrumentalness",
            "liveness",
            "valence",
            "tempo",
            "duration_ms",
        ]
    ]

    music_feature.loc[:] = min_max_scaler.fit_transform(music_feature.loc[:])

    # plot size
    fig = plt.figure(figsize=(12, 8))

    # convert column names into a list
    categories = list(music_feature.columns)
    # number of categories
    N = len(categories)

    # create a list with the average of all features
    value = list(music_feature.mean())

    # repeat first value to close the circle
    # the plot is a circle, so we need to "complete the loop"
    # and append the start value to the end.
    value += value[:1]
    # calculate angle for each category
    angles = [n / float(N) * 2 * pi for n in range(N)]
    angles += angles[:1]

    # plot
    plt.polar(angles, value)
    plt.fill(angles, value, alpha=0.3)

    plt.xticks(angles[:-1], categories, size=15)
    plt.yticks(color="grey", size=15)
    # plt.plot()
    plt.savefig("templates/images/new_plot.pdf")