FinalProject_group144.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction and Background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Music is a very popular part of our culture and it has become more and more accessible to listeners. Many individuals purchase these different music streaming services such as Spotify, Apple music, Amazon music, Google play, etc. Songs at the top of the charts are streamed millions of times. This brings into question what makes a song more popular and a worthwhile listen? There are many components that make up a song, such as acoustics, danceability, energy, tempo, loudness, etc. We set out to figure out if there is a relationship between these different components and a song’s popularity in terms of streams on Spotify. \n",
    "\n",
    "Past studies have shown us that the tempo of a song is directly correlated with our emotional response to that song. They found that a slower tempo makes us feel sadder while a higher tempo makes us feel happier [1]. Another study found that songs with a higher tempo are associated with higher energy and thus, are more likely to make us want to dance [2]. These studies seem to imply that a songs makeup can have a big effect on our mood. The extent to which this translates to a song’s popularity is our main focus for this project.\n",
    "\n",
    "References:\n",
    "\n",
    "[1] https://sonicscoop.com/2014/06/19/the-resonant-human-the-science-of-how-tempo-affects-us/\n",
    "\n",
    "[2]https://www.washingtonpost.com/news/to-your-health/wp/2015/10/30/the-mathematical-formula-behind-feel-good-songs/?noredirect=on&utm_term=.4af1c700d58a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Group Members and Contributions "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Michael Zuazo__ (XXXXXXXXX)\n",
    "- Background and Prior Work\n",
    "- Data Cleaning / Pre-processing\n",
    "- Data Visualization\n",
    "\n",
    "__Yash Garde__ (XXXXXXXXX)\n",
    "- Data Cleaning / Pre-processing\n",
    "- Data Analysis\n",
    "- Conclusion\n",
    "\n",
    "__Wang Linxuan__ (XXXXXXXXX)\n",
    "- Data Cleaning / Pre-processing\n",
    "- Data Visualization\n",
    "- Data Analysis\n",
    "- Prediction Models\n",
    "\n",
    "__Juan Tapia__ (XXXXXXXXX)\n",
    "- Introduction\n",
    "- Ethics and Privacy\n",
    "- Discussion / Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To what extent do the different components that make up a song allow us to predict its popularity, in terms of streams on Spotify?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We predict that components that are associated with happiness, such as tempo, energy, and danceability will allow us to predict a song’s popularity. Specifically we predict that as tempo/energy/danceability increases, so will the number of streams."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Datasets Used"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Spotify Song Attributes Dataset\n",
    "  - Number of Observations: 2017\n",
    "  - Source: https://www.kaggle.com/geomack/spotifyclassification\n",
    "  - This dataset contains the attributes of the top 2017 songs of the year 2017 such as loudness, acousticness, tempo, energy etc. We will be using this data to determine which of these features impacted the popularity of any given song in 2017.\n",
    "  \n",
    "- Spotify Worldwide Daily Song Rankings Dataset\n",
    "  - Number of Observations: 3.44 million\n",
    "  - Source: https://www.kaggle.com/edumucelli/spotifys-worldwide-daily-song-ranking\n",
    "  - This dataset contains information about the number of daily streams the top 200 songs got for every day in the years 2017 and 2018. We will be accumulating the number of streams each song got in order to guage the popularity of any given song.\n",
    "  \n",
    "We will merge this data by creating a unique key in the form of the song name followed by the artist. In order to get the total number of streams for any given song, we will have to iterate through the daily song ranking dataset and sum up all the streams it accumulated over the year.\n",
    "Our final dataset will contain the name of the song, the artist, the attributes of that song and the number of streams it accumulated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will begin by importing all the required packages that we will use for our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import patsy\n",
    "import statsmodels.api as sm\n",
    "import os\n",
    "import bs4\n",
    "import urllib.request\n",
    "from urllib.request import urlopen\n",
    "from bs4 import BeautifulSoup\n",
    "import requests\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Wrangling and Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We begin by reading in our two datasets specified above. The dataset containing the information about the features of the songs will be read into the DataFrame df_tempo and the large dataset containing information about the number of daily streams will be read into the DataFrame df_pop1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tempo = pd.read_csv('data.csv')\n",
    "df_pop1 = pd.read_csv('popularity_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now have to remove any columns that are not critical to our analysis.\n",
    "\n",
    "Our streams dataset (df_pop1) has many extraneous columns such as Position, URL, Date and Region of the song. We will ignore these fields and only focus on the Track Name, Artist and Streams field. In addition, we will also rename the fields to be consistent with the df_tempo dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pop1 = df_pop1[['Track Name', 'Artist', 'Streams']]\n",
    "df_pop1.columns = ['song_title', 'artist', 'streams']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step will be to merge these two datasets on a common column. We could not simply merge the dataframe on the song title as different artists may put out songs with the same title.\n",
    "To combat this, we create a unique key using the song title and the artist put together in both the datasets so that we may merge the datasets later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "      <th>uniq_id</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</td>\n",
       "      <td>0.286</td>\n",
       "      <td>1</td>\n",
       "      <td>Mask Off</td>\n",
       "      <td>Future</td>\n",
       "      <td>Future - Mask Off</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</td>\n",
       "      <td>0.588</td>\n",
       "      <td>1</td>\n",
       "      <td>Redbone</td>\n",
       "      <td>Childish Gambino</td>\n",
       "      <td>Childish Gambino - Redbone</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</td>\n",
       "      <td>0.173</td>\n",
       "      <td>1</td>\n",
       "      <td>Xanny Family</td>\n",
       "      <td>Future</td>\n",
       "      <td>Future - Xanny Family</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</td>\n",
       "      <td>0.230</td>\n",
       "      <td>1</td>\n",
       "      <td>Master Of None</td>\n",
       "      <td>Beach House</td>\n",
       "      <td>Beach House - Master Of None</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</td>\n",
       "      <td>0.904</td>\n",
       "      <td>1</td>\n",
       "      <td>Parallel Lines</td>\n",
       "      <td>Junior Boys</td>\n",
       "      <td>Junior Boys - Parallel Lines</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</td>\n",
       "      <td>0.264</td>\n",
       "      <td>1</td>\n",
       "      <td>Sneakin’</td>\n",
       "      <td>Drake</td>\n",
       "      <td>Drake - Sneakin’</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</td>\n",
       "      <td>0.308</td>\n",
       "      <td>1</td>\n",
       "      <td>Childs Play</td>\n",
       "      <td>Drake</td>\n",
       "      <td>Drake - Childs Play</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</td>\n",
       "      <td>0.393</td>\n",
       "      <td>1</td>\n",
       "      <td>Gyöngyhajú lány</td>\n",
       "      <td>Omega</td>\n",
       "      <td>Omega - Gyöngyhajú lány</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</td>\n",
       "      <td>0.398</td>\n",
       "      <td>1</td>\n",
       "      <td>I've Seen Footage</td>\n",
       "      <td>Death Grips</td>\n",
       "      <td>Death Grips - I've Seen Footage</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</td>\n",
       "      <td>0.386</td>\n",
       "      <td>1</td>\n",
       "      <td>Digital Animal</td>\n",
       "      <td>Honey Claws</td>\n",
       "      <td>Honey Claws - Digital Animal</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</td>\n",
       "      <td>0.524</td>\n",
       "      <td>1</td>\n",
       "      <td>Subways - In Flagranti Extended Edit</td>\n",
       "      <td>The Avalanches</td>\n",
       "      <td>The Avalanches - Subways - In Flagranti Extend...</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</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",
       "      <td>Modern Folk Üçlüsü - Donme Dolap - Baris K Edit</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</td>\n",
       "      <td>0.381</td>\n",
       "      <td>1</td>\n",
       "      <td>Cemalim</td>\n",
       "      <td>Erkin Koray</td>\n",
       "      <td>Erkin Koray - Cemalim</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</td>\n",
       "      <td>0.367</td>\n",
       "      <td>1</td>\n",
       "      <td>One Night</td>\n",
       "      <td>Lil Yachty</td>\n",
       "      <td>Lil Yachty - One Night</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</td>\n",
       "      <td>0.351</td>\n",
       "      <td>1</td>\n",
       "      <td>Oh lala</td>\n",
       "      <td>PNL</td>\n",
       "      <td>PNL - Oh lala</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</td>\n",
       "      <td>0.317</td>\n",
       "      <td>1</td>\n",
       "      <td>Char</td>\n",
       "      <td>Crystal Castles</td>\n",
       "      <td>Crystal Castles - Char</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</td>\n",
       "      <td>0.773</td>\n",
       "      <td>1</td>\n",
       "      <td>World In Motion</td>\n",
       "      <td>New Order</td>\n",
       "      <td>New Order - World In Motion</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</td>\n",
       "      <td>0.842</td>\n",
       "      <td>1</td>\n",
       "      <td>One Nation Under a Groove</td>\n",
       "      <td>Funkadelic</td>\n",
       "      <td>Funkadelic - One Nation Under a Groove</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</td>\n",
       "      <td>0.783</td>\n",
       "      <td>1</td>\n",
       "      <td>Bouncin</td>\n",
       "      <td>Chief Keef</td>\n",
       "      <td>Chief Keef - Bouncin</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</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",
       "      <td>Le Youth - C O O L - Radio Edit</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</td>\n",
       "      <td>0.471</td>\n",
       "      <td>1</td>\n",
       "      <td>Percolator (Jamie Jones Vault Mix) - mixed</td>\n",
       "      <td>Cajmere</td>\n",
       "      <td>Cajmere - Percolator (Jamie Jones Vault Mix) -...</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</td>\n",
       "      <td>0.770</td>\n",
       "      <td>1</td>\n",
       "      <td>House of Jealous Lovers</td>\n",
       "      <td>The Rapture</td>\n",
       "      <td>The Rapture - House of Jealous Lovers</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</td>\n",
       "      <td>0.441</td>\n",
       "      <td>1</td>\n",
       "      <td>Imma Ride</td>\n",
       "      <td>Young Thug</td>\n",
       "      <td>Young Thug - Imma Ride</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</td>\n",
       "      <td>0.364</td>\n",
       "      <td>1</td>\n",
       "      <td>Girlfriend</td>\n",
       "      <td>Ty Segall</td>\n",
       "      <td>Ty Segall - Girlfriend</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</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",
       "      <td>Myron &amp; E - If I Gave You My Love</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</td>\n",
       "      <td>0.795</td>\n",
       "      <td>1</td>\n",
       "      <td>This Ready Flesh</td>\n",
       "      <td>TR/ST</td>\n",
       "      <td>TR/ST - This Ready Flesh</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</td>\n",
       "      <td>0.598</td>\n",
       "      <td>1</td>\n",
       "      <td>Lose My Mind</td>\n",
       "      <td>A-Trak</td>\n",
       "      <td>A-Trak - Lose My Mind</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</td>\n",
       "      <td>0.320</td>\n",
       "      <td>1</td>\n",
       "      <td>Char</td>\n",
       "      <td>Crystal Castles</td>\n",
       "      <td>Crystal Castles - Char</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</td>\n",
       "      <td>0.237</td>\n",
       "      <td>1</td>\n",
       "      <td>Look Alive</td>\n",
       "      <td>Rae Sremmurd</td>\n",
       "      <td>Rae Sremmurd - Look Alive</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</td>\n",
       "      <td>0.326</td>\n",
       "      <td>1</td>\n",
       "      <td>Please Stop Making Fake Versace</td>\n",
       "      <td>Father</td>\n",
       "      <td>Father - Please Stop Making Fake Versace</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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</td>\n",
       "      <td>0.489</td>\n",
       "      <td>0</td>\n",
       "      <td>Work This Body</td>\n",
       "      <td>WALK THE MOON</td>\n",
       "      <td>WALK THE MOON - Work This Body</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</td>\n",
       "      <td>0.577</td>\n",
       "      <td>0</td>\n",
       "      <td>Tightrope</td>\n",
       "      <td>WALK THE MOON</td>\n",
       "      <td>WALK THE MOON - Tightrope</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</td>\n",
       "      <td>0.701</td>\n",
       "      <td>0</td>\n",
       "      <td>Different Colors</td>\n",
       "      <td>WALK THE MOON</td>\n",
       "      <td>WALK THE MOON - Different Colors</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</td>\n",
       "      <td>0.646</td>\n",
       "      <td>0</td>\n",
       "      <td>Avalanche</td>\n",
       "      <td>WALK THE MOON</td>\n",
       "      <td>WALK THE MOON - Avalanche</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</td>\n",
       "      <td>0.821</td>\n",
       "      <td>0</td>\n",
       "      <td>Jenny</td>\n",
       "      <td>WALK THE MOON</td>\n",
       "      <td>WALK THE MOON - Jenny</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</td>\n",
       "      <td>0.428</td>\n",
       "      <td>0</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>WALK THE MOON</td>\n",
       "      <td>WALK THE MOON - Portugal</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</td>\n",
       "      <td>0.628</td>\n",
       "      <td>0</td>\n",
       "      <td>Quesadilla</td>\n",
       "      <td>WALK THE MOON</td>\n",
       "      <td>WALK THE MOON - Quesadilla</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</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0</td>\n",
       "      <td>Aquaman</td>\n",
       "      <td>WALK THE MOON</td>\n",
       "      <td>WALK THE MOON - Aquaman</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</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0</td>\n",
       "      <td>Paris - Pegboard Nerds Remix</td>\n",
       "      <td>The Chainsmokers</td>\n",
       "      <td>The Chainsmokers - Paris - Pegboard Nerds Remix</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</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0</td>\n",
       "      <td>Quit You - Wuki Remix</td>\n",
       "      <td>Lost Kings</td>\n",
       "      <td>Lost Kings - Quit You - Wuki Remix</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</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",
       "      <td>The Chainsmokers - Don't Let Me Down - Zomboy ...</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</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0</td>\n",
       "      <td>Faded - Slushii Remix</td>\n",
       "      <td>Alan Walker</td>\n",
       "      <td>Alan Walker - Faded - Slushii Remix</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</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",
       "      <td>Tommie Sunshine - Can't Get Enough - Pegboard ...</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</td>\n",
       "      <td>0.362</td>\n",
       "      <td>0</td>\n",
       "      <td>Light - Loosid Remix</td>\n",
       "      <td>San Holo</td>\n",
       "      <td>San Holo - Light - Loosid Remix</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</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",
       "      <td>The Chainsmokers - Something Just Like This - ...</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</td>\n",
       "      <td>0.321</td>\n",
       "      <td>0</td>\n",
       "      <td>Without U</td>\n",
       "      <td>Steve Aoki</td>\n",
       "      <td>Steve Aoki - Without U</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</td>\n",
       "      <td>0.311</td>\n",
       "      <td>0</td>\n",
       "      <td>Hold Up</td>\n",
       "      <td>Borgeous</td>\n",
       "      <td>Borgeous - Hold Up</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</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",
       "      <td>Lost Kings - Phone Down - Dodge &amp; Fuski Remix</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</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0</td>\n",
       "      <td>Spoon Me - Slushii Remix</td>\n",
       "      <td>Elliphant</td>\n",
       "      <td>Elliphant - Spoon Me - Slushii Remix</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</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",
       "      <td>Martin Garrix - Scared To Be Lonely (Conro Remix)</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</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",
       "      <td>NERVO - Let It Go - Scott Melker &amp; Mister Gray...</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</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",
       "      <td>Starley - Call On Me - EDWYNN X TIKAL, Spirix ...</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</td>\n",
       "      <td>0.331</td>\n",
       "      <td>0</td>\n",
       "      <td>Aamon</td>\n",
       "      <td>Kuuro</td>\n",
       "      <td>Kuuro - Aamon</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</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",
       "      <td>Dimitri Vegas &amp; Like Mike - Hey Baby - Steve A...</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</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0</td>\n",
       "      <td>Brightside - Borgeous Remix</td>\n",
       "      <td>Icona Pop</td>\n",
       "      <td>Icona Pop - Brightside - Borgeous Remix</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</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",
       "      <td>Kill The Noise - Like A Bitch - 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</td>\n",
       "      <td>0.867</td>\n",
       "      <td>0</td>\n",
       "      <td>Candy</td>\n",
       "      <td>Dillon Francis</td>\n",
       "      <td>Dillon Francis - Candy</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</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",
       "      <td>Rain Man - Habit - Dack Janiels &amp; Wenzday Remix</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</td>\n",
       "      <td>0.623</td>\n",
       "      <td>0</td>\n",
       "      <td>First Contact</td>\n",
       "      <td>Twin Moons</td>\n",
       "      <td>Twin Moons - First Contact</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</td>\n",
       "      <td>0.402</td>\n",
       "      <td>0</td>\n",
       "      <td>I Wanna Get Better</td>\n",
       "      <td>Bleachers</td>\n",
       "      <td>Bleachers - I Wanna Get Better</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2017 rows × 18 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    0.286       1   \n",
       "1                  4    0.588       1   \n",
       "2                  4    0.173       1   \n",
       "3                  4    0.230       1   \n",
       "4                  4    0.904       1   \n",
       "5                  4    0.264       1   \n",
       "6                  4    0.308       1   \n",
       "7                  4    0.393       1   \n",
       "8                  4    0.398       1   \n",
       "9                  4    0.386       1   \n",
       "10                 3    0.524       1   \n",
       "11                 4    0.642       1   \n",
       "12                 4    0.381       1   \n",
       "13                 4    0.367       1   \n",
       "14                 4    0.351       1   \n",
       "15                 4    0.317       1   \n",
       "16                 4    0.773       1   \n",
       "17                 4    0.842       1   \n",
       "18                 4    0.783       1   \n",
       "19                 4    0.763       1   \n",
       "20                 4    0.471       1   \n",
       "21                 4    0.770       1   \n",
       "22                 4    0.441       1   \n",
       "23                 4    0.364       1   \n",
       "24                 4    0.401       1   \n",
       "25                 4    0.795       1   \n",
       "26                 4    0.598       1   \n",
       "27                 4    0.320       1   \n",
       "28                 4    0.237       1   \n",
       "29                 4    0.326       1   \n",
       "...              ...      ...     ...   \n",
       "1987               4    0.489       0   \n",
       "1988               4    0.577       0   \n",
       "1989               4    0.701       0   \n",
       "1990               4    0.646       0   \n",
       "1991               4    0.821       0   \n",
       "1992               4    0.428       0   \n",
       "1993               4    0.628       0   \n",
       "1994               4    0.714       0   \n",
       "1995               4    0.129       0   \n",
       "1996               4    0.186       0   \n",
       "1997               4    0.554       0   \n",
       "1998               4    0.260       0   \n",
       "1999               4    0.257       0   \n",
       "2000               4    0.362       0   \n",
       "2001               4    0.266       0   \n",
       "2002               4    0.321       0   \n",
       "2003               4    0.311       0   \n",
       "2004               4    0.183       0   \n",
       "2005               4    0.108       0   \n",
       "2006               4    0.307       0   \n",
       "2007               4    0.494       0   \n",
       "2008               4    0.589       0   \n",
       "2009               4    0.331       0   \n",
       "2010               4    0.442       0   \n",
       "2011               4    0.327       0   \n",
       "2012               4    0.211       0   \n",
       "2013               4    0.867       0   \n",
       "2014               4    0.470       0   \n",
       "2015               4    0.623       0   \n",
       "2016               4    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",
       "                                                uniq_id  \n",
       "0                                     Future - Mask Off  \n",
       "1                            Childish Gambino - Redbone  \n",
       "2                                 Future - Xanny Family  \n",
       "3                          Beach House - Master Of None  \n",
       "4                          Junior Boys - Parallel Lines  \n",
       "5                                      Drake - Sneakin’  \n",
       "6                                   Drake - Childs Play  \n",
       "7                               Omega - Gyöngyhajú lány  \n",
       "8                       Death Grips - I've Seen Footage  \n",
       "9                          Honey Claws - Digital Animal  \n",
       "10    The Avalanches - Subways - In Flagranti Extend...  \n",
       "11      Modern Folk Üçlüsü - Donme Dolap - Baris K Edit  \n",
       "12                                Erkin Koray - Cemalim  \n",
       "13                               Lil Yachty - One Night  \n",
       "14                                        PNL - Oh lala  \n",
       "15                               Crystal Castles - Char  \n",
       "16                          New Order - World In Motion  \n",
       "17               Funkadelic - One Nation Under a Groove  \n",
       "18                                 Chief Keef - Bouncin  \n",
       "19                      Le Youth - C O O L - Radio Edit  \n",
       "20    Cajmere - Percolator (Jamie Jones Vault Mix) -...  \n",
       "21                The Rapture - House of Jealous Lovers  \n",
       "22                               Young Thug - Imma Ride  \n",
       "23                               Ty Segall - Girlfriend  \n",
       "24                    Myron & E - If I Gave You My Love  \n",
       "25                             TR/ST - This Ready Flesh  \n",
       "26                                A-Trak - Lose My Mind  \n",
       "27                               Crystal Castles - Char  \n",
       "28                            Rae Sremmurd - Look Alive  \n",
       "29             Father - Please Stop Making Fake Versace  \n",
       "...                                                 ...  \n",
       "1987                     WALK THE MOON - Work This Body  \n",
       "1988                          WALK THE MOON - Tightrope  \n",
       "1989                   WALK THE MOON - Different Colors  \n",
       "1990                          WALK THE MOON - Avalanche  \n",
       "1991                              WALK THE MOON - Jenny  \n",
       "1992                           WALK THE MOON - Portugal  \n",
       "1993                         WALK THE MOON - Quesadilla  \n",
       "1994                            WALK THE MOON - Aquaman  \n",
       "1995    The Chainsmokers - Paris - Pegboard Nerds Remix  \n",
       "1996                 Lost Kings - Quit You - Wuki Remix  \n",
       "1997  The Chainsmokers - Don't Let Me Down - Zomboy ...  \n",
       "1998                Alan Walker - Faded - Slushii Remix  \n",
       "1999  Tommie Sunshine - Can't Get Enough - Pegboard ...  \n",
       "2000                    San Holo - Light - Loosid Remix  \n",
       "2001  The Chainsmokers - Something Just Like This - ...  \n",
       "2002                             Steve Aoki - Without U  \n",
       "2003                                 Borgeous - Hold Up  \n",
       "2004      Lost Kings - Phone Down - Dodge & Fuski Remix  \n",
       "2005               Elliphant - Spoon Me - Slushii Remix  \n",
       "2006  Martin Garrix - Scared To Be Lonely (Conro Remix)  \n",
       "2007  NERVO - Let It Go - Scott Melker & Mister Gray...  \n",
       "2008  Starley - Call On Me - EDWYNN X TIKAL, Spirix ...  \n",
       "2009                                      Kuuro - Aamon  \n",
       "2010  Dimitri Vegas & Like Mike - Hey Baby - Steve A...  \n",
       "2011            Icona Pop - Brightside - Borgeous Remix  \n",
       "2012  Kill The Noise - Like A Bitch - Kill The Noise...  \n",
       "2013                             Dillon Francis - Candy  \n",
       "2014    Rain Man - Habit - Dack Janiels & Wenzday Remix  \n",
       "2015                         Twin Moons - First Contact  \n",
       "2016                     Bleachers - I Wanna Get Better  \n",
       "\n",
       "[2017 rows x 18 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tempo['uniq_id'] = df_tempo['artist'].str.cat(df_tempo[['song_title']], sep=' - ')\n",
    "df_pop1['uniq_id'] = df_pop1['artist'].str.cat(df_pop1[['song_title']], sep=' - ')\n",
    "df_tempo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we can merge the dataframes on this new unique identity column we created, we have to accumulate the number of streams for every song between all the days that are present in the dataset. This is so that we can have a total count of the number of streams a given song had in the year 2017 which is our measure of the popularity of the song."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pop1 = df_pop1.groupby(['uniq_id'])['streams'].sum()\n",
    "df_pop1 = df_pop1.to_frame().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the number of streams for each song has been summed up, we are ready to merge the two dataframes together on our unique identifer column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "      <th>uniq_id</th>\n",
       "      <th>streams</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</td>\n",
       "      <td>0.286</td>\n",
       "      <td>1</td>\n",
       "      <td>Mask Off</td>\n",
       "      <td>Future</td>\n",
       "      <td>Future - Mask Off</td>\n",
       "      <td>1006341171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1173</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</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0</td>\n",
       "      <td>Mask Off</td>\n",
       "      <td>Future</td>\n",
       "      <td>Future - Mask Off</td>\n",
       "      <td>1006341171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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</td>\n",
       "      <td>0.588</td>\n",
       "      <td>1</td>\n",
       "      <td>Redbone</td>\n",
       "      <td>Childish Gambino</td>\n",
       "      <td>Childish Gambino - Redbone</td>\n",
       "      <td>611080489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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</td>\n",
       "      <td>0.264</td>\n",
       "      <td>1</td>\n",
       "      <td>Sneakin’</td>\n",
       "      <td>Drake</td>\n",
       "      <td>Drake - Sneakin’</td>\n",
       "      <td>86903911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</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</td>\n",
       "      <td>0.308</td>\n",
       "      <td>1</td>\n",
       "      <td>Childs Play</td>\n",
       "      <td>Drake</td>\n",
       "      <td>Drake - Childs Play</td>\n",
       "      <td>1817339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</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</td>\n",
       "      <td>0.367</td>\n",
       "      <td>1</td>\n",
       "      <td>One Night</td>\n",
       "      <td>Lil Yachty</td>\n",
       "      <td>Lil Yachty - One Night</td>\n",
       "      <td>22048775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</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</td>\n",
       "      <td>0.351</td>\n",
       "      <td>1</td>\n",
       "      <td>Oh lala</td>\n",
       "      <td>PNL</td>\n",
       "      <td>PNL - Oh lala</td>\n",
       "      <td>125301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</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</td>\n",
       "      <td>0.237</td>\n",
       "      <td>1</td>\n",
       "      <td>Look Alive</td>\n",
       "      <td>Rae Sremmurd</td>\n",
       "      <td>Rae Sremmurd - Look Alive</td>\n",
       "      <td>270233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>37</td>\n",
       "      <td>0.056500</td>\n",
       "      <td>0.853</td>\n",
       "      <td>205879</td>\n",
       "      <td>0.547</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3410</td>\n",
       "      <td>-7.535</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>142.065</td>\n",
       "      <td>4</td>\n",
       "      <td>0.677</td>\n",
       "      <td>1</td>\n",
       "      <td>Jumpman</td>\n",
       "      <td>Drake</td>\n",
       "      <td>Drake - Jumpman</td>\n",
       "      <td>46905737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>45</td>\n",
       "      <td>0.006310</td>\n",
       "      <td>0.715</td>\n",
       "      <td>213093</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1640</td>\n",
       "      <td>-5.379</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1080</td>\n",
       "      <td>95.487</td>\n",
       "      <td>4</td>\n",
       "      <td>0.607</td>\n",
       "      <td>1</td>\n",
       "      <td>Return Of The Mack - C &amp; J Street Mix</td>\n",
       "      <td>Mark Morrison</td>\n",
       "      <td>Mark Morrison - Return Of The Mack - C &amp; J Str...</td>\n",
       "      <td>92362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>54</td>\n",
       "      <td>0.246000</td>\n",
       "      <td>0.676</td>\n",
       "      <td>254769</td>\n",
       "      <td>0.528</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0803</td>\n",
       "      <td>-8.699</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0366</td>\n",
       "      <td>130.073</td>\n",
       "      <td>4</td>\n",
       "      <td>0.301</td>\n",
       "      <td>1</td>\n",
       "      <td>White Iverson</td>\n",
       "      <td>Post Malone</td>\n",
       "      <td>Post Malone - White Iverson</td>\n",
       "      <td>311510902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>117</td>\n",
       "      <td>0.002570</td>\n",
       "      <td>0.896</td>\n",
       "      <td>267024</td>\n",
       "      <td>0.623</td>\n",
       "      <td>0.000258</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0390</td>\n",
       "      <td>-8.008</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0571</td>\n",
       "      <td>134.962</td>\n",
       "      <td>4</td>\n",
       "      <td>0.564</td>\n",
       "      <td>1</td>\n",
       "      <td>Hotline Bling</td>\n",
       "      <td>Drake</td>\n",
       "      <td>Drake - Hotline Bling</td>\n",
       "      <td>29434501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>118</td>\n",
       "      <td>0.002560</td>\n",
       "      <td>0.618</td>\n",
       "      <td>196693</td>\n",
       "      <td>0.717</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>0.6250</td>\n",
       "      <td>-5.738</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3180</td>\n",
       "      <td>190.050</td>\n",
       "      <td>4</td>\n",
       "      <td>0.601</td>\n",
       "      <td>1</td>\n",
       "      <td>679 (feat. Remy Boyz)</td>\n",
       "      <td>Fetty Wap</td>\n",
       "      <td>Fetty Wap - 679 (feat. Remy Boyz)</td>\n",
       "      <td>644654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>119</td>\n",
       "      <td>0.024400</td>\n",
       "      <td>0.746</td>\n",
       "      <td>222093</td>\n",
       "      <td>0.873</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>0.3540</td>\n",
       "      <td>-3.803</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1280</td>\n",
       "      <td>148.075</td>\n",
       "      <td>4</td>\n",
       "      <td>0.808</td>\n",
       "      <td>1</td>\n",
       "      <td>Trap Queen</td>\n",
       "      <td>Fetty Wap</td>\n",
       "      <td>Fetty Wap - Trap Queen</td>\n",
       "      <td>584929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>120</td>\n",
       "      <td>0.013000</td>\n",
       "      <td>0.888</td>\n",
       "      <td>200080</td>\n",
       "      <td>0.483</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0926</td>\n",
       "      <td>-7.797</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1540</td>\n",
       "      <td>125.008</td>\n",
       "      <td>4</td>\n",
       "      <td>0.221</td>\n",
       "      <td>1</td>\n",
       "      <td>No Type</td>\n",
       "      <td>Rae Sremmurd</td>\n",
       "      <td>Rae Sremmurd - No Type</td>\n",
       "      <td>5416809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>125</td>\n",
       "      <td>0.016100</td>\n",
       "      <td>0.526</td>\n",
       "      <td>241440</td>\n",
       "      <td>0.712</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>0.1790</td>\n",
       "      <td>-6.525</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0356</td>\n",
       "      <td>105.009</td>\n",
       "      <td>4</td>\n",
       "      <td>0.299</td>\n",
       "      <td>1</td>\n",
       "      <td>Midnight City</td>\n",
       "      <td>M83</td>\n",
       "      <td>M83 - Midnight City</td>\n",
       "      <td>156504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>433</td>\n",
       "      <td>0.019800</td>\n",
       "      <td>0.517</td>\n",
       "      <td>245013</td>\n",
       "      <td>0.491</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0786</td>\n",
       "      <td>-13.742</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0341</td>\n",
       "      <td>104.996</td>\n",
       "      <td>4</td>\n",
       "      <td>0.331</td>\n",
       "      <td>1</td>\n",
       "      <td>Midnight City</td>\n",
       "      <td>M83</td>\n",
       "      <td>M83 - Midnight City</td>\n",
       "      <td>156504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>133</td>\n",
       "      <td>0.066800</td>\n",
       "      <td>0.704</td>\n",
       "      <td>199246</td>\n",
       "      <td>0.647</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>10</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>-7.874</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1710</td>\n",
       "      <td>92.002</td>\n",
       "      <td>4</td>\n",
       "      <td>0.694</td>\n",
       "      <td>1</td>\n",
       "      <td>Magnets</td>\n",
       "      <td>Disclosure</td>\n",
       "      <td>Disclosure - Magnets</td>\n",
       "      <td>14273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>149</td>\n",
       "      <td>0.334000</td>\n",
       "      <td>0.726</td>\n",
       "      <td>221787</td>\n",
       "      <td>0.583</td>\n",
       "      <td>0.001350</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1220</td>\n",
       "      <td>-6.434</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2300</td>\n",
       "      <td>140.943</td>\n",
       "      <td>4</td>\n",
       "      <td>0.480</td>\n",
       "      <td>1</td>\n",
       "      <td>The Buzz (feat. Mataya &amp; Young Tapz)</td>\n",
       "      <td>Hermitude</td>\n",
       "      <td>Hermitude - The Buzz (feat. Mataya &amp; Young Tapz)</td>\n",
       "      <td>20287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>152</td>\n",
       "      <td>0.689000</td>\n",
       "      <td>0.658</td>\n",
       "      <td>238560</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>0.1700</td>\n",
       "      <td>-10.866</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0448</td>\n",
       "      <td>128.128</td>\n",
       "      <td>4</td>\n",
       "      <td>0.197</td>\n",
       "      <td>1</td>\n",
       "      <td>River</td>\n",
       "      <td>Leon Bridges</td>\n",
       "      <td>Leon Bridges - River</td>\n",
       "      <td>50478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>170</td>\n",
       "      <td>0.006890</td>\n",
       "      <td>0.717</td>\n",
       "      <td>224933</td>\n",
       "      <td>0.862</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>0.3210</td>\n",
       "      <td>-4.736</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0540</td>\n",
       "      <td>130.021</td>\n",
       "      <td>4</td>\n",
       "      <td>0.512</td>\n",
       "      <td>1</td>\n",
       "      <td>We Dem Boyz</td>\n",
       "      <td>Wiz Khalifa</td>\n",
       "      <td>Wiz Khalifa - We Dem Boyz</td>\n",
       "      <td>21132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>176</td>\n",
       "      <td>0.141000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>256093</td>\n",
       "      <td>0.593</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0969</td>\n",
       "      <td>-4.340</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0898</td>\n",
       "      <td>68.456</td>\n",
       "      <td>4</td>\n",
       "      <td>0.325</td>\n",
       "      <td>1</td>\n",
       "      <td>New Slaves</td>\n",
       "      <td>Kanye West</td>\n",
       "      <td>Kanye West - New Slaves</td>\n",
       "      <td>3681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>191</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.430</td>\n",
       "      <td>154414</td>\n",
       "      <td>0.436</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>0.1280</td>\n",
       "      <td>-4.816</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1190</td>\n",
       "      <td>90.053</td>\n",
       "      <td>4</td>\n",
       "      <td>0.220</td>\n",
       "      <td>1</td>\n",
       "      <td>Holdin On</td>\n",
       "      <td>Flume</td>\n",
       "      <td>Flume - Holdin On</td>\n",
       "      <td>13142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>262</td>\n",
       "      <td>0.108000</td>\n",
       "      <td>0.530</td>\n",
       "      <td>194613</td>\n",
       "      <td>0.433</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1350</td>\n",
       "      <td>-10.598</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1850</td>\n",
       "      <td>160.140</td>\n",
       "      <td>4</td>\n",
       "      <td>0.272</td>\n",
       "      <td>1</td>\n",
       "      <td>Exchange</td>\n",
       "      <td>Bryson Tiller</td>\n",
       "      <td>Bryson Tiller - Exchange</td>\n",
       "      <td>66985135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>297</td>\n",
       "      <td>0.086100</td>\n",
       "      <td>0.356</td>\n",
       "      <td>242253</td>\n",
       "      <td>0.567</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1370</td>\n",
       "      <td>-7.011</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0798</td>\n",
       "      <td>135.553</td>\n",
       "      <td>5</td>\n",
       "      <td>0.116</td>\n",
       "      <td>1</td>\n",
       "      <td>The Hills</td>\n",
       "      <td>The Weeknd</td>\n",
       "      <td>The Weeknd - The Hills</td>\n",
       "      <td>41254667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>303</td>\n",
       "      <td>0.407000</td>\n",
       "      <td>0.688</td>\n",
       "      <td>192040</td>\n",
       "      <td>0.488</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>0.4300</td>\n",
       "      <td>-14.195</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3570</td>\n",
       "      <td>69.770</td>\n",
       "      <td>4</td>\n",
       "      <td>0.708</td>\n",
       "      <td>1</td>\n",
       "      <td>Monster Mash</td>\n",
       "      <td>Bobby \"Boris\" Pickett</td>\n",
       "      <td>Bobby \"Boris\" Pickett - Monster Mash</td>\n",
       "      <td>214616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>333</td>\n",
       "      <td>0.119000</td>\n",
       "      <td>0.716</td>\n",
       "      <td>263817</td>\n",
       "      <td>0.647</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1210</td>\n",
       "      <td>-4.008</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0345</td>\n",
       "      <td>131.040</td>\n",
       "      <td>4</td>\n",
       "      <td>0.159</td>\n",
       "      <td>1</td>\n",
       "      <td>Antidote</td>\n",
       "      <td>Travis Scott</td>\n",
       "      <td>Travis Scott - Antidote</td>\n",
       "      <td>13144570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>345</td>\n",
       "      <td>0.089300</td>\n",
       "      <td>0.636</td>\n",
       "      <td>235813</td>\n",
       "      <td>0.720</td>\n",
       "      <td>0.017800</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0868</td>\n",
       "      <td>-6.195</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0432</td>\n",
       "      <td>124.944</td>\n",
       "      <td>4</td>\n",
       "      <td>0.444</td>\n",
       "      <td>1</td>\n",
       "      <td>Shooting Stars</td>\n",
       "      <td>Bag Raiders</td>\n",
       "      <td>Bag Raiders - Shooting Stars</td>\n",
       "      <td>30958799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>434</td>\n",
       "      <td>0.004170</td>\n",
       "      <td>0.356</td>\n",
       "      <td>193653</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.009750</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0997</td>\n",
       "      <td>-7.837</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0680</td>\n",
       "      <td>166.300</td>\n",
       "      <td>4</td>\n",
       "      <td>0.592</td>\n",
       "      <td>1</td>\n",
       "      <td>Lonely Boy</td>\n",
       "      <td>The Black Keys</td>\n",
       "      <td>The Black Keys - Lonely Boy</td>\n",
       "      <td>13438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>440</td>\n",
       "      <td>0.369000</td>\n",
       "      <td>0.662</td>\n",
       "      <td>236733</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0908</td>\n",
       "      <td>-7.382</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1060</td>\n",
       "      <td>152.009</td>\n",
       "      <td>4</td>\n",
       "      <td>0.442</td>\n",
       "      <td>1</td>\n",
       "      <td>Headlines</td>\n",
       "      <td>Drake</td>\n",
       "      <td>Drake - Headlines</td>\n",
       "      <td>907204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>1889</td>\n",
       "      <td>0.006700</td>\n",
       "      <td>0.420</td>\n",
       "      <td>238667</td>\n",
       "      <td>0.672</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1080</td>\n",
       "      <td>-7.902</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0270</td>\n",
       "      <td>168.019</td>\n",
       "      <td>4</td>\n",
       "      <td>0.459</td>\n",
       "      <td>0</td>\n",
       "      <td>Push</td>\n",
       "      <td>Matchbox Twenty</td>\n",
       "      <td>Matchbox Twenty - Push</td>\n",
       "      <td>15977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>1890</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.561</td>\n",
       "      <td>295493</td>\n",
       "      <td>0.555</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>-9.734</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0287</td>\n",
       "      <td>134.369</td>\n",
       "      <td>4</td>\n",
       "      <td>0.509</td>\n",
       "      <td>0</td>\n",
       "      <td>What's Up?</td>\n",
       "      <td>4 Non Blondes</td>\n",
       "      <td>4 Non Blondes - What's Up?</td>\n",
       "      <td>61852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>1897</td>\n",
       "      <td>0.001330</td>\n",
       "      <td>0.291</td>\n",
       "      <td>289907</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0811</td>\n",
       "      <td>-5.683</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0363</td>\n",
       "      <td>155.667</td>\n",
       "      <td>3</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0</td>\n",
       "      <td>Iris</td>\n",
       "      <td>The Goo Goo Dolls</td>\n",
       "      <td>The Goo Goo Dolls - Iris</td>\n",
       "      <td>10973912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>1900</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.926</td>\n",
       "      <td>199853</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0234</td>\n",
       "      <td>-2.221</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0929</td>\n",
       "      <td>110.007</td>\n",
       "      <td>4</td>\n",
       "      <td>0.903</td>\n",
       "      <td>0</td>\n",
       "      <td>Hollaback Girl</td>\n",
       "      <td>Gwen Stefani</td>\n",
       "      <td>Gwen Stefani - Hollaback Girl</td>\n",
       "      <td>232917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>1901</td>\n",
       "      <td>0.058400</td>\n",
       "      <td>0.967</td>\n",
       "      <td>242733</td>\n",
       "      <td>0.583</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0519</td>\n",
       "      <td>-5.562</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0789</td>\n",
       "      <td>117.000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0</td>\n",
       "      <td>SexyBack</td>\n",
       "      <td>Justin Timberlake</td>\n",
       "      <td>Justin Timberlake - SexyBack</td>\n",
       "      <td>109955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>1902</td>\n",
       "      <td>0.082300</td>\n",
       "      <td>0.741</td>\n",
       "      <td>289133</td>\n",
       "      <td>0.748</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3400</td>\n",
       "      <td>-6.299</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0264</td>\n",
       "      <td>127.965</td>\n",
       "      <td>4</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0</td>\n",
       "      <td>I Gotta Feeling</td>\n",
       "      <td>The Black Eyed Peas</td>\n",
       "      <td>The Black Eyed Peas - I Gotta Feeling</td>\n",
       "      <td>431869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>1903</td>\n",
       "      <td>0.185000</td>\n",
       "      <td>0.757</td>\n",
       "      <td>211160</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5680</td>\n",
       "      <td>-4.860</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0315</td>\n",
       "      <td>92.960</td>\n",
       "      <td>4</td>\n",
       "      <td>0.887</td>\n",
       "      <td>0</td>\n",
       "      <td>...Baby One More Time</td>\n",
       "      <td>Britney Spears</td>\n",
       "      <td>Britney Spears - ...Baby One More Time</td>\n",
       "      <td>156658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>1905</td>\n",
       "      <td>0.139000</td>\n",
       "      <td>0.671</td>\n",
       "      <td>243493</td>\n",
       "      <td>0.727</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>0.1230</td>\n",
       "      <td>-7.585</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0350</td>\n",
       "      <td>102.358</td>\n",
       "      <td>4</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>Mariah Carey</td>\n",
       "      <td>Mariah Carey - Fantasy</td>\n",
       "      <td>33008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>1906</td>\n",
       "      <td>0.025100</td>\n",
       "      <td>0.743</td>\n",
       "      <td>214400</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.000717</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0685</td>\n",
       "      <td>-4.267</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0953</td>\n",
       "      <td>92.909</td>\n",
       "      <td>4</td>\n",
       "      <td>0.604</td>\n",
       "      <td>0</td>\n",
       "      <td>No Scrubs</td>\n",
       "      <td>TLC</td>\n",
       "      <td>TLC - No Scrubs</td>\n",
       "      <td>10280215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>1909</td>\n",
       "      <td>0.101000</td>\n",
       "      <td>0.768</td>\n",
       "      <td>173027</td>\n",
       "      <td>0.864</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>0.1620</td>\n",
       "      <td>-6.145</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0324</td>\n",
       "      <td>110.008</td>\n",
       "      <td>4</td>\n",
       "      <td>0.876</td>\n",
       "      <td>0</td>\n",
       "      <td>Wannabe - Radio Edit</td>\n",
       "      <td>Spice Girls</td>\n",
       "      <td>Spice Girls - Wannabe - Radio Edit</td>\n",
       "      <td>545802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>1913</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.439</td>\n",
       "      <td>234800</td>\n",
       "      <td>0.931</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>11</td>\n",
       "      <td>0.1930</td>\n",
       "      <td>-2.590</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0473</td>\n",
       "      <td>135.944</td>\n",
       "      <td>4</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0</td>\n",
       "      <td>Learn to Fly</td>\n",
       "      <td>Foo Fighters</td>\n",
       "      <td>Foo Fighters - Learn to Fly</td>\n",
       "      <td>70595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>1916</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>0.566</td>\n",
       "      <td>279400</td>\n",
       "      <td>0.705</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1090</td>\n",
       "      <td>-7.569</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0311</td>\n",
       "      <td>168.152</td>\n",
       "      <td>4</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0</td>\n",
       "      <td>Truly Madly Deeply</td>\n",
       "      <td>Savage Garden</td>\n",
       "      <td>Savage Garden - Truly Madly Deeply</td>\n",
       "      <td>89078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>1919</td>\n",
       "      <td>0.044900</td>\n",
       "      <td>0.706</td>\n",
       "      <td>225253</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>10</td>\n",
       "      <td>0.3010</td>\n",
       "      <td>-4.355</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0449</td>\n",
       "      <td>108.006</td>\n",
       "      <td>4</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0</td>\n",
       "      <td>Everybody (Backstreet's Back) - Radio Edit</td>\n",
       "      <td>Backstreet Boys</td>\n",
       "      <td>Backstreet Boys - Everybody (Backstreet's Back...</td>\n",
       "      <td>209470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>1927</td>\n",
       "      <td>0.284000</td>\n",
       "      <td>0.778</td>\n",
       "      <td>218093</td>\n",
       "      <td>0.824</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>0.4050</td>\n",
       "      <td>-5.892</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0712</td>\n",
       "      <td>100.024</td>\n",
       "      <td>4</td>\n",
       "      <td>0.767</td>\n",
       "      <td>0</td>\n",
       "      <td>Hips Don't Lie</td>\n",
       "      <td>Shakira</td>\n",
       "      <td>Shakira - Hips Don't Lie</td>\n",
       "      <td>2902013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>1929</td>\n",
       "      <td>0.002810</td>\n",
       "      <td>0.633</td>\n",
       "      <td>197124</td>\n",
       "      <td>0.976</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>8</td>\n",
       "      <td>0.3820</td>\n",
       "      <td>-5.362</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0519</td>\n",
       "      <td>142.027</td>\n",
       "      <td>4</td>\n",
       "      <td>0.471</td>\n",
       "      <td>0</td>\n",
       "      <td>Every time we touch</td>\n",
       "      <td>Cascada</td>\n",
       "      <td>Cascada - Every time we touch</td>\n",
       "      <td>42158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>1930</td>\n",
       "      <td>0.101000</td>\n",
       "      <td>0.835</td>\n",
       "      <td>272533</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1320</td>\n",
       "      <td>-3.180</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1840</td>\n",
       "      <td>94.059</td>\n",
       "      <td>4</td>\n",
       "      <td>0.826</td>\n",
       "      <td>0</td>\n",
       "      <td>Where Is The Love?</td>\n",
       "      <td>The Black Eyed Peas</td>\n",
       "      <td>The Black Eyed Peas - Where Is The Love?</td>\n",
       "      <td>5151011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>1931</td>\n",
       "      <td>0.044600</td>\n",
       "      <td>0.853</td>\n",
       "      <td>267080</td>\n",
       "      <td>0.676</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0514</td>\n",
       "      <td>-5.533</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0715</td>\n",
       "      <td>122.650</td>\n",
       "      <td>4</td>\n",
       "      <td>0.558</td>\n",
       "      <td>0</td>\n",
       "      <td>Don't Stop The Music</td>\n",
       "      <td>Rihanna</td>\n",
       "      <td>Rihanna - Don't Stop The Music</td>\n",
       "      <td>306673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>1932</td>\n",
       "      <td>0.055900</td>\n",
       "      <td>0.514</td>\n",
       "      <td>254027</td>\n",
       "      <td>0.911</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7750</td>\n",
       "      <td>-2.027</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4100</td>\n",
       "      <td>161.109</td>\n",
       "      <td>4</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0</td>\n",
       "      <td>Survivor</td>\n",
       "      <td>Destiny's Child</td>\n",
       "      <td>Destiny's Child - Survivor</td>\n",
       "      <td>151867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>1933</td>\n",
       "      <td>0.001250</td>\n",
       "      <td>0.649</td>\n",
       "      <td>230747</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>3</td>\n",
       "      <td>0.6710</td>\n",
       "      <td>-3.796</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0415</td>\n",
       "      <td>126.030</td>\n",
       "      <td>4</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0</td>\n",
       "      <td>Last Friday Night (T.G.I.F.)</td>\n",
       "      <td>Katy Perry</td>\n",
       "      <td>Katy Perry - Last Friday Night (T.G.I.F.)</td>\n",
       "      <td>8740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>1934</td>\n",
       "      <td>0.265000</td>\n",
       "      <td>0.793</td>\n",
       "      <td>197027</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2070</td>\n",
       "      <td>-4.861</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0405</td>\n",
       "      <td>107.647</td>\n",
       "      <td>4</td>\n",
       "      <td>0.859</td>\n",
       "      <td>0</td>\n",
       "      <td>Whenever, Wherever</td>\n",
       "      <td>Shakira</td>\n",
       "      <td>Shakira - Whenever, Wherever</td>\n",
       "      <td>79881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>1935</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.851</td>\n",
       "      <td>237200</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>4</td>\n",
       "      <td>0.1220</td>\n",
       "      <td>-4.618</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0788</td>\n",
       "      <td>119.002</td>\n",
       "      <td>4</td>\n",
       "      <td>0.769</td>\n",
       "      <td>0</td>\n",
       "      <td>Poker Face</td>\n",
       "      <td>Lady Gaga</td>\n",
       "      <td>Lady Gaga - Poker Face</td>\n",
       "      <td>1121697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>1936</td>\n",
       "      <td>0.057200</td>\n",
       "      <td>0.585</td>\n",
       "      <td>244507</td>\n",
       "      <td>0.776</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>5</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>-5.898</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0459</td>\n",
       "      <td>77.987</td>\n",
       "      <td>4</td>\n",
       "      <td>0.430</td>\n",
       "      <td>0</td>\n",
       "      <td>Complicated</td>\n",
       "      <td>Avril Lavigne</td>\n",
       "      <td>Avril Lavigne - Complicated</td>\n",
       "      <td>123641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>1942</td>\n",
       "      <td>0.007330</td>\n",
       "      <td>0.845</td>\n",
       "      <td>187840</td>\n",
       "      <td>0.768</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0575</td>\n",
       "      <td>-5.448</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1880</td>\n",
       "      <td>100.000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.954</td>\n",
       "      <td>0</td>\n",
       "      <td>Jenny from the Block - Track Masters Remix</td>\n",
       "      <td>Jennifer Lopez</td>\n",
       "      <td>Jennifer Lopez - Jenny from the Block - Track ...</td>\n",
       "      <td>214657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>1944</td>\n",
       "      <td>0.080300</td>\n",
       "      <td>0.524</td>\n",
       "      <td>393827</td>\n",
       "      <td>0.429</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0726</td>\n",
       "      <td>-10.064</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0279</td>\n",
       "      <td>131.206</td>\n",
       "      <td>4</td>\n",
       "      <td>0.237</td>\n",
       "      <td>0</td>\n",
       "      <td>(Everything I Do) I Do It For You</td>\n",
       "      <td>Bryan Adams</td>\n",
       "      <td>Bryan Adams - (Everything I Do) I Do It For You</td>\n",
       "      <td>151773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>1962</td>\n",
       "      <td>0.058800</td>\n",
       "      <td>0.653</td>\n",
       "      <td>216613</td>\n",
       "      <td>0.748</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>-6.323</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2110</td>\n",
       "      <td>172.008</td>\n",
       "      <td>4</td>\n",
       "      <td>0.809</td>\n",
       "      <td>0</td>\n",
       "      <td>Mysterious Girl</td>\n",
       "      <td>Peter Andre</td>\n",
       "      <td>Peter Andre - Mysterious Girl</td>\n",
       "      <td>48945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>1973</td>\n",
       "      <td>0.001350</td>\n",
       "      <td>0.446</td>\n",
       "      <td>223840</td>\n",
       "      <td>0.764</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0990</td>\n",
       "      <td>-5.042</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0330</td>\n",
       "      <td>172.094</td>\n",
       "      <td>4</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0</td>\n",
       "      <td>How You Remind Me - LP Mix</td>\n",
       "      <td>Nickelback</td>\n",
       "      <td>Nickelback - How You Remind Me - LP Mix</td>\n",
       "      <td>202288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>1980</td>\n",
       "      <td>0.005740</td>\n",
       "      <td>0.682</td>\n",
       "      <td>192000</td>\n",
       "      <td>0.812</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>-2.691</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1180</td>\n",
       "      <td>104.891</td>\n",
       "      <td>4</td>\n",
       "      <td>0.347</td>\n",
       "      <td>0</td>\n",
       "      <td>Shape of You (Major Lazer Remix) [feat. Nyla &amp;...</td>\n",
       "      <td>Ed Sheeran</td>\n",
       "      <td>Ed Sheeran - Shape of You (Major Lazer Remix) ...</td>\n",
       "      <td>6141094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>1981</td>\n",
       "      <td>0.181000</td>\n",
       "      <td>0.585</td>\n",
       "      <td>249213</td>\n",
       "      <td>0.612</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2930</td>\n",
       "      <td>-8.341</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0806</td>\n",
       "      <td>77.487</td>\n",
       "      <td>4</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0</td>\n",
       "      <td>Party Monster</td>\n",
       "      <td>The Weeknd</td>\n",
       "      <td>The Weeknd - Party Monster</td>\n",
       "      <td>159304820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>1985</td>\n",
       "      <td>0.007010</td>\n",
       "      <td>0.578</td>\n",
       "      <td>199080</td>\n",
       "      <td>0.866</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2570</td>\n",
       "      <td>-3.804</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0619</td>\n",
       "      <td>128.038</td>\n",
       "      <td>4</td>\n",
       "      <td>0.619</td>\n",
       "      <td>0</td>\n",
       "      <td>Shut Up and Dance</td>\n",
       "      <td>WALK THE MOON</td>\n",
       "      <td>WALK THE MOON - Shut Up and Dance</td>\n",
       "      <td>3474724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</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</td>\n",
       "      <td>0.321</td>\n",
       "      <td>0</td>\n",
       "      <td>Without U</td>\n",
       "      <td>Steve Aoki</td>\n",
       "      <td>Steve Aoki - Without U</td>\n",
       "      <td>4491</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>330 rows × 19 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          1173      0.010200         0.833       204600   0.434   \n",
       "2             1      0.199000         0.743       326933   0.359   \n",
       "3             5      0.004790         0.804       251333   0.560   \n",
       "4             6      0.014500         0.739       241400   0.472   \n",
       "5            13      0.366000         0.762       243270   0.476   \n",
       "6            14      0.440000         0.662       247288   0.603   \n",
       "7            28      0.051600         0.782       228562   0.572   \n",
       "8            37      0.056500         0.853       205879   0.547   \n",
       "9            45      0.006310         0.715       213093   0.833   \n",
       "10           54      0.246000         0.676       254769   0.528   \n",
       "11          117      0.002570         0.896       267024   0.623   \n",
       "12          118      0.002560         0.618       196693   0.717   \n",
       "13          119      0.024400         0.746       222093   0.873   \n",
       "14          120      0.013000         0.888       200080   0.483   \n",
       "15          125      0.016100         0.526       241440   0.712   \n",
       "16          433      0.019800         0.517       245013   0.491   \n",
       "17          133      0.066800         0.704       199246   0.647   \n",
       "18          149      0.334000         0.726       221787   0.583   \n",
       "19          152      0.689000         0.658       238560   0.179   \n",
       "20          170      0.006890         0.717       224933   0.862   \n",
       "21          176      0.141000         0.500       256093   0.593   \n",
       "22          191      0.170000         0.430       154414   0.436   \n",
       "23          262      0.108000         0.530       194613   0.433   \n",
       "24          297      0.086100         0.356       242253   0.567   \n",
       "25          303      0.407000         0.688       192040   0.488   \n",
       "26          333      0.119000         0.716       263817   0.647   \n",
       "27          345      0.089300         0.636       235813   0.720   \n",
       "28          434      0.004170         0.356       193653   0.872   \n",
       "29          440      0.369000         0.662       236733   0.571   \n",
       "..          ...           ...           ...          ...     ...   \n",
       "300        1889      0.006700         0.420       238667   0.672   \n",
       "301        1890      0.160000         0.561       295493   0.555   \n",
       "302        1897      0.001330         0.291       289907   0.790   \n",
       "303        1900      0.350000         0.926       199853   0.916   \n",
       "304        1901      0.058400         0.967       242733   0.583   \n",
       "305        1902      0.082300         0.741       289133   0.748   \n",
       "306        1903      0.185000         0.757       211160   0.725   \n",
       "307        1905      0.139000         0.671       243493   0.727   \n",
       "308        1906      0.025100         0.743       214400   0.675   \n",
       "309        1909      0.101000         0.768       173027   0.864   \n",
       "310        1913      0.000019         0.439       234800   0.931   \n",
       "311        1916      0.430000         0.566       279400   0.705   \n",
       "312        1919      0.044900         0.706       225253   0.893   \n",
       "313        1927      0.284000         0.778       218093   0.824   \n",
       "314        1929      0.002810         0.633       197124   0.976   \n",
       "315        1930      0.101000         0.835       272533   0.687   \n",
       "316        1931      0.044600         0.853       267080   0.676   \n",
       "317        1932      0.055900         0.514       254027   0.911   \n",
       "318        1933      0.001250         0.649       230747   0.815   \n",
       "319        1934      0.265000         0.793       197027   0.832   \n",
       "320        1935      0.120000         0.851       237200   0.806   \n",
       "321        1936      0.057200         0.585       244507   0.776   \n",
       "322        1942      0.007330         0.845       187840   0.768   \n",
       "323        1944      0.080300         0.524       393827   0.429   \n",
       "324        1962      0.058800         0.653       216613   0.748   \n",
       "325        1973      0.001350         0.446       223840   0.764   \n",
       "326        1980      0.005740         0.682       192000   0.812   \n",
       "327        1981      0.181000         0.585       249213   0.612   \n",
       "328        1985      0.007010         0.578       199080   0.866   \n",
       "329        2002      0.030100         0.583       224092   0.891   \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            0.021900    2    0.1650    -8.795     1       0.4310  150.062   \n",
       "2            0.006110    1    0.1370   -10.401     1       0.0794  160.083   \n",
       "3            0.000000    8    0.1640    -6.682     1       0.1850   85.023   \n",
       "4            0.000007    1    0.2070   -11.204     1       0.1560   80.030   \n",
       "5            0.000000    0    0.1030   -12.686     1       0.1140  130.007   \n",
       "6            0.000000    9    0.0972    -8.317     0       0.0793  125.011   \n",
       "7            0.000000    4    0.3300    -7.467     0       0.0385  120.021   \n",
       "8            0.000000    1    0.3410    -7.535     1       0.1940  142.065   \n",
       "9            0.000000    2    0.1640    -5.379     1       0.1080   95.487   \n",
       "10           0.000000    7    0.0803    -8.699     1       0.0366  130.073   \n",
       "11           0.000258    2    0.0390    -8.008     1       0.0571  134.962   \n",
       "12           0.000000    7    0.6250    -5.738     1       0.3180  190.050   \n",
       "13           0.000000    7    0.3540    -3.803     1       0.1280  148.075   \n",
       "14           0.000005    6    0.0926    -7.797     1       0.1540  125.008   \n",
       "15           0.000000   11    0.1790    -6.525     0       0.0356  105.009   \n",
       "16           0.000001   11    0.0786   -13.742     0       0.0341  104.996   \n",
       "17           0.000002   10    0.1010    -7.874     0       0.1710   92.002   \n",
       "18           0.001350    0    0.1220    -6.434     0       0.2300  140.943   \n",
       "19           0.000000    8    0.1700   -10.866     1       0.0448  128.128   \n",
       "20           0.000000    8    0.3210    -4.736     1       0.0540  130.021   \n",
       "21           0.000006    1    0.0969    -4.340     1       0.0898   68.456   \n",
       "22           0.000000    8    0.1280    -4.816     1       0.1190   90.053   \n",
       "23           0.000000    6    0.1350   -10.598     1       0.1850  160.140   \n",
       "24           0.000000    0    0.1370    -7.011     0       0.0798  135.553   \n",
       "25           0.000000   11    0.4300   -14.195     0       0.3570   69.770   \n",
       "26           0.000014    1    0.1210    -4.008     1       0.0345  131.040   \n",
       "27           0.017800   11    0.0868    -6.195     1       0.0432  124.944   \n",
       "28           0.009750    2    0.0997    -7.837     1       0.0680  166.300   \n",
       "29           0.000197    6    0.0908    -7.382     0       0.1060  152.009   \n",
       "..                ...  ...       ...       ...   ...          ...      ...   \n",
       "300          0.000000    6    0.1080    -7.902     1       0.0270  168.019   \n",
       "301          0.000000   11    0.1140    -9.734     0       0.0287  134.369   \n",
       "302          0.000006   11    0.0811    -5.683     0       0.0363  155.667   \n",
       "303          0.000006   10    0.0234    -2.221     0       0.0929  110.007   \n",
       "304          0.000000    7    0.0519    -5.562     0       0.0789  117.000   \n",
       "305          0.000000    0    0.3400    -6.299     1       0.0264  127.965   \n",
       "306          0.000056    0    0.5680    -4.860     0       0.0315   92.960   \n",
       "307          0.000000    7    0.1230    -7.585     1       0.0350  102.358   \n",
       "308          0.000717    8    0.0685    -4.267     0       0.0953   92.909   \n",
       "309          0.000000   11    0.1620    -6.145     1       0.0324  110.008   \n",
       "310          0.000012   11    0.1930    -2.590     0       0.0473  135.944   \n",
       "311          0.000000    0    0.1090    -7.569     1       0.0311  168.152   \n",
       "312          0.000001   10    0.3010    -4.355     0       0.0449  108.006   \n",
       "313          0.000000   10    0.4050    -5.892     0       0.0712  100.024   \n",
       "314          0.000013    8    0.3820    -5.362     1       0.0519  142.027   \n",
       "315          0.000000    5    0.1320    -3.180     1       0.1840   94.059   \n",
       "316          0.000034    6    0.0514    -5.533     0       0.0715  122.650   \n",
       "317          0.000000    1    0.7750    -2.027     0       0.4100  161.109   \n",
       "318          0.000043    3    0.6710    -3.796     0       0.0415  126.030   \n",
       "319          0.000007    1    0.2070    -4.861     0       0.0405  107.647   \n",
       "320          0.000001    4    0.1220    -4.618     1       0.0788  119.002   \n",
       "321          0.000008    5    0.3000    -5.898     1       0.0459   77.987   \n",
       "322          0.000005    6    0.0575    -5.448     1       0.1880  100.000   \n",
       "323          0.000014    1    0.0726   -10.064     1       0.0279  131.206   \n",
       "324          0.000000   10    0.1940    -6.323     0       0.2110  172.008   \n",
       "325          0.000000   10    0.0990    -5.042     1       0.0330  172.094   \n",
       "326          0.000000    1    0.0553    -2.691     0       0.1180  104.891   \n",
       "327          0.000010    1    0.2930    -8.341     1       0.0806   77.487   \n",
       "328          0.000000    1    0.2570    -3.804     1       0.0619  128.038   \n",
       "329          0.000003    7    0.1290    -3.495     1       0.4470  149.843   \n",
       "\n",
       "     time_signature  valence  target  \\\n",
       "0                 4    0.286       1   \n",
       "1                 4    0.286       0   \n",
       "2                 4    0.588       1   \n",
       "3                 4    0.264       1   \n",
       "4                 4    0.308       1   \n",
       "5                 4    0.367       1   \n",
       "6                 4    0.351       1   \n",
       "7                 4    0.237       1   \n",
       "8                 4    0.677       1   \n",
       "9                 4    0.607       1   \n",
       "10                4    0.301       1   \n",
       "11                4    0.564       1   \n",
       "12                4    0.601       1   \n",
       "13                4    0.808       1   \n",
       "14                4    0.221       1   \n",
       "15                4    0.299       1   \n",
       "16                4    0.331       1   \n",
       "17                4    0.694       1   \n",
       "18                4    0.480       1   \n",
       "19                4    0.197       1   \n",
       "20                4    0.512       1   \n",
       "21                4    0.325       1   \n",
       "22                4    0.220       1   \n",
       "23                4    0.272       1   \n",
       "24                5    0.116       1   \n",
       "25                4    0.708       1   \n",
       "26                4    0.159       1   \n",
       "27                4    0.444       1   \n",
       "28                4    0.592       1   \n",
       "29                4    0.442       1   \n",
       "..              ...      ...     ...   \n",
       "300               4    0.459       0   \n",
       "301               4    0.509       0   \n",
       "302               3    0.493       0   \n",
       "303               4    0.903       0   \n",
       "304               4    0.965       0   \n",
       "305               4    0.616       0   \n",
       "306               4    0.887       0   \n",
       "307               4    0.820       0   \n",
       "308               4    0.604       0   \n",
       "309               4    0.876       0   \n",
       "310               4    0.516       0   \n",
       "311               4    0.785       0   \n",
       "312               4    0.616       0   \n",
       "313               4    0.767       0   \n",
       "314               4    0.471       0   \n",
       "315               4    0.826       0   \n",
       "316               4    0.558       0   \n",
       "317               4    0.614       0   \n",
       "318               4    0.774       0   \n",
       "319               4    0.859       0   \n",
       "320               4    0.769       0   \n",
       "321               4    0.430       0   \n",
       "322               4    0.954       0   \n",
       "323               4    0.237       0   \n",
       "324               4    0.809       0   \n",
       "325               4    0.520       0   \n",
       "326               4    0.347       0   \n",
       "327               4    0.173       0   \n",
       "328               4    0.619       0   \n",
       "329               4    0.321       0   \n",
       "\n",
       "                                            song_title                 artist  \\\n",
       "0                                             Mask Off                 Future   \n",
       "1                                             Mask Off                 Future   \n",
       "2                                              Redbone       Childish Gambino   \n",
       "3                                             Sneakin’                  Drake   \n",
       "4                                          Childs Play                  Drake   \n",
       "5                                            One Night             Lil Yachty   \n",
       "6                                              Oh lala                    PNL   \n",
       "7                                           Look Alive           Rae Sremmurd   \n",
       "8                                              Jumpman                  Drake   \n",
       "9                Return Of The Mack - C & J Street Mix          Mark Morrison   \n",
       "10                                       White Iverson            Post Malone   \n",
       "11                                       Hotline Bling                  Drake   \n",
       "12                               679 (feat. Remy Boyz)              Fetty Wap   \n",
       "13                                          Trap Queen              Fetty Wap   \n",
       "14                                             No Type           Rae Sremmurd   \n",
       "15                                       Midnight City                    M83   \n",
       "16                                       Midnight City                    M83   \n",
       "17                                             Magnets             Disclosure   \n",
       "18                The Buzz (feat. Mataya & Young Tapz)              Hermitude   \n",
       "19                                               River           Leon Bridges   \n",
       "20                                         We Dem Boyz            Wiz Khalifa   \n",
       "21                                          New Slaves             Kanye West   \n",
       "22                                           Holdin On                  Flume   \n",
       "23                                            Exchange          Bryson Tiller   \n",
       "24                                           The Hills             The Weeknd   \n",
       "25                                        Monster Mash  Bobby \"Boris\" Pickett   \n",
       "26                                            Antidote           Travis Scott   \n",
       "27                                      Shooting Stars            Bag Raiders   \n",
       "28                                          Lonely Boy         The Black Keys   \n",
       "29                                           Headlines                  Drake   \n",
       "..                                                 ...                    ...   \n",
       "300                                               Push        Matchbox Twenty   \n",
       "301                                         What's Up?          4 Non Blondes   \n",
       "302                                               Iris      The Goo Goo Dolls   \n",
       "303                                     Hollaback Girl           Gwen Stefani   \n",
       "304                                           SexyBack      Justin Timberlake   \n",
       "305                                    I Gotta Feeling    The Black Eyed Peas   \n",
       "306                              ...Baby One More Time         Britney Spears   \n",
       "307                                            Fantasy           Mariah Carey   \n",
       "308                                          No Scrubs                    TLC   \n",
       "309                               Wannabe - Radio Edit            Spice Girls   \n",
       "310                                       Learn to Fly           Foo Fighters   \n",
       "311                                 Truly Madly Deeply          Savage Garden   \n",
       "312         Everybody (Backstreet's Back) - Radio Edit        Backstreet Boys   \n",
       "313                                     Hips Don't Lie                Shakira   \n",
       "314                                Every time we touch                Cascada   \n",
       "315                                 Where Is The Love?    The Black Eyed Peas   \n",
       "316                               Don't Stop The Music                Rihanna   \n",
       "317                                           Survivor        Destiny's Child   \n",
       "318                       Last Friday Night (T.G.I.F.)             Katy Perry   \n",
       "319                                 Whenever, Wherever                Shakira   \n",
       "320                                         Poker Face              Lady Gaga   \n",
       "321                                        Complicated          Avril Lavigne   \n",
       "322         Jenny from the Block - Track Masters Remix         Jennifer Lopez   \n",
       "323                  (Everything I Do) I Do It For You            Bryan Adams   \n",
       "324                                    Mysterious Girl            Peter Andre   \n",
       "325                         How You Remind Me - LP Mix             Nickelback   \n",
       "326  Shape of You (Major Lazer Remix) [feat. Nyla &...             Ed Sheeran   \n",
       "327                                      Party Monster             The Weeknd   \n",
       "328                                  Shut Up and Dance          WALK THE MOON   \n",
       "329                                          Without U             Steve Aoki   \n",
       "\n",
       "                                               uniq_id     streams  \n",
       "0                                    Future - Mask Off  1006341171  \n",
       "1                                    Future - Mask Off  1006341171  \n",
       "2                           Childish Gambino - Redbone   611080489  \n",
       "3                                     Drake - Sneakin’    86903911  \n",
       "4                                  Drake - Childs Play     1817339  \n",
       "5                               Lil Yachty - One Night    22048775  \n",
       "6                                        PNL - Oh lala      125301  \n",
       "7                            Rae Sremmurd - Look Alive      270233  \n",
       "8                                      Drake - Jumpman    46905737  \n",
       "9    Mark Morrison - Return Of The Mack - C & J Str...       92362  \n",
       "10                         Post Malone - White Iverson   311510902  \n",
       "11                               Drake - Hotline Bling    29434501  \n",
       "12                   Fetty Wap - 679 (feat. Remy Boyz)      644654  \n",
       "13                              Fetty Wap - Trap Queen      584929  \n",
       "14                              Rae Sremmurd - No Type     5416809  \n",
       "15                                 M83 - Midnight City      156504  \n",
       "16                                 M83 - Midnight City      156504  \n",
       "17                                Disclosure - Magnets       14273  \n",
       "18    Hermitude - The Buzz (feat. Mataya & Young Tapz)       20287  \n",
       "19                                Leon Bridges - River       50478  \n",
       "20                           Wiz Khalifa - We Dem Boyz       21132  \n",
       "21                             Kanye West - New Slaves        3681  \n",
       "22                                   Flume - Holdin On       13142  \n",
       "23                            Bryson Tiller - Exchange    66985135  \n",
       "24                              The Weeknd - The Hills    41254667  \n",
       "25                Bobby \"Boris\" Pickett - Monster Mash      214616  \n",
       "26                             Travis Scott - Antidote    13144570  \n",
       "27                        Bag Raiders - Shooting Stars    30958799  \n",
       "28                         The Black Keys - Lonely Boy       13438  \n",
       "29                                   Drake - Headlines      907204  \n",
       "..                                                 ...         ...  \n",
       "300                             Matchbox Twenty - Push       15977  \n",
       "301                         4 Non Blondes - What's Up?       61852  \n",
       "302                           The Goo Goo Dolls - Iris    10973912  \n",
       "303                      Gwen Stefani - Hollaback Girl      232917  \n",
       "304                       Justin Timberlake - SexyBack      109955  \n",
       "305              The Black Eyed Peas - I Gotta Feeling      431869  \n",
       "306             Britney Spears - ...Baby One More Time      156658  \n",
       "307                             Mariah Carey - Fantasy       33008  \n",
       "308                                    TLC - No Scrubs    10280215  \n",
       "309                 Spice Girls - Wannabe - Radio Edit      545802  \n",
       "310                        Foo Fighters - Learn to Fly       70595  \n",
       "311                 Savage Garden - Truly Madly Deeply       89078  \n",
       "312  Backstreet Boys - Everybody (Backstreet's Back...      209470  \n",
       "313                           Shakira - Hips Don't Lie     2902013  \n",
       "314                      Cascada - Every time we touch       42158  \n",
       "315           The Black Eyed Peas - Where Is The Love?     5151011  \n",
       "316                     Rihanna - Don't Stop The Music      306673  \n",
       "317                         Destiny's Child - Survivor      151867  \n",
       "318          Katy Perry - Last Friday Night (T.G.I.F.)        8740  \n",
       "319                       Shakira - Whenever, Wherever       79881  \n",
       "320                             Lady Gaga - Poker Face     1121697  \n",
       "321                        Avril Lavigne - Complicated      123641  \n",
       "322  Jennifer Lopez - Jenny from the Block - Track ...      214657  \n",
       "323    Bryan Adams - (Everything I Do) I Do It For You      151773  \n",
       "324                      Peter Andre - Mysterious Girl       48945  \n",
       "325            Nickelback - How You Remind Me - LP Mix      202288  \n",
       "326  Ed Sheeran - Shape of You (Major Lazer Remix) ...     6141094  \n",
       "327                         The Weeknd - Party Monster   159304820  \n",
       "328                  WALK THE MOON - Shut Up and Dance     3474724  \n",
       "329                             Steve Aoki - Without U        4491  \n",
       "\n",
       "[330 rows x 19 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final = df_tempo.merge(df_pop1, on = 'uniq_id')\n",
    "df_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upon merging, we noticed two things:\n",
    "- The total number of observation dropped significantly as our two datasets were independently maintained and hence the overlap may not have been very large.\n",
    "- There are a few cases of duplicate songs being present in the merged dataframe which we will have to handle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So our analysis is not skewed, we must first remove the duplicates from the merged dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = df_final.drop_duplicates(subset='uniq_id', keep='first')\n",
    "df_final = df_final.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to be working in more conventional units, we decided to change the unit of music duration from ms to second."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['duration_ms'] = df_final['duration_ms']/1000\n",
    "df_final.rename(columns={'duration_ms':'duration'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to prevent having to run the cleaning code every single time, we decided to store our merged dataset into an independent csv for efficiency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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</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",
       "      <th>uniq_id</th>\n",
       "      <th>streams</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>204.600</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</td>\n",
       "      <td>0.286</td>\n",
       "      <td>1</td>\n",
       "      <td>Mask Off</td>\n",
       "      <td>Future</td>\n",
       "      <td>Future - Mask Off</td>\n",
       "      <td>1006341171</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>326.933</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</td>\n",
       "      <td>0.588</td>\n",
       "      <td>1</td>\n",
       "      <td>Redbone</td>\n",
       "      <td>Childish Gambino</td>\n",
       "      <td>Childish Gambino - Redbone</td>\n",
       "      <td>611080489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>0.004790</td>\n",
       "      <td>0.804</td>\n",
       "      <td>251.333</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</td>\n",
       "      <td>0.264</td>\n",
       "      <td>1</td>\n",
       "      <td>Sneakin’</td>\n",
       "      <td>Drake</td>\n",
       "      <td>Drake - Sneakin’</td>\n",
       "      <td>86903911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>0.014500</td>\n",
       "      <td>0.739</td>\n",
       "      <td>241.400</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</td>\n",
       "      <td>0.308</td>\n",
       "      <td>1</td>\n",
       "      <td>Childs Play</td>\n",
       "      <td>Drake</td>\n",
       "      <td>Drake - Childs Play</td>\n",
       "      <td>1817339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>0.366000</td>\n",
       "      <td>0.762</td>\n",
       "      <td>243.270</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</td>\n",
       "      <td>0.367</td>\n",
       "      <td>1</td>\n",
       "      <td>One Night</td>\n",
       "      <td>Lil Yachty</td>\n",
       "      <td>Lil Yachty - One Night</td>\n",
       "      <td>22048775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.662</td>\n",
       "      <td>247.288</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</td>\n",
       "      <td>0.351</td>\n",
       "      <td>1</td>\n",
       "      <td>Oh lala</td>\n",
       "      <td>PNL</td>\n",
       "      <td>PNL - Oh lala</td>\n",
       "      <td>125301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>28</td>\n",
       "      <td>0.051600</td>\n",
       "      <td>0.782</td>\n",
       "      <td>228.562</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</td>\n",
       "      <td>0.237</td>\n",
       "      <td>1</td>\n",
       "      <td>Look Alive</td>\n",
       "      <td>Rae Sremmurd</td>\n",
       "      <td>Rae Sremmurd - Look Alive</td>\n",
       "      <td>270233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>37</td>\n",
       "      <td>0.056500</td>\n",
       "      <td>0.853</td>\n",
       "      <td>205.879</td>\n",
       "      <td>0.547</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3410</td>\n",
       "      <td>-7.535</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>142.065</td>\n",
       "      <td>4</td>\n",
       "      <td>0.677</td>\n",
       "      <td>1</td>\n",
       "      <td>Jumpman</td>\n",
       "      <td>Drake</td>\n",
       "      <td>Drake - Jumpman</td>\n",
       "      <td>46905737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>45</td>\n",
       "      <td>0.006310</td>\n",
       "      <td>0.715</td>\n",
       "      <td>213.093</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1640</td>\n",
       "      <td>-5.379</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1080</td>\n",
       "      <td>95.487</td>\n",
       "      <td>4</td>\n",
       "      <td>0.607</td>\n",
       "      <td>1</td>\n",
       "      <td>Return Of The Mack - C &amp; J Street Mix</td>\n",
       "      <td>Mark Morrison</td>\n",
       "      <td>Mark Morrison - Return Of The Mack - C &amp; J Str...</td>\n",
       "      <td>92362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>54</td>\n",
       "      <td>0.246000</td>\n",
       "      <td>0.676</td>\n",
       "      <td>254.769</td>\n",
       "      <td>0.528</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0803</td>\n",
       "      <td>-8.699</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0366</td>\n",
       "      <td>130.073</td>\n",
       "      <td>4</td>\n",
       "      <td>0.301</td>\n",
       "      <td>1</td>\n",
       "      <td>White Iverson</td>\n",
       "      <td>Post Malone</td>\n",
       "      <td>Post Malone - White Iverson</td>\n",
       "      <td>311510902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>117</td>\n",
       "      <td>0.002570</td>\n",
       "      <td>0.896</td>\n",
       "      <td>267.024</td>\n",
       "      <td>0.623</td>\n",
       "      <td>0.000258</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0390</td>\n",
       "      <td>-8.008</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0571</td>\n",
       "      <td>134.962</td>\n",
       "      <td>4</td>\n",
       "      <td>0.564</td>\n",
       "      <td>1</td>\n",
       "      <td>Hotline Bling</td>\n",
       "      <td>Drake</td>\n",
       "      <td>Drake - Hotline Bling</td>\n",
       "      <td>29434501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>118</td>\n",
       "      <td>0.002560</td>\n",
       "      <td>0.618</td>\n",
       "      <td>196.693</td>\n",
       "      <td>0.717</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>0.6250</td>\n",
       "      <td>-5.738</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3180</td>\n",
       "      <td>190.050</td>\n",
       "      <td>4</td>\n",
       "      <td>0.601</td>\n",
       "      <td>1</td>\n",
       "      <td>679 (feat. Remy Boyz)</td>\n",
       "      <td>Fetty Wap</td>\n",
       "      <td>Fetty Wap - 679 (feat. Remy Boyz)</td>\n",
       "      <td>644654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>119</td>\n",
       "      <td>0.024400</td>\n",
       "      <td>0.746</td>\n",
       "      <td>222.093</td>\n",
       "      <td>0.873</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>0.3540</td>\n",
       "      <td>-3.803</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1280</td>\n",
       "      <td>148.075</td>\n",
       "      <td>4</td>\n",
       "      <td>0.808</td>\n",
       "      <td>1</td>\n",
       "      <td>Trap Queen</td>\n",
       "      <td>Fetty Wap</td>\n",
       "      <td>Fetty Wap - Trap Queen</td>\n",
       "      <td>584929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>120</td>\n",
       "      <td>0.013000</td>\n",
       "      <td>0.888</td>\n",
       "      <td>200.080</td>\n",
       "      <td>0.483</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0926</td>\n",
       "      <td>-7.797</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1540</td>\n",
       "      <td>125.008</td>\n",
       "      <td>4</td>\n",
       "      <td>0.221</td>\n",
       "      <td>1</td>\n",
       "      <td>No Type</td>\n",
       "      <td>Rae Sremmurd</td>\n",
       "      <td>Rae Sremmurd - No Type</td>\n",
       "      <td>5416809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>125</td>\n",
       "      <td>0.016100</td>\n",
       "      <td>0.526</td>\n",
       "      <td>241.440</td>\n",
       "      <td>0.712</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>0.1790</td>\n",
       "      <td>-6.525</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0356</td>\n",
       "      <td>105.009</td>\n",
       "      <td>4</td>\n",
       "      <td>0.299</td>\n",
       "      <td>1</td>\n",
       "      <td>Midnight City</td>\n",
       "      <td>M83</td>\n",
       "      <td>M83 - Midnight City</td>\n",
       "      <td>156504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>133</td>\n",
       "      <td>0.066800</td>\n",
       "      <td>0.704</td>\n",
       "      <td>199.246</td>\n",
       "      <td>0.647</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>10</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>-7.874</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1710</td>\n",
       "      <td>92.002</td>\n",
       "      <td>4</td>\n",
       "      <td>0.694</td>\n",
       "      <td>1</td>\n",
       "      <td>Magnets</td>\n",
       "      <td>Disclosure</td>\n",
       "      <td>Disclosure - Magnets</td>\n",
       "      <td>14273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>149</td>\n",
       "      <td>0.334000</td>\n",
       "      <td>0.726</td>\n",
       "      <td>221.787</td>\n",
       "      <td>0.583</td>\n",
       "      <td>0.001350</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1220</td>\n",
       "      <td>-6.434</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2300</td>\n",
       "      <td>140.943</td>\n",
       "      <td>4</td>\n",
       "      <td>0.480</td>\n",
       "      <td>1</td>\n",
       "      <td>The Buzz (feat. Mataya &amp; Young Tapz)</td>\n",
       "      <td>Hermitude</td>\n",
       "      <td>Hermitude - The Buzz (feat. Mataya &amp; Young Tapz)</td>\n",
       "      <td>20287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>152</td>\n",
       "      <td>0.689000</td>\n",
       "      <td>0.658</td>\n",
       "      <td>238.560</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>0.1700</td>\n",
       "      <td>-10.866</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0448</td>\n",
       "      <td>128.128</td>\n",
       "      <td>4</td>\n",
       "      <td>0.197</td>\n",
       "      <td>1</td>\n",
       "      <td>River</td>\n",
       "      <td>Leon Bridges</td>\n",
       "      <td>Leon Bridges - River</td>\n",
       "      <td>50478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>170</td>\n",
       "      <td>0.006890</td>\n",
       "      <td>0.717</td>\n",
       "      <td>224.933</td>\n",
       "      <td>0.862</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>0.3210</td>\n",
       "      <td>-4.736</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0540</td>\n",
       "      <td>130.021</td>\n",
       "      <td>4</td>\n",
       "      <td>0.512</td>\n",
       "      <td>1</td>\n",
       "      <td>We Dem Boyz</td>\n",
       "      <td>Wiz Khalifa</td>\n",
       "      <td>Wiz Khalifa - We Dem Boyz</td>\n",
       "      <td>21132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>176</td>\n",
       "      <td>0.141000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>256.093</td>\n",
       "      <td>0.593</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0969</td>\n",
       "      <td>-4.340</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0898</td>\n",
       "      <td>68.456</td>\n",
       "      <td>4</td>\n",
       "      <td>0.325</td>\n",
       "      <td>1</td>\n",
       "      <td>New Slaves</td>\n",
       "      <td>Kanye West</td>\n",
       "      <td>Kanye West - New Slaves</td>\n",
       "      <td>3681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>191</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.430</td>\n",
       "      <td>154.414</td>\n",
       "      <td>0.436</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>0.1280</td>\n",
       "      <td>-4.816</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1190</td>\n",
       "      <td>90.053</td>\n",
       "      <td>4</td>\n",
       "      <td>0.220</td>\n",
       "      <td>1</td>\n",
       "      <td>Holdin On</td>\n",
       "      <td>Flume</td>\n",
       "      <td>Flume - Holdin On</td>\n",
       "      <td>13142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>262</td>\n",
       "      <td>0.108000</td>\n",
       "      <td>0.530</td>\n",
       "      <td>194.613</td>\n",
       "      <td>0.433</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1350</td>\n",
       "      <td>-10.598</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1850</td>\n",
       "      <td>160.140</td>\n",
       "      <td>4</td>\n",
       "      <td>0.272</td>\n",
       "      <td>1</td>\n",
       "      <td>Exchange</td>\n",
       "      <td>Bryson Tiller</td>\n",
       "      <td>Bryson Tiller - Exchange</td>\n",
       "      <td>66985135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>297</td>\n",
       "      <td>0.086100</td>\n",
       "      <td>0.356</td>\n",
       "      <td>242.253</td>\n",
       "      <td>0.567</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1370</td>\n",
       "      <td>-7.011</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0798</td>\n",
       "      <td>135.553</td>\n",
       "      <td>5</td>\n",
       "      <td>0.116</td>\n",
       "      <td>1</td>\n",
       "      <td>The Hills</td>\n",
       "      <td>The Weeknd</td>\n",
       "      <td>The Weeknd - The Hills</td>\n",
       "      <td>41254667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>303</td>\n",
       "      <td>0.407000</td>\n",
       "      <td>0.688</td>\n",
       "      <td>192.040</td>\n",
       "      <td>0.488</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>0.4300</td>\n",
       "      <td>-14.195</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3570</td>\n",
       "      <td>69.770</td>\n",
       "      <td>4</td>\n",
       "      <td>0.708</td>\n",
       "      <td>1</td>\n",
       "      <td>Monster Mash</td>\n",
       "      <td>Bobby \"Boris\" Pickett</td>\n",
       "      <td>Bobby \"Boris\" Pickett - Monster Mash</td>\n",
       "      <td>214616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>333</td>\n",
       "      <td>0.119000</td>\n",
       "      <td>0.716</td>\n",
       "      <td>263.817</td>\n",
       "      <td>0.647</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1210</td>\n",
       "      <td>-4.008</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0345</td>\n",
       "      <td>131.040</td>\n",
       "      <td>4</td>\n",
       "      <td>0.159</td>\n",
       "      <td>1</td>\n",
       "      <td>Antidote</td>\n",
       "      <td>Travis Scott</td>\n",
       "      <td>Travis Scott - Antidote</td>\n",
       "      <td>13144570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>345</td>\n",
       "      <td>0.089300</td>\n",
       "      <td>0.636</td>\n",
       "      <td>235.813</td>\n",
       "      <td>0.720</td>\n",
       "      <td>0.017800</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0868</td>\n",
       "      <td>-6.195</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0432</td>\n",
       "      <td>124.944</td>\n",
       "      <td>4</td>\n",
       "      <td>0.444</td>\n",
       "      <td>1</td>\n",
       "      <td>Shooting Stars</td>\n",
       "      <td>Bag Raiders</td>\n",
       "      <td>Bag Raiders - Shooting Stars</td>\n",
       "      <td>30958799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>434</td>\n",
       "      <td>0.004170</td>\n",
       "      <td>0.356</td>\n",
       "      <td>193.653</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.009750</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0997</td>\n",
       "      <td>-7.837</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0680</td>\n",
       "      <td>166.300</td>\n",
       "      <td>4</td>\n",
       "      <td>0.592</td>\n",
       "      <td>1</td>\n",
       "      <td>Lonely Boy</td>\n",
       "      <td>The Black Keys</td>\n",
       "      <td>The Black Keys - Lonely Boy</td>\n",
       "      <td>13438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>440</td>\n",
       "      <td>0.369000</td>\n",
       "      <td>0.662</td>\n",
       "      <td>236.733</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0908</td>\n",
       "      <td>-7.382</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1060</td>\n",
       "      <td>152.009</td>\n",
       "      <td>4</td>\n",
       "      <td>0.442</td>\n",
       "      <td>1</td>\n",
       "      <td>Headlines</td>\n",
       "      <td>Drake</td>\n",
       "      <td>Drake - Headlines</td>\n",
       "      <td>907204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>519</td>\n",
       "      <td>0.145000</td>\n",
       "      <td>0.844</td>\n",
       "      <td>265.400</td>\n",
       "      <td>0.688</td>\n",
       "      <td>0.003510</td>\n",
       "      <td>4</td>\n",
       "      <td>0.1040</td>\n",
       "      <td>-8.594</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2910</td>\n",
       "      <td>96.671</td>\n",
       "      <td>4</td>\n",
       "      <td>0.747</td>\n",
       "      <td>1</td>\n",
       "      <td>Can I Kick It?</td>\n",
       "      <td>A Tribe Called Quest</td>\n",
       "      <td>A Tribe Called Quest - Can I Kick It?</td>\n",
       "      <td>1102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>526</td>\n",
       "      <td>0.072600</td>\n",
       "      <td>0.739</td>\n",
       "      <td>386.907</td>\n",
       "      <td>0.526</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.2150</td>\n",
       "      <td>-7.384</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>143.948</td>\n",
       "      <td>4</td>\n",
       "      <td>0.374</td>\n",
       "      <td>1</td>\n",
       "      <td>Money Trees</td>\n",
       "      <td>Kendrick Lamar</td>\n",
       "      <td>Kendrick Lamar - Money Trees</td>\n",
       "      <td>416120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>1889</td>\n",
       "      <td>0.006700</td>\n",
       "      <td>0.420</td>\n",
       "      <td>238.667</td>\n",
       "      <td>0.672</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1080</td>\n",
       "      <td>-7.902</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0270</td>\n",
       "      <td>168.019</td>\n",
       "      <td>4</td>\n",
       "      <td>0.459</td>\n",
       "      <td>0</td>\n",
       "      <td>Push</td>\n",
       "      <td>Matchbox Twenty</td>\n",
       "      <td>Matchbox Twenty - Push</td>\n",
       "      <td>15977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>1890</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.561</td>\n",
       "      <td>295.493</td>\n",
       "      <td>0.555</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>-9.734</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0287</td>\n",
       "      <td>134.369</td>\n",
       "      <td>4</td>\n",
       "      <td>0.509</td>\n",
       "      <td>0</td>\n",
       "      <td>What's Up?</td>\n",
       "      <td>4 Non Blondes</td>\n",
       "      <td>4 Non Blondes - What's Up?</td>\n",
       "      <td>61852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>1897</td>\n",
       "      <td>0.001330</td>\n",
       "      <td>0.291</td>\n",
       "      <td>289.907</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0811</td>\n",
       "      <td>-5.683</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0363</td>\n",
       "      <td>155.667</td>\n",
       "      <td>3</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0</td>\n",
       "      <td>Iris</td>\n",
       "      <td>The Goo Goo Dolls</td>\n",
       "      <td>The Goo Goo Dolls - Iris</td>\n",
       "      <td>10973912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>1900</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.926</td>\n",
       "      <td>199.853</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0234</td>\n",
       "      <td>-2.221</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0929</td>\n",
       "      <td>110.007</td>\n",
       "      <td>4</td>\n",
       "      <td>0.903</td>\n",
       "      <td>0</td>\n",
       "      <td>Hollaback Girl</td>\n",
       "      <td>Gwen Stefani</td>\n",
       "      <td>Gwen Stefani - Hollaback Girl</td>\n",
       "      <td>232917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>1901</td>\n",
       "      <td>0.058400</td>\n",
       "      <td>0.967</td>\n",
       "      <td>242.733</td>\n",
       "      <td>0.583</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0519</td>\n",
       "      <td>-5.562</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0789</td>\n",
       "      <td>117.000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0</td>\n",
       "      <td>SexyBack</td>\n",
       "      <td>Justin Timberlake</td>\n",
       "      <td>Justin Timberlake - SexyBack</td>\n",
       "      <td>109955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>1902</td>\n",
       "      <td>0.082300</td>\n",
       "      <td>0.741</td>\n",
       "      <td>289.133</td>\n",
       "      <td>0.748</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3400</td>\n",
       "      <td>-6.299</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0264</td>\n",
       "      <td>127.965</td>\n",
       "      <td>4</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0</td>\n",
       "      <td>I Gotta Feeling</td>\n",
       "      <td>The Black Eyed Peas</td>\n",
       "      <td>The Black Eyed Peas - I Gotta Feeling</td>\n",
       "      <td>431869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>1903</td>\n",
       "      <td>0.185000</td>\n",
       "      <td>0.757</td>\n",
       "      <td>211.160</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5680</td>\n",
       "      <td>-4.860</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0315</td>\n",
       "      <td>92.960</td>\n",
       "      <td>4</td>\n",
       "      <td>0.887</td>\n",
       "      <td>0</td>\n",
       "      <td>...Baby One More Time</td>\n",
       "      <td>Britney Spears</td>\n",
       "      <td>Britney Spears - ...Baby One More Time</td>\n",
       "      <td>156658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>1905</td>\n",
       "      <td>0.139000</td>\n",
       "      <td>0.671</td>\n",
       "      <td>243.493</td>\n",
       "      <td>0.727</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>0.1230</td>\n",
       "      <td>-7.585</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0350</td>\n",
       "      <td>102.358</td>\n",
       "      <td>4</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>Mariah Carey</td>\n",
       "      <td>Mariah Carey - Fantasy</td>\n",
       "      <td>33008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>1906</td>\n",
       "      <td>0.025100</td>\n",
       "      <td>0.743</td>\n",
       "      <td>214.400</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.000717</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0685</td>\n",
       "      <td>-4.267</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0953</td>\n",
       "      <td>92.909</td>\n",
       "      <td>4</td>\n",
       "      <td>0.604</td>\n",
       "      <td>0</td>\n",
       "      <td>No Scrubs</td>\n",
       "      <td>TLC</td>\n",
       "      <td>TLC - No Scrubs</td>\n",
       "      <td>10280215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>1909</td>\n",
       "      <td>0.101000</td>\n",
       "      <td>0.768</td>\n",
       "      <td>173.027</td>\n",
       "      <td>0.864</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>0.1620</td>\n",
       "      <td>-6.145</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0324</td>\n",
       "      <td>110.008</td>\n",
       "      <td>4</td>\n",
       "      <td>0.876</td>\n",
       "      <td>0</td>\n",
       "      <td>Wannabe - Radio Edit</td>\n",
       "      <td>Spice Girls</td>\n",
       "      <td>Spice Girls - Wannabe - Radio Edit</td>\n",
       "      <td>545802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>1913</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.439</td>\n",
       "      <td>234.800</td>\n",
       "      <td>0.931</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>11</td>\n",
       "      <td>0.1930</td>\n",
       "      <td>-2.590</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0473</td>\n",
       "      <td>135.944</td>\n",
       "      <td>4</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0</td>\n",
       "      <td>Learn to Fly</td>\n",
       "      <td>Foo Fighters</td>\n",
       "      <td>Foo Fighters - Learn to Fly</td>\n",
       "      <td>70595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>1916</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>0.566</td>\n",
       "      <td>279.400</td>\n",
       "      <td>0.705</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1090</td>\n",
       "      <td>-7.569</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0311</td>\n",
       "      <td>168.152</td>\n",
       "      <td>4</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0</td>\n",
       "      <td>Truly Madly Deeply</td>\n",
       "      <td>Savage Garden</td>\n",
       "      <td>Savage Garden - Truly Madly Deeply</td>\n",
       "      <td>89078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>1919</td>\n",
       "      <td>0.044900</td>\n",
       "      <td>0.706</td>\n",
       "      <td>225.253</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>10</td>\n",
       "      <td>0.3010</td>\n",
       "      <td>-4.355</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0449</td>\n",
       "      <td>108.006</td>\n",
       "      <td>4</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0</td>\n",
       "      <td>Everybody (Backstreet's Back) - Radio Edit</td>\n",
       "      <td>Backstreet Boys</td>\n",
       "      <td>Backstreet Boys - Everybody (Backstreet's Back...</td>\n",
       "      <td>209470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>1927</td>\n",
       "      <td>0.284000</td>\n",
       "      <td>0.778</td>\n",
       "      <td>218.093</td>\n",
       "      <td>0.824</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>0.4050</td>\n",
       "      <td>-5.892</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0712</td>\n",
       "      <td>100.024</td>\n",
       "      <td>4</td>\n",
       "      <td>0.767</td>\n",
       "      <td>0</td>\n",
       "      <td>Hips Don't Lie</td>\n",
       "      <td>Shakira</td>\n",
       "      <td>Shakira - Hips Don't Lie</td>\n",
       "      <td>2902013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>1929</td>\n",
       "      <td>0.002810</td>\n",
       "      <td>0.633</td>\n",
       "      <td>197.124</td>\n",
       "      <td>0.976</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>8</td>\n",
       "      <td>0.3820</td>\n",
       "      <td>-5.362</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0519</td>\n",
       "      <td>142.027</td>\n",
       "      <td>4</td>\n",
       "      <td>0.471</td>\n",
       "      <td>0</td>\n",
       "      <td>Every time we touch</td>\n",
       "      <td>Cascada</td>\n",
       "      <td>Cascada - Every time we touch</td>\n",
       "      <td>42158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>1930</td>\n",
       "      <td>0.101000</td>\n",
       "      <td>0.835</td>\n",
       "      <td>272.533</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1320</td>\n",
       "      <td>-3.180</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1840</td>\n",
       "      <td>94.059</td>\n",
       "      <td>4</td>\n",
       "      <td>0.826</td>\n",
       "      <td>0</td>\n",
       "      <td>Where Is The Love?</td>\n",
       "      <td>The Black Eyed Peas</td>\n",
       "      <td>The Black Eyed Peas - Where Is The Love?</td>\n",
       "      <td>5151011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>1931</td>\n",
       "      <td>0.044600</td>\n",
       "      <td>0.853</td>\n",
       "      <td>267.080</td>\n",
       "      <td>0.676</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0514</td>\n",
       "      <td>-5.533</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0715</td>\n",
       "      <td>122.650</td>\n",
       "      <td>4</td>\n",
       "      <td>0.558</td>\n",
       "      <td>0</td>\n",
       "      <td>Don't Stop The Music</td>\n",
       "      <td>Rihanna</td>\n",
       "      <td>Rihanna - Don't Stop The Music</td>\n",
       "      <td>306673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>1932</td>\n",
       "      <td>0.055900</td>\n",
       "      <td>0.514</td>\n",
       "      <td>254.027</td>\n",
       "      <td>0.911</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7750</td>\n",
       "      <td>-2.027</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4100</td>\n",
       "      <td>161.109</td>\n",
       "      <td>4</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0</td>\n",
       "      <td>Survivor</td>\n",
       "      <td>Destiny's Child</td>\n",
       "      <td>Destiny's Child - Survivor</td>\n",
       "      <td>151867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>1933</td>\n",
       "      <td>0.001250</td>\n",
       "      <td>0.649</td>\n",
       "      <td>230.747</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>3</td>\n",
       "      <td>0.6710</td>\n",
       "      <td>-3.796</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0415</td>\n",
       "      <td>126.030</td>\n",
       "      <td>4</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0</td>\n",
       "      <td>Last Friday Night (T.G.I.F.)</td>\n",
       "      <td>Katy Perry</td>\n",
       "      <td>Katy Perry - Last Friday Night (T.G.I.F.)</td>\n",
       "      <td>8740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>1934</td>\n",
       "      <td>0.265000</td>\n",
       "      <td>0.793</td>\n",
       "      <td>197.027</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2070</td>\n",
       "      <td>-4.861</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0405</td>\n",
       "      <td>107.647</td>\n",
       "      <td>4</td>\n",
       "      <td>0.859</td>\n",
       "      <td>0</td>\n",
       "      <td>Whenever, Wherever</td>\n",
       "      <td>Shakira</td>\n",
       "      <td>Shakira - Whenever, Wherever</td>\n",
       "      <td>79881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>1935</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.851</td>\n",
       "      <td>237.200</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>4</td>\n",
       "      <td>0.1220</td>\n",
       "      <td>-4.618</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0788</td>\n",
       "      <td>119.002</td>\n",
       "      <td>4</td>\n",
       "      <td>0.769</td>\n",
       "      <td>0</td>\n",
       "      <td>Poker Face</td>\n",
       "      <td>Lady Gaga</td>\n",
       "      <td>Lady Gaga - Poker Face</td>\n",
       "      <td>1121697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>1936</td>\n",
       "      <td>0.057200</td>\n",
       "      <td>0.585</td>\n",
       "      <td>244.507</td>\n",
       "      <td>0.776</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>5</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>-5.898</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0459</td>\n",
       "      <td>77.987</td>\n",
       "      <td>4</td>\n",
       "      <td>0.430</td>\n",
       "      <td>0</td>\n",
       "      <td>Complicated</td>\n",
       "      <td>Avril Lavigne</td>\n",
       "      <td>Avril Lavigne - Complicated</td>\n",
       "      <td>123641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>1942</td>\n",
       "      <td>0.007330</td>\n",
       "      <td>0.845</td>\n",
       "      <td>187.840</td>\n",
       "      <td>0.768</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0575</td>\n",
       "      <td>-5.448</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1880</td>\n",
       "      <td>100.000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.954</td>\n",
       "      <td>0</td>\n",
       "      <td>Jenny from the Block - Track Masters Remix</td>\n",
       "      <td>Jennifer Lopez</td>\n",
       "      <td>Jennifer Lopez - Jenny from the Block - Track ...</td>\n",
       "      <td>214657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>1944</td>\n",
       "      <td>0.080300</td>\n",
       "      <td>0.524</td>\n",
       "      <td>393.827</td>\n",
       "      <td>0.429</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0726</td>\n",
       "      <td>-10.064</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0279</td>\n",
       "      <td>131.206</td>\n",
       "      <td>4</td>\n",
       "      <td>0.237</td>\n",
       "      <td>0</td>\n",
       "      <td>(Everything I Do) I Do It For You</td>\n",
       "      <td>Bryan Adams</td>\n",
       "      <td>Bryan Adams - (Everything I Do) I Do It For You</td>\n",
       "      <td>151773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>1962</td>\n",
       "      <td>0.058800</td>\n",
       "      <td>0.653</td>\n",
       "      <td>216.613</td>\n",
       "      <td>0.748</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>-6.323</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2110</td>\n",
       "      <td>172.008</td>\n",
       "      <td>4</td>\n",
       "      <td>0.809</td>\n",
       "      <td>0</td>\n",
       "      <td>Mysterious Girl</td>\n",
       "      <td>Peter Andre</td>\n",
       "      <td>Peter Andre - Mysterious Girl</td>\n",
       "      <td>48945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>1973</td>\n",
       "      <td>0.001350</td>\n",
       "      <td>0.446</td>\n",
       "      <td>223.840</td>\n",
       "      <td>0.764</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0990</td>\n",
       "      <td>-5.042</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0330</td>\n",
       "      <td>172.094</td>\n",
       "      <td>4</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0</td>\n",
       "      <td>How You Remind Me - LP Mix</td>\n",
       "      <td>Nickelback</td>\n",
       "      <td>Nickelback - How You Remind Me - LP Mix</td>\n",
       "      <td>202288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>1980</td>\n",
       "      <td>0.005740</td>\n",
       "      <td>0.682</td>\n",
       "      <td>192.000</td>\n",
       "      <td>0.812</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>-2.691</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1180</td>\n",
       "      <td>104.891</td>\n",
       "      <td>4</td>\n",
       "      <td>0.347</td>\n",
       "      <td>0</td>\n",
       "      <td>Shape of You (Major Lazer Remix) [feat. Nyla &amp;...</td>\n",
       "      <td>Ed Sheeran</td>\n",
       "      <td>Ed Sheeran - Shape of You (Major Lazer Remix) ...</td>\n",
       "      <td>6141094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>1981</td>\n",
       "      <td>0.181000</td>\n",
       "      <td>0.585</td>\n",
       "      <td>249.213</td>\n",
       "      <td>0.612</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2930</td>\n",
       "      <td>-8.341</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0806</td>\n",
       "      <td>77.487</td>\n",
       "      <td>4</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0</td>\n",
       "      <td>Party Monster</td>\n",
       "      <td>The Weeknd</td>\n",
       "      <td>The Weeknd - Party Monster</td>\n",
       "      <td>159304820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>1985</td>\n",
       "      <td>0.007010</td>\n",
       "      <td>0.578</td>\n",
       "      <td>199.080</td>\n",
       "      <td>0.866</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2570</td>\n",
       "      <td>-3.804</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0619</td>\n",
       "      <td>128.038</td>\n",
       "      <td>4</td>\n",
       "      <td>0.619</td>\n",
       "      <td>0</td>\n",
       "      <td>Shut Up and Dance</td>\n",
       "      <td>WALK THE MOON</td>\n",
       "      <td>WALK THE MOON - Shut Up and Dance</td>\n",
       "      <td>3474724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>2002</td>\n",
       "      <td>0.030100</td>\n",
       "      <td>0.583</td>\n",
       "      <td>224.092</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</td>\n",
       "      <td>0.321</td>\n",
       "      <td>0</td>\n",
       "      <td>Without U</td>\n",
       "      <td>Steve Aoki</td>\n",
       "      <td>Steve Aoki - Without U</td>\n",
       "      <td>4491</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>323 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  acousticness  danceability  duration  energy  \\\n",
       "0             0      0.010200         0.833   204.600   0.434   \n",
       "1             1      0.199000         0.743   326.933   0.359   \n",
       "2             5      0.004790         0.804   251.333   0.560   \n",
       "3             6      0.014500         0.739   241.400   0.472   \n",
       "4            13      0.366000         0.762   243.270   0.476   \n",
       "5            14      0.440000         0.662   247.288   0.603   \n",
       "6            28      0.051600         0.782   228.562   0.572   \n",
       "7            37      0.056500         0.853   205.879   0.547   \n",
       "8            45      0.006310         0.715   213.093   0.833   \n",
       "9            54      0.246000         0.676   254.769   0.528   \n",
       "10          117      0.002570         0.896   267.024   0.623   \n",
       "11          118      0.002560         0.618   196.693   0.717   \n",
       "12          119      0.024400         0.746   222.093   0.873   \n",
       "13          120      0.013000         0.888   200.080   0.483   \n",
       "14          125      0.016100         0.526   241.440   0.712   \n",
       "15          133      0.066800         0.704   199.246   0.647   \n",
       "16          149      0.334000         0.726   221.787   0.583   \n",
       "17          152      0.689000         0.658   238.560   0.179   \n",
       "18          170      0.006890         0.717   224.933   0.862   \n",
       "19          176      0.141000         0.500   256.093   0.593   \n",
       "20          191      0.170000         0.430   154.414   0.436   \n",
       "21          262      0.108000         0.530   194.613   0.433   \n",
       "22          297      0.086100         0.356   242.253   0.567   \n",
       "23          303      0.407000         0.688   192.040   0.488   \n",
       "24          333      0.119000         0.716   263.817   0.647   \n",
       "25          345      0.089300         0.636   235.813   0.720   \n",
       "26          434      0.004170         0.356   193.653   0.872   \n",
       "27          440      0.369000         0.662   236.733   0.571   \n",
       "28          519      0.145000         0.844   265.400   0.688   \n",
       "29          526      0.072600         0.739   386.907   0.526   \n",
       "..          ...           ...           ...       ...     ...   \n",
       "293        1889      0.006700         0.420   238.667   0.672   \n",
       "294        1890      0.160000         0.561   295.493   0.555   \n",
       "295        1897      0.001330         0.291   289.907   0.790   \n",
       "296        1900      0.350000         0.926   199.853   0.916   \n",
       "297        1901      0.058400         0.967   242.733   0.583   \n",
       "298        1902      0.082300         0.741   289.133   0.748   \n",
       "299        1903      0.185000         0.757   211.160   0.725   \n",
       "300        1905      0.139000         0.671   243.493   0.727   \n",
       "301        1906      0.025100         0.743   214.400   0.675   \n",
       "302        1909      0.101000         0.768   173.027   0.864   \n",
       "303        1913      0.000019         0.439   234.800   0.931   \n",
       "304        1916      0.430000         0.566   279.400   0.705   \n",
       "305        1919      0.044900         0.706   225.253   0.893   \n",
       "306        1927      0.284000         0.778   218.093   0.824   \n",
       "307        1929      0.002810         0.633   197.124   0.976   \n",
       "308        1930      0.101000         0.835   272.533   0.687   \n",
       "309        1931      0.044600         0.853   267.080   0.676   \n",
       "310        1932      0.055900         0.514   254.027   0.911   \n",
       "311        1933      0.001250         0.649   230.747   0.815   \n",
       "312        1934      0.265000         0.793   197.027   0.832   \n",
       "313        1935      0.120000         0.851   237.200   0.806   \n",
       "314        1936      0.057200         0.585   244.507   0.776   \n",
       "315        1942      0.007330         0.845   187.840   0.768   \n",
       "316        1944      0.080300         0.524   393.827   0.429   \n",
       "317        1962      0.058800         0.653   216.613   0.748   \n",
       "318        1973      0.001350         0.446   223.840   0.764   \n",
       "319        1980      0.005740         0.682   192.000   0.812   \n",
       "320        1981      0.181000         0.585   249.213   0.612   \n",
       "321        1985      0.007010         0.578   199.080   0.866   \n",
       "322        2002      0.030100         0.583   224.092   0.891   \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            0.000000    8    0.1640    -6.682     1       0.1850   85.023   \n",
       "3            0.000007    1    0.2070   -11.204     1       0.1560   80.030   \n",
       "4            0.000000    0    0.1030   -12.686     1       0.1140  130.007   \n",
       "5            0.000000    9    0.0972    -8.317     0       0.0793  125.011   \n",
       "6            0.000000    4    0.3300    -7.467     0       0.0385  120.021   \n",
       "7            0.000000    1    0.3410    -7.535     1       0.1940  142.065   \n",
       "8            0.000000    2    0.1640    -5.379     1       0.1080   95.487   \n",
       "9            0.000000    7    0.0803    -8.699     1       0.0366  130.073   \n",
       "10           0.000258    2    0.0390    -8.008     1       0.0571  134.962   \n",
       "11           0.000000    7    0.6250    -5.738     1       0.3180  190.050   \n",
       "12           0.000000    7    0.3540    -3.803     1       0.1280  148.075   \n",
       "13           0.000005    6    0.0926    -7.797     1       0.1540  125.008   \n",
       "14           0.000000   11    0.1790    -6.525     0       0.0356  105.009   \n",
       "15           0.000002   10    0.1010    -7.874     0       0.1710   92.002   \n",
       "16           0.001350    0    0.1220    -6.434     0       0.2300  140.943   \n",
       "17           0.000000    8    0.1700   -10.866     1       0.0448  128.128   \n",
       "18           0.000000    8    0.3210    -4.736     1       0.0540  130.021   \n",
       "19           0.000006    1    0.0969    -4.340     1       0.0898   68.456   \n",
       "20           0.000000    8    0.1280    -4.816     1       0.1190   90.053   \n",
       "21           0.000000    6    0.1350   -10.598     1       0.1850  160.140   \n",
       "22           0.000000    0    0.1370    -7.011     0       0.0798  135.553   \n",
       "23           0.000000   11    0.4300   -14.195     0       0.3570   69.770   \n",
       "24           0.000014    1    0.1210    -4.008     1       0.0345  131.040   \n",
       "25           0.017800   11    0.0868    -6.195     1       0.0432  124.944   \n",
       "26           0.009750    2    0.0997    -7.837     1       0.0680  166.300   \n",
       "27           0.000197    6    0.0908    -7.382     0       0.1060  152.009   \n",
       "28           0.003510    4    0.1040    -8.594     0       0.2910   96.671   \n",
       "29           0.000000    4    0.2150    -7.384     0       0.1010  143.948   \n",
       "..                ...  ...       ...       ...   ...          ...      ...   \n",
       "293          0.000000    6    0.1080    -7.902     1       0.0270  168.019   \n",
       "294          0.000000   11    0.1140    -9.734     0       0.0287  134.369   \n",
       "295          0.000006   11    0.0811    -5.683     0       0.0363  155.667   \n",
       "296          0.000006   10    0.0234    -2.221     0       0.0929  110.007   \n",
       "297          0.000000    7    0.0519    -5.562     0       0.0789  117.000   \n",
       "298          0.000000    0    0.3400    -6.299     1       0.0264  127.965   \n",
       "299          0.000056    0    0.5680    -4.860     0       0.0315   92.960   \n",
       "300          0.000000    7    0.1230    -7.585     1       0.0350  102.358   \n",
       "301          0.000717    8    0.0685    -4.267     0       0.0953   92.909   \n",
       "302          0.000000   11    0.1620    -6.145     1       0.0324  110.008   \n",
       "303          0.000012   11    0.1930    -2.590     0       0.0473  135.944   \n",
       "304          0.000000    0    0.1090    -7.569     1       0.0311  168.152   \n",
       "305          0.000001   10    0.3010    -4.355     0       0.0449  108.006   \n",
       "306          0.000000   10    0.4050    -5.892     0       0.0712  100.024   \n",
       "307          0.000013    8    0.3820    -5.362     1       0.0519  142.027   \n",
       "308          0.000000    5    0.1320    -3.180     1       0.1840   94.059   \n",
       "309          0.000034    6    0.0514    -5.533     0       0.0715  122.650   \n",
       "310          0.000000    1    0.7750    -2.027     0       0.4100  161.109   \n",
       "311          0.000043    3    0.6710    -3.796     0       0.0415  126.030   \n",
       "312          0.000007    1    0.2070    -4.861     0       0.0405  107.647   \n",
       "313          0.000001    4    0.1220    -4.618     1       0.0788  119.002   \n",
       "314          0.000008    5    0.3000    -5.898     1       0.0459   77.987   \n",
       "315          0.000005    6    0.0575    -5.448     1       0.1880  100.000   \n",
       "316          0.000014    1    0.0726   -10.064     1       0.0279  131.206   \n",
       "317          0.000000   10    0.1940    -6.323     0       0.2110  172.008   \n",
       "318          0.000000   10    0.0990    -5.042     1       0.0330  172.094   \n",
       "319          0.000000    1    0.0553    -2.691     0       0.1180  104.891   \n",
       "320          0.000010    1    0.2930    -8.341     1       0.0806   77.487   \n",
       "321          0.000000    1    0.2570    -3.804     1       0.0619  128.038   \n",
       "322          0.000003    7    0.1290    -3.495     1       0.4470  149.843   \n",
       "\n",
       "     time_signature  valence  target  \\\n",
       "0                 4    0.286       1   \n",
       "1                 4    0.588       1   \n",
       "2                 4    0.264       1   \n",
       "3                 4    0.308       1   \n",
       "4                 4    0.367       1   \n",
       "5                 4    0.351       1   \n",
       "6                 4    0.237       1   \n",
       "7                 4    0.677       1   \n",
       "8                 4    0.607       1   \n",
       "9                 4    0.301       1   \n",
       "10                4    0.564       1   \n",
       "11                4    0.601       1   \n",
       "12                4    0.808       1   \n",
       "13                4    0.221       1   \n",
       "14                4    0.299       1   \n",
       "15                4    0.694       1   \n",
       "16                4    0.480       1   \n",
       "17                4    0.197       1   \n",
       "18                4    0.512       1   \n",
       "19                4    0.325       1   \n",
       "20                4    0.220       1   \n",
       "21                4    0.272       1   \n",
       "22                5    0.116       1   \n",
       "23                4    0.708       1   \n",
       "24                4    0.159       1   \n",
       "25                4    0.444       1   \n",
       "26                4    0.592       1   \n",
       "27                4    0.442       1   \n",
       "28                4    0.747       1   \n",
       "29                4    0.374       1   \n",
       "..              ...      ...     ...   \n",
       "293               4    0.459       0   \n",
       "294               4    0.509       0   \n",
       "295               3    0.493       0   \n",
       "296               4    0.903       0   \n",
       "297               4    0.965       0   \n",
       "298               4    0.616       0   \n",
       "299               4    0.887       0   \n",
       "300               4    0.820       0   \n",
       "301               4    0.604       0   \n",
       "302               4    0.876       0   \n",
       "303               4    0.516       0   \n",
       "304               4    0.785       0   \n",
       "305               4    0.616       0   \n",
       "306               4    0.767       0   \n",
       "307               4    0.471       0   \n",
       "308               4    0.826       0   \n",
       "309               4    0.558       0   \n",
       "310               4    0.614       0   \n",
       "311               4    0.774       0   \n",
       "312               4    0.859       0   \n",
       "313               4    0.769       0   \n",
       "314               4    0.430       0   \n",
       "315               4    0.954       0   \n",
       "316               4    0.237       0   \n",
       "317               4    0.809       0   \n",
       "318               4    0.520       0   \n",
       "319               4    0.347       0   \n",
       "320               4    0.173       0   \n",
       "321               4    0.619       0   \n",
       "322               4    0.321       0   \n",
       "\n",
       "                                            song_title                 artist  \\\n",
       "0                                             Mask Off                 Future   \n",
       "1                                              Redbone       Childish Gambino   \n",
       "2                                             Sneakin’                  Drake   \n",
       "3                                          Childs Play                  Drake   \n",
       "4                                            One Night             Lil Yachty   \n",
       "5                                              Oh lala                    PNL   \n",
       "6                                           Look Alive           Rae Sremmurd   \n",
       "7                                              Jumpman                  Drake   \n",
       "8                Return Of The Mack - C & J Street Mix          Mark Morrison   \n",
       "9                                        White Iverson            Post Malone   \n",
       "10                                       Hotline Bling                  Drake   \n",
       "11                               679 (feat. Remy Boyz)              Fetty Wap   \n",
       "12                                          Trap Queen              Fetty Wap   \n",
       "13                                             No Type           Rae Sremmurd   \n",
       "14                                       Midnight City                    M83   \n",
       "15                                             Magnets             Disclosure   \n",
       "16                The Buzz (feat. Mataya & Young Tapz)              Hermitude   \n",
       "17                                               River           Leon Bridges   \n",
       "18                                         We Dem Boyz            Wiz Khalifa   \n",
       "19                                          New Slaves             Kanye West   \n",
       "20                                           Holdin On                  Flume   \n",
       "21                                            Exchange          Bryson Tiller   \n",
       "22                                           The Hills             The Weeknd   \n",
       "23                                        Monster Mash  Bobby \"Boris\" Pickett   \n",
       "24                                            Antidote           Travis Scott   \n",
       "25                                      Shooting Stars            Bag Raiders   \n",
       "26                                          Lonely Boy         The Black Keys   \n",
       "27                                           Headlines                  Drake   \n",
       "28                                      Can I Kick It?   A Tribe Called Quest   \n",
       "29                                         Money Trees         Kendrick Lamar   \n",
       "..                                                 ...                    ...   \n",
       "293                                               Push        Matchbox Twenty   \n",
       "294                                         What's Up?          4 Non Blondes   \n",
       "295                                               Iris      The Goo Goo Dolls   \n",
       "296                                     Hollaback Girl           Gwen Stefani   \n",
       "297                                           SexyBack      Justin Timberlake   \n",
       "298                                    I Gotta Feeling    The Black Eyed Peas   \n",
       "299                              ...Baby One More Time         Britney Spears   \n",
       "300                                            Fantasy           Mariah Carey   \n",
       "301                                          No Scrubs                    TLC   \n",
       "302                               Wannabe - Radio Edit            Spice Girls   \n",
       "303                                       Learn to Fly           Foo Fighters   \n",
       "304                                 Truly Madly Deeply          Savage Garden   \n",
       "305         Everybody (Backstreet's Back) - Radio Edit        Backstreet Boys   \n",
       "306                                     Hips Don't Lie                Shakira   \n",
       "307                                Every time we touch                Cascada   \n",
       "308                                 Where Is The Love?    The Black Eyed Peas   \n",
       "309                               Don't Stop The Music                Rihanna   \n",
       "310                                           Survivor        Destiny's Child   \n",
       "311                       Last Friday Night (T.G.I.F.)             Katy Perry   \n",
       "312                                 Whenever, Wherever                Shakira   \n",
       "313                                         Poker Face              Lady Gaga   \n",
       "314                                        Complicated          Avril Lavigne   \n",
       "315         Jenny from the Block - Track Masters Remix         Jennifer Lopez   \n",
       "316                  (Everything I Do) I Do It For You            Bryan Adams   \n",
       "317                                    Mysterious Girl            Peter Andre   \n",
       "318                         How You Remind Me - LP Mix             Nickelback   \n",
       "319  Shape of You (Major Lazer Remix) [feat. Nyla &...             Ed Sheeran   \n",
       "320                                      Party Monster             The Weeknd   \n",
       "321                                  Shut Up and Dance          WALK THE MOON   \n",
       "322                                          Without U             Steve Aoki   \n",
       "\n",
       "                                               uniq_id     streams  \n",
       "0                                    Future - Mask Off  1006341171  \n",
       "1                           Childish Gambino - Redbone   611080489  \n",
       "2                                     Drake - Sneakin’    86903911  \n",
       "3                                  Drake - Childs Play     1817339  \n",
       "4                               Lil Yachty - One Night    22048775  \n",
       "5                                        PNL - Oh lala      125301  \n",
       "6                            Rae Sremmurd - Look Alive      270233  \n",
       "7                                      Drake - Jumpman    46905737  \n",
       "8    Mark Morrison - Return Of The Mack - C & J Str...       92362  \n",
       "9                          Post Malone - White Iverson   311510902  \n",
       "10                               Drake - Hotline Bling    29434501  \n",
       "11                   Fetty Wap - 679 (feat. Remy Boyz)      644654  \n",
       "12                              Fetty Wap - Trap Queen      584929  \n",
       "13                              Rae Sremmurd - No Type     5416809  \n",
       "14                                 M83 - Midnight City      156504  \n",
       "15                                Disclosure - Magnets       14273  \n",
       "16    Hermitude - The Buzz (feat. Mataya & Young Tapz)       20287  \n",
       "17                                Leon Bridges - River       50478  \n",
       "18                           Wiz Khalifa - We Dem Boyz       21132  \n",
       "19                             Kanye West - New Slaves        3681  \n",
       "20                                   Flume - Holdin On       13142  \n",
       "21                            Bryson Tiller - Exchange    66985135  \n",
       "22                              The Weeknd - The Hills    41254667  \n",
       "23                Bobby \"Boris\" Pickett - Monster Mash      214616  \n",
       "24                             Travis Scott - Antidote    13144570  \n",
       "25                        Bag Raiders - Shooting Stars    30958799  \n",
       "26                         The Black Keys - Lonely Boy       13438  \n",
       "27                                   Drake - Headlines      907204  \n",
       "28               A Tribe Called Quest - Can I Kick It?        1102  \n",
       "29                        Kendrick Lamar - Money Trees      416120  \n",
       "..                                                 ...         ...  \n",
       "293                             Matchbox Twenty - Push       15977  \n",
       "294                         4 Non Blondes - What's Up?       61852  \n",
       "295                           The Goo Goo Dolls - Iris    10973912  \n",
       "296                      Gwen Stefani - Hollaback Girl      232917  \n",
       "297                       Justin Timberlake - SexyBack      109955  \n",
       "298              The Black Eyed Peas - I Gotta Feeling      431869  \n",
       "299             Britney Spears - ...Baby One More Time      156658  \n",
       "300                             Mariah Carey - Fantasy       33008  \n",
       "301                                    TLC - No Scrubs    10280215  \n",
       "302                 Spice Girls - Wannabe - Radio Edit      545802  \n",
       "303                        Foo Fighters - Learn to Fly       70595  \n",
       "304                 Savage Garden - Truly Madly Deeply       89078  \n",
       "305  Backstreet Boys - Everybody (Backstreet's Back...      209470  \n",
       "306                           Shakira - Hips Don't Lie     2902013  \n",
       "307                      Cascada - Every time we touch       42158  \n",
       "308           The Black Eyed Peas - Where Is The Love?     5151011  \n",
       "309                     Rihanna - Don't Stop The Music      306673  \n",
       "310                         Destiny's Child - Survivor      151867  \n",
       "311          Katy Perry - Last Friday Night (T.G.I.F.)        8740  \n",
       "312                       Shakira - Whenever, Wherever       79881  \n",
       "313                             Lady Gaga - Poker Face     1121697  \n",
       "314                        Avril Lavigne - Complicated      123641  \n",
       "315  Jennifer Lopez - Jenny from the Block - Track ...      214657  \n",
       "316    Bryan Adams - (Everything I Do) I Do It For You      151773  \n",
       "317                      Peter Andre - Mysterious Girl       48945  \n",
       "318            Nickelback - How You Remind Me - LP Mix      202288  \n",
       "319  Ed Sheeran - Shape of You (Major Lazer Remix) ...     6141094  \n",
       "320                         The Weeknd - Party Monster   159304820  \n",
       "321                  WALK THE MOON - Shut Up and Dance     3474724  \n",
       "322                             Steve Aoki - Without U        4491  \n",
       "\n",
       "[323 rows x 19 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.to_csv('finaldata.csv')\n",
    "df_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also want to gain a better understanding of which genre of music is most popular amongst our data. To do this, we will do\n",
    "some web scaping to get the genre off of Wikipedia. Since not every song has a Wikipedia page, we decided to instead look at\n",
    "the Wikipedia pages of each of the artists in our dataframe. This gave us info on which genres each particular artist falls \n",
    "under. Since each artist produces music under multiple genres, our counts will not match exactly with the number of rows in \n",
    "our dataframe. However, we will still be able to see how popular each genre is relative to one another. \n",
    "\n",
    "Since there are hundereds of nuanced genres, we lumped together genres with slight variations such as 'electro house' and \n",
    "'future house' under the umberella genre of 'house'. This makes the results easier to interpret while still highlighting the \n",
    "differences among major genres. The list of genres we kept track of was made by looking at the genres of the artists in our\n",
    "dataset and noting down the most common ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Initialize the genre counts to 0\n",
    "edm, pop, house, alternative, rock, trap, hip_hop, r_b, country, reggaeton, indie, dance, funk, soul, reggae, other = 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_artists = df_final[['artist']].copy()\n",
    "\n",
    "#Drop the following rows in the genre dataframe because the artists either do not have Wikipedia pages or their name \n",
    "#does not match their Wikipedia page\n",
    "df_artists = df_artists.drop(df_artists[(df_artists['artist'] == 'IAmChino')].index)\n",
    "df_artists = df_artists.drop(df_artists[(df_artists.artist == 'Xantos')].index)\n",
    "df_artists = df_artists.drop(df_artists[(df_artists.artist == 'J HUS')].index)\n",
    "df_artists = df_artists.drop(df_artists[(df_artists.artist == 'Logic')].index)\n",
    "df_artists = df_artists.drop(df_artists[(df_artists.artist == 'Cheat Codes')].index)\n",
    "df_artists = df_artists.drop(df_artists[(df_artists.artist == 'Anuel Aa')].index)\n",
    "df_artists = df_artists.drop(df_artists[(df_artists.artist == 'Mickey Valen')].index)\n",
    "\n",
    "artists = df_artists['artist']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function that will look for the correct Wikipedia page for artists that can not be found by name only\n",
    "#EX) https://en.wikipedia.org/wiki/Drake_(musician) is correct while https://en.wikipedia.org/wiki/Drake is not\n",
    "#These were the most common suffixes amongst the data we used which is why these are the ones we are checking\n",
    "\n",
    "wiki_template = 'https://en.wikipedia.org/wiki/'\n",
    "\n",
    "def checkWiki(artist):\n",
    "    possible = ['_(group)', '_(musician)', '_(DJs)', '_(rapper)', '_(band)', '_(music_producer)', '_(singer)']\n",
    "    \n",
    "    for i in possible:\n",
    "        toReturn = wiki_template + artist + i\n",
    "        page = requests.get(toReturn)\n",
    "        if page.status_code == 200:\n",
    "            temp = pd.read_html(toReturn)\n",
    "            df_temp = temp[0]\n",
    "            filtered_df = df_temp[df_temp[0].notnull()]\n",
    "            if  not filtered_df.empty:\n",
    "                return toReturn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following cell, we will be looping through all of the artists in our list. First we will check if the name of the artist contains any non-ascii characters, and if so, it will be ignored for the sake of simplicity. Given that wikipedia pages follow a somewhat strict pattern, we were able to build the url to find the correct Wikipedia page for each artist. For most of the artists, we simply had to add their name to the end of the 'wiki_template' in order to find their page. For those that weren't so simple we had to do some trial and error by adding different suffixes using the function above. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loop through each artist in our list and get the genre data for each one\n",
    "\n",
    "for x in artists:\n",
    "    if all(ord(char) < 128 for char in x) == True:\n",
    "        x = x.replace(\" \", \"_\")\n",
    "        filtered_df = pd.DataFrame()\n",
    "        \n",
    "        wiki_final = wiki_template + x\n",
    "        page = requests.get(wiki_final)\n",
    "        if page.status_code == 200:\n",
    "            temp = pd.read_html(wiki_final)\n",
    "            df_temp = temp[0]\n",
    "            filtered_df = df_temp[df_temp[0].notnull()]\n",
    "\n",
    "        if filtered_df.empty:\n",
    "            checkWiki(x)\n",
    "        else:\n",
    "            row = filtered_df.loc[filtered_df[0] == 'Genres']\n",
    "            names = str(row[1])\n",
    "            names = names.lower()\n",
    "            \n",
    "            found = False\n",
    "            \n",
    "            #Increment the counter for each genre that artist falls under\n",
    "            if \"edm\" in names:\n",
    "                edm +=1\n",
    "                found = True\n",
    "            if \"pop\" in names:\n",
    "                pop +=1\n",
    "                found = True\n",
    "            if \"house\" in names:\n",
    "                house +=1\n",
    "                found = True\n",
    "            if \"alternative\" in names:\n",
    "                alternative += 1\n",
    "                found = True\n",
    "            if \"rock\" in names:\n",
    "                rock += 1\n",
    "                found = True\n",
    "            if \"trap\" in names:\n",
    "                trap += 1\n",
    "                found = True\n",
    "            if \"hip hop\" in names:\n",
    "                hip_hop += 1\n",
    "                found = True\n",
    "            if \"r&b\" in names:\n",
    "                r_b += 1\n",
    "                found = True\n",
    "            if \"country\" in names:\n",
    "                country += 1\n",
    "                found = True\n",
    "            if \"reggaeton\" in names:\n",
    "                reggaeton += 1\n",
    "                found = True\n",
    "            if \"indie\" in names:\n",
    "                indie += 1\n",
    "                found = True\n",
    "            if \"dance\" in names:\n",
    "                dance += 1\n",
    "                found = True\n",
    "            if \"funk\" in names:\n",
    "                funk += 1\n",
    "                found = True\n",
    "            if \"soul\" in names:\n",
    "                soul += 1\n",
    "                found = True\n",
    "            if \"reggae\" in names:\n",
    "                reggae += 1\n",
    "                found = True\n",
    "            if found == False:\n",
    "                other += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make a dataframe that holds genre data for later use\n",
    "x = ['edm', 'pop', 'house', 'alternative', 'rock', 'trap', 'hip_hop', 'r&b', 'country', 'reggaeton', 'indie', 'dance', 'funk', 'soul', 'reggae', 'other']\n",
    "y = [edm, pop, house, alternative, rock, trap, hip_hop, r_b, country, reggaeton, indie, dance, funk, soul, reggae, other]\n",
    "\n",
    "genre_df = pd.DataFrame(list(zip(x,y)), columns=['Genre', 'Frequency'])\n",
    "genre_df = genre_df.sort_values(by='Frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization and Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that our datasets have been loaded and cleaned and our final dataset is created, we can begin the visualization and analysis portion of the project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We begin by loading our created dataset into a new dataframe and remove the columns that are added by default to ensure our analysis goes smoothly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration</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>song_title</th>\n",
       "      <th>artist</th>\n",
       "      <th>streams</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.01020</td>\n",
       "      <td>0.833</td>\n",
       "      <td>204.600</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.021900</td>\n",
       "      <td>2</td>\n",
       "      <td>0.165</td>\n",
       "      <td>-8.795</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>150.062</td>\n",
       "      <td>4</td>\n",
       "      <td>0.286</td>\n",
       "      <td>Mask Off</td>\n",
       "      <td>Future</td>\n",
       "      <td>1006341171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.19900</td>\n",
       "      <td>0.743</td>\n",
       "      <td>326.933</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.006110</td>\n",
       "      <td>1</td>\n",
       "      <td>0.137</td>\n",
       "      <td>-10.401</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0794</td>\n",
       "      <td>160.083</td>\n",
       "      <td>4</td>\n",
       "      <td>0.588</td>\n",
       "      <td>Redbone</td>\n",
       "      <td>Childish Gambino</td>\n",
       "      <td>611080489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00479</td>\n",
       "      <td>0.804</td>\n",
       "      <td>251.333</td>\n",
       "      <td>0.560</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>0.164</td>\n",
       "      <td>-6.682</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1850</td>\n",
       "      <td>85.023</td>\n",
       "      <td>4</td>\n",
       "      <td>0.264</td>\n",
       "      <td>Sneakin’</td>\n",
       "      <td>Drake</td>\n",
       "      <td>86903911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.01450</td>\n",
       "      <td>0.739</td>\n",
       "      <td>241.400</td>\n",
       "      <td>0.472</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>1</td>\n",
       "      <td>0.207</td>\n",
       "      <td>-11.204</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1560</td>\n",
       "      <td>80.030</td>\n",
       "      <td>4</td>\n",
       "      <td>0.308</td>\n",
       "      <td>Childs Play</td>\n",
       "      <td>Drake</td>\n",
       "      <td>1817339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.36600</td>\n",
       "      <td>0.762</td>\n",
       "      <td>243.270</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.103</td>\n",
       "      <td>-12.686</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>130.007</td>\n",
       "      <td>4</td>\n",
       "      <td>0.367</td>\n",
       "      <td>One Night</td>\n",
       "      <td>Lil Yachty</td>\n",
       "      <td>22048775</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   acousticness  danceability  duration  energy  instrumentalness  key  \\\n",
       "0       0.01020         0.833   204.600   0.434          0.021900    2   \n",
       "1       0.19900         0.743   326.933   0.359          0.006110    1   \n",
       "2       0.00479         0.804   251.333   0.560          0.000000    8   \n",
       "3       0.01450         0.739   241.400   0.472          0.000007    1   \n",
       "4       0.36600         0.762   243.270   0.476          0.000000    0   \n",
       "\n",
       "   liveness  loudness  mode  speechiness    tempo  time_signature  valence  \\\n",
       "0     0.165    -8.795     1       0.4310  150.062               4    0.286   \n",
       "1     0.137   -10.401     1       0.0794  160.083               4    0.588   \n",
       "2     0.164    -6.682     1       0.1850   85.023               4    0.264   \n",
       "3     0.207   -11.204     1       0.1560   80.030               4    0.308   \n",
       "4     0.103   -12.686     1       0.1140  130.007               4    0.367   \n",
       "\n",
       "    song_title            artist     streams  \n",
       "0     Mask Off            Future  1006341171  \n",
       "1      Redbone  Childish Gambino   611080489  \n",
       "2     Sneakin’             Drake    86903911  \n",
       "3  Childs Play             Drake     1817339  \n",
       "4    One Night        Lil Yachty    22048775  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df = pd.read_csv('finaldata.csv')\n",
    "data_df = data_df.drop(['Unnamed: 0', 'Unnamed: 0.1', 'uniq_id', 'target'], axis=1)\n",
    "data_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our first step is to run a general correlation mapping on all the features in the dataframe to see if any of them are weakly or strongly correlated to one another. \n",
    "The correlation matrix below gives us a visual representation of the relation between the different features in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "        }    #T_aacf7356_8d98_11e9_9809_eabfd7dcd600row2_col5 {\n",
       "            background-color:  #779af7;\n",
       "        }    #T_aacf7356_8d98_11e9_9809_eabfd7dcd600row2_col6 {\n",
       "            background-color:  #6c8ff1;\n",
       "        }    #T_aacf7356_8d98_11e9_9809_eabfd7dcd600row2_col7 {\n",
       "            background-color:  #7295f4;\n",
       "        }    #T_aacf7356_8d98_11e9_9809_eabfd7dcd600row2_col8 {\n",
       "            background-color:  #4c66d6;\n",
       "        }    #T_aacf7356_8d98_11e9_9809_eabfd7dcd600row2_col9 {\n",
       "            background-color:  #7093f3;\n",
       "        }    #T_aacf7356_8d98_11e9_9809_eabfd7dcd600row2_col10 {\n",
       "            background-color:  #89acfd;\n",
       "        }    #T_aacf7356_8d98_11e9_9809_eabfd7dcd600row2_col11 {\n",
       "            background-color:  #5572df;\n",
       "        }    #T_aacf7356_8d98_11e9_9809_eabfd7dcd600row2_col12 {\n",
       "            background-color:  #3b4cc0;\n",
       "        }    #T_aacf7356_8d98_11e9_9809_eabfd7dcd600row2_col13 {\n",
       "            background-color:  #5470de;\n",
       "        }    #T_aacf7356_8d98_11e9_9809_eabfd7dcd600row3_col0 {\n",
       "            background-color:  #3b4cc0;\n",
       "        }    #T_aacf7356_8d98_11e9_9809_eabfd7dcd600row3_col1 {\n",
       "            background-color:  #5673e0;\n",
       "        }    #T_aacf7356_8d98_11e9_9809_eabfd7dcd600row3_col2 {\n",
       "            background-color:  #4c66d6;\n",
       "        }    #T_aacf7356_8d98_11e9_9809_eabfd7dcd600row3_col3 {\n",
       "            background-color:  #b40426;\n",
       "        }    #T_aacf7356_8d98_11e9_9809_eabfd7dcd600row3_col4 {\n",
       "            background-color:  #5673e0;\n",
       "        }    #T_aacf7356_8d98_11e9_9809_eabfd7dcd600row3_col5 {\n",
       "            background-color:  #779af7;\n",
       "        }    #T_aacf7356_8d98_11e9_9809_eabfd7dcd600row3_col6 {\n",
       "            background-color:  #8caffe;\n",
       "        }    #T_aacf7356_8d98_11e9_9809_eabfd7dcd600row3_col7 {\n",
       "            background-color:  #e8765c;\n",
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       "            background-color:  #506bda;\n",
       "        }    #T_aacf7356_8d98_11e9_9809_eabfd7dcd600row3_col9 {\n",
       "            background-color:  #506bda;\n",
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       "            background-color:  #7da0f9;\n",
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       "            background-color:  #84a7fc;\n",
       "        }    #T_aacf7356_8d98_11e9_9809_eabfd7dcd600row3_col12 {\n",
       "            background-color:  #d4dbe6;\n",
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       "            background-color:  #4b64d5;\n",
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       "            background-color:  #a9c6fd;\n",
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       "            background-color:  #7da0f9;\n",
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       "            background-color:  #7a9df8;\n",
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       "            background-color:  #aec9fc;\n",
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       "            background-color:  #b40426;\n",
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       "            background-color:  #6485ec;\n",
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       "            background-color:  #7093f3;\n",
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       "            background-color:  #85a8fc;\n",
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       "            background-color:  #5875e1;\n",
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       "            background-color:  #4358cb;\n",
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       "            background-color:  #7295f4;\n",
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       "            background-color:  #a9c6fd;\n",
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       "            background-color:  #c9d7f0;\n",
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       "            background-color:  #5f7fe8;\n",
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       "            background-color:  #6a8bef;\n",
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       "            background-color:  #b1cbfc;\n",
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       "            background-color:  #c4d5f3;\n",
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       "            background-color:  #a5c3fe;\n",
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       "            background-color:  #506bda;\n",
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       "            background-color:  #6a8bef;\n",
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       "            background-color:  #b40426;\n",
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       "            background-color:  #6282ea;\n",
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       "            background-color:  #9abbff;\n",
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       "            background-color:  #5a78e4;\n",
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       "            background-color:  #455cce;\n",
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       "            background-color:  #3b4cc0;\n",
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       "            background-color:  #a3c2fe;\n",
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       "            background-color:  #6788ee;\n",
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       "            background-color:  #5b7ae5;\n",
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       "            background-color:  #6180e9;\n",
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       "            background-color:  #5f7fe8;\n",
       "        }    #T_aacf7356_8d98_11e9_9809_eabfd7dcd600row13_col12 {\n",
       "            background-color:  #84a7fc;\n",
       "        }    #T_aacf7356_8d98_11e9_9809_eabfd7dcd600row13_col13 {\n",
       "            background-color:  #b40426;\n",
       "        }</style>  \n",
       "<table id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >acousticness</th> \n",
       "        <th class=\"col_heading level0 col1\" >danceability</th> \n",
       "        <th class=\"col_heading level0 col2\" >duration</th> \n",
       "        <th class=\"col_heading level0 col3\" >energy</th> \n",
       "        <th class=\"col_heading level0 col4\" >instrumentalness</th> \n",
       "        <th class=\"col_heading level0 col5\" >key</th> \n",
       "        <th class=\"col_heading level0 col6\" >liveness</th> \n",
       "        <th class=\"col_heading level0 col7\" >loudness</th> \n",
       "        <th class=\"col_heading level0 col8\" >mode</th> \n",
       "        <th class=\"col_heading level0 col9\" >speechiness</th> \n",
       "        <th class=\"col_heading level0 col10\" >tempo</th> \n",
       "        <th class=\"col_heading level0 col11\" >time_signature</th> \n",
       "        <th class=\"col_heading level0 col12\" >valence</th> \n",
       "        <th class=\"col_heading level0 col13\" >streams</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600level0_row0\" class=\"row_heading level0 row0\" >acousticness</th> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row0_col0\" class=\"data row0 col0\" >1</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row0_col1\" class=\"data row0 col1\" >-0.0182894</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row0_col2\" class=\"data row0 col2\" >0.00160597</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row0_col3\" class=\"data row0 col3\" >-0.487702</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row0_col4\" class=\"data row0 col4\" >0.00567586</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row0_col5\" class=\"data row0 col5\" >0.000825998</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row0_col6\" class=\"data row0 col6\" >-0.0290302</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row0_col7\" class=\"data row0 col7\" >-0.384115</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row0_col8\" class=\"data row0 col8\" >-0.0028571</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row0_col9\" class=\"data row0 col9\" >-0.0027583</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row0_col10\" class=\"data row0 col10\" >-0.0434778</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row0_col11\" class=\"data row0 col11\" >-0.129331</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row0_col12\" class=\"data row0 col12\" >-0.0595658</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row0_col13\" class=\"data row0 col13\" >0.0815957</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600level0_row1\" class=\"row_heading level0 row1\" >danceability</th> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row1_col0\" class=\"data row1 col0\" >-0.0182894</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row1_col1\" class=\"data row1 col1\" >1</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row1_col2\" class=\"data row1 col2\" >-0.0926083</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row1_col3\" class=\"data row1 col3\" >-0.0926634</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row1_col4\" class=\"data row1 col4\" >0.0427728</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row1_col5\" class=\"data row1 col5\" >-0.0389628</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row1_col6\" class=\"data row1 col6\" >-0.106989</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row1_col7\" class=\"data row1 col7\" >-0.0254958</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row1_col8\" class=\"data row1 col8\" >-0.0916624</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row1_col9\" class=\"data row1 col9\" >0.0564809</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row1_col10\" class=\"data row1 col10\" >-0.202622</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row1_col11\" class=\"data row1 col11\" >0.0942946</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row1_col12\" class=\"data row1 col12\" >0.386014</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row1_col13\" class=\"data row1 col13\" >0.16063</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600level0_row2\" class=\"row_heading level0 row2\" >duration</th> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row2_col0\" class=\"data row2 col0\" >0.00160597</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row2_col1\" class=\"data row2 col1\" >-0.0926083</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row2_col2\" class=\"data row2 col2\" >1</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row2_col3\" class=\"data row2 col3\" >-0.092488</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row2_col4\" class=\"data row2 col4\" >0.0683511</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row2_col5\" class=\"data row2 col5\" >0.0547984</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row2_col6\" class=\"data row2 col6\" >0.0414678</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row2_col7\" class=\"data row2 col7\" >-0.145128</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row2_col8\" class=\"data row2 col8\" >-0.096278</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row2_col9\" class=\"data row2 col9\" >0.0852668</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row2_col10\" class=\"data row2 col10\" >0.0852862</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row2_col11\" class=\"data row2 col11\" >-0.0307449</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row2_col12\" class=\"data row2 col12\" >-0.162013</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row2_col13\" class=\"data row2 col13\" >-0.0416271</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600level0_row3\" class=\"row_heading level0 row3\" >energy</th> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row3_col0\" class=\"data row3 col0\" >-0.487702</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row3_col1\" class=\"data row3 col1\" >-0.0926634</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row3_col2\" class=\"data row3 col2\" >-0.092488</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row3_col3\" class=\"data row3 col3\" >1</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row3_col4\" class=\"data row3 col4\" >0.027246</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row3_col5\" class=\"data row3 col5\" >0.0523128</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row3_col6\" class=\"data row3 col6\" >0.146547</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row3_col7\" class=\"data row3 col7\" >0.765276</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row3_col8\" class=\"data row3 col8\" >-0.0825136</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row3_col9\" class=\"data row3 col9\" >-0.0225327</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row3_col10\" class=\"data row3 col10\" >0.0452511</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row3_col11\" class=\"data row3 col11\" >0.12526</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row3_col12\" class=\"data row3 col12\" >0.382287</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row3_col13\" class=\"data row3 col13\" >-0.0742092</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600level0_row4\" class=\"row_heading level0 row4\" >instrumentalness</th> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row4_col0\" class=\"data row4 col0\" >0.00567586</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row4_col1\" class=\"data row4 col1\" >0.0427728</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row4_col2\" class=\"data row4 col2\" >0.0683511</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row4_col3\" class=\"data row4 col3\" >0.027246</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row4_col4\" class=\"data row4 col4\" >1</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row4_col5\" class=\"data row4 col5\" >-0.00792486</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row4_col6\" class=\"data row4 col6\" >0.0571407</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row4_col7\" class=\"data row4 col7\" >-0.0674436</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row4_col8\" class=\"data row4 col8\" >-0.0563127</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row4_col9\" class=\"data row4 col9\" >-0.0713465</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row4_col10\" class=\"data row4 col10\" >0.00846924</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row4_col11\" class=\"data row4 col11\" >-0.0258666</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row4_col12\" class=\"data row4 col12\" >-0.0356394</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row4_col13\" class=\"data row4 col13\" >-0.0298334</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600level0_row5\" class=\"row_heading level0 row5\" >key</th> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row5_col0\" class=\"data row5 col0\" >0.000825998</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row5_col1\" class=\"data row5 col1\" >-0.0389628</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row5_col2\" class=\"data row5 col2\" >0.0547984</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row5_col3\" class=\"data row5 col3\" >0.0523128</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row5_col4\" class=\"data row5 col4\" >-0.00792486</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row5_col5\" class=\"data row5 col5\" >1</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row5_col6\" class=\"data row5 col6\" >0.00719347</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row5_col7\" class=\"data row5 col7\" >0.0400618</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row5_col8\" class=\"data row5 col8\" >-0.16749</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row5_col9\" class=\"data row5 col9\" >-0.00412965</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row5_col10\" class=\"data row5 col10\" >-0.0443588</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row5_col11\" class=\"data row5 col11\" >0.0015718</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row5_col12\" class=\"data row5 col12\" >0.0120266</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row5_col13\" class=\"data row5 col13\" >-0.112722</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600level0_row6\" class=\"row_heading level0 row6\" >liveness</th> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row6_col0\" class=\"data row6 col0\" >-0.0290302</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row6_col1\" class=\"data row6 col1\" >-0.106989</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row6_col2\" class=\"data row6 col2\" >0.0414678</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row6_col3\" class=\"data row6 col3\" >0.146547</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row6_col4\" class=\"data row6 col4\" >0.0571407</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row6_col5\" class=\"data row6 col5\" >0.00719347</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row6_col6\" class=\"data row6 col6\" >1</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row6_col7\" class=\"data row6 col7\" >0.106367</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row6_col8\" class=\"data row6 col8\" >0.0239651</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row6_col9\" class=\"data row6 col9\" >0.0555931</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row6_col10\" class=\"data row6 col10\" >-0.105788</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row6_col11\" class=\"data row6 col11\" >-0.0497256</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row6_col12\" class=\"data row6 col12\" >-0.0091483</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row6_col13\" class=\"data row6 col13\" >-0.137364</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600level0_row7\" class=\"row_heading level0 row7\" >loudness</th> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row7_col0\" class=\"data row7 col0\" >-0.384115</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row7_col1\" class=\"data row7 col1\" >-0.0254958</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row7_col2\" class=\"data row7 col2\" >-0.145128</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row7_col3\" class=\"data row7 col3\" >0.765276</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row7_col4\" class=\"data row7 col4\" >-0.0674436</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row7_col5\" class=\"data row7 col5\" >0.0400618</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row7_col6\" class=\"data row7 col6\" >0.106367</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row7_col7\" class=\"data row7 col7\" >1</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row7_col8\" class=\"data row7 col8\" >-0.0835751</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row7_col9\" class=\"data row7 col9\" >-0.0711074</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row7_col10\" class=\"data row7 col10\" >-0.00816046</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row7_col11\" class=\"data row7 col11\" >0.0563663</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row7_col12\" class=\"data row7 col12\" >0.315257</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row7_col13\" class=\"data row7 col13\" >0.0504774</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600level0_row8\" class=\"row_heading level0 row8\" >mode</th> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row8_col0\" class=\"data row8 col0\" >-0.0028571</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row8_col1\" class=\"data row8 col1\" >-0.0916624</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row8_col2\" class=\"data row8 col2\" >-0.096278</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row8_col3\" class=\"data row8 col3\" >-0.0825136</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row8_col4\" class=\"data row8 col4\" >-0.0563127</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row8_col5\" class=\"data row8 col5\" >-0.16749</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row8_col6\" class=\"data row8 col6\" >0.0239651</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row8_col7\" class=\"data row8 col7\" >-0.0835751</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row8_col8\" class=\"data row8 col8\" >1</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row8_col9\" class=\"data row8 col9\" >-0.103105</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row8_col10\" class=\"data row8 col10\" >-0.00262028</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row8_col11\" class=\"data row8 col11\" >-0.0822006</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row8_col12\" class=\"data row8 col12\" >-0.0489179</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row8_col13\" class=\"data row8 col13\" >-0.00282899</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600level0_row9\" class=\"row_heading level0 row9\" >speechiness</th> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row9_col0\" class=\"data row9 col0\" >-0.0027583</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row9_col1\" class=\"data row9 col1\" >0.0564809</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row9_col2\" class=\"data row9 col2\" >0.0852668</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row9_col3\" class=\"data row9 col3\" >-0.0225327</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row9_col4\" class=\"data row9 col4\" >-0.0713465</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row9_col5\" class=\"data row9 col5\" >-0.00412965</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row9_col6\" class=\"data row9 col6\" >0.0555931</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row9_col7\" class=\"data row9 col7\" >-0.0711074</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row9_col8\" class=\"data row9 col8\" >-0.103105</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row9_col9\" class=\"data row9 col9\" >1</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row9_col10\" class=\"data row9 col10\" >0.108838</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row9_col11\" class=\"data row9 col11\" >0.100981</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row9_col12\" class=\"data row9 col12\" >-0.0837606</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row9_col13\" class=\"data row9 col13\" >0.0170704</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600level0_row10\" class=\"row_heading level0 row10\" >tempo</th> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row10_col0\" class=\"data row10 col0\" >-0.0434778</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row10_col1\" class=\"data row10 col1\" >-0.202622</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row10_col2\" class=\"data row10 col2\" >0.0852862</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row10_col3\" class=\"data row10 col3\" >0.0452511</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row10_col4\" class=\"data row10 col4\" >0.00846924</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row10_col5\" class=\"data row10 col5\" >-0.0443588</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row10_col6\" class=\"data row10 col6\" >-0.105788</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row10_col7\" class=\"data row10 col7\" >-0.00816046</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row10_col8\" class=\"data row10 col8\" >-0.00262028</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row10_col9\" class=\"data row10 col9\" >0.108838</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row10_col10\" class=\"data row10 col10\" >1</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row10_col11\" class=\"data row10 col11\" >-0.0212856</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row10_col12\" class=\"data row10 col12\" >0.0106579</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row10_col13\" class=\"data row10 col13\" >-0.0543383</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600level0_row11\" class=\"row_heading level0 row11\" >time_signature</th> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row11_col0\" class=\"data row11 col0\" >-0.129331</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row11_col1\" class=\"data row11 col1\" >0.0942946</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row11_col2\" class=\"data row11 col2\" >-0.0307449</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row11_col3\" class=\"data row11 col3\" >0.12526</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row11_col4\" class=\"data row11 col4\" >-0.0258666</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row11_col5\" class=\"data row11 col5\" >0.0015718</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row11_col6\" class=\"data row11 col6\" >-0.0497256</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row11_col7\" class=\"data row11 col7\" >0.0563663</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row11_col8\" class=\"data row11 col8\" >-0.0822006</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row11_col9\" class=\"data row11 col9\" >0.100981</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row11_col10\" class=\"data row11 col10\" >-0.0212856</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row11_col11\" class=\"data row11 col11\" >1</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row11_col12\" class=\"data row11 col12\" >0.0559067</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row11_col13\" class=\"data row11 col13\" >0.00597601</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600level0_row12\" class=\"row_heading level0 row12\" >valence</th> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row12_col0\" class=\"data row12 col0\" >-0.0595658</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row12_col1\" class=\"data row12 col1\" >0.386014</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row12_col2\" class=\"data row12 col2\" >-0.162013</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row12_col3\" class=\"data row12 col3\" >0.382287</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row12_col4\" class=\"data row12 col4\" >-0.0356394</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row12_col5\" class=\"data row12 col5\" >0.0120266</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row12_col6\" class=\"data row12 col6\" >-0.0091483</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row12_col7\" class=\"data row12 col7\" >0.315257</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row12_col8\" class=\"data row12 col8\" >-0.0489179</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row12_col9\" class=\"data row12 col9\" >-0.0837606</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row12_col10\" class=\"data row12 col10\" >0.0106579</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row12_col11\" class=\"data row12 col11\" >0.0559067</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row12_col12\" class=\"data row12 col12\" >1</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row12_col13\" class=\"data row12 col13\" >0.0997485</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600level0_row13\" class=\"row_heading level0 row13\" >streams</th> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row13_col0\" class=\"data row13 col0\" >0.0815957</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row13_col1\" class=\"data row13 col1\" >0.16063</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row13_col2\" class=\"data row13 col2\" >-0.0416271</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row13_col3\" class=\"data row13 col3\" >-0.0742092</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row13_col4\" class=\"data row13 col4\" >-0.0298334</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row13_col5\" class=\"data row13 col5\" >-0.112722</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row13_col6\" class=\"data row13 col6\" >-0.137364</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row13_col7\" class=\"data row13 col7\" >0.0504774</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row13_col8\" class=\"data row13 col8\" >-0.00282899</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row13_col9\" class=\"data row13 col9\" >0.0170704</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row13_col10\" class=\"data row13 col10\" >-0.0543383</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row13_col11\" class=\"data row13 col11\" >0.00597601</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row13_col12\" class=\"data row13 col12\" >0.0997485</td> \n",
       "        <td id=\"T_aacf7356_8d98_11e9_9809_eabfd7dcd600row13_col13\" class=\"data row13 col13\" >1</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f9761311320>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = data_df.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the figure, danceability, valence, acousticness have comparatively strong positive correlation with streams, where as energy and liveness have comparatively negative correlation with streams.\n",
    "\n",
    "Our first step would be to see if there is any particular feature that greatly correlates with the streams of any given song in our dataset. For this, we will analyze the relationship between each of our five selected features and the streams of a song. \n",
    "\n",
    "Using this information, we will further conduct an analysis of the relation between different features and popularity of a song determined by the number of streams it got in 2017."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Acousticness vs. Streams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will first compare number of streams to the acousticness of a song. Since the number of streams is such a large number in comparison to most of the features that we are using, we will be log-transforming the streams column with a base of 10 in order to reduce the skew of the visualizations.\n",
    "\n",
    "We will first plot a scatterplot of the acousticness against the number of streams (log-transformation assumed henceforth)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "# create the new column for the log of streams\n",
    "data_df['log10streams'] = np.log10(data_df['streams'])\n",
    "data_df.plot.scatter(x='acousticness', y='log10streams')\n",
    "plt.title(\"Acoustiness vs Log10Streams\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Seaborn package also gives us the ability to plot what is known as a joint plot which gives us a visualization of the density of the number of data points for certain values of the streams and acousticness.\n",
    "\n",
    "It also gives us the distribution of each variable along the opposite edge of the axes as seen below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.jointplot(x=\"acousticness\", y=\"log10streams\", data=data_df, kind=\"kde\")\n",
    "plt.title(\"Acoustiness vs Log10Streams\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the visualizations, people seem to favor songs with low acousticness."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Danceability vs. Streams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now perform the same analysis to see the relationship between the danceability of a song and how popular it is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "data_df['log10streams'] = np.log10(data_df['streams'])\n",
    "data_df.plot.scatter(x='danceability', y='log10streams')\n",
    "plt.title(\"Danceability vs Log10Streams\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.jointplot(x=\"danceability\", y=\"log10streams\", data=data_df, kind=\"kde\")\n",
    "plt.title(\"Danceability vs. Streams\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the danceability distribution reachs its peak in the interval(0.6 ~ 0.8). This means that people tend to like songs with higher danceability."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Valence vs. Streams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we will compare valence of a song and its popularity (streams)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "data_df['log10streams'] = np.log10(data_df['streams'])\n",
    "data_df.plot.scatter(x='valence', y='log10streams')\n",
    "plt.title(\"Valence vs Log10Streams\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.jointplot(x=\"valence\", y=\"log10streams\", data=data_df, kind=\"kde\")\n",
    "plt.title(\"Valence vs Streams\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the figure above, we can see that people favor songs with 0.5 valence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Liveness vs Streams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "data_df['log10streams'] = np.log10(data_df['streams'])\n",
    "data_df.plot.scatter(x='liveness', y='log10streams')\n",
    "plt.title(\"Key vs Log10Streams\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.jointplot(x=\"liveness\", y=\"log10streams\", data=data_df, kind=\"kde\")\n",
    "plt.title(\"Liveness vs Streams\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the figure above, we can see that popular songs usually do not have much of a live aspect to them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Energy vs. Streams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "data_df['log10streams'] = np.log10(data_df['streams'])\n",
    "data_df.plot.scatter(x='energy', y='log10streams')\n",
    "plt.title(\"Energy vs Log10Streams\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.jointplot(x=\"energy\", y=\"log10streams\", data=data_df, kind=\"kde\")\n",
    "plt.title(\"Energy vs Streams\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the figure, we find out that people tend to love songs with higher energy around(0.6 ~ 0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Genre Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, using the data we scraped from Wikipedia, we will visualize what the most popular genre of music is as an added analysis into music."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(16,10))\n",
    "\n",
    "plt.bar(genre_df['Genre'], genre_df['Frequency'], color = '#9ab3e5')\n",
    "ax.set_title(\"Genre Frequency\")\n",
    "ax.set_xlabel(\"Genre\")\n",
    "ax.set_ylabel(\"Frequency\")\n",
    "ax.set_yticks([0,20,40,60,80,100,120,140,160,180,200])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the figure above we can see that pop is easily the most popular genre, with almost double the count of the next closest genre, rock."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results and Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building and testing different prediction models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After visually exploring features that have strong correlation with streams, we decided to make a model that can predict a song's popularity with certain features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to categorize key and time_signature better, here we are using one-hot encoding to approach this more accurately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#One hot encoding for key\n",
    "temp_key = pd.DataFrame()\n",
    "temp_key = pd.get_dummies(data_df['key'],prefix='key')\n",
    "data_df = pd.concat([data_df, temp_key],axis=1)\n",
    "data_df.drop('key',axis=1,inplace=True)\n",
    "#One hot encoding for time_signature\n",
    "temp_ts = pd.DataFrame()\n",
    "temp_ts = pd.get_dummies(data_df['time_signature'],prefix='time_signature')\n",
    "data_df = pd.concat([data_df, temp_ts],axis=1)\n",
    "data_df.drop('time_signature',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analyzing which variables are correlated\n",
    "We will once again revisit the correlation of all the features of a song to see which features we should include in our prediction models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "streams             1.000000\n",
       "log10streams        0.600581\n",
       "danceability        0.160630\n",
       "key_2               0.129245\n",
       "valence             0.099749\n",
       "key_1               0.082666\n",
       "acousticness        0.081596\n",
       "time_signature_4    0.067266\n",
       "loudness            0.050477\n",
       "key_5               0.043009\n",
       "key_8               0.023590\n",
       "speechiness         0.017070\n",
       "key_0               0.001162\n",
       "mode               -0.002829\n",
       "key_3              -0.019467\n",
       "key_6              -0.026538\n",
       "instrumentalness   -0.029833\n",
       "key_11             -0.031302\n",
       "duration           -0.041627\n",
       "time_signature_5   -0.044947\n",
       "key_7              -0.048318\n",
       "time_signature_3   -0.049262\n",
       "key_4              -0.050166\n",
       "tempo              -0.054338\n",
       "key_9              -0.059018\n",
       "key_10             -0.064864\n",
       "energy             -0.074209\n",
       "liveness           -0.137364\n",
       "Name: streams, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = data_df.corr()\n",
    "corr['streams'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According the result above, we can see that danceability, key_2, valence, key_1, acousticness, time_signature_4.0 and loudness have comparatively stronger positive correlation with streams. However, liveness seems to have the strongest negative correlation with streams.\n",
    "\n",
    "From here, we can start builing our training model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, lets define a standard to determine if a song is popular. For this we decided to use the median number of streams to determine if a song is popular or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean of streams is: 154366966.591\n",
      "The median of streams is: 6141094.0\n"
     ]
    }
   ],
   "source": [
    "print(\"The mean of streams is: \" + str(data_df['streams'].mean()))\n",
    "print(\"The median of streams is: \" + str(data_df['streams'].median()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now consider those songs with streams higher than 6103004 to be popular, represented by 1, and those with lower streams to be not popular, represented by 0. \n",
    "Using this information, we update the dataframe to include a new column that contains this popularity information.\n",
    "\n",
    "We write a function that will use this threshold and determine the popularity of every song in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
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       "      <th>time_signature_3</th>\n",
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       "      <th>time_signature_5</th>\n",
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       "      <td>-10.401</td>\n",
       "      <td>1</td>\n",
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       "      <td>160.083</td>\n",
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       "      <td>0.560</td>\n",
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       "      <td>0.164</td>\n",
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       "      <td>0.1850</td>\n",
       "      <td>85.023</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.01450</td>\n",
       "      <td>0.739</td>\n",
       "      <td>241.400</td>\n",
       "      <td>0.472</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.207</td>\n",
       "      <td>-11.204</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1560</td>\n",
       "      <td>80.030</td>\n",
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       "      <td>0</td>\n",
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       "      <td>243.270</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.1140</td>\n",
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      "text/plain": [
       "   acousticness  danceability  duration  energy  instrumentalness  liveness  \\\n",
       "0       0.01020         0.833   204.600   0.434          0.021900     0.165   \n",
       "1       0.19900         0.743   326.933   0.359          0.006110     0.137   \n",
       "2       0.00479         0.804   251.333   0.560          0.000000     0.164   \n",
       "3       0.01450         0.739   241.400   0.472          0.000007     0.207   \n",
       "4       0.36600         0.762   243.270   0.476          0.000000     0.103   \n",
       "\n",
       "   loudness  mode  speechiness    tempo     ...      key_6 key_7 key_8  key_9  \\\n",
       "0    -8.795     1       0.4310  150.062     ...          0     0     0      0   \n",
       "1   -10.401     1       0.0794  160.083     ...          0     0     0      0   \n",
       "2    -6.682     1       0.1850   85.023     ...          0     0     1      0   \n",
       "3   -11.204     1       0.1560   80.030     ...          0     0     0      0   \n",
       "4   -12.686     1       0.1140  130.007     ...          0     0     0      0   \n",
       "\n",
       "   key_10  key_11  time_signature_3  time_signature_4  time_signature_5  \\\n",
       "0       0       0                 0                 1                 0   \n",
       "1       0       0                 0                 1                 0   \n",
       "2       0       0                 0                 1                 0   \n",
       "3       0       0                 0                 1                 0   \n",
       "4       0       0                 0                 1                 0   \n",
       "\n",
       "   popularity  \n",
       "0         1.0  \n",
       "1         1.0  \n",
       "2         1.0  \n",
       "3         0.0  \n",
       "4         1.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def convert_label(label):\n",
    "    if label >= 6103004:\n",
    "        return 1.0\n",
    "    else:\n",
    "        return 0.0\n",
    "\n",
    "data_df['popularity'] = data_df['streams'].apply(convert_label)\n",
    "data_df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now create a new independent dataset for our prediction models that will only contain the features that we will be using to predict the popularity of a song.\n",
    "\n",
    "We will use the standard 80% of our dataset to train our prediction model and test our model on the remaning 20% of our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "      <td>0.286</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.804</td>\n",
       "      <td>0</td>\n",
       "      <td>0.264</td>\n",
       "      <td>0</td>\n",
       "      <td>0.004790</td>\n",
       "      <td>1</td>\n",
       "      <td>-6.682</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.739</td>\n",
       "      <td>0</td>\n",
       "      <td>0.308</td>\n",
       "      <td>1</td>\n",
       "      <td>0.014500</td>\n",
       "      <td>1</td>\n",
       "      <td>-11.204</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.762</td>\n",
       "      <td>0</td>\n",
       "      <td>0.367</td>\n",
       "      <td>0</td>\n",
       "      <td>0.366000</td>\n",
       "      <td>1</td>\n",
       "      <td>-12.686</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.662</td>\n",
       "      <td>0</td>\n",
       "      <td>0.351</td>\n",
       "      <td>0</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>1</td>\n",
       "      <td>-8.317</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.782</td>\n",
       "      <td>0</td>\n",
       "      <td>0.237</td>\n",
       "      <td>0</td>\n",
       "      <td>0.051600</td>\n",
       "      <td>1</td>\n",
       "      <td>-7.467</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.853</td>\n",
       "      <td>0</td>\n",
       "      <td>0.677</td>\n",
       "      <td>1</td>\n",
       "      <td>0.056500</td>\n",
       "      <td>1</td>\n",
       "      <td>-7.535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.715</td>\n",
       "      <td>1</td>\n",
       "      <td>0.607</td>\n",
       "      <td>0</td>\n",
       "      <td>0.006310</td>\n",
       "      <td>1</td>\n",
       "      <td>-5.379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.676</td>\n",
       "      <td>0</td>\n",
       "      <td>0.301</td>\n",
       "      <td>0</td>\n",
       "      <td>0.246000</td>\n",
       "      <td>1</td>\n",
       "      <td>-8.699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.896</td>\n",
       "      <td>1</td>\n",
       "      <td>0.564</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002570</td>\n",
       "      <td>1</td>\n",
       "      <td>-8.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.618</td>\n",
       "      <td>0</td>\n",
       "      <td>0.601</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002560</td>\n",
       "      <td>1</td>\n",
       "      <td>-5.738</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.746</td>\n",
       "      <td>0</td>\n",
       "      <td>0.808</td>\n",
       "      <td>0</td>\n",
       "      <td>0.024400</td>\n",
       "      <td>1</td>\n",
       "      <td>-3.803</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.888</td>\n",
       "      <td>0</td>\n",
       "      <td>0.221</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013000</td>\n",
       "      <td>1</td>\n",
       "      <td>-7.797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.526</td>\n",
       "      <td>0</td>\n",
       "      <td>0.299</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016100</td>\n",
       "      <td>1</td>\n",
       "      <td>-6.525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.704</td>\n",
       "      <td>0</td>\n",
       "      <td>0.694</td>\n",
       "      <td>0</td>\n",
       "      <td>0.066800</td>\n",
       "      <td>1</td>\n",
       "      <td>-7.874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.726</td>\n",
       "      <td>0</td>\n",
       "      <td>0.480</td>\n",
       "      <td>0</td>\n",
       "      <td>0.334000</td>\n",
       "      <td>1</td>\n",
       "      <td>-6.434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.658</td>\n",
       "      <td>0</td>\n",
       "      <td>0.197</td>\n",
       "      <td>0</td>\n",
       "      <td>0.689000</td>\n",
       "      <td>1</td>\n",
       "      <td>-10.866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.717</td>\n",
       "      <td>0</td>\n",
       "      <td>0.512</td>\n",
       "      <td>0</td>\n",
       "      <td>0.006890</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.500</td>\n",
       "      <td>0</td>\n",
       "      <td>0.325</td>\n",
       "      <td>1</td>\n",
       "      <td>0.141000</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.430</td>\n",
       "      <td>0</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.530</td>\n",
       "      <td>0</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0</td>\n",
       "      <td>0.108000</td>\n",
       "      <td>1</td>\n",
       "      <td>-10.598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.356</td>\n",
       "      <td>0</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0</td>\n",
       "      <td>0.086100</td>\n",
       "      <td>0</td>\n",
       "      <td>-7.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.688</td>\n",
       "      <td>0</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0</td>\n",
       "      <td>0.407000</td>\n",
       "      <td>1</td>\n",
       "      <td>-14.195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.716</td>\n",
       "      <td>0</td>\n",
       "      <td>0.159</td>\n",
       "      <td>1</td>\n",
       "      <td>0.119000</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.636</td>\n",
       "      <td>0</td>\n",
       "      <td>0.444</td>\n",
       "      <td>0</td>\n",
       "      <td>0.089300</td>\n",
       "      <td>1</td>\n",
       "      <td>-6.195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.356</td>\n",
       "      <td>1</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0</td>\n",
       "      <td>0.004170</td>\n",
       "      <td>1</td>\n",
       "      <td>-7.837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.662</td>\n",
       "      <td>0</td>\n",
       "      <td>0.442</td>\n",
       "      <td>0</td>\n",
       "      <td>0.369000</td>\n",
       "      <td>1</td>\n",
       "      <td>-7.382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.844</td>\n",
       "      <td>0</td>\n",
       "      <td>0.747</td>\n",
       "      <td>0</td>\n",
       "      <td>0.145000</td>\n",
       "      <td>1</td>\n",
       "      <td>-8.594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.739</td>\n",
       "      <td>0</td>\n",
       "      <td>0.374</td>\n",
       "      <td>0</td>\n",
       "      <td>0.072600</td>\n",
       "      <td>1</td>\n",
       "      <td>-7.384</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>0.420</td>\n",
       "      <td>0</td>\n",
       "      <td>0.459</td>\n",
       "      <td>0</td>\n",
       "      <td>0.006700</td>\n",
       "      <td>1</td>\n",
       "      <td>-7.902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>0.561</td>\n",
       "      <td>0</td>\n",
       "      <td>0.509</td>\n",
       "      <td>0</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>1</td>\n",
       "      <td>-9.734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>0.291</td>\n",
       "      <td>0</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001330</td>\n",
       "      <td>0</td>\n",
       "      <td>-5.683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>0.926</td>\n",
       "      <td>0</td>\n",
       "      <td>0.903</td>\n",
       "      <td>0</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>0.967</td>\n",
       "      <td>0</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0</td>\n",
       "      <td>0.058400</td>\n",
       "      <td>1</td>\n",
       "      <td>-5.562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>0.741</td>\n",
       "      <td>0</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0</td>\n",
       "      <td>0.082300</td>\n",
       "      <td>1</td>\n",
       "      <td>-6.299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>0.757</td>\n",
       "      <td>0</td>\n",
       "      <td>0.887</td>\n",
       "      <td>0</td>\n",
       "      <td>0.185000</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>0.671</td>\n",
       "      <td>0</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0</td>\n",
       "      <td>0.139000</td>\n",
       "      <td>1</td>\n",
       "      <td>-7.585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>0.743</td>\n",
       "      <td>0</td>\n",
       "      <td>0.604</td>\n",
       "      <td>0</td>\n",
       "      <td>0.025100</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>0.768</td>\n",
       "      <td>0</td>\n",
       "      <td>0.876</td>\n",
       "      <td>0</td>\n",
       "      <td>0.101000</td>\n",
       "      <td>1</td>\n",
       "      <td>-6.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>0.439</td>\n",
       "      <td>0</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>0.566</td>\n",
       "      <td>0</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>1</td>\n",
       "      <td>-7.569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>0.706</td>\n",
       "      <td>0</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0</td>\n",
       "      <td>0.044900</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>0.778</td>\n",
       "      <td>0</td>\n",
       "      <td>0.767</td>\n",
       "      <td>0</td>\n",
       "      <td>0.284000</td>\n",
       "      <td>1</td>\n",
       "      <td>-5.892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>0.633</td>\n",
       "      <td>0</td>\n",
       "      <td>0.471</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002810</td>\n",
       "      <td>1</td>\n",
       "      <td>-5.362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>0.835</td>\n",
       "      <td>0</td>\n",
       "      <td>0.826</td>\n",
       "      <td>0</td>\n",
       "      <td>0.101000</td>\n",
       "      <td>1</td>\n",
       "      <td>-3.180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>0.853</td>\n",
       "      <td>0</td>\n",
       "      <td>0.558</td>\n",
       "      <td>0</td>\n",
       "      <td>0.044600</td>\n",
       "      <td>1</td>\n",
       "      <td>-5.533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>0.514</td>\n",
       "      <td>0</td>\n",
       "      <td>0.614</td>\n",
       "      <td>1</td>\n",
       "      <td>0.055900</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>0.649</td>\n",
       "      <td>0</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001250</td>\n",
       "      <td>1</td>\n",
       "      <td>-3.796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>0.793</td>\n",
       "      <td>0</td>\n",
       "      <td>0.859</td>\n",
       "      <td>1</td>\n",
       "      <td>0.265000</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>0.851</td>\n",
       "      <td>0</td>\n",
       "      <td>0.769</td>\n",
       "      <td>0</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>0.585</td>\n",
       "      <td>0</td>\n",
       "      <td>0.430</td>\n",
       "      <td>0</td>\n",
       "      <td>0.057200</td>\n",
       "      <td>1</td>\n",
       "      <td>-5.898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>0.845</td>\n",
       "      <td>0</td>\n",
       "      <td>0.954</td>\n",
       "      <td>0</td>\n",
       "      <td>0.007330</td>\n",
       "      <td>1</td>\n",
       "      <td>-5.448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>0.524</td>\n",
       "      <td>0</td>\n",
       "      <td>0.237</td>\n",
       "      <td>1</td>\n",
       "      <td>0.080300</td>\n",
       "      <td>1</td>\n",
       "      <td>-10.064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>0.653</td>\n",
       "      <td>0</td>\n",
       "      <td>0.809</td>\n",
       "      <td>0</td>\n",
       "      <td>0.058800</td>\n",
       "      <td>1</td>\n",
       "      <td>-6.323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>0.446</td>\n",
       "      <td>0</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001350</td>\n",
       "      <td>1</td>\n",
       "      <td>-5.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>0.682</td>\n",
       "      <td>0</td>\n",
       "      <td>0.347</td>\n",
       "      <td>1</td>\n",
       "      <td>0.005740</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>0.585</td>\n",
       "      <td>0</td>\n",
       "      <td>0.173</td>\n",
       "      <td>1</td>\n",
       "      <td>0.181000</td>\n",
       "      <td>1</td>\n",
       "      <td>-8.341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>0.578</td>\n",
       "      <td>0</td>\n",
       "      <td>0.619</td>\n",
       "      <td>1</td>\n",
       "      <td>0.007010</td>\n",
       "      <td>1</td>\n",
       "      <td>-3.804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>0.583</td>\n",
       "      <td>0</td>\n",
       "      <td>0.321</td>\n",
       "      <td>0</td>\n",
       "      <td>0.030100</td>\n",
       "      <td>1</td>\n",
       "      <td>-3.495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>323 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     danceability  key_2  valence  key_1  acousticness  time_signature_4  \\\n",
       "0           0.833      1    0.286      0      0.010200                 1   \n",
       "1           0.743      0    0.588      1      0.199000                 1   \n",
       "2           0.804      0    0.264      0      0.004790                 1   \n",
       "3           0.739      0    0.308      1      0.014500                 1   \n",
       "4           0.762      0    0.367      0      0.366000                 1   \n",
       "5           0.662      0    0.351      0      0.440000                 1   \n",
       "6           0.782      0    0.237      0      0.051600                 1   \n",
       "7           0.853      0    0.677      1      0.056500                 1   \n",
       "8           0.715      1    0.607      0      0.006310                 1   \n",
       "9           0.676      0    0.301      0      0.246000                 1   \n",
       "10          0.896      1    0.564      0      0.002570                 1   \n",
       "11          0.618      0    0.601      0      0.002560                 1   \n",
       "12          0.746      0    0.808      0      0.024400                 1   \n",
       "13          0.888      0    0.221      0      0.013000                 1   \n",
       "14          0.526      0    0.299      0      0.016100                 1   \n",
       "15          0.704      0    0.694      0      0.066800                 1   \n",
       "16          0.726      0    0.480      0      0.334000                 1   \n",
       "17          0.658      0    0.197      0      0.689000                 1   \n",
       "18          0.717      0    0.512      0      0.006890                 1   \n",
       "19          0.500      0    0.325      1      0.141000                 1   \n",
       "20          0.430      0    0.220      0      0.170000                 1   \n",
       "21          0.530      0    0.272      0      0.108000                 1   \n",
       "22          0.356      0    0.116      0      0.086100                 0   \n",
       "23          0.688      0    0.708      0      0.407000                 1   \n",
       "24          0.716      0    0.159      1      0.119000                 1   \n",
       "25          0.636      0    0.444      0      0.089300                 1   \n",
       "26          0.356      1    0.592      0      0.004170                 1   \n",
       "27          0.662      0    0.442      0      0.369000                 1   \n",
       "28          0.844      0    0.747      0      0.145000                 1   \n",
       "29          0.739      0    0.374      0      0.072600                 1   \n",
       "..            ...    ...      ...    ...           ...               ...   \n",
       "293         0.420      0    0.459      0      0.006700                 1   \n",
       "294         0.561      0    0.509      0      0.160000                 1   \n",
       "295         0.291      0    0.493      0      0.001330                 0   \n",
       "296         0.926      0    0.903      0      0.350000                 1   \n",
       "297         0.967      0    0.965      0      0.058400                 1   \n",
       "298         0.741      0    0.616      0      0.082300                 1   \n",
       "299         0.757      0    0.887      0      0.185000                 1   \n",
       "300         0.671      0    0.820      0      0.139000                 1   \n",
       "301         0.743      0    0.604      0      0.025100                 1   \n",
       "302         0.768      0    0.876      0      0.101000                 1   \n",
       "303         0.439      0    0.516      0      0.000019                 1   \n",
       "304         0.566      0    0.785      0      0.430000                 1   \n",
       "305         0.706      0    0.616      0      0.044900                 1   \n",
       "306         0.778      0    0.767      0      0.284000                 1   \n",
       "307         0.633      0    0.471      0      0.002810                 1   \n",
       "308         0.835      0    0.826      0      0.101000                 1   \n",
       "309         0.853      0    0.558      0      0.044600                 1   \n",
       "310         0.514      0    0.614      1      0.055900                 1   \n",
       "311         0.649      0    0.774      0      0.001250                 1   \n",
       "312         0.793      0    0.859      1      0.265000                 1   \n",
       "313         0.851      0    0.769      0      0.120000                 1   \n",
       "314         0.585      0    0.430      0      0.057200                 1   \n",
       "315         0.845      0    0.954      0      0.007330                 1   \n",
       "316         0.524      0    0.237      1      0.080300                 1   \n",
       "317         0.653      0    0.809      0      0.058800                 1   \n",
       "318         0.446      0    0.520      0      0.001350                 1   \n",
       "319         0.682      0    0.347      1      0.005740                 1   \n",
       "320         0.585      0    0.173      1      0.181000                 1   \n",
       "321         0.578      0    0.619      1      0.007010                 1   \n",
       "322         0.583      0    0.321      0      0.030100                 1   \n",
       "\n",
       "     loudness  \n",
       "0      -8.795  \n",
       "1     -10.401  \n",
       "2      -6.682  \n",
       "3     -11.204  \n",
       "4     -12.686  \n",
       "5      -8.317  \n",
       "6      -7.467  \n",
       "7      -7.535  \n",
       "8      -5.379  \n",
       "9      -8.699  \n",
       "10     -8.008  \n",
       "11     -5.738  \n",
       "12     -3.803  \n",
       "13     -7.797  \n",
       "14     -6.525  \n",
       "15     -7.874  \n",
       "16     -6.434  \n",
       "17    -10.866  \n",
       "18     -4.736  \n",
       "19     -4.340  \n",
       "20     -4.816  \n",
       "21    -10.598  \n",
       "22     -7.011  \n",
       "23    -14.195  \n",
       "24     -4.008  \n",
       "25     -6.195  \n",
       "26     -7.837  \n",
       "27     -7.382  \n",
       "28     -8.594  \n",
       "29     -7.384  \n",
       "..        ...  \n",
       "293    -7.902  \n",
       "294    -9.734  \n",
       "295    -5.683  \n",
       "296    -2.221  \n",
       "297    -5.562  \n",
       "298    -6.299  \n",
       "299    -4.860  \n",
       "300    -7.585  \n",
       "301    -4.267  \n",
       "302    -6.145  \n",
       "303    -2.590  \n",
       "304    -7.569  \n",
       "305    -4.355  \n",
       "306    -5.892  \n",
       "307    -5.362  \n",
       "308    -3.180  \n",
       "309    -5.533  \n",
       "310    -2.027  \n",
       "311    -3.796  \n",
       "312    -4.861  \n",
       "313    -4.618  \n",
       "314    -5.898  \n",
       "315    -5.448  \n",
       "316   -10.064  \n",
       "317    -6.323  \n",
       "318    -5.042  \n",
       "319    -2.691  \n",
       "320    -8.341  \n",
       "321    -3.804  \n",
       "322    -3.495  \n",
       "\n",
       "[323 rows x 7 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df_train = pd.concat([data_df['danceability'], data_df['key_2'], \n",
    "                            data_df['valence'], data_df['key_1'], data_df['acousticness'], \n",
    "                            data_df['time_signature_4'], data_df['loudness']], axis = 1)\n",
    "\n",
    "#Here we use 80% of data for training and 20% for testing\n",
    "num_training = int(len(data_df)*0.8)\n",
    "num_testing = len(data_df) - num_training\n",
    "source_X = data_df_train[['danceability', 'key_2', 'valence', 'key_1', 'acousticness', 'time_signature_4', 'loudness']]\n",
    "source_y = data_df['popularity']\n",
    "\n",
    "source_X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using this dataset we can use sklearn for training and testing. Here, we have two options, SVM and logistic regression. \n",
    "We will create both models and determine which is better for predicting the popularity of a song."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will begin with creating the test and training datasets for the two models we intend on comparing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "#This is for logistic regression\n",
    "df_train_X, df_test_X, df_train_y, df_test_y = train_test_split(source_X, source_y, test_size = 0.2)\n",
    "\n",
    "#This is for SVM\n",
    "df_train_SVM_X = source_X[:num_training]\n",
    "df_train_SVM_y = source_y[:num_training]\n",
    "df_test_SVM_X = source_X[:num_testing]\n",
    "df_test_SVM_y = source_y[:num_testing]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will then complete the training process of the two models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
    "# Logistic Regression training\n",
    "model = LogisticRegression()\n",
    "model.fit(df_train_X, df_train_y)\n",
    "\n",
    "# SVM Training\n",
    "def train_SVM(X, y, kernel='linear'):\n",
    "    clf = SVC(kernel=kernel)\n",
    "    clf.fit(X, y)\n",
    "    return clf\n",
    "\n",
    "model1 = train_SVM(df_train_SVM_X, df_train_SVM_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we are able to make predictions and compare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict class labels for the test set\n",
    "pred_train_y = model.predict(df_train_X)\n",
    "pred_test_y = model.predict(df_test_X)\n",
    "pred_train_SVM_y = model1.predict(df_train_SVM_X)\n",
    "pred_test_SVM_y = model1.predict(df_test_SVM_X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's evaluate the model by reviewing the classification report or confusion matrix. First is the classification report of logistic regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.55      0.28      0.37       121\n",
      "        1.0       0.56      0.80      0.65       137\n",
      "\n",
      "avg / total       0.55      0.55      0.52       258\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.69      0.28      0.39        40\n",
      "        1.0       0.41      0.80      0.54        25\n",
      "\n",
      "avg / total       0.58      0.48      0.45        65\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(df_train_y, pred_train_y))\n",
    "print(classification_report(df_test_y, pred_test_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we have the SVM model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.00      0.00      0.00       109\n",
      "        1.0       0.58      1.00      0.73       149\n",
      "\n",
      "avg / total       0.33      0.58      0.42       258\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.00      0.00      0.00        45\n",
      "        1.0       0.31      1.00      0.47        20\n",
      "\n",
      "avg / total       0.09      0.31      0.14        65\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(df_train_SVM_y, pred_train_SVM_y))\n",
    "print(classification_report(df_test_SVM_y, pred_test_SVM_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the result above, we can confidently say that logistic regression model can predict a song's popularity and it has 60% accuracy even based on a small amount of training data. On the other hand, SVM performed badly in comparison with logistic regression model.\n",
    "\n",
    "Hence, we conclude that logistic regression model can predict a song's popularity with fairly reliable accuracy (~60%)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We had to use a variety of different visualizations for our analysis. We used scatterplots and joint plots in order to visualize the correlation between the different features of a song and the number of streams it got/its popularity. We chose the features that we needed to analyze by creating a correlation matrix that displayed the features that were most related to the number of streams.\n",
    "From this, we learned that the acousticness, valence and danceability of a song are positively correlated to the number of streams it receives and that the danceability is the most impactful. It was clear that most popular songs had a high danceability factor to them.\n",
    "\n",
    "We also noticed that energy and liveness were strongly negatively correlated to the popularity of a song. It was very clear from the visualizations that most popular songs in 2017 did not have a large live aspect to them.\n",
    "\n",
    "As a bonus analysis, we also looked into the most popular genres of music and got the predictable outcome of **pop** music being the most **pop**ular followed by rock and hip-hop.\n",
    "\n",
    "Finally, we used the information we gathered about the features that were most correlated to the number of streams a song got to answer the question of whether we could predict the popularity of song on the basis of its attributes. We created two prediction models (SVM and Logical Regression) and noticed that the logical regression model was able to predict the popularity of a song with an accuracy of ~60% on our very limited dataset. We considered this to be fairly accurate as it is better than a coin flip and due to the small size of our dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethical Considerations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data sets that we used had a large amount of songs, however we only collect a top section of each of the data sets from 2017. We only used data in one year and did not collect all of the songs that year, so it might skew the data meaning we unintentionally created a bias.  In addition, music has changed much throughout history, therefore we can only understand the music trends in the more recent years due to are selective data. There is no guarantee that these variables we find will continue to hold the same weight on music in the future as they due now. There is reason to believe that these findings could ultimately be misleading in the near future because of the data we have selected. Consider the case that we are solely using 2017 spotify music data, our findings may be limited to possibly this decade and spotify web media player. Other music players may have different trends due to their users identity, music taste or income level. However, Spotify is one of the most used streaming services in the United States and the world, so its data can not be completely disregarded. Spotify is a quality streaming service to collect data from and make generalizations for the population. Another consideration is that music companies and artists may benefit from our research by tailoring their songs to the public's taste in order to make money. Music types may be limited to certain types after we publish our research results. If students in a Cogs data science course can draw these conclusions, then other entities may be able to use our analysis and change the music industry as a whole. The industry has already been criticised for not being a very diverse culture and we may be unfortunately benefiting that narrative. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion and Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our project investigated the features of a song that made it popular such as its energy, loudness, acoustic ness, etc. We strived to find whether these features could be used to accurately predict the popularity of a given song in 2017. We used a dataset that contained the attribute information for about 2000 songs from Spotify in the year 2017 and combined that with stream data we found about the number of daily streams the top 200 songs on Spotify got for the year 2017. We accumulated the number of streams for each song and finally merged the datasets to create our working dataset. This made the data set smaller, but it also focused our analysis. As an additional analysis, we also web scraped off Wikipedia to see which genre of music is the most popular. This was an added question because we wanted to understand which kind of music genre is better at using these different variables inside their music.\n",
    "\n",
    "From our analysis, we learned that there are certain features of a song in 2017 that made it more popular than others. We hypothesized that the danceability, tempo and energy of a song would be the most significant features that would predict the popularity of a song. Upon crunching the data, while we were correct in our assumption of danceability playing a major role, tempo and energy played a less significant role than we anticipated. As it turned out, the liveness and acoustic ness of a song played a bigger role in the popularity of a song. This was a bit of a surprise because it does not look like the obvious choice. People were not as interested in dancing or tempo but the acoustic ness. The sound seems to provide a more happy feel that listeners seemed to engage with more. Also, to answer our interesting question, pop was by far, according to our definition of popularity, the most popular musical genre by far. Rock was second, but it was only at about half of the popularity as pop. All the other musical categories were not really that close. \n",
    "\n",
    "In addition, our hypothesis regarding whether the popularity of a song can be predicted using its features was proven partially correct as using our logical regression prediction model, we were able to predict the popularity of a song using features such as danceability, acoustic ness and valence with an accuracy of ~60%. While this is not a very accurate, it has a lot to do with the limited size of our dataset and we believe that given a larger dataset, the prediction accuracy would rise to 80% or 90%. Despite our initial hypothesis being somewhat wrong the variables that we thought would not contribute to popularity did. We were able to construct a reliable prediction model based off of these components, despite the fact we thought other factors would contribute more. \n",
    "\n",
    "All in all, our analysis had some very useful conclusions to draw from. Looking back on it there could have been some extra steps to improve this project and we would probably start with the data itself. We had used a relatively small data set because it was only significant for one year and it was not the full list of songs in that year. There definitely was some more data out there that we could have found to create a better analysis and prediction, but we made our data work. Also, to add some more complexity we could have added some different types of streaming services. This would allow for a better analysis because we could create some better generalizations for the population. Also maybe choosing a different variable to account for popularity. Streams looks to be an easy choice, but just because someone streams it doesn't make it the popular. Some streams could have been someone accidentally streamed it or maybe someone has left it on repeat while away. While it is possible that it is the most popular song, maybe a like to dislike ratio for the song would have been a more useful variable to consider. Even though this was not a perfect project we were still able to draw some valuable conclusions about it. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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