MartinWeiss12

full_grid(album_images, 'Album')
full_grid(artist_images, 'Artist')

mini_grid(album_images, 'Album')
mini_grid(artist_images, 'Artist')

MartinWeiss12

import os
import requests
import pandas as pd
from PIL import Image

# path for the excel file for your top albums
top_albums = pd.read_excel('')
# path for the excel file for your top artists
top_artists = pd.read_excel('')
# path for an empty folder named 'Album-Images' where the album images will be saved to

MartinWeiss12

track_artist_album_df['Artist Image URL'] = track_artist_album_df['Artist URI'].map(unique_artist_image_url_dict)
merged_track_artist_album_url_df = pd.merge(cleaned_df, track_artist_album_df, on='Track URI', how='left')

MartinWeiss12

artist_uris = track_artist_album_df['Artist URI']
unique_artist_uris = list(set(artist_uris))
n_unique_artist_sublists = len(unique_artist_uris) // 50 + (len(unique_artist_uris) % 50 > 0)
unique_artist_sublists = [unique_artist_uris[i * 50 : (i + 1) * 50] for i in range(n_unique_artist_sublists)]
unique_artist_image_url_dict = {}
start_time = time.time()

for artist_list in unique_artist_sublists:
    params = {'ids': ','.join(artist_list)}
    response = requests.get('https://api.spotify.com/v1/artists?', headers=headers, params=params)

MartinWeiss12

track_artist_df['Duplicate URI'] = track_artist_df['URI']
groups = track_artist_df.groupby(['Track', 'Artist'])

for group_name, group_df in groups:
	if len(group_df) > 1:
		unique_uris = group_df['URI'].unique()
		track_artist_df.loc[group_df.index, 'Duplicate URI'] = unique_uris[0]

MartinWeiss12

cleaned_df = pd.DataFrame(list(zip(
	filtered_data['master_metadata_track_name'],
	filtered_data['master_metadata_album_album_name'],
	filtered_data['master_metadata_album_artist_name'],
	filtered_data['spotify_track_uri'])),
	columns=['Track', 'Album', 'Artist', 'Track URI']
)

cleaned_df = (
	cleaned_df[~cleaned_df[['Track', 'Album', 'Artist', 'Track URI']]

MartinWeiss12

import math
import time
import spotipy
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn import tree
import spotipy.util as util
from datetime import datetime
import matplotlib.pyplot as plt

MartinWeiss12

library(png)
library(grid)
library(units)
library(imager)
library(readxl)
library(ggplot2)
# install.packages("ggimage")

# source: https://www.nba.com/stats/teams/advanced?dir=A&sort=NET_RATING

MartinWeiss12

spotifyArtistGrid = Image.new('RGB', (1000, 1000))
for i in range(1, 2):
    imagePath = imageFolderPath + '/artist_' + str(i) + '.png'
    artistImage = Image.open(imagePath)
    artistImage = artistImage.resize((400, 400))
    spotifyArtistGrid.paste(artistImage, (300, 300))
for i in range(2, 6):
    imagePath = imageFolderPath + '/artist_' + str(i) + '.png'
    artistImage = Image.open(imagePath)
    artistImage = artistImage.resize((200, 200))

MartinWeiss12

for image in range(len(artistImageUrlList)):
    artistImageUrl = requests.get(artistImageUrlList[image])
    filename = 'artist_' + str(image+1) + '.png'
    with open(os.path.join(imageFolderPath, filename), 'wb') as out:
        out.write(artistImageUrl.content)