Exploration_Clustering_M.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploration & Clustering of my Music Taste\n",
    "\n",
    "This notebook is not key to the project or the problems I tried to solve.\n",
    "It's simply an exploration and some clustering and visualization of my music taste.\n",
    "\n",
    "I created similar notebooks for my girlfriend and my sister to give them some small insights into the topic as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spotipy\n",
    "from spotipy.oauth2 import SpotifyClientCredentials\n",
    "import spotipy.util as util\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from spotifuncs import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = Path(\"C:/Users/ms101/OneDrive/DataScience_ML/projects/spotify_app\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(path / \"client_s.txt\") as f:\n",
    "    content = f.readlines()\n",
    "content = [x.strip() for x in content]\n",
    "\n",
    "client_id = content[0]\n",
    "client_secret = content[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(path / \"usernames.txt\") as f:\n",
    "    usernames = f.readlines()\n",
    "usernames = [x.strip() for x in usernames]\n",
    "\n",
    "username = usernames[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "scope = \"user-library-read user-read-recently-played user-top-read playlist-modify-public playlist-read-private playlist-read-collaborative\"\n",
    "\n",
    "redirect_uri = \"https://developer.spotify.com/dashboard/applications/4a4e029d299a4241873db8300038bf0a\"\n",
    "\n",
    "client_credentials_manager = SpotifyClientCredentials(client_id=client_id, \n",
    "                                                      client_secret=client_secret)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp = authenticate(redirect_uri, client_credentials_manager, username, scope, client_id, client_secret)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieving the Data\n",
    "\n",
    "I will mainly use my top tracks (short,medium and long term) in this analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>genres</th>\n",
       "      <th>popularity</th>\n",
       "      <th>uri</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rammstein</td>\n",
       "      <td>6wWVKhxIU2cEi0K81v7HvP</td>\n",
       "      <td>alternative metal,german metal,industrial,indu...</td>\n",
       "      <td>79</td>\n",
       "      <td>spotify:artist:6wWVKhxIU2cEi0K81v7HvP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fynn Kliemann</td>\n",
       "      <td>2YIxQoGvBukvoC1CbJ7opS</td>\n",
       "      <td>german pop</td>\n",
       "      <td>65</td>\n",
       "      <td>spotify:artist:2YIxQoGvBukvoC1CbJ7opS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Joyner Lucas</td>\n",
       "      <td>6C1ohJrd5VydigQtaGy5Wa</td>\n",
       "      <td>boston hip hop,pop rap,rap</td>\n",
       "      <td>78</td>\n",
       "      <td>spotify:artist:6C1ohJrd5VydigQtaGy5Wa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Frank Sinatra</td>\n",
       "      <td>1Mxqyy3pSjf8kZZL4QVxS0</td>\n",
       "      <td>adult standards,easy listening,lounge</td>\n",
       "      <td>87</td>\n",
       "      <td>spotify:artist:1Mxqyy3pSjf8kZZL4QVxS0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Geegun</td>\n",
       "      <td>5W7N6u4EjCEMKj7bDyzPEC</td>\n",
       "      <td>russian dance,russian dance pop,russian hip ho...</td>\n",
       "      <td>57</td>\n",
       "      <td>spotify:artist:5W7N6u4EjCEMKj7bDyzPEC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>KSHMR</td>\n",
       "      <td>2wX6xSig4Rig5kZU6ePlWe</td>\n",
       "      <td>big room,deep big room,edm,electro house,india...</td>\n",
       "      <td>73</td>\n",
       "      <td>spotify:artist:2wX6xSig4Rig5kZU6ePlWe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Chris Rock</td>\n",
       "      <td>36eSjIksD6fehqxyDUHDA3</td>\n",
       "      <td>black comedy,comedy</td>\n",
       "      <td>48</td>\n",
       "      <td>spotify:artist:36eSjIksD6fehqxyDUHDA3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Carnage</td>\n",
       "      <td>7CCjtD0hCK005Bvg2WG1a7</td>\n",
       "      <td>edm,electro house,electronic trap,pop rap,rap,...</td>\n",
       "      <td>64</td>\n",
       "      <td>spotify:artist:7CCjtD0hCK005Bvg2WG1a7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Wardruna</td>\n",
       "      <td>0NJ6wlOAsAJ1PN4VRdTPKA</td>\n",
       "      <td>medieval folk,nordic folk,rune folk,viking folk</td>\n",
       "      <td>62</td>\n",
       "      <td>spotify:artist:0NJ6wlOAsAJ1PN4VRdTPKA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Motörhead</td>\n",
       "      <td>1DFr97A9HnbV3SKTJFu62M</td>\n",
       "      <td>album rock,alternative metal,hard rock,metal,r...</td>\n",
       "      <td>69</td>\n",
       "      <td>spotify:artist:1DFr97A9HnbV3SKTJFu62M</td>\n",
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       "</div>"
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      "text/plain": [
       "             name                      id  \\\n",
       "0       Rammstein  6wWVKhxIU2cEi0K81v7HvP   \n",
       "1   Fynn Kliemann  2YIxQoGvBukvoC1CbJ7opS   \n",
       "2    Joyner Lucas  6C1ohJrd5VydigQtaGy5Wa   \n",
       "3   Frank Sinatra  1Mxqyy3pSjf8kZZL4QVxS0   \n",
       "4          Geegun  5W7N6u4EjCEMKj7bDyzPEC   \n",
       "..            ...                     ...   \n",
       "94          KSHMR  2wX6xSig4Rig5kZU6ePlWe   \n",
       "95     Chris Rock  36eSjIksD6fehqxyDUHDA3   \n",
       "96        Carnage  7CCjtD0hCK005Bvg2WG1a7   \n",
       "97       Wardruna  0NJ6wlOAsAJ1PN4VRdTPKA   \n",
       "98      Motörhead  1DFr97A9HnbV3SKTJFu62M   \n",
       "\n",
       "                                               genres  popularity  \\\n",
       "0   alternative metal,german metal,industrial,indu...          79   \n",
       "1                                          german pop          65   \n",
       "2                          boston hip hop,pop rap,rap          78   \n",
       "3               adult standards,easy listening,lounge          87   \n",
       "4   russian dance,russian dance pop,russian hip ho...          57   \n",
       "..                                                ...         ...   \n",
       "94  big room,deep big room,edm,electro house,india...          73   \n",
       "95                                black comedy,comedy          48   \n",
       "96  edm,electro house,electronic trap,pop rap,rap,...          64   \n",
       "97    medieval folk,nordic folk,rune folk,viking folk          62   \n",
       "98  album rock,alternative metal,hard rock,metal,r...          69   \n",
       "\n",
       "                                      uri  \n",
       "0   spotify:artist:6wWVKhxIU2cEi0K81v7HvP  \n",
       "1   spotify:artist:2YIxQoGvBukvoC1CbJ7opS  \n",
       "2   spotify:artist:6C1ohJrd5VydigQtaGy5Wa  \n",
       "3   spotify:artist:1Mxqyy3pSjf8kZZL4QVxS0  \n",
       "4   spotify:artist:5W7N6u4EjCEMKj7bDyzPEC  \n",
       "..                                    ...  \n",
       "94  spotify:artist:2wX6xSig4Rig5kZU6ePlWe  \n",
       "95  spotify:artist:36eSjIksD6fehqxyDUHDA3  \n",
       "96  spotify:artist:7CCjtD0hCK005Bvg2WG1a7  \n",
       "97  spotify:artist:0NJ6wlOAsAJ1PN4VRdTPKA  \n",
       "98  spotify:artist:1DFr97A9HnbV3SKTJFu62M  \n",
       "\n",
       "[99 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_tracks_short = sp.current_user_top_tracks(limit = 50,offset=0,time_range='short_term')\n",
    "top_tracks_med = sp.current_user_top_tracks(limit = 50,offset=0,time_range='medium_term')\n",
    "top_tracks_long = sp.current_user_top_tracks(limit = 50,offset=0,time_range='long_term')\n",
    "\n",
    "#combine the top_tracks\n",
    "top_tracks_short_df = append_audio_features(create_df_top_songs(top_tracks_short),sp)\n",
    "top_tracks_med_df = append_audio_features(create_df_top_songs(top_tracks_med),sp)\n",
    "top_tracks_long_df = append_audio_features(create_df_top_songs(top_tracks_long),sp)\n",
    "\n",
    "top_tracks_short_df[\"Timeframe\"] = \"short term\"\n",
    "top_tracks_med_df[\"Timeframe\"] = \"medium term\"\n",
    "top_tracks_long_df[\"Timeframe\"] = \"long term\"\n",
    "\n",
    "top_tracks = pd.concat([top_tracks_short_df, top_tracks_med_df, top_tracks_long_df])\n",
    "top_tracks = top_tracks.reset_index(drop = True)\n",
    "\n",
    "top_artists_long = sp.current_user_top_artists(limit = 50, time_range = \"long_term\")\n",
    "top_artists_med = sp.current_user_top_artists(limit = 50, time_range = \"medium_term\")\n",
    "top_artists_short = sp.current_user_top_artists(limit = 50, time_range = \"short_term\")\n",
    "\n",
    "\n",
    "artists_short_df = top_artists_from_API(top_artists_short)\n",
    "artists_med_df = top_artists_from_API(top_artists_med)\n",
    "artists_long_df = top_artists_from_API(top_artists_long)\n",
    "\n",
    "\n",
    "artists_df = pd.concat([artists_short_df,artists_med_df,artists_long_df])\n",
    "artists_df[\"genres\"] = artists_df[\"genres\"].apply(lambda x: \",\".join(x))\n",
    "artists_df.drop_duplicates().reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>track_name</th>\n",
       "      <th>album</th>\n",
       "      <th>track_id</th>\n",
       "      <th>artist</th>\n",
       "      <th>duration</th>\n",
       "      <th>popularity</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>Timeframe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Who - Single Version</td>\n",
       "      <td>Who</td>\n",
       "      <td>0CjBORMsmiQNe3vPDcNIvk</td>\n",
       "      <td>Modeselektor</td>\n",
       "      <td>207988</td>\n",
       "      <td>43</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.910</td>\n",
       "      <td>7</td>\n",
       "      <td>-8.472</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0484</td>\n",
       "      <td>0.044200</td>\n",
       "      <td>0.006620</td>\n",
       "      <td>0.1080</td>\n",
       "      <td>0.3920</td>\n",
       "      <td>142.982</td>\n",
       "      <td>short term</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lambo Lambo</td>\n",
       "      <td>KitschKrieg</td>\n",
       "      <td>7oqvRZNv4dUV8CgQWtIAMe</td>\n",
       "      <td>KitschKrieg</td>\n",
       "      <td>214991</td>\n",
       "      <td>60</td>\n",
       "      <td>0.876</td>\n",
       "      <td>0.400</td>\n",
       "      <td>5</td>\n",
       "      <td>-9.748</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1290</td>\n",
       "      <td>0.494000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.4840</td>\n",
       "      <td>144.938</td>\n",
       "      <td>short term</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Oh Junge</td>\n",
       "      <td>KitschKrieg</td>\n",
       "      <td>0JqbpesudPE6j901fBEzo2</td>\n",
       "      <td>KitschKrieg</td>\n",
       "      <td>193946</td>\n",
       "      <td>58</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.515</td>\n",
       "      <td>1</td>\n",
       "      <td>-7.715</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1880</td>\n",
       "      <td>0.029200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0835</td>\n",
       "      <td>0.2690</td>\n",
       "      <td>156.000</td>\n",
       "      <td>short term</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Zuhause</td>\n",
       "      <td>Nie</td>\n",
       "      <td>5vC4M4GjYLkgDaUwQcL7WA</td>\n",
       "      <td>Fynn Kliemann</td>\n",
       "      <td>197887</td>\n",
       "      <td>66</td>\n",
       "      <td>0.656</td>\n",
       "      <td>0.448</td>\n",
       "      <td>9</td>\n",
       "      <td>-8.676</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0442</td>\n",
       "      <td>0.860000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.1030</td>\n",
       "      <td>0.4030</td>\n",
       "      <td>95.626</td>\n",
       "      <td>short term</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fall Slowly (feat. Ashanti) - Extended Version</td>\n",
       "      <td>Evolution</td>\n",
       "      <td>7wK4pOTZKVaAJ00rziu901</td>\n",
       "      <td>Joyner Lucas</td>\n",
       "      <td>292056</td>\n",
       "      <td>58</td>\n",
       "      <td>0.542</td>\n",
       "      <td>0.453</td>\n",
       "      <td>6</td>\n",
       "      <td>-11.208</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2400</td>\n",
       "      <td>0.204000</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.1370</td>\n",
       "      <td>0.0375</td>\n",
       "      <td>88.310</td>\n",
       "      <td>short term</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>The Beautiful &amp; Damned</td>\n",
       "      <td>The Beautiful &amp; Damned</td>\n",
       "      <td>2WWruw7ul9N7eqoHELyMc2</td>\n",
       "      <td>G-Eazy</td>\n",
       "      <td>189306</td>\n",
       "      <td>61</td>\n",
       "      <td>0.656</td>\n",
       "      <td>0.804</td>\n",
       "      <td>8</td>\n",
       "      <td>-5.191</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3630</td>\n",
       "      <td>0.173000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.8370</td>\n",
       "      <td>0.3140</td>\n",
       "      <td>125.882</td>\n",
       "      <td>long term</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Like A G6</td>\n",
       "      <td>Like a G6</td>\n",
       "      <td>5S1yeuswEPofFOCkMFLNLc</td>\n",
       "      <td>Far East Movement</td>\n",
       "      <td>218346</td>\n",
       "      <td>0</td>\n",
       "      <td>0.629</td>\n",
       "      <td>0.869</td>\n",
       "      <td>7</td>\n",
       "      <td>-7.013</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3140</td>\n",
       "      <td>0.006570</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1910</td>\n",
       "      <td>0.7150</td>\n",
       "      <td>125.024</td>\n",
       "      <td>long term</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Halftime</td>\n",
       "      <td>Wolke 7</td>\n",
       "      <td>1N9ZAbqVw5o0m7wccSgRIt</td>\n",
       "      <td>Gzuz</td>\n",
       "      <td>181060</td>\n",
       "      <td>1</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.783</td>\n",
       "      <td>2</td>\n",
       "      <td>-6.490</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3250</td>\n",
       "      <td>0.181000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.4710</td>\n",
       "      <td>129.844</td>\n",
       "      <td>long term</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Push It</td>\n",
       "      <td>Wisconsin Death Trip</td>\n",
       "      <td>43WFwjiWFHc8ZryT1Tz1aY</td>\n",
       "      <td>Static-X</td>\n",
       "      <td>154906</td>\n",
       "      <td>37</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.977</td>\n",
       "      <td>0</td>\n",
       "      <td>-3.965</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0493</td>\n",
       "      <td>0.000393</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.2910</td>\n",
       "      <td>0.5040</td>\n",
       "      <td>149.879</td>\n",
       "      <td>long term</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Talk Shit, Get Shot</td>\n",
       "      <td>Manslaughter</td>\n",
       "      <td>2rsho1vI3L5o65QOjM5Mwx</td>\n",
       "      <td>Body Count</td>\n",
       "      <td>227147</td>\n",
       "      <td>40</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.993</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.952</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2930</td>\n",
       "      <td>0.002350</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1490</td>\n",
       "      <td>0.3840</td>\n",
       "      <td>175.004</td>\n",
       "      <td>long term</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         track_name                   album  \\\n",
       "0                              Who - Single Version                     Who   \n",
       "1                                       Lambo Lambo             KitschKrieg   \n",
       "2                                          Oh Junge             KitschKrieg   \n",
       "3                                           Zuhause                     Nie   \n",
       "4    Fall Slowly (feat. Ashanti) - Extended Version               Evolution   \n",
       "..                                              ...                     ...   \n",
       "145                          The Beautiful & Damned  The Beautiful & Damned   \n",
       "146                                       Like A G6               Like a G6   \n",
       "147                                        Halftime                 Wolke 7   \n",
       "148                                         Push It    Wisconsin Death Trip   \n",
       "149                             Talk Shit, Get Shot            Manslaughter   \n",
       "\n",
       "                   track_id             artist  duration  popularity  \\\n",
       "0    0CjBORMsmiQNe3vPDcNIvk       Modeselektor    207988          43   \n",
       "1    7oqvRZNv4dUV8CgQWtIAMe        KitschKrieg    214991          60   \n",
       "2    0JqbpesudPE6j901fBEzo2        KitschKrieg    193946          58   \n",
       "3    5vC4M4GjYLkgDaUwQcL7WA      Fynn Kliemann    197887          66   \n",
       "4    7wK4pOTZKVaAJ00rziu901       Joyner Lucas    292056          58   \n",
       "..                      ...                ...       ...         ...   \n",
       "145  2WWruw7ul9N7eqoHELyMc2             G-Eazy    189306          61   \n",
       "146  5S1yeuswEPofFOCkMFLNLc  Far East Movement    218346           0   \n",
       "147  1N9ZAbqVw5o0m7wccSgRIt               Gzuz    181060           1   \n",
       "148  43WFwjiWFHc8ZryT1Tz1aY           Static-X    154906          37   \n",
       "149  2rsho1vI3L5o65QOjM5Mwx         Body Count    227147          40   \n",
       "\n",
       "     danceability  energy  key  loudness  mode  speechiness  acousticness  \\\n",
       "0           0.760   0.910    7    -8.472     1       0.0484      0.044200   \n",
       "1           0.876   0.400    5    -9.748     0       0.1290      0.494000   \n",
       "2           0.878   0.515    1    -7.715     1       0.1880      0.029200   \n",
       "3           0.656   0.448    9    -8.676     0       0.0442      0.860000   \n",
       "4           0.542   0.453    6   -11.208     0       0.2400      0.204000   \n",
       "..            ...     ...  ...       ...   ...          ...           ...   \n",
       "145         0.656   0.804    8    -5.191     0       0.3630      0.173000   \n",
       "146         0.629   0.869    7    -7.013     0       0.3140      0.006570   \n",
       "147         0.533   0.783    2    -6.490     1       0.3250      0.181000   \n",
       "148         0.557   0.977    0    -3.965     1       0.0493      0.000393   \n",
       "149         0.455   0.993    3    -1.952     0       0.2930      0.002350   \n",
       "\n",
       "     instrumentalness  liveness  valence    tempo   Timeframe  \n",
       "0            0.006620    0.1080   0.3920  142.982  short term  \n",
       "1            0.000009    0.1000   0.4840  144.938  short term  \n",
       "2            0.000000    0.0835   0.2690  156.000  short term  \n",
       "3            0.000005    0.1030   0.4030   95.626  short term  \n",
       "4            0.000007    0.1370   0.0375   88.310  short term  \n",
       "..                ...       ...      ...      ...         ...  \n",
       "145          0.000000    0.8370   0.3140  125.882   long term  \n",
       "146          0.000000    0.1910   0.7150  125.024   long term  \n",
       "147          0.000000    0.1010   0.4710  129.844   long term  \n",
       "148          0.000004    0.2910   0.5040  149.879   long term  \n",
       "149          0.000000    0.1490   0.3840  175.004   long term  \n",
       "\n",
       "[150 rows x 18 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_tracks.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['danceability',\n",
       " 'energy',\n",
       " 'key',\n",
       " 'loudness',\n",
       " 'mode',\n",
       " 'speechiness',\n",
       " 'acousticness',\n",
       " 'instrumentalness',\n",
       " 'liveness',\n",
       " 'valence']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(top_tracks_short_df.columns[6:-2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average Audio Features for the three Timeframes\n",
    "\n",
    "I wanted to explore the average audio feature of my top tracks to get a kind of \"top track profile\" I also wanted to see whether there were any major changes between the three timeframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "audio_features = list(top_tracks_short_df.columns[6:-2])\n",
    "audio_features.append(\"Timeframe\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timeframe</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>long term</th>\n",
       "      <td>0.61314</td>\n",
       "      <td>0.78496</td>\n",
       "      <td>5.78</td>\n",
       "      <td>-5.44072</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.248948</td>\n",
       "      <td>0.092208</td>\n",
       "      <td>0.064601</td>\n",
       "      <td>0.212500</td>\n",
       "      <td>0.422921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medium term</th>\n",
       "      <td>0.61784</td>\n",
       "      <td>0.75652</td>\n",
       "      <td>5.18</td>\n",
       "      <td>-6.04674</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.145466</td>\n",
       "      <td>0.106589</td>\n",
       "      <td>0.039815</td>\n",
       "      <td>0.213790</td>\n",
       "      <td>0.362230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>short term</th>\n",
       "      <td>0.68280</td>\n",
       "      <td>0.62192</td>\n",
       "      <td>5.58</td>\n",
       "      <td>-8.27152</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.152014</td>\n",
       "      <td>0.215322</td>\n",
       "      <td>0.209815</td>\n",
       "      <td>0.239122</td>\n",
       "      <td>0.362832</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             danceability   energy   key  loudness  mode  speechiness  \\\n",
       "Timeframe                                                               \n",
       "long term         0.61314  0.78496  5.78  -5.44072  0.52     0.248948   \n",
       "medium term       0.61784  0.75652  5.18  -6.04674  0.52     0.145466   \n",
       "short term        0.68280  0.62192  5.58  -8.27152  0.48     0.152014   \n",
       "\n",
       "             acousticness  instrumentalness  liveness   valence  \n",
       "Timeframe                                                        \n",
       "long term        0.092208          0.064601  0.212500  0.422921  \n",
       "medium term      0.106589          0.039815  0.213790  0.362230  \n",
       "short term       0.215322          0.209815  0.239122  0.362832  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "audio_overtime_df = top_tracks[audio_features].groupby(\"Timeframe\").mean()\n",
    "audio_overtime_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "audio_overtime_df.reset_index(inplace = True)\n",
    "df_loud_key = audio_overtime_df[[\"loudness\",\"key\"]] #for better viz these need to be plotted seperately\n",
    "df_loud_key.set_index(pd.Index([\"long term\", \"medium term\", \"short term\"]), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "audio_overtime_df.drop([\"loudness\",\"key\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting\n",
    "\n",
    "The plot requires a certain dataformat which is created through `pd.melt()` here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timeframe</th>\n",
       "      <th>audio_feature</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>long term</td>\n",
       "      <td>danceability</td>\n",
       "      <td>0.613140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>medium term</td>\n",
       "      <td>danceability</td>\n",
       "      <td>0.617840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>short term</td>\n",
       "      <td>danceability</td>\n",
       "      <td>0.682800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>long term</td>\n",
       "      <td>energy</td>\n",
       "      <td>0.784960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>medium term</td>\n",
       "      <td>energy</td>\n",
       "      <td>0.756520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>short term</td>\n",
       "      <td>energy</td>\n",
       "      <td>0.621920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>long term</td>\n",
       "      <td>mode</td>\n",
       "      <td>0.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>medium term</td>\n",
       "      <td>mode</td>\n",
       "      <td>0.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>short term</td>\n",
       "      <td>mode</td>\n",
       "      <td>0.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>long term</td>\n",
       "      <td>speechiness</td>\n",
       "      <td>0.248948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>medium term</td>\n",
       "      <td>speechiness</td>\n",
       "      <td>0.145466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>short term</td>\n",
       "      <td>speechiness</td>\n",
       "      <td>0.152014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>long term</td>\n",
       "      <td>acousticness</td>\n",
       "      <td>0.092208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>medium term</td>\n",
       "      <td>acousticness</td>\n",
       "      <td>0.106589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>short term</td>\n",
       "      <td>acousticness</td>\n",
       "      <td>0.215322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>long term</td>\n",
       "      <td>instrumentalness</td>\n",
       "      <td>0.064601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>medium term</td>\n",
       "      <td>instrumentalness</td>\n",
       "      <td>0.039815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>short term</td>\n",
       "      <td>instrumentalness</td>\n",
       "      <td>0.209815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>long term</td>\n",
       "      <td>liveness</td>\n",
       "      <td>0.212500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>medium term</td>\n",
       "      <td>liveness</td>\n",
       "      <td>0.213790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>short term</td>\n",
       "      <td>liveness</td>\n",
       "      <td>0.239122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>long term</td>\n",
       "      <td>valence</td>\n",
       "      <td>0.422921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>medium term</td>\n",
       "      <td>valence</td>\n",
       "      <td>0.362230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>short term</td>\n",
       "      <td>valence</td>\n",
       "      <td>0.362832</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Timeframe     audio_feature      mean\n",
       "0     long term      danceability  0.613140\n",
       "1   medium term      danceability  0.617840\n",
       "2    short term      danceability  0.682800\n",
       "3     long term            energy  0.784960\n",
       "4   medium term            energy  0.756520\n",
       "5    short term            energy  0.621920\n",
       "6     long term              mode  0.520000\n",
       "7   medium term              mode  0.520000\n",
       "8    short term              mode  0.480000\n",
       "9     long term       speechiness  0.248948\n",
       "10  medium term       speechiness  0.145466\n",
       "11   short term       speechiness  0.152014\n",
       "12    long term      acousticness  0.092208\n",
       "13  medium term      acousticness  0.106589\n",
       "14   short term      acousticness  0.215322\n",
       "15    long term  instrumentalness  0.064601\n",
       "16  medium term  instrumentalness  0.039815\n",
       "17   short term  instrumentalness  0.209815\n",
       "18    long term          liveness  0.212500\n",
       "19  medium term          liveness  0.213790\n",
       "20   short term          liveness  0.239122\n",
       "21    long term           valence  0.422921\n",
       "22  medium term           valence  0.362230\n",
       "23   short term           valence  0.362832"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_df = pd.melt(audio_overtime_df, id_vars = \"Timeframe\",\n",
    "                           var_name = \"audio_feature\", value_name = \"mean\")\n",
    "plot_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "sns.catplot(data = plot_df, kind = \"bar\", x = \"Timeframe\",\n",
    "            y = \"mean\", hue = \"audio_feature\"\n",
    ")\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(10, 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Some features have changed a lot on average especially between the long term and short term top tracks. It seems my taste has shifted to less energetic and more acoustic and instrumental music.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering my Top Tracks\n",
    "\n",
    "I wanted to see whether my top tracks could be sensibly clustered. I first looked at the individual distributions and than applied k-means clustering to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['danceability',\n",
       " 'energy',\n",
       " 'key',\n",
       " 'loudness',\n",
       " 'mode',\n",
       " 'speechiness',\n",
       " 'acousticness',\n",
       " 'instrumentalness',\n",
       " 'liveness',\n",
       " 'valence']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_feat = audio_features[:-1]\n",
    "cluster_feat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'danceability'}>,\n",
       "        <AxesSubplot:title={'center':'energy'}>,\n",
       "        <AxesSubplot:title={'center':'key'}>],\n",
       "       [<AxesSubplot:title={'center':'loudness'}>,\n",
       "        <AxesSubplot:title={'center':'speechiness'}>,\n",
       "        <AxesSubplot:title={'center':'acousticness'}>],\n",
       "       [<AxesSubplot:title={'center':'instrumentalness'}>,\n",
       "        <AxesSubplot:title={'center':'liveness'}>,\n",
       "        <AxesSubplot:title={'center':'valence'}>]], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cluster = top_tracks[cluster_feat].drop(\"mode\",axis = 1)\n",
    "df_cluster.hist(figsize= (15,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "X = np.array(df_cluster)\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X)\n",
    "X = scaler.transform(X)\n",
    "\n",
    "\n",
    "ss_dist = []\n",
    "for k in range(1,21):\n",
    "    km = KMeans(n_clusters=k,max_iter = 10000 ,random_state=13)\n",
    "    km = km.fit(X)\n",
    "    ss_dist.append(km.inertia_)\n",
    "    \n",
    "plt.plot(range(1,21), ss_dist, 'bx-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Sum of squared distances')\n",
    "plt.title('Elbow Method For Optimal k')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "kmeans_per_k = [KMeans(n_clusters=k, random_state=42).fit(X)\n",
    "                for k in range(1, 10)]\n",
    "silhouette_scores = [silhouette_score(X, model.labels_)\n",
    "                     for model in kmeans_per_k[1:]]\n",
    "plt.figure(figsize=(8, 3))\n",
    "sns.lineplot(x = range(2, 10), y = silhouette_scores,marker=\"o\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Both plots don't really show a clear number of clusters, as there are 9 features the number should be a bit below that and above only 2/3 clusters. I choose 6 here, which worked okay.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_cluster = 6\n",
    "km = KMeans(n_clusters = n_cluster, max_iter = 10000, random_state=13).fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_pred = km.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150,)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_tracks[\"cluster\"] = X_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "              track_name              artist\n",
      "5            By My Side              Ecepta\n",
      "8                Zunder       Marek Hemmann\n",
      "15       Waiting Around             BoomBox\n",
      "18         Blooming Era  Enrico Sangiuliano\n",
      "19                  Neo   Christian Löffler\n",
      "29   Zwitscherzwitscher              Rampue\n",
      "30       Bottom Feeders      Isaac Chambers\n",
      "33        Can't Take It           AKKI (DE)\n",
      "37         Crispy Bacon     Laurent Garnier\n",
      "39                Siren             Tourist\n",
      "42               So Far      Ólafur Arnalds\n",
      "88            Magnifico                Kilo\n",
      "118          Light Rain          Chill Bees\n",
      "127          Until Dawn              Jaeger \n",
      "\n",
      "\n",
      "                                          track_name          artist\n",
      "0                              Who - Single Version    Modeselektor\n",
      "2                                          Oh Junge     KitschKrieg\n",
      "4    Fall Slowly (feat. Ashanti) - Extended Version    Joyner Lucas\n",
      "6                                        Coco L'Eau      Egor Kreed\n",
      "9                                               069      Haftbefehl\n",
      "13                                       Дни и ночи          Geegun\n",
      "24                                        Lady Lady          Masego\n",
      "28                                           Twingo   Fynn Kliemann\n",
      "36                   What About Me (feat. Sosamann)       Lil Wayne\n",
      "40                                         Handsome         Stormzy\n",
      "41                                           Wealth    Modeselektor\n",
      "43                                      Fall Slowly    Joyner Lucas\n",
      "47                                     Devil's Work    Joyner Lucas\n",
      "48             What's Up Danger (with Black Caviar)        Blackway\n",
      "53                                      ALLES HELAL            ELIF\n",
      "54                                    Beifahrersitz             LEA\n",
      "55                        KANN DAS BITTE SO BLEIBEN            ELIF\n",
      "60                                            FEUER            ELIF\n",
      "64                                 ALL OF MY DREAMS            ELIF\n",
      "67                                           ALASKA            ELIF\n",
      "73                                  EIN LETZTES MAL            ELIF\n",
      "74                                     10 Feet Down              NF\n",
      "75                         DOA [Feat. Joyner Lucas]       I Prevail\n",
      "82                                          Testo E            SSIO\n",
      "83                                          NUR MIR            ELIF\n",
      "86                           AUGEN ZU (feat. Samra)            ELIF\n",
      "87                                          GITARRE            ELIF\n",
      "91                                             1975           MAJAN\n",
      "98                                      God of Fire  Fit For A King\n",
      "99                               FEIND (feat. Azad)            ELIF\n",
      "101                                    m.A.A.d city  Kendrick Lamar\n",
      "103                                     Berlin lebt     Capital Bra\n",
      "105                          INTERNATIONAL GANGSTAS      Farid Bang\n",
      "116                                The Next Episode         Dr. Dre\n",
      "129                          I Mean It (feat. Remo)          G-Eazy\n",
      "130   Crank That (Soulja Boy) [Travis Barker Remix]      Soulja Boy\n",
      "137                                    Back To Back           Drake \n",
      "\n",
      "\n",
      "                                   track_name             artist\n",
      "12                                 Zim Zimma       Joyner Lucas\n",
      "16                             Kkallabassass  Spark Master Tape\n",
      "22                  Обними (feat. Ани Лорак)             Geegun\n",
      "26        Demons & Goblins (feat. Meek Mill)      Fivio Foreign\n",
      "32                                  Bläulich         Apache 207\n",
      "38                     Schüttel deinen Speck          Peter Fox\n",
      "45                                 Evolution       Joyner Lucas\n",
      "49                                     Scary            Stormzy\n",
      "51                                      YAYA            6ix9ine\n",
      "57                                      RADW         Haftbefehl\n",
      "66                                   SCHWARZ               ELIF\n",
      "68                           ABER WO BIST DU               ELIF\n",
      "78                   Movin' Bass - GTA Remix          Rick Ross\n",
      "102                                     Only        Nicki Minaj\n",
      "104  Ich rolle mit meim Besten - Babos Remix         Haftbefehl\n",
      "107                     X Gon' Give It To Ya                DMX\n",
      "110                                    BILLY            6ix9ine\n",
      "114                                    Panda          Desiigner\n",
      "119                         (Rock) Superstar       Cypress Hill\n",
      "122                   HaifischNikez Allstars                 LX\n",
      "126                                ¿ Warum ?               Gzuz\n",
      "128                         Juice (feat. YG)        Bhad Bhabie\n",
      "131                                Hot N*gga      Bobby Shmurda\n",
      "134                             Still D.R.E.            Dr. Dre\n",
      "135                                Big Rings              Drake\n",
      "138                                    Mercy         Kanye West\n",
      "141          Hit the Quan - Original Version       iLoveMemphis\n",
      "143                     Bricks (feat. Migos)            Carnage\n",
      "146                                Like A G6  Far East Movement \n",
      "\n",
      "\n",
      "                               track_name              artist\n",
      "10                     Alles was ich hab       Fynn Kliemann\n",
      "20                The Beautiful & Damned              G-Eazy\n",
      "23                  Black Bimmer - Remix              Linius\n",
      "25                                   Raw             Ellipso\n",
      "31                                  ADHD        Joyner Lucas\n",
      "35                  LOCO SMOCO FREESTYLE            OBLADAET\n",
      "44      Lily Moon - Isaac Chambers Remix           Wildlight\n",
      "77                                Stitch            Wage War\n",
      "96                           Holy Roller           Spiritbox\n",
      "100  Purple Lamborghini (with Rick Ross)            Skrillex\n",
      "115                            The Beast           Tech N9ne\n",
      "125                       NEW MAGIC WAND  Tyler, The Creator \n",
      "\n",
      "\n",
      "                                track_name         artist\n",
      "1                             Lambo Lambo    KitschKrieg\n",
      "3                                 Zuhause  Fynn Kliemann\n",
      "11                         A Child's Tale       Bukahara\n",
      "17                     Come Waltz With Me  Frank Sinatra\n",
      "27                                 Mirror      Lil Wayne\n",
      "34                        That Other Girl      Sevdaliza\n",
      "85              Aretha Franklin Freestyle            RIN\n",
      "93                  DU TUST MIR NICHT GUT           ELIF\n",
      "117  Gordo (feat. Rick Ross & Araabmuzik)        Carnage \n",
      "\n",
      "\n",
      " Empty DataFrame\n",
      "Columns: [track_name, artist]\n",
      "Index: [] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,n_cluster+1):\n",
    "    print(\"\\n\",top_tracks[[\"track_name\",\"artist\"]][top_tracks[\"cluster\"] == i].drop_duplicates(), \"\\n\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Unfortunately the clusters differ a lot in size and don't seem to have any obvious overarching characteristics, such as just one genre. It's time to visualize them and explore them further.**\n",
    "\n",
    "### T-SNE (2-D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>D1</th>\n",
       "      <th>D2</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.823404</td>\n",
       "      <td>0.728941</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.389895</td>\n",
       "      <td>-1.680083</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.750389</td>\n",
       "      <td>-0.054148</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.669502</td>\n",
       "      <td>-2.830237</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.925185</td>\n",
       "      <td>-3.194834</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         D1        D2  label\n",
       "0  4.823404  0.728941      2\n",
       "1 -0.389895 -1.680083      5\n",
       "2  0.750389 -0.054148      2\n",
       "3 -0.669502 -2.830237      5\n",
       "4  1.925185 -3.194834      2"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "tsne = TSNE(n_components=2, perplexity=50, n_iter=5000, random_state=13)\n",
    "tsne_results = tsne.fit_transform(X)\n",
    "\n",
    "df_tsne = pd.DataFrame(tsne_results)\n",
    "df_tsne.columns = ['D1', 'D2']\n",
    "df_tsne['label'] = km.labels_\n",
    "df_tsne.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data = df_tsne, x = \"D1\", y = \"D2\", hue = \"label\", style = \"label\",\n",
    "                palette = \"tab10\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This worked quite well the clusters are mostly well seperated from one another and can be intuitively understood.\n",
    "\n",
    "### T-SNE (3-D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsne3 = TSNE(n_components=3, n_iter=5000, random_state=13, perplexity=50)\n",
    "tsne_results = tsne3.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>D1</th>\n",
       "      <th>D2</th>\n",
       "      <th>D3</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>624.382935</td>\n",
       "      <td>-93.204903</td>\n",
       "      <td>63.264420</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-185.143341</td>\n",
       "      <td>-209.190048</td>\n",
       "      <td>-363.956879</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33.380653</td>\n",
       "      <td>-153.681870</td>\n",
       "      <td>-350.942291</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-195.708130</td>\n",
       "      <td>-441.540436</td>\n",
       "      <td>-255.700180</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-66.709648</td>\n",
       "      <td>-604.682678</td>\n",
       "      <td>-114.184532</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           D1          D2          D3  label\n",
       "0  624.382935  -93.204903   63.264420      2\n",
       "1 -185.143341 -209.190048 -363.956879      5\n",
       "2   33.380653 -153.681870 -350.942291      2\n",
       "3 -195.708130 -441.540436 -255.700180      5\n",
       "4  -66.709648 -604.682678 -114.184532      2"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tsne3 = pd.DataFrame(tsne_results)\n",
    "df_tsne3.columns = ['D1', 'D2', 'D3']\n",
    "df_tsne3['label'] = km.labels_\n",
    "df_tsne3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = Axes3D(fig)\n",
    "\n",
    "ax.scatter(df_tsne3['D1'], df_tsne3['D2'], df_tsne3['D3'],\n",
    "           c=df_tsne3['label'], cmap='tab10')\n",
    "\n",
    "ax.set_xlabel('D1')\n",
    "ax.set_ylabel('D2')\n",
    "ax.set_zlabel('D3')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is quite a mess, the 2D visualization worked much better.\n",
    "\n",
    "### PCA (2-D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5768743066837753\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.26281223, 0.44661126, 0.57687431])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=3, random_state=123)\n",
    "pca_results = pca.fit_transform(X)\n",
    "print(pca.explained_variance_ratio_.sum())\n",
    "pca.explained_variance_ratio_.cumsum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is really not enough variance explained by PCA with 2 components for a sensible visualisation.\n",
    "\n",
    "### kernel PCA (2-D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>D1</th>\n",
       "      <th>D2</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.067540</td>\n",
       "      <td>-0.009966</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.523905</td>\n",
       "      <td>-0.057627</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.256085</td>\n",
       "      <td>0.045457</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.453155</td>\n",
       "      <td>0.071320</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.353061</td>\n",
       "      <td>0.215603</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         D1        D2  label\n",
       "0 -0.067540 -0.009966      2\n",
       "1  0.523905 -0.057627      5\n",
       "2  0.256085  0.045457      2\n",
       "3  0.453155  0.071320      5\n",
       "4  0.353061  0.215603      2"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import KernelPCA\n",
    "rbf_pca = KernelPCA(n_components=2, kernel = \"rbf\", gamma = 0.04)\n",
    "rbf_pca_results = rbf_pca.fit_transform(X)\n",
    "\n",
    "df_rbf_pca = pd.DataFrame(rbf_pca_results)\n",
    "df_rbf_pca.columns = ['D1', 'D2']\n",
    "df_rbf_pca['label'] = km.labels_\n",
    "df_rbf_pca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data = df_rbf_pca, x = \"D1\", y = \"D2\", hue = \"label\", style = \"label\",\n",
    "                palette = \"tab10\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>D1</th>\n",
       "      <th>D2</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.026207</td>\n",
       "      <td>0.025889</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.020199</td>\n",
       "      <td>0.001351</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.027577</td>\n",
       "      <td>0.098385</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.072817</td>\n",
       "      <td>-0.064120</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.025086</td>\n",
       "      <td>-0.005538</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         D1        D2  label\n",
       "0  0.026207  0.025889      2\n",
       "1  0.020199  0.001351      5\n",
       "2 -0.027577  0.098385      2\n",
       "3  0.072817 -0.064120      5\n",
       "4 -0.025086 -0.005538      2"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.manifold import LocallyLinearEmbedding\n",
    "lle = LocallyLinearEmbedding(n_components=2, n_neighbors=5)\n",
    "lle_results = lle.fit_transform(X)\n",
    "\n",
    "df_lle = pd.DataFrame(lle_results)\n",
    "df_lle.columns = ['D1', 'D2']\n",
    "df_lle['label'] = km.labels_\n",
    "df_lle.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cdy/hs38D45JhSiXH33YcE7fsTZ7d/gz7Djs6t+MC4sk4FscLX7SMLBuXYGbu9ACysk4xf2Ich/KrWfzhPh67wszv12Zw89RErhwXy2VR9/H9tCDCA9w3aun8ERE8NtfMC+uP0Kg1d1+UxKzUIW2O0VrzzZFSHnt/N3mVFkYNCeTZGyYwITHUbXUI4Qlaa+655x5SU1N5+OGH3XpuCYz2lAKbBd65wdEJ3Gh17Bsg2vcZeCenoK02kn77IJwqhcTpcOjfWEoC8R82E691P3McePGj+I523GJ1Z/5xlFKkRTr6BKZHfYdH38pq8z4H8+p4OPhSDtqK2HGinIlDQ7l6fCyVpxp4akEa27PLueNvW9AapiWH8+gcM6H+3gyPDMDQwb00zkV4gA8/uGQE10yIo7FREx/mf9o5j5XUcu/ft1FnddxvPLOwhh++vYMPf3gB0cG+3Xp/IVwVYAw4rQ+ju7755huWL1/OuHHjmDhxIgBPP/008+bN6/a5JTDaixkH177smFxnq3cMaU26yNNV9RjvoYmOPIyd6Nhh8oUxCwhstMHmQ3DiS+eB/sQ8/n2e2vEnQrNz2blvJjATgLLgGBrsbe+wd8OUBH7z7yMcLXb8MOTtLSCjoJrHrjCTX2nhgx0tl7k2Z5Xx6d58Nh0tYdH5SVw/OQHfVmtC5ZSd4nhpLYE+RkZGBxLo2/mkPqUUCWFnvrtfdtmp5rBocrKijryKOuxas+NEBVnFNYyJC2bS0FC3toCEaOLODu4mF110UZubnrmTBEZ7+Xvhw/vB6ONoYXz+C4geM2AuSbWnAf8F36Vo/SYad3xLzOLFjj6WfR/A1886RmvZ6h0TCKNTyavJIzSolJ3ZvtgaHd+UfiYDM0ZG8vXhkubzxof6saK4bQfe0eJalFLsOVl5Wh3/OVzCzFFRLPlwH+PiQxifEArArpxy7vzbVipOOUaJ3XxeIo9cYSaiG7/Atdb4mAz8eFYKBi8vKk81sOzb43gbvQjwMfLzj/ax7mDLcOEfzBzB/1w+Em+jrIIrBjcJjPYUYPKHG5Y5fnGuuGVAXZJqP4Gu9h9fUrPuc0eH96xWTdaEqTB7KaRdD7qR2hOHsQWOJz5oJ0dPfcsvr7uV59eWUFxdT0lNA0sXpJEY7s/Xh4sZGR3E2LiOR2Wc0idJjQkD2nakTx4WxoUpkRRW15NbXsf4hFBqLFZ+s+pgc1gAvLslhyvTYpkxquuDATYfK+P21zdjtTsCLzkygHsuSiYlKoDyUw1twgLgla+zuG5SfI/cMVCI/kQCo72YcXDX6paZ1a23B4DFo++g9MN1NGTn0JCViWXffnzMjhDJLAohrdxCUJivY2TY+Q+Al4Haqno276mhPP84D9x4GyHhJgiI5vKYbCrxJzgwkC3HyokO8mHGyCgO5FWxdn8h8yfG8fGuvOb3njsuhAPVG7h6+D1MGRrG9uxyABLC/Bga7s/D/9zNI3NGER3kaD1U1lnZnVvB6OhAwnxMbMxxHJ9fWdflf3+txcYf1mY0hwVAVkkt90cHsGBiPJuPlZ32GnujxmKzn7ZfiMFGAqMjrQNiAIUFAFX5RBj+DTPP53iGDR+zmezvPElJbg2lX+aRPDHaERjQEhYfZ3HwG8cw1c/ftnD5nHpCAncQ8/5dlC1Yy93vV3GwoJoQPxP3XpyMxWbHx+SFtbGR/7l8JEXV9YxN8GV39T/JqMhg8flh/HHheHbnVJJVUktFnZU/f56J1a75KrOEI0U1pMWHEB7gw81TErjINwBrrQ1voxdfHSslMfzMfROdOWW1c6Ls1Gn7qyxWfEwGkqMCiAr0obimZeLflGFhDO3GewoxUEhgDDaJ6ZB+F2x8Ed/RFwJQkltD/pFKrn5wPK8XvkjGwZZlyO+N+1FzWAAUZDeQXRhG2sb7qRp/N4u/quZgQTXgaBH8cV0GL9w8iQffcQzVNXgpQv1MZJX4MmGcHyEBjtZMUmQgf9+YzRvfHGtT3tHiGgwKSmrqiQ30ZWFMJF8tPwQaFs6KZ8GkeNLiQrr8z48M9ObG9ARe3HC0zf7x8aEAJIT58+bdU3lxwxG2nyhnzpgY7rwgiRA/maMhhATGYFOwF1Y9BFYLMUO9scZOZ3hjCGMujCN+dBgZ69oOuw2b4s+lt41mw/JDAEy8dAgplf+HKs2kaNpSdn3bdla11lBc3fLXub1RU1rbwJZjVp694f42I5emJ4efFhiXjIwk0MdIkI+R/KMVzWEBcHz9SWbdkUqQb9e/bZVS3HTeUGrq7byzOZsQfxNPXpXKhMSWEBoTF8KfbpxIjcVGiJ9JZocL4SSBMdhYLY7PN70NJj9MK25hzHevxBA/HGMHo4CMRgMjz3NMeivPq2Sy/QX8Cr+ByXcQUriZ6KALKWoVEABDOpjHMD4+BEO7wQPmmCBuO38Y/9iSg7WxkctGR6O8FGPiQwjx98Yr2p+kcREc31MKQGRiEDEjQrq9gGBCmD8/uyqV7108HG+jF9FBp9frYzTgEyijokT/Y7FYmDFjBvX19dhsNm644QZ+9atfueXc8qfTYJM4Fb63HpIvgcTz4Hvr8UmadMZ7YZcuW4bJ5AiNKbNi8UscDbe8B7N/RXTUEJ6Zb8bYalLctZPiiQ/1ZdG0lhV/w/xNXDMxjpW789qc296o2XS0lO9dPJwfXZbCqXo7r/3nGPXO+RFB4b7MuMlM0vgIIhODuOLesYRGu6cvwWjwIiHMv8OwEKI/8/Hx4YsvvmD37t3s2rWLNWvWsGmTe+Z7SAtjMAob1rJKbbv7X4yJGMOosFEAGL2MNGzNAcBkMmAKD3XcO8TonDiXfg+XeBl5/Q4ftp4ox9vgxY4T5Vz//zby5t1TiQ72xWZvxGJt5I+fZWI0KBZMim9ugUQH+xAR4M1fvmzpT1AKhke2zHZtCg27rdFtYeEO9VY7+/IqySquxd/bQFp8CMMiuj9LVww+GVPSaaytxSsgAPP2bd0+n1KKwMBAwLGmlNVqdduy/hIYg1RHy5cDPH7e481hgsFO5cqV1Gdk4Dva7AgXY6tZ1kYTlnobf/gsg70n2/ZlbDxaxjtbsgEoq3XcLXBIsE+by1KBPiZ+NX8sP/1gLzuyKwj1N/Gb+WmYY9rOdwgK73utgG+PlvLQP3ZRWeeYIzI2Npjnb55ISrTM1RDnprG2ts1nd7Db7UyZMoUjR47wwAMPDIzlzZVSc4HnAQPwmtb6mXbPK+fz84BTwJ1aa/fePGGQKVi6tM1S5U3Ll2MwOGZ50xImvmPH4ps2Fsdsxo4pBQav069s+hi9SAr345m5Q7l1RSZF1fU8OsdMZFDbGdrmmGDevOs88istBPgaiA/tO62IM8kpq+XvG080hwXA/vwqNmWVSWAIlzW1LDAYwG4Hg4GDo1Pd0tIwGAzs2rWLiooKrr32Wvbt20daWvfvB+OxwFBKGYCXgNlALrBVKbVSa32g1WFXAiOdH9OA/+f8LLpJeXvjmzYWn+HDqVqzBh+z+bQwAVpaFkCDrZGs4hoKqizEhvhhb2xkQ0YxN6YnsCunovk1PkYvLhkVxdVDvVj7+yU8d+8T2IKiSB8W1mEtQX4mgvw6Xx+qr6itt5FVUnPa/mMl7vsLUQx8zS0Ku73NZ3e2NEJDQ5k5cyZr1qzp34EBnAcc0VpnASilVgDzgdaBMR/4u3aspLVJKRWqlIrVWueffjrhiqZf/k0ti6o1a7Ds3YsyGfEZMeKMr7PZG/l410me+GAv9kaN0Uvxk1kj+ce2HCIDffjplaPZe7KSyAAfrh4XTUDJYf67Yjm1FeVkvfcyF3z3FqgzgzI5bj3bj8WG+DJjZBTLS0+02T/lDIEoREe8AgJOa2Fgt+MV0L2+sOLiYkwmE6GhodTV1fH555/z+OOPu6dmt5yla+KBnFaPc537zvUY0QXew5PaPLbs24/WEH7rInzHjcN33Dgsz75AxE+XAI7lMxZ/6AgLAFuj5sUNR5g/MZ5dORU8s+YQJi/F41eaGRPhDY26ecVMpRsxemkMDRXwyf9Cccf9J2fTYLGRd7gCS23P3Kr2XIT4+3DDlARmpUajlKNF9ZNZIzk/OdzTpYl+xLx9G6mHDrZpYaQeOtjty1H5+flceumljB8/nqlTpzJ79myuvvpqN1Ts2RZGRxfG26/J68oxjgOVug+4D2Do0KEdHSJaibjtNkqXLydw9mxKXngRgGMLv8fqQyU8MNoxG/uxfx/k5gv8uHbcJEpq6tusvwRQb2tsXpdRa8d1fFujJigkhBFTziMgLJwPn/klV82bSPg3D4JPMOTvhFFzIcr1O+s1WGwc+G8e37x/hClXDmPi5UPxDfDsJawJiaE8e/14TpSdws9kYNSQILy6eR8PMTg1tTS627JoMn78eHbu3Nn5gV3gycDIBVrf+zQByOvCMQBorV8BXgFIT0/vmcXgBxhjRAQK8B07BlNcHFVWyC6r5eGY2VjtjezJqeTiMkcndUywL34mQ5t7SAT5GLHZG5sff/+SEQQ571VRW1HOkOEjuPnXzxJatg2+cg6dnb0UzK7fyKV1WABsX+24DNQXQiM80IfwQLlPhugedwyl7S2evCS1FRiplBqulPIGbgJWtjtmJXC7cpgOVEr/hfvUbd9Oyct/BcCak8PEj17jGbPCx+jF1uPl1Nsa8fV2ZO/wyABeuGUSwX6OvzFC/U38/rvjKai0MDExhOdvmsis0Y4Z4VXFRax/7S9YqqsI9bXCxhda3nT7MqjMdrlGa72d4pzqNvtKcmqw1svqsUL0No+1MLTWNqXUg8BaHMNq39Ba71dKfd/5/MvApziG1B7BMaz2Lk/VO9BUfvopGAx4X3ABlllXws+fAOD98xYyf2IADfZGSmvqGRYayeHCalKiA7k8dQif/OhiSmrqiQz0ITHcn8tHD8GudfMd8srz89j12Scc3rqRmJGjGJ2WQnDhfvjO/4F/BPzzdsqrbQQEWvD27Xx+RUCID+dfmwJA5uZChqVFcMkt5j45N0OIgU711K38PCk9PV1v29Z/mnmeUPr22ygvAw1HDrcZSus7bhy+o81kLLyfLzOKWbbxON4GL968+zymJ0d0eK76U7X4+AdQW1HO3g2f8e0/3kbrRgwmE5ff80NSUpPxjYgDgzeWwixWvvo3LvjuLSSkuj7Mr6ainqM7ikieGCVhIfqsgwcPkpqa6ukyOtVRnUqp7Vrr9LO9TtaSGmQKli6lYOlStNVKTVTMGY/blVvB+kNFaO3o3P7Fyv2Un2o47bia8jIOb9lIY6OdgNAwpn7nOq594hcoLy9u/MUzjL1kFr4xI9BGX0pyc/h27QZy9u/h0xef4+iOLdRVV3Xw7qcLDPVh/MwECQshPEiWBhlkmmZxZ9x2Hl72AL6uWcCo2UFc/MLPAHhj4RMczK9igsVGlaVlCOvhwmpqLDbC/FvuC2GpreHwlm/58s3XCI9LYEhyCgajiThzKgsefZLo4SNQzlngdpsNa309FQWOMQsNp2o5VVGBreH0EDoTJaOQhPAoaWEMEgVLl3J80a1Y9u7Fsncv5iXLGffOGrxNjYT7e+M72ozvaDMH86vIKq4l2NfU5l7al6cOIbLViKDainI2LHuFL954mUa7jRW/eIwj2zbRYLHg4+dP4tjxGIwtf48YTSZiR5qZ+4OHiBqWzFU/eYxxl80hKCKyV78OQgwGdrudSZMmuW3+RRNpYQxyacPL+fqglfqr7kJrzQJbI2jHTY8CfYzU1NuYmhTOo1eY8fNuWQI9IDSMCxfein9wCNtWfcjVDz3B0LTxzR3ZJp+Oh5v6h4Ry5YMPS1AI0YOef/55UlNTqapy7ZKvqyQwBommJUGOL7oVgIynbmNbwTaO5bzFBSNnMC1xDP7eRspq61m1J5/Mwmr+tHACkUE+pEQHNs+vaC04KpoJc+bhZTSSPGkKRm/X5iREDU1y279LiP4uN6Ocz17dx5x700gwd395mdzcXD755BOWLFnCc88954YKW0hgDHDt73vh65zFva1gG5nlmYwKG4XRAL/Y8mOSQ5NZPG0xP7nc9bWeQofEMnnefJfDQgjRIjejnE9e2o2toZFPXtrNVQ9M6HZoPPTQQ/z+97+nurq684PPkfRhDHCWQxlt7n0R8+STrL8hmWOVjntpK6VYn72ezQWbySg79zWeAAJCQl0+tv6UrOgqBLQNC6A5NHIzyrt8zlWrVhEdHc2UKVPcVWYb0sIYoNovVX580a34jjZjSkxk0Z13ctk/swD4/IZEsiqy8PbyPtvp3KKuqpIj27eQetFMjKb+s5y5ED3hs1f3NYdFE1tDI5+9uo+7/3Bxl875zTffsHLlSj799FMsFgtVVVXceuutvPXWW+4oWVoYg01Ddg6ly96kIesoVStXcvFTa/nNlmFcN/I6zOGuLwh4rurrTpG1cxvr/voC+YcPYbN6ftVZITxpzr1pGL3b/go2ensx596u37fit7/9Lbm5uRw/fpwVK1Zw2WWXuS0sQFoYA1b7Tu6g2bOp+XIDln378TGbm/sytNWGV1AQS6Y/1GO1nKqqZNO/VrBzzb8BeO/Xi5lz/48ZNe0CfPzlPthicEowh3HVAxOaL0sZvb3c0ofRk6SFMcA1za9oyM6mbsdOdKuJcgEzL8X+p5d5N20eN7+ykbc2nSCvos7tNfgHh5B+zfWcf8MtAFz5w/9hRPo0CQsx6DWFhl+gye1hMXPmTFatWuW284G0MAa8ppZG3YED1Gc4OrWjfvJjtN3OF+EN/PHNzZwotQCwMauMG6Yk8JsFY/E1ufdbIzgikrTL5mA5VUPK1PPx9vNz6/mF6K8SzGFd7rPobRIYg0DB0qVo3TKktmrNWpSCyNtuoaAys82x/9qRy70XD8ccE+z2OoIjIpm+4EYJCyH6KQmMQaIhK4uWmxUqfEYkY/7yP4xPGMXW4+2H8fXcmk3+ISE9dm4hRM+SPoxBwJSYSNiNC9FWG9pqI/rRR9Aaar78iotHRjA1KQwfo+NbYeGUBJIi/D1csRCiL5IWxiBQve7zNiOjKv71AZXvvw/ANdalLBxt5o3zr2d4VCAzR0XhYzKc7XRCiEFKAmMAaz15rz4jg4gHH0DhaFm0t/iqMQDYGzV7civILKwm0MdIWnwICWEtLY6nNz/dPCPcHG5m8bTFXaqturQEu81K6JDYLr1eCNH7JDAGCd3QQO2XXxE48xKGLftb8/yMpLfbTurZeLSEO/62FXujo79jdEwgr96eTmK4YwhsRlkGO4p2dLueE3t3YampZspVC1BK7nMhhDslJSURFBSEwWDAaDTirjuQSmAMYO0n7w1b9rfm55ouT7VWWWfl6dWHmsMC4FBBDXtyK5sD41xYTlnJy6xgyPBgAkIcixMWZxdQmHWQDcv+it1mw8ffn2HjJxMcGeXiOWupq6oiLEZaJkKczYYNG4iMdO9tBKTTexBomrzXWsyTTzYHShOL1d7hxL2y2pbJfuZwM5OjJzM5evJZlxKxnLKy54scVr+8l40fHKW2sp6yglryMgvJPXgIq6Ueu9VK/pFM6mtrXP63FB49zPZVH2BrqHf5NUL0ZdWlJbz2o3uoLi3xdCmdkhbGINA6GOoa7JTW1hPoYyTUv+2Cg1GBPtyYnshfv85qs39MXMucDFf6LCy1VvZsyGHrquMAHN5ayNC0cDZ/lEVVqYXoYZOZODeARvspLrntLkwuLI1uqa2h4EgG6159iaqSYsJi40lJn07IkDPfl1yI/uCr5a9TVVzEV2+9wdU/ecwt51RKMWfOHJRS3H///dx3331uOa8ExiByuLCaZ9dmsP5QEaOGBPLL74xlWnJE8/NeXopbzx9Gva2Rd7dkExnow8+vTiUt7tznTtitLZe1jD4GGm2amgpHq6DoRC1DU8cSOzLIpbAAsNTUkJd5iJqyMtCao9u3EjcqlaDIKLwMMqpL9D/VpSV8tfx1jmzbhNaaI1s3surPv+OS2+7p9h0pv/nmG+Li4igqKmL27NmMHj2aGTNmdLtmpbXu/Kh+Jj09Xburk2egqK6zcteyrWw70TJJz9fkxaofXURKdNsbJtnsjRRWWfA1GYgI7NqNkSy1VnZ+ls2OtScYmR7NBTekUHqylk//sgc0XPXD8SSkhuFlcP2qaGOjnYP/+ZI9n69h/qM/wz9YJgGKvuXgwYOkpqa6dOxrP7qHquIiWv8OVkoRHBXN91543W01/fKXvyQwMJBHHnnkrHUqpbZrrdPPdi5pYQwSJyvq2oQFgMXayLGS2tMCw2jwIj6se5P3fANMTJozlKjEQGJSQggM9SUg2Id5PxyPbtQkjglHeZ3b6CgvLwPDxk0kImGohIXo92785e/46q03OLJ1I3arFYPJRMrU87nk1ru7dd7a2loaGxsJCgqitraWzz77jJ///OduqVkCY5Dw8zYQ4G2gtsHeZn9H9+p2F98AEynpQ5ofKy/F0NTw5u2uCAyPICC07y7/LISrgiIiufonj7Hqz78jc9N/SZl6vlv6MAoLC7n22msBsNls3HLLLcydO7fb5wUJjEFjaLg/S65KZfGH+5r3XTUuBvMQ1+/f7Q5dDYq255DBfWLguOS2eyg4mtntlkWT5ORkdu/e7ZZztSeBMUgopVgwKZ6R0UEcL60lMsiHtLhgwgJ6/tasQogzC4qIdGufRU+SwBhE/L2NTB0eztTh4Z4uRQjRD0lgDEDuWu9JCHHutNZ9ermb7oyMlcAYgNy13pMQ4tz4+vpSWlpKREREnwwNrTWlpaX4+vp26fUSGEII4SYJCQnk5uZSXFzs6VLOyNfXl4SEhC69VgJjEFh+YDm3jbnN02UIMeCZTCaGDx/u6TJ6jATGANR6UUBzuJnsqmwPViOEGCg8MqBdKRWulFqnlDrs/NzhTCyl1BtKqSKl1L6Onhcda92plVGWwbHKYx6sRggxUHhqBtQTwHqt9UhgvfNxR5YB7pmiOIi072xLDk32UCVCiIHEU5ek5gMzndtvAl8Cj7c/SGv9tVIqqdeqGiBkGK0Qoid4qoUxRGudD+D8HN3dEyql7lNKbVNKbevLIxSEEKK/cqmFoZQyaa2t7fZFaq3PeIsopdTnQEd3t1lybiW6Rmv9CvAKOJY374n3EEKIweysgaGUuhRYDvgopXYC92mtjzuf/gyYfKbXaq0vP8t5C5VSsVrrfKVULFB0zpULIYToVZ1dkvo9cIXWOgrHX+/rlFLTnc91ZxrjSuAO5/YdwMfdOJcQQohe0FlgeGut9wNord8HFgBvKqWuBbpz2ecZYLZS6jAw2/kYpVScUurTpoOUUu8CGwGzUipXKXVPN95TCCFEN3TWh2FVSsVorQsAtNb7lVKzgFXAiK6+qda6FJjVwf48YF6rxzd39T2EaK/BYsG7i2voCCE6b2E8AQxpvUNrnYtjSOwzPVSTEG5XmptNwZFMT5chRL921sDQWn+utd4NoJSKUkpFOfdXaK2f6o0CxcBSX3eKBktdr75nXXUV+778nA3L/kpViYyvEKKrOp2HoZT6hVKqBDgEZCqlipVS7rmjuBh0irKOUJh1pNfer6qkmI//8BTb/v0BJTkneOdnj1B07Ci6sbHXahBioDhrYCil/ge4CJiqtY7QWocB04ALnc8J4bLy/JN89fbf+Pqtv1GWf7JX3jMwLJxL77yPmJRRmHz9uPKBhwkZEiv3BReiC9TZ7r7knHsxu/0EPeelqc+01pN6uL4uSU9P19u2bfN0GaKVyqJC3vnZ/3KqsgKAgLBwbl76B0Kiuj3J3yW5B/dTnH2cSVdc1SvvJ0R/o5TarrVOP9sxnf2ZZepoNrfWuhgwdac4MbiERA9h4S9+S9Sw4UQPH8F3n3yq18ICYEjyCFLSp/Xa+wkxEHU2rLahi88JcZqI+ERm3v49QBERn9ir723y8cXkI0NqheiOzgJjglKqqoP9CpCfPnHOhiSnQB+817EQonNnDQyttaG3ChGDg49/gKdLEEJ0kQwVEUII4RIJDCGEEC7x1B33RBc8vflpMsoyADCHm+XOekKIXiWB0Y9klGWwo2iHp8sQQgxScklKCCGES6SF0Y+Yw80dbgshRG+QwOhHpM9CCOFJcklKCCGESyQwhBBCuEQCQwghhEskMIQQQrhEAkMIIYRLJDCEEEK4RAJDCCGESyQwhBBCuEQCQwghhEskMPqwxkbt6RKEEKKZLA3SB50sr2P9oUL+vTuP84aHs2BiPCOHBHm6LCHEICeB0cfUW+08vz6T97blArD1eDkf7zzJe9+/gLhQPw9XJ4QYzOSSVB+TXXaKf27PbbMvt8JCZmG1hyoSQggHCYy+RoHqYLdXRzuFEKIXySWpPmZouD+Lpg1l+abs5n3DI/0Y1UN9GHLbVyGEqyQw+hgfo4EHL0shMaqRbzNPMSrWSNowOzEhPdN/Ibd9FUK4yiOBoZQKB/4BJAHHgYVa6/J2xyQCfwdigEbgFa31871bqWcMCfbjvgsncN+Fnq5ECCFaeKqF8QSwXmv9jFLqCefjx9sdYwP+V2u9QykVBGxXSq3TWh/o7WIHMrntqxDCVZ4KjPnATOf2m8CXtAsMrXU+kO/crlZKHQTiAQkMN5I+CyGEqzw1SmqIMxCagiH6bAcrpZKAScDmni9NCCFER3qshaGU+hxH/0N7S87xPIHAv4CHtNZVZznuPuA+gKFDh57LWwghhHBBjwWG1vryMz2nlCpUSsVqrfOVUrFA0RmOM+EIi7e11h908n6vAK8ApKenyyJMQgjhZp66JLUSuMO5fQfwcfsDlFIKeB04qLV+rhdrE0II0QFPBcYzwGyl1GFgtvMxSqk4pdSnzmMuBG4DLlNK7XJ+zPNMuUIIITwySkprXQrM6mB/HjDPuf1fOl4lQwghhAfITG8PkmU5hBD9iQSGB8myHEKI/kRWqxVCCOESaWF4kCzLIYToTyQwPEj6LMSAZKmCU6UQPrzttuj35JKUEMJ9LFWw+RV492YoOQKbX3ZuH/Z0ZcINpIUhhHAfkx9EjYSSDHj5ArDVw5gF4B3g6cqEG0gLQwjhPgYTDJ8JyZc6wgJg5k8hOM6TVQk3kcAQQriPpQq2vApH10OkGZQXvHeHXJIaIOSSlBDCfZouSY29DuY+DTlbYP9HcklqgJDAEEK4j8EE5nkw9HwIjG67Lfo9uSQlhHAvg6klIFpvi35PAkMIIYRLJDCEEEK4RAJDCCGESyQwhBBCuEQCQwghhEskMIQQQrhEAkMIIYRLJDCEEEK4RAJDCCGESyQwhBBCuEQCQwghhEskMIQQQrhEAkMIIYRLJDCEEEK4RAJDCCGESyQwhBBCuEQCQwghhEskMIQQQrhEAkMIIYRLJDCEEEK4RAJDCCGESzwSGEqpcKXUOqXUYefnsA6O8VVKbVFK7VZK7VdK/coTtQohhHDwVAvjCWC91noksN75uL164DKt9QRgIjBXKTW990oUQgjRmqcCYz7wpnP7TWBB+wO0Q43zocn5oXulOiGEEKfxVGAM0VrnAzg/R3d0kFLKoJTaBRQB67TWm890QqXUfUqpbUqpbcXFxT1RsxBCDGrGnjqxUupzIKaDp5a4eg6ttR2YqJQKBT5USqVprfed4dhXgFcA0tPTpSUihBBu1mOBobW+/EzPKaUKlVKxWut8pVQsjhbE2c5VoZT6EpgLdBgYQgghepanLkmtBO5wbt8BfNz+AKVUlLNlgVLKD7gcONRbBQohhGjLU4HxDDBbKXUYmO18jFIqTin1qfOYWGCDUmoPsBVHH8Yqj1QrhBCi5y5JnY3WuhSY1cH+PGCec3sPMKmXSxNCCHEGHgmMgeTpzU+TUZYBgDnczOJpiz1ckRBC9AwJjG7KKMtgR9EOT5chhBA9TtaSEkII4RJpYXSTOdzc4bYQQgw0EhjdJH0WQojBQi5JCSGEcIkEhhBCCJdIYAghhHCJBIYQQgiXSGAIIYRwiQSGEEIIl0hgCCFEf6d1x9tuJoEhhBD9WV0F7FgGZVmtto/1yFtJYAghRH9WdhT+/RC8fw989XvH9p73wGZ1+1tJYAghRH8WnQbXvAh5O2DTSzD6Gph8OxhNbn8rCQwhhOjPbHVQcaLlcWWOY18PkMAQQoj+rOwofP0spH0Xrn4e8nfCnn/2yCUpWXxQCCH6s+g0uGMVRKSAXzhEjHBs98AlKQkMIYToz0w+MPzilsett91MLkkJIYRwiQSGEEIIl0hgCCGEcIkEhhBCCJdIYAghhHCJBIYQQgiXKN2DKxt6ilKqGDjR6YFtRQIlPVCOO0htXdeX6+vLtUHfrq8v1wZ9u74z1TZMax11thcOyMDoCqXUNq11uqfr6IjU1nV9ub6+XBv07fr6cm3Qt+vrTm1ySUoIIYRLJDCEEEK4RAKjxSueLuAspLau68v19eXaoG/X15drg75dX5drkz4MIYQQLpEWhhBCCJdIYAghhHDJoA0MpVS4UmqdUuqw83NYB8ckKqU2KKUOKqX2K6V+0ldqcx73hlKqSCm1rxdqmquUylBKHVFKPdHB80op9X/O5/copSb3dE3nWN9opdRGpVS9UuqRPlbbIufXbI9S6lul1IQ+VNt8Z127lFLblFIX9VZtrtTX6ripSim7UuqGvlKbUmqmUqrS+bXbpZT6eW/V5kp9rWrc5fz99lWnJ9VaD8oP4PfAE87tJ4DfdXBMLDDZuR0EZAJj+kJtzudmAJOBfT1cjwE4CiQD3sDu9l8HYB6wGlDAdGBzL/5fulJfNDAVeAp4pI/VdgEQ5ty+sre+di7WFkhLX+d44FBf+tq1Ou4L4FPghr5SGzATWNVbX68u1BcKHACGOh9Hd3beQdvCAOYDbzq33wQWtD9Aa52vtd7h3K4GDgLxfaE2Z01fA2W9UM95wBGtdZbWugFY4ayxtfnA37XDJiBUKRXbC7W5VJ/WukhrvRVw/30ru1/bt1rrcufDTUBCH6qtRjt/mwABQG+OknHl+w7gR8C/gKI+WJunuFLfLcAHWutscPyMdHbSwRwYQ7TW+eAIBhx/gZ6RUioJmARs7vnSzq22XhAP5LR6nMvpwenKMT3Fk+/dmXOt7R4cLbXe4FJtSqlrlVKHgE+Au3upNnChPqVUPHAt8HIv1gWu/7+er5TarZRarZQa2zulAa7VNwoIU0p9qZTarpS6vbOTDuhbtCqlPgdiOnhqyTmeJxDHXzAPaa2r+lJtvUR1sK/9X5quHNNTPPnenXG5NqXUpTgCo7f6CVyqTWv9IfChUmoGsBS4vKcLc3Klvj8Dj2ut7Up1dHiPcaW2HTjWZ6pRSs0DPgJG9nRhTq7UZwSmALMAP2CjUmqT1jrzTCcd0IGhtT7jN7ZSqlApFau1zndeOumwOaaUMuEIi7e11h/0pdp6US6Q2OpxApDXhWN6iiffuzMu1aaUGg+8BlyptS7tS7U10Vp/rZQaoZSK1Fr3xsJ6rtSXDqxwhkUkME8pZdNaf+Tp2lr/cam1/lQp9Zc+9rXLBUq01rVArVLqa2ACjr7aDg3mS1IrgTuc23cAH7c/QDm+C18HDmqtn+tLtfWyrcBIpdRwpZQ3cBOOGltbCdzuHC01HahsuqzWR+rzlE5rU0oNBT4AbjvbX3ceqi3F+XOAc+SbN9BbgdZpfVrr4VrrJK11EvA+8MNeCAuXalNKxbT62p2H4/dtn/na4fi9crFSyqiU8gem4einPTNP9OD3hQ8gAlgPHHZ+DnfujwM+dW5fhKMZtwfY5fyY1xdqcz5+F8jH0ZGbC9zTgzXNw/GXx1FgiXPf94HvO7cV8JLz+b1Aei//f3ZWX4zza1QFVDi3g/tIba8B5a2+x7b1oa/b48B+Z10bgYv60v9ru2OX0UujpFz82j3o/NrtxjGY4YK+9rUDHsUxUmofjkvuZz2nLA0ihBDCJYP5kpQQQohzIIEhhBDCJRIYQgghXCKBIYQQwiUSGEIIIVwigSFED3CunNq0CuhupdTDSikv53MRyrEKco1S6kVP1yqEqwb0TG8hPKhOaz0RQCkVDbwDhAC/ACzAk0Ca80OIfkFaGEL0MO1YBfQ+4EGllNJa12qt/4sjOIToNyQwhOgFWussHD9vnl55WIguk8AQovf06nKqQribBIYQvUAplQzY8fzKw0J0mQSGED1MKRWF4wY/L2pZvE30Y7L4oBA9QCllx7FqrwmwAcuB57TWjc7njwPBOJYLrwDmaK0PeKRYIVwkgSGEEMIlcklKCCGESyQwhBBCuEQCQwghhEskMIQQQrhEAkMIIYRLJDCEEEK4RAJDCCGES/4/SxSmvW5TRPQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data = df_lle, x = \"D1\", y = \"D2\", hue = \"label\", style = \"label\",\n",
    "                palette = \"tab10\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "that did not work well.\n",
    "\n",
    "**t-sne seems to be a good solution for visualisation**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average song feature per cluster (what are the clusters representing?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.760</td>\n",
       "      <td>0.910</td>\n",
       "      <td>0.0484</td>\n",
       "      <td>0.044200</td>\n",
       "      <td>0.006620</td>\n",
       "      <td>0.1080</td>\n",
       "      <td>0.3920</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.876</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.1290</td>\n",
       "      <td>0.494000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.4840</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.878</td>\n",
       "      <td>0.515</td>\n",
       "      <td>0.1880</td>\n",
       "      <td>0.029200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0835</td>\n",
       "      <td>0.2690</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.656</td>\n",
       "      <td>0.448</td>\n",
       "      <td>0.0442</td>\n",
       "      <td>0.860000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.1030</td>\n",
       "      <td>0.4030</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.542</td>\n",
       "      <td>0.453</td>\n",
       "      <td>0.2400</td>\n",
       "      <td>0.204000</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.1370</td>\n",
       "      <td>0.0375</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>0.656</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.3630</td>\n",
       "      <td>0.173000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.8370</td>\n",
       "      <td>0.3140</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>0.629</td>\n",
       "      <td>0.869</td>\n",
       "      <td>0.3140</td>\n",
       "      <td>0.006570</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1910</td>\n",
       "      <td>0.7150</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>0.533</td>\n",
       "      <td>0.783</td>\n",
       "      <td>0.3250</td>\n",
       "      <td>0.181000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.4710</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>0.557</td>\n",
       "      <td>0.977</td>\n",
       "      <td>0.0493</td>\n",
       "      <td>0.000393</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.2910</td>\n",
       "      <td>0.5040</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>0.455</td>\n",
       "      <td>0.993</td>\n",
       "      <td>0.2930</td>\n",
       "      <td>0.002350</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1490</td>\n",
       "      <td>0.3840</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     danceability  energy  speechiness  acousticness  instrumentalness  \\\n",
       "0           0.760   0.910       0.0484      0.044200          0.006620   \n",
       "1           0.876   0.400       0.1290      0.494000          0.000009   \n",
       "2           0.878   0.515       0.1880      0.029200          0.000000   \n",
       "3           0.656   0.448       0.0442      0.860000          0.000005   \n",
       "4           0.542   0.453       0.2400      0.204000          0.000007   \n",
       "..            ...     ...          ...           ...               ...   \n",
       "145         0.656   0.804       0.3630      0.173000          0.000000   \n",
       "146         0.629   0.869       0.3140      0.006570          0.000000   \n",
       "147         0.533   0.783       0.3250      0.181000          0.000000   \n",
       "148         0.557   0.977       0.0493      0.000393          0.000004   \n",
       "149         0.455   0.993       0.2930      0.002350          0.000000   \n",
       "\n",
       "     liveness  valence  cluster  \n",
       "0      0.1080   0.3920        0  \n",
       "1      0.1000   0.4840        1  \n",
       "2      0.0835   0.2690        0  \n",
       "3      0.1030   0.4030        1  \n",
       "4      0.1370   0.0375        1  \n",
       "..        ...      ...      ...  \n",
       "145    0.8370   0.3140        0  \n",
       "146    0.1910   0.7150        0  \n",
       "147    0.1010   0.4710        0  \n",
       "148    0.2910   0.5040        0  \n",
       "149    0.1490   0.3840        0  \n",
       "\n",
       "[150 rows x 8 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['key' 'loudness'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-56-963d53fc3eb0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mdf_cluster\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"cluster\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX_pred\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdf_cluster\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"key\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"loudness\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0minplace\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\envs\\spotify_app\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   4161\u001b[0m                 \u001b[0mweight\u001b[0m  \u001b[1;36m1.0\u001b[0m     \u001b[1;36m0.8\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4162\u001b[0m         \"\"\"\n\u001b[1;32m-> 4163\u001b[1;33m         return super().drop(\n\u001b[0m\u001b[0;32m   4164\u001b[0m             \u001b[0mlabels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4165\u001b[0m             \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\spotify_app\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   3885\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3886\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3887\u001b[1;33m                 \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3888\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3889\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\spotify_app\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[1;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[0;32m   3919\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3920\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3921\u001b[1;33m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3922\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3923\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\spotify_app\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   5280\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5281\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m\"ignore\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5282\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{labels[mask]} not found in axis\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5283\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5284\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['key' 'loudness'] not found in axis\""
     ]
    }
   ],
   "source": [
    "df_cluster[\"cluster\"] = X_pred\n",
    "df_cluster.drop([\"key\",\"loudness\"],axis = 1,inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Radar Plot to better understand and visualize the features of each cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cluster</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.477108</td>\n",
       "      <td>0.913459</td>\n",
       "      <td>0.151605</td>\n",
       "      <td>0.033007</td>\n",
       "      <td>0.062214</td>\n",
       "      <td>0.178316</td>\n",
       "      <td>0.414595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.642133</td>\n",
       "      <td>0.600667</td>\n",
       "      <td>0.084240</td>\n",
       "      <td>0.146256</td>\n",
       "      <td>0.786667</td>\n",
       "      <td>0.220920</td>\n",
       "      <td>0.254150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.708024</td>\n",
       "      <td>0.645476</td>\n",
       "      <td>0.154948</td>\n",
       "      <td>0.115980</td>\n",
       "      <td>0.000730</td>\n",
       "      <td>0.160081</td>\n",
       "      <td>0.299786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.729594</td>\n",
       "      <td>0.706594</td>\n",
       "      <td>0.338272</td>\n",
       "      <td>0.135199</td>\n",
       "      <td>0.002868</td>\n",
       "      <td>0.177453</td>\n",
       "      <td>0.484625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.638714</td>\n",
       "      <td>0.795857</td>\n",
       "      <td>0.161721</td>\n",
       "      <td>0.107236</td>\n",
       "      <td>0.056171</td>\n",
       "      <td>0.675357</td>\n",
       "      <td>0.420286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.637800</td>\n",
       "      <td>0.449900</td>\n",
       "      <td>0.085180</td>\n",
       "      <td>0.659200</td>\n",
       "      <td>0.070084</td>\n",
       "      <td>0.150220</td>\n",
       "      <td>0.426390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cluster  danceability    energy  speechiness  acousticness  \\\n",
       "0        0      0.477108  0.913459     0.151605      0.033007   \n",
       "1        1      0.642133  0.600667     0.084240      0.146256   \n",
       "2        2      0.708024  0.645476     0.154948      0.115980   \n",
       "3        3      0.729594  0.706594     0.338272      0.135199   \n",
       "4        4      0.638714  0.795857     0.161721      0.107236   \n",
       "5        5      0.637800  0.449900     0.085180      0.659200   \n",
       "\n",
       "   instrumentalness  liveness   valence  \n",
       "0          0.062214  0.178316  0.414595  \n",
       "1          0.786667  0.220920  0.254150  \n",
       "2          0.000730  0.160081  0.299786  \n",
       "3          0.002868  0.177453  0.484625  \n",
       "4          0.056171  0.675357  0.420286  \n",
       "5          0.070084  0.150220  0.426390  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_radar = df_cluster.groupby(\"cluster\").mean().reset_index()\n",
    "df_radar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The following plot was take from the address below and changed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://python-graph-gallery.com/392-use-faceting-for-radar-chart/\n",
    "from math import pi, ceil\n",
    "\n",
    "def make_radar(row, title, color, dframe, num_clusters):\n",
    "    # number of variable\n",
    "    categories=list(dframe)[1:]\n",
    "    N = len(categories)\n",
    "    \n",
    "    # What will be the angle of each axis in the plot? (we divide the plot / number of variable)\n",
    "    angles = [n / float(N) * 2 * pi for n in range(N)]\n",
    "    angles += angles[:1]\n",
    "    # Initialise the radar plot\n",
    "    ax = plt.subplot(2,ceil(num_clusters/2),row+1, polar=True)\n",
    "    \n",
    "    # If you want the first axis to be on top:\n",
    "    ax.set_theta_offset(pi / 2)\n",
    "    ax.set_theta_direction(-1)\n",
    "\n",
    "    # Draw one axe per variable + add labels labels yet\n",
    "    plt.xticks(angles[:-1], categories, color='grey', size=12)\n",
    "\n",
    "    # Draw ylabels\n",
    "    ax.set_rlabel_position(0)\n",
    "    plt.yticks([0.2,0.4,0.6,0.8], [\"0.2\",\"0.4\",\"0.6\",\"0.8\"], color=\"grey\", size=8)\n",
    "    plt.ylim(0,1)\n",
    "\n",
    "    # Ind1\n",
    "    values=dframe.loc[row].drop('cluster').values.flatten().tolist()\n",
    "    values += values[:1]\n",
    "    ax.plot(angles, values, color=color, linewidth=2, linestyle='solid')\n",
    "    ax.fill(angles, values, color=color, alpha=0.4)\n",
    "\n",
    "    # Add a title\n",
    "    plt.title(title, size=16, color=color, y=1.06)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,10))\n",
    "my_palette = plt.cm.get_cmap(\"tab10\", len(df_radar.index))\n",
    "title_list = [\"high energy\", \"instrumental dance\",\"dance\",\"dance again\",\"live energy\",\"acoustic\"] #the naming was performed after looking at the graph the first time\n",
    "\n",
    "for row in range(0, len(df_radar.index)):\n",
    "    make_radar(row=row, title= title_list[row], \n",
    "               color=my_palette(row), dframe=df_radar, num_clusters=len(df_radar.index))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interesting, it seems there are some distinct clusters that also intuitively match the songs that were put in each cluster**"
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}