pycaret-clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data science approach to organizing my playlist\n",
    "[Complete Post](https://arjon.es/2020/data-science-approach-to-organizing-my-playlist/)\n",
    "\n",
    "## Clustering with pycaret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('my-playlist.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['track_id', 'track_name', 'danceability', 'energy', 'tempo', 'loudness',\n",
       "       'valence', 'speechiness', 'instrumentalness', 'liveness',\n",
       "       'acousticness'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['track_id', 'track_name', 'danceability', 'energy', 'tempo', 'valence'], dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select columns to keep on dataset\n",
    "COLUMNS_KEEP = ['track_id', 'track_name', 'danceability', 'energy', 'tempo', 'valence'] #, 'liveness', 'acousticness']\n",
    "\n",
    "data = data[COLUMNS_KEEP]\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>tempo</th>\n",
       "      <th>valence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.598886</td>\n",
       "      <td>0.629162</td>\n",
       "      <td>119.993211</td>\n",
       "      <td>0.574082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.146012</td>\n",
       "      <td>0.213013</td>\n",
       "      <td>26.798519</td>\n",
       "      <td>0.246125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.128000</td>\n",
       "      <td>0.040600</td>\n",
       "      <td>65.179000</td>\n",
       "      <td>0.059100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.497500</td>\n",
       "      <td>0.479000</td>\n",
       "      <td>99.934500</td>\n",
       "      <td>0.383500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.615000</td>\n",
       "      <td>0.655000</td>\n",
       "      <td>119.344000</td>\n",
       "      <td>0.588000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.704000</td>\n",
       "      <td>0.801000</td>\n",
       "      <td>138.004000</td>\n",
       "      <td>0.766500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.941000</td>\n",
       "      <td>0.977000</td>\n",
       "      <td>189.892000</td>\n",
       "      <td>0.985000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       danceability      energy       tempo     valence\n",
       "count    299.000000  299.000000  299.000000  299.000000\n",
       "mean       0.598886    0.629162  119.993211    0.574082\n",
       "std        0.146012    0.213013   26.798519    0.246125\n",
       "min        0.128000    0.040600   65.179000    0.059100\n",
       "25%        0.497500    0.479000   99.934500    0.383500\n",
       "50%        0.615000    0.655000  119.344000    0.588000\n",
       "75%        0.704000    0.801000  138.004000    0.766500\n",
       "max        0.941000    0.977000  189.892000    0.985000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['url'] = data[['track_id']].apply(lambda x: f\"https://open.spotify.com/track/{x['track_id']}\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Setup Succesfully Completed!\n"
     ]
    },
    {
     "data": {
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       "        }</style><table id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66f\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66flevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
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       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow0_col1\" class=\"data row0 col1\" >42</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66flevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow1_col0\" class=\"data row1 col0\" >Original Data </td>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow1_col1\" class=\"data row1 col1\" >(299, 7)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66flevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow2_col0\" class=\"data row2 col0\" >Missing Values </td>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow2_col1\" class=\"data row2 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66flevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow3_col0\" class=\"data row3 col0\" >Numeric Features </td>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow3_col1\" class=\"data row3 col1\" >4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66flevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow4_col0\" class=\"data row4 col0\" >Categorical Features </td>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow4_col1\" class=\"data row4 col1\" >3</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66flevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow5_col0\" class=\"data row5 col0\" >Ordinal Features </td>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow5_col1\" class=\"data row5 col1\" >False</td>\n",
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       "                        <th id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66flevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow6_col0\" class=\"data row6 col0\" >High Cardinality Features </td>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow6_col1\" class=\"data row6 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66flevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow7_col0\" class=\"data row7 col0\" >Transformed Data </td>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow7_col1\" class=\"data row7 col1\" >(299, 4)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66flevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow8_col0\" class=\"data row8 col0\" >Numeric Imputer </td>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow8_col1\" class=\"data row8 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66flevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow9_col0\" class=\"data row9 col0\" >Categorical Imputer </td>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow9_col1\" class=\"data row9 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66flevel0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow10_col0\" class=\"data row10 col0\" >Normalize </td>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow10_col1\" class=\"data row10 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66flevel0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow11_col0\" class=\"data row11 col0\" >Normalize Method </td>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow11_col1\" class=\"data row11 col1\" >zscore</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66flevel0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow12_col0\" class=\"data row12 col0\" >Transformation </td>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow12_col1\" class=\"data row12 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66flevel0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow13_col0\" class=\"data row13 col0\" >Transformation Method </td>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow13_col1\" class=\"data row13 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66flevel0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow14_col0\" class=\"data row14 col0\" >PCA </td>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow14_col1\" class=\"data row14 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66flevel0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow15_col0\" class=\"data row15 col0\" >PCA Method </td>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow15_col1\" class=\"data row15 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66flevel0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow16_col0\" class=\"data row16 col0\" >PCA components </td>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow16_col1\" class=\"data row16 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66flevel0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow17_col0\" class=\"data row17 col0\" >Ignore Low Variance </td>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow17_col1\" class=\"data row17 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66flevel0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow18_col0\" class=\"data row18 col0\" >Combine Rare Levels </td>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow18_col1\" class=\"data row18 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66flevel0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow19_col0\" class=\"data row19 col0\" >Rare Level Threshold </td>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow19_col1\" class=\"data row19 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66flevel0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow20_col0\" class=\"data row20 col0\" >Numeric Binning </td>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow20_col1\" class=\"data row20 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66flevel0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow21_col0\" class=\"data row21 col0\" >Remove Multicollinearity </td>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow21_col1\" class=\"data row21 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66flevel0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow22_col0\" class=\"data row22 col0\" >Multicollinearity Threshold </td>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow22_col1\" class=\"data row22 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66flevel0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow23_col0\" class=\"data row23 col0\" >Group Features </td>\n",
       "                        <td id=\"T_adacfeae_9ecc_11ea_a047_a683e7bfb66frow23_col1\" class=\"data row23 col1\" >False</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
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      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.clustering import *\n",
    "s = setup(data, normalize = True, \n",
    "                   ignore_features = ['track_name', 'track_id', 'url'],\n",
    "                   session_id = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create_model?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Clustering modules](https://scikit-learn.org/stable/modules/clustering.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Model\n",
    "m = create_model('kmeans', num_clusters=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
      "       n_clusters=6, n_init=10, n_jobs=None, precompute_distances='auto',\n",
      "       random_state=42, tol=0.0001, verbose=0)\n"
     ]
    }
   ],
   "source": [
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = assign_model(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Find best value of K\n",
    "plot_model(m, plot = 'elbow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
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    "plot_model(m, feature='track_name')"
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   "execution_count": 34,
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       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html\n",
    "plot_model(m, plot = 'silhouette')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "bingroup": "x",
         "hoverlabel": {
          "namelength": 0
         },
         "hovertemplate": "Cluster=%{x}<br>count=%{y}",
         "legendgroup": "Cluster=Cluster 0",
         "marker": {
          "color": "#636efa",
          "opacity": 0.7
         },
         "name": "Cluster=Cluster 0",
         "offsetgroup": "Cluster=Cluster 0",
         "orientation": "v",
         "showlegend": true,
         "type": "histogram",
         "x": [
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0",
          "Cluster 0"
         ],
         "xaxis": "x",
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "customdata": [
          [
           "4uYwlMp841PLJmj1gJJwIq",
           "Lay, Lady, Lay",
           0.52,
           0.179,
           155.746,
           0.40700000000000003,
           "https://open.spotify.com/track/4uYwlMp841PLJmj1gJJwIq",
           "Cluster 0"
          ],
          [
           "4y6P6eic8hhTcFqtaV5ftn",
           "Carta Ao Tom 74",
           0.621,
           0.369,
           143.1,
           0.517,
           "https://open.spotify.com/track/4y6P6eic8hhTcFqtaV5ftn",
           "Cluster 0"
          ],
          [
           "6nbyjI9tJUITph7LN7uZPj",
           "O Trem das 7 (Ao Vivo)",
           0.396,
           0.47600000000000003,
           122.02,
           0.39399999999999996,
           "https://open.spotify.com/track/6nbyjI9tJUITph7LN7uZPj",
           "Cluster 0"
          ],
          [
           "1Ow7BwZB6NKfxham67mLH7",
           "Acontecimentos",
           0.634,
           0.495,
           137.879,
           0.45399999999999996,
           "https://open.spotify.com/track/1Ow7BwZB6NKfxham67mLH7",
           "Cluster 0"
          ],
          [
           "5aGhtbviqUqjtno5vcUmCN",
           "Colo de Menina",
           0.654,
           0.46399999999999997,
           145.371,
           0.72,
           "https://open.spotify.com/track/5aGhtbviqUqjtno5vcUmCN",
           "Cluster 0"
          ],
          [
           "0X3wPQutw80NtG4PQGolvT",
           "Let's Call the Whole Thing Off",
           0.644,
           0.312,
           123.396,
           0.5820000000000001,
           "https://open.spotify.com/track/0X3wPQutw80NtG4PQGolvT",
           "Cluster 0"
          ],
          [
           "2kzEEsKiUPTvct5pGflQVF",
           "Greenback Boogie (Theme from \"Suits\")",
           0.629,
           0.67,
           142.997,
           0.418,
           "https://open.spotify.com/track/2kzEEsKiUPTvct5pGflQVF",
           "Cluster 0"
          ],
          [
           "2Yk0HvfTaijA47aM0Fj88u",
           "Metamorfose Ambulante",
           0.498,
           0.5870000000000001,
           135.91299999999998,
           0.597,
           "https://open.spotify.com/track/2Yk0HvfTaijA47aM0Fj88u",
           "Cluster 0"
          ],
          [
           "333v4s9EybUEFOY6ComIC2",
           "Amor e Sexo",
           0.6409999999999999,
           0.37,
           129.843,
           0.17600000000000002,
           "https://open.spotify.com/track/333v4s9EybUEFOY6ComIC2",
           "Cluster 0"
          ],
          [
           "3YKptz29AsOlm7WAVnztBh",
           "Kiss from a Rose",
           0.583,
           0.532,
           131.741,
           0.223,
           "https://open.spotify.com/track/3YKptz29AsOlm7WAVnztBh",
           "Cluster 0"
          ],
          [
           "6Nabz5Gsy80XL0MJoOmgF8",
           "Beds Are Burning - Remastered",
           0.754,
           0.496,
           119.186,
           0.341,
           "https://open.spotify.com/track/6Nabz5Gsy80XL0MJoOmgF8",
           "Cluster 0"
          ],
          [
           "1BIDHSkkst8nckyBaHVP6A",
           "Águas De Março",
           0.637,
           0.551,
           143.10399999999998,
           0.46799999999999997,
           "https://open.spotify.com/track/1BIDHSkkst8nckyBaHVP6A",
           "Cluster 0"
          ],
          [
           "6mFkJmJqdDVQ1REhVfGgd1",
           "Wish You Were Here",
           0.48100000000000004,
           0.262,
           122.883,
           0.375,
           "https://open.spotify.com/track/6mFkJmJqdDVQ1REhVfGgd1",
           "Cluster 0"
          ],
          [
           "5jrdCoLpJSvHHorevXBATy",
           "Dark Horse",
           0.645,
           0.585,
           131.931,
           0.353,
           "https://open.spotify.com/track/5jrdCoLpJSvHHorevXBATy",
           "Cluster 0"
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       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(m, plot = 'distribution') #to see size of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cluster</th>\n",
       "      <th>track_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Have You Ever Seen The Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cluster 4</td>\n",
       "      <td>Break It Down Again</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Stay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>A Visita</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>O Que É O Que É?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Loving Every Minute</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Nosso Pequeno Castelo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Cluster 0</td>\n",
       "      <td>Voce Abusou</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>I Follow Rivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Relicário</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Cluster                   track_name\n",
       "0  Cluster 1  Have You Ever Seen The Rain\n",
       "1  Cluster 4          Break It Down Again\n",
       "2  Cluster 1                         Stay\n",
       "3  Cluster 1                     A Visita\n",
       "4  Cluster 1             O Que É O Que É?\n",
       "5  Cluster 1          Loving Every Minute\n",
       "6  Cluster 1        Nosso Pequeno Castelo\n",
       "7  Cluster 0                  Voce Abusou\n",
       "8  Cluster 1              I Follow Rivers\n",
       "9  Cluster 3                    Relicário"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[['Cluster', 'track_name']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ddisplay(df):\n",
    "    def make_clickable(url):\n",
    "        return f'<a target=\"_blank\" href=\"{url}\">listen</a>'\n",
    "\n",
    "    display(df.style.format({'url': make_clickable}))\n",
    "    pass\n",
    "\n",
    "# Check results\n",
    "def select_cluster(id):\n",
    "    _df = results[results['Cluster']==f'Cluster {id}'][['track_name', 'url']]\n",
    "    return _df.sample(n=15, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66f\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >track_name</th>        <th class=\"col_heading level0 col1\" >url</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66flevel0_row0\" class=\"row_heading level0 row0\" >288</th>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow0_col0\" class=\"data row0 col0\" >18 and Life</td>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow0_col1\" class=\"data row0 col1\" ><a target=\"_blank\" href=\"https://open.spotify.com/track/0qgrrDnUUhyxpxbBznUnzg\">listen</a></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66flevel0_row1\" class=\"row_heading level0 row1\" >100</th>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow1_col0\" class=\"data row1 col0\" >These Boots Are Made For Walkin'</td>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow1_col1\" class=\"data row1 col1\" ><a target=\"_blank\" href=\"https://open.spotify.com/track/2nwCO1PqpvyoFIvq3Vrj8N\">listen</a></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66flevel0_row2\" class=\"row_heading level0 row2\" >233</th>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow2_col0\" class=\"data row2 col0\" >Smiled At Me (Sorriu Para Mim)</td>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow2_col1\" class=\"data row2 col1\" ><a target=\"_blank\" href=\"https://open.spotify.com/track/6IVjnPfMGjQeUXvqpzLLdY\">listen</a></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66flevel0_row3\" class=\"row_heading level0 row3\" >118</th>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow3_col0\" class=\"data row3 col0\" >Dream A Little Dream Of Me</td>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow3_col1\" class=\"data row3 col1\" ><a target=\"_blank\" href=\"https://open.spotify.com/track/3Bbbz0IGORWZSLf9UqsAL4\">listen</a></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66flevel0_row4\" class=\"row_heading level0 row4\" >50</th>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow4_col0\" class=\"data row4 col0\" >Non, je ne regrette rien - Remasterisé En 2015</td>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow4_col1\" class=\"data row4 col1\" ><a target=\"_blank\" href=\"https://open.spotify.com/track/0qukw1Cr7FADBAgzU55MQK\">listen</a></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66flevel0_row5\" class=\"row_heading level0 row5\" >54</th>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow5_col0\" class=\"data row5 col0\" >The Thrill Is Gone</td>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow5_col1\" class=\"data row5 col1\" ><a target=\"_blank\" href=\"https://open.spotify.com/track/5gEuWtxcn5Vz4xpT7dBoBU\">listen</a></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66flevel0_row6\" class=\"row_heading level0 row6\" >157</th>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow6_col0\" class=\"data row6 col0\" >Dona Cila</td>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow6_col1\" class=\"data row6 col1\" ><a target=\"_blank\" href=\"https://open.spotify.com/track/2FYE5aTxHX5lMJZ8HKmiQn\">listen</a></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66flevel0_row7\" class=\"row_heading level0 row7\" >174</th>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow7_col0\" class=\"data row7 col0\" >Telegrama</td>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow7_col1\" class=\"data row7 col1\" ><a target=\"_blank\" href=\"https://open.spotify.com/track/7xpDGoUGbtaquk5xMvzwTh\">listen</a></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66flevel0_row8\" class=\"row_heading level0 row8\" >84</th>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow8_col0\" class=\"data row8 col0\" >Dream a Little Dream of Me</td>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow8_col1\" class=\"data row8 col1\" ><a target=\"_blank\" href=\"https://open.spotify.com/track/00hwJU6jHS9C1LW4PLWXId\">listen</a></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66flevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow9_col0\" class=\"data row9 col0\" >Relicário</td>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow9_col1\" class=\"data row9 col1\" ><a target=\"_blank\" href=\"https://open.spotify.com/track/2kMvM1l45ZjpOv73qPefxA\">listen</a></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66flevel0_row10\" class=\"row_heading level0 row10\" >24</th>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow10_col0\" class=\"data row10 col0\" >Skyfall</td>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow10_col1\" class=\"data row10 col1\" ><a target=\"_blank\" href=\"https://open.spotify.com/track/3mr584ZFSkZrWja9kJAzlb\">listen</a></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66flevel0_row11\" class=\"row_heading level0 row11\" >117</th>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow11_col0\" class=\"data row11 col0\" >La vie en rose - Single Version</td>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow11_col1\" class=\"data row11 col1\" ><a target=\"_blank\" href=\"https://open.spotify.com/track/0zDVclPMSsjeyvbnqhWOmt\">listen</a></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66flevel0_row12\" class=\"row_heading level0 row12\" >35</th>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow12_col0\" class=\"data row12 col0\" >Aquarela</td>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow12_col1\" class=\"data row12 col1\" ><a target=\"_blank\" href=\"https://open.spotify.com/track/4rq3CoA3hrw3LjgtItwSu5\">listen</a></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66flevel0_row13\" class=\"row_heading level0 row13\" >85</th>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow13_col0\" class=\"data row13 col0\" >What a Wonderful World</td>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow13_col1\" class=\"data row13 col1\" ><a target=\"_blank\" href=\"https://open.spotify.com/track/6tjZNDZo24C8gGelsozCzB\">listen</a></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66flevel0_row14\" class=\"row_heading level0 row14\" >58</th>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow14_col0\" class=\"data row14 col0\" >O bêbado e a equilibrista</td>\n",
       "                        <td id=\"T_48ac70fa_9ece_11ea_a047_a683e7bfb66frow14_col1\" class=\"data row14 col1\" ><a target=\"_blank\" href=\"https://open.spotify.com/track/7KFUj2MunvS4cbauF04r6w\">listen</a></td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1355eb290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ddisplay(select_cluster(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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   "nbconvert_exporter": "python",
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