BaselineModel_and_EDA_on_Million_Playlists_and_Spotify_API.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "import spotipy\n",
    "\n",
    "from tools import *\n",
    "from spotipy.oauth2 import SpotifyClientCredentials\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.utils import resample\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from collections import Counter\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(style='whitegrid')\n",
    "pd.set_option('display.width', 1500)\n",
    "pd.set_option('display.max_columns', 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the data\n",
    "# I downloaded the data from the url given below and put it in my project directory in a folder called data\n",
    "\n",
    "url = \"https://raw.githubusercontent.com/vaslnk/Spotify-Song-Recommendation-ML/master/data/mpd.slice.0-999.json\"\n",
    "data = json.load(open('data/mpd.slice.0-999.json'))\n",
    "\n",
    "# We want the playlists data\n",
    "df = pd.DataFrame(data[\"playlists\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>tracks</th>\n",
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       "      <th>description</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>Throwbacks</td>\n",
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       "      <td>2</td>\n",
       "      <td>[{'pos': 0, 'artist_name': 'The Smashing Pumpk...</td>\n",
       "      <td>7</td>\n",
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       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>5</th>\n",
       "      <td>Wedding</td>\n",
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       "      <td>5</td>\n",
       "      <td>1430956800</td>\n",
       "      <td>80</td>\n",
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       "      <td>1</td>\n",
       "      <td>[{'pos': 0, 'artist_name': 'Cali Swag District...</td>\n",
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       "      <td>2</td>\n",
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      ],
      "text/plain": [
       "                   name collaborative  pid  modified_at  num_tracks  num_albums  num_followers                                             tracks  num_edits  duration_ms  num_artists description\n",
       "0            Throwbacks         false    0   1493424000          52          47              1  [{'pos': 0, 'artist_name': 'Missy Elliott', 't...          6     11532414           37         NaN\n",
       "1      Awesome Playlist         false    1   1506556800          39          23              1  [{'pos': 0, 'artist_name': 'Survivor', 'track_...          5     11656470           21         NaN\n",
       "2               korean          false    2   1505692800          64          51              1  [{'pos': 0, 'artist_name': 'Hoody', 'track_uri...         18     14039958           31         NaN\n",
       "3                   mat         false    3   1501027200         126         107              1  [{'pos': 0, 'artist_name': 'Camille Saint-Saën...          4     28926058           86         NaN\n",
       "4                   90s         false    4   1401667200          17          16              2  [{'pos': 0, 'artist_name': 'The Smashing Pumpk...          7      4335282           16         NaN\n",
       "5               Wedding         false    5   1430956800          80          71              1  [{'pos': 0, 'artist_name': 'Cali Swag District...          3     19156557           56         NaN\n",
       "6  I Put A Spell On You         false    6   1477094400          16          15              1  [{'pos': 0, 'artist_name': 'Creedence Clearwat...          2      3408479           13         NaN\n",
       "7                  2017         false    7   1509321600          53          52              1  [{'pos': 0, 'artist_name': 'Fink', 'track_uri'...         38     12674796           48         NaN\n",
       "8                   BOP         false    8   1508976000          46          37              2  [{'pos': 0, 'artist_name': 'Catfish and the Bo...         21      9948921           23         NaN\n",
       "9          old country          false    9   1501804800          21          20              1  [{'pos': 0, 'artist_name': 'Willie Nelson', 't...         10      4297488           18         NaN"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the data we have\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1000.000000</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1000.00000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1000.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>499.500000</td>\n",
       "      <td>1.476247e+09</td>\n",
       "      <td>67.50300</td>\n",
       "      <td>50.526000</td>\n",
       "      <td>2.583000</td>\n",
       "      <td>17.546000</td>\n",
       "      <td>1.574061e+07</td>\n",
       "      <td>38.746000</td>\n",
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       "      <th>std</th>\n",
       "      <td>288.819436</td>\n",
       "      <td>3.719567e+07</td>\n",
       "      <td>55.00334</td>\n",
       "      <td>41.448997</td>\n",
       "      <td>32.813501</td>\n",
       "      <td>20.978751</td>\n",
       "      <td>1.290575e+07</td>\n",
       "      <td>31.245091</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.322611e+09</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.002971e+06</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>249.750000</td>\n",
       "      <td>1.459944e+09</td>\n",
       "      <td>25.00000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.810390e+06</td>\n",
       "      <td>16.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>499.500000</td>\n",
       "      <td>1.491048e+09</td>\n",
       "      <td>50.00000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.163116e+07</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>749.250000</td>\n",
       "      <td>1.505779e+09</td>\n",
       "      <td>92.50000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>2.181915e+07</td>\n",
       "      <td>53.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>999.000000</td>\n",
       "      <td>1.509494e+09</td>\n",
       "      <td>245.00000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>1038.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>5.781713e+07</td>\n",
       "      <td>175.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "               pid   modified_at  num_tracks   num_albums  num_followers    num_edits   duration_ms  num_artists\n",
       "count  1000.000000  1.000000e+03  1000.00000  1000.000000    1000.000000  1000.000000  1.000000e+03  1000.000000\n",
       "mean    499.500000  1.476247e+09    67.50300    50.526000       2.583000    17.546000  1.574061e+07    38.746000\n",
       "std     288.819436  3.719567e+07    55.00334    41.448997      32.813501    20.978751  1.290575e+07    31.245091\n",
       "min       0.000000  1.322611e+09     5.00000     2.000000       1.000000     2.000000  1.002971e+06     3.000000\n",
       "25%     249.750000  1.459944e+09    25.00000    19.000000       1.000000     4.000000  5.810390e+06    16.000000\n",
       "50%     499.500000  1.491048e+09    50.00000    38.000000       1.000000    10.000000  1.163116e+07    30.000000\n",
       "75%     749.250000  1.505779e+09    92.50000    70.000000       1.250000    22.000000  2.181915e+07    53.000000\n",
       "max     999.000000  1.509494e+09   245.00000   225.000000    1038.000000   178.000000  5.781713e+07   175.000000"
      ]
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     "execution_count": 6,
     "metadata": {},
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    }
   ],
   "source": [
    "df.describe()\n",
    "# It seems there are outliers in num_tracks, num_albums, num_followers, num_artists\n",
    "# I will check if they are for the same playlist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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      "text/plain": [
       "              name collaborative  pid  modified_at  num_tracks  num_albums  num_followers                                             tracks  num_edits  duration_ms  num_artists description\n",
       "123  w o r k o u t         false  123   1500681600         245         225              1  [{'pos': 0, 'artist_name': 'Trap Beckham', 'tr...         26     55676545          171         NaN\n",
       "743        New ish         false  743   1509148800         245         168              1  [{'pos': 0, 'artist_name': 'Big Sean', 'track_...        131     57648694          103         NaN"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It seems num_tracks, num_albums, num_artists outliers are for the same playlists\n",
    "df[df['num_tracks'] == 245]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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      "765    Tangled\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>collaborative</th>\n",
       "      <th>pid</th>\n",
       "      <th>modified_at</th>\n",
       "      <th>num_tracks</th>\n",
       "      <th>num_albums</th>\n",
       "      <th>num_followers</th>\n",
       "      <th>tracks</th>\n",
       "      <th>num_edits</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>num_artists</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>Tangled</td>\n",
       "      <td>false</td>\n",
       "      <td>765</td>\n",
       "      <td>1499040000</td>\n",
       "      <td>81</td>\n",
       "      <td>37</td>\n",
       "      <td>1038</td>\n",
       "      <td>[{'pos': 0, 'artist_name': 'Mandy Moore', 'tra...</td>\n",
       "      <td>15</td>\n",
       "      <td>15112167</td>\n",
       "      <td>59</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name collaborative  pid  modified_at  num_tracks  num_albums  num_followers                                             tracks  num_edits  duration_ms  num_artists description\n",
       "765  Tangled         false  765   1499040000          81          37           1038  [{'pos': 0, 'artist_name': 'Mandy Moore', 'tra...         15     15112167           59         NaN"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check the playlist with the highest followers to see if we have something insightful\n",
    "best_playlist = df[df['num_followers'] == 1038]['name']\n",
    "print(best_playlist)\n",
    "df[df['num_followers'] == 1038]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Playlist_pos</th>\n",
       "      <th>artist_name</th>\n",
       "      <th>track_uri</th>\n",
       "      <th>artist_uri</th>\n",
       "      <th>track_name</th>\n",
       "      <th>album_uri</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>album_name</th>\n",
       "      <th>playlist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Missy Elliott</td>\n",
       "      <td>spotify:track:0UaMYEvWZi0ZqiDOoHU3YI</td>\n",
       "      <td>spotify:artist:2wIVse2owClT7go1WT98tk</td>\n",
       "      <td>Lose Control (feat. Ciara &amp; Fat Man Scoop)</td>\n",
       "      <td>spotify:album:6vV5UrXcfyQD1wu4Qo2I9K</td>\n",
       "      <td>226863</td>\n",
       "      <td>The Cookbook</td>\n",
       "      <td>Throwbacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Britney Spears</td>\n",
       "      <td>spotify:track:6I9VzXrHxO9rA9A5euc8Ak</td>\n",
       "      <td>spotify:artist:26dSoYclwsYLMAKD3tpOr4</td>\n",
       "      <td>Toxic</td>\n",
       "      <td>spotify:album:0z7pVBGOD7HCIB7S8eLkLI</td>\n",
       "      <td>198800</td>\n",
       "      <td>In The Zone</td>\n",
       "      <td>Throwbacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Beyoncé</td>\n",
       "      <td>spotify:track:0WqIKmW4BTrj3eJFmnCKMv</td>\n",
       "      <td>spotify:artist:6vWDO969PvNqNYHIOW5v0m</td>\n",
       "      <td>Crazy In Love</td>\n",
       "      <td>spotify:album:25hVFAxTlDvXbx2X2QkUkE</td>\n",
       "      <td>235933</td>\n",
       "      <td>Dangerously In Love (Alben für die Ewigkeit)</td>\n",
       "      <td>Throwbacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Justin Timberlake</td>\n",
       "      <td>spotify:track:1AWQoqb9bSvzTjaLralEkT</td>\n",
       "      <td>spotify:artist:31TPClRtHm23RisEBtV3X7</td>\n",
       "      <td>Rock Your Body</td>\n",
       "      <td>spotify:album:6QPkyl04rXwTGlGlcYaRoW</td>\n",
       "      <td>267266</td>\n",
       "      <td>Justified</td>\n",
       "      <td>Throwbacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Shaggy</td>\n",
       "      <td>spotify:track:1lzr43nnXAijIGYnCT8M8H</td>\n",
       "      <td>spotify:artist:5EvFsr3kj42KNv97ZEnqij</td>\n",
       "      <td>It Wasn't Me</td>\n",
       "      <td>spotify:album:6NmFmPX56pcLBOFMhIiKvF</td>\n",
       "      <td>227600</td>\n",
       "      <td>Hot Shot</td>\n",
       "      <td>Throwbacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Usher</td>\n",
       "      <td>spotify:track:0XUfyU2QviPAs6bxSpXYG4</td>\n",
       "      <td>spotify:artist:23zg3TcAtWQy7J6upgbUnj</td>\n",
       "      <td>Yeah!</td>\n",
       "      <td>spotify:album:0vO0b1AvY49CPQyVisJLj0</td>\n",
       "      <td>250373</td>\n",
       "      <td>Confessions</td>\n",
       "      <td>Throwbacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Usher</td>\n",
       "      <td>spotify:track:68vgtRHr7iZHpzGpon6Jlo</td>\n",
       "      <td>spotify:artist:23zg3TcAtWQy7J6upgbUnj</td>\n",
       "      <td>My Boo</td>\n",
       "      <td>spotify:album:1RM6MGv6bcl6NrAG8PGoZk</td>\n",
       "      <td>223440</td>\n",
       "      <td>Confessions</td>\n",
       "      <td>Throwbacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>The Pussycat Dolls</td>\n",
       "      <td>spotify:track:3BxWKCI06eQ5Od8TY2JBeA</td>\n",
       "      <td>spotify:artist:6wPhSqRtPu1UhRCDX5yaDJ</td>\n",
       "      <td>Buttons</td>\n",
       "      <td>spotify:album:5x8e8UcCeOgrOzSnDGuPye</td>\n",
       "      <td>225560</td>\n",
       "      <td>PCD</td>\n",
       "      <td>Throwbacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Destiny's Child</td>\n",
       "      <td>spotify:track:7H6ev70Weq6DdpZyyTmUXk</td>\n",
       "      <td>spotify:artist:1Y8cdNmUJH7yBTd9yOvr5i</td>\n",
       "      <td>Say My Name</td>\n",
       "      <td>spotify:album:283NWqNsCA9GwVHrJk59CG</td>\n",
       "      <td>271333</td>\n",
       "      <td>The Writing's On The Wall</td>\n",
       "      <td>Throwbacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>OutKast</td>\n",
       "      <td>spotify:track:2PpruBYCo4H7WOBJ7Q2EwM</td>\n",
       "      <td>spotify:artist:1G9G7WwrXka3Z1r7aIDjI7</td>\n",
       "      <td>Hey Ya! - Radio Mix / Club Mix</td>\n",
       "      <td>spotify:album:1UsmQ3bpJTyK6ygoOOjG1r</td>\n",
       "      <td>235213</td>\n",
       "      <td>Speakerboxxx/The Love Below</td>\n",
       "      <td>Throwbacks</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Playlist_pos         artist_name                             track_uri                             artist_uri                                  track_name                             album_uri  duration_ms                                    album_name    playlist\n",
       "0             0       Missy Elliott  spotify:track:0UaMYEvWZi0ZqiDOoHU3YI  spotify:artist:2wIVse2owClT7go1WT98tk  Lose Control (feat. Ciara & Fat Man Scoop)  spotify:album:6vV5UrXcfyQD1wu4Qo2I9K       226863                                  The Cookbook  Throwbacks\n",
       "1             1      Britney Spears  spotify:track:6I9VzXrHxO9rA9A5euc8Ak  spotify:artist:26dSoYclwsYLMAKD3tpOr4                                       Toxic  spotify:album:0z7pVBGOD7HCIB7S8eLkLI       198800                                   In The Zone  Throwbacks\n",
       "2             2             Beyoncé  spotify:track:0WqIKmW4BTrj3eJFmnCKMv  spotify:artist:6vWDO969PvNqNYHIOW5v0m                               Crazy In Love  spotify:album:25hVFAxTlDvXbx2X2QkUkE       235933  Dangerously In Love (Alben für die Ewigkeit)  Throwbacks\n",
       "3             3   Justin Timberlake  spotify:track:1AWQoqb9bSvzTjaLralEkT  spotify:artist:31TPClRtHm23RisEBtV3X7                              Rock Your Body  spotify:album:6QPkyl04rXwTGlGlcYaRoW       267266                                     Justified  Throwbacks\n",
       "4             4              Shaggy  spotify:track:1lzr43nnXAijIGYnCT8M8H  spotify:artist:5EvFsr3kj42KNv97ZEnqij                                It Wasn't Me  spotify:album:6NmFmPX56pcLBOFMhIiKvF       227600                                      Hot Shot  Throwbacks\n",
       "5             5               Usher  spotify:track:0XUfyU2QviPAs6bxSpXYG4  spotify:artist:23zg3TcAtWQy7J6upgbUnj                                       Yeah!  spotify:album:0vO0b1AvY49CPQyVisJLj0       250373                                   Confessions  Throwbacks\n",
       "6             6               Usher  spotify:track:68vgtRHr7iZHpzGpon6Jlo  spotify:artist:23zg3TcAtWQy7J6upgbUnj                                      My Boo  spotify:album:1RM6MGv6bcl6NrAG8PGoZk       223440                                   Confessions  Throwbacks\n",
       "7             7  The Pussycat Dolls  spotify:track:3BxWKCI06eQ5Od8TY2JBeA  spotify:artist:6wPhSqRtPu1UhRCDX5yaDJ                                     Buttons  spotify:album:5x8e8UcCeOgrOzSnDGuPye       225560                                           PCD  Throwbacks\n",
       "8             8     Destiny's Child  spotify:track:7H6ev70Weq6DdpZyyTmUXk  spotify:artist:1Y8cdNmUJH7yBTd9yOvr5i                                 Say My Name  spotify:album:283NWqNsCA9GwVHrJk59CG       271333                     The Writing's On The Wall  Throwbacks\n",
       "9             9             OutKast  spotify:track:2PpruBYCo4H7WOBJ7Q2EwM  spotify:artist:1G9G7WwrXka3Z1r7aIDjI7              Hey Ya! - Radio Mix / Club Mix  spotify:album:1UsmQ3bpJTyK6ygoOOjG1r       235213                   Speakerboxxx/The Love Below  Throwbacks"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Taking the tracks for each playlist and creating a separate dataframe for it\n",
    "track_df = []\n",
    "pl_counter = 0\n",
    "for track in df['tracks']:\n",
    "    for one_track in track:\n",
    "        x = list(one_track.values())\n",
    "        \n",
    "        # Making sure each track in the dataframe has a column to indicate which playlist they came from\n",
    "        x.append(df['name'][pl_counter])\n",
    "        track_df.append(x)\n",
    "    pl_counter += 1\n",
    "\n",
    "track_df = pd.DataFrame(track_df, columns = ['Playlist_pos', 'artist_name','track_uri','artist_uri','track_name','album_uri','duration_ms','album_name','playlist'])  \n",
    "\n",
    "# Checking if our data frame is correct\n",
    "track_df.head(10)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "# Exploratory Data Analysis\n",
    "\n",
    "plt.plot( df['num_edits'], df['num_followers'], 'o')\n",
    "\n",
    "# Set limits to avoid the outlier\n",
    "plt.ylim(-1,50)\n",
    "plt.title(\"No relation between # of playlist followers and # of edits\")\n",
    "plt.xlabel(\"Edits\")\n",
    "plt.ylabel(\"Followers\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1       750\n",
       "2       138\n",
       "3        57\n",
       "4        24\n",
       "5         8\n",
       "6         6\n",
       "7         4\n",
       "8         3\n",
       "9         2\n",
       "18        1\n",
       "10        1\n",
       "11        1\n",
       "13        1\n",
       "1038      1\n",
       "15        1\n",
       "17        1\n",
       "22        1\n",
       "Name: num_followers, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most playlists have 1-4 followers\n",
    "df['num_followers'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Total songs across playlists')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Checking which artists are featured most across all playlists\n",
    "values = list(track_df['artist_name'].value_counts())\n",
    "names = track_df['artist_name'].value_counts().index.tolist()\n",
    "\n",
    "ax = sns.barplot(y=names[0:10], x=values[0:10])\n",
    "ax.set_title('Artists with most featured songs across all playlists')\n",
    "ax.set_xlabel('Total songs across playlists')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "# Checking the distribution of song durations\n",
    "track_df['duration_sec'] = track_df['duration_ms']/1000\n",
    "plt.title(\"Most songs across playlists are between 3-4 minutes\")\n",
    "plt.xlabel(\"Song Duration (sec)\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.hist(track_df.drop_duplicates(['track_name'], keep='first')['duration_sec'], bins = 100)\n",
    "plt.xlim(0,500)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the uri for each track\n",
    "mah_tracks = track_df.track_uri.unique()\n",
    "mah_tracks = list(mah_tracks)\n",
    "mah_tracks_uri = []\n",
    "for track in mah_tracks:\n",
    "    uri = track.split(\"track:\")\n",
    "    mah_tracks_uri.append(uri[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting data from Spotify api\n",
    "\n",
    "client_credentials_manager = SpotifyClientCredentials('ca4bc09f475c428ea4a78ac1149aa78b', '72743d3e010d44d0b904b259695194d3', None)\n",
    "sp = spotipy.Spotify(client_credentials_manager=client_credentials_manager)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Got track info on a thousand songs\n",
    "track_info = []\n",
    "counts = list(np.arange(0,1001,100))\n",
    "for count in counts:\n",
    "    for x in mah_tracks_uri[count:count+100]:\n",
    "        song = sp.audio_features(tracks= x)[0]\n",
    "        song[\"track\"] = track_df.loc[track_df[\"track_uri\"] == song[\"uri\"]][\"track_name\"].iloc[0]\n",
    "        track_info.append(song)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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",
       "      <th>tempo</th>\n",
       "      <th>type</th>\n",
       "      <th>id</th>\n",
       "      <th>uri</th>\n",
       "      <th>track_href</th>\n",
       "      <th>analysis_url</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>time_signature</th>\n",
       "      <th>track</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.904</td>\n",
       "      <td>0.813</td>\n",
       "      <td>4</td>\n",
       "      <td>-7.105</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1210</td>\n",
       "      <td>0.03110</td>\n",
       "      <td>0.006970</td>\n",
       "      <td>0.0471</td>\n",
       "      <td>0.810</td>\n",
       "      <td>125.461</td>\n",
       "      <td>audio_features</td>\n",
       "      <td>0UaMYEvWZi0ZqiDOoHU3YI</td>\n",
       "      <td>spotify:track:0UaMYEvWZi0ZqiDOoHU3YI</td>\n",
       "      <td>https://api.spotify.com/v1/tracks/0UaMYEvWZi0Z...</td>\n",
       "      <td>https://api.spotify.com/v1/audio-analysis/0UaM...</td>\n",
       "      <td>226864</td>\n",
       "      <td>4</td>\n",
       "      <td>Lose Control (feat. Ciara &amp; Fat Man Scoop)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.774</td>\n",
       "      <td>0.838</td>\n",
       "      <td>5</td>\n",
       "      <td>-3.914</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.02490</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.2420</td>\n",
       "      <td>0.924</td>\n",
       "      <td>143.040</td>\n",
       "      <td>audio_features</td>\n",
       "      <td>6I9VzXrHxO9rA9A5euc8Ak</td>\n",
       "      <td>spotify:track:6I9VzXrHxO9rA9A5euc8Ak</td>\n",
       "      <td>https://api.spotify.com/v1/tracks/6I9VzXrHxO9r...</td>\n",
       "      <td>https://api.spotify.com/v1/audio-analysis/6I9V...</td>\n",
       "      <td>198800</td>\n",
       "      <td>4</td>\n",
       "      <td>Toxic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.664</td>\n",
       "      <td>0.758</td>\n",
       "      <td>2</td>\n",
       "      <td>-6.583</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2100</td>\n",
       "      <td>0.00238</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0598</td>\n",
       "      <td>0.701</td>\n",
       "      <td>99.259</td>\n",
       "      <td>audio_features</td>\n",
       "      <td>0WqIKmW4BTrj3eJFmnCKMv</td>\n",
       "      <td>spotify:track:0WqIKmW4BTrj3eJFmnCKMv</td>\n",
       "      <td>https://api.spotify.com/v1/tracks/0WqIKmW4BTrj...</td>\n",
       "      <td>https://api.spotify.com/v1/audio-analysis/0WqI...</td>\n",
       "      <td>235933</td>\n",
       "      <td>4</td>\n",
       "      <td>Crazy In Love</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.891</td>\n",
       "      <td>0.714</td>\n",
       "      <td>4</td>\n",
       "      <td>-6.055</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1400</td>\n",
       "      <td>0.20200</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>0.0521</td>\n",
       "      <td>0.818</td>\n",
       "      <td>100.972</td>\n",
       "      <td>audio_features</td>\n",
       "      <td>1AWQoqb9bSvzTjaLralEkT</td>\n",
       "      <td>spotify:track:1AWQoqb9bSvzTjaLralEkT</td>\n",
       "      <td>https://api.spotify.com/v1/tracks/1AWQoqb9bSvz...</td>\n",
       "      <td>https://api.spotify.com/v1/audio-analysis/1AWQ...</td>\n",
       "      <td>267267</td>\n",
       "      <td>4</td>\n",
       "      <td>Rock Your Body</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.853</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0</td>\n",
       "      <td>-4.596</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0713</td>\n",
       "      <td>0.05610</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.3130</td>\n",
       "      <td>0.654</td>\n",
       "      <td>94.759</td>\n",
       "      <td>audio_features</td>\n",
       "      <td>1lzr43nnXAijIGYnCT8M8H</td>\n",
       "      <td>spotify:track:1lzr43nnXAijIGYnCT8M8H</td>\n",
       "      <td>https://api.spotify.com/v1/tracks/1lzr43nnXAij...</td>\n",
       "      <td>https://api.spotify.com/v1/audio-analysis/1lzr...</td>\n",
       "      <td>227600</td>\n",
       "      <td>4</td>\n",
       "      <td>It Wasn't Me</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.881</td>\n",
       "      <td>0.788</td>\n",
       "      <td>2</td>\n",
       "      <td>-4.669</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1680</td>\n",
       "      <td>0.02120</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0377</td>\n",
       "      <td>0.592</td>\n",
       "      <td>104.997</td>\n",
       "      <td>audio_features</td>\n",
       "      <td>0XUfyU2QviPAs6bxSpXYG4</td>\n",
       "      <td>spotify:track:0XUfyU2QviPAs6bxSpXYG4</td>\n",
       "      <td>https://api.spotify.com/v1/tracks/0XUfyU2QviPA...</td>\n",
       "      <td>https://api.spotify.com/v1/audio-analysis/0XUf...</td>\n",
       "      <td>250373</td>\n",
       "      <td>4</td>\n",
       "      <td>Yeah!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.662</td>\n",
       "      <td>0.507</td>\n",
       "      <td>5</td>\n",
       "      <td>-8.238</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1180</td>\n",
       "      <td>0.25700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0465</td>\n",
       "      <td>0.676</td>\n",
       "      <td>86.412</td>\n",
       "      <td>audio_features</td>\n",
       "      <td>68vgtRHr7iZHpzGpon6Jlo</td>\n",
       "      <td>spotify:track:68vgtRHr7iZHpzGpon6Jlo</td>\n",
       "      <td>https://api.spotify.com/v1/tracks/68vgtRHr7iZH...</td>\n",
       "      <td>https://api.spotify.com/v1/audio-analysis/68vg...</td>\n",
       "      <td>223440</td>\n",
       "      <td>4</td>\n",
       "      <td>My Boo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.570</td>\n",
       "      <td>0.821</td>\n",
       "      <td>2</td>\n",
       "      <td>-4.380</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2670</td>\n",
       "      <td>0.17800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2890</td>\n",
       "      <td>0.408</td>\n",
       "      <td>210.857</td>\n",
       "      <td>audio_features</td>\n",
       "      <td>3BxWKCI06eQ5Od8TY2JBeA</td>\n",
       "      <td>spotify:track:3BxWKCI06eQ5Od8TY2JBeA</td>\n",
       "      <td>https://api.spotify.com/v1/tracks/3BxWKCI06eQ5...</td>\n",
       "      <td>https://api.spotify.com/v1/audio-analysis/3BxW...</td>\n",
       "      <td>225560</td>\n",
       "      <td>4</td>\n",
       "      <td>Buttons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.713</td>\n",
       "      <td>0.678</td>\n",
       "      <td>5</td>\n",
       "      <td>-3.525</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>0.27300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1490</td>\n",
       "      <td>0.734</td>\n",
       "      <td>138.009</td>\n",
       "      <td>audio_features</td>\n",
       "      <td>7H6ev70Weq6DdpZyyTmUXk</td>\n",
       "      <td>spotify:track:7H6ev70Weq6DdpZyyTmUXk</td>\n",
       "      <td>https://api.spotify.com/v1/tracks/7H6ev70Weq6D...</td>\n",
       "      <td>https://api.spotify.com/v1/audio-analysis/7H6e...</td>\n",
       "      <td>271333</td>\n",
       "      <td>4</td>\n",
       "      <td>Say My Name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.728</td>\n",
       "      <td>0.974</td>\n",
       "      <td>4</td>\n",
       "      <td>-2.261</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0665</td>\n",
       "      <td>0.10300</td>\n",
       "      <td>0.000532</td>\n",
       "      <td>0.1750</td>\n",
       "      <td>0.965</td>\n",
       "      <td>79.526</td>\n",
       "      <td>audio_features</td>\n",
       "      <td>2PpruBYCo4H7WOBJ7Q2EwM</td>\n",
       "      <td>spotify:track:2PpruBYCo4H7WOBJ7Q2EwM</td>\n",
       "      <td>https://api.spotify.com/v1/tracks/2PpruBYCo4H7...</td>\n",
       "      <td>https://api.spotify.com/v1/audio-analysis/2Ppr...</td>\n",
       "      <td>235213</td>\n",
       "      <td>4</td>\n",
       "      <td>Hey Ya! - Radio Mix / Club Mix</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   danceability  energy  key  loudness  mode  speechiness  acousticness  instrumentalness  liveness  valence    tempo            type                      id                                   uri                                         track_href                                       analysis_url  duration_ms  time_signature                                       track\n",
       "0         0.904   0.813    4    -7.105     0       0.1210       0.03110          0.006970    0.0471    0.810  125.461  audio_features  0UaMYEvWZi0ZqiDOoHU3YI  spotify:track:0UaMYEvWZi0ZqiDOoHU3YI  https://api.spotify.com/v1/tracks/0UaMYEvWZi0Z...  https://api.spotify.com/v1/audio-analysis/0UaM...       226864               4  Lose Control (feat. Ciara & Fat Man Scoop)\n",
       "1         0.774   0.838    5    -3.914     0       0.1140       0.02490          0.025000    0.2420    0.924  143.040  audio_features  6I9VzXrHxO9rA9A5euc8Ak  spotify:track:6I9VzXrHxO9rA9A5euc8Ak  https://api.spotify.com/v1/tracks/6I9VzXrHxO9r...  https://api.spotify.com/v1/audio-analysis/6I9V...       198800               4                                       Toxic\n",
       "2         0.664   0.758    2    -6.583     0       0.2100       0.00238          0.000000    0.0598    0.701   99.259  audio_features  0WqIKmW4BTrj3eJFmnCKMv  spotify:track:0WqIKmW4BTrj3eJFmnCKMv  https://api.spotify.com/v1/tracks/0WqIKmW4BTrj...  https://api.spotify.com/v1/audio-analysis/0WqI...       235933               4                               Crazy In Love\n",
       "3         0.891   0.714    4    -6.055     0       0.1400       0.20200          0.000234    0.0521    0.818  100.972  audio_features  1AWQoqb9bSvzTjaLralEkT  spotify:track:1AWQoqb9bSvzTjaLralEkT  https://api.spotify.com/v1/tracks/1AWQoqb9bSvz...  https://api.spotify.com/v1/audio-analysis/1AWQ...       267267               4                              Rock Your Body\n",
       "4         0.853   0.606    0    -4.596     1       0.0713       0.05610          0.000000    0.3130    0.654   94.759  audio_features  1lzr43nnXAijIGYnCT8M8H  spotify:track:1lzr43nnXAijIGYnCT8M8H  https://api.spotify.com/v1/tracks/1lzr43nnXAij...  https://api.spotify.com/v1/audio-analysis/1lzr...       227600               4                                It Wasn't Me\n",
       "5         0.881   0.788    2    -4.669     1       0.1680       0.02120          0.000000    0.0377    0.592  104.997  audio_features  0XUfyU2QviPAs6bxSpXYG4  spotify:track:0XUfyU2QviPAs6bxSpXYG4  https://api.spotify.com/v1/tracks/0XUfyU2QviPA...  https://api.spotify.com/v1/audio-analysis/0XUf...       250373               4                                       Yeah!\n",
       "6         0.662   0.507    5    -8.238     1       0.1180       0.25700          0.000000    0.0465    0.676   86.412  audio_features  68vgtRHr7iZHpzGpon6Jlo  spotify:track:68vgtRHr7iZHpzGpon6Jlo  https://api.spotify.com/v1/tracks/68vgtRHr7iZH...  https://api.spotify.com/v1/audio-analysis/68vg...       223440               4                                      My Boo\n",
       "7         0.570   0.821    2    -4.380     1       0.2670       0.17800          0.000000    0.2890    0.408  210.857  audio_features  3BxWKCI06eQ5Od8TY2JBeA  spotify:track:3BxWKCI06eQ5Od8TY2JBeA  https://api.spotify.com/v1/tracks/3BxWKCI06eQ5...  https://api.spotify.com/v1/audio-analysis/3BxW...       225560               4                                     Buttons\n",
       "8         0.713   0.678    5    -3.525     0       0.1020       0.27300          0.000000    0.1490    0.734  138.009  audio_features  7H6ev70Weq6DdpZyyTmUXk  spotify:track:7H6ev70Weq6DdpZyyTmUXk  https://api.spotify.com/v1/tracks/7H6ev70Weq6D...  https://api.spotify.com/v1/audio-analysis/7H6e...       271333               4                                 Say My Name\n",
       "9         0.728   0.974    4    -2.261     0       0.0665       0.10300          0.000532    0.1750    0.965   79.526  audio_features  2PpruBYCo4H7WOBJ7Q2EwM  spotify:track:2PpruBYCo4H7WOBJ7Q2EwM  https://api.spotify.com/v1/tracks/2PpruBYCo4H7...  https://api.spotify.com/v1/audio-analysis/2Ppr...       235213               4              Hey Ya! - Radio Mix / Club Mix"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We have a dataframe with info on each track\n",
    "track_info_df = pd.DataFrame(track_info)\n",
    "track_info_df.head(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": [
    "# Let's do some EDA on our tracks\n",
    "plt.plot(track_info_df['danceability'], track_info_df['energy'], 'o')\n",
    "plt.title(\"No signification linear relation between danceability and energy\")\n",
    "plt.ylabel(\"Energy\")\n",
    "plt.xlabel(\"Danceability\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": [
    "plt.hist(track_info_df['danceability'])\n",
    "plt.title(\"Distribution of song danceability of some songs from our playlist subset\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xlabel(\"Danceability\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": [
    "plt.hist(track_info_df['tempo'])\n",
    "plt.title(\"Distribution of song tempo of some songs from our playlist subset\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xlabel(\"Tempo\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Decision on baseline Model based on data initial EDA:**\n",
    "\n",
    "So we are taking info of tracks of songs that we got from our initial subset of playlists. Our assumption is that these playlists are representative of all playlists in Spotify.\n",
    "\n",
    "Once we do some EDA, we realize that track info like \"Danceability\" and \"Tempo\" are pretty normally distributed can be used as good metrics for our baseline model. We would have looked at similar type of info like danceability and energy and create a baseline model of song recommendation that combines the two. However, we didn't find any relationship between those two metrics and thus it might not be a good idea to recommend songs using a combination of those two metrics.\n",
    "\n",
    "So our baseline model: Use 'Danceability' of a song that user listens to recommend future songs. \n",
    "\n",
    "Since this is a simple model, we will break any ties based on Python's automatic list sorting (so, alphabetically within ties). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here is our baseline model\n",
    "def baseline(tracks, df, n_pred):\n",
    "    '''\n",
    "    Input:\n",
    "    1. tracks = Takes a list of track titles as input (strings)\n",
    "    2. df = Dataframe containing all track info\n",
    "    Output:\n",
    "    recs = Recommends 10 track uri based on info of that string (List of strings)\n",
    "    '''\n",
    "    recs = []\n",
    "    for t in tracks:\n",
    "        danceability = df[df['track_name'] == t]['danceability']\n",
    "        df['rec_df_dance'] = np.abs(df['danceability'] - danceability.values[0])\n",
    "        one_recs = list(df.sort_values(by=['rec_df_dance'])['track_name'][1:].values)\n",
    "        recs = [track for track in one_recs if track not in tracks][0:n_pred]\n",
    "    preds = Counter(recs).most_common(n_pred)\n",
    "    pred_titles = [p[0] for p in preds]\n",
    "    return pred_titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test baseline using 100_Sample_MilPlaylist data\n",
    "all_info = pd.read_csv('data/100_Sample_MilPlay_Spotify.csv')\n",
    "raw_data = pd.read_csv('data/sparse_df.csv')\n",
    "\n",
    "obscured_df = pd.DataFrame.copy(raw_data)\n",
    "\n",
    "# Obscure each playlist in set by removing half the songs\n",
    "obscureds = [] #indexes of obscured lists\n",
    "n_obscured = []\n",
    "\n",
    "#We need to add in the user's seed playlist to this dataset.\n",
    "for i, playlist in raw_data.iterrows():\n",
    "    obscured_list = remove_songs(list(playlist), 0.5)\n",
    "    obscured_df.iloc[i] = obscured_list\n",
    "    n_obscured.append(int(sum(obscured_list)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.004852633067927875\n",
      "0.03378128025850286\n"
     ]
    }
   ],
   "source": [
    "pred_songs_bool = []\n",
    "\n",
    "for index, n in enumerate(n_obscured):\n",
    "    pred_songs = baseline(get_titles(obscured_df.iloc[index], raw_data), all_info, n)\n",
    "    pred_songs_bool.append(get_song_bool_list(pred_songs, raw_data))\n",
    "\n",
    "# Score the model's performance\n",
    "r2 = []\n",
    "ndcgs = []\n",
    "for i, pred in enumerate(pred_songs_bool):\n",
    "    pred_idx = [idx for idx, p in enumerate(pred) if p == 1]\n",
    "    right = np.array(raw_data.iloc[i]) - np.array(obscured_df.iloc[i])\n",
    "    right_idx = [idx for idx, r in enumerate(right) if r == 1]\n",
    "    r2.append(sum([1 for pidx in pred_idx if pidx in right_idx])/len(pred_idx))\n",
    "    r = [1 if idx in pred_idx else 0 for idx in right_idx]\n",
    "    ndcgs.append(ndcg_at_k(r, len(r)))\n",
    "print(np.mean(r2))\n",
    "print(np.mean(ndcgs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score for baseline: 0.004852633067927875\n",
      "NDCGS score for baseline: 0.03378128025850286\n"
     ]
    }
   ],
   "source": [
    "print(\"R2 score for baseline: {}\".format(np.mean(r2)))\n",
    "print(\"NDCGS score for baseline: {}\".format(np.mean(ndcgs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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