1.2-Machine_Learning-FeaturesAndVisualization_solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Humans are really good a finding patterns. Even from a young age, they can group together items that are similar. Grouping similar things is an essential step in learning concepts, even before language comes into play. \n",
    "\n",
    "In machine learning, we do things like find *similar things*, *classify*, *predict*, *generate*, and more. \n",
    "\n",
    "Today we'll learn about things called \"features\", measuring the difference between things, and finding similarities!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# In Daily Life..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Machine learning today has taken off thanks to the large amounts of data available, thanks to the internet. One example includes Netflix being able to suggest new shows that you might like. \n",
    "\n",
    "Let's explore this idea more. The main concept behind it is that you might like things that are similar to what you already like. This has some caveats (we'll talk about it in the Ethics for AI section), but it is behind much of the technology we have around us today."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's talk about music."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imagine that we want to make an AI program that suggests new song that you might like. Let's say you are interested in discovering songs that you like from other countries, or in other languages.\n",
    "\n",
    "But how do you quantify or measure what you like? Could you even describe it?\n",
    "\n",
    "Let's explore the Spotify Dataset 2017 songs of 2017 to learn how an algorithm to suggest new songs might work.\n",
    "\n",
    "Consider this dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "data = pandas.read_csv('data/spotify.csv')  # Top 2017 songs of 2017 from kaggle.com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>key</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "      <th>valence</th>\n",
       "      <th>target</th>\n",
       "      <th>song_title</th>\n",
       "      <th>artist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.010200</td>\n",
       "      <td>0.833</td>\n",
       "      <td>204600</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.021900</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1650</td>\n",
       "      <td>-8.795</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>150.062</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.286</td>\n",
       "      <td>1</td>\n",
       "      <td>Mask Off</td>\n",
       "      <td>Future</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.199000</td>\n",
       "      <td>0.743</td>\n",
       "      <td>326933</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.006110</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1370</td>\n",
       "      <td>-10.401</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0794</td>\n",
       "      <td>160.083</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.588</td>\n",
       "      <td>1</td>\n",
       "      <td>Redbone</td>\n",
       "      <td>Childish Gambino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.034400</td>\n",
       "      <td>0.838</td>\n",
       "      <td>185707</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>-7.148</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2890</td>\n",
       "      <td>75.044</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.173</td>\n",
       "      <td>1</td>\n",
       "      <td>Xanny Family</td>\n",
       "      <td>Future</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.604000</td>\n",
       "      <td>0.494</td>\n",
       "      <td>199413</td>\n",
       "      <td>0.338</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0922</td>\n",
       "      <td>-15.236</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0261</td>\n",
       "      <td>86.468</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.230</td>\n",
       "      <td>1</td>\n",
       "      <td>Master Of None</td>\n",
       "      <td>Beach House</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.678</td>\n",
       "      <td>392893</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.512000</td>\n",
       "      <td>5</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>-11.648</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0694</td>\n",
       "      <td>174.004</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.904</td>\n",
       "      <td>1</td>\n",
       "      <td>Parallel Lines</td>\n",
       "      <td>Junior Boys</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.004790</td>\n",
       "      <td>0.804</td>\n",
       "      <td>251333</td>\n",
       "      <td>0.560</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>0.1640</td>\n",
       "      <td>-6.682</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1850</td>\n",
       "      <td>85.023</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.264</td>\n",
       "      <td>1</td>\n",
       "      <td>Sneakin’</td>\n",
       "      <td>Drake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.014500</td>\n",
       "      <td>0.739</td>\n",
       "      <td>241400</td>\n",
       "      <td>0.472</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2070</td>\n",
       "      <td>-11.204</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1560</td>\n",
       "      <td>80.030</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.308</td>\n",
       "      <td>1</td>\n",
       "      <td>Childs Play</td>\n",
       "      <td>Drake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.020200</td>\n",
       "      <td>0.266</td>\n",
       "      <td>349667</td>\n",
       "      <td>0.348</td>\n",
       "      <td>0.664000</td>\n",
       "      <td>10</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>-11.609</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0371</td>\n",
       "      <td>144.154</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.393</td>\n",
       "      <td>1</td>\n",
       "      <td>Gyöngyhajú lány</td>\n",
       "      <td>Omega</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.048100</td>\n",
       "      <td>0.603</td>\n",
       "      <td>202853</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>0.3420</td>\n",
       "      <td>-3.626</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3470</td>\n",
       "      <td>130.035</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.398</td>\n",
       "      <td>1</td>\n",
       "      <td>I've Seen Footage</td>\n",
       "      <td>Death Grips</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.002080</td>\n",
       "      <td>0.836</td>\n",
       "      <td>226840</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>0.5710</td>\n",
       "      <td>-7.792</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2370</td>\n",
       "      <td>99.994</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.386</td>\n",
       "      <td>1</td>\n",
       "      <td>Digital Animal</td>\n",
       "      <td>Honey Claws</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>0.057200</td>\n",
       "      <td>0.525</td>\n",
       "      <td>358187</td>\n",
       "      <td>0.855</td>\n",
       "      <td>0.014300</td>\n",
       "      <td>5</td>\n",
       "      <td>0.6490</td>\n",
       "      <td>-7.372</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0548</td>\n",
       "      <td>111.951</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>1</td>\n",
       "      <td>Subways - In Flagranti Extended Edit</td>\n",
       "      <td>The Avalanches</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0.091500</td>\n",
       "      <td>0.753</td>\n",
       "      <td>324880</td>\n",
       "      <td>0.748</td>\n",
       "      <td>0.003480</td>\n",
       "      <td>10</td>\n",
       "      <td>0.2120</td>\n",
       "      <td>-8.620</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0494</td>\n",
       "      <td>104.322</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.642</td>\n",
       "      <td>1</td>\n",
       "      <td>Donme Dolap - Baris K Edit</td>\n",
       "      <td>Modern Folk Üçlüsü</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>0.253000</td>\n",
       "      <td>0.603</td>\n",
       "      <td>356973</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.061900</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1080</td>\n",
       "      <td>-11.062</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0342</td>\n",
       "      <td>127.681</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.381</td>\n",
       "      <td>1</td>\n",
       "      <td>Cemalim</td>\n",
       "      <td>Erkin Koray</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>0.366000</td>\n",
       "      <td>0.762</td>\n",
       "      <td>243270</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1030</td>\n",
       "      <td>-12.686</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>130.007</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.367</td>\n",
       "      <td>1</td>\n",
       "      <td>One Night</td>\n",
       "      <td>Lil Yachty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.662</td>\n",
       "      <td>247288</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0972</td>\n",
       "      <td>-8.317</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0793</td>\n",
       "      <td>125.011</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.351</td>\n",
       "      <td>1</td>\n",
       "      <td>Oh lala</td>\n",
       "      <td>PNL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>0.019000</td>\n",
       "      <td>0.637</td>\n",
       "      <td>188333</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.056300</td>\n",
       "      <td>6</td>\n",
       "      <td>0.3160</td>\n",
       "      <td>-6.637</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1630</td>\n",
       "      <td>99.988</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.317</td>\n",
       "      <td>1</td>\n",
       "      <td>Char</td>\n",
       "      <td>Crystal Castles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0.023900</td>\n",
       "      <td>0.603</td>\n",
       "      <td>270827</td>\n",
       "      <td>0.955</td>\n",
       "      <td>0.045100</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1190</td>\n",
       "      <td>-4.111</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0458</td>\n",
       "      <td>123.922</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.773</td>\n",
       "      <td>1</td>\n",
       "      <td>World In Motion</td>\n",
       "      <td>New Order</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>0.233000</td>\n",
       "      <td>0.789</td>\n",
       "      <td>447907</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.000490</td>\n",
       "      <td>4</td>\n",
       "      <td>0.1840</td>\n",
       "      <td>-12.654</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0429</td>\n",
       "      <td>122.415</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.842</td>\n",
       "      <td>1</td>\n",
       "      <td>One Nation Under a Groove</td>\n",
       "      <td>Funkadelic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>0.314000</td>\n",
       "      <td>0.713</td>\n",
       "      <td>195429</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1170</td>\n",
       "      <td>-6.702</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2410</td>\n",
       "      <td>140.061</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.783</td>\n",
       "      <td>1</td>\n",
       "      <td>Bouncin</td>\n",
       "      <td>Chief Keef</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>0.024200</td>\n",
       "      <td>0.735</td>\n",
       "      <td>214347</td>\n",
       "      <td>0.759</td>\n",
       "      <td>0.185000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0966</td>\n",
       "      <td>-6.914</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0449</td>\n",
       "      <td>109.982</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.763</td>\n",
       "      <td>1</td>\n",
       "      <td>C O O L - Radio Edit</td>\n",
       "      <td>Le Youth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>0.000702</td>\n",
       "      <td>0.854</td>\n",
       "      <td>249253</td>\n",
       "      <td>0.719</td>\n",
       "      <td>0.308000</td>\n",
       "      <td>10</td>\n",
       "      <td>0.4280</td>\n",
       "      <td>-9.335</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0655</td>\n",
       "      <td>128.049</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.471</td>\n",
       "      <td>1</td>\n",
       "      <td>Percolator (Jamie Jones Vault Mix) - mixed</td>\n",
       "      <td>Cajmere</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>0.000240</td>\n",
       "      <td>0.747</td>\n",
       "      <td>307680</td>\n",
       "      <td>0.740</td>\n",
       "      <td>0.369000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>-4.134</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0323</td>\n",
       "      <td>130.031</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.770</td>\n",
       "      <td>1</td>\n",
       "      <td>House of Jealous Lovers</td>\n",
       "      <td>The Rapture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>0.118000</td>\n",
       "      <td>0.854</td>\n",
       "      <td>287086</td>\n",
       "      <td>0.401</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>0.5270</td>\n",
       "      <td>-8.553</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3950</td>\n",
       "      <td>139.922</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.441</td>\n",
       "      <td>1</td>\n",
       "      <td>Imma Ride</td>\n",
       "      <td>Young Thug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.224</td>\n",
       "      <td>132760</td>\n",
       "      <td>0.925</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0663</td>\n",
       "      <td>-1.710</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0834</td>\n",
       "      <td>138.022</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.364</td>\n",
       "      <td>1</td>\n",
       "      <td>Girlfriend</td>\n",
       "      <td>Ty Segall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>0.279000</td>\n",
       "      <td>0.512</td>\n",
       "      <td>203400</td>\n",
       "      <td>0.564</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>10</td>\n",
       "      <td>0.1330</td>\n",
       "      <td>-5.892</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0316</td>\n",
       "      <td>94.498</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.401</td>\n",
       "      <td>1</td>\n",
       "      <td>If I Gave You My Love</td>\n",
       "      <td>Myron &amp; E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>0.002190</td>\n",
       "      <td>0.781</td>\n",
       "      <td>205160</td>\n",
       "      <td>0.795</td>\n",
       "      <td>0.269000</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0673</td>\n",
       "      <td>-6.758</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0360</td>\n",
       "      <td>109.977</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.795</td>\n",
       "      <td>1</td>\n",
       "      <td>This Ready Flesh</td>\n",
       "      <td>TR/ST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>0.341000</td>\n",
       "      <td>0.411</td>\n",
       "      <td>199500</td>\n",
       "      <td>0.684</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>11</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>-6.889</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3830</td>\n",
       "      <td>110.016</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.598</td>\n",
       "      <td>1</td>\n",
       "      <td>Lose My Mind</td>\n",
       "      <td>A-Trak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>0.018000</td>\n",
       "      <td>0.646</td>\n",
       "      <td>188333</td>\n",
       "      <td>0.837</td>\n",
       "      <td>0.040300</td>\n",
       "      <td>6</td>\n",
       "      <td>0.3160</td>\n",
       "      <td>-6.615</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1340</td>\n",
       "      <td>100.040</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.320</td>\n",
       "      <td>1</td>\n",
       "      <td>Char</td>\n",
       "      <td>Crystal Castles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>0.051600</td>\n",
       "      <td>0.782</td>\n",
       "      <td>228562</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.3300</td>\n",
       "      <td>-7.467</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0385</td>\n",
       "      <td>120.021</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.237</td>\n",
       "      <td>1</td>\n",
       "      <td>Look Alive</td>\n",
       "      <td>Rae Sremmurd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>0.282000</td>\n",
       "      <td>0.735</td>\n",
       "      <td>186096</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0811</td>\n",
       "      <td>-12.766</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>129.004</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.326</td>\n",
       "      <td>1</td>\n",
       "      <td>Please Stop Making Fake Versace</td>\n",
       "      <td>Father</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>1987</td>\n",
       "      <td>0.028300</td>\n",
       "      <td>0.421</td>\n",
       "      <td>175907</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.4640</td>\n",
       "      <td>-5.128</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1070</td>\n",
       "      <td>134.027</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.489</td>\n",
       "      <td>0</td>\n",
       "      <td>Work This Body</td>\n",
       "      <td>WALK THE MOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>1988</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.467</td>\n",
       "      <td>209187</td>\n",
       "      <td>0.794</td>\n",
       "      <td>0.002040</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1030</td>\n",
       "      <td>-6.174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0349</td>\n",
       "      <td>162.435</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.577</td>\n",
       "      <td>0</td>\n",
       "      <td>Tightrope</td>\n",
       "      <td>WALK THE MOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>1989</td>\n",
       "      <td>0.000797</td>\n",
       "      <td>0.480</td>\n",
       "      <td>222053</td>\n",
       "      <td>0.826</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>-4.602</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0397</td>\n",
       "      <td>96.000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.701</td>\n",
       "      <td>0</td>\n",
       "      <td>Different Colors</td>\n",
       "      <td>WALK THE MOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>1990</td>\n",
       "      <td>0.000590</td>\n",
       "      <td>0.594</td>\n",
       "      <td>218720</td>\n",
       "      <td>0.858</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0642</td>\n",
       "      <td>-4.339</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0387</td>\n",
       "      <td>138.021</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.646</td>\n",
       "      <td>0</td>\n",
       "      <td>Avalanche</td>\n",
       "      <td>WALK THE MOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>1991</td>\n",
       "      <td>0.000366</td>\n",
       "      <td>0.544</td>\n",
       "      <td>245133</td>\n",
       "      <td>0.788</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1410</td>\n",
       "      <td>-5.593</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0474</td>\n",
       "      <td>148.121</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.821</td>\n",
       "      <td>0</td>\n",
       "      <td>Jenny</td>\n",
       "      <td>WALK THE MOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>1992</td>\n",
       "      <td>0.007490</td>\n",
       "      <td>0.446</td>\n",
       "      <td>241253</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>-4.368</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0545</td>\n",
       "      <td>169.889</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.428</td>\n",
       "      <td>0</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>WALK THE MOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>1993</td>\n",
       "      <td>0.001900</td>\n",
       "      <td>0.551</td>\n",
       "      <td>195280</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0764</td>\n",
       "      <td>-6.081</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0627</td>\n",
       "      <td>145.107</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.628</td>\n",
       "      <td>0</td>\n",
       "      <td>Quesadilla</td>\n",
       "      <td>WALK THE MOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>1994</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>0.630</td>\n",
       "      <td>240000</td>\n",
       "      <td>0.772</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0881</td>\n",
       "      <td>-6.986</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0297</td>\n",
       "      <td>99.964</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0</td>\n",
       "      <td>Aquaman</td>\n",
       "      <td>WALK THE MOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>1995</td>\n",
       "      <td>0.020200</td>\n",
       "      <td>0.333</td>\n",
       "      <td>174240</td>\n",
       "      <td>0.913</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1460</td>\n",
       "      <td>-3.722</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0808</td>\n",
       "      <td>99.889</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0</td>\n",
       "      <td>Paris - Pegboard Nerds Remix</td>\n",
       "      <td>The Chainsmokers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>1996</td>\n",
       "      <td>0.003630</td>\n",
       "      <td>0.743</td>\n",
       "      <td>259889</td>\n",
       "      <td>0.889</td>\n",
       "      <td>0.143000</td>\n",
       "      <td>10</td>\n",
       "      <td>0.3830</td>\n",
       "      <td>-5.567</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0409</td>\n",
       "      <td>103.963</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0</td>\n",
       "      <td>Quit You - Wuki Remix</td>\n",
       "      <td>Lost Kings</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>1997</td>\n",
       "      <td>0.000870</td>\n",
       "      <td>0.465</td>\n",
       "      <td>265600</td>\n",
       "      <td>0.953</td>\n",
       "      <td>0.014600</td>\n",
       "      <td>8</td>\n",
       "      <td>0.2260</td>\n",
       "      <td>-4.684</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2950</td>\n",
       "      <td>160.064</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.554</td>\n",
       "      <td>0</td>\n",
       "      <td>Don't Let Me Down - Zomboy Remix</td>\n",
       "      <td>The Chainsmokers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>1998</td>\n",
       "      <td>0.031600</td>\n",
       "      <td>0.680</td>\n",
       "      <td>211853</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1120</td>\n",
       "      <td>-3.920</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0611</td>\n",
       "      <td>140.048</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0</td>\n",
       "      <td>Faded - Slushii Remix</td>\n",
       "      <td>Alan Walker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>1999</td>\n",
       "      <td>0.004990</td>\n",
       "      <td>0.619</td>\n",
       "      <td>245656</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1790</td>\n",
       "      <td>-4.640</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0658</td>\n",
       "      <td>149.935</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.257</td>\n",
       "      <td>0</td>\n",
       "      <td>Can't Get Enough - Pegboard Nerds Remix</td>\n",
       "      <td>Tommie Sunshine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>2000</td>\n",
       "      <td>0.093500</td>\n",
       "      <td>0.520</td>\n",
       "      <td>231667</td>\n",
       "      <td>0.809</td>\n",
       "      <td>0.003500</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0939</td>\n",
       "      <td>-4.180</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>170.032</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.362</td>\n",
       "      <td>0</td>\n",
       "      <td>Light - Loosid Remix</td>\n",
       "      <td>San Holo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>2001</td>\n",
       "      <td>0.002620</td>\n",
       "      <td>0.484</td>\n",
       "      <td>224320</td>\n",
       "      <td>0.609</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>0.3390</td>\n",
       "      <td>-5.652</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0303</td>\n",
       "      <td>131.049</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.266</td>\n",
       "      <td>0</td>\n",
       "      <td>Something Just Like This - ARMNHMR Remix</td>\n",
       "      <td>The Chainsmokers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>2002</td>\n",
       "      <td>0.030100</td>\n",
       "      <td>0.583</td>\n",
       "      <td>224092</td>\n",
       "      <td>0.891</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>7</td>\n",
       "      <td>0.1290</td>\n",
       "      <td>-3.495</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4470</td>\n",
       "      <td>149.843</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.321</td>\n",
       "      <td>0</td>\n",
       "      <td>Without U</td>\n",
       "      <td>Steve Aoki</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>2003</td>\n",
       "      <td>0.000461</td>\n",
       "      <td>0.562</td>\n",
       "      <td>201031</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.038300</td>\n",
       "      <td>9</td>\n",
       "      <td>0.7160</td>\n",
       "      <td>-2.981</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0692</td>\n",
       "      <td>159.927</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.311</td>\n",
       "      <td>0</td>\n",
       "      <td>Hold Up</td>\n",
       "      <td>Borgeous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>2004</td>\n",
       "      <td>0.096900</td>\n",
       "      <td>0.595</td>\n",
       "      <td>198400</td>\n",
       "      <td>0.975</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>-2.339</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3640</td>\n",
       "      <td>150.001</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0</td>\n",
       "      <td>Phone Down - Dodge &amp; Fuski Remix</td>\n",
       "      <td>Lost Kings</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>2005</td>\n",
       "      <td>0.021600</td>\n",
       "      <td>0.526</td>\n",
       "      <td>216072</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>5</td>\n",
       "      <td>0.4260</td>\n",
       "      <td>-3.506</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0875</td>\n",
       "      <td>150.181</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0</td>\n",
       "      <td>Spoon Me - Slushii Remix</td>\n",
       "      <td>Elliphant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>2006</td>\n",
       "      <td>0.178000</td>\n",
       "      <td>0.501</td>\n",
       "      <td>202839</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3850</td>\n",
       "      <td>-4.261</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0651</td>\n",
       "      <td>154.901</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.307</td>\n",
       "      <td>0</td>\n",
       "      <td>Scared To Be Lonely (Conro Remix)</td>\n",
       "      <td>Martin Garrix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>2007</td>\n",
       "      <td>0.002170</td>\n",
       "      <td>0.639</td>\n",
       "      <td>296276</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.112000</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0838</td>\n",
       "      <td>-2.376</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0730</td>\n",
       "      <td>144.986</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.494</td>\n",
       "      <td>0</td>\n",
       "      <td>Let It Go - Scott Melker &amp; Mister Gray Remix</td>\n",
       "      <td>NERVO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>2008</td>\n",
       "      <td>0.054100</td>\n",
       "      <td>0.646</td>\n",
       "      <td>160408</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1340</td>\n",
       "      <td>-6.511</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0378</td>\n",
       "      <td>97.957</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.589</td>\n",
       "      <td>0</td>\n",
       "      <td>Call On Me - EDWYNN X TIKAL, Spirix Remix</td>\n",
       "      <td>Starley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>2009</td>\n",
       "      <td>0.004990</td>\n",
       "      <td>0.595</td>\n",
       "      <td>224000</td>\n",
       "      <td>0.837</td>\n",
       "      <td>0.035900</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>-6.011</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0696</td>\n",
       "      <td>149.964</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.331</td>\n",
       "      <td>0</td>\n",
       "      <td>Aamon</td>\n",
       "      <td>Kuuro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>2010</td>\n",
       "      <td>0.002310</td>\n",
       "      <td>0.624</td>\n",
       "      <td>206013</td>\n",
       "      <td>0.971</td>\n",
       "      <td>0.121000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.2550</td>\n",
       "      <td>-0.935</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0643</td>\n",
       "      <td>102.003</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.442</td>\n",
       "      <td>0</td>\n",
       "      <td>Hey Baby - Steve Aoki Remix</td>\n",
       "      <td>Dimitri Vegas &amp; Like Mike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>2011</td>\n",
       "      <td>0.000586</td>\n",
       "      <td>0.528</td>\n",
       "      <td>245053</td>\n",
       "      <td>0.879</td>\n",
       "      <td>0.004890</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0432</td>\n",
       "      <td>-5.891</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1200</td>\n",
       "      <td>128.268</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0</td>\n",
       "      <td>Brightside - Borgeous Remix</td>\n",
       "      <td>Icona Pop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>2012</td>\n",
       "      <td>0.001060</td>\n",
       "      <td>0.584</td>\n",
       "      <td>274404</td>\n",
       "      <td>0.932</td>\n",
       "      <td>0.002690</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1290</td>\n",
       "      <td>-3.501</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3330</td>\n",
       "      <td>74.976</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0</td>\n",
       "      <td>Like A Bitch - Kill The Noise Remix</td>\n",
       "      <td>Kill The Noise</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>2013</td>\n",
       "      <td>0.087700</td>\n",
       "      <td>0.894</td>\n",
       "      <td>182182</td>\n",
       "      <td>0.892</td>\n",
       "      <td>0.001670</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0528</td>\n",
       "      <td>-2.663</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1310</td>\n",
       "      <td>110.041</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.867</td>\n",
       "      <td>0</td>\n",
       "      <td>Candy</td>\n",
       "      <td>Dillon Francis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>2014</td>\n",
       "      <td>0.008570</td>\n",
       "      <td>0.637</td>\n",
       "      <td>207200</td>\n",
       "      <td>0.935</td>\n",
       "      <td>0.003990</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2140</td>\n",
       "      <td>-2.467</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1070</td>\n",
       "      <td>150.082</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.470</td>\n",
       "      <td>0</td>\n",
       "      <td>Habit - Dack Janiels &amp; Wenzday Remix</td>\n",
       "      <td>Rain Man</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>2015</td>\n",
       "      <td>0.001640</td>\n",
       "      <td>0.557</td>\n",
       "      <td>185600</td>\n",
       "      <td>0.992</td>\n",
       "      <td>0.677000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0913</td>\n",
       "      <td>-2.735</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1330</td>\n",
       "      <td>150.011</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.623</td>\n",
       "      <td>0</td>\n",
       "      <td>First Contact</td>\n",
       "      <td>Twin Moons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>2016</td>\n",
       "      <td>0.002810</td>\n",
       "      <td>0.446</td>\n",
       "      <td>204520</td>\n",
       "      <td>0.915</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>9</td>\n",
       "      <td>0.2180</td>\n",
       "      <td>-6.221</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1410</td>\n",
       "      <td>190.013</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.402</td>\n",
       "      <td>0</td>\n",
       "      <td>I Wanna Get Better</td>\n",
       "      <td>Bleachers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2017 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0  acousticness  danceability  duration_ms  energy  \\\n",
       "0              0      0.010200         0.833       204600   0.434   \n",
       "1              1      0.199000         0.743       326933   0.359   \n",
       "2              2      0.034400         0.838       185707   0.412   \n",
       "3              3      0.604000         0.494       199413   0.338   \n",
       "4              4      0.180000         0.678       392893   0.561   \n",
       "5              5      0.004790         0.804       251333   0.560   \n",
       "6              6      0.014500         0.739       241400   0.472   \n",
       "7              7      0.020200         0.266       349667   0.348   \n",
       "8              8      0.048100         0.603       202853   0.944   \n",
       "9              9      0.002080         0.836       226840   0.603   \n",
       "10            10      0.057200         0.525       358187   0.855   \n",
       "11            11      0.091500         0.753       324880   0.748   \n",
       "12            12      0.253000         0.603       356973   0.434   \n",
       "13            13      0.366000         0.762       243270   0.476   \n",
       "14            14      0.440000         0.662       247288   0.603   \n",
       "15            15      0.019000         0.637       188333   0.832   \n",
       "16            16      0.023900         0.603       270827   0.955   \n",
       "17            17      0.233000         0.789       447907   0.659   \n",
       "18            18      0.314000         0.713       195429   0.611   \n",
       "19            19      0.024200         0.735       214347   0.759   \n",
       "20            20      0.000702         0.854       249253   0.719   \n",
       "21            21      0.000240         0.747       307680   0.740   \n",
       "22            22      0.118000         0.854       287086   0.401   \n",
       "23            23      0.000596         0.224       132760   0.925   \n",
       "24            24      0.279000         0.512       203400   0.564   \n",
       "25            25      0.002190         0.781       205160   0.795   \n",
       "26            26      0.341000         0.411       199500   0.684   \n",
       "27            27      0.018000         0.646       188333   0.837   \n",
       "28            28      0.051600         0.782       228562   0.572   \n",
       "29            29      0.282000         0.735       186096   0.400   \n",
       "...          ...           ...           ...          ...     ...   \n",
       "1987        1987      0.028300         0.421       175907   0.831   \n",
       "1988        1988      0.000084         0.467       209187   0.794   \n",
       "1989        1989      0.000797         0.480       222053   0.826   \n",
       "1990        1990      0.000590         0.594       218720   0.858   \n",
       "1991        1991      0.000366         0.544       245133   0.788   \n",
       "1992        1992      0.007490         0.446       241253   0.881   \n",
       "1993        1993      0.001900         0.551       195280   0.732   \n",
       "1994        1994      0.510000         0.630       240000   0.772   \n",
       "1995        1995      0.020200         0.333       174240   0.913   \n",
       "1996        1996      0.003630         0.743       259889   0.889   \n",
       "1997        1997      0.000870         0.465       265600   0.953   \n",
       "1998        1998      0.031600         0.680       211853   0.725   \n",
       "1999        1999      0.004990         0.619       245656   0.949   \n",
       "2000        2000      0.093500         0.520       231667   0.809   \n",
       "2001        2001      0.002620         0.484       224320   0.609   \n",
       "2002        2002      0.030100         0.583       224092   0.891   \n",
       "2003        2003      0.000461         0.562       201031   0.960   \n",
       "2004        2004      0.096900         0.595       198400   0.975   \n",
       "2005        2005      0.021600         0.526       216072   0.825   \n",
       "2006        2006      0.178000         0.501       202839   0.683   \n",
       "2007        2007      0.002170         0.639       296276   0.978   \n",
       "2008        2008      0.054100         0.646       160408   0.714   \n",
       "2009        2009      0.004990         0.595       224000   0.837   \n",
       "2010        2010      0.002310         0.624       206013   0.971   \n",
       "2011        2011      0.000586         0.528       245053   0.879   \n",
       "2012        2012      0.001060         0.584       274404   0.932   \n",
       "2013        2013      0.087700         0.894       182182   0.892   \n",
       "2014        2014      0.008570         0.637       207200   0.935   \n",
       "2015        2015      0.001640         0.557       185600   0.992   \n",
       "2016        2016      0.002810         0.446       204520   0.915   \n",
       "\n",
       "      instrumentalness  key  liveness  loudness  mode  speechiness    tempo  \\\n",
       "0             0.021900    2    0.1650    -8.795     1       0.4310  150.062   \n",
       "1             0.006110    1    0.1370   -10.401     1       0.0794  160.083   \n",
       "2             0.000234    2    0.1590    -7.148     1       0.2890   75.044   \n",
       "3             0.510000    5    0.0922   -15.236     1       0.0261   86.468   \n",
       "4             0.512000    5    0.4390   -11.648     0       0.0694  174.004   \n",
       "5             0.000000    8    0.1640    -6.682     1       0.1850   85.023   \n",
       "6             0.000007    1    0.2070   -11.204     1       0.1560   80.030   \n",
       "7             0.664000   10    0.1600   -11.609     0       0.0371  144.154   \n",
       "8             0.000000   11    0.3420    -3.626     0       0.3470  130.035   \n",
       "9             0.000000    7    0.5710    -7.792     1       0.2370   99.994   \n",
       "10            0.014300    5    0.6490    -7.372     0       0.0548  111.951   \n",
       "11            0.003480   10    0.2120    -8.620     1       0.0494  104.322   \n",
       "12            0.061900    0    0.1080   -11.062     1       0.0342  127.681   \n",
       "13            0.000000    0    0.1030   -12.686     1       0.1140  130.007   \n",
       "14            0.000000    9    0.0972    -8.317     0       0.0793  125.011   \n",
       "15            0.056300    6    0.3160    -6.637     1       0.1630   99.988   \n",
       "16            0.045100    1    0.1190    -4.111     1       0.0458  123.922   \n",
       "17            0.000490    4    0.1840   -12.654     0       0.0429  122.415   \n",
       "18            0.000000    1    0.1170    -6.702     0       0.2410  140.061   \n",
       "19            0.185000    1    0.0966    -6.914     0       0.0449  109.982   \n",
       "20            0.308000   10    0.4280    -9.335     0       0.0655  128.049   \n",
       "21            0.369000    1    0.0995    -4.134     1       0.0323  130.031   \n",
       "22            0.000000    9    0.5270    -8.553     1       0.3950  139.922   \n",
       "23            0.000001   11    0.0663    -1.710     0       0.0834  138.022   \n",
       "24            0.000015   10    0.1330    -5.892     1       0.0316   94.498   \n",
       "25            0.269000    7    0.0673    -6.758     1       0.0360  109.977   \n",
       "26            0.000001   11    0.1980    -6.889     0       0.3830  110.016   \n",
       "27            0.040300    6    0.3160    -6.615     1       0.1340  100.040   \n",
       "28            0.000000    4    0.3300    -7.467     0       0.0385  120.021   \n",
       "29            0.125000    1    0.0811   -12.766     1       0.1020  129.004   \n",
       "...                ...  ...       ...       ...   ...          ...      ...   \n",
       "1987          0.000000    4    0.4640    -5.128     1       0.1070  134.027   \n",
       "1988          0.002040    1    0.1030    -6.174     0       0.0349  162.435   \n",
       "1989          0.000001    0    0.1250    -4.602     1       0.0397   96.000   \n",
       "1990          0.000003    9    0.0642    -4.339     1       0.0387  138.021   \n",
       "1991          0.000000    5    0.1410    -5.593     0       0.0474  148.121   \n",
       "1992          0.000000    2    0.1300    -4.368     1       0.0545  169.889   \n",
       "1993          0.000000    9    0.0764    -6.081     1       0.0627  145.107   \n",
       "1994          0.000023    1    0.0881    -6.986     1       0.0297   99.964   \n",
       "1995          0.000000    2    0.1460    -3.722     1       0.0808   99.889   \n",
       "1996          0.143000   10    0.3830    -5.567     1       0.0409  103.963   \n",
       "1997          0.014600    8    0.2260    -4.684     0       0.2950  160.064   \n",
       "1998          0.000018    6    0.1120    -3.920     1       0.0611  140.048   \n",
       "1999          0.000418    6    0.1790    -4.640     0       0.0658  149.935   \n",
       "2000          0.003500    1    0.0939    -4.180     1       0.0500  170.032   \n",
       "2001          0.000000   11    0.3390    -5.652     0       0.0303  131.049   \n",
       "2002          0.000003    7    0.1290    -3.495     1       0.4470  149.843   \n",
       "2003          0.038300    9    0.7160    -2.981     1       0.0692  159.927   \n",
       "2004          0.000000    8    0.1020    -2.339     0       0.3640  150.001   \n",
       "2005          0.000001    5    0.4260    -3.506     0       0.0875  150.181   \n",
       "2006          0.000003    1    0.3850    -4.261     0       0.0651  154.901   \n",
       "2007          0.112000   11    0.0838    -2.376     1       0.0730  144.986   \n",
       "2008          0.000000    0    0.1340    -6.511     1       0.0378   97.957   \n",
       "2009          0.035900    1    0.1010    -6.011     1       0.0696  149.964   \n",
       "2010          0.121000    6    0.2550    -0.935     0       0.0643  102.003   \n",
       "2011          0.004890    6    0.0432    -5.891     0       0.1200  128.268   \n",
       "2012          0.002690    1    0.1290    -3.501     1       0.3330   74.976   \n",
       "2013          0.001670    1    0.0528    -2.663     1       0.1310  110.041   \n",
       "2014          0.003990    0    0.2140    -2.467     1       0.1070  150.082   \n",
       "2015          0.677000    1    0.0913    -2.735     1       0.1330  150.011   \n",
       "2016          0.000039    9    0.2180    -6.221     1       0.1410  190.013   \n",
       "\n",
       "      time_signature  valence  target  \\\n",
       "0                4.0    0.286       1   \n",
       "1                4.0    0.588       1   \n",
       "2                4.0    0.173       1   \n",
       "3                4.0    0.230       1   \n",
       "4                4.0    0.904       1   \n",
       "5                4.0    0.264       1   \n",
       "6                4.0    0.308       1   \n",
       "7                4.0    0.393       1   \n",
       "8                4.0    0.398       1   \n",
       "9                4.0    0.386       1   \n",
       "10               3.0    0.524       1   \n",
       "11               4.0    0.642       1   \n",
       "12               4.0    0.381       1   \n",
       "13               4.0    0.367       1   \n",
       "14               4.0    0.351       1   \n",
       "15               4.0    0.317       1   \n",
       "16               4.0    0.773       1   \n",
       "17               4.0    0.842       1   \n",
       "18               4.0    0.783       1   \n",
       "19               4.0    0.763       1   \n",
       "20               4.0    0.471       1   \n",
       "21               4.0    0.770       1   \n",
       "22               4.0    0.441       1   \n",
       "23               4.0    0.364       1   \n",
       "24               4.0    0.401       1   \n",
       "25               4.0    0.795       1   \n",
       "26               4.0    0.598       1   \n",
       "27               4.0    0.320       1   \n",
       "28               4.0    0.237       1   \n",
       "29               4.0    0.326       1   \n",
       "...              ...      ...     ...   \n",
       "1987             4.0    0.489       0   \n",
       "1988             4.0    0.577       0   \n",
       "1989             4.0    0.701       0   \n",
       "1990             4.0    0.646       0   \n",
       "1991             4.0    0.821       0   \n",
       "1992             4.0    0.428       0   \n",
       "1993             4.0    0.628       0   \n",
       "1994             4.0    0.714       0   \n",
       "1995             4.0    0.129       0   \n",
       "1996             4.0    0.186       0   \n",
       "1997             4.0    0.554       0   \n",
       "1998             4.0    0.260       0   \n",
       "1999             4.0    0.257       0   \n",
       "2000             4.0    0.362       0   \n",
       "2001             4.0    0.266       0   \n",
       "2002             4.0    0.321       0   \n",
       "2003             4.0    0.311       0   \n",
       "2004             4.0    0.183       0   \n",
       "2005             4.0    0.108       0   \n",
       "2006             4.0    0.307       0   \n",
       "2007             4.0    0.494       0   \n",
       "2008             4.0    0.589       0   \n",
       "2009             4.0    0.331       0   \n",
       "2010             4.0    0.442       0   \n",
       "2011             4.0    0.327       0   \n",
       "2012             4.0    0.211       0   \n",
       "2013             4.0    0.867       0   \n",
       "2014             4.0    0.470       0   \n",
       "2015             4.0    0.623       0   \n",
       "2016             4.0    0.402       0   \n",
       "\n",
       "                                        song_title                     artist  \n",
       "0                                         Mask Off                     Future  \n",
       "1                                          Redbone           Childish Gambino  \n",
       "2                                     Xanny Family                     Future  \n",
       "3                                   Master Of None                Beach House  \n",
       "4                                   Parallel Lines                Junior Boys  \n",
       "5                                         Sneakin’                      Drake  \n",
       "6                                      Childs Play                      Drake  \n",
       "7                                  Gyöngyhajú lány                      Omega  \n",
       "8                                I've Seen Footage                Death Grips  \n",
       "9                                   Digital Animal                Honey Claws  \n",
       "10            Subways - In Flagranti Extended Edit             The Avalanches  \n",
       "11                      Donme Dolap - Baris K Edit         Modern Folk Üçlüsü  \n",
       "12                                         Cemalim                Erkin Koray  \n",
       "13                                       One Night                 Lil Yachty  \n",
       "14                                         Oh lala                        PNL  \n",
       "15                                            Char            Crystal Castles  \n",
       "16                                 World In Motion                  New Order  \n",
       "17                       One Nation Under a Groove                 Funkadelic  \n",
       "18                                         Bouncin                 Chief Keef  \n",
       "19                            C O O L - Radio Edit                   Le Youth  \n",
       "20      Percolator (Jamie Jones Vault Mix) - mixed                    Cajmere  \n",
       "21                         House of Jealous Lovers                The Rapture  \n",
       "22                                       Imma Ride                 Young Thug  \n",
       "23                                      Girlfriend                  Ty Segall  \n",
       "24                           If I Gave You My Love                  Myron & E  \n",
       "25                                This Ready Flesh                      TR/ST  \n",
       "26                                    Lose My Mind                     A-Trak  \n",
       "27                                            Char            Crystal Castles  \n",
       "28                                      Look Alive               Rae Sremmurd  \n",
       "29                 Please Stop Making Fake Versace                     Father  \n",
       "...                                            ...                        ...  \n",
       "1987                                Work This Body              WALK THE MOON  \n",
       "1988                                     Tightrope              WALK THE MOON  \n",
       "1989                              Different Colors              WALK THE MOON  \n",
       "1990                                     Avalanche              WALK THE MOON  \n",
       "1991                                         Jenny              WALK THE MOON  \n",
       "1992                                      Portugal              WALK THE MOON  \n",
       "1993                                    Quesadilla              WALK THE MOON  \n",
       "1994                                       Aquaman              WALK THE MOON  \n",
       "1995                  Paris - Pegboard Nerds Remix           The Chainsmokers  \n",
       "1996                         Quit You - Wuki Remix                 Lost Kings  \n",
       "1997              Don't Let Me Down - Zomboy Remix           The Chainsmokers  \n",
       "1998                         Faded - Slushii Remix                Alan Walker  \n",
       "1999       Can't Get Enough - Pegboard Nerds Remix            Tommie Sunshine  \n",
       "2000                          Light - Loosid Remix                   San Holo  \n",
       "2001      Something Just Like This - ARMNHMR Remix           The Chainsmokers  \n",
       "2002                                     Without U                 Steve Aoki  \n",
       "2003                                       Hold Up                   Borgeous  \n",
       "2004              Phone Down - Dodge & Fuski Remix                 Lost Kings  \n",
       "2005                      Spoon Me - Slushii Remix                  Elliphant  \n",
       "2006             Scared To Be Lonely (Conro Remix)              Martin Garrix  \n",
       "2007  Let It Go - Scott Melker & Mister Gray Remix                      NERVO  \n",
       "2008     Call On Me - EDWYNN X TIKAL, Spirix Remix                    Starley  \n",
       "2009                                         Aamon                      Kuuro  \n",
       "2010                   Hey Baby - Steve Aoki Remix  Dimitri Vegas & Like Mike  \n",
       "2011                   Brightside - Borgeous Remix                  Icona Pop  \n",
       "2012           Like A Bitch - Kill The Noise Remix             Kill The Noise  \n",
       "2013                                         Candy             Dillon Francis  \n",
       "2014          Habit - Dack Janiels & Wenzday Remix                   Rain Man  \n",
       "2015                                 First Contact                 Twin Moons  \n",
       "2016                            I Wanna Get Better                  Bleachers  \n",
       "\n",
       "[2017 rows x 17 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tempo          150.062\n",
      "song_title    Mask Off\n",
      "Name: 0, dtype: object\n",
      "tempo         160.083\n",
      "song_title    Redbone\n",
      "Name: 1, dtype: object\n",
      "tempo               75.044\n",
      "song_title    Xanny Family\n",
      "Name: 2, dtype: object\n",
      "tempo                 86.468\n",
      "song_title    Master Of None\n",
      "Name: 3, dtype: object\n",
      "tempo                174.004\n",
      "song_title    Parallel Lines\n",
      "Name: 4, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# indexing example using .loc\n",
    "for k in range(5):\n",
    "    print(data.loc[k,[\"tempo\",\"song_title\"]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this dataset of 2017 songs, you will see that, alongside every song title, there are many characteristics or **features** extracted for each song. These features include numerical information like tempo, acousticness, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imagine that you were to search this entire database for a song that you might like. Some features will be useful for finding similar songs. Which features do you think would be most useful? Least useful?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What can we do with Features?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using features, we can already:\n",
    "\n",
    "- visualize our data, and\n",
    "- find similar items.\n",
    "\n",
    "We'll use a dataset to suggest songs that are similar to our favourite song. For the sake of this example, let's say that we like the song \"Mask Off\" by \"Future\" and would like to find songs that are similar. Let's just look at 2 features for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>tempo</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>song_title</th>\n",
       "      <th>artist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150.062</td>\n",
       "      <td>0.01020</td>\n",
       "      <td>Mask Off</td>\n",
       "      <td>Future</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>160.083</td>\n",
       "      <td>0.19900</td>\n",
       "      <td>Redbone</td>\n",
       "      <td>Childish Gambino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75.044</td>\n",
       "      <td>0.03440</td>\n",
       "      <td>Xanny Family</td>\n",
       "      <td>Future</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>86.468</td>\n",
       "      <td>0.60400</td>\n",
       "      <td>Master Of None</td>\n",
       "      <td>Beach House</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>174.004</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>Parallel Lines</td>\n",
       "      <td>Junior Boys</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>85.023</td>\n",
       "      <td>0.00479</td>\n",
       "      <td>Sneakin’</td>\n",
       "      <td>Drake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>80.030</td>\n",
       "      <td>0.01450</td>\n",
       "      <td>Childs Play</td>\n",
       "      <td>Drake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>144.154</td>\n",
       "      <td>0.02020</td>\n",
       "      <td>Gyöngyhajú lány</td>\n",
       "      <td>Omega</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>130.035</td>\n",
       "      <td>0.04810</td>\n",
       "      <td>I've Seen Footage</td>\n",
       "      <td>Death Grips</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>99.994</td>\n",
       "      <td>0.00208</td>\n",
       "      <td>Digital Animal</td>\n",
       "      <td>Honey Claws</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>111.951</td>\n",
       "      <td>0.05720</td>\n",
       "      <td>Subways - In Flagranti Extended Edit</td>\n",
       "      <td>The Avalanches</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>104.322</td>\n",
       "      <td>0.09150</td>\n",
       "      <td>Donme Dolap - Baris K Edit</td>\n",
       "      <td>Modern Folk Üçlüsü</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>127.681</td>\n",
       "      <td>0.25300</td>\n",
       "      <td>Cemalim</td>\n",
       "      <td>Erkin Koray</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>130.007</td>\n",
       "      <td>0.36600</td>\n",
       "      <td>One Night</td>\n",
       "      <td>Lil Yachty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>125.011</td>\n",
       "      <td>0.44000</td>\n",
       "      <td>Oh lala</td>\n",
       "      <td>PNL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>99.988</td>\n",
       "      <td>0.01900</td>\n",
       "      <td>Char</td>\n",
       "      <td>Crystal Castles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>123.922</td>\n",
       "      <td>0.02390</td>\n",
       "      <td>World In Motion</td>\n",
       "      <td>New Order</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>122.415</td>\n",
       "      <td>0.23300</td>\n",
       "      <td>One Nation Under a Groove</td>\n",
       "      <td>Funkadelic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>140.061</td>\n",
       "      <td>0.31400</td>\n",
       "      <td>Bouncin</td>\n",
       "      <td>Chief Keef</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>109.982</td>\n",
       "      <td>0.02420</td>\n",
       "      <td>C O O L - Radio Edit</td>\n",
       "      <td>Le Youth</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tempo  acousticness                            song_title  \\\n",
       "0   150.062       0.01020                              Mask Off   \n",
       "1   160.083       0.19900                               Redbone   \n",
       "2    75.044       0.03440                          Xanny Family   \n",
       "3    86.468       0.60400                        Master Of None   \n",
       "4   174.004       0.18000                        Parallel Lines   \n",
       "5    85.023       0.00479                              Sneakin’   \n",
       "6    80.030       0.01450                           Childs Play   \n",
       "7   144.154       0.02020                       Gyöngyhajú lány   \n",
       "8   130.035       0.04810                     I've Seen Footage   \n",
       "9    99.994       0.00208                        Digital Animal   \n",
       "10  111.951       0.05720  Subways - In Flagranti Extended Edit   \n",
       "11  104.322       0.09150            Donme Dolap - Baris K Edit   \n",
       "12  127.681       0.25300                               Cemalim   \n",
       "13  130.007       0.36600                             One Night   \n",
       "14  125.011       0.44000                               Oh lala   \n",
       "15   99.988       0.01900                                  Char   \n",
       "16  123.922       0.02390                       World In Motion   \n",
       "17  122.415       0.23300             One Nation Under a Groove   \n",
       "18  140.061       0.31400                               Bouncin   \n",
       "19  109.982       0.02420                  C O O L - Radio Edit   \n",
       "\n",
       "                artist  \n",
       "0               Future  \n",
       "1     Childish Gambino  \n",
       "2               Future  \n",
       "3          Beach House  \n",
       "4          Junior Boys  \n",
       "5                Drake  \n",
       "6                Drake  \n",
       "7                Omega  \n",
       "8          Death Grips  \n",
       "9          Honey Claws  \n",
       "10      The Avalanches  \n",
       "11  Modern Folk Üçlüsü  \n",
       "12         Erkin Koray  \n",
       "13          Lil Yachty  \n",
       "14                 PNL  \n",
       "15     Crystal Castles  \n",
       "16           New Order  \n",
       "17          Funkadelic  \n",
       "18          Chief Keef  \n",
       "19            Le Youth  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "raw_data = pandas.read_csv('data/spotify.csv')  # Top 2017 songs of 2017 from kaggle.com\n",
    "\n",
    "# Just get the columns we want\n",
    "dataset = pandas.DataFrame(raw_data, columns = ['tempo', 'acousticness', 'song_title', 'artist'])\n",
    "\n",
    "# Let's just get the top 20 songs\n",
    "dataset = dataset[:20]\n",
    "\n",
    "# Display the dataset\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I want to find songs with a similar tempo!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by thinking about how to find similar songs if you only know one thing about each song, e.g. tempo. \n",
    "We'll compare each song to Mask Off.\n",
    "\n",
    "Let's add a new column that finds the differences in tempo from the top song, Mask Off (tempo=150.062)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     50.062\n",
       "1     60.083\n",
       "2     24.956\n",
       "3     13.532\n",
       "4     74.004\n",
       "5     14.977\n",
       "6     19.970\n",
       "7     44.154\n",
       "8     30.035\n",
       "9      0.006\n",
       "10    11.951\n",
       "11     4.322\n",
       "12    27.681\n",
       "13    30.007\n",
       "14    25.011\n",
       "15     0.012\n",
       "16    23.922\n",
       "17    22.415\n",
       "18    40.061\n",
       "19     9.982\n",
       "Name: tempo, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(dataset.tempo - 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Frome hereon we'll use the song Mask Off to calculate its distance to other songs**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tempo</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>song_title</th>\n",
       "      <th>artist</th>\n",
       "      <th>tempo_difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150.062</td>\n",
       "      <td>0.01020</td>\n",
       "      <td>Mask Off</td>\n",
       "      <td>Future</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>160.083</td>\n",
       "      <td>0.19900</td>\n",
       "      <td>Redbone</td>\n",
       "      <td>Childish Gambino</td>\n",
       "      <td>10.021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75.044</td>\n",
       "      <td>0.03440</td>\n",
       "      <td>Xanny Family</td>\n",
       "      <td>Future</td>\n",
       "      <td>75.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>86.468</td>\n",
       "      <td>0.60400</td>\n",
       "      <td>Master Of None</td>\n",
       "      <td>Beach House</td>\n",
       "      <td>63.594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>174.004</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>Parallel Lines</td>\n",
       "      <td>Junior Boys</td>\n",
       "      <td>23.942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>85.023</td>\n",
       "      <td>0.00479</td>\n",
       "      <td>Sneakin’</td>\n",
       "      <td>Drake</td>\n",
       "      <td>65.039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>80.030</td>\n",
       "      <td>0.01450</td>\n",
       "      <td>Childs Play</td>\n",
       "      <td>Drake</td>\n",
       "      <td>70.032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>144.154</td>\n",
       "      <td>0.02020</td>\n",
       "      <td>Gyöngyhajú lány</td>\n",
       "      <td>Omega</td>\n",
       "      <td>5.908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>130.035</td>\n",
       "      <td>0.04810</td>\n",
       "      <td>I've Seen Footage</td>\n",
       "      <td>Death Grips</td>\n",
       "      <td>20.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>99.994</td>\n",
       "      <td>0.00208</td>\n",
       "      <td>Digital Animal</td>\n",
       "      <td>Honey Claws</td>\n",
       "      <td>50.068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>111.951</td>\n",
       "      <td>0.05720</td>\n",
       "      <td>Subways - In Flagranti Extended Edit</td>\n",
       "      <td>The Avalanches</td>\n",
       "      <td>38.111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>104.322</td>\n",
       "      <td>0.09150</td>\n",
       "      <td>Donme Dolap - Baris K Edit</td>\n",
       "      <td>Modern Folk Üçlüsü</td>\n",
       "      <td>45.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>127.681</td>\n",
       "      <td>0.25300</td>\n",
       "      <td>Cemalim</td>\n",
       "      <td>Erkin Koray</td>\n",
       "      <td>22.381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>130.007</td>\n",
       "      <td>0.36600</td>\n",
       "      <td>One Night</td>\n",
       "      <td>Lil Yachty</td>\n",
       "      <td>20.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>125.011</td>\n",
       "      <td>0.44000</td>\n",
       "      <td>Oh lala</td>\n",
       "      <td>PNL</td>\n",
       "      <td>25.051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>99.988</td>\n",
       "      <td>0.01900</td>\n",
       "      <td>Char</td>\n",
       "      <td>Crystal Castles</td>\n",
       "      <td>50.074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>123.922</td>\n",
       "      <td>0.02390</td>\n",
       "      <td>World In Motion</td>\n",
       "      <td>New Order</td>\n",
       "      <td>26.140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>122.415</td>\n",
       "      <td>0.23300</td>\n",
       "      <td>One Nation Under a Groove</td>\n",
       "      <td>Funkadelic</td>\n",
       "      <td>27.647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>140.061</td>\n",
       "      <td>0.31400</td>\n",
       "      <td>Bouncin</td>\n",
       "      <td>Chief Keef</td>\n",
       "      <td>10.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>109.982</td>\n",
       "      <td>0.02420</td>\n",
       "      <td>C O O L - Radio Edit</td>\n",
       "      <td>Le Youth</td>\n",
       "      <td>40.080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tempo  acousticness                            song_title  \\\n",
       "0   150.062       0.01020                              Mask Off   \n",
       "1   160.083       0.19900                               Redbone   \n",
       "2    75.044       0.03440                          Xanny Family   \n",
       "3    86.468       0.60400                        Master Of None   \n",
       "4   174.004       0.18000                        Parallel Lines   \n",
       "5    85.023       0.00479                              Sneakin’   \n",
       "6    80.030       0.01450                           Childs Play   \n",
       "7   144.154       0.02020                       Gyöngyhajú lány   \n",
       "8   130.035       0.04810                     I've Seen Footage   \n",
       "9    99.994       0.00208                        Digital Animal   \n",
       "10  111.951       0.05720  Subways - In Flagranti Extended Edit   \n",
       "11  104.322       0.09150            Donme Dolap - Baris K Edit   \n",
       "12  127.681       0.25300                               Cemalim   \n",
       "13  130.007       0.36600                             One Night   \n",
       "14  125.011       0.44000                               Oh lala   \n",
       "15   99.988       0.01900                                  Char   \n",
       "16  123.922       0.02390                       World In Motion   \n",
       "17  122.415       0.23300             One Nation Under a Groove   \n",
       "18  140.061       0.31400                               Bouncin   \n",
       "19  109.982       0.02420                  C O O L - Radio Edit   \n",
       "\n",
       "                artist  tempo_difference  \n",
       "0               Future             0.000  \n",
       "1     Childish Gambino            10.021  \n",
       "2               Future            75.018  \n",
       "3          Beach House            63.594  \n",
       "4          Junior Boys            23.942  \n",
       "5                Drake            65.039  \n",
       "6                Drake            70.032  \n",
       "7                Omega             5.908  \n",
       "8          Death Grips            20.027  \n",
       "9          Honey Claws            50.068  \n",
       "10      The Avalanches            38.111  \n",
       "11  Modern Folk Üçlüsü            45.740  \n",
       "12         Erkin Koray            22.381  \n",
       "13          Lil Yachty            20.055  \n",
       "14                 PNL            25.051  \n",
       "15     Crystal Castles            50.074  \n",
       "16           New Order            26.140  \n",
       "17          Funkadelic            27.647  \n",
       "18          Chief Keef            10.001  \n",
       "19            Le Youth            40.080  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a column tempo_difference. 150.062 refers to Mask Off's tempo\n",
    "#dataset['tempo_difference'] = abs(150.062 - dataset.tempo)\n",
    "dataset['tempo_difference'] = abs(150.062 - dataset.tempo)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's visualize that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now let's visualize that \n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plot\n",
    "plot.rcParams[\"figure.figsize\"] = 8,8\n",
    "\n",
    "plot.scatter(dataset['tempo_difference'], dataset['song_title'])\n",
    "#ax = plot.scatter(dataset['song_title'], dataset['tempo_difference'])\n",
    "plot.xlabel('Absolute difference in Tempo')\n",
    "plot.grid()\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now let's find how different they are in terms of acousticness!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "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>tempo</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>song_title</th>\n",
       "      <th>artist</th>\n",
       "      <th>tempo_difference</th>\n",
       "      <th>acoustic_difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150.062</td>\n",
       "      <td>0.01020</td>\n",
       "      <td>Mask Off</td>\n",
       "      <td>Future</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>160.083</td>\n",
       "      <td>0.19900</td>\n",
       "      <td>Redbone</td>\n",
       "      <td>Childish Gambino</td>\n",
       "      <td>10.021</td>\n",
       "      <td>0.18880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75.044</td>\n",
       "      <td>0.03440</td>\n",
       "      <td>Xanny Family</td>\n",
       "      <td>Future</td>\n",
       "      <td>75.018</td>\n",
       "      <td>0.02420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>86.468</td>\n",
       "      <td>0.60400</td>\n",
       "      <td>Master Of None</td>\n",
       "      <td>Beach House</td>\n",
       "      <td>63.594</td>\n",
       "      <td>0.59380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>174.004</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>Parallel Lines</td>\n",
       "      <td>Junior Boys</td>\n",
       "      <td>23.942</td>\n",
       "      <td>0.16980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>85.023</td>\n",
       "      <td>0.00479</td>\n",
       "      <td>Sneakin’</td>\n",
       "      <td>Drake</td>\n",
       "      <td>65.039</td>\n",
       "      <td>0.00541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>80.030</td>\n",
       "      <td>0.01450</td>\n",
       "      <td>Childs Play</td>\n",
       "      <td>Drake</td>\n",
       "      <td>70.032</td>\n",
       "      <td>0.00430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>144.154</td>\n",
       "      <td>0.02020</td>\n",
       "      <td>Gyöngyhajú lány</td>\n",
       "      <td>Omega</td>\n",
       "      <td>5.908</td>\n",
       "      <td>0.01000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>130.035</td>\n",
       "      <td>0.04810</td>\n",
       "      <td>I've Seen Footage</td>\n",
       "      <td>Death Grips</td>\n",
       "      <td>20.027</td>\n",
       "      <td>0.03790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>99.994</td>\n",
       "      <td>0.00208</td>\n",
       "      <td>Digital Animal</td>\n",
       "      <td>Honey Claws</td>\n",
       "      <td>50.068</td>\n",
       "      <td>0.00812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>111.951</td>\n",
       "      <td>0.05720</td>\n",
       "      <td>Subways - In Flagranti Extended Edit</td>\n",
       "      <td>The Avalanches</td>\n",
       "      <td>38.111</td>\n",
       "      <td>0.04700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>104.322</td>\n",
       "      <td>0.09150</td>\n",
       "      <td>Donme Dolap - Baris K Edit</td>\n",
       "      <td>Modern Folk Üçlüsü</td>\n",
       "      <td>45.740</td>\n",
       "      <td>0.08130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>127.681</td>\n",
       "      <td>0.25300</td>\n",
       "      <td>Cemalim</td>\n",
       "      <td>Erkin Koray</td>\n",
       "      <td>22.381</td>\n",
       "      <td>0.24280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>130.007</td>\n",
       "      <td>0.36600</td>\n",
       "      <td>One Night</td>\n",
       "      <td>Lil Yachty</td>\n",
       "      <td>20.055</td>\n",
       "      <td>0.35580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>125.011</td>\n",
       "      <td>0.44000</td>\n",
       "      <td>Oh lala</td>\n",
       "      <td>PNL</td>\n",
       "      <td>25.051</td>\n",
       "      <td>0.42980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>99.988</td>\n",
       "      <td>0.01900</td>\n",
       "      <td>Char</td>\n",
       "      <td>Crystal Castles</td>\n",
       "      <td>50.074</td>\n",
       "      <td>0.00880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>123.922</td>\n",
       "      <td>0.02390</td>\n",
       "      <td>World In Motion</td>\n",
       "      <td>New Order</td>\n",
       "      <td>26.140</td>\n",
       "      <td>0.01370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>122.415</td>\n",
       "      <td>0.23300</td>\n",
       "      <td>One Nation Under a Groove</td>\n",
       "      <td>Funkadelic</td>\n",
       "      <td>27.647</td>\n",
       "      <td>0.22280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>140.061</td>\n",
       "      <td>0.31400</td>\n",
       "      <td>Bouncin</td>\n",
       "      <td>Chief Keef</td>\n",
       "      <td>10.001</td>\n",
       "      <td>0.30380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>109.982</td>\n",
       "      <td>0.02420</td>\n",
       "      <td>C O O L - Radio Edit</td>\n",
       "      <td>Le Youth</td>\n",
       "      <td>40.080</td>\n",
       "      <td>0.01400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tempo  acousticness                            song_title  \\\n",
       "0   150.062       0.01020                              Mask Off   \n",
       "1   160.083       0.19900                               Redbone   \n",
       "2    75.044       0.03440                          Xanny Family   \n",
       "3    86.468       0.60400                        Master Of None   \n",
       "4   174.004       0.18000                        Parallel Lines   \n",
       "5    85.023       0.00479                              Sneakin’   \n",
       "6    80.030       0.01450                           Childs Play   \n",
       "7   144.154       0.02020                       Gyöngyhajú lány   \n",
       "8   130.035       0.04810                     I've Seen Footage   \n",
       "9    99.994       0.00208                        Digital Animal   \n",
       "10  111.951       0.05720  Subways - In Flagranti Extended Edit   \n",
       "11  104.322       0.09150            Donme Dolap - Baris K Edit   \n",
       "12  127.681       0.25300                               Cemalim   \n",
       "13  130.007       0.36600                             One Night   \n",
       "14  125.011       0.44000                               Oh lala   \n",
       "15   99.988       0.01900                                  Char   \n",
       "16  123.922       0.02390                       World In Motion   \n",
       "17  122.415       0.23300             One Nation Under a Groove   \n",
       "18  140.061       0.31400                               Bouncin   \n",
       "19  109.982       0.02420                  C O O L - Radio Edit   \n",
       "\n",
       "                artist  tempo_difference  acoustic_difference  \n",
       "0               Future             0.000              0.00000  \n",
       "1     Childish Gambino            10.021              0.18880  \n",
       "2               Future            75.018              0.02420  \n",
       "3          Beach House            63.594              0.59380  \n",
       "4          Junior Boys            23.942              0.16980  \n",
       "5                Drake            65.039              0.00541  \n",
       "6                Drake            70.032              0.00430  \n",
       "7                Omega             5.908              0.01000  \n",
       "8          Death Grips            20.027              0.03790  \n",
       "9          Honey Claws            50.068              0.00812  \n",
       "10      The Avalanches            38.111              0.04700  \n",
       "11  Modern Folk Üçlüsü            45.740              0.08130  \n",
       "12         Erkin Koray            22.381              0.24280  \n",
       "13          Lil Yachty            20.055              0.35580  \n",
       "14                 PNL            25.051              0.42980  \n",
       "15     Crystal Castles            50.074              0.00880  \n",
       "16           New Order            26.140              0.01370  \n",
       "17          Funkadelic            27.647              0.22280  \n",
       "18          Chief Keef            10.001              0.30380  \n",
       "19            Le Youth            40.080              0.01400  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a new column that finds the differences in acousticness from top song, Mask Off (acousticness=0.01020)\n",
    "\n",
    "dataset['acoustic_difference'] = abs(0.01020 - dataset.acousticness)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now let's visualize that with a histogram\n",
    "import matplotlib.pyplot as plot\n",
    "\n",
    "plot.scatter(dataset['acoustic_difference'], dataset['song_title'])\n",
    "\n",
    "plot.xlabel('Difference in Acousticness')\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perceptually Similar Songs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we manually look at which song looked the closest over both **tempo** and **acousticness** to Mask Off, we get ________________. \n",
    "\n",
    "\n",
    "Did you make your guess? Let's see if that sounds right! \n",
    "    \n",
    "https://youtu.be/CGt-rTDkMcM\n",
    "    \n",
    "https://youtu.be/xvZqHgFz51I\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing The Songs in 2D Space"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also visualize the two features simultaneously in a 2D plot, like this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now let's visualize that by plotting all the original data points\n",
    "import matplotlib.pyplot as plot\n",
    "plot.rcParams[\"figure.figsize\"] = 8,8\n",
    "\n",
    "plot.scatter(dataset['tempo'], dataset['acousticness'])\n",
    "plot.xlabel('Tempo')\n",
    "plot.ylabel('Acousticness')\n",
    "\n",
    "for index, row in dataset.iterrows():\n",
    "    plot.annotate(str(row['song_title']), xy=(row['tempo'], row['acousticness']))\n",
    "plot.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculating Distance in 2D Space"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now that we plot the songs in 2D space, we can properly calculate the distances in 2D mathematically, using the **Euclidean distance** function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "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>tempo</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>song_title</th>\n",
       "      <th>artist</th>\n",
       "      <th>tempo_difference</th>\n",
       "      <th>euclidean_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150.062</td>\n",
       "      <td>0.01020</td>\n",
       "      <td>Mask Off</td>\n",
       "      <td>Future</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>144.154</td>\n",
       "      <td>0.02020</td>\n",
       "      <td>Gyöngyhajú lány</td>\n",
       "      <td>Omega</td>\n",
       "      <td>5.908</td>\n",
       "      <td>5.908008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>140.061</td>\n",
       "      <td>0.31400</td>\n",
       "      <td>Bouncin</td>\n",
       "      <td>Chief Keef</td>\n",
       "      <td>10.001</td>\n",
       "      <td>10.005613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>160.083</td>\n",
       "      <td>0.19900</td>\n",
       "      <td>Redbone</td>\n",
       "      <td>Childish Gambino</td>\n",
       "      <td>10.021</td>\n",
       "      <td>10.022778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>130.035</td>\n",
       "      <td>0.04810</td>\n",
       "      <td>I've Seen Footage</td>\n",
       "      <td>Death Grips</td>\n",
       "      <td>20.027</td>\n",
       "      <td>20.027036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>130.007</td>\n",
       "      <td>0.36600</td>\n",
       "      <td>One Night</td>\n",
       "      <td>Lil Yachty</td>\n",
       "      <td>20.055</td>\n",
       "      <td>20.058156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>127.681</td>\n",
       "      <td>0.25300</td>\n",
       "      <td>Cemalim</td>\n",
       "      <td>Erkin Koray</td>\n",
       "      <td>22.381</td>\n",
       "      <td>22.382317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>174.004</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>Parallel Lines</td>\n",
       "      <td>Junior Boys</td>\n",
       "      <td>23.942</td>\n",
       "      <td>23.942602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>125.011</td>\n",
       "      <td>0.44000</td>\n",
       "      <td>Oh lala</td>\n",
       "      <td>PNL</td>\n",
       "      <td>25.051</td>\n",
       "      <td>25.054687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>123.922</td>\n",
       "      <td>0.02390</td>\n",
       "      <td>World In Motion</td>\n",
       "      <td>New Order</td>\n",
       "      <td>26.140</td>\n",
       "      <td>26.140004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>122.415</td>\n",
       "      <td>0.23300</td>\n",
       "      <td>One Nation Under a Groove</td>\n",
       "      <td>Funkadelic</td>\n",
       "      <td>27.647</td>\n",
       "      <td>27.647898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>111.951</td>\n",
       "      <td>0.05720</td>\n",
       "      <td>Subways - In Flagranti Extended Edit</td>\n",
       "      <td>The Avalanches</td>\n",
       "      <td>38.111</td>\n",
       "      <td>38.111029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>109.982</td>\n",
       "      <td>0.02420</td>\n",
       "      <td>C O O L - Radio Edit</td>\n",
       "      <td>Le Youth</td>\n",
       "      <td>40.080</td>\n",
       "      <td>40.080002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>104.322</td>\n",
       "      <td>0.09150</td>\n",
       "      <td>Donme Dolap - Baris K Edit</td>\n",
       "      <td>Modern Folk Üçlüsü</td>\n",
       "      <td>45.740</td>\n",
       "      <td>45.740072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>99.994</td>\n",
       "      <td>0.00208</td>\n",
       "      <td>Digital Animal</td>\n",
       "      <td>Honey Claws</td>\n",
       "      <td>50.068</td>\n",
       "      <td>50.068001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>99.988</td>\n",
       "      <td>0.01900</td>\n",
       "      <td>Char</td>\n",
       "      <td>Crystal Castles</td>\n",
       "      <td>50.074</td>\n",
       "      <td>50.074001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>86.468</td>\n",
       "      <td>0.60400</td>\n",
       "      <td>Master Of None</td>\n",
       "      <td>Beach House</td>\n",
       "      <td>63.594</td>\n",
       "      <td>63.596772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>85.023</td>\n",
       "      <td>0.00479</td>\n",
       "      <td>Sneakin’</td>\n",
       "      <td>Drake</td>\n",
       "      <td>65.039</td>\n",
       "      <td>65.039000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>80.030</td>\n",
       "      <td>0.01450</td>\n",
       "      <td>Childs Play</td>\n",
       "      <td>Drake</td>\n",
       "      <td>70.032</td>\n",
       "      <td>70.032000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75.044</td>\n",
       "      <td>0.03440</td>\n",
       "      <td>Xanny Family</td>\n",
       "      <td>Future</td>\n",
       "      <td>75.018</td>\n",
       "      <td>75.018004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tempo  acousticness                            song_title  \\\n",
       "0   150.062       0.01020                              Mask Off   \n",
       "7   144.154       0.02020                       Gyöngyhajú lány   \n",
       "18  140.061       0.31400                               Bouncin   \n",
       "1   160.083       0.19900                               Redbone   \n",
       "8   130.035       0.04810                     I've Seen Footage   \n",
       "13  130.007       0.36600                             One Night   \n",
       "12  127.681       0.25300                               Cemalim   \n",
       "4   174.004       0.18000                        Parallel Lines   \n",
       "14  125.011       0.44000                               Oh lala   \n",
       "16  123.922       0.02390                       World In Motion   \n",
       "17  122.415       0.23300             One Nation Under a Groove   \n",
       "10  111.951       0.05720  Subways - In Flagranti Extended Edit   \n",
       "19  109.982       0.02420                  C O O L - Radio Edit   \n",
       "11  104.322       0.09150            Donme Dolap - Baris K Edit   \n",
       "9    99.994       0.00208                        Digital Animal   \n",
       "15   99.988       0.01900                                  Char   \n",
       "3    86.468       0.60400                        Master Of None   \n",
       "5    85.023       0.00479                              Sneakin’   \n",
       "6    80.030       0.01450                           Childs Play   \n",
       "2    75.044       0.03440                          Xanny Family   \n",
       "\n",
       "                artist  tempo_difference  euclidean_distance  \n",
       "0               Future             0.000            0.000000  \n",
       "7                Omega             5.908            5.908008  \n",
       "18          Chief Keef            10.001           10.005613  \n",
       "1     Childish Gambino            10.021           10.022778  \n",
       "8          Death Grips            20.027           20.027036  \n",
       "13          Lil Yachty            20.055           20.058156  \n",
       "12         Erkin Koray            22.381           22.382317  \n",
       "4          Junior Boys            23.942           23.942602  \n",
       "14                 PNL            25.051           25.054687  \n",
       "16           New Order            26.140           26.140004  \n",
       "17          Funkadelic            27.647           27.647898  \n",
       "10      The Avalanches            38.111           38.111029  \n",
       "19            Le Youth            40.080           40.080002  \n",
       "11  Modern Folk Üçlüsü            45.740           45.740072  \n",
       "9          Honey Claws            50.068           50.068001  \n",
       "15     Crystal Castles            50.074           50.074001  \n",
       "3          Beach House            63.594           63.596772  \n",
       "5                Drake            65.039           65.039000  \n",
       "6                Drake            70.032           70.032000  \n",
       "2               Future            75.018           75.018004  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy\n",
    "dataset['euclidean_distance'] = numpy.sqrt( (0.01020 - dataset.acousticness)**2 + (150.062 - dataset.tempo)**2)\n",
    "\n",
    "# Sort the dataset\n",
    "sorted_dataset_distance = dataset.sort_values('euclidean_distance')\n",
    "\n",
    "# Display sorted dataset\n",
    "sorted_dataset_distance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interesting..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the last plot above, it suddenly looks like **I've Seen Footage** is really *close* to Mask Off, and **One Night** is really *far* from Mask Off. But in our calculations of Euclidean distance above, both are ~20 units away from **Mask Off**. Can you guess what's wrong?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we calculated the distance, the tempo was on a scale of 100's, while the acousticness was on a scale from 0 to 1. This means that the distance in tempo completely overshadowed the minute differences in acousticness.\n",
    "\n",
    "So, we need to make them both on the same ___. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also known as *normalizing* or standardizing data, we should scale our data to make our comparisons fair. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "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>tempo</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>song_title</th>\n",
       "      <th>artist</th>\n",
       "      <th>tempo_difference</th>\n",
       "      <th>euclidean_distance</th>\n",
       "      <th>tempo_normalized</th>\n",
       "      <th>acoustic_normalized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150.062</td>\n",
       "      <td>0.01020</td>\n",
       "      <td>Mask Off</td>\n",
       "      <td>Future</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.758064</td>\n",
       "      <td>0.013490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>160.083</td>\n",
       "      <td>0.19900</td>\n",
       "      <td>Redbone</td>\n",
       "      <td>Childish Gambino</td>\n",
       "      <td>10.021</td>\n",
       "      <td>10.022778</td>\n",
       "      <td>0.859327</td>\n",
       "      <td>0.327153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75.044</td>\n",
       "      <td>0.03440</td>\n",
       "      <td>Xanny Family</td>\n",
       "      <td>Future</td>\n",
       "      <td>75.018</td>\n",
       "      <td>75.018004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.053695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>86.468</td>\n",
       "      <td>0.60400</td>\n",
       "      <td>Master Of None</td>\n",
       "      <td>Beach House</td>\n",
       "      <td>63.594</td>\n",
       "      <td>63.596772</td>\n",
       "      <td>0.115441</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>174.004</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>Parallel Lines</td>\n",
       "      <td>Junior Boys</td>\n",
       "      <td>23.942</td>\n",
       "      <td>23.942602</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.295587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>85.023</td>\n",
       "      <td>0.00479</td>\n",
       "      <td>Sneakin’</td>\n",
       "      <td>Drake</td>\n",
       "      <td>65.039</td>\n",
       "      <td>65.039000</td>\n",
       "      <td>0.100839</td>\n",
       "      <td>0.004502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>80.030</td>\n",
       "      <td>0.01450</td>\n",
       "      <td>Childs Play</td>\n",
       "      <td>Drake</td>\n",
       "      <td>70.032</td>\n",
       "      <td>70.032000</td>\n",
       "      <td>0.050384</td>\n",
       "      <td>0.020634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>144.154</td>\n",
       "      <td>0.02020</td>\n",
       "      <td>Gyöngyhajú lány</td>\n",
       "      <td>Omega</td>\n",
       "      <td>5.908</td>\n",
       "      <td>5.908008</td>\n",
       "      <td>0.698363</td>\n",
       "      <td>0.030104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>130.035</td>\n",
       "      <td>0.04810</td>\n",
       "      <td>I've Seen Footage</td>\n",
       "      <td>Death Grips</td>\n",
       "      <td>20.027</td>\n",
       "      <td>20.027036</td>\n",
       "      <td>0.555689</td>\n",
       "      <td>0.076455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>99.994</td>\n",
       "      <td>0.00208</td>\n",
       "      <td>Digital Animal</td>\n",
       "      <td>Honey Claws</td>\n",
       "      <td>50.068</td>\n",
       "      <td>50.068001</td>\n",
       "      <td>0.252122</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>111.951</td>\n",
       "      <td>0.05720</td>\n",
       "      <td>Subways - In Flagranti Extended Edit</td>\n",
       "      <td>The Avalanches</td>\n",
       "      <td>38.111</td>\n",
       "      <td>38.111029</td>\n",
       "      <td>0.372949</td>\n",
       "      <td>0.091574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>104.322</td>\n",
       "      <td>0.09150</td>\n",
       "      <td>Donme Dolap - Baris K Edit</td>\n",
       "      <td>Modern Folk Üçlüsü</td>\n",
       "      <td>45.740</td>\n",
       "      <td>45.740072</td>\n",
       "      <td>0.295857</td>\n",
       "      <td>0.148558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>127.681</td>\n",
       "      <td>0.25300</td>\n",
       "      <td>Cemalim</td>\n",
       "      <td>Erkin Koray</td>\n",
       "      <td>22.381</td>\n",
       "      <td>22.382317</td>\n",
       "      <td>0.531902</td>\n",
       "      <td>0.416866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>130.007</td>\n",
       "      <td>0.36600</td>\n",
       "      <td>One Night</td>\n",
       "      <td>Lil Yachty</td>\n",
       "      <td>20.055</td>\n",
       "      <td>20.058156</td>\n",
       "      <td>0.555406</td>\n",
       "      <td>0.604599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>125.011</td>\n",
       "      <td>0.44000</td>\n",
       "      <td>Oh lala</td>\n",
       "      <td>PNL</td>\n",
       "      <td>25.051</td>\n",
       "      <td>25.054687</td>\n",
       "      <td>0.504921</td>\n",
       "      <td>0.727539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>99.988</td>\n",
       "      <td>0.01900</td>\n",
       "      <td>Char</td>\n",
       "      <td>Crystal Castles</td>\n",
       "      <td>50.074</td>\n",
       "      <td>50.074001</td>\n",
       "      <td>0.252061</td>\n",
       "      <td>0.028110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>123.922</td>\n",
       "      <td>0.02390</td>\n",
       "      <td>World In Motion</td>\n",
       "      <td>New Order</td>\n",
       "      <td>26.140</td>\n",
       "      <td>26.140004</td>\n",
       "      <td>0.493917</td>\n",
       "      <td>0.036251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>122.415</td>\n",
       "      <td>0.23300</td>\n",
       "      <td>One Nation Under a Groove</td>\n",
       "      <td>Funkadelic</td>\n",
       "      <td>27.647</td>\n",
       "      <td>27.647898</td>\n",
       "      <td>0.478688</td>\n",
       "      <td>0.383639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>140.061</td>\n",
       "      <td>0.31400</td>\n",
       "      <td>Bouncin</td>\n",
       "      <td>Chief Keef</td>\n",
       "      <td>10.001</td>\n",
       "      <td>10.005613</td>\n",
       "      <td>0.657003</td>\n",
       "      <td>0.518208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>109.982</td>\n",
       "      <td>0.02420</td>\n",
       "      <td>C O O L - Radio Edit</td>\n",
       "      <td>Le Youth</td>\n",
       "      <td>40.080</td>\n",
       "      <td>40.080002</td>\n",
       "      <td>0.353052</td>\n",
       "      <td>0.036749</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tempo  acousticness                            song_title  \\\n",
       "0   150.062       0.01020                              Mask Off   \n",
       "1   160.083       0.19900                               Redbone   \n",
       "2    75.044       0.03440                          Xanny Family   \n",
       "3    86.468       0.60400                        Master Of None   \n",
       "4   174.004       0.18000                        Parallel Lines   \n",
       "5    85.023       0.00479                              Sneakin’   \n",
       "6    80.030       0.01450                           Childs Play   \n",
       "7   144.154       0.02020                       Gyöngyhajú lány   \n",
       "8   130.035       0.04810                     I've Seen Footage   \n",
       "9    99.994       0.00208                        Digital Animal   \n",
       "10  111.951       0.05720  Subways - In Flagranti Extended Edit   \n",
       "11  104.322       0.09150            Donme Dolap - Baris K Edit   \n",
       "12  127.681       0.25300                               Cemalim   \n",
       "13  130.007       0.36600                             One Night   \n",
       "14  125.011       0.44000                               Oh lala   \n",
       "15   99.988       0.01900                                  Char   \n",
       "16  123.922       0.02390                       World In Motion   \n",
       "17  122.415       0.23300             One Nation Under a Groove   \n",
       "18  140.061       0.31400                               Bouncin   \n",
       "19  109.982       0.02420                  C O O L - Radio Edit   \n",
       "\n",
       "                artist  tempo_difference  euclidean_distance  \\\n",
       "0               Future             0.000            0.000000   \n",
       "1     Childish Gambino            10.021           10.022778   \n",
       "2               Future            75.018           75.018004   \n",
       "3          Beach House            63.594           63.596772   \n",
       "4          Junior Boys            23.942           23.942602   \n",
       "5                Drake            65.039           65.039000   \n",
       "6                Drake            70.032           70.032000   \n",
       "7                Omega             5.908            5.908008   \n",
       "8          Death Grips            20.027           20.027036   \n",
       "9          Honey Claws            50.068           50.068001   \n",
       "10      The Avalanches            38.111           38.111029   \n",
       "11  Modern Folk Üçlüsü            45.740           45.740072   \n",
       "12         Erkin Koray            22.381           22.382317   \n",
       "13          Lil Yachty            20.055           20.058156   \n",
       "14                 PNL            25.051           25.054687   \n",
       "15     Crystal Castles            50.074           50.074001   \n",
       "16           New Order            26.140           26.140004   \n",
       "17          Funkadelic            27.647           27.647898   \n",
       "18          Chief Keef            10.001           10.005613   \n",
       "19            Le Youth            40.080           40.080002   \n",
       "\n",
       "    tempo_normalized  acoustic_normalized  \n",
       "0           0.758064             0.013490  \n",
       "1           0.859327             0.327153  \n",
       "2           0.000000             0.053695  \n",
       "3           0.115441             1.000000  \n",
       "4           1.000000             0.295587  \n",
       "5           0.100839             0.004502  \n",
       "6           0.050384             0.020634  \n",
       "7           0.698363             0.030104  \n",
       "8           0.555689             0.076455  \n",
       "9           0.252122             0.000000  \n",
       "10          0.372949             0.091574  \n",
       "11          0.295857             0.148558  \n",
       "12          0.531902             0.416866  \n",
       "13          0.555406             0.604599  \n",
       "14          0.504921             0.727539  \n",
       "15          0.252061             0.028110  \n",
       "16          0.493917             0.036251  \n",
       "17          0.478688             0.383639  \n",
       "18          0.657003             0.518208  \n",
       "19          0.353052             0.036749  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scale data to [0,1]\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Scale tempo to [0,1]\n",
    "tempo_column = dataset[['tempo']].values.astype(float)\n",
    "tempo_scaler = MinMaxScaler().fit(tempo_column)\n",
    "tempo_scaled = tempo_scaler.transform(tempo_column)\n",
    "dataset['tempo_normalized'] = pandas.DataFrame(tempo_scaled)\n",
    "\n",
    "# Scale acousticness to [0,1] (just in case)\n",
    "acoustic_column = dataset[['acousticness']].values.astype(float)\n",
    "acoustic_scaler = MinMaxScaler().fit(acoustic_column)\n",
    "acousticness_scaled = acoustic_scaler.transform(acoustic_column)\n",
    "dataset['acoustic_normalized'] = pandas.DataFrame(acousticness_scaled)\n",
    "\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "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>tempo</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>song_title</th>\n",
       "      <th>artist</th>\n",
       "      <th>tempo_difference</th>\n",
       "      <th>acoustic_difference</th>\n",
       "      <th>euclidean_distance</th>\n",
       "      <th>tempo_normalized</th>\n",
       "      <th>acoustic_normalized</th>\n",
       "      <th>norm_euclidean_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150.062</td>\n",
       "      <td>0.01020</td>\n",
       "      <td>Mask Off</td>\n",
       "      <td>Future</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.758064</td>\n",
       "      <td>0.013490</td>\n",
       "      <td>0.009326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>144.154</td>\n",
       "      <td>0.02020</td>\n",
       "      <td>Gyöngyhajú lány</td>\n",
       "      <td>Omega</td>\n",
       "      <td>5.908</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>5.908008</td>\n",
       "      <td>0.698363</td>\n",
       "      <td>0.030104</td>\n",
       "      <td>0.053762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>130.035</td>\n",
       "      <td>0.04810</td>\n",
       "      <td>I've Seen Footage</td>\n",
       "      <td>Death Grips</td>\n",
       "      <td>20.027</td>\n",
       "      <td>0.03790</td>\n",
       "      <td>20.027036</td>\n",
       "      <td>0.555689</td>\n",
       "      <td>0.076455</td>\n",
       "      <td>0.203753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>123.922</td>\n",
       "      <td>0.02390</td>\n",
       "      <td>World In Motion</td>\n",
       "      <td>New Order</td>\n",
       "      <td>26.140</td>\n",
       "      <td>0.01370</td>\n",
       "      <td>26.140004</td>\n",
       "      <td>0.493917</td>\n",
       "      <td>0.036251</td>\n",
       "      <td>0.256156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>160.083</td>\n",
       "      <td>0.19900</td>\n",
       "      <td>Redbone</td>\n",
       "      <td>Childish Gambino</td>\n",
       "      <td>10.021</td>\n",
       "      <td>0.18880</td>\n",
       "      <td>10.022778</td>\n",
       "      <td>0.859327</td>\n",
       "      <td>0.327153</td>\n",
       "      <td>0.334164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>174.004</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>Parallel Lines</td>\n",
       "      <td>Junior Boys</td>\n",
       "      <td>23.942</td>\n",
       "      <td>0.16980</td>\n",
       "      <td>23.942602</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.295587</td>\n",
       "      <td>0.378957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>111.951</td>\n",
       "      <td>0.05720</td>\n",
       "      <td>Subways - In Flagranti Extended Edit</td>\n",
       "      <td>The Avalanches</td>\n",
       "      <td>38.111</td>\n",
       "      <td>0.04700</td>\n",
       "      <td>38.111029</td>\n",
       "      <td>0.372949</td>\n",
       "      <td>0.091574</td>\n",
       "      <td>0.384329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>109.982</td>\n",
       "      <td>0.02420</td>\n",
       "      <td>C O O L - Radio Edit</td>\n",
       "      <td>Le Youth</td>\n",
       "      <td>40.080</td>\n",
       "      <td>0.01400</td>\n",
       "      <td>40.080002</td>\n",
       "      <td>0.353052</td>\n",
       "      <td>0.036749</td>\n",
       "      <td>0.396635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>127.681</td>\n",
       "      <td>0.25300</td>\n",
       "      <td>Cemalim</td>\n",
       "      <td>Erkin Koray</td>\n",
       "      <td>22.381</td>\n",
       "      <td>0.24280</td>\n",
       "      <td>22.382317</td>\n",
       "      <td>0.531902</td>\n",
       "      <td>0.416866</td>\n",
       "      <td>0.459562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>122.415</td>\n",
       "      <td>0.23300</td>\n",
       "      <td>One Nation Under a Groove</td>\n",
       "      <td>Funkadelic</td>\n",
       "      <td>27.647</td>\n",
       "      <td>0.22280</td>\n",
       "      <td>27.647898</td>\n",
       "      <td>0.478688</td>\n",
       "      <td>0.383639</td>\n",
       "      <td>0.459681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>104.322</td>\n",
       "      <td>0.09150</td>\n",
       "      <td>Donme Dolap - Baris K Edit</td>\n",
       "      <td>Modern Folk Üçlüsü</td>\n",
       "      <td>45.740</td>\n",
       "      <td>0.08130</td>\n",
       "      <td>45.740072</td>\n",
       "      <td>0.295857</td>\n",
       "      <td>0.148558</td>\n",
       "      <td>0.473244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>99.994</td>\n",
       "      <td>0.00208</td>\n",
       "      <td>Digital Animal</td>\n",
       "      <td>Honey Claws</td>\n",
       "      <td>50.068</td>\n",
       "      <td>0.00812</td>\n",
       "      <td>50.068001</td>\n",
       "      <td>0.252122</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.496916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>99.988</td>\n",
       "      <td>0.01900</td>\n",
       "      <td>Char</td>\n",
       "      <td>Crystal Castles</td>\n",
       "      <td>50.074</td>\n",
       "      <td>0.00880</td>\n",
       "      <td>50.074001</td>\n",
       "      <td>0.252061</td>\n",
       "      <td>0.028110</td>\n",
       "      <td>0.497106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>140.061</td>\n",
       "      <td>0.31400</td>\n",
       "      <td>Bouncin</td>\n",
       "      <td>Chief Keef</td>\n",
       "      <td>10.001</td>\n",
       "      <td>0.30380</td>\n",
       "      <td>10.005613</td>\n",
       "      <td>0.657003</td>\n",
       "      <td>0.518208</td>\n",
       "      <td>0.514716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>130.007</td>\n",
       "      <td>0.36600</td>\n",
       "      <td>One Night</td>\n",
       "      <td>Lil Yachty</td>\n",
       "      <td>20.055</td>\n",
       "      <td>0.35580</td>\n",
       "      <td>20.058156</td>\n",
       "      <td>0.555406</td>\n",
       "      <td>0.604599</td>\n",
       "      <td>0.623614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>85.023</td>\n",
       "      <td>0.00479</td>\n",
       "      <td>Sneakin’</td>\n",
       "      <td>Drake</td>\n",
       "      <td>65.039</td>\n",
       "      <td>0.00541</td>\n",
       "      <td>65.039000</td>\n",
       "      <td>0.100839</td>\n",
       "      <td>0.004502</td>\n",
       "      <td>0.648101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>80.030</td>\n",
       "      <td>0.01450</td>\n",
       "      <td>Childs Play</td>\n",
       "      <td>Drake</td>\n",
       "      <td>70.032</td>\n",
       "      <td>0.00430</td>\n",
       "      <td>70.032000</td>\n",
       "      <td>0.050384</td>\n",
       "      <td>0.020634</td>\n",
       "      <td>0.698571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75.044</td>\n",
       "      <td>0.03440</td>\n",
       "      <td>Xanny Family</td>\n",
       "      <td>Future</td>\n",
       "      <td>75.018</td>\n",
       "      <td>0.02420</td>\n",
       "      <td>75.018004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.053695</td>\n",
       "      <td>0.750072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>125.011</td>\n",
       "      <td>0.44000</td>\n",
       "      <td>Oh lala</td>\n",
       "      <td>PNL</td>\n",
       "      <td>25.051</td>\n",
       "      <td>0.42980</td>\n",
       "      <td>25.054687</td>\n",
       "      <td>0.504921</td>\n",
       "      <td>0.727539</td>\n",
       "      <td>0.756215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>86.468</td>\n",
       "      <td>0.60400</td>\n",
       "      <td>Master Of None</td>\n",
       "      <td>Beach House</td>\n",
       "      <td>63.594</td>\n",
       "      <td>0.59380</td>\n",
       "      <td>63.596772</td>\n",
       "      <td>0.115441</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.173833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tempo  acousticness                            song_title  \\\n",
       "0   150.062       0.01020                              Mask Off   \n",
       "7   144.154       0.02020                       Gyöngyhajú lány   \n",
       "8   130.035       0.04810                     I've Seen Footage   \n",
       "16  123.922       0.02390                       World In Motion   \n",
       "1   160.083       0.19900                               Redbone   \n",
       "4   174.004       0.18000                        Parallel Lines   \n",
       "10  111.951       0.05720  Subways - In Flagranti Extended Edit   \n",
       "19  109.982       0.02420                  C O O L - Radio Edit   \n",
       "12  127.681       0.25300                               Cemalim   \n",
       "17  122.415       0.23300             One Nation Under a Groove   \n",
       "11  104.322       0.09150            Donme Dolap - Baris K Edit   \n",
       "9    99.994       0.00208                        Digital Animal   \n",
       "15   99.988       0.01900                                  Char   \n",
       "18  140.061       0.31400                               Bouncin   \n",
       "13  130.007       0.36600                             One Night   \n",
       "5    85.023       0.00479                              Sneakin’   \n",
       "6    80.030       0.01450                           Childs Play   \n",
       "2    75.044       0.03440                          Xanny Family   \n",
       "14  125.011       0.44000                               Oh lala   \n",
       "3    86.468       0.60400                        Master Of None   \n",
       "\n",
       "                artist  tempo_difference  acoustic_difference  \\\n",
       "0               Future             0.000              0.00000   \n",
       "7                Omega             5.908              0.01000   \n",
       "8          Death Grips            20.027              0.03790   \n",
       "16           New Order            26.140              0.01370   \n",
       "1     Childish Gambino            10.021              0.18880   \n",
       "4          Junior Boys            23.942              0.16980   \n",
       "10      The Avalanches            38.111              0.04700   \n",
       "19            Le Youth            40.080              0.01400   \n",
       "12         Erkin Koray            22.381              0.24280   \n",
       "17          Funkadelic            27.647              0.22280   \n",
       "11  Modern Folk Üçlüsü            45.740              0.08130   \n",
       "9          Honey Claws            50.068              0.00812   \n",
       "15     Crystal Castles            50.074              0.00880   \n",
       "18          Chief Keef            10.001              0.30380   \n",
       "13          Lil Yachty            20.055              0.35580   \n",
       "5                Drake            65.039              0.00541   \n",
       "6                Drake            70.032              0.00430   \n",
       "2               Future            75.018              0.02420   \n",
       "14                 PNL            25.051              0.42980   \n",
       "3          Beach House            63.594              0.59380   \n",
       "\n",
       "    euclidean_distance  tempo_normalized  acoustic_normalized  \\\n",
       "0             0.000000          0.758064             0.013490   \n",
       "7             5.908008          0.698363             0.030104   \n",
       "8            20.027036          0.555689             0.076455   \n",
       "16           26.140004          0.493917             0.036251   \n",
       "1            10.022778          0.859327             0.327153   \n",
       "4            23.942602          1.000000             0.295587   \n",
       "10           38.111029          0.372949             0.091574   \n",
       "19           40.080002          0.353052             0.036749   \n",
       "12           22.382317          0.531902             0.416866   \n",
       "17           27.647898          0.478688             0.383639   \n",
       "11           45.740072          0.295857             0.148558   \n",
       "9            50.068001          0.252122             0.000000   \n",
       "15           50.074001          0.252061             0.028110   \n",
       "18           10.005613          0.657003             0.518208   \n",
       "13           20.058156          0.555406             0.604599   \n",
       "5            65.039000          0.100839             0.004502   \n",
       "6            70.032000          0.050384             0.020634   \n",
       "2            75.018004          0.000000             0.053695   \n",
       "14           25.054687          0.504921             0.727539   \n",
       "3            63.596772          0.115441             1.000000   \n",
       "\n",
       "    norm_euclidean_distance  \n",
       "0                  0.009326  \n",
       "7                  0.053762  \n",
       "8                  0.203753  \n",
       "16                 0.256156  \n",
       "1                  0.334164  \n",
       "4                  0.378957  \n",
       "10                 0.384329  \n",
       "19                 0.396635  \n",
       "12                 0.459562  \n",
       "17                 0.459681  \n",
       "11                 0.473244  \n",
       "9                  0.496916  \n",
       "15                 0.497106  \n",
       "18                 0.514716  \n",
       "13                 0.623614  \n",
       "5                  0.648101  \n",
       "6                  0.698571  \n",
       "2                  0.750072  \n",
       "14                 0.756215  \n",
       "3                  1.173833  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now calculate the distances again using these normalized values\n",
    "dataset['norm_euclidean_distance'] = numpy.sqrt( (0.011762 - dataset.acoustic_normalized)**2 \n",
    "                                                + (0.748899 - dataset.tempo_normalized)**2)\n",
    "\n",
    "# Sort the dataset from closest to farthest\n",
    "sorted_dataset_distance = dataset.sort_values('norm_euclidean_distance')\n",
    "\n",
    "# Display sorted dataset\n",
    "sorted_dataset_distance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can see the closest songs are different! Which are the most similar songs to Mask Off?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing Our Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can start to see our data and notice that there are songs that are similar to Mask Off, but also very different! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now let's visualize that by plotting all the original data points\n",
    "import matplotlib.pyplot as plot\n",
    "plot.rcParams[\"figure.figsize\"] = 8,8\n",
    "\n",
    "plot.scatter(dataset['tempo_normalized'], dataset['acoustic_normalized'])\n",
    "plot.xlabel('Tempo')\n",
    "plot.ylabel('Acousticness')\n",
    "\n",
    "plot.axis('equal')\n",
    "\n",
    "for index, row in dataset.iterrows():\n",
    "    plot.annotate(str(row['song_title']), xy=(row['tempo_normalized'], row['acoustic_normalized']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's finish by visualizing even more songs from our database."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's put everything together! And this time, let's use lots more data. 100 songs!\n",
    "\n",
    "**1. Pre-processing**\n",
    "- Reading the file\n",
    "- Getting the columns (features) we want\n",
    "- Choosing the rows (songs) we want\n",
    "- Scaling the features to [0,1]\n",
    "\n",
    "**2. Visualizing Data in Feature Space**\n",
    "- Plotting the songs in 2D space\n",
    "\n",
    "**3. Calculating Differences and Similarities**\n",
    "- Calculating distances and sorting them to find the most similar songs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import matplotlib.pyplot as plot\n",
    "\n",
    "# Read the data from the spreadsheet\n",
    "raw_data = pandas.read_csv('data/spotify.csv')  # Top 2017 songs of 2017 from kaggle.com\n",
    "\n",
    "# Just get the columns we want\n",
    "dataset = pandas.DataFrame(raw_data, columns = ['tempo', 'acousticness', 'song_title', 'artist'])\n",
    "\n",
    "# Let's get the top 100 songs\n",
    "dataset = dataset[:100]\n",
    "\n",
    "# Scale tempo to [0,1]\n",
    "tempo_column = dataset[['tempo']].values.astype(float)\n",
    "tempo_scaler = MinMaxScaler().fit(tempo_column)\n",
    "tempo_scaled = tempo_scaler.transform(tempo_column)\n",
    "dataset['tempo_normalized'] = pandas.DataFrame(tempo_scaled)\n",
    "\n",
    "# Scale acousticness to [0,1] (just in case)\n",
    "acoustic_column = dataset[['acousticness']].values.astype(float)\n",
    "acoustic_scaler = MinMaxScaler().fit(acoustic_column)\n",
    "acousticness_scaled = acoustic_scaler.transform(acoustic_column)\n",
    "dataset['acoustic_normalized'] = pandas.DataFrame(acousticness_scaled)\\"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now let's visualize that by plotting all the original data points\n",
    "plot.rcParams[\"figure.figsize\"] = 20,20\n",
    "\n",
    "plot.scatter(dataset['tempo_normalized'], dataset['acoustic_normalized'])\n",
    "plot.xlabel('Tempo')\n",
    "plot.ylabel('Acousticness')\n",
    "\n",
    "plot.axis('equal')\n",
    "\n",
    "for index, row in dataset.iterrows():\n",
    "    plot.annotate(str(row['song_title']), xy=(row['tempo_normalized'], row['acoustic_normalized']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "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>tempo</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>song_title</th>\n",
       "      <th>artist</th>\n",
       "      <th>tempo_difference</th>\n",
       "      <th>euclidean_distance</th>\n",
       "      <th>tempo_normalized</th>\n",
       "      <th>acoustic_normalized</th>\n",
       "      <th>norm_euclidean_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150.062</td>\n",
       "      <td>0.01020</td>\n",
       "      <td>Mask Off</td>\n",
       "      <td>Future</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.758064</td>\n",
       "      <td>0.013490</td>\n",
       "      <td>0.009326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>144.154</td>\n",
       "      <td>0.02020</td>\n",
       "      <td>Gyöngyhajú lány</td>\n",
       "      <td>Omega</td>\n",
       "      <td>5.908</td>\n",
       "      <td>5.908008</td>\n",
       "      <td>0.698363</td>\n",
       "      <td>0.030104</td>\n",
       "      <td>0.053762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>130.035</td>\n",
       "      <td>0.04810</td>\n",
       "      <td>I've Seen Footage</td>\n",
       "      <td>Death Grips</td>\n",
       "      <td>20.027</td>\n",
       "      <td>20.027036</td>\n",
       "      <td>0.555689</td>\n",
       "      <td>0.076455</td>\n",
       "      <td>0.203753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>123.922</td>\n",
       "      <td>0.02390</td>\n",
       "      <td>World In Motion</td>\n",
       "      <td>New Order</td>\n",
       "      <td>26.140</td>\n",
       "      <td>26.140004</td>\n",
       "      <td>0.493917</td>\n",
       "      <td>0.036251</td>\n",
       "      <td>0.256156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>160.083</td>\n",
       "      <td>0.19900</td>\n",
       "      <td>Redbone</td>\n",
       "      <td>Childish Gambino</td>\n",
       "      <td>10.021</td>\n",
       "      <td>10.022778</td>\n",
       "      <td>0.859327</td>\n",
       "      <td>0.327153</td>\n",
       "      <td>0.334164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>174.004</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>Parallel Lines</td>\n",
       "      <td>Junior Boys</td>\n",
       "      <td>23.942</td>\n",
       "      <td>23.942602</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.295587</td>\n",
       "      <td>0.378957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>111.951</td>\n",
       "      <td>0.05720</td>\n",
       "      <td>Subways - In Flagranti Extended Edit</td>\n",
       "      <td>The Avalanches</td>\n",
       "      <td>38.111</td>\n",
       "      <td>38.111029</td>\n",
       "      <td>0.372949</td>\n",
       "      <td>0.091574</td>\n",
       "      <td>0.384329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>109.982</td>\n",
       "      <td>0.02420</td>\n",
       "      <td>C O O L - Radio Edit</td>\n",
       "      <td>Le Youth</td>\n",
       "      <td>40.080</td>\n",
       "      <td>40.080002</td>\n",
       "      <td>0.353052</td>\n",
       "      <td>0.036749</td>\n",
       "      <td>0.396635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>127.681</td>\n",
       "      <td>0.25300</td>\n",
       "      <td>Cemalim</td>\n",
       "      <td>Erkin Koray</td>\n",
       "      <td>22.381</td>\n",
       "      <td>22.382317</td>\n",
       "      <td>0.531902</td>\n",
       "      <td>0.416866</td>\n",
       "      <td>0.459562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>122.415</td>\n",
       "      <td>0.23300</td>\n",
       "      <td>One Nation Under a Groove</td>\n",
       "      <td>Funkadelic</td>\n",
       "      <td>27.647</td>\n",
       "      <td>27.647898</td>\n",
       "      <td>0.478688</td>\n",
       "      <td>0.383639</td>\n",
       "      <td>0.459681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>104.322</td>\n",
       "      <td>0.09150</td>\n",
       "      <td>Donme Dolap - Baris K Edit</td>\n",
       "      <td>Modern Folk Üçlüsü</td>\n",
       "      <td>45.740</td>\n",
       "      <td>45.740072</td>\n",
       "      <td>0.295857</td>\n",
       "      <td>0.148558</td>\n",
       "      <td>0.473244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>99.994</td>\n",
       "      <td>0.00208</td>\n",
       "      <td>Digital Animal</td>\n",
       "      <td>Honey Claws</td>\n",
       "      <td>50.068</td>\n",
       "      <td>50.068001</td>\n",
       "      <td>0.252122</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.496916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>99.988</td>\n",
       "      <td>0.01900</td>\n",
       "      <td>Char</td>\n",
       "      <td>Crystal Castles</td>\n",
       "      <td>50.074</td>\n",
       "      <td>50.074001</td>\n",
       "      <td>0.252061</td>\n",
       "      <td>0.028110</td>\n",
       "      <td>0.497106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>140.061</td>\n",
       "      <td>0.31400</td>\n",
       "      <td>Bouncin</td>\n",
       "      <td>Chief Keef</td>\n",
       "      <td>10.001</td>\n",
       "      <td>10.005613</td>\n",
       "      <td>0.657003</td>\n",
       "      <td>0.518208</td>\n",
       "      <td>0.514716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>130.007</td>\n",
       "      <td>0.36600</td>\n",
       "      <td>One Night</td>\n",
       "      <td>Lil Yachty</td>\n",
       "      <td>20.055</td>\n",
       "      <td>20.058156</td>\n",
       "      <td>0.555406</td>\n",
       "      <td>0.604599</td>\n",
       "      <td>0.623614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>85.023</td>\n",
       "      <td>0.00479</td>\n",
       "      <td>Sneakin’</td>\n",
       "      <td>Drake</td>\n",
       "      <td>65.039</td>\n",
       "      <td>65.039000</td>\n",
       "      <td>0.100839</td>\n",
       "      <td>0.004502</td>\n",
       "      <td>0.648101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>80.030</td>\n",
       "      <td>0.01450</td>\n",
       "      <td>Childs Play</td>\n",
       "      <td>Drake</td>\n",
       "      <td>70.032</td>\n",
       "      <td>70.032000</td>\n",
       "      <td>0.050384</td>\n",
       "      <td>0.020634</td>\n",
       "      <td>0.698571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75.044</td>\n",
       "      <td>0.03440</td>\n",
       "      <td>Xanny Family</td>\n",
       "      <td>Future</td>\n",
       "      <td>75.018</td>\n",
       "      <td>75.018004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.053695</td>\n",
       "      <td>0.750072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>125.011</td>\n",
       "      <td>0.44000</td>\n",
       "      <td>Oh lala</td>\n",
       "      <td>PNL</td>\n",
       "      <td>25.051</td>\n",
       "      <td>25.054687</td>\n",
       "      <td>0.504921</td>\n",
       "      <td>0.727539</td>\n",
       "      <td>0.756215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>86.468</td>\n",
       "      <td>0.60400</td>\n",
       "      <td>Master Of None</td>\n",
       "      <td>Beach House</td>\n",
       "      <td>63.594</td>\n",
       "      <td>63.596772</td>\n",
       "      <td>0.115441</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.173833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tempo  acousticness                            song_title  \\\n",
       "0   150.062       0.01020                              Mask Off   \n",
       "7   144.154       0.02020                       Gyöngyhajú lány   \n",
       "8   130.035       0.04810                     I've Seen Footage   \n",
       "16  123.922       0.02390                       World In Motion   \n",
       "1   160.083       0.19900                               Redbone   \n",
       "4   174.004       0.18000                        Parallel Lines   \n",
       "10  111.951       0.05720  Subways - In Flagranti Extended Edit   \n",
       "19  109.982       0.02420                  C O O L - Radio Edit   \n",
       "12  127.681       0.25300                               Cemalim   \n",
       "17  122.415       0.23300             One Nation Under a Groove   \n",
       "11  104.322       0.09150            Donme Dolap - Baris K Edit   \n",
       "9    99.994       0.00208                        Digital Animal   \n",
       "15   99.988       0.01900                                  Char   \n",
       "18  140.061       0.31400                               Bouncin   \n",
       "13  130.007       0.36600                             One Night   \n",
       "5    85.023       0.00479                              Sneakin’   \n",
       "6    80.030       0.01450                           Childs Play   \n",
       "2    75.044       0.03440                          Xanny Family   \n",
       "14  125.011       0.44000                               Oh lala   \n",
       "3    86.468       0.60400                        Master Of None   \n",
       "\n",
       "                artist  tempo_difference  euclidean_distance  \\\n",
       "0               Future             0.000            0.000000   \n",
       "7                Omega             5.908            5.908008   \n",
       "8          Death Grips            20.027           20.027036   \n",
       "16           New Order            26.140           26.140004   \n",
       "1     Childish Gambino            10.021           10.022778   \n",
       "4          Junior Boys            23.942           23.942602   \n",
       "10      The Avalanches            38.111           38.111029   \n",
       "19            Le Youth            40.080           40.080002   \n",
       "12         Erkin Koray            22.381           22.382317   \n",
       "17          Funkadelic            27.647           27.647898   \n",
       "11  Modern Folk Üçlüsü            45.740           45.740072   \n",
       "9          Honey Claws            50.068           50.068001   \n",
       "15     Crystal Castles            50.074           50.074001   \n",
       "18          Chief Keef            10.001           10.005613   \n",
       "13          Lil Yachty            20.055           20.058156   \n",
       "5                Drake            65.039           65.039000   \n",
       "6                Drake            70.032           70.032000   \n",
       "2               Future            75.018           75.018004   \n",
       "14                 PNL            25.051           25.054687   \n",
       "3          Beach House            63.594           63.596772   \n",
       "\n",
       "    tempo_normalized  acoustic_normalized  norm_euclidean_distance  \n",
       "0           0.758064             0.013490                 0.009326  \n",
       "7           0.698363             0.030104                 0.053762  \n",
       "8           0.555689             0.076455                 0.203753  \n",
       "16          0.493917             0.036251                 0.256156  \n",
       "1           0.859327             0.327153                 0.334164  \n",
       "4           1.000000             0.295587                 0.378957  \n",
       "10          0.372949             0.091574                 0.384329  \n",
       "19          0.353052             0.036749                 0.396635  \n",
       "12          0.531902             0.416866                 0.459562  \n",
       "17          0.478688             0.383639                 0.459681  \n",
       "11          0.295857             0.148558                 0.473244  \n",
       "9           0.252122             0.000000                 0.496916  \n",
       "15          0.252061             0.028110                 0.497106  \n",
       "18          0.657003             0.518208                 0.514716  \n",
       "13          0.555406             0.604599                 0.623614  \n",
       "5           0.100839             0.004502                 0.648101  \n",
       "6           0.050384             0.020634                 0.698571  \n",
       "2           0.000000             0.053695                 0.750072  \n",
       "14          0.504921             0.727539                 0.756215  \n",
       "3           0.115441             1.000000                 1.173833  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now calculate the distances again using these normalized values\n",
    "import numpy\n",
    "dataset['norm_euclidean_distance'] = numpy.sqrt( (0.011762 - dataset.acoustic_normalized)**2 \n",
    "                                                + (0.748899 - dataset.tempo_normalized)**2)\n",
    "# Sort the dataset from closest to farthest\n",
    "sorted_dataset_distance = dataset.sort_values('norm_euclidean_distance')\n",
    "\n",
    "# Display sorted dataset\n",
    "sorted_dataset_distance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Your Turn!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Play with this a little bit -- find a song you like, \n",
    "and change the code above to find other songs in the top 100 songs that you might also like!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What can you say about the songs now that you've visualized it?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can you try to add another feature? How would you modify the Euclidean Distance to accomodate another feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discussion - Ethics and Bias in AI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do you think about how features are chosen? Are all features equally important? Who chooses the features to use?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  }
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