Create Radar Chart to analyze Playlist
Tags: #spotify #python #spotipy #analytics #operations #image
Description: This notebook provides a step-by-step guide to creating a Radar Chart to analyze a Spotify Playlist.
First you need to set up Spotify API at https://developer.spotify.com to get clientID and clientSecret
!pip install spotify
!pip install spotipy
import json
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
# Retrieve Client credentials from Spotify Developer Page
client_id = ""
client_secret = ""
client_credentials_manager = SpotifyClientCredentials(client_id, client_secret)
sp = spotipy.Spotify(client_credentials_manager=client_credentials_manager)
# Retrieve playlist_id by getting the Spotify URI of any playlist
playlist_id = "spotify:playlist:5fqIcaihygJQGberg0wy0G"
results = sp.playlist(playlist_id)
min_max_scaler = MinMaxScaler()
# Function to Convert JSON to Dataframe
# 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)
# 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)
# Function for Data Pre-Processing
music_features = features_df[
[
"danceability",
"energy",
"loudness",
"speechiness",
"acousticness",
"instrumentalness",
"liveness",
"valence",
"tempo",
"duration_ms",
]
]
music_features.describe()
# Transforming Data so that all values are in the range 0 to 1
# To turn of warning run below command
pd.set_option("mode.chained_assignment", None)
music_features.loc[:] = min_max_scaler.fit_transform(music_features.loc[:])
# Radar Chart with several heads from DataFrame
# Creating Radar Chart
fig = plt.figure(figsize=(10, 10))
categories = list(music_features.columns)
N = len(categories)
value = list(music_features.mean())
value += value[:1]
angles = [n / float(N) * 2 * pi for n in range(N)]
angles += angles[:1]
plt.polar(angles, value, color="red")
plt.fill(angles, value, alpha=0.7, color="purple")
plt.title("Playlist Audio Features", size=20, y=1.05)
plt.xticks(angles[:-1], categories, size=15, color="purple")
plt.yticks(color="black", size=15)
plt.show()
Last modified 1mo ago