{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import spotipy\n", "import pandas as pd\n", "import numpy as np\n", "import requests\n", "from spotipy import oauth2\n", "\n", "SPOTIPY_CLIENT_ID = '7094b9a237704754bcf84abad7f4fd07'\n", "SPOTIPY_CLIENT_SECRET = '4f88772687154fcbb263aa909ca39a90'\n", "SCOPE = ('user-read-recently-played,user-library-read,user-read-currently-playing,playlist-read-private,playlist-modify-private,playlist-modify-public,user-read-email,user-modify-playback-state,streaming,app-remote-control,user-read-private,user-read-playback-state')\n", "SPOTIPY_REDIRECT_URI = 'https://daftjoe.github.io/'\n", "sp_oauth = oauth2.SpotifyOAuth( SPOTIPY_CLIENT_ID, SPOTIPY_CLIENT_SECRET,SPOTIPY_REDIRECT_URI,scope=SCOPE )\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter the URL you were redirected to: https://daftjoe.github.io/?code=AQA1R5aEDV5u1xWObhqZA4CGJNSUwKH9nMiHcStCVdxJhBsFcqxCU5BkFcxT1jMO5MuSq5lnpRX_NvsjUWWibPbOWHKK5G-7-ndccfiVtFeGP11yZhuZGo3Bj-nIel0RYCoxU9vPKY4BiL861RduMWAJlUPBACXIAwgTQVVYIbCWOeJnElOFaR7eQZ4yytxT4cZouSvBBP1aP1asNYPj1veGAoq-ON-O1Uv64IXeB0DgGEHcHNKjoDCUWrewkMvTXsk4NiM5O77wo86xPAmLq-Gf77WTpLl1CuZ5H81ow8DDZCnchU4-84KNUWRVsNDbwgPhxJVwGSNLQoyuMznoDbj_hK_90RBcGGNUfnYzdZMUotES-adkEFT2-y39I_gSXqkPBm7xMCZGsRGuoNEHudY6HkVuGsj4pwTmZ7WzeyKNgcO61I2KcImQ_IZBrBD4VoZV2QBGuY8EdTxuZNCtunTiG8KQLGFj2HJdtazYpBuQEv4XL3EPBZzVjOflzPnhxCYODVC5SnEpiw\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ ":3: DeprecationWarning: You're using 'as_dict = True'.get_access_token will return the token string directly in future versions. Please adjust your code accordingly, or use get_cached_token instead.\n", " get_cached_token = sp_oauth.get_access_token(code)\n" ] } ], "source": [ "#browser will open and you have to copy and paste the url after clicking \"Accept\"\n", "code = sp_oauth.get_auth_response(open_browser=True)\n", "token = sp_oauth.get_access_token(code)\n", "refresh_token = token['refresh_token']\n", "sp = spotipy.Spotify(token['access_token'])\n", "username = sp.current_user()['id']" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "#create playlist and save playlist id\n", "pl_name = 'myEndlessPlaylist1'\n", "result = sp.user_playlist_create(username, name=pl_name)\n", "pl_id = result['id']" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#ID for the public spotify playlist we're getting all the albums from \n", "top_album_pl = '70n5zfYco8wG777Ua2LlNv'\n", "\n", "#top_albums is list we'll use to make the top albums playlist\n", "top_albums = []\n", "offset = 0\n", "while True:\n", " response = sp.playlist_items(top_album_pl,\n", " offset=offset)\n", " \n", " if len(response['items']) == 0:\n", " break\n", " top_albums +=response['items']\n", " offset = offset + len(response['items'])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "#Here we make a DataFrame of all the top albums by looping through that list of response of dictionaries\n", "album_df = []\n", "for album in top_albums:\n", " \n", " track = album['track']['name']\n", " artist = album['track']['artists'][0]['name']\n", " album_name = album['track']['album']['name']\n", " track_id = album['track']['id']\n", " album_id = album['track']['album']['id']\n", " album_df.append([track,artist,album_name, track_id,album_id])\n", "album_df = pd.DataFrame(album_df, columns =['track','artist','album','track_id','album_id'])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ":28: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n", " album_df.loc[(album_df['track'].str.contains(r\"\\\\\")),'track'] = album_df.track.str.replace('\\\\','')\n" ] } ], "source": [ "\n", "#random seed so others can get the same album order as me\n", "np.random.seed(10)\n", "\n", "#make a DataFrame of all albums and add a random number between 0-1 \"rand_key\" for each album\n", "all_albums = album_df.drop_duplicates('album_id').album_id\n", "all_albums = pd.DataFrame(all_albums).reset_index(drop=True)\n", "all_albums['rand_key'] = np.random.rand(len(all_albums))\n", "\n", "#merge the albums DataFrame back with the full top albums DataFrame\n", "album_df = pd.merge(album_df, all_albums, how='inner', on='album_id')\n", "\n", "#sort by the rand_key (and by index so the songs within albums stay in order)\n", "album_df['idx'] = album_df.index\n", "album_df = album_df.sort_values(['rand_key','idx']).reset_index(drop=True)\n", "\n", "#run the code below to exclude one-track albums\n", "album_df_piv = pd.pivot_table(album_df, index='album_id',values='track_id',aggfunc='count')\n", "album_df_piv.columns = ['num_tracks']\n", "album_df = pd.merge(album_df, album_df_piv, how='left',left_on='album_id',right_index=True)\n", "album_df = album_df[album_df.num_tracks>1].reset_index(drop=True)\n", "\n", "album_df['idx'] = album_df.index\n", "\n", "#do some data cleaning (getting rid of quotes, backslashes) so it will play nice \n", "album_df.loc[(album_df['track'].str.contains('\"')),'track'] = album_df.track.str.replace('\"','')\n", "album_df.loc[(album_df['artist'].str.contains('\"')),'artist'] = album_df.artist.str.replace('\"','')\n", "album_df.loc[(album_df['album'].str.contains('\"')),'album'] = album_df.album.str.replace('\"','')\n", "album_df.loc[(album_df['track'].str.contains(r\"\\\\\")),'track'] = album_df.track.str.replace('\\\\','')\n", "\n", "#copy this album df to your clipboard to paste into google sheets or elsewhere.\n", "album_df.to_clipboard(index=False)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'snapshot_id': 'MixhZWY0ODdjMjExNDg5ZWUzMDc3ZDgxYWU3YWIzMzVjZjA4NDAwZDU3'}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#initialize playlist of length 30\n", "pl_length = 30\n", "last_tracks_added = album_df.loc[0:pl_length-1]\n", "tracks_to_add = last_tracks_added.track_id.tolist()\n", "sp.playlist_add_items(pl_id,tracks_to_add )" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.5" } }, "nbformat": 4, "nbformat_minor": 2 }