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 "cells": [
  {
   "source": [
    "# Nigerian Music scraped from Spotify - an analysis"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
      "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
      "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "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  "
      ],
      "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>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>"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top genres')"
      ]
     },
     "metadata": {},
     "execution_count": 6
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "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": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "wcss = []\n",
    "\n",
    "X = df[['popularity','danceability']].values\n",
    "\n",
    "\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
    "    kmeans.fit(X)\n",
    "    # inertia method returns wcss for that model\n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.lineplot(range(1, 11), wcss,marker='o',color='red')\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "     popularity  danceability\n1            30         0.710\n3            14         0.894\n4            25         0.702\n5            26         0.803\n6            29         0.818\n..          ...           ...\n514          20         0.838\n515          14         0.786\n519           2         0.879\n522          26         0.863\n525          10         0.735\n\n[286 rows x 2 columns]\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "selection = df[['popularity','danceability']]\n",
    "print(selection)\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "kmeans = KMeans(n_clusters = 3)\n",
    "kmeans.fit(selection)\n",
    "labels = kmeans.predict(selection)\n",
    "plt.scatter(df['popularity'],df['danceability'],c = labels)\n",
    "plt.xlabel('danceability')\n",
    "plt.xlabel('popularity')\n",
    "plt.show()\n"
   ]
  }
 ]
}