spotify_feat_playlist_nb.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3hOo5ikXgntJ"
      },
      "source": [
        "# Playlist Creation via Spotify Audio Features Classification\n",
        "This is an example notebook that uses the SpotifyFeatPlaylist script to create an ensemble model that uses audio features as input and popularity as the target variable\n",
        "to ultimately create a playlist with similar audio features."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "Q4NzKJV8gntM"
      },
      "outputs": [],
      "source": [
        "import config\n",
        "import SpotFeatPlaylist as spa\n",
        "import glob\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N-Ku7zBFgntO"
      },
      "source": [
        "Get stream history metadata from Spotify and get user client id and secret for Spotify Web API to access Spotify music data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "nEvwA3BbgntO"
      },
      "outputs": [],
      "source": [
        "cid = config.API_CID\n",
        "secret = config.API_SECRET\n",
        "stream_data = glob.glob(\"./*Audio*.json\")\n",
        "\n",
        "sdata = spa.SpotifyAudioAnalysis(cid, secret)\n",
        "\n",
        "df = sdata.to_pandasdf(stream_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TuYfmPlLgntO",
        "outputId": "e15c4071-a8bd-4e9d-8020-c4a9dd062d8a"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>master_metadata_track_name</th>\n",
              "      <th>master_metadata_album_artist_name</th>\n",
              "      <th>spotify_track_uri</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Kaktüs</td>\n",
              "      <td>Norm Ender</td>\n",
              "      <td>spotify:track:4xDLgHEIa8RHwEbXMkxT7l</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Benim Derdim</td>\n",
              "      <td>Ezhel</td>\n",
              "      <td>spotify:track:3RCy0AV1CKCF2h7IAvjfpU</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Yalanlar</td>\n",
              "      <td>Aga B</td>\n",
              "      <td>spotify:track:4TV5415yZ3fIFasqfFLbHN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Soygun</td>\n",
              "      <td>Tepki</td>\n",
              "      <td>spotify:track:4Ohsjd3wz5ZuI21HCKZ5e6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Şehrimin Tadı</td>\n",
              "      <td>Ezhel</td>\n",
              "      <td>spotify:track:5JaXKUIcNxwSuUyD4CsphL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>539</th>\n",
              "      <td>The Perfect Girl</td>\n",
              "      <td>Mareux</td>\n",
              "      <td>spotify:track:5RBOcBpJXaNnHCGViJmYhh</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>540</th>\n",
              "      <td>GigaChad Theme - Phonk House Version</td>\n",
              "      <td>g3ox_em</td>\n",
              "      <td>spotify:track:7mLWNwcvwRdEviz6SfYp8A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>541</th>\n",
              "      <td>The Beautiful People</td>\n",
              "      <td>Marilyn Manson</td>\n",
              "      <td>spotify:track:2aIB1CdRRG7YLBu9hNw9nR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>542</th>\n",
              "      <td>The Beautiful People</td>\n",
              "      <td>Marilyn Manson</td>\n",
              "      <td>spotify:track:2aIB1CdRRG7YLBu9hNw9nR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>543</th>\n",
              "      <td>Killing Loneliness</td>\n",
              "      <td>HIM</td>\n",
              "      <td>spotify:track:6R3jzxmUp5OSDlHsv6vyAg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>32358 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               master_metadata_track_name master_metadata_album_artist_name  \\\n",
              "0                                  Kaktüs                        Norm Ender   \n",
              "1                            Benim Derdim                             Ezhel   \n",
              "2                                Yalanlar                             Aga B   \n",
              "3                                  Soygun                             Tepki   \n",
              "4                          Şehrimin Tadı                             Ezhel   \n",
              "..                                    ...                               ...   \n",
              "539                      The Perfect Girl                            Mareux   \n",
              "540  GigaChad Theme - Phonk House Version                           g3ox_em   \n",
              "541                  The Beautiful People                    Marilyn Manson   \n",
              "542                  The Beautiful People                    Marilyn Manson   \n",
              "543                    Killing Loneliness                               HIM   \n",
              "\n",
              "                        spotify_track_uri  \n",
              "0    spotify:track:4xDLgHEIa8RHwEbXMkxT7l  \n",
              "1    spotify:track:3RCy0AV1CKCF2h7IAvjfpU  \n",
              "2    spotify:track:4TV5415yZ3fIFasqfFLbHN  \n",
              "3    spotify:track:4Ohsjd3wz5ZuI21HCKZ5e6  \n",
              "4    spotify:track:5JaXKUIcNxwSuUyD4CsphL  \n",
              "..                                    ...  \n",
              "539  spotify:track:5RBOcBpJXaNnHCGViJmYhh  \n",
              "540  spotify:track:7mLWNwcvwRdEviz6SfYp8A  \n",
              "541  spotify:track:2aIB1CdRRG7YLBu9hNw9nR  \n",
              "542  spotify:track:2aIB1CdRRG7YLBu9hNw9nR  \n",
              "543  spotify:track:6R3jzxmUp5OSDlHsv6vyAg  \n",
              "\n",
              "[32358 rows x 3 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-qilnCZogntQ",
        "outputId": "f5f2c6a8-29a4-431a-ee89-66bc47f7d99c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>master_metadata_track_name</th>\n",
              "      <th>master_metadata_album_artist_name</th>\n",
              "      <th>spotify_track_uri</th>\n",
              "      <th>track_artist</th>\n",
              "      <th>track_listen_count</th>\n",
              "      <th>popularity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Şehrimin Tadı</td>\n",
              "      <td>Ezhel</td>\n",
              "      <td>spotify:track:5JaXKUIcNxwSuUyD4CsphL</td>\n",
              "      <td>Şehrimin Tadı - Ezhel</td>\n",
              "      <td>39</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Geceler</td>\n",
              "      <td>Ezhel</td>\n",
              "      <td>spotify:track:0R0o8uOkDz40XsR6uwThfQ</td>\n",
              "      <td>Geceler - Ezhel</td>\n",
              "      <td>55</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mono</td>\n",
              "      <td>Courtney Love</td>\n",
              "      <td>spotify:track:7zL1KgkpPb2Gi5Q2Inypl5</td>\n",
              "      <td>Mono - Courtney Love</td>\n",
              "      <td>72</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Kazıdık Tırnaklarla</td>\n",
              "      <td>Ezhel</td>\n",
              "      <td>spotify:track:27OWNTEEtJCFVejrh5mqgv</td>\n",
              "      <td>Kazıdık Tırnaklarla - Ezhel</td>\n",
              "      <td>134</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Mahallemiz Esmer</td>\n",
              "      <td>Ben Fero</td>\n",
              "      <td>spotify:track:3qOxkhNytyb6ifxNzxxULn</td>\n",
              "      <td>Mahallemiz Esmer - Ben Fero</td>\n",
              "      <td>98</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>Walk</td>\n",
              "      <td>Kyddiekafka</td>\n",
              "      <td>spotify:track:2cVKHQTLJbgs8TUqQXgVDU</td>\n",
              "      <td>Walk - Kyddiekafka</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>0% Angel</td>\n",
              "      <td>Mr.Kitty</td>\n",
              "      <td>spotify:track:0l2Ob0zMIQ21gJn642HyQ1</td>\n",
              "      <td>0% Angel - Mr.Kitty</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>Glitter</td>\n",
              "      <td>GRXGVR</td>\n",
              "      <td>spotify:track:7ogu6FZbymEdWuofKabkOu</td>\n",
              "      <td>Glitter - GRXGVR</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>One Step Closer</td>\n",
              "      <td>Linkin Park</td>\n",
              "      <td>spotify:track:3K4HG9evC7dg3N0R9cYqk4</td>\n",
              "      <td>One Step Closer - Linkin Park</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>Lost</td>\n",
              "      <td>Linkin Park</td>\n",
              "      <td>spotify:track:3RQHAFkEHiFHQ7qmMpKRdv</td>\n",
              "      <td>Lost - Linkin Park</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    master_metadata_track_name master_metadata_album_artist_name  \\\n",
              "0               Şehrimin Tadı                             Ezhel   \n",
              "1                      Geceler                             Ezhel   \n",
              "2                         Mono                     Courtney Love   \n",
              "3          Kazıdık Tırnaklarla                             Ezhel   \n",
              "4             Mahallemiz Esmer                          Ben Fero   \n",
              "..                         ...                               ...   \n",
              "495                       Walk                       Kyddiekafka   \n",
              "496                   0% Angel                          Mr.Kitty   \n",
              "497                    Glitter                            GRXGVR   \n",
              "498            One Step Closer                       Linkin Park   \n",
              "499                       Lost                       Linkin Park   \n",
              "\n",
              "                        spotify_track_uri                   track_artist  \\\n",
              "0    spotify:track:5JaXKUIcNxwSuUyD4CsphL         Şehrimin Tadı - Ezhel   \n",
              "1    spotify:track:0R0o8uOkDz40XsR6uwThfQ                Geceler - Ezhel   \n",
              "2    spotify:track:7zL1KgkpPb2Gi5Q2Inypl5           Mono - Courtney Love   \n",
              "3    spotify:track:27OWNTEEtJCFVejrh5mqgv    Kazıdık Tırnaklarla - Ezhel   \n",
              "4    spotify:track:3qOxkhNytyb6ifxNzxxULn    Mahallemiz Esmer - Ben Fero   \n",
              "..                                    ...                            ...   \n",
              "495  spotify:track:2cVKHQTLJbgs8TUqQXgVDU             Walk - Kyddiekafka   \n",
              "496  spotify:track:0l2Ob0zMIQ21gJn642HyQ1            0% Angel - Mr.Kitty   \n",
              "497  spotify:track:7ogu6FZbymEdWuofKabkOu               Glitter - GRXGVR   \n",
              "498  spotify:track:3K4HG9evC7dg3N0R9cYqk4  One Step Closer - Linkin Park   \n",
              "499  spotify:track:3RQHAFkEHiFHQ7qmMpKRdv             Lost - Linkin Park   \n",
              "\n",
              "     track_listen_count  popularity  \n",
              "0                    39           1  \n",
              "1                    55           1  \n",
              "2                    72           1  \n",
              "3                   134           1  \n",
              "4                    98           1  \n",
              "..                  ...         ...  \n",
              "495                   1           0  \n",
              "496                   1           0  \n",
              "497                   1           0  \n",
              "498                   1           0  \n",
              "499                   1           0  \n",
              "\n",
              "[500 rows x 6 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_pop = sdata.create_popularity(df, 250)\n",
        "df_pop"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RqeMNit4gntR"
      },
      "source": [
        "Get genre data from Spotify API and add it to the df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XNCXLYCdgntR"
      },
      "outputs": [],
      "source": [
        "df_genre = sdata.create_genre(df_pop)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_kiuQn-_gntR",
        "outputId": "90f1de9c-a197-4479-a3cd-4a25a22036ec"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>master_metadata_track_name</th>\n",
              "      <th>master_metadata_album_artist_name</th>\n",
              "      <th>spotify_track_uri</th>\n",
              "      <th>track_artist</th>\n",
              "      <th>track_listen_count</th>\n",
              "      <th>popularity</th>\n",
              "      <th>genre</th>\n",
              "      <th>track_uri</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Şehrimin Tadı</td>\n",
              "      <td>Ezhel</td>\n",
              "      <td>spotify:track:5JaXKUIcNxwSuUyD4CsphL</td>\n",
              "      <td>Şehrimin Tadı - Ezhel</td>\n",
              "      <td>39</td>\n",
              "      <td>1</td>\n",
              "      <td>turkish hip hop</td>\n",
              "      <td>5JaXKUIcNxwSuUyD4CsphL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Geceler</td>\n",
              "      <td>Ezhel</td>\n",
              "      <td>spotify:track:0R0o8uOkDz40XsR6uwThfQ</td>\n",
              "      <td>Geceler - Ezhel</td>\n",
              "      <td>55</td>\n",
              "      <td>1</td>\n",
              "      <td>turkish hip hop</td>\n",
              "      <td>0R0o8uOkDz40XsR6uwThfQ</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mono</td>\n",
              "      <td>Courtney Love</td>\n",
              "      <td>spotify:track:7zL1KgkpPb2Gi5Q2Inypl5</td>\n",
              "      <td>Mono - Courtney Love</td>\n",
              "      <td>72</td>\n",
              "      <td>1</td>\n",
              "      <td>alternative rock</td>\n",
              "      <td>7zL1KgkpPb2Gi5Q2Inypl5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Kazıdık Tırnaklarla</td>\n",
              "      <td>Ezhel</td>\n",
              "      <td>spotify:track:27OWNTEEtJCFVejrh5mqgv</td>\n",
              "      <td>Kazıdık Tırnaklarla - Ezhel</td>\n",
              "      <td>134</td>\n",
              "      <td>1</td>\n",
              "      <td>turkish hip hop</td>\n",
              "      <td>27OWNTEEtJCFVejrh5mqgv</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Mahallemiz Esmer</td>\n",
              "      <td>Ben Fero</td>\n",
              "      <td>spotify:track:3qOxkhNytyb6ifxNzxxULn</td>\n",
              "      <td>Mahallemiz Esmer - Ben Fero</td>\n",
              "      <td>98</td>\n",
              "      <td>1</td>\n",
              "      <td>turkce drill</td>\n",
              "      <td>3qOxkhNytyb6ifxNzxxULn</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  master_metadata_track_name master_metadata_album_artist_name  \\\n",
              "0             Şehrimin Tadı                             Ezhel   \n",
              "1                    Geceler                             Ezhel   \n",
              "2                       Mono                     Courtney Love   \n",
              "3        Kazıdık Tırnaklarla                             Ezhel   \n",
              "4           Mahallemiz Esmer                          Ben Fero   \n",
              "\n",
              "                      spotify_track_uri                 track_artist  \\\n",
              "0  spotify:track:5JaXKUIcNxwSuUyD4CsphL       Şehrimin Tadı - Ezhel   \n",
              "1  spotify:track:0R0o8uOkDz40XsR6uwThfQ              Geceler - Ezhel   \n",
              "2  spotify:track:7zL1KgkpPb2Gi5Q2Inypl5         Mono - Courtney Love   \n",
              "3  spotify:track:27OWNTEEtJCFVejrh5mqgv  Kazıdık Tırnaklarla - Ezhel   \n",
              "4  spotify:track:3qOxkhNytyb6ifxNzxxULn  Mahallemiz Esmer - Ben Fero   \n",
              "\n",
              "   track_listen_count  popularity             genre               track_uri  \n",
              "0                  39           1   turkish hip hop  5JaXKUIcNxwSuUyD4CsphL  \n",
              "1                  55           1   turkish hip hop  0R0o8uOkDz40XsR6uwThfQ  \n",
              "2                  72           1  alternative rock  7zL1KgkpPb2Gi5Q2Inypl5  \n",
              "3                 134           1   turkish hip hop  27OWNTEEtJCFVejrh5mqgv  \n",
              "4                  98           1      turkce drill  3qOxkhNytyb6ifxNzxxULn  "
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_genre.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BNL3--9igntR"
      },
      "source": [
        "Lastly get audio features for the complete dataframe. This will take some time.<br>\n",
        "We will save the dataframe and use it for further analyses"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zIftC7iNgntR",
        "outputId": "d58f722f-9acf-4b5e-e447-10e8054df22b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "HTTP Error for GET to https://api.spotify.com/v1/audio-analysis/56V9n2xZDsxX2pJO7qE5Re with Params: {} returned 404 due to analysis not found\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Error processing track 56V9n2xZDsxX2pJO7qE5Re: http status: 404, code:-1 - https://api.spotify.com/v1/audio-analysis/56V9n2xZDsxX2pJO7qE5Re:\n",
            " analysis not found, reason: None\n"
          ]
        },
        {
          "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>master_metadata_track_name</th>\n",
              "      <th>master_metadata_album_artist_name</th>\n",
              "      <th>spotify_track_uri</th>\n",
              "      <th>track_artist</th>\n",
              "      <th>track_listen_count</th>\n",
              "      <th>popularity</th>\n",
              "      <th>genre</th>\n",
              "      <th>track_uri</th>\n",
              "      <th>mean_bar_duration</th>\n",
              "      <th>mean_pitches</th>\n",
              "      <th>...</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Şehrimin Tadı</td>\n",
              "      <td>Ezhel</td>\n",
              "      <td>spotify:track:5JaXKUIcNxwSuUyD4CsphL</td>\n",
              "      <td>Şehrimin Tadı - Ezhel</td>\n",
              "      <td>39</td>\n",
              "      <td>1</td>\n",
              "      <td>turkish hip hop</td>\n",
              "      <td>5JaXKUIcNxwSuUyD4CsphL</td>\n",
              "      <td>2.010277</td>\n",
              "      <td>0.383483</td>\n",
              "      <td>...</td>\n",
              "      <td>0.3220</td>\n",
              "      <td>0.302</td>\n",
              "      <td>125.055</td>\n",
              "      <td>audio_features</td>\n",
              "      <td>5JaXKUIcNxwSuUyD4CsphL</td>\n",
              "      <td>spotify:track:5JaXKUIcNxwSuUyD4CsphL</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/5JaXKUIcNxwS...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/5JaX...</td>\n",
              "      <td>253450.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Geceler</td>\n",
              "      <td>Ezhel</td>\n",
              "      <td>spotify:track:0R0o8uOkDz40XsR6uwThfQ</td>\n",
              "      <td>Geceler - Ezhel</td>\n",
              "      <td>55</td>\n",
              "      <td>1</td>\n",
              "      <td>turkish hip hop</td>\n",
              "      <td>0R0o8uOkDz40XsR6uwThfQ</td>\n",
              "      <td>1.845690</td>\n",
              "      <td>0.346793</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0939</td>\n",
              "      <td>0.339</td>\n",
              "      <td>130.020</td>\n",
              "      <td>audio_features</td>\n",
              "      <td>0R0o8uOkDz40XsR6uwThfQ</td>\n",
              "      <td>spotify:track:0R0o8uOkDz40XsR6uwThfQ</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/0R0o8uOkDz40...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/0R0o...</td>\n",
              "      <td>222928.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mono</td>\n",
              "      <td>Courtney Love</td>\n",
              "      <td>spotify:track:7zL1KgkpPb2Gi5Q2Inypl5</td>\n",
              "      <td>Mono - Courtney Love</td>\n",
              "      <td>72</td>\n",
              "      <td>1</td>\n",
              "      <td>alternative rock</td>\n",
              "      <td>7zL1KgkpPb2Gi5Q2Inypl5</td>\n",
              "      <td>1.527554</td>\n",
              "      <td>0.430793</td>\n",
              "      <td>...</td>\n",
              "      <td>0.3350</td>\n",
              "      <td>0.442</td>\n",
              "      <td>155.967</td>\n",
              "      <td>audio_features</td>\n",
              "      <td>7zL1KgkpPb2Gi5Q2Inypl5</td>\n",
              "      <td>spotify:track:7zL1KgkpPb2Gi5Q2Inypl5</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/7zL1KgkpPb2G...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/7zL1...</td>\n",
              "      <td>219693.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Kazıdık Tırnaklarla</td>\n",
              "      <td>Ezhel</td>\n",
              "      <td>spotify:track:27OWNTEEtJCFVejrh5mqgv</td>\n",
              "      <td>Kazıdık Tırnaklarla - Ezhel</td>\n",
              "      <td>134</td>\n",
              "      <td>1</td>\n",
              "      <td>turkish hip hop</td>\n",
              "      <td>27OWNTEEtJCFVejrh5mqgv</td>\n",
              "      <td>1.718326</td>\n",
              "      <td>0.292124</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0998</td>\n",
              "      <td>0.358</td>\n",
              "      <td>139.821</td>\n",
              "      <td>audio_features</td>\n",
              "      <td>27OWNTEEtJCFVejrh5mqgv</td>\n",
              "      <td>spotify:track:27OWNTEEtJCFVejrh5mqgv</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/27OWNTEEtJCF...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/27OW...</td>\n",
              "      <td>172609.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Mahallemiz Esmer</td>\n",
              "      <td>Ben Fero</td>\n",
              "      <td>spotify:track:3qOxkhNytyb6ifxNzxxULn</td>\n",
              "      <td>Mahallemiz Esmer - Ben Fero</td>\n",
              "      <td>98</td>\n",
              "      <td>1</td>\n",
              "      <td>turkce drill</td>\n",
              "      <td>3qOxkhNytyb6ifxNzxxULn</td>\n",
              "      <td>1.675778</td>\n",
              "      <td>0.362264</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0746</td>\n",
              "      <td>0.388</td>\n",
              "      <td>140.070</td>\n",
              "      <td>audio_features</td>\n",
              "      <td>3qOxkhNytyb6ifxNzxxULn</td>\n",
              "      <td>spotify:track:3qOxkhNytyb6ifxNzxxULn</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/3qOxkhNytyb6...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/3qOx...</td>\n",
              "      <td>226768.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "  master_metadata_track_name master_metadata_album_artist_name  \\\n",
              "0             Şehrimin Tadı                             Ezhel   \n",
              "1                    Geceler                             Ezhel   \n",
              "2                       Mono                     Courtney Love   \n",
              "3        Kazıdık Tırnaklarla                             Ezhel   \n",
              "4           Mahallemiz Esmer                          Ben Fero   \n",
              "\n",
              "                      spotify_track_uri                 track_artist  \\\n",
              "0  spotify:track:5JaXKUIcNxwSuUyD4CsphL       Şehrimin Tadı - Ezhel   \n",
              "1  spotify:track:0R0o8uOkDz40XsR6uwThfQ              Geceler - Ezhel   \n",
              "2  spotify:track:7zL1KgkpPb2Gi5Q2Inypl5         Mono - Courtney Love   \n",
              "3  spotify:track:27OWNTEEtJCFVejrh5mqgv  Kazıdık Tırnaklarla - Ezhel   \n",
              "4  spotify:track:3qOxkhNytyb6ifxNzxxULn  Mahallemiz Esmer - Ben Fero   \n",
              "\n",
              "   track_listen_count  popularity             genre               track_uri  \\\n",
              "0                  39           1   turkish hip hop  5JaXKUIcNxwSuUyD4CsphL   \n",
              "1                  55           1   turkish hip hop  0R0o8uOkDz40XsR6uwThfQ   \n",
              "2                  72           1  alternative rock  7zL1KgkpPb2Gi5Q2Inypl5   \n",
              "3                 134           1   turkish hip hop  27OWNTEEtJCFVejrh5mqgv   \n",
              "4                  98           1      turkce drill  3qOxkhNytyb6ifxNzxxULn   \n",
              "\n",
              "   mean_bar_duration  mean_pitches  ...  liveness  valence    tempo  \\\n",
              "0           2.010277      0.383483  ...    0.3220    0.302  125.055   \n",
              "1           1.845690      0.346793  ...    0.0939    0.339  130.020   \n",
              "2           1.527554      0.430793  ...    0.3350    0.442  155.967   \n",
              "3           1.718326      0.292124  ...    0.0998    0.358  139.821   \n",
              "4           1.675778      0.362264  ...    0.0746    0.388  140.070   \n",
              "\n",
              "             type                      id  \\\n",
              "0  audio_features  5JaXKUIcNxwSuUyD4CsphL   \n",
              "1  audio_features  0R0o8uOkDz40XsR6uwThfQ   \n",
              "2  audio_features  7zL1KgkpPb2Gi5Q2Inypl5   \n",
              "3  audio_features  27OWNTEEtJCFVejrh5mqgv   \n",
              "4  audio_features  3qOxkhNytyb6ifxNzxxULn   \n",
              "\n",
              "                                    uri  \\\n",
              "0  spotify:track:5JaXKUIcNxwSuUyD4CsphL   \n",
              "1  spotify:track:0R0o8uOkDz40XsR6uwThfQ   \n",
              "2  spotify:track:7zL1KgkpPb2Gi5Q2Inypl5   \n",
              "3  spotify:track:27OWNTEEtJCFVejrh5mqgv   \n",
              "4  spotify:track:3qOxkhNytyb6ifxNzxxULn   \n",
              "\n",
              "                                          track_href  \\\n",
              "0  https://api.spotify.com/v1/tracks/5JaXKUIcNxwS...   \n",
              "1  https://api.spotify.com/v1/tracks/0R0o8uOkDz40...   \n",
              "2  https://api.spotify.com/v1/tracks/7zL1KgkpPb2G...   \n",
              "3  https://api.spotify.com/v1/tracks/27OWNTEEtJCF...   \n",
              "4  https://api.spotify.com/v1/tracks/3qOxkhNytyb6...   \n",
              "\n",
              "                                        analysis_url  duration_ms  \\\n",
              "0  https://api.spotify.com/v1/audio-analysis/5JaX...     253450.0   \n",
              "1  https://api.spotify.com/v1/audio-analysis/0R0o...     222928.0   \n",
              "2  https://api.spotify.com/v1/audio-analysis/7zL1...     219693.0   \n",
              "3  https://api.spotify.com/v1/audio-analysis/27OW...     172609.0   \n",
              "4  https://api.spotify.com/v1/audio-analysis/3qOx...     226768.0   \n",
              "\n",
              "   time_signature  \n",
              "0             4.0  \n",
              "1             4.0  \n",
              "2             4.0  \n",
              "3             4.0  \n",
              "4             4.0  \n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_audio = sdata.get_audio_features(df_genre, save_df=True)\n",
        "df_audio.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "id": "wx7TSLSOgntS",
        "outputId": "c7010a41-a485-4db8-83fa-cf77adef5dea"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    master_metadata_track_name master_metadata_album_artist_name  \\\n",
              "0               Şehrimin Tadı                             Ezhel   \n",
              "1                      Geceler                             Ezhel   \n",
              "2                         Mono                     Courtney Love   \n",
              "3          Kazıdık Tırnaklarla                             Ezhel   \n",
              "4             Mahallemiz Esmer                          Ben Fero   \n",
              "..                         ...                               ...   \n",
              "495                       Walk                       Kyddiekafka   \n",
              "496                   0% Angel                          Mr.Kitty   \n",
              "497                    Glitter                            GRXGVR   \n",
              "498            One Step Closer                       Linkin Park   \n",
              "499                       Lost                       Linkin Park   \n",
              "\n",
              "                        spotify_track_uri                   track_artist  \\\n",
              "0    spotify:track:5JaXKUIcNxwSuUyD4CsphL         Şehrimin Tadı - Ezhel   \n",
              "1    spotify:track:0R0o8uOkDz40XsR6uwThfQ                Geceler - Ezhel   \n",
              "2    spotify:track:7zL1KgkpPb2Gi5Q2Inypl5           Mono - Courtney Love   \n",
              "3    spotify:track:27OWNTEEtJCFVejrh5mqgv    Kazıdık Tırnaklarla - Ezhel   \n",
              "4    spotify:track:3qOxkhNytyb6ifxNzxxULn    Mahallemiz Esmer - Ben Fero   \n",
              "..                                    ...                            ...   \n",
              "495  spotify:track:2cVKHQTLJbgs8TUqQXgVDU             Walk - Kyddiekafka   \n",
              "496  spotify:track:0l2Ob0zMIQ21gJn642HyQ1            0% Angel - Mr.Kitty   \n",
              "497  spotify:track:7ogu6FZbymEdWuofKabkOu               Glitter - GRXGVR   \n",
              "498  spotify:track:3K4HG9evC7dg3N0R9cYqk4  One Step Closer - Linkin Park   \n",
              "499  spotify:track:3RQHAFkEHiFHQ7qmMpKRdv             Lost - Linkin Park   \n",
              "\n",
              "     track_listen_count  popularity              genre  \\\n",
              "0                    39           1    turkish hip hop   \n",
              "1                    55           1    turkish hip hop   \n",
              "2                    72           1   alternative rock   \n",
              "3                   134           1    turkish hip hop   \n",
              "4                    98           1       turkce drill   \n",
              "..                  ...         ...                ...   \n",
              "495                   1           0               wave   \n",
              "496                   1           0          weirdcore   \n",
              "497                   1           0          cloud rap   \n",
              "498                   1           0  alternative metal   \n",
              "499                   1           0  alternative metal   \n",
              "\n",
              "                  track_uri  mean_bar_duration  mean_pitches  ...  liveness  \\\n",
              "0    5JaXKUIcNxwSuUyD4CsphL           2.010277      0.383483  ...    0.3220   \n",
              "1    0R0o8uOkDz40XsR6uwThfQ           1.845690      0.346793  ...    0.0939   \n",
              "2    7zL1KgkpPb2Gi5Q2Inypl5           1.527554      0.430793  ...    0.3350   \n",
              "3    27OWNTEEtJCFVejrh5mqgv           1.718326      0.292124  ...    0.0998   \n",
              "4    3qOxkhNytyb6ifxNzxxULn           1.675778      0.362264  ...    0.0746   \n",
              "..                      ...                ...           ...  ...       ...   \n",
              "495  2cVKHQTLJbgs8TUqQXgVDU           1.773178      0.337286  ...    0.1100   \n",
              "496  0l2Ob0zMIQ21gJn642HyQ1           1.784976      0.355810  ...    0.0797   \n",
              "497  7ogu6FZbymEdWuofKabkOu           1.792892      0.227856  ...    0.1120   \n",
              "498  3K4HG9evC7dg3N0R9cYqk4           2.502925      0.402978  ...    0.0787   \n",
              "499  3RQHAFkEHiFHQ7qmMpKRdv           2.285346      0.348945  ...    0.3380   \n",
              "\n",
              "     valence    tempo            type                      id  \\\n",
              "0     0.3020  125.055  audio_features  5JaXKUIcNxwSuUyD4CsphL   \n",
              "1     0.3390  130.020  audio_features  0R0o8uOkDz40XsR6uwThfQ   \n",
              "2     0.4420  155.967  audio_features  7zL1KgkpPb2Gi5Q2Inypl5   \n",
              "3     0.3580  139.821  audio_features  27OWNTEEtJCFVejrh5mqgv   \n",
              "4     0.3880  140.070  audio_features  3qOxkhNytyb6ifxNzxxULn   \n",
              "..       ...      ...             ...                     ...   \n",
              "495   0.0849  135.026  audio_features  2cVKHQTLJbgs8TUqQXgVDU   \n",
              "496   0.7660  135.013  audio_features  0l2Ob0zMIQ21gJn642HyQ1   \n",
              "497   0.1910  132.006  audio_features  7ogu6FZbymEdWuofKabkOu   \n",
              "498   0.5380   95.136  audio_features  3K4HG9evC7dg3N0R9cYqk4   \n",
              "499   0.4590  105.010  audio_features  3RQHAFkEHiFHQ7qmMpKRdv   \n",
              "\n",
              "                                      uri  \\\n",
              "0    spotify:track:5JaXKUIcNxwSuUyD4CsphL   \n",
              "1    spotify:track:0R0o8uOkDz40XsR6uwThfQ   \n",
              "2    spotify:track:7zL1KgkpPb2Gi5Q2Inypl5   \n",
              "3    spotify:track:27OWNTEEtJCFVejrh5mqgv   \n",
              "4    spotify:track:3qOxkhNytyb6ifxNzxxULn   \n",
              "..                                    ...   \n",
              "495  spotify:track:2cVKHQTLJbgs8TUqQXgVDU   \n",
              "496  spotify:track:0l2Ob0zMIQ21gJn642HyQ1   \n",
              "497  spotify:track:7ogu6FZbymEdWuofKabkOu   \n",
              "498  spotify:track:3K4HG9evC7dg3N0R9cYqk4   \n",
              "499  spotify:track:3RQHAFkEHiFHQ7qmMpKRdv   \n",
              "\n",
              "                                            track_href  \\\n",
              "0    https://api.spotify.com/v1/tracks/5JaXKUIcNxwS...   \n",
              "1    https://api.spotify.com/v1/tracks/0R0o8uOkDz40...   \n",
              "2    https://api.spotify.com/v1/tracks/7zL1KgkpPb2G...   \n",
              "3    https://api.spotify.com/v1/tracks/27OWNTEEtJCF...   \n",
              "4    https://api.spotify.com/v1/tracks/3qOxkhNytyb6...   \n",
              "..                                                 ...   \n",
              "495  https://api.spotify.com/v1/tracks/2cVKHQTLJbgs...   \n",
              "496  https://api.spotify.com/v1/tracks/0l2Ob0zMIQ21...   \n",
              "497  https://api.spotify.com/v1/tracks/7ogu6FZbymEd...   \n",
              "498  https://api.spotify.com/v1/tracks/3K4HG9evC7dg...   \n",
              "499  https://api.spotify.com/v1/tracks/3RQHAFkEHiFH...   \n",
              "\n",
              "                                          analysis_url  duration_ms  \\\n",
              "0    https://api.spotify.com/v1/audio-analysis/5JaX...     253450.0   \n",
              "1    https://api.spotify.com/v1/audio-analysis/0R0o...     222928.0   \n",
              "2    https://api.spotify.com/v1/audio-analysis/7zL1...     219693.0   \n",
              "3    https://api.spotify.com/v1/audio-analysis/27OW...     172609.0   \n",
              "4    https://api.spotify.com/v1/audio-analysis/3qOx...     226768.0   \n",
              "..                                                 ...          ...   \n",
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              "      <td>https://api.spotify.com/v1/audio-analysis/0R0o...</td>\n",
              "      <td>222928.0</td>\n",
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              "      <th>2</th>\n",
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              "      <td>219693.0</td>\n",
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              "      <th>4</th>\n",
              "      <td>Mahallemiz Esmer</td>\n",
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              "      <td>136364.0</td>\n",
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              "      <th>498</th>\n",
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              "      <td>spotify:track:3K4HG9evC7dg3N0R9cYqk4</td>\n",
              "      <td>One Step Closer - Linkin Park</td>\n",
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              "      <td>alternative metal</td>\n",
              "      <td>3K4HG9evC7dg3N0R9cYqk4</td>\n",
              "      <td>2.502925</td>\n",
              "      <td>0.402978</td>\n",
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              "      <td>0.5380</td>\n",
              "      <td>95.136</td>\n",
              "      <td>audio_features</td>\n",
              "      <td>3K4HG9evC7dg3N0R9cYqk4</td>\n",
              "      <td>spotify:track:3K4HG9evC7dg3N0R9cYqk4</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/3K4HG9evC7dg...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/3K4H...</td>\n",
              "      <td>157333.0</td>\n",
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              "      <th>499</th>\n",
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              "      <td>0.348945</td>\n",
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              "      <td>https://api.spotify.com/v1/audio-analysis/3RQH...</td>\n",
              "      <td>199342.0</td>\n",
              "      <td>4.0</td>\n",
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              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-71275251-f4e0-442a-ae1d-c066f330271b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "df_audio = pd.read_csv(\"./df_with_audio_features.csv\")\n",
        "df_audio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p0MgEV2SgntS"
      },
      "source": [
        "Drop any NaN samples"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "bK8_mIQbgntS"
      },
      "outputs": [],
      "source": [
        "df_audio.dropna(inplace=True)\n",
        "df_audio.reset_index(drop=True, inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bNz0bDLwgntS"
      },
      "source": [
        "Instantiate plotting class to create various plots"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NvcK053lgntS"
      },
      "outputs": [],
      "source": [
        "plotting = spa.CreatePlots(df_audio)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dg73sem-gntT",
        "outputId": "b5ab8dab-9e7d-4eb7-c0c3-bce4681da213"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plotting.barplot_streamcount(xlabel=\"artist\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WMLesp2WgntT",
        "outputId": "9c9205ad-5a27-473c-e47b-9824f4367cc3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plotting.barplot_streamcount(xlabel=\"genre\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xd_rhlwpgntT",
        "outputId": "730e6f1c-0147-4e20-840b-f1e87e375733"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plotting.plot_features()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zvvnwQv0gntT",
        "outputId": "ca684c6b-fc30-4c80-8a46-58fbe2948b55"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plotting.plot_features(groupby='popularity')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IPvgM4RxgntT"
      },
      "source": [
        "Instantiate the classifier class to create our model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "H-9Xs5u0gntU"
      },
      "outputs": [],
      "source": [
        "cls = spa.RunClassifier(df_audio)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 712
        },
        "id": "C2eRxNp_gntU",
        "outputId": "d07ff04f-9892-409b-bcc6-a35a775385af"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Voting Classifier:\n",
            "Accuracy: 0.759493670886076\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.67      0.72        36\n",
            "           1       0.75      0.84      0.79        43\n",
            "\n",
            "    accuracy                           0.76        79\n",
            "   macro avg       0.76      0.75      0.75        79\n",
            "weighted avg       0.76      0.76      0.76        79\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "model = cls.create_model(max_iter=400)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lnFW7lWWgntU",
        "outputId": "826d469c-1981-4f75-fcbd-fd18b3f741e3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model saved successfully.\n"
          ]
        }
      ],
      "source": [
        "cls.save_model(model, 'spotify_mlp_model')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ciLTVd8QgntU"
      },
      "source": [
        "Instantiate playlist class to create our playlist based on our model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "2AF9Q5fZgntU"
      },
      "outputs": [],
      "source": [
        "playlist = spa.CreatePlaylist(sdata, model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "f0dOeoT2gntU",
        "outputId": "144cd739-a24f-49df-cbe4-a0dcc13b2f20"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     track             artist                     uri\n",
              "0                    Drive            Incubus  7nnWIPM5hwE3DaUBkvOIpy\n",
              "1          One Last Breath              Creed  42T2QQv3xgBlpQxaSP7lnK\n",
              "2        Seven Nation Army  The White Stripes  3dPQuX8Gs42Y7b454ybpMR\n",
              "3         It's Been Awhile             Staind  25CMmGsl22APKhfuj4Tp7j\n",
              "4         Somebody Told Me        The Killers  6PwjJ58I4t7Mae9xfZ9l9v\n",
              "5       You Get Me So High  The Neighbourhood  7zwn1eykZtZ5LODrf7c0tS\n",
              "6  The Kids Aren't Alright      The Offspring  4EchqUKQ3qAQuRNKmeIpnf\n",
              "7              Break Stuff        Limp Bizkit  5cZqsjVs6MevCnAkasbEOX\n",
              "8          Pumped Up Kicks  Foster The People  7w87IxuO7BDcJ3YUqCyMTT\n",
              "9                  Dragula         Rob Zombie  6Nm8h73ycDG2saCnZV8poF"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c95823a1-38ff-4f90-b941-83ff545d0b08\" class=\"colab-df-container\">\n",
              "    <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>track</th>\n",
              "      <th>artist</th>\n",
              "      <th>uri</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Drive</td>\n",
              "      <td>Incubus</td>\n",
              "      <td>7nnWIPM5hwE3DaUBkvOIpy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>One Last Breath</td>\n",
              "      <td>Creed</td>\n",
              "      <td>42T2QQv3xgBlpQxaSP7lnK</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Seven Nation Army</td>\n",
              "      <td>The White Stripes</td>\n",
              "      <td>3dPQuX8Gs42Y7b454ybpMR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>It's Been Awhile</td>\n",
              "      <td>Staind</td>\n",
              "      <td>25CMmGsl22APKhfuj4Tp7j</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Somebody Told Me</td>\n",
              "      <td>The Killers</td>\n",
              "      <td>6PwjJ58I4t7Mae9xfZ9l9v</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>You Get Me So High</td>\n",
              "      <td>The Neighbourhood</td>\n",
              "      <td>7zwn1eykZtZ5LODrf7c0tS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>The Kids Aren't Alright</td>\n",
              "      <td>The Offspring</td>\n",
              "      <td>4EchqUKQ3qAQuRNKmeIpnf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Break Stuff</td>\n",
              "      <td>Limp Bizkit</td>\n",
              "      <td>5cZqsjVs6MevCnAkasbEOX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Pumped Up Kicks</td>\n",
              "      <td>Foster The People</td>\n",
              "      <td>7w87IxuO7BDcJ3YUqCyMTT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Dragula</td>\n",
              "      <td>Rob Zombie</td>\n",
              "      <td>6Nm8h73ycDG2saCnZV8poF</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c95823a1-38ff-4f90-b941-83ff545d0b08')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c95823a1-38ff-4f90-b941-83ff545d0b08 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c95823a1-38ff-4f90-b941-83ff545d0b08');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8caed6ba-d050-49b6-99a3-9731afb5bc8a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8caed6ba-d050-49b6-99a3-9731afb5bc8a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8caed6ba-d050-49b6-99a3-9731afb5bc8a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "playlist.create_playlist(genre=\"alternative\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UoTb6ZMXgntV"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": ".venv",
      "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": "3.12.1"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}