ML-For-Beginners/5-Clustering/1-Visualize/solution/notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nigerian Music scraped from Spotify - an analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: seaborn in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (0.11.2)\n",
      "Requirement already satisfied: matplotlib>=2.2 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (3.5.0)\n",
      "Requirement already satisfied: numpy>=1.15 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.21.4)\n",
      "Requirement already satisfied: pandas>=0.23 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.3.4)\n",
      "Requirement already satisfied: scipy>=1.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.7.2)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (4.28.1)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (2.4.7)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (1.3.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (8.4.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (21.2)\n",
      "Requirement already satisfied: setuptools-scm>=4 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (6.3.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from pandas>=0.23->seaborn) (2021.3)\n",
      "Requirement already satisfied: six>=1.5 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn) (1.16.0)\n",
      "Requirement already satisfied: tomli>=1.0.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from setuptools-scm>=4->matplotlib>=2.2->seaborn) (1.2.2)\n",
      "Requirement already satisfied: setuptools in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from setuptools-scm>=4->matplotlib>=2.2->seaborn) (59.1.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": [
    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get information about the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 530 entries, 0 to 529\n",
      "Data columns (total 16 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   name              530 non-null    object \n",
      " 1   album             530 non-null    object \n",
      " 2   artist            530 non-null    object \n",
      " 3   artist_top_genre  530 non-null    object \n",
      " 4   release_date      530 non-null    int64  \n",
      " 5   length            530 non-null    int64  \n",
      " 6   popularity        530 non-null    int64  \n",
      " 7   danceability      530 non-null    float64\n",
      " 8   acousticness      530 non-null    float64\n",
      " 9   energy            530 non-null    float64\n",
      " 10  instrumentalness  530 non-null    float64\n",
      " 11  liveness          530 non-null    float64\n",
      " 12  loudness          530 non-null    float64\n",
      " 13  speechiness       530 non-null    float64\n",
      " 14  tempo             530 non-null    float64\n",
      " 15  time_signature    530 non-null    int64  \n",
      "dtypes: float64(8), int64(4), object(4)\n",
      "memory usage: 66.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Double-check for null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                0\n",
       "album               0\n",
       "artist              0\n",
       "artist_top_genre    0\n",
       "release_date        0\n",
       "length              0\n",
       "popularity          0\n",
       "danceability        0\n",
       "acousticness        0\n",
       "energy              0\n",
       "instrumentalness    0\n",
       "liveness            0\n",
       "loudness            0\n",
       "speechiness         0\n",
       "tempo               0\n",
       "time_signature      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the general values of the data. Note that popularity can be '0' - and there are many rows with that value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>count</th>\n",
       "      <td>530.000000</td>\n",
       "      <td>530.000000</td>\n",
       "      <td>530.000000</td>\n",
       "      <td>530.000000</td>\n",
       "      <td>530.000000</td>\n",
       "      <td>530.000000</td>\n",
       "      <td>530.000000</td>\n",
       "      <td>530.000000</td>\n",
       "      <td>530.000000</td>\n",
       "      <td>530.000000</td>\n",
       "      <td>530.000000</td>\n",
       "      <td>530.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2015.390566</td>\n",
       "      <td>222298.169811</td>\n",
       "      <td>17.507547</td>\n",
       "      <td>0.741619</td>\n",
       "      <td>0.265412</td>\n",
       "      <td>0.760623</td>\n",
       "      <td>0.016305</td>\n",
       "      <td>0.147308</td>\n",
       "      <td>-4.953011</td>\n",
       "      <td>0.130748</td>\n",
       "      <td>116.487864</td>\n",
       "      <td>3.986792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.131688</td>\n",
       "      <td>39696.822259</td>\n",
       "      <td>18.992212</td>\n",
       "      <td>0.117522</td>\n",
       "      <td>0.208342</td>\n",
       "      <td>0.148533</td>\n",
       "      <td>0.090321</td>\n",
       "      <td>0.123588</td>\n",
       "      <td>2.464186</td>\n",
       "      <td>0.092939</td>\n",
       "      <td>23.518601</td>\n",
       "      <td>0.333701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1998.000000</td>\n",
       "      <td>89488.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.255000</td>\n",
       "      <td>0.000665</td>\n",
       "      <td>0.111000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028300</td>\n",
       "      <td>-19.362000</td>\n",
       "      <td>0.027800</td>\n",
       "      <td>61.695000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>199305.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.681000</td>\n",
       "      <td>0.089525</td>\n",
       "      <td>0.669000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.075650</td>\n",
       "      <td>-6.298750</td>\n",
       "      <td>0.059100</td>\n",
       "      <td>102.961250</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>218509.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.761000</td>\n",
       "      <td>0.220500</td>\n",
       "      <td>0.784500</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.103500</td>\n",
       "      <td>-4.558500</td>\n",
       "      <td>0.097950</td>\n",
       "      <td>112.714500</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>242098.500000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0.829500</td>\n",
       "      <td>0.403000</td>\n",
       "      <td>0.875750</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>0.164000</td>\n",
       "      <td>-3.331000</td>\n",
       "      <td>0.177000</td>\n",
       "      <td>125.039250</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>511738.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>0.966000</td>\n",
       "      <td>0.954000</td>\n",
       "      <td>0.995000</td>\n",
       "      <td>0.910000</td>\n",
       "      <td>0.811000</td>\n",
       "      <td>0.582000</td>\n",
       "      <td>0.514000</td>\n",
       "      <td>206.007000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       release_date         length  popularity  danceability  acousticness  \\\n",
       "count    530.000000     530.000000  530.000000    530.000000    530.000000   \n",
       "mean    2015.390566  222298.169811   17.507547      0.741619      0.265412   \n",
       "std        3.131688   39696.822259   18.992212      0.117522      0.208342   \n",
       "min     1998.000000   89488.000000    0.000000      0.255000      0.000665   \n",
       "25%     2014.000000  199305.000000    0.000000      0.681000      0.089525   \n",
       "50%     2016.000000  218509.000000   13.000000      0.761000      0.220500   \n",
       "75%     2017.000000  242098.500000   31.000000      0.829500      0.403000   \n",
       "max     2020.000000  511738.000000   73.000000      0.966000      0.954000   \n",
       "\n",
       "           energy  instrumentalness    liveness    loudness  speechiness  \\\n",
       "count  530.000000        530.000000  530.000000  530.000000   530.000000   \n",
       "mean     0.760623          0.016305    0.147308   -4.953011     0.130748   \n",
       "std      0.148533          0.090321    0.123588    2.464186     0.092939   \n",
       "min      0.111000          0.000000    0.028300  -19.362000     0.027800   \n",
       "25%      0.669000          0.000000    0.075650   -6.298750     0.059100   \n",
       "50%      0.784500          0.000004    0.103500   -4.558500     0.097950   \n",
       "75%      0.875750          0.000234    0.164000   -3.331000     0.177000   \n",
       "max      0.995000          0.910000    0.811000    0.582000     0.514000   \n",
       "\n",
       "            tempo  time_signature  \n",
       "count  530.000000      530.000000  \n",
       "mean   116.487864        3.986792  \n",
       "std     23.518601        0.333701  \n",
       "min     61.695000        3.000000  \n",
       "25%    102.961250        4.000000  \n",
       "50%    112.714500        4.000000  \n",
       "75%    125.039250        4.000000  \n",
       "max    206.007000        5.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's examine the genres. Quite a few are listed as 'Missing' which means they aren't categorized in the dataset with a genre "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top genres')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAAHbCAYAAAAJY9SEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAsIElEQVR4nO3debyt9dz/8ddbpzI20EETGRKZwpHMGVJyU6ZUJN25C0Uh81CmW4RkCLmLTCXTLXT/lFA3bkPSQETRdKSOBkMpOn1+f3yvzXKcOtN3t9Y++/V8PPbjrH2ta639qWvvtd7rO6aqkCRJ0oq72bgLkCRJWlkYrCRJkjoxWEmSJHVisJIkSerEYCVJktSJwUqSJKkTg5UkSVInBitJ0ybhzyNf1yf8ZeT7Z4+7PknqLS4QKummkHAe8PwqvjHuWpZHwpwqrht3HZImmy1Wkm5yCasnvDfht8PXexNWH+7bMuGihNcm/D7hvBtr3Uq4S8LJCX9K+EbCBxM+NXL/FgnfS7gy4fSELUfu+3bCWxK+Ozz++IR1hvs2SqiE3RMuAL45HP/3hJ8nXJHw9YQ7D8eTcHDCpQl/TDgz4T7T839Q0qQyWEkah9cBWwCbAfcHNgdeP3L/HYF1gPWBXYHDEja5gef6DPBD4HbAAcAuU3ckrA98DXgrcFtgP+ALCXNHHr8zsBtwe2C14ZxRjwbuBWydsB3wWuBpwFzgf4GjhvOeADwKuAewJrADcNkS/j9IWskYrCSNw7OBN1dxaRULgDcxEogGb6ji2ipOooWjHRZ9koQ7AQ8G3ljFX6v4DnDsyCnPAY6r4rgqrq/iBOAUYNuRcz5WxS+r+AtwDC3sjTqgiquG+18AvL2Knw/dgv8JbDa0Wv0NuA1wTyDDORcv+/8aSTOZwUrSOKwHnD/y/fnDsSlXVHHVjdw/+jyXV3H1yLELR27fGXjm0A14ZcKVwCOAdUfO+d3I7auBWy/yMxZ9vkNGnutyIMD6VXwT+ADwQeDShMMS1lhMzZJWYgYrSePwW1pImXKn4diUtRNudSP3T7kYuG3CLUeObThy+0Lgk1WsNfJ1qyoOXIZaR2f4XAjsucjz3aKK7wFU8b4qHgRsSusSfMUy/BxJKwGDlaRxOAp4fcLcYbD4G+EfA84Hb0pYLeGRwL8Bn1v0Sao4n9a1d8Bw7kOBJ4+c8ingyQlbJ6yScPNhcPwGy1n3h4HXJNwbIGHNhGcOtx+c8JCEVYGrgGuA65fz50iaoeaMuwBJs9JbgTWAM4bvPzccm/I74ApaK9XVwAuq+MUNPNezgY/TBor/EPgssApAFRcOA87fSQtzC4dzXrg8RVfxpYRbA0cP46r+AJww1L8GcDBwV1qo+jpw0PL8HEkzl+tYSZoow3IIn6pavlalhM8Cv6hi/66FSdJSsCtQ0ow2dMHdLeFmCdsA2wH/PeayJM1SdgVKmunuCHyRto7VRcALq/jJeEuSNFvZFShJktSJXYGSJEmdGKwkSZI6mYgxVuuss05ttNFG4y5DkiRpiX784x//vqrmLu6+iQhWG220Eaeccsq4y5AkSVqiJOff0H12BUqSJHVisJIkSerEYCVJktSJwUqSJKkTg5UkSVInBitJkqRODFaSJEmdGKwkSZI6MVhJkiR1YrCSJEnqxGAlSZLUicFKkiSpE4OVJElSJwYrSZKkTgxWkiRJnRisJEmSOpkz7gKW1YNe8YlxlzAr/Pig5467BEmSZhxbrCRJkjoxWEmSJHVisJIkSerEYCVJktSJwUqSJKkTg5UkSVInBitJkqRODFaSJEmdGKwkSZI6MVhJkiR1YrCSJEnqxGAlSZLUicFKkiSpE4OVJElSJwYrSZKkTgxWkiRJnRisJEmSOjFYSZIkdWKwkiRJ6sRgJUmS1InBSpIkqRODlSRJUicGK0mSpE4MVpIkSZ0YrCRJkjoxWEmSJHWyxGCV5OZJfpjk9CQ/S/Km4fhdkvwgyTlJPptkteH46sP35wz3bzTN/w2SJEkTYWlarK4FHltV9wc2A7ZJsgXwDuDgqro7cAWw+3D+7sAVw/GDh/MkSZJWeksMVtX8efh21eGrgMcCnx+OHwlsP9zebvie4f7HJUmvgiVJkibVUo2xSrJKktOAS4ETgHOBK6vquuGUi4D1h9vrAxcCDPf/Abhdx5olSZIm0lIFq6paWFWbARsAmwP3XNEfnGSPJKckOWXBggUr+nSSJEljt0yzAqvqSuBbwEOBtZLMGe7aAJg/3J4PbAgw3L8mcNlinuuwqppXVfPmzp27fNVLkiRNkKWZFTg3yVrD7VsAWwE/pwWsZwyn7Qp8ebh97PA9w/3frKrqWLMkSdJEmrPkU1gXODLJKrQgdkxVfTXJWcDRSd4K/AQ4fDj/cOCTSc4BLgd2nIa6JUmSJs4Sg1VVnQE8YDHHf00bb7Xo8WuAZ3apTpIkaQZx5XVJkqRODFaSJEmdGKwkSZI6MVhJkiR1YrCSJEnqxGAlSZLUicFKkiSpE4OVJElSJwYrSZKkTgxWkiRJnRisJEmSOjFYSZIkdWKwkiRJ6sRgJUmS1InBSpIkqRODlSRJUicGK0mSpE4MVpIkSZ0YrCRJkjoxWEmSJHVisJIkSerEYCVJktSJwUqSJKkTg5UkSVInBitJkqRODFaSJEmdGKwkSZI6MVhJkiR1YrCSJEnqxGAlSZLUicFKkiSpE4OVJElSJwYrSZKkTgxWkiRJnRisJEmSOjFYSZIkdWKwkiRJ6sRgJUmS1InBSpIkqRODlSRJUicGK0mSpE4MVpIkSZ0YrCRJkjoxWEmSJHVisJIkSerEYCVJktSJwUqSJKmTJQarJBsm+VaSs5L8LMk+w/EDksxPctrwte3IY16T5JwkZyfZejr/AyRJkibFnKU45zrg5VV1apLbAD9OcsJw38FV9a7Rk5NsCuwI3BtYD/hGkntU1cKehUuSJE2aJbZYVdXFVXXqcPtPwM+B9W/kIdsBR1fVtVX1G+AcYPMexUqSJE2yZRpjlWQj4AHAD4ZDeyc5I8kRSdYejq0PXDjysItYTBBLskeSU5KcsmDBgmWvXJIkacIsdbBKcmvgC8C+VfVH4EPA3YDNgIuBdy/LD66qw6pqXlXNmzt37rI8VJIkaSItVbBKsiotVH26qr4IUFWXVNXCqroe+Cj/6O6bD2w48vANhmOSJEkrtaWZFRjgcODnVfWekePrjpz2VOCnw+1jgR2TrJ7kLsDGwA/7lSxJkjSZlmZW4MOBXYAzk5w2HHstsFOSzYACzgP2BKiqnyU5BjiLNqNwL2cESpKk2WCJwaqqvgNkMXcddyOPeRvwthWoS5IkacZx5XVJkqRODFaSJEmdGKwkSZI6MVhJkiR1YrCSJEnqxGAlSZLUicFKkiSpE4OVJElSJwYrSZKkTgxWkiRJnRisJEmSOjFYSZIkdWKwkiRJ6sRgJUmS1InBSpIkqRODlSRJUicGK0mSpE4MVpIkSZ0YrCRJkjoxWEmSJHVisJIkSerEYCVJktSJwUqSJKkTg5UkSVInBitJkqRODFaSJEmdGKwkSZI6MVhJkiR1YrCSJEnqxGAlSZLUicFKkiSpE4OVJElSJwYrSZKkTgxWkiRJnRisJEmSOjFYSZIkdWKwkiRJ6sRgJUmS1InBSpIkqRODlSRJUicGK0mSpE4MVpIkSZ0YrCRJkjoxWEmSJHVisJIkSerEYCVJktTJEoNVkg2TfCvJWUl+lmSf4fhtk5yQ5FfDv2sPx5PkfUnOSXJGkgdO93+EJEnSJFiaFqvrgJdX1abAFsBeSTYFXg2cWFUbAycO3wM8Edh4+NoD+FD3qiVJkibQEoNVVV1cVacOt/8E/BxYH9gOOHI47Uhg++H2dsAnqvk+sFaSdXsXLkmSNGmWaYxVko2ABwA/AO5QVRcPd/0OuMNwe33gwpGHXTQcW/S59khySpJTFixYsKx1S5IkTZylDlZJbg18Adi3qv44el9VFVDL8oOr6rCqmldV8+bOnbssD5UkSZpISxWskqxKC1WfrqovDocvmeriG/69dDg+H9hw5OEbDMckSZJWakszKzDA4cDPq+o
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "top = df['artist_top_genre'].value_counts()\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.barplot(x=top[:5].index,y=top[:5].values)\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Top genres',color = 'blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove 'Missing' genres, as it's not classified in Spotify\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top genres')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df[df['artist_top_genre'] != 'Missing']\n",
    "top = df['artist_top_genre'].value_counts()\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.barplot(x=top.index,y=top.values)\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Top genres',color = 'blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The top three genres comprise the greatest part of the dataset, so let's focus on those"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top genres')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
    "df = df[(df['popularity'] > 0)]\n",
    "top = df['artist_top_genre'].value_counts()\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.barplot(x=top.index,y=top.values)\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Top genres',color = 'blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is not strongly correlated except between energy and loudness, which makes sense. Popularity has a correspondence to release data, which also makes sense, as more recent songs are probably more popular. Length and energy seem to have a correlation - perhaps shorter songs are more energetic?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat = df.corr()\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "sns.heatmap(corrmat, vmax=.8, square=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are the genres significantly different in the perception of their danceability, based on their popularity? Examine our top three genres data distribution for popularity and danceability along a given x and y axis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaQAAAGkCAYAAAB+TFE1AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOydd3gc1dWH39m+q9Xuqvde3eTeG7bBdAwm9A6BBEJJvtASSggJCYQk1IQSCBBIqKaYjgvuvTfZktV71/Y+8/2x0tpCkrst28z7PPvM7MydmbNl7m/uveeeI0iSJCEjIyMjIzPAKAbaABkZGRkZGZAFSUZGRkbmJEEWJBkZGRmZkwJZkGRkZGRkTgpkQZKRkZGROSmQBUlGRkZG5qRAFiQZGRkZmZMC1UAbICNzuIiSiD8YwBf04Q368AX9+AKhZfd7b8CHL9j98qNRqjFpIzHrIkmOTMCkjUQQhIH+KDIyMvshC5LMgODyuWlyttLhttLpsdLhtuL0uXAHvLgDHjx+D+6AF4/fgyfg3U9sQgJztBg1EeREZzAsoZCihEFkWFJkgZKRGWAEOVKDzPGm1dXO7pa9lLZVUmtroNbWQIfb2qucVqVFr9KiV+nQqbXoVDr0Ki06lRaNSoNWqUGjVKNVadB0rytD6z22qfbbp9KgUajxBX3YvA7a3Vbq7Y3UWBvY3bqXOlsjAKmmJM7ImsD0zAmYdaYT/RXJyMggC5LMccDld7O1cReb6newq6WUFmcbEBKc1MhEUsyJpJqSSDTGEa23YNGbsehMaJTqE25ru6uTjfXbWVq5hpK2ctRKNWflTGVO4Wyi9OYTbo+MzI8ZWZBkjgl2r4PVNZtYX7eVHc17CIpBIjURDI7PZ1BcLoWxuWRaUlEoTl4/mlpbA/OLF7Csai1KhZI5hbO5uHA2GpVmoE2TkflRIAuSzBHjC/rZVL+dZZVr2dywg6AkkmSMZ0xKEWNThpMfk31SC1B/NDpaeG/7fFZVbyAhIpZbRl/FiKTBA22WjMxpjyxIMoeFJEkUt+xledU6VtdsxOV3E6UzMyVjLFMzxp9WzgE7mnbz+sb3qbM3MjtnGteNuBSt3FqSkTluyIIkc0jYvA6WVqxhYflyGuzNaFVaxqeOYFrGeIbGF5ySLaFDwR/08972+Xy+ZyHJkQn8cuJPyYxKHWizZGROS2RBkukXSZLY3bqXBWUrWFOziYAYoCA2h7NypjIudQQ6lXagTTxh7GjazQtr38Tpc/GzMdcyNXPcQJskI3PaIQuSTC9EUWRt3WY+Lf6Wio4aDGo90zLGc2bOFNItKQNt3oDR6bHxzKrXKG4p5by8GVw74lJUCuVAmyUjc9ogC5JMmIAYZFnlGj7b/R0N9maSjPFcWHgWUzLG/qhaQwciIAZ5Z+vHfFWymEFxufxq0q1Y5HlLMjLHBFmQZJAkic0NO3h7y8fU2RvJikrjkkHnMC5lxGk7NnS0rKhax8vr3yFSY+S+KT8jOzpjoE2SkTnlkQXpR06rs51XN/yXLY27SIqM57rhcxmdXHTaeModTyo6anh6xctYvXZuH3stUzLkcSUZmaNBFqQfKZIk8X3FKt7a/BEiElcOvZCzc6ejUsrhDQ8Hq8fG31f9i+KWvVxUeBZXD7tYblXKyBwhsiD9CPEEvLy07m1W12xkSHw+t4+9jnhj7ECbdcoSCAZ4c/OHfFe2jBGJg7l74s0YNREDbZaMzCmHLEg/MhrtzTy98hVqbQ1cNWwOFxWehUKQn+iPBQvLlvP6pveJM0Rz/5TbSTUnDbRJMjKnFLIg/YjY21bJn5a9CMAvJ95CUeKgAbbo9GN3y17+tvJVfEE/d064kbEpwwfaJBmZUwZZkH4k7GjazV9WvIxJa+ThM+4h0Rg30CadtrS62vnrilco76jmnLwzuHb43AGJZC4jc6ohC9KPgE31O/jbyldIjIznoel3Ea23DLRJpz2+oJ//bfuUr0oWk25O4c7xN5AZlTbQZsnInNTIgnSas6u5hCeWvUiqKZFHpt+DUSsPtp9INjfs4J/r3sbudXBR4Vn8ZPB5cjoLGZl+kAXpNKasvYrHv3+WaIOF38/8NSatcaBN+lHi8Dp5e+vHfF+xiviIGK4ZfgkTUkfJc71kZH6ALEinKY2OFh5a+Bd0Ki1/mHkv0QbLQJv0o2dH0x7e3Pwh1dY68mOyuXLYhQyJL5CFSUamC1mQTkOcPhcPL3waq9fOE2feT1Jk/ECbJNOFKIosqVzD+9vn0+GxkhudycWDzmZ08jCUcqBWmR85siCdZgTEIE8u+wc7W0p4ZPrdDI7PH2iTZPrAF/SzpGI183d/R7OzjWi9hZnZk5iZNZnYiOiBNk9GZkCQBek049+b3ueb0iXcPvY6ZmRPGmhzZA5CUAyysX47i8pXsKVhFwgwInEw0zLHMzZ5uOwAIfOjQhak04hllWt5ce2bnJ8/ixtG/mSgzZE5TFqcbSwqX8nSyjW0uTrQq3VMSB3F9MzxFMblyhE1ZE57ZEE6TajsqOXhRX8hNzqTR864Rx6POIURJZHilr0srVzDmppNeAJe4gzRTM0cx7SM8SSbEgfaRBmZ44IsSKcBDp+T33z3JD7Rz1OzfysnjDuN8AZ8rK/bwrLKtWxtKkaSJPKiM5maOZ7J6WOIlF35ZU4jZEE6xRElkaeWv8S2pmJ+P+P/yI/NHmiTZI4THW4rK6rWs6xyDVXWOtQKFVMzxnF+wSzSzMkDbZ6MzFEjC9Ipzkc7v+SDHV9w86grOCfvjIE2R+YEUdlRy4KyZSytXIMv6Gd44mAuLDiTYQmF8rwmmVMWWZBOYbY07OTPy/7BlIyx3Dn+Rrki+hFi9zpYULacb0qX0OmxURibw2VDL2CoPOFW5hREFqRTlGZnGw989ydi9VH88cz70cruwT9q/EE/i8tX8UnxN7S7OxkUl8tlQy5gaELBQJsmI3PIyIJ0CuIL+nl00V9pdLTw5FkPkihHYpDpwhf0s7h8JZ8Uf0OH28qQ+HyuGHoRhXE5A22ajMxBkQXpFOTl9e+wuHwl90/5OWPkBHAyfeAL+llYtpxPir/F6rExMmkIVwy9iOzo9IE2TUamX2RBOsVYXL6Kl9e/zSWDzuGqojkDbY7MSY4n4OWb0iV8tvs7nD4X41NHcsXQC+X06jInJbIgnUKUt1fxyKK/UhiXy0PT7kKhkGfuyxwaLp+bL0oW8sWeRXiDPqZmjOOyIeeTIGcOljmJkAXpFKHd1clvFj6JUlDy5OzfyLmNZI4Im9fBZ8Xf8s3epYhikJnZk7l08HlyehKZkwJZkE4BPAEvv1v8Nxrszfxx1n2kW1IG2iSZU5x2dycf7/qaReUrUQgKzs6ZxsWDzsakixxo02R+xMiCdJIjiiJ/X/0v1tdt5YEptzMqedhAmyRzGtHsaOWjnV+xtGoNWqWGM7OncF7BTGINcgoMmROPLEgnMZIk8eqG/7GofAU3jryM8/JnDrRJMqcpdbZG5u38ilU1GxGAieljuLDgTLKi0gbaNJkfEbIgnaRIksQ7Wz/m8z0LmTv4HK4cdnw86oJBkU6Hlzarh3abhw6bB5vLh9sTwOUN4PYG8PqCiKLUZReIXX8ZhSCgUSvQqJVdLwUaVWhd27Vdq1ai1SjD65qu99of7NOolSgVB48sIIoSgaCIPyASCIrh9e6XLxDE7xfx+oP4A0F8fhGfP4gvIPZ+7w92lQsd173PH+h5vD8QRBQP7ftUKEClVIReqtBSHV4Xwvu6P7tOq0Kn6VrXdK+r0He912qV6DUqIvRqjHo1Wo3yuEZgaHW281XJYhaWr8AT8DIsoYBz8mYwKmmoHEFe5rgjC9JJiCRJfLDjC+bt+oqzc6dz86grjroS8ngDVDbYqGiwUd/ioLbZQX2Lg8Z2V1hs9ketUqDXqtBrVWg1ShSCgCCA0L0ERJEeFXf3eiB4ZH8phUIgpEn7rkHX9QACAZFgH7YeLoIAalVINNUq5T5RVSm6titRh8U1tO1QxBJCYr2/WAYCUg/h3CegQTy+rpc3cMifS6UUwuJk1GuIMITWTREaok06oiK1RJl0REXqiDJ
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"ticks\")\n",
    "\n",
    "# Show the joint distribution using kernel density estimation\n",
    "g = sns.jointplot(\n",
    "    data=df,\n",
    "    x=\"popularity\", y=\"danceability\", hue=\"artist_top_genre\",\n",
    "    kind=\"kde\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general, the three genres align in terms of their popularity and danceability.  A scatterplot of the same axes shows a similar pattern of convergence. Try a scatterplot to check the distribution of data per genre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1350b1a90>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 468.975x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(df, hue=\"artist_top_genre\", size=5) \\\n",
    "   .map(plt.scatter, \"popularity\", \"danceability\") \\\n",
    "   .add_legend()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
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 },
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}