Nigerian Spotify DT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Necessary Libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting Styles\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_palette(\"magma\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading Data\n",
    "df = pd.read_csv(\"nigerian_spotify_songs1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>name</th>\n",
       "      <th>album</th>\n",
       "      <th>artist</th>\n",
       "      <th>artist_top_genre</th>\n",
       "      <th>release_date</th>\n",
       "      <th>length</th>\n",
       "      <th>popularity</th>\n",
       "      <th>danceability</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sparky</td>\n",
       "      <td>Mandy &amp; The Jungle</td>\n",
       "      <td>Cruel Santino</td>\n",
       "      <td>alternative r&amp;b</td>\n",
       "      <td>2019</td>\n",
       "      <td>144000</td>\n",
       "      <td>48</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.8510</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.534000</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>-6.699</td>\n",
       "      <td>0.0829</td>\n",
       "      <td>133.015</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>shuga rush</td>\n",
       "      <td>EVERYTHING YOU HEARD IS TRUE</td>\n",
       "      <td>Odunsi (The Engine)</td>\n",
       "      <td>afropop</td>\n",
       "      <td>2020</td>\n",
       "      <td>89488</td>\n",
       "      <td>30</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.0822</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>-5.640</td>\n",
       "      <td>0.3600</td>\n",
       "      <td>129.993</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LITT!</td>\n",
       "      <td>LITT!</td>\n",
       "      <td>AYLØ</td>\n",
       "      <td>indie r&amp;b</td>\n",
       "      <td>2018</td>\n",
       "      <td>207758</td>\n",
       "      <td>40</td>\n",
       "      <td>0.836</td>\n",
       "      <td>0.2720</td>\n",
       "      <td>0.564</td>\n",
       "      <td>0.000537</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>-7.127</td>\n",
       "      <td>0.0424</td>\n",
       "      <td>130.005</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Confident / Feeling Cool</td>\n",
       "      <td>Enjoy Your Life</td>\n",
       "      <td>Lady Donli</td>\n",
       "      <td>nigerian pop</td>\n",
       "      <td>2019</td>\n",
       "      <td>175135</td>\n",
       "      <td>14</td>\n",
       "      <td>0.894</td>\n",
       "      <td>0.7980</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>0.0964</td>\n",
       "      <td>-4.961</td>\n",
       "      <td>0.1130</td>\n",
       "      <td>111.087</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>wanted you</td>\n",
       "      <td>rare.</td>\n",
       "      <td>Odunsi (The Engine)</td>\n",
       "      <td>afropop</td>\n",
       "      <td>2018</td>\n",
       "      <td>152049</td>\n",
       "      <td>25</td>\n",
       "      <td>0.702</td>\n",
       "      <td>0.1160</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.910000</td>\n",
       "      <td>0.3480</td>\n",
       "      <td>-6.044</td>\n",
       "      <td>0.0447</td>\n",
       "      <td>105.115</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       name                         album  \\\n",
       "0                    Sparky            Mandy & The Jungle   \n",
       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
       "2                     LITT!                         LITT!   \n",
       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
       "4                wanted you                         rare.   \n",
       "\n",
       "                artist artist_top_genre  release_date  length  popularity  \\\n",
       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
       "2                 AYLØ        indie r&b          2018  207758          40   \n",
       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
       "\n",
       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
       "\n",
       "   speechiness    tempo  time_signature  \n",
       "0       0.0829  133.015               5  \n",
       "1       0.3600  129.993               3  \n",
       "2       0.0424  130.005               4  \n",
       "3       0.1130  111.087               4  \n",
       "4       0.0447  105.115               4  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Collecting First 5 Rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What are the 10 Latest Songs in this DataFrame?"
   ]
  },
  {
   "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>name</th>\n",
       "      <th>album</th>\n",
       "      <th>artist</th>\n",
       "      <th>artist_top_genre</th>\n",
       "      <th>release_date</th>\n",
       "      <th>length</th>\n",
       "      <th>popularity</th>\n",
       "      <th>danceability</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>Ginger Me</td>\n",
       "      <td>Ginger Me</td>\n",
       "      <td>Rema</td>\n",
       "      <td>nigerian pop</td>\n",
       "      <td>2020</td>\n",
       "      <td>205892</td>\n",
       "      <td>60</td>\n",
       "      <td>0.567</td>\n",
       "      <td>0.1340</td>\n",
       "      <td>0.577</td>\n",
       "      <td>0.000397</td>\n",
       "      <td>0.1450</td>\n",
       "      <td>-7.833</td>\n",
       "      <td>0.1340</td>\n",
       "      <td>200.008</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Amen</td>\n",
       "      <td>Boo of the Booless</td>\n",
       "      <td>chike</td>\n",
       "      <td>nigerian pop</td>\n",
       "      <td>2020</td>\n",
       "      <td>220203</td>\n",
       "      <td>30</td>\n",
       "      <td>0.480</td>\n",
       "      <td>0.4940</td>\n",
       "      <td>0.709</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1270</td>\n",
       "      <td>-5.963</td>\n",
       "      <td>0.1040</td>\n",
       "      <td>90.826</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>Won Le Ba</td>\n",
       "      <td>Won Le Ba</td>\n",
       "      <td>Shizzi</td>\n",
       "      <td>nigerian pop</td>\n",
       "      <td>2020</td>\n",
       "      <td>229655</td>\n",
       "      <td>45</td>\n",
       "      <td>0.866</td>\n",
       "      <td>0.1470</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.3090</td>\n",
       "      <td>-5.562</td>\n",
       "      <td>0.0681</td>\n",
       "      <td>115.999</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>Abule</td>\n",
       "      <td>Abule</td>\n",
       "      <td>Patoranking</td>\n",
       "      <td>afro dancehall</td>\n",
       "      <td>2020</td>\n",
       "      <td>199198</td>\n",
       "      <td>50</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.1190</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.1040</td>\n",
       "      <td>-7.122</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>99.669</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Duduke</td>\n",
       "      <td>Duduke</td>\n",
       "      <td>Simi</td>\n",
       "      <td>afropop</td>\n",
       "      <td>2020</td>\n",
       "      <td>172000</td>\n",
       "      <td>60</td>\n",
       "      <td>0.781</td>\n",
       "      <td>0.5520</td>\n",
       "      <td>0.656</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.1310</td>\n",
       "      <td>-4.105</td>\n",
       "      <td>0.1060</td>\n",
       "      <td>96.083</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>Igbotic</td>\n",
       "      <td>Igbotic</td>\n",
       "      <td>Anyidons</td>\n",
       "      <td>Missing</td>\n",
       "      <td>2020</td>\n",
       "      <td>204146</td>\n",
       "      <td>19</td>\n",
       "      <td>0.512</td>\n",
       "      <td>0.3460</td>\n",
       "      <td>0.842</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.3560</td>\n",
       "      <td>-3.692</td>\n",
       "      <td>0.2530</td>\n",
       "      <td>88.374</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>4 Life</td>\n",
       "      <td>4 Life</td>\n",
       "      <td>Famous Bobson</td>\n",
       "      <td>Missing</td>\n",
       "      <td>2020</td>\n",
       "      <td>165746</td>\n",
       "      <td>0</td>\n",
       "      <td>0.578</td>\n",
       "      <td>0.0102</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.006890</td>\n",
       "      <td>0.1730</td>\n",
       "      <td>-7.367</td>\n",
       "      <td>0.2020</td>\n",
       "      <td>103.969</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>Mummy Pray for Me</td>\n",
       "      <td>Mummy Pray for Me</td>\n",
       "      <td>Mister Versace</td>\n",
       "      <td>Missing</td>\n",
       "      <td>2020</td>\n",
       "      <td>208378</td>\n",
       "      <td>35</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.5650</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.000322</td>\n",
       "      <td>0.1270</td>\n",
       "      <td>-8.173</td>\n",
       "      <td>0.2040</td>\n",
       "      <td>105.999</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>FEM</td>\n",
       "      <td>FEM</td>\n",
       "      <td>DaVido</td>\n",
       "      <td>afropop</td>\n",
       "      <td>2020</td>\n",
       "      <td>202222</td>\n",
       "      <td>61</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.4930</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.000085</td>\n",
       "      <td>0.0958</td>\n",
       "      <td>-6.174</td>\n",
       "      <td>0.0725</td>\n",
       "      <td>108.017</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Dami Duro</td>\n",
       "      <td>Best Of Davido</td>\n",
       "      <td>DaVido</td>\n",
       "      <td>afropop</td>\n",
       "      <td>2020</td>\n",
       "      <td>250250</td>\n",
       "      <td>8</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.1880</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2160</td>\n",
       "      <td>-0.735</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>124.983</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  name               album          artist artist_top_genre  \\\n",
       "444          Ginger Me           Ginger Me            Rema     nigerian pop   \n",
       "48                Amen  Boo of the Booless           chike     nigerian pop   \n",
       "443          Won Le Ba           Won Le Ba          Shizzi     nigerian pop   \n",
       "352              Abule               Abule     Patoranking   afro dancehall   \n",
       "40              Duduke              Duduke            Simi          afropop   \n",
       "356            Igbotic             Igbotic        Anyidons          Missing   \n",
       "450             4 Life              4 Life   Famous Bobson          Missing   \n",
       "449  Mummy Pray for Me   Mummy Pray for Me  Mister Versace          Missing   \n",
       "357                FEM                 FEM          DaVido          afropop   \n",
       "179          Dami Duro      Best Of Davido          DaVido          afropop   \n",
       "\n",
       "     release_date  length  popularity  danceability  acousticness  energy  \\\n",
       "444          2020  205892          60         0.567        0.1340   0.577   \n",
       "48           2020  220203          30         0.480        0.4940   0.709   \n",
       "443          2020  229655          45         0.866        0.1470   0.710   \n",
       "352          2020  199198          50         0.687        0.1190   0.790   \n",
       "40           2020  172000          60         0.781        0.5520   0.656   \n",
       "356          2020  204146          19         0.512        0.3460   0.842   \n",
       "450          2020  165746           0         0.578        0.0102   0.561   \n",
       "449          2020  208378          35         0.885        0.5650   0.506   \n",
       "357          2020  202222          61         0.775        0.4930   0.687   \n",
       "179          2020  250250           8         0.659        0.1880   0.995   \n",
       "\n",
       "     instrumentalness  liveness  loudness  speechiness    tempo  \\\n",
       "444          0.000397    0.1450    -7.833       0.1340  200.008   \n",
       "48           0.000000    0.1270    -5.963       0.1040   90.826   \n",
       "443          0.000000    0.3090    -5.562       0.0681  115.999   \n",
       "352          0.000026    0.1040    -7.122       0.1940   99.669   \n",
       "40           0.000052    0.1310    -4.105       0.1060   96.083   \n",
       "356          0.000000    0.3560    -3.692       0.2530   88.374   \n",
       "450          0.006890    0.1730    -7.367       0.2020  103.969   \n",
       "449          0.000322    0.1270    -8.173       0.2040  105.999   \n",
       "357          0.000085    0.0958    -6.174       0.0725  108.017   \n",
       "179          0.000000    0.2160    -0.735       0.1940  124.983   \n",
       "\n",
       "     time_signature  \n",
       "444               5  \n",
       "48                4  \n",
       "443               4  \n",
       "352               4  \n",
       "40                4  \n",
       "356               4  \n",
       "450               4  \n",
       "449               4  \n",
       "357               4  \n",
       "179               4  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(\"release_date\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How Many Genres are there?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 19 Genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['alternative r&b', 'afropop', 'indie r&b', 'nigerian pop',\n",
       "       'afro r&b', 'afro dancehall', 'Missing', 'azonto', 'soft rock',\n",
       "       'hiplife', 'erotica', 'nigerian hip hop', 'highlife',\n",
       "       'azontobeats', 'south african house', 'bongo flava', 'afroswing',\n",
       "       'gqom', 'christian afrobeat'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of Unique Genres\n",
    "# 19 Genres\n",
    "df[\"artist_top_genre\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How Many Artistes are there?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 171 Artists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Cruel Santino', 'Odunsi (The Engine)', 'AYLØ', 'Lady Donli',\n",
       "       'DRB Lasgidi', 'prettyboydo', 'Tay Iwar', 'Amaarae', 'Joeboy',\n",
       "       'DaVido', 'Bracket', 'DJ Neptune', 'Sean Tizzle', 'Teni', 'KiDi',\n",
       "       'T-Classic', 'Simi', 'Runtown', 'WizKid', 'Patoranking',\n",
       "       'P-Square', 'Stormrex', 'chike', 'Chidinma', 'Banky W.', 'Phyno',\n",
       "       'Tekno', 'Mayorkun', 'Peruzzi', 'Kiss Daniel', 'Korede Bello',\n",
       "       'DMW', 'Kizz Daniel', 'L.A.X', 'E.L', 'Tiwa Savage',\n",
       "       'Reekado Banks', 'Iyanya', 'Bisa Kdei', 'Juls', 'Kuami Eugene',\n",
       "       'Sugarboy', 'Adekunle Gold', 'Various Artists', 'Master Kraft',\n",
       "       'Flavour', 'Wande Coal', 'Boj', 'This Is Africa', 'Skales',\n",
       "       'Kayswitch', 'M.I. Abaga', 'Naeto C', 'Eldee', 'Olamide', 'Eazzy',\n",
       "       'Wizboyy', 'Essence', 'The Jungle Collective', 'Lord Of Ajasa',\n",
       "       'X Project', '9ice', 'K9', \"Mo' Hits All Stars\", 'Magic System',\n",
       "       'Tony Tetuila', 'Kas', 'Ice Prince', 'Brymo', 'May D', 'Styl-Plus',\n",
       "       '2Baba', 'Eedris Abdulkareem', 'Sunny Neji', 'Ikechukwu',\n",
       "       'Olu Maintain', 'Dr SID', 'KCee', 'Burna Boy', 'Goldie',\n",
       "       'Reminisce', 'The Three Wisemen', 'Skiibii', 'Lil Kesh', 'Falz',\n",
       "       'Niniola', 'Moelogo', 'Johnny Drille', 'Jilex Anderson', 'Ycee',\n",
       "       'Jilex', 'Nonso Amadi', 'Koker', 'Maleek Berry', 'Mr Eazi',\n",
       "       'DJ Henry X', 'Legendury Beatz', 'Cynthia Morgan', 'Sarkodie',\n",
       "       'Kwamz & Flava', 'Timaya', 'Selebobo', 'Dammy Krane', 'Orezi',\n",
       "       'CDQ', 'Skuki', 'Julz', 'J. Martins', 'Yemi Alade', 'Tjan',\n",
       "       'Pheelz', 'Toofan', 'Seyi Shay', 'DJ Spinall',\n",
       "       'SUPERSTAR DJ Xclusive', 'Illbliss', 'Magnito',\n",
       "       'Davido x Dammy Krane', 'Rema', 'Zlatan', 'Starboy',\n",
       "       'Naira Marley', 'Rudeboy', 'Victor AD', 'HumbleSmith',\n",
       "       'Duncan Mighty', 'Ruffcoin', 'Shatta Wale', 'Master KG', 'Singah',\n",
       "       'Otigba Agulu', 'Anyidons', 'Solidstar', 'HarrySong', 'MC Galaxy',\n",
       "       'Awilo Longomba', 'Eugy', \"Flavour N'abania\", 'Eff-Jay', 'Mut4y',\n",
       "       'Wizboyy Ofuasia', 'Afro B', 'Praiz', 'DJ Tunez', 'Chidokeyz',\n",
       "       'Shizzi', 'Basketmouth', 'YBNL MaFia Family', 'Mister Versace',\n",
       "       'Famous Bobson', 'OmoAkin', 'Tytanium', 'Minz', 'WizzyWee',\n",
       "       'T-Obay', 'Baci', 'Yung L', 'Starboy L.a.X', 'Tekno Miles',\n",
       "       'Fally Ipupa', 'BEZ', 'Blackmagic', 'Tee Blaq', 'Mavins',\n",
       "       'Teddy-A', 'Tobby Potter', 'Triplemg', 'DJ Xclusive', 'Nero X',\n",
       "       'R2Bees', 'BM'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of Unique Artists\n",
    "# 171 Genres\n",
    "df[\"artist\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the Trend of Music Over the Years?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>album</th>\n",
       "      <th>artist</th>\n",
       "      <th>artist_top_genre</th>\n",
       "      <th>length</th>\n",
       "      <th>popularity</th>\n",
       "      <th>danceability</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              name  album  artist  artist_top_genre  length  popularity  \\\n",
       "release_date                                                              \n",
       "1998             1      1       1                 1       1           1   \n",
       "2000             1      1       1                 1       1           1   \n",
       "2001             1      1       1                 1       1           1   \n",
       "2003             2      2       2                 2       2           2   \n",
       "2004             2      2       2                 2       2           2   \n",
       "2006             1      1       1                 1       1           1   \n",
       "2007             1      1       1                 1       1           1   \n",
       "2008             7      7       7                 7       7           7   \n",
       "2009            11     11      11                11      11          11   \n",
       "2010             9      9       9                 9       9           9   \n",
       "2011            16     16      16                16      16          16   \n",
       "2012            22     22      22                22      22          22   \n",
       "2013            25     25      25                25      25          25   \n",
       "2014            75     75      75                75      75          75   \n",
       "2015            55     55      55                55      55          55   \n",
       "2016            95     95      95                95      95          95   \n",
       "2017            83     83      83                83      83          83   \n",
       "2018            47     47      47                47      47          47   \n",
       "2019            51     51      51                51      51          51   \n",
       "2020            25     25      25                25      25          25   \n",
       "\n",
       "              danceability  acousticness  energy  instrumentalness  liveness  \\\n",
       "release_date                                                                   \n",
       "1998                     1             1       1                 1         1   \n",
       "2000                     1             1       1                 1         1   \n",
       "2001                     1             1       1                 1         1   \n",
       "2003                     2             2       2                 2         2   \n",
       "2004                     2             2       2                 2         2   \n",
       "2006                     1             1       1                 1         1   \n",
       "2007                     1             1       1                 1         1   \n",
       "2008                     7             7       7                 7         7   \n",
       "2009                    11            11      11                11        11   \n",
       "2010                     9             9       9                 9         9   \n",
       "2011                    16            16      16                16        16   \n",
       "2012                    22            22      22                22        22   \n",
       "2013                    25            25      25                25        25   \n",
       "2014                    75            75      75                75        75   \n",
       "2015                    55            55      55                55        55   \n",
       "2016                    95            95      95                95        95   \n",
       "2017                    83            83      83                83        83   \n",
       "2018                    47            47      47                47        47   \n",
       "2019                    51            51      51                51        51   \n",
       "2020                    25            25      25                25        25   \n",
       "\n",
       "              loudness  speechiness  tempo  time_signature  \n",
       "release_date                                                \n",
       "1998                 1            1      1               1  \n",
       "2000                 1            1      1               1  \n",
       "2001                 1            1      1               1  \n",
       "2003                 2            2      2               2  \n",
       "2004                 2            2      2               2  \n",
       "2006                 1            1      1               1  \n",
       "2007                 1            1      1               1  \n",
       "2008                 7            7      7               7  \n",
       "2009                11           11     11              11  \n",
       "2010                 9            9      9               9  \n",
       "2011                16           16     16              16  \n",
       "2012                22           22     22              22  \n",
       "2013                25           25     25              25  \n",
       "2014                75           75     75              75  \n",
       "2015                55           55     55              55  \n",
       "2016                95           95     95              95  \n",
       "2017                83           83     83              83  \n",
       "2018                47           47     47              47  \n",
       "2019                51           51     51              51  \n",
       "2020                25           25     25              25  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Trends in Music per year\n",
    "# Group Data by their Release Date and count them\n",
    "release_date = df.groupby(\"release_date\").count()\n",
    "release_date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the Plot below, 2016 has the highest number of songs, with most songs in the range between 2013 and 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[16,8])\n",
    "plt.ylabel(\"Number of Songs\")\n",
    "plt.title(\"Number of Songs Released per year\")\n",
    "release_date[\"artist\"].plot(kind=\"bar\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What are the Top 10 Popular Songs?"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Sort the DF by Popularity, with the highest ones on top\n",
    "top_songs = df.sort_values(\"popularity\",ascending=False).head(10)\n",
    "# top_songs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8,8])\n",
    "sns.barplot(y=\"name\", x=\"popularity\", data=top_songs)\n",
    "plt.title(\"Top 10 Songs\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which Artists have some of  the most popular Songs?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8,12])\n",
    "plt.title(\"Artists with High Rated Songs\")\n",
    "\n",
    "# Get The Top 50 popular songs and group them by their artists\n",
    "popular_songs = df.sort_values(\"popularity\",ascending=False).head(50).groupby(\"artist\").count()\n",
    "\n",
    "# Sort the top songs of the artists from highest to lowest\n",
    "popular_songs = popular_songs.sort_values(\"name\",ascending=False)\n",
    "\n",
    "sns.barplot(y=\"artist\", x=\"name\", data=popular_songs.reset_index());"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What are the top 10 genres?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>album</th>\n",
       "      <th>artist</th>\n",
       "      <th>release_date</th>\n",
       "      <th>length</th>\n",
       "      <th>popularity</th>\n",
       "      <th>danceability</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>artist_top_genre</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>afro dancehall</th>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>afropop</th>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missing</th>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nigerian pop</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>azontobeats</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alternative r&amp;b</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>indie r&amp;b</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>afro r&amp;b</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nigerian hip hop</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highlife</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  name  album  artist  release_date  length  popularity  \\\n",
       "artist_top_genre                                                          \n",
       "afro dancehall     328    328     328           328     328         328   \n",
       "afropop             90     90      90            90      90          90   \n",
       "Missing             53     53      53            53      53          53   \n",
       "nigerian pop        24     24      24            24      24          24   \n",
       "azontobeats          8      8       8             8       8           8   \n",
       "alternative r&b      7      7       7             7       7           7   \n",
       "indie r&b            4      4       4             4       4           4   \n",
       "afro r&b             3      3       3             3       3           3   \n",
       "nigerian hip hop     3      3       3             3       3           3   \n",
       "highlife             1      1       1             1       1           1   \n",
       "\n",
       "                  danceability  acousticness  energy  instrumentalness  \\\n",
       "artist_top_genre                                                         \n",
       "afro dancehall             328           328     328               328   \n",
       "afropop                     90            90      90                90   \n",
       "Missing                     53            53      53                53   \n",
       "nigerian pop                24            24      24                24   \n",
       "azontobeats                  8             8       8                 8   \n",
       "alternative r&b              7             7       7                 7   \n",
       "indie r&b                    4             4       4                 4   \n",
       "afro r&b                     3             3       3                 3   \n",
       "nigerian hip hop             3             3       3                 3   \n",
       "highlife                     1             1       1                 1   \n",
       "\n",
       "                  liveness  loudness  speechiness  tempo  time_signature  \n",
       "artist_top_genre                                                          \n",
       "afro dancehall         328       328          328    328             328  \n",
       "afropop                 90        90           90     90              90  \n",
       "Missing                 53        53           53     53              53  \n",
       "nigerian pop            24        24           24     24              24  \n",
       "azontobeats              8         8            8      8               8  \n",
       "alternative r&b          7         7            7      7               7  \n",
       "indie r&b                4         4            4      4               4  \n",
       "afro r&b                 3         3            3      3               3  \n",
       "nigerian hip hop         3         3            3      3               3  \n",
       "highlife                 1         1            1      1               1  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group DF Data by their Genre, Count All their values each, and Sort them through Descending Order\n",
    "top_genres = df.groupby(\"artist_top_genre\").count().sort_values(\"name\", ascending=False).head(10)\n",
    "top_genres"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the Plot Below we see that Afro DanceHall has the highest data. Missing data accounts for artists who input their own \"form of genre\", that may haven't been recognised by Spotify"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8,8])\n",
    "plt.title(\"Top 10 Genres\")\n",
    "\n",
    "sns.barplot(y=\"artist_top_genre\", x=\"name\", data=top_genres.reset_index())\n",
    "plt.xlabel(\"Count\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 20 Artists with the Highest number of songs?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>album</th>\n",
       "      <th>artist_top_genre</th>\n",
       "      <th>release_date</th>\n",
       "      <th>length</th>\n",
       "      <th>popularity</th>\n",
       "      <th>danceability</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>artist</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Various Artists</th>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flavour</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WizKid</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P-Square</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DaVido</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tekno</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timaya</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iyanya</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patoranking</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KCee</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Olamide</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr Eazi</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Odunsi (The Engine)</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moelogo</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemi Alade</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kiss Daniel</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skales</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2Baba</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lady Donli</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banky W.</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     name  album  artist_top_genre  release_date  length  \\\n",
       "artist                                                                     \n",
       "Various Artists        37     37                37            37      37   \n",
       "Flavour                19     19                19            19      19   \n",
       "WizKid                 18     18                18            18      18   \n",
       "P-Square               18     18                18            18      18   \n",
       "DaVido                 17     17                17            17      17   \n",
       "Tekno                  16     16                16            16      16   \n",
       "Timaya                 10     10                10            10      10   \n",
       "Iyanya                 10     10                10            10      10   \n",
       "Patoranking            10     10                10            10      10   \n",
       "KCee                    9      9                 9             9       9   \n",
       "Olamide                 9      9                 9             9       9   \n",
       "Mr Eazi                 8      8                 8             8       8   \n",
       "Odunsi (The Engine)     8      8                 8             8       8   \n",
       "Moelogo                 7      7                 7             7       7   \n",
       "Yemi Alade              7      7                 7             7       7   \n",
       "Kiss Daniel             7      7                 7             7       7   \n",
       "Skales                  6      6                 6             6       6   \n",
       "2Baba                   6      6                 6             6       6   \n",
       "Lady Donli              6      6                 6             6       6   \n",
       "Banky W.                6      6                 6             6       6   \n",
       "\n",
       "                     popularity  danceability  acousticness  energy  \\\n",
       "artist                                                                \n",
       "Various Artists              37            37            37      37   \n",
       "Flavour                      19            19            19      19   \n",
       "WizKid                       18            18            18      18   \n",
       "P-Square                     18            18            18      18   \n",
       "DaVido                       17            17            17      17   \n",
       "Tekno                        16            16            16      16   \n",
       "Timaya                       10            10            10      10   \n",
       "Iyanya                       10            10            10      10   \n",
       "Patoranking                  10            10            10      10   \n",
       "KCee                          9             9             9       9   \n",
       "Olamide                       9             9             9       9   \n",
       "Mr Eazi                       8             8             8       8   \n",
       "Odunsi (The Engine)           8             8             8       8   \n",
       "Moelogo                       7             7             7       7   \n",
       "Yemi Alade                    7             7             7       7   \n",
       "Kiss Daniel                   7             7             7       7   \n",
       "Skales                        6             6             6       6   \n",
       "2Baba                         6             6             6       6   \n",
       "Lady Donli                    6             6             6       6   \n",
       "Banky W.                      6             6             6       6   \n",
       "\n",
       "                     instrumentalness  liveness  loudness  speechiness  tempo  \\\n",
       "artist                                                                          \n",
       "Various Artists                    37        37        37           37     37   \n",
       "Flavour                            19        19        19           19     19   \n",
       "WizKid                             18        18        18           18     18   \n",
       "P-Square                           18        18        18           18     18   \n",
       "DaVido                             17        17        17           17     17   \n",
       "Tekno                              16        16        16           16     16   \n",
       "Timaya                             10        10        10           10     10   \n",
       "Iyanya                             10        10        10           10     10   \n",
       "Patoranking                        10        10        10           10     10   \n",
       "KCee                                9         9         9            9      9   \n",
       "Olamide                             9         9         9            9      9   \n",
       "Mr Eazi                             8         8         8            8      8   \n",
       "Odunsi (The Engine)                 8         8         8            8      8   \n",
       "Moelogo                             7         7         7            7      7   \n",
       "Yemi Alade                          7         7         7            7      7   \n",
       "Kiss Daniel                         7         7         7            7      7   \n",
       "Skales                              6         6         6            6      6   \n",
       "2Baba                               6         6         6            6      6   \n",
       "Lady Donli                          6         6         6            6      6   \n",
       "Banky W.                            6         6         6            6      6   \n",
       "\n",
       "                     time_signature  \n",
       "artist                               \n",
       "Various Artists                  37  \n",
       "Flavour                          19  \n",
       "WizKid                           18  \n",
       "P-Square                         18  \n",
       "DaVido                           17  \n",
       "Tekno                            16  \n",
       "Timaya                           10  \n",
       "Iyanya                           10  \n",
       "Patoranking                      10  \n",
       "KCee                              9  \n",
       "Olamide                           9  \n",
       "Mr Eazi                           8  \n",
       "Odunsi (The Engine)               8  \n",
       "Moelogo                           7  \n",
       "Yemi Alade                        7  \n",
       "Kiss Daniel                       7  \n",
       "Skales                            6  \n",
       "2Baba                             6  \n",
       "Lady Donli                        6  \n",
       "Banky W.                          6  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "artist_no_of_songs = df.groupby(\"artist\").count().sort_values(\"name\",ascending=False).head(20)\n",
    "artist_no_of_songs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that Various Artists stands out, this are Artistes who did collaborations with each other to make the songs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IiOQ3bhF6ChQoQPPmzVm/fj1t27Zl1apVDBgwgCNHjvDMM88QGBjI7t27iYqKokWLFly6dIkXX3yRGjVqEBUVBcCmTZtISEhg2bJlZGZm0r17d+rXr3/V/SUnJ7Nt2zZWrlwJwObNm7O97nA4+Oyzz1i6dCmenp60bt2aV199lYIFC3L8+HFWrVqFr68vq1at4siRIzRv3pz58+cDsGHDBp544gn69OnD119/zYULF+jXrx8HDx6kf//+TJw48YrXFXpERERuzC0ObwGEhITwySefkJiYyIULF6hZsyZlypRh6dKlDBkyhCVLlpCZmekc7+fnl+39cXFx1KtXD4vFgpeXF7Vr1yYuLi7bmL8vXu3j48OIESMYMWIEAwcOJD09Pdu47777jpSUFF577TVeffVVHA4Ha9asAcDX1xdfX99r9tGvXz/OnDlDr169WLduHZ6enrf0uoiIiFyd24SeatWqkZKSwsKFC+nUqRMAU6dOpV27dkRGRhIYGMg/77hhtWZvPSAgwHloKyMjgx9//JHKlSvj7e1NUlISAL/++isAJ0+e5JdffuH999/ngw8+IDIyMlugWrFiBWPGjGHOnDnMmTOHd999l8WLF1+xX6vVisPhyFbHmjVr6NChA9HR0VStWpVly5ZlG3e110VEROTG3GqaoFOnTkRGRrJp0ybgr7U+Y8eOZdasWZQrV46zZ89e871NmzZl+/btdOnShYyMDIKCgqhZsyYhISG88cYbrFmzhnvvvReAMmXKkJSURPv27SlcuDC9e/d2zricPn2an3/+mSlTpji3/fDDD5OWluZctPy3++67jxkzZlDzH/fGevDBBwkLC6Nw4cJ4eXkxatQoSpUqRUZGBpGRkTz55JNXvC4iIiI3phuOujm73U7pb3a5ugwREZEr5Ma9t653w1G3ObwlIiIicj0KPSIiImIKCj0iIiJiCm61kFmuZDgcuXLMVERE5HYZmVlYPD3ybH+a6XFzaf9zDSF3ZLfbXV1CnjBDn2boEczRpxl6BHP0mZs95mXgAYUeERERMQmFHhERETEFhR4RERExBYUeN1fA29vVJeS6a12Eyt2Yoc/82qPxj9vQiMidS2dvuTmL1cqJGWNcXYaIW7v7heGuLkFEboJmekRERMQUFHpERETEFBR6RERExBS0puc2JSQk0LZtW2rWrOl8LjAwkLi4OKZMmeLCykREROSfFHpyQJUqVYiOjnY+3rZtG3FxcS6sSERERP6XDm/lskWLFtGzZ0+6d+/O888/T3p6Ov3792f79u0A7NmzhxdeeIGMjAyGDBlC165dCQkJYe3atQDYbDZngIqJiSEqKoqEhASCg4Ox2WzMnj3bZb2JiIjkJ5rpyQGHDx/GZrM5H4eEhADgcDg4d+4c8+fPx2q10qdPH/bu3UtISAirV6/m0UcfZfXq1YSGhrJ06VJ8fX2JjIwkOTmZjh07Ur9+/WvuMykpiZUrV+JtguvwiIiI5ASFnhxwtcNbAFarFS8vLwYNGkThwoU5ceIEmZmZPPbYY0RGRnLu3Dl27tzJ8OHDGTNmDA0bNgTAx8eHgIAA4uPjs+3HMAznzxUqVFDgERERuQU6vJWL9u/fz4YNG3j33XcZMWIEDocDwzCwWq0EBQURERFBixYt8PDwICAggJ07dwKQnJzMwYMHncEmKSkJgF9//dW5batVX52IiMit0ExPLqpcuTKFChWiY8eOeHt7U6ZMGU6ePAlAp06daNGiBV9++SUAoaGhjBgxgm7dupGWlkb//v0pVaoUPXv2ZNSoUZQrV4677rrLle2IiIjkaxbjn8dMxO3Y7XZ8v1np6jJE3Nqt3obCbrfn2/uM3Swz9Ajm6DO/9Xi9enWMRERERExBoUdERERMQaFHRERETEGhR0RERExBZ2+5OcPhuOVFliJya4zMTCye+nUqcqfTTI+bS0tPd3UJuc5ut7u6hDxhhj7za48KPCL5g0KPiIiImIJCj4iIiJiCQo+IiIiYgkKPmytggpuS5qcrhd4OM/R5J/XoyHT/9XAiZqPVd27OYrWy//12ri5DJN+p/tInri5BRHKYZnpERETEFBR6RERExBQUekRERMQUFHpySM+ePdmzZw8A6enpPPzww8yZM8f5eo8ePXjppZdIv8bFAqOiooiJiXE+Hj9+PC+++CLp6en079//ivExMTFERUXlcBciIiLuS6EnhzRu3JidO3cCsGvXLho3bsw333wDQFpaGsePH+f999/H+wZnUxmGwejRozl9+jTTpk3D29ub9957L7fLFxERcXsKPTmkYcOGztDz7bffEhISwsWLF7l48SI//vgjjz76KM2aNSMtLY0333wTm82GzWbjoYceYvXq1cBfgWfkyJFcvnyZSZMm4fn/L23fqFEjAHbu3EnHjh155pln2LBhg2saFRERyacUenJIjRo1OHLkCIZhsGPHDh599FEaNGjADz/8wPbt23nsscecY0eNGkV0dDStW7emRYsWtG/fHoBZs2bxxx9/kJiYiMViuWIf48eP5+2332bevHlUqFAhr1oTERFxCwo9OcRqtVK9enViY2MpU6YM3t7eNGnShN27d7Nr1y4aNmyYbfzatWvZuHEj48aNcwac5s2bM3/+fIoUKcKMGTOu2EdiYiJ+fn4A1K1bN/ebEhERcSMKPTmoUaNGzJo1yzmr8/DDD/Prr78CUKJECee42NhYFi1axNSpU/Hy8nI+X7VqVQBGjx7NihUr2LZtW7btlylThri4OAD27t2bm62IiIi4HYWeHNSwYUN27drF448/DoC3tzdFixblkUceyTbulVdeITMzk+effx6bzca8efOyvV68eHEmTpzIkCFDOHXqlPP5yMhIhg4dSq9evTh27FjuNyQiIuJGdBuKHHTPPfdw4MCBbM9Nnz7d+fPXX38NwE8//XTDbT3yyCPExsYCsHnzZgCqVKnCihUrcqhaERERc9FMj4iIiJiCQo+IiIiYgkKPiIiImIJCj4iIiJiCFjK7OcPh4P6XPnF1GSL5jiMzHavn9W8bIyL5i2Z63FzaNW5w6k7sdrurS8gTZujzTupRgUfE/Sj0iIiIiCko9IiIiIgpKPS4uQLe7j9Ff//997u6hDxhhj7vpB6zMt3/0LCI2Wghs5uzWK18M7u1q8sQyXf+8+znri5BRHKYZnpERETEFBR6RERExBQUekRERMQUFHpERETEFLSQ+Tq2bdvGgAEDqFKlCgBpaWkEBwdjs9myjfvggw/44YcfsFqtWCwWBg4cyAMPPOCKkkVEROQaFHpuoH79+kyZMgWA9PR0goKCaNeuHcWKFQPg8OHDfP3118TExGCxWLDb7QwdOpRPP/3UlWWLiIjI/1DouQXJyclYrVY8PDycz5UsWZJjx46xYsUKmjRpwv3338+KFSsA2LFjB+PGjaN48eKULl2aypUr06FDBwYNGsSyZcsACA0N5Z133sHT05OIiAjS0tI4d+4cL730Ei1atKBNmzbce++9eHt789ZbbxEeHs7Zs2cBGD58ONWqVcv7D0JERCQfUui5ga1bt2Kz2bBYLHh5eTFixAiKFCnifL1kyZLMmDGDRYsW8f7771OwYEEGDhxIy5Yteeutt5g2bRr+/v6MGDHiuvs5cuQIzzzzDIGBgezevZuoqChatGjBpUuXePHFF6lRowaRkZHUr1+f7t278/vvvzNs2DBiYmJy+yMQERFxCwo9N/DPw1sAKSkpzjU9DRs2pFWrVvj4+DB+/HgA9u7dy3PPPUdgYCDnzp3D398fgMDAQH777bcrtm8YBgBlypRhxowZrFixAovFQmZmpnOMn58fAAcPHmTr1q188cUXAFy4cCEXOhYREXFPCj23qEiRIkRHRzsff/XVV8TExDBz5kwKFCiAn58fRYsWxcPDg/Lly3Po0CGqVq3Kvn37KFKkCAUKFOD06dNkZWWRkpJCQkICAFOnTiUkJITHH3+clStXsnr1auc+rNa/TrLz9/enbdu2BAcHc/r0aZYvX563zYuIiORjCj236cknnyQuLo6QkBAKFy6MYRi8/vrrFC1alFGjRvHmm29SoEABHA4HjzzyCGXKlKFRo0Z07tyZSpUqUblyZQCCgoIYO3Yss2bNoly5cs51O//Ur18/wsPDWbZsGcnJyfTv3z+v2xUREcm3LMbfx1ckV8XExHDq1ClefvnlPN2v3W4n8fvBebpPEXeQm/festvtd9TNVXODGXoEc/SZ33q8Xr26OKGIiIiYgg5v5ZFu3bq5ugQRERFT00yPiIiImIJCj4iIiJiCDm+5OcPhyNUFmSLuKiszHQ9Pb1eXISI5SDM9bi4tPd3VJeQ6u93u6hLyhBn6vJN6VOARcT8KPSIiImIKCj0iIiJiCgo9bs7b2/2n6PPTRbNuhxn6zM0eszLd/1CviFyfFjK7OavVyop5Qa4uQ8TlOj+zztUliIiLaaZHRERETEGhR0RERExBoUdERERMQaFHRERETEGhJwds27aNBg0aYLPZ6NGjB127dmXt2rVXHZucnEzDhg1JSUnJ9ny7du3YuXMnERERV7xn8uTJrFq1KjdKFxERMQ2dvZVD6tevz5QpUwBISUnBZrPh5+d3xSm4Pj4+NG3alC+//JKOHTsCsG/fPooXL069evWoV69entcuIiJiBprpyQVFihShS5curF27lvDwcPr06UPHjh159913AQgNDeXjjz92jl+5ciVdunQhISGB0NBQAL788kvat29P7969+fnnn51jJ0yYQEhICCEhISxYsCAv2xIREcnXFHpySalSpfj111+pU6cOc+bMISYmhpiYGABq167N+fPnOX78OOnp6fzwww888cQT2d4fGRnJvHnzmDNnDgULFgRg06ZNJCQksGzZMhYvXsxnn33GgQMH8rw3ERGR/EiHt3LJsWPHeOihh9i7dy9bt27Fx8eH9H/c/LNz5858+umnVKhQgWbNmmW7cvKpU6fw8fHB19cXgIceegiAuLg46tWrh8ViwcvLi9q1axMXF0e1atXytjkREZF8SDM9uSA5OZnly5dTtGhRihYtyttvv03v3r1JTU3FMAwA2rZty4YNG1izZo3zkNbfSpQowcWLFzlz5gwAe/fuBSAgIIBdu3YBkJGRwY8//kjlypXzsDMREZH8SzM9OWTr1q3YbDasVitZWVm8/PLL+Pn5MWjQIHbt2kWhQoWoXLkyJ0+epGzZshQvXhw/Pz9OnTqFn59ftm15enoyfvx4+vTpQ/HixfH0/Otratq0Kdu3b6dLly5kZGQQFBREzZo1XdGuiIhIvmMx/p56ELdkt9v5ZetAV5ch4nJ30r237Ha7299A1gw9gjn6zG89Xq9eHd4SERERU1DoEREREVNQ6BERERFT0EJmN+dwOO6otQwirpKVmY6Hp/eNB4qI29JMj5v757WB3JXdbnd1CXnCDH3mZo8KPCKi0CMiIiKmoNAjIiIipqDQIyIiIqag0OPm/nlPL3eVny6adTvM0Oet9piZ5f5r1kQk5+jsLTdntVqZFd3S1WWI5IrnbV+6ugQRyUc00yMiIiKmoNAjIiIipqDQIyIiIqagNT25ZMKECfzyyy8kJSWRmppKxYoV8fX1Zdq0adnGNWvWjC+++IICBQq4qFIRERFzUOjJJWFhYQCsWrWKI0eOMHjwYBdXJCIiYm4KPXkkIyODkSNHcvToURwOBwMGDCAwMND5ekxMDJs3b+add96hU6dOPProoxw4cACLxcL06dMpWrQoEyZMYNeuXQC0adOGXr16uaodERGRfEdrevLI8uXL8fX15aOPPmL69OmMGjXK+Vp0dDQ7d+5k6tSpeHt7k5KSQuvWrVm0aBF33XUXsbGxbNq0iYSEBJYtW8bixYv57LPPOHDggAs7EhERyV8005NHDh48yK5du9izZw8AmZmZnD17FoAtW7bg4eGBh4eHc3yNGjUAKFeuHGlpaRw/fpx69ephsVjw8vKidu3axMXFUa1atbxvRkREJB/STE8e8ff3p3Xr1kRHRzN79myCgoIoXrw4ANOnT6dYsWLExMQ4x1sslmzvDwgIcB7aysjI4Mcff6Ry5cp514CIiEg+p9CTR7p27cqRI0fo0aMHXbt25Z577sFq/e/HP3z4cObOncvvv/9+1fc3bdqUChUq0KVLF7p06ULLli2pWbNmHlUvIiKS/1kMwzBcXYTkHrvdTuzOAa4uQyRX5NfbUNjtdre/l5oZegRz9JnferxevZrpEREREVNQ6BERERFTUOgRERERU1DoEREREVPQdXrcnMPhyLeLPUVuJDMrHU8Pb1eXISL5hGZ63Fx6erqrS8h1drvd1SXkCTP0eas9KvCIyK1Q6BERERFTUOgRERERU1DoEREREVNQ6HFz3t7uv+YhP10p9Hb4+eteayIit0Nnb7k5q9VKxLKWri5DckBEqM7CExG5HZrpEREREVNQ6BERERFTUOgRERERU9Cants0YcIEfvnlF5KSkkhNTaVixYrs3r2bmJgYatWq5eryRERE5P9T6LlNYWFhAKxatYojR44wePBgF1ckIiIiV6PQkwvCwsJo1aoVp06dYtOmTaSmppKUlETPnj3ZuHEjhw4d4vXXX6dFixYsWrSIr776iszMTIoWLUpUVBTDhg0jODiY//znP8TFxTFx4kTeeecdwsPDuXjxImfPniUkJITu3bu7ulUREZF8Q2t6cllKSgqzZ8/m2WefJSYmhvfee49Ro0axatUqHA4H586dY/78+SxevJjMzEz27t1LSEgIq1evBmDFihV07tyZo0eP0rp1a+bOncvMmTOZP3++axsTERHJZzTTk8v+vnBe0aJFCQgIwGKxULx4cdLS0rBarXh5eTFo0CAKFy7MiRMnyMzMJDAwkLFjx3L69Gk2b97MoEGDOHPmDAsWLOCrr77Cx8eHzMxMF3cmIiKSvyj05DKLxXLN1/bv38+GDRtYvnw5ly9fpmPHjhiGgcViITg4mLFjx9KoUSO8vLyYO3cuderUoXv37mzdupVvv/02D7sQERHJ/xR6XKhy5coUKlSIjh074u3tTZkyZTh58iQAHTt25D//+Q+ffPIJAE2bNiUiIoI1a9ZQokQJPDw8SE9PN8VtJkRERHKCQk8O6dixo/PnCRMmXPF6kyZNaNKkCfDXIa85c+YAsHDhwqtuLysri4cffpiAgAAA6tevz7p163K6bBEREdPQQuY70Jdffknfvn157bXXXF2KiIiI29BMzx2oZcuWtGypm4SKiIjkJM30iIiIiCko9IiIiIgp6PCWm3M4HESEfunqMiQHpKZdomCBwq4uQ0Qk39JMj5tLT093dQm5zm63u7qEPPHbkaOuLkFEJF9T6BERERFTUOgRERERU1DocXNmuGLz3/c3c3eV/Su7ugQRkXxNC5ndnNVq5f8+6eTqMiQHfNFupatLEBHJ1zTTIyIiIqag0CMiIiKmoNAjIiIipqDQIyIiIqag0JPDEhISCA0NdXUZIiIi8j8UekRERMQUdMp6LkhPT6dFixZ8+eWXeHh4EBkZyQMPPECpUqV47733AEhNTWXixIl4eXnx2muvcffddxMfH8+DDz7IW2+9RdeuXRk9ejRVq1bl22+/5ZtvvuH5558nIiKCtLQ0zp07x0svvUSLFi1c3K2IiEj+oJmeXODt7c3DDz/M999/T1ZWFrGxsTRv3pxDhw4RGRnJwoULadasGevWrQPg999/Z+zYsSxfvpzY2FiSkpIICQlh9erVAKxcuZLOnTtz5MgRnnnmGebNm8eIESP46KOPXNmmiIhIvqKZnlwSEhJCdHQ0DoeDhg0b4u3tTdmyZRk7diyFCxcmMTGRunXrAlCpUiV8fHwAKFOmDGlpabRq1YoOHTrQp08fTpw4Qc2aNTl06BAzZsxgxYoVWCwWMjMzXdmiiIhIvqKZnlxSr1494uPjWbFiBZ07dwZg+PDhjBs3jgkTJnDXXXdhGAYAFovlivcXKlSIwMBAxo4dS7t27QCYOnUq7dq1IzIyksDAQOf7RURE5MYUenJRcHAwp06domrVqgC0a9eO0NBQunbtSkpKCidPnrzu+0NDQ9mwYQPBwcEABAUFMXbsWLp3784PP/zA2bNnc70HERERd6HDWzmsQoUKLFu2DIDMzExCQkKcrw0bNoxhw4Zd8Z6/x//vz1lZWQQFBVGsWDEA2rRpQ5s2bXKrdBEREbem0JNLwsLCOHv2LFFRUf/q/YsWLWLlypVMmzYthysTERExJ4WeXDJhwoTben+PHj3o0aNHDlUjIiIiWtMjIiIipqDQIyIiIqagw1tuzuFw8EW7la4uQ3LApbRLFC5Q2NVliIjkW5rpcXPp6emuLiHX2e12V5eQJ44eOerqEkRE8jWFHhERETEFhR4RERExBYUeN+ft7e3qEnLd/fff7+oS8kRlfz9XlyAikq9pIbObs1qttFo9xtVlSA5Y22G4q0sQEcnXNNMjIiIipqDQIyIiIqag0CMiIiKmoNAjIiIipmC60LNt2zYaNGiAzWbDZrMRGhpKdHT0NccvXbqUjIyMXKmlUaNGVzzXv3//XNmXiIiI2Zny7K369eszZcoU4K8rFgcFBdGuXTuKFSt2xdhZs2bRvn37PKvtvffey7N9iYiImIkpQ88/JScnY7Va2b9/vzNwpKamMnHiRHbu3ElSUhIDBw5k+vTpTJgwgV27dgHQpk0bevXqRVhYGOfOnePcuXPMmDGDyZMnc+LECc6ePUuTJk0YMGAAYWFheHt78+eff3Ly5EkmTJhAzZo1nTW88847XLx4kTfffJPGjRuzefNmbDYb1atX59ChQyQnJzN16lTuuece3n//fTZs2EDJkiW5fPkyr776KoGBgS757ERERPIT0x3eAti6dSs2m42ePXsyZMgQRowYwaFDh4iMjGThwoU0a9aMdevWERISQpkyZZgyZQqbNm0iISGBZcuWsXjxYj777DMOHDgA/DVztGTJElJSUqhTpw5z5swhJiaGmJgY5z7Lly/PnDlzsNlsLF261Pn8xIkTyczMZOTIkVgslmx11qpVi/nz59OoUSM+//xz9u/fz3fffceKFSt4//33SUpKypsPTERExA2Ycqbnn4e3/rZhwwbGjh1L4cKFSUxMpG7dutlej4uLo169elgsFry8vKhduzZxcXEA+Pn9daXcEiVKsHfvXrZu3YqPj0+2m33+fdXgu+++m927dwNw6tQpDhw4QKVKla5aZ40aNZzvOXXqFHFxcTz44IN4eHjg4eHBAw88kAOfhoiIiDmYcqbnaoYPH864ceOYMGECd911F4ZhAGCxWHA4HAQEBDgPbWVkZPDjjz9SuXJl5xiAVatWUbRoUd5++2169+5Nampqtu38r9KlSzNnzhwOHz5MbGzsDWusUqUKe/fuxeFwkJ6ezq+//pojvYuIiJiBKWd6rqZdu3aEhoZSrFgxSpcuzcmTJwGoV68ezz33HAsXLmT79u106dKFjIwMgoKCsq3LAWjQoAGDBg1i165dFCpUiMqVKzu3cy0Wi4Vx48bRp08fli1bdt2x1apV4/HHHyc0NBRfX1+8vLzw9NRXKCIicjMsxt9TEXLHO336NOvWreOpp54iPT2d1q1bs2DBAsqXL3/N99jtdl7bvzIPq5TcYoZ7b9ntdlPcQNYMfZqhRzBHn/mtx+vVq2mCfMTX15d9+/bRqVMnLBYLISEh1w08IiIi8l8KPfmI1Wpl/Pjxri5DREQkX9JCZhERETEFzfS4OYfDYYq1IGZwKS2VwgUKuroMEZF8SzM9bu6f1wpyV3a73dUl5ImjR35zdQkiIvmaQo+IiIiYgkKPiIiImIJCj4iIiJiCQo+b8/b2dnUJuS4/XTTrn9KzMl1dgoiIqejsLTdntVppvWqGq8uQq/i84wuuLkFExFQ00yMiIiKmoNAjIiIipqDQIyIiIqag0JMHtm3bxsCBA52P161bR5s2bTh27BhLly7lqaeewmaz0bVrV7Zt2+bCSkVERNyXFjLnsc8//5w5c+Ywf/58tm3bxubNm5k/fz5eXl7Ex8fTo0cPVq9eTcmSJV1dqoiIiFtR6MlDH3/8MYsWLWLevHkUL16cJUuWMGzYMLy8vACoWLEiH3/8Mb6+vhw/fpwRI0aQlpZGgQIFGD16NOXKlSM6OprPPvsMi8VCq1at6Nmzp4u7EhERyR8UevLIzp07SUxM5Pz582RlZQFw8uRJKlasmG2cr68vABMnTsRms/H444+zZcsWJk+ezAsvvMDatWtZvHgxFouFp59+msaNG+Pv75/n/YiIiOQ3Cj15pEyZMsybN4/ly5czZMgQZs+ezT333MPx48cpWrSoc9z3339PtWrVOHjwILNmzeLDDz/EMAy8vLw4ePAgx44d4+mnnwbg/Pnz/PHHHwo9IiIiN0ELmfNI5cqVKVCgAD169MDLy4sZM2bQqVMnpk+fTmbmX1fm/e233wgPD8dqteLv78/gwYOJjo7mrbfeomXLlvj7+1OlShUWLlxIdHQ0HTt25L777nNxZyIiIvnDdWd6srKyyMrKYtCgQUyZMgXDMDAMg2effZaFCxfmVY1uZ9y4cbRv355JkyZRp04dunfvjpeXF1lZWURGRlKqVCmGDh1KREQEaWlppKamEh4eTvXq1WnQoAHdunUjPT2dWrVqUbZsWVe3IyIiki9cN/SsXLmSmTNncurUKYKCgjAMA6vVSr169fKqPrcQGBhIYGCg83HJkiWJjY0FoH79+s7DVf9UsWJF5syZc8Xzffv2pW/fvrlWq4iIiLu6bugJDQ0lNDSUFStW0Llz57yqSURERCTH3dSantq1a/Pjjz/y888/06tXL7Zs2ZLbdYmIiIjkqJsKPSNHjsTb25sZM2YwcOBA3nvvvdyuS0RERCRH3VTo8fT0pGrVqmRkZFCnTh3ndWZERERE8oubuk6PxWLhtddeo0mTJqxdu5ZChQrldl2SQxwOB593fMHVZchVpGdl4u2hS2WJiOSVm5rpmTJlCp07d6ZXr16ULFmSKVOm5HZdkkPS09NdXUKus9vtri7hX1HgERHJW9cNPZs2bQJg/fr1nDhxgqVLl3L06FG+/PLLPClOREREJKdc95+a586dAyApKSkvahERERHJNdcNPR06dADAarXy4osvOp9/++23c7cqERERkRx23dCzfPlyVqxYQVxcnPMKwg6Hg4yMDF577bU8KVBuj7e3t6tLyHX333+/q0v4V9KzsvD28HB1GSIipnHd0NOuXTsaNGjArFmz6NevH/DXrE+pUqXypDi5fVarlTYrPnJ1GXIVn3V+ytUliIiYynVDj7e3NxUqVODPP//knnvuyauaRERERHLcTZ0zW7RoUTZs2ICfnx9W618nfPn5+eVqYSIiIiI56aZCz5kzZ1i4cKHzcXp6OkuWLMm1okRERERy2k1dnLBVq1YkJiaSkJBAQkICZ86cye267mjx8fG8/PLL2Gw2unbtSkREBMnJyYSFhTkXfP8bsbGxLF269IrnQ0NDSUhIuJ2SRURETO+mZnqWL19OdHQ0M2bMICgoKNusj9mkpqby4osvMmbMGGrXrg3A6tWree211/D19b2tbTdp0iQnShQREZGruKnQ4+vry1133UVKSgqBgYFMmzYtt+u6Y33zzTc88sgjzsADf13PKCYmhuLFiwOQnJxMeHg4Fy9e5OzZs4SEhNC9e3dsNhvVqlXj0KFDFC5cmHr16vH9999z4cIF5s6dy8aNGzly5AiDBw9mypQpfPfdd9x9992cPXsWgIsXLxIeHu58PHz4cKpVq5b3H4KIiEg+dFOHt/5eyGyxWFiyZImpD2/Fx8dTqVKlK56vUKECO3fuBODo0aO0bt2auXPnMnPmTObPn+8cV6tWLRYsWEB6ejoFCxZk3rx5VKlShR07djjHHDx4kB07drBixQomTZpESkoKADNnzqR+/fpER0czevRoIiIicrVXERERd3JTMz1jxozhjz/+4LXXXmPu3Lmm/su2bNmy7Nmz54rnf//9d+rVqwdA6dKlWbBgAV999RU+Pj5kZmY6x9WsWROAYsWKUaVKFefPaWlpzjGHDx/mgQcewGq14uPjw3333Qf8FYa2bt3KF198AcCFCxdyp0kRERE3dFOhx8fHhxo1agAQFhaWqwXd6Zo3b87MmTPZs2cPtWrVAv5a81SyZEnn6fxz586lTp06dO/ena1bt/Ltt9/e0j78/PxYuHAhDoeD1NRUDh8+DIC/vz9t27YlODiY06dPs3z58pxtTkRExI3dVOiR/ypSpAgzZ85k3LhxnDt3jqysLKpVq8Y777zDuHHjAGjatCkRERGsWbOGEiVK4OHhQXp6+k3v4/777ycoKIjOnTtz1113Oa+A3a9fP8LDw1m2bBnJycn0798/V3oUERFxRxbDMAxXFyG5x263M+SX3a4uQ67iVm9DYbfb8+19xm6WGXoEc/Rphh7BHH3mtx6vV+9NLWQWERERye8UekRERMQUFHpERETEFBR6RERExBR09pabczgct7xgVvJGelYW3h4eri5DRMQ0NNPj5m7lVPn8ym63u7qEf0WBR0Qkbyn0iIiIiCko9IiIiIgpKPS4OW9vb1eXkOvupItmpWdluboEERG5Bi1kdnNWq5W2K9a4ugzT+LRzsKtLEBGRa9BMj4iIiJiCQo+IiIiYgkKPiIiImIJCj4iIiJiCQs9t2LZtG9WqVWPt2rXZng8ODiYsLOy677XZbHTu3Bmbzeb875tvvrml/X/wwQfs2bPnVssWERExJZ29dZv8/f357LPPaNWqFQAHDhzg8uXLN/XeiRMnEhAQ8K/3/dxzz/3r94qIiJiNQs9tql69Or///jsXLlygWLFifPrppwQHB3P8+HEAmjZtir+/P/7+/oSHh99we1lZWbz55pucOHGCs2fP0qRJEwYMGMCbb77Jb7/9BsC+fft488032bZtG61ataJJkya52qOIiIg7UOjJAU888QTr16+nY8eO7Nmzh2effdYZeo4fP86qVavw9fW94n1Dhw6lUKFCzsdTp07l0qVL1KlTh5CQENLS0pyhZ9SoUQAsWbKEu+++m/bt27Nt27a8aVBERMQNKPTkgODgYCIiIqhYsSL16tXL9pqvr+9VAw9c/fCWt7c3e/fuZevWrfj4+GS7YejatWvZuHEj06dPx2Kx5HwjIiIibkwLmXNAxYoVuXTpEtHR0bRt2zbba1brrX3Eq1atomjRorz99tv07t2b1NRUDMMgNjaWRYsWMXXqVLy8vHKyfBEREVPQTE8OadWqFZ988gl+fn7Ex8ff1Hv+9/DW//3f/9GgQQMGDRrErl27KFSoEJUrV+bkyZO88sor3HfffTz//PMANGvWLFf6EBERcVcKPbchMDCQwMBAAOdp5wBNmjRxLi7evHnzVd8bHR19ze2uWXPlvbJ++umn26xWRETE3HR4S0RERExBoUdERERMQaFHRERETEGhR0RERExBC5ndnMPh4NPOwa4uwzTSs7Lw9vBwdRkiInIVmulxc/+8uKG7stvtri7BSYFHROTOpdAjIiIipqDQIyIiIqag0OPmvL0LuLqEXHf//fe7ugSn9CyHq0sQEZFr0EJmN2e1Wuiw8ntXl2Eaqzs1dnUJIiJyDZrpEREREVNQ6BERERFTUOgRERERU1DoEREREVPIlYXM8fHxTJo0iXPnzpGRkUH16tUZPHgwPj4+2cbFxsaydu1aJkyYcNv7/OCDD6hfvz61atXK9vzOnTv55ZdfyMjI4Ntvv+XChQucPHmSKlWqADB27FgGDx7MsmXLbml/27ZtY8CAAc7tAPj6+jJt2rRb2s6qVasoXrw4zZs3v+n3GIZBWFgYb731FgULFryl/YmIiJhVjoee1NRUXnzxRcaMGUPt2rUBWL16Na+99hqzZs3K6d05Pffcc1c8ZxgGUVFRzJ49G29vb/r27cu2bdtYsmQJU6ZMASAhIeFf77N+/frO7fxbHTt2vOX3WCwW2rRpw4cffkj//v1va/8iIiJmkeOh55tvvuGRRx5xBh6ADh06EBMTQ3x8POnp6bzxxhsUKlSIQoUKUbx4cQAaNWrE5s2bARg4cCBdu3blzz//5NtvvyU1NZU//viDZ599lo4dO/LRRx/x8ccfY7VaqVu3LkOHDiUsLIxWrVrRpEkT5343b95MlSpV8Pb2vm7NZ86c4cUXXyQpKYlq1aoxZswYjh8/zogRI0hLS6NAgQKMHj2acuXK3dRnYLPZqF69OocOHSI5OZmpU6dyzz338P7777NhwwZKlizJ5cuXefXVV9m+fTulS5fG39+f2bNn4+XlRUJCAq1ateKFF164Zh0NGzZkwoQJvPjii1itOkopIiJyIzn+t2V8fDyVKlW64vkKFSpw7Ngxpk6dyiuvvML8+fN56KGHbri95ORkZs2axYwZM/jggw+Avw4JhYeHs3TpUipWrEhmZuZV37t9+3aqVat2U/sYP348S5cuZcuWLZw+fZqJEydis9mIjo6mT58+TJ48+Yr3bd26FZvN5vzvww8/dL5Wq1Yt5s+fT6NGjfj888/Zv38/3333HStWrOD9998nKSnpiu0dO3aMqKgoli5d6tzWterw8PCgZMmSHDx48Ib9iYiISC7M9JQtW5Y9e/Zc8fzvv/9O+fLlOXTokHPdTd26dTly5MgVYw3DcP5cvXp1AMqVK+e8eeb48eOZO3cukydPpk6dOtnG/9PZs2ezzThdS8WKFZ0zTqVKleLy5cscPHiQWbNm8eGHH2IYBl5eXle873qHt2rUqAHA3XffzalTp4iLi+PBBx/Ew8MDDw8PHnjggSvec9999+Hp6Ymnp6dzrc716rjrrrs4d+7cDfsTERGRXAg9zZs3Z+bMmezZs8cZbpYvX07JkiWpWLEi/v7+/PjjjzRp0oR9+/Y535eZmUlKSgpeXl4cPnzY+bzFYrliH8uWLeOtt96iQIEC9OnThx9//PGqtZQsWZKLFy/esOar7cPf35/evXtTt25d4uLi2LFjxw23cz1VqlQhOjoah8NBZmYmv/76623Xcf78eUqVKnVbdYmIiJhFjoeeIkWKMHPmTMaNG8e5c+fIysqiWrVqvPPOOwCMHDmSgQMHMmfOHEqWLEmBAn/dG6pnz5506dKFChUqUL58+evuo1q1anTu3BlfX1/Kli1L7dq1WbVq1RXjAgMDWb9+Pe3bt7/lPoYOHUpERARpaWmkpqYSHh5+xZi/D2/90+zZs69Z8+OPP05oaCi+vr54eXnh6Xnjj/9adTgcDhITE7OdPSYiIiLXZjGudWzIDTgcDnr16sWcOXNuuJg5t50+fZp169bx1FNPkZ6eTuvWrVmwYMENA961fPvtt/zyyy+8+OKL1x1nt9t549fT/2ofcuty895bdrv9jrq5am4wQ49gjj7N0COYo8/81uP16nXr036sVisvvfQSixcvdnUp+Pr6sm/fPjp16kT37t0JCQn514HHMAzWrFnD008/nbNFioiIuDG3v8t6/fr1qV+/vqvLwGq1Mn78+BzZlsViuerZZCIiInJtbj3TIyIiIvI3t5/pMTuHw8jVdSaSXXqWA28P/VtCROROpN/Obi49Pc3VJeQ6u93u6hKcFHhERO5c+g0tIiIipqDQIyIiIqag0CMiIiKmoNDj5ry9C7i6hFyXmxfNSs9y5Nq2RUQkb+nsLTdntVrosurwjQfKVS3tqNt8iIi4C830iIiIiCko9IiIiIgpKPSIiIiIKSj03IJt27ZRrVo11q5dm+354OBgwsLCbmlbzZo1Iy3N/S8cKCIicqdQ6LlF/v7+fPbZZ87HBw4c4PLlyy6sSERERG6Gzt66RdWrV+f333/nwoULFCtWjE8//ZTg4GCOHz/Op59+yoIFC/D29ubee+9l1KhRAIwcOZKjR4/icDgYMGAAgYGBzu0lJCQQHh5OZmYmFouF4cOHU716dZYvX85HH31E8eLF8fLyolWrVgQHB/PGG28QHx9PVlYWzzzzDK1atXLVRyEiIpKvaKbnX3jiiSdYv349hmGwZ88eHnroIc6dO0dUVBQLFiwgJiaGokWLsnTpUpYvX46vry8fffQR06dPdwahv02aNAmbzcZHH31EeHg4b7zxBmfOnOHDDz8kJiaGuXPnOmeSli5diq+vL0uWLGHevHm8++67nDlzxhUfgYiISL6jmZ5/ITg4mIiICCpWrEi9evUAcDgcVKlSBR8fHwAeeeQRvv/+eywWC7t27WLPnj0AZGZmcvbsWee24uLieOSRR4C/LrJ34sQJ/vjjDwICAihUqBAADz30kHNsw4YNAfDx8SEgIID4+HhKliyZN42LiIjkY5rp+RcqVqzIpUuXiI6Opm3btgBYLBbi4uK4dOkSANu3b8fPzw9/f39at25NdHQ0s2fPJigoiOLFizu3FRAQwM6dO4G/7hZeunRpKlWqxJEjR0hNTcXhcDgD0z/HJicnc/DgQSpUqJCXrYuIiORbmun5l1q1asUnn3yCn58f8fHx+Pr60qZNG3r27InVaqVSpUoMHjzYuU6nR48eJCcn0717d6zW/2bN119/nREjRjB37lwyMzMZO3YsJUuW5Nlnn6V79+6UKFGCtLQ0PD09CQ0NZcSIEXTr1o20tDT69+9PqVKlXPgpiIiI5B8KPbcgMDDQuQjZZrNhs9kAaNKkCU2aNAH+OvT1vyZNmnTFc19//TUAFSpUYN68edley8zM5OTJk6xatQqAp556inLlyuHt7c3EiRNzriERERETUei5A3l6enL58mU6dOiAl5cXtWrVcq4dEhERkX9HoecONWjQIAYNGuTqMkRERNyGFjKLiIiIKSj0iIiIiCno8JabczgMlnas4uoy8q30LAfeHvq3gYiIO9BvczeXnu7+NzW12+25tm0FHhER96Hf6CIiImIKCj0iIiJiCgo9IiIiYgoKPW7O27uAq0vIdffff3+ubTszy8i1bYuISN7S2Vtuzmq18P7qRFeXkW+91KGsq0sQEZEcopkeERERMQWFHhERETEFhR4RERExBdOHnldeeYUPPvjA+TglJYWWLVuyf//+W97W2LFjOXbs2BXPr127ljp16pCY+N+1NWFhYcTGxt7UdmNiYoiKirrlekREROS/TB96IiIiiImJ4fDhwwBMnDiRLl26UL169VveVnh4OOXLl7/i+eXLl9OjRw+WLVt22/WKiIjIv2P6s7dKlizJiBEjGD58OIMGDSI+Pp4XXniBvn37kpaWRoECBRg9ejRZWVkMHDiQcuXKkZCQQOvWrTl06BC//vor//nPfxg0aBA2m42IiAgCAgKc24+Pj+f8+fM8//zzdOjQgX79+uHl5eV8PTk5mfDwcC5evMjZs2cJCQmhe/fu7Ny5k3HjxlG8eHGsVit16tQBIDo6ms8++wyLxUKrVq3o2bNnXn9kIiIi+ZLpQw9As2bNWL9+PWFhYcTExDB+/HhsNhuPP/44W7ZsYfLkyQwcOJD4+Hjmzp1LamoqzZs3JzY2lkKFCtG0aVMGDRp01W2vWLGCTp06UbRoUerUqcP69etp1aqV8/WjR4/SunVrnnzySRITE7HZbHTv3p3x48fz9ttv4+fnx8iRIwE4fPgwa9euZfHixVgsFp5++mkaN26Mv79/nnxOIiIi+ZlCz//Xvn17UlNTKVu2LAcPHmTWrFl8+OGHGIbhnJmpWLEiRYsWxdvbm9KlS1OiRAkALBbLVbeZlZXFmjVruOeee/j66685f/48ixYtyhZ6SpcuzYIFC/jqq6/w8fEhMzMTgMTERPz8/ACoW7cuf/zxBwcPHuTYsWM8/fTTAJw/f54//vhDoUdEROQmKPRchb+/P71796Zu3brExcWxY8cO4Nrh5lq+/fZbHnjgAaZNm+Z87n8XSc+dO5c6derQvXt3tm7dyrfffgtAmTJliIuLIyAggL1791K8eHH8/f2pUqUKH374IRaLhfnz53PfffflQMciIiLuT6HnKoYOHUpERARpaWmkpqYSHh7+r7azbNkyQkJCsj3XuXNnPvroI+fjpk2bEhERwZo1ayhRogQeHh6kp6cTGRnJ0KFDKVKkCEWKFKF48eJUr16dBg0a0K1bN9LT06lVqxZly+qKwSIiIjfDYhiGbi7kxux2O1/vL+nqMvKtO+k2FHa7PVfvM3YnMEOPYI4+zdAjmKPP/Nbj9eo1/SnrIiIiYg4KPSIiImIKCj0iIiJiCgo9IiIiYgo6e8vNORzGHbUYN7/JzDLw9Li1SxWIiMidSTM9bi49Pc3VJeQ6u92ea9tW4BERcR8KPSIiImIKCj0iIiJiCgo9bs7bu4CrS8h1uXnRrKwsXbtTRMRdaCGzm7NaLXyx9JSry8i3/q9LaVeXICIiOUQzPSIiImIKCj0iIiJiCgo9IiIiYgoKPSIiImIKpg4927ZtY+DAgc7H69ato02bNhw7doyxY8dy7Nixf7XdVatW8Z///AebzcZTTz1Fjx492LJlyy1vJykpiYiIiOvuZ/Lkyf+qRhEREbPR2Vv/3+eff86cOXOYP38+pUuXJjw8/La216ZNGwYPHgzAqVOneOqpp1i0aBFlypS56W2UKVPmuqFHREREbp5CD/Dxxx+zaNEi5s2bR/HixQGw2WxERERw7tw5Jk6ciKenJ8WKFWPy5MkkJSUxbNgwPD098fDwYNKkSZQte+37W5UuXZqWLVvyzTff8H//93+Eh4dz8eJFzp49S0hICN27d8dms1G9enUOHTpEcnIyU6dOxTAMBg0axLJly9i+fTtTpkzBw8ODihUrMmrUqLz6eERERNyCqQ9vAezcuZNly5Zx/vx5srKyrnh9w4YNPPHEEyxatIjOnTtz4cIFfvjhB2rWrMm8efPo168f58+fv+F+SpUqxdmzZzl69CitW7dm7ty5zJw5k/nz5zvH1KpVi/nz59OoUSM+//xz5/OGYTBixAjee+89Fi1aRNmyZVm9enWO9C8iImIWpg89ZcqUYd68efTq1YshQ4bgcDiyvd6vXz/OnDlDr169WLduHZ6ennTu3BlfX1/69u3LRx99hIeHxw33c+zYMcqWLUvp0qXZsGEDgwcPZsaMGWRmZjrH1KhRA4C7776btLT/3ij0zJkznDx5kgEDBmCz2di8efO/Xm8kIiJiVqYPPZUrV6ZAgQL06NEDLy8vZsyYke31NWvW0KFDB6Kjo6latSrLli1j48aNPPzwwyxYsICgoCA+/PDD6+7j5MmTbNy4kccff5y5c+dSp04dJk+eTFBQEIZx49sc+Pr6cvfddzN9+nSio6Pp168fgYGBt9W3iIiI2WhNzz+MGzeO9u3b8/DDDzufe/DBBwkLC6Nw4cJ4eXkxatQoDMNgyJAhREVFYbVaGTZs2BXb+uyzz/j555+xWq0YhsH48eMpUaIETZs2JSIigjVr1lCiRAk8PDxIT0+/bl1Wq5Xw8HCee+45DMOgSJEiTJo0iePHj+f4ZyAiIuKuLMbNTDVIvmW32/l9z82fMSbZ3Un33rLb7bl6c9U7gRl6BHP0aYYewRx95rcer1ev6Q9viYiIiDko9IiIiIgpKPSIiIiIKSj0iIiIiCno7C0353AYd9Ri3PwmK8vAw8Pi6jJERCQHaKbHzaWnp914UD5nt9tzbdsKPCIi7kOhR0RERExBoUdERERMQaHHzRXwLuDqEnLdrV40y5Gp63GKiJiRFjK7OYvVwo8fnnR1GXeUh/re5eoSRETEBTTTIyIiIqag0CMiIiKmoNAjIiIipqDQIyIiIqag0JPDPvjgA55++ml69+5Nnz592LdvHzabjbi4uBu+NyEhgdDQ0DyoUkRExHx09lYOOnz4MF9//TUxMTFYLBbsdjtDhw6lePHiri5NRETE9DTTk4NKlizJsWPHWLFiBYmJidx///2sWLHC+frXX3+NzWbjwoULbN++nZ49e9KzZ09CQ0P57bffsm1r+/btdOvWjR49ejBs2DAyMjL47bff6Nq1Kz169KBXr14kJibmdYsiIiL5lmZ6clDJkiWZMWMGixYt4v3336dgwYIMHDgQgPXr17Njxw5mzZpF4cKFOXToEJGRkZQtW5aZM2eybt06goODATAMgxEjRrB48WJKlSrFu+++y+rVq8nIyKBmzZqEhYWxc+dOzp8/T9myZV3ZsoiISL6h0JODjh49io+PD+PHjwdg7969PPfcc5QuXZotW7aQnJyMp+dfH3nZsmUZO3YshQsXJjExkbp16zq3c+bMGU6ePMmAAQMASE1NpVGjRrzwwgvMnj2bvn37UrRoUWegEhERkRvT4a0cdODAASIiIkhL++vO5n5+fhQtWhQPDw/efPNNGjduzLRp0wAYPnw448aNY8KECdx1110Yxn9vjeDr68vdd9/N9OnTiY6Opl+/fgQGBrJx40YefvhhFixYQFBQEB9++KFL+hQREcmPNNOTg5588kni4uIICQmhcOHCGIbB66+/zoIFCwB46aWXCAkJ4T//+Q/t2rUjNDSUYsWKUbp0aU6e/O+tIqxWK+Hh4Tz33HMYhkGRIkWYNGkSKSkpDBkyhKioKKxWK8OGDXNVqyIiIvmOxfjnFIO4HbvdTurmUq4u446SX++9Zbfbb/nmqvmNGXoEc/Rphh7BHH3mtx6vV68Ob4mIiIgpKPSIiIiIKSj0iIiIiCloIbObMxxGvl3DklscmQZWT4uryxARkTymmR43l5ae5uoScp3dbr+l8Qo8IiLmpNAjIiIipqDQIyIiIqag0CMiIiKmoNDj5gp4F3B1CbnuVi+aZWTqepwiImaks7fcnMVq4fikP11dxh2l3Ov3uLoEERFxAc30iIiIiCko9IiIiIgpKPSIiIiIKWhNTw7LyMjgjTfe4M8//yQ9PZ0XXniB9evX88svv1CiRAnS09OpWrUqI0eOxMvL66rbiIqKonTp0nTr1i2PqxcREXFfmunJYZ9++iklSpRg8eLFzJ49m9GjRwMwZMgQoqOjWbp0KZcvX2bjxo0urlRERMRcNNOTw4KCgmjZsqXzsYeHR7bXs7KySElJoXz58gC8/fbb7Nu3j5SUFAICAhg/fjwAGzZs4IsvviA1NZXhw4dTq1YtFi1axFdffUVmZiZFixYlKioKb2/vvGtOREQkH9NMTw4rUqQIPj4+JCcn88orrzBgwAAAIiMjsdlstGrVilOnTuHn50dycjLFihVj3rx5LFmyhJ9++onExEQA7rnnHhYuXMjYsWMZOXIkDoeDc+fOMX/+fBYvXkxmZiZ79+51YaciIiL5i2Z6csHx48d56aWX6N69O8HBwWzevJkhQ4bQpEkTAKZOncqECROIiIjgzJkzDBo0iMKFC3Pp0iUyMjIAeOSRRwCoWrUqSUlJWK1WvLy8nGNPnDhBZmamy3oUERHJbxR6ctipU6fo3bs3b775Jg0aNLjqmHLlyvHnn38SGxvL8ePHeffddzlz5gzr16/HMP66WvCePXsIDg7mwIEDlC9fnv3797NhwwaWL1/O5cuX6dixo3OsiIiI3JhCTw6bOXMmFy5cYPr06UyfPh2AUqVKERkZyezZs7FarTgcDsaNG0fBggWZPn06oaGheHt7U7FiRU6ePAlAQkICPXv2JD09nVGjRlG5cmUKFSpEx44d8fb2pkyZMs6xIiIicmMKPTls+PDhDB8+/KbHr1y58ornHn744auOXbhw4b+uS0RExOy0kFlERERMQaFHRERETEGhR0RERExBoUdERERMQQuZ3ZzhMCj3+j2uLuOOYmQaWDwtri5DRETymGZ63FxaepqrS8h1drv9lsYr8IiImJNCj4iIiJiCQo+IiIiYgkKPmyvgXcDVJeS6+++//5bGG5mOXKpERETuZFrI7OYsVguJ7+5ydRl3lLIDrn7FaxERcW+a6RERERFTUOgRERERU1DoEREREVNQ6BERERFT0EJmYNu2bSxZsoQpU6bc8nsbNWrE5s2bbzjOZrNx+fJlChUqREZGBhUqVCA8PBxfX99b2l9UVBSlS5emTp06bNy4kf79+99yzSIiImak0JOHJk6cSEBAAACffvopb775JlFRUf9qW/fff/8tn6otIiJiZgo917Fu3To++ugj5+OpU6dSvHhxRowYweHDh6lYsSLp6ek4HA5atmzJ8uXLKVGiBIsXL+bSpUv07dv3mttu27Yt7777LmlpacTFxTF69Gg8PDwoUKAAo0ePxuFw8Nprr3H33XcTHx/Pgw8+yFtvveV8/+3MTomIiJiR1vRcx++//84HH3xAdHQ0fn5+fP/998TGxpKWlsayZct47bXXuHz5MlarleDgYD7//HPgr1mc9u3b33D7xYoV48KFCwwfPpw333yTRYsW0a1bNyZMmODc/9ixY1m+fDmxsbEkJSXlZrsiIiJuTaHnOkqVKsXQoUMZNmwYBw4cIDMzk0OHDlGrVi0AypcvT7ly5QDo3Lkzn376KQcPHqR06dKULl36uts2DINTp05RqlQpTp486TxU9cgjj3Do0CEAKlWqhI+PDx4eHpQpU4a0NPe/eaiIiEhuUei5hosXLzJt2jSmTJnCmDFjKFCgAIZh4O/vz08//QRAYmIiiYmJwF8BqGjRosycOZPOnTvfcPsrVqygfv36WK1W7rrrLvbv3w/Ajh07uPfeewGwWHQ3cBERkZyiNT3/3+bNm+nYsaPz8eTJk6lbty4dOnSgcOHCFCtWjJMnT9KpUyd27dpFSEgI5cuXz3b2VWhoKGPGjCEyMvKq+xg6dCiFChUCoGzZsowcORKAMWPGMHr0aAzDwMPDg3HjxuVipyIiIuZkMQzDcHUR7mLt2rUcOnSIV1991dWlONntdkp+ecnVZdxR8uu9t+x2u9ufsWeGHsEcfZqhRzBHn/mtx+vVq5meHPLOO++wc+dOpk+f7upSRERE5CoUenLIoEGDXF2CiIiIXIcWMouIiIgpKPSIiIiIKejwlpszHEa+XbibW4xMBxZP5X0REbPRb343l5bu/hc0tNvttzRegUdExJz0219ERERMQaFHRERETEGhx80V8PZ2dQm57lYvmmVkZuVSJSIicifTQmY3Z7FaOfneV64u445yV/8nXV2CiIi4gGZ6RERExBQUekRERMQUFHpERETEFBR6RERExBQUeq5h27ZtNGjQAJvNRo8ePejatStxcXG3vJ2oqChiYmJuevyxY8do3bq18/Fnn31GjRo1OH36NAAJCQm0b9/+lusQERExO4We66hfvz7R0dEsWrSI/v37M2nSpFzfZ/ny5XE4HJw5cwaAb7/9lieffJLY2FjgrzD22GOP5XodIiIi7kah5yZduHCBe+65B4Dt27fTs2dPevbsSWhoKL/99hsJCQl06dKFV199lY4dOzJy5Mhs7z969CidOnVi//79dO3alUOHDgF/hZq33nor29iGDRuye/duHA4HBw8epHfv3nzzzTfOfSv0iIiI3Dpdp+c6tm7dis1mIz09nQMHDjBr1iwADh06RGRkJGXLlmXmzJmsW7eO4OBgfv/9d+bMmUOhQoVo0aIFSUlJAPz222+sXLmSt99+m3vvvZeQkBBWr17N66+/zsqVK3n++eez7bdhw4bs2LGDu+66i5o1a/Lggw+yf/9+HA4Hv/76K2PGjMnzz0JERCS/U+i5jvr16zNlyhQAjhw5QteuXYmNjaVs2bKMHTuWwoULk5iYSN26dQGoVKkSPj4+AJQpU4a0tL9u9hkbG4unpyceHh4AtGrVig4dOtCnTx9OnDhBzZo1s+03MDCQ2bNn4+Pjw+OPP47FYqF27dps2rSJSpUq4eXllVcfgYiIiNtQ6LlJpUuXdv48fPhwNmzYgI+PD0OHDsUwDAAsFstV39urVy8qV67M66+/zqJFiyhUqBCBgYGMHTuWdu3aXTHex8cHb29vNm/eTK9evQBo0qQJH3744VXHi4iIyI1pTc91/H14q1evXvTu3ZuwsDAKFixIu3btCA0NpWvXrqSkpHDy5Mkbbqthw4ZUrVqV2bNnAxAaGsqGDRsIDg6+6vhHH30UT09PihYtCkCjRo346aefnOt51qxZw9KlS3OoUxEREfdnMf6eppA8tWfPHhYtWpTrZ4TZ7XZKbYzP1X3kN/n13lt2u/2Wb66a35ihRzBHn2boEczRZ37r8Xr16vCWCyxatIiVK1cybdo0V5ciIiJiGgo9LtCjRw969Ojh6jJERERMRWt6RERExBQUekRERMQUdHjLzRkOR75duJtbjMwsLJ4eri5DRETymGZ63FxaerqrS8h1drv9lsYr8IiImJNOWXdzP/30EwUKFHB1GSIiInkiLS2NOnXqXPU1hR4RERExBR3eEhEREVNQ6BERERFTUOgRERERU1DoEREREVNQ6BERERFT0MUJ3ZDD4SAiIoIDBw7g7e3NmDFjqFy5sqvLyhXt27enaNGiAFSoUIHx48e7uKKc8/PPPzN58mSio6M5evQoYWFhWCwWqlatysiRI7Fa3ePfLP/s85dffqFfv37ce++9AHTr1o1WrVq5tsDbkJGRwRtvvMGff/5Jeno6L7zwAlWqVHG77/Jqfd59991u9V1mZWUxfPhwfvvtNzw8PBg/fjyGYbjdd3m1Pi9evOg236VCjxvasGED6enpLF26lJ9++okJEyYwY8YMV5eV49LS0gCIjo52cSU5b/bs2Xz66acUKlQIgPHjxzNgwAACAwN588032bhxI0888YSLq7x9/9vnr7/+yjPPPEPv3r1dXFnO+PTTTylRogSRkZGcPXuWDh06UL16dbf7Lq/W50svveRW3+WmTZsAWLJkCdu2bXOGHnf7Lq/WZ7Nmzdzmu8zfkVSuateuXTz22GMA1KlTh3379rm4otyxf/9+Ll++TO/evenZsyc//fSTq0vKMZUqVSIqKsr5+JdffuHRRx8FoEmTJvzwww+uKi1H/W+f+/bt45tvvuGpp57ijTfeIDk52YXV3b6goCBeffVV52MPDw+3/C6v1qe7fZctWrRg9OjRABw7dozSpUu75Xd5tT7d6btU6HFDycnJ+Pj4OB97eHiQmZnpwopyR8GCBenTpw9z5szhrbfeYvDgwW7TZ8uWLfH0/O9ErGEYWCwWAIoUKcLFixddVVqO+t8+a9Wqxeuvv85HH31ExYoVef/9911Y3e0rUqQIPj4+JCcn88orrzBgwAC3/C6v1qe7fZcAnp6eDB06lNGjR9OyZUu3/C7hyj7d6btU6HFDPj4+pKSkOB87HI5sf7G4Cz8/P9q2bYvFYsHPz48SJUqQlJTk6rJyxT/XCaSkpFCsWDEXVpN7nnjiCR544AHnz7/++quLK7p9x48fp2fPnrRr147g4GC3/S7/t093/C4BJk6cyJdffsmIESOch9jBvb5LyN5n48aN3ea7VOhxQ3Xr1iU2Nhb4695b9913n4sryh0rVqxgwoQJACQmJpKcnEyZMmVcXFXuqFGjBtu2bQMgNjaWevXqubii3NGnTx/27NkDwJYtW6hZs6aLK7o9p06donfv3gwZMoTOnTsD7vldXq1Pd/suP/74Y2bNmgVAoUKFsFgsPPDAA273XV6tz/79+7vNd6l7b7mhv8/eOnjwIIZhMG7cOAICAlxdVo5LT09n2LBhHDt2DIvFwuDBg6lbt66ry8oxCQkJDBo0iGXLlvHbb78xYsQIMjIy8Pf3Z8yYMXh4uMfd4v/Z5y+//MLo0aPx8vKidOnSjB49Otuh2vxmzJgxfPHFF/j7+zufCw8PZ8yYMW71XV6tzwEDBhAZGek23+WlS5cYNmwYp06dIjMzk2effZaAgAC3+//yan2WK1fObf6/VOgRERERU9DhLRERETEFhR4RERExBYUeERERMQWFHhERETEFhR4RERExBfe7Yp2ISC44dOgQkZGRXL58mUuXLvH444/z8ssvO6/I+2+lpaXx6aefEhISkkOVisi1aKZHROQGLly4wKBBg3jjjTeIjo5m2bJlHDx4kCVLltz2tpOSkli+fHkOVCkiN6LQIyJyAxs3biQwMJB7770X+Ot+dhMnTqRTp05MmDCBkJAQQkJCWLBgAQBhYWHOq6LHxsYSFhYGwJNPPklYWBhdunThxRdfJCsri5kzZ3L48GHee+89l/QmYiY6vCUicgMnT56kYsWK2Z4rUqQImzZtIiEhgWXLlpGZmUn37t2pX7/+NbcTHx/PggULKFeuHF27dmXv3r3069ePgwcP0r9//9xuQ8T0NNMjInID5cuX58SJE9mei4+P55dffqFevXpYLBa8vLyoXbs2cXFx2cb986L3vr6+lCtXDoBy5cplu2GliOQ+hR4RkRto2rQp3333HX/88QcAGRkZTJgwgWLFirFr1y7ncz/++COVK1fG29ubpKQkgGx3pL7aomer1YrD4ciDLkREh7dERG7Ax8eHCRMmMHz4cAzDICUlhaZNm2Kz2Th+/DhdunQhIyODoKAgatasSUhICG+88QZr1qxxrgO6llKlSpGRkUFkZCRDhgzJm4ZETEo3HBURERFT0OEtERERMQWFHhERETEFhR4RERExBYUeERERMQWFHhERETEFhR4RERExBYUeERERMQWFHhERETGF/wcYoe6igaQ01gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8,8])\n",
    "plt.title(\"Artistes with the Highest No of Songs\")\n",
    "\n",
    "# Resetting Index so that the Artist Index would be a column itself\n",
    "sns.barplot(y=\"artist\", x=\"name\", data=artist_no_of_songs.reset_index())\n",
    "plt.xlabel(\"Count\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What are the top 20 albums with the highest songs?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Top Albums\n",
    "album_no_of_songs = df.groupby(\"album\").count().sort_values(\"name\",ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8,8])\n",
    "plt.title(\"Albums with the Highest No of Songs\")\n",
    "\n",
    "sns.barplot(y=\"album\", x=\"name\", data=album_no_of_songs.reset_index())\n",
    "plt.xlabel(\"Count\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What are the top 30 Most Danceable Songs?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>album</th>\n",
       "      <th>artist</th>\n",
       "      <th>artist_top_genre</th>\n",
       "      <th>release_date</th>\n",
       "      <th>length</th>\n",
       "      <th>popularity</th>\n",
       "      <th>danceability</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>Drogba (Joanna)</td>\n",
       "      <td>Drogba (Joanna)</td>\n",
       "      <td>Afro B</td>\n",
       "      <td>afroswing</td>\n",
       "      <td>2018</td>\n",
       "      <td>199000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.966</td>\n",
       "      <td>0.0206</td>\n",
       "      <td>0.633</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.0715</td>\n",
       "      <td>-6.392</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>108.011</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>Pass The Agbara</td>\n",
       "      <td>Pass The Agbara</td>\n",
       "      <td>Skuki</td>\n",
       "      <td>Missing</td>\n",
       "      <td>2017</td>\n",
       "      <td>215021</td>\n",
       "      <td>0</td>\n",
       "      <td>0.962</td>\n",
       "      <td>0.5780</td>\n",
       "      <td>0.613</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.0656</td>\n",
       "      <td>-3.921</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>110.021</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>If</td>\n",
       "      <td>If</td>\n",
       "      <td>DaVido</td>\n",
       "      <td>afropop</td>\n",
       "      <td>2017</td>\n",
       "      <td>237714</td>\n",
       "      <td>57</td>\n",
       "      <td>0.951</td>\n",
       "      <td>0.3970</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0686</td>\n",
       "      <td>-2.678</td>\n",
       "      <td>0.0612</td>\n",
       "      <td>104.997</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>Koffi Anan</td>\n",
       "      <td>Koffi Anan</td>\n",
       "      <td>Yemi Alade</td>\n",
       "      <td>afro dancehall</td>\n",
       "      <td>2016</td>\n",
       "      <td>205008</td>\n",
       "      <td>37</td>\n",
       "      <td>0.937</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.000466</td>\n",
       "      <td>0.1640</td>\n",
       "      <td>-1.430</td>\n",
       "      <td>0.1330</td>\n",
       "      <td>112.983</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>Sikiru</td>\n",
       "      <td>Sikiru</td>\n",
       "      <td>Magnito</td>\n",
       "      <td>nigerian hip hop</td>\n",
       "      <td>2017</td>\n",
       "      <td>213183</td>\n",
       "      <td>0</td>\n",
       "      <td>0.934</td>\n",
       "      <td>0.3370</td>\n",
       "      <td>0.762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0604</td>\n",
       "      <td>-6.247</td>\n",
       "      <td>0.2700</td>\n",
       "      <td>109.973</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Bizzy Body</td>\n",
       "      <td>Get Squared</td>\n",
       "      <td>P-Square</td>\n",
       "      <td>afro dancehall</td>\n",
       "      <td>2009</td>\n",
       "      <td>289080</td>\n",
       "      <td>31</td>\n",
       "      <td>0.931</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>0.559</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0398</td>\n",
       "      <td>-7.434</td>\n",
       "      <td>0.0787</td>\n",
       "      <td>100.062</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>Malo</td>\n",
       "      <td>Malo</td>\n",
       "      <td>Bracket</td>\n",
       "      <td>afro dancehall</td>\n",
       "      <td>2017</td>\n",
       "      <td>205191</td>\n",
       "      <td>0</td>\n",
       "      <td>0.928</td>\n",
       "      <td>0.4200</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0667</td>\n",
       "      <td>-5.465</td>\n",
       "      <td>0.1290</td>\n",
       "      <td>105.005</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>Give It to Me</td>\n",
       "      <td>Give It to Me</td>\n",
       "      <td>Skales</td>\n",
       "      <td>afro dancehall</td>\n",
       "      <td>2014</td>\n",
       "      <td>227473</td>\n",
       "      <td>9</td>\n",
       "      <td>0.928</td>\n",
       "      <td>0.1160</td>\n",
       "      <td>0.845</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1330</td>\n",
       "      <td>-5.100</td>\n",
       "      <td>0.2990</td>\n",
       "      <td>126.912</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Fall</td>\n",
       "      <td>Fall</td>\n",
       "      <td>DaVido</td>\n",
       "      <td>afropop</td>\n",
       "      <td>2017</td>\n",
       "      <td>240000</td>\n",
       "      <td>61</td>\n",
       "      <td>0.928</td>\n",
       "      <td>0.3790</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0533</td>\n",
       "      <td>-3.535</td>\n",
       "      <td>0.0566</td>\n",
       "      <td>105.952</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>Baby (Chop Kiss)</td>\n",
       "      <td>Baby (Chop Kiss)</td>\n",
       "      <td>Shatta Wale</td>\n",
       "      <td>afropop</td>\n",
       "      <td>2016</td>\n",
       "      <td>198973</td>\n",
       "      <td>0</td>\n",
       "      <td>0.923</td>\n",
       "      <td>0.0523</td>\n",
       "      <td>0.842</td>\n",
       "      <td>0.000620</td>\n",
       "      <td>0.0434</td>\n",
       "      <td>-5.365</td>\n",
       "      <td>0.1460</td>\n",
       "      <td>123.006</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>First of All</td>\n",
       "      <td>YBNL</td>\n",
       "      <td>Olamide</td>\n",
       "      <td>afro dancehall</td>\n",
       "      <td>2016</td>\n",
       "      <td>186666</td>\n",
       "      <td>0</td>\n",
       "      <td>0.923</td>\n",
       "      <td>0.0660</td>\n",
       "      <td>0.702</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.0999</td>\n",
       "      <td>-6.031</td>\n",
       "      <td>0.2460</td>\n",
       "      <td>124.980</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>Dumebi</td>\n",
       "      <td>Rema</td>\n",
       "      <td>Rema</td>\n",
       "      <td>nigerian pop</td>\n",
       "      <td>2019</td>\n",
       "      <td>179775</td>\n",
       "      <td>18</td>\n",
       "      <td>0.922</td>\n",
       "      <td>0.2020</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.003860</td>\n",
       "      <td>0.1050</td>\n",
       "      <td>-4.970</td>\n",
       "      <td>0.0609</td>\n",
       "      <td>110.015</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>Yawa</td>\n",
       "      <td>Yawa</td>\n",
       "      <td>Tekno</td>\n",
       "      <td>afropop</td>\n",
       "      <td>2017</td>\n",
       "      <td>236355</td>\n",
       "      <td>46</td>\n",
       "      <td>0.920</td>\n",
       "      <td>0.2230</td>\n",
       "      <td>0.584</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>0.0422</td>\n",
       "      <td>-4.789</td>\n",
       "      <td>0.0629</td>\n",
       "      <td>107.965</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>My Car</td>\n",
       "      <td>My Car</td>\n",
       "      <td>Tony Tetuila</td>\n",
       "      <td>afro dancehall</td>\n",
       "      <td>2001</td>\n",
       "      <td>248426</td>\n",
       "      <td>24</td>\n",
       "      <td>0.917</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.707</td>\n",
       "      <td>0.006020</td>\n",
       "      <td>0.3230</td>\n",
       "      <td>-10.512</td>\n",
       "      <td>0.0933</td>\n",
       "      <td>110.049</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Fi Mi Le</td>\n",
       "      <td>a.k.a Fi Mi Le</td>\n",
       "      <td>Kas</td>\n",
       "      <td>Missing</td>\n",
       "      <td>2011</td>\n",
       "      <td>218331</td>\n",
       "      <td>22</td>\n",
       "      <td>0.917</td>\n",
       "      <td>0.0352</td>\n",
       "      <td>0.829</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.2410</td>\n",
       "      <td>-5.129</td>\n",
       "      <td>0.1950</td>\n",
       "      <td>104.946</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>Teré teré</td>\n",
       "      <td>Teré teré</td>\n",
       "      <td>Toofan</td>\n",
       "      <td>azontobeats</td>\n",
       "      <td>2016</td>\n",
       "      <td>207333</td>\n",
       "      <td>45</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.1130</td>\n",
       "      <td>0.744</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.0494</td>\n",
       "      <td>-1.724</td>\n",
       "      <td>0.0747</td>\n",
       "      <td>109.995</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>Sisi Maria (feat. Skales &amp; Koker)</td>\n",
       "      <td>Sisi Maria (feat. Skales &amp; Koker)</td>\n",
       "      <td>OmoAkin</td>\n",
       "      <td>Missing</td>\n",
       "      <td>2016</td>\n",
       "      <td>223451</td>\n",
       "      <td>33</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.0684</td>\n",
       "      <td>0.729</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>-3.825</td>\n",
       "      <td>0.0457</td>\n",
       "      <td>111.982</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>Maradona</td>\n",
       "      <td>Maradona</td>\n",
       "      <td>Niniola</td>\n",
       "      <td>afro dancehall</td>\n",
       "      <td>2017</td>\n",
       "      <td>191242</td>\n",
       "      <td>30</td>\n",
       "      <td>0.914</td>\n",
       "      <td>0.0444</td>\n",
       "      <td>0.613</td>\n",
       "      <td>0.069000</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>-6.808</td>\n",
       "      <td>0.0548</td>\n",
       "      <td>118.043</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>Sukus</td>\n",
       "      <td>Sukus</td>\n",
       "      <td>MC Galaxy</td>\n",
       "      <td>afropop</td>\n",
       "      <td>2014</td>\n",
       "      <td>209826</td>\n",
       "      <td>0</td>\n",
       "      <td>0.913</td>\n",
       "      <td>0.0652</td>\n",
       "      <td>0.894</td>\n",
       "      <td>0.006920</td>\n",
       "      <td>0.0837</td>\n",
       "      <td>-2.948</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>137.253</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>Reason with me</td>\n",
       "      <td>Reason with me</td>\n",
       "      <td>Rudeboy</td>\n",
       "      <td>azontobeats</td>\n",
       "      <td>2019</td>\n",
       "      <td>251305</td>\n",
       "      <td>28</td>\n",
       "      <td>0.913</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.560</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>-6.782</td>\n",
       "      <td>0.0913</td>\n",
       "      <td>102.014</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>Jacuzzi (feat. Ice Prince)</td>\n",
       "      <td>Lovely Afrolife Tunes</td>\n",
       "      <td>Various Artists</td>\n",
       "      <td>afro dancehall</td>\n",
       "      <td>2014</td>\n",
       "      <td>237244</td>\n",
       "      <td>0</td>\n",
       "      <td>0.911</td>\n",
       "      <td>0.3190</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.0400</td>\n",
       "      <td>-3.635</td>\n",
       "      <td>0.0422</td>\n",
       "      <td>123.022</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>Waka Waka</td>\n",
       "      <td>Waka Waka (feat. Davido)</td>\n",
       "      <td>Selebobo</td>\n",
       "      <td>afro dancehall</td>\n",
       "      <td>2017</td>\n",
       "      <td>206496</td>\n",
       "      <td>39</td>\n",
       "      <td>0.909</td>\n",
       "      <td>0.1990</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>0.0380</td>\n",
       "      <td>-2.905</td>\n",
       "      <td>0.0375</td>\n",
       "      <td>110.033</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>Shake Your Bum</td>\n",
       "      <td>Naija Hits 2012-2013</td>\n",
       "      <td>This Is Africa</td>\n",
       "      <td>afro dancehall</td>\n",
       "      <td>2013</td>\n",
       "      <td>216633</td>\n",
       "      <td>0</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.1390</td>\n",
       "      <td>0.522</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.0842</td>\n",
       "      <td>-4.896</td>\n",
       "      <td>0.1390</td>\n",
       "      <td>126.075</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>My Woman, My Everything (feat. Wandecoal)</td>\n",
       "      <td>My Woman, My Everything (feat. Wandecoal) - Si...</td>\n",
       "      <td>Patoranking</td>\n",
       "      <td>afro dancehall</td>\n",
       "      <td>2015</td>\n",
       "      <td>233717</td>\n",
       "      <td>54</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.0438</td>\n",
       "      <td>0.845</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0867</td>\n",
       "      <td>-2.695</td>\n",
       "      <td>0.0644</td>\n",
       "      <td>112.005</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Bastard</td>\n",
       "      <td>Bastard</td>\n",
       "      <td>The Three Wisemen</td>\n",
       "      <td>Missing</td>\n",
       "      <td>2012</td>\n",
       "      <td>230714</td>\n",
       "      <td>18</td>\n",
       "      <td>0.901</td>\n",
       "      <td>0.1450</td>\n",
       "      <td>0.668</td>\n",
       "      <td>0.001070</td>\n",
       "      <td>0.1440</td>\n",
       "      <td>-8.597</td>\n",
       "      <td>0.2630</td>\n",
       "      <td>105.011</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>Soapy</td>\n",
       "      <td>Soapy</td>\n",
       "      <td>Naira Marley</td>\n",
       "      <td>afro dancehall</td>\n",
       "      <td>2019</td>\n",
       "      <td>174043</td>\n",
       "      <td>52</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.009710</td>\n",
       "      <td>0.0811</td>\n",
       "      <td>-4.816</td>\n",
       "      <td>0.2460</td>\n",
       "      <td>123.038</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>My Woman My Everything</td>\n",
       "      <td>Mid Year Hitz Selection</td>\n",
       "      <td>Various Artists</td>\n",
       "      <td>afro dancehall</td>\n",
       "      <td>2015</td>\n",
       "      <td>233770</td>\n",
       "      <td>0</td>\n",
       "      <td>0.899</td>\n",
       "      <td>0.0497</td>\n",
       "      <td>0.846</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0668</td>\n",
       "      <td>-2.654</td>\n",
       "      <td>0.0634</td>\n",
       "      <td>112.009</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>Bend Down Pause</td>\n",
       "      <td>Bend Down Pause</td>\n",
       "      <td>Runtown</td>\n",
       "      <td>afro dancehall</td>\n",
       "      <td>2015</td>\n",
       "      <td>197276</td>\n",
       "      <td>0</td>\n",
       "      <td>0.895</td>\n",
       "      <td>0.2200</td>\n",
       "      <td>0.956</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.3400</td>\n",
       "      <td>-2.599</td>\n",
       "      <td>0.1820</td>\n",
       "      <td>100.030</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>Audio Money</td>\n",
       "      <td>Audio Money</td>\n",
       "      <td>Rudeboy</td>\n",
       "      <td>azontobeats</td>\n",
       "      <td>2019</td>\n",
       "      <td>224092</td>\n",
       "      <td>14</td>\n",
       "      <td>0.894</td>\n",
       "      <td>0.2810</td>\n",
       "      <td>0.787</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0341</td>\n",
       "      <td>-2.881</td>\n",
       "      <td>0.0882</td>\n",
       "      <td>104.942</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Confident / Feeling Cool</td>\n",
       "      <td>Enjoy Your Life</td>\n",
       "      <td>Lady Donli</td>\n",
       "      <td>nigerian pop</td>\n",
       "      <td>2019</td>\n",
       "      <td>175135</td>\n",
       "      <td>14</td>\n",
       "      <td>0.894</td>\n",
       "      <td>0.7980</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>0.0964</td>\n",
       "      <td>-4.961</td>\n",
       "      <td>0.1130</td>\n",
       "      <td>111.087</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          name  \\\n",
       "432                            Drogba (Joanna)   \n",
       "258                            Pass The Agbara   \n",
       "46                                          If   \n",
       "266                                 Koffi Anan   \n",
       "289                                     Sikiru   \n",
       "192                                 Bizzy Body   \n",
       "399                                       Malo   \n",
       "482                              Give It to Me   \n",
       "67                                        Fall   \n",
       "389                           Baby (Chop Kiss)   \n",
       "181                               First of All   \n",
       "294                                     Dumebi   \n",
       "324                                       Yawa   \n",
       "145                                     My Car   \n",
       "147                                   Fi Mi Le   \n",
       "276                                  Teré teré   \n",
       "451          Sisi Maria (feat. Skales & Koker)   \n",
       "277                                   Maradona   \n",
       "498                                      Sukus   \n",
       "329                             Reason with me   \n",
       "484                 Jacuzzi (feat. Ice Prince)   \n",
       "321                                  Waka Waka   \n",
       "458                             Shake Your Bum   \n",
       "75   My Woman, My Everything (feat. Wandecoal)   \n",
       "196                                    Bastard   \n",
       "305                                      Soapy   \n",
       "523                     My Woman My Everything   \n",
       "360                            Bend Down Pause   \n",
       "314                                Audio Money   \n",
       "3                     Confident / Feeling Cool   \n",
       "\n",
       "                                                 album             artist  \\\n",
       "432                                    Drogba (Joanna)             Afro B   \n",
       "258                                    Pass The Agbara              Skuki   \n",
       "46                                                  If             DaVido   \n",
       "266                                         Koffi Anan         Yemi Alade   \n",
       "289                                             Sikiru            Magnito   \n",
       "192                                        Get Squared           P-Square   \n",
       "399                                               Malo            Bracket   \n",
       "482                                      Give It to Me             Skales   \n",
       "67                                                Fall             DaVido   \n",
       "389                                   Baby (Chop Kiss)        Shatta Wale   \n",
       "181                                               YBNL            Olamide   \n",
       "294                                               Rema               Rema   \n",
       "324                                               Yawa              Tekno   \n",
       "145                                             My Car       Tony Tetuila   \n",
       "147                                     a.k.a Fi Mi Le                Kas   \n",
       "276                                          Teré teré             Toofan   \n",
       "451                  Sisi Maria (feat. Skales & Koker)            OmoAkin   \n",
       "277                                           Maradona            Niniola   \n",
       "498                                              Sukus          MC Galaxy   \n",
       "329                                     Reason with me            Rudeboy   \n",
       "484                              Lovely Afrolife Tunes    Various Artists   \n",
       "321                           Waka Waka (feat. Davido)           Selebobo   \n",
       "458                               Naija Hits 2012-2013     This Is Africa   \n",
       "75   My Woman, My Everything (feat. Wandecoal) - Si...        Patoranking   \n",
       "196                                            Bastard  The Three Wisemen   \n",
       "305                                              Soapy       Naira Marley   \n",
       "523                            Mid Year Hitz Selection    Various Artists   \n",
       "360                                    Bend Down Pause            Runtown   \n",
       "314                                        Audio Money            Rudeboy   \n",
       "3                                      Enjoy Your Life         Lady Donli   \n",
       "\n",
       "     artist_top_genre  release_date  length  popularity  danceability  \\\n",
       "432         afroswing          2018  199000           0         0.966   \n",
       "258           Missing          2017  215021           0         0.962   \n",
       "46            afropop          2017  237714          57         0.951   \n",
       "266    afro dancehall          2016  205008          37         0.937   \n",
       "289  nigerian hip hop          2017  213183           0         0.934   \n",
       "192    afro dancehall          2009  289080          31         0.931   \n",
       "399    afro dancehall          2017  205191           0         0.928   \n",
       "482    afro dancehall          2014  227473           9         0.928   \n",
       "67            afropop          2017  240000          61         0.928   \n",
       "389           afropop          2016  198973           0         0.923   \n",
       "181    afro dancehall          2016  186666           0         0.923   \n",
       "294      nigerian pop          2019  179775          18         0.922   \n",
       "324           afropop          2017  236355          46         0.920   \n",
       "145    afro dancehall          2001  248426          24         0.917   \n",
       "147           Missing          2011  218331          22         0.917   \n",
       "276       azontobeats          2016  207333          45         0.916   \n",
       "451           Missing          2016  223451          33         0.916   \n",
       "277    afro dancehall          2017  191242          30         0.914   \n",
       "498           afropop          2014  209826           0         0.913   \n",
       "329       azontobeats          2019  251305          28         0.913   \n",
       "484    afro dancehall          2014  237244           0         0.911   \n",
       "321    afro dancehall          2017  206496          39         0.909   \n",
       "458    afro dancehall          2013  216633           0         0.907   \n",
       "75     afro dancehall          2015  233717          54         0.902   \n",
       "196           Missing          2012  230714          18         0.901   \n",
       "305    afro dancehall          2019  174043          52         0.900   \n",
       "523    afro dancehall          2015  233770           0         0.899   \n",
       "360    afro dancehall          2015  197276           0         0.895   \n",
       "314       azontobeats          2019  224092          14         0.894   \n",
       "3        nigerian pop          2019  175135          14         0.894   \n",
       "\n",
       "     acousticness  energy  instrumentalness  liveness  loudness  speechiness  \\\n",
       "432        0.0206   0.633          0.000004    0.0715    -6.392       0.1010   \n",
       "258        0.5780   0.613          0.000003    0.0656    -3.921       0.2050   \n",
       "46         0.3970   0.661          0.000000    0.0686    -2.678       0.0612   \n",
       "266        0.2650   0.872          0.000466    0.1640    -1.430       0.1330   \n",
       "289        0.3370   0.762          0.000000    0.0604    -6.247       0.2700   \n",
       "192        0.3000   0.559          0.000000    0.0398    -7.434       0.0787   \n",
       "399        0.4200   0.737          0.000000    0.0667    -5.465       0.1290   \n",
       "482        0.1160   0.845          0.000000    0.1330    -5.100       0.2990   \n",
       "67         0.3790   0.675          0.000000    0.0533    -3.535       0.0566   \n",
       "389        0.0523   0.842          0.000620    0.0434    -5.365       0.1460   \n",
       "181        0.0660   0.702          0.000031    0.0999    -6.031       0.2460   \n",
       "294        0.2020   0.666          0.003860    0.1050    -4.970       0.0609   \n",
       "324        0.2230   0.584          0.000274    0.0422    -4.789       0.0629   \n",
       "145        0.2500   0.707          0.006020    0.3230   -10.512       0.0933   \n",
       "147        0.0352   0.829          0.000026    0.2410    -5.129       0.1950   \n",
       "276        0.1130   0.744          0.000022    0.0494    -1.724       0.0747   \n",
       "451        0.0684   0.729          0.000014    0.1100    -3.825       0.0457   \n",
       "277        0.0444   0.613          0.069000    0.1100    -6.808       0.0548   \n",
       "498        0.0652   0.894          0.006920    0.0837    -2.948       0.2650   \n",
       "329        0.4310   0.560          0.000002    0.1100    -6.782       0.0913   \n",
       "484        0.3190   0.833          0.000054    0.0400    -3.635       0.0422   \n",
       "321        0.1990   0.735          0.000220    0.0380    -2.905       0.0375   \n",
       "458        0.1390   0.522          0.000001    0.0842    -4.896       0.1390   \n",
       "75         0.0438   0.845          0.000000    0.0867    -2.695       0.0644   \n",
       "196        0.1450   0.668          0.001070    0.1440    -8.597       0.2630   \n",
       "305        0.1590   0.806          0.009710    0.0811    -4.816       0.2460   \n",
       "523        0.0497   0.846          0.000000    0.0668    -2.654       0.0634   \n",
       "360        0.2200   0.956          0.000000    0.3400    -2.599       0.1820   \n",
       "314        0.2810   0.787          0.000000    0.0341    -2.881       0.0882   \n",
       "3          0.7980   0.611          0.000187    0.0964    -4.961       0.1130   \n",
       "\n",
       "       tempo  time_signature  \n",
       "432  108.011               4  \n",
       "258  110.021               4  \n",
       "46   104.997               4  \n",
       "266  112.983               4  \n",
       "289  109.973               4  \n",
       "192  100.062               4  \n",
       "399  105.005               4  \n",
       "482  126.912               4  \n",
       "67   105.952               4  \n",
       "389  123.006               4  \n",
       "181  124.980               4  \n",
       "294  110.015               4  \n",
       "324  107.965               4  \n",
       "145  110.049               4  \n",
       "147  104.946               4  \n",
       "276  109.995               4  \n",
       "451  111.982               4  \n",
       "277  118.043               4  \n",
       "498  137.253               4  \n",
       "329  102.014               4  \n",
       "484  123.022               4  \n",
       "321  110.033               4  \n",
       "458  126.075               4  \n",
       "75   112.005               4  \n",
       "196  105.011               4  \n",
       "305  123.038               4  \n",
       "523  112.009               4  \n",
       "360  100.030               4  \n",
       "314  104.942               4  \n",
       "3    111.087               4  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most Danceable songs\n",
    "danceable_songs = df.sort_values(\"danceability\", ascending=False).head(30)\n",
    "danceable_songs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1008 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8,14])\n",
    "plt.title(\"Top 30 Most Danceable Songs\")\n",
    "\n",
    "sns.barplot(x=\"danceability\", y=\"name\", data=danceable_songs);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do Songs Need to Have HIgh Energy to be Danceable?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the Plot below is Almost No, as some Danceable Songs have low energy, but some of the most danceable songs are within average energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Songs with High Danceability dont all need to have high energy\n",
    "plt.figure(figsize=[8,6])\n",
    "plt.title(\"Energy Against Danceabilty\")\n",
    "sns.scatterplot(x=\"danceability\", y=\"energy\", data=df.head(30));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do Songs Need to Have Long to be Popular?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the Plot below is the answer is No, As Some Popular Songs are below 220000 milliseconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Songs dont need to be really long to be popular\n",
    "plt.figure(figsize=[8,6])\n",
    "plt.title(\"Length Vs Popularity\")\n",
    "\n",
    "top_30_songs = df.sort_values(\"popularity\",ascending=False).head(30)\n",
    "sns.scatterplot(x=\"popularity\", y=\"length\", data=top_30_songs)\n",
    "\n",
    "plt.ylabel(\"Length (milliseconds)\");"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}