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 "cells": [
  {
   "source": [
    "# Nigerian Music scraped from Spotify - an analysis"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 33
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 530 entries, 0 to 529\nData 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  \ndtypes: float64(8), int64(4), object(4)\nmemory usage: 66.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "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"
      ]
     },
     "metadata": {},
     "execution_count": 34
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "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  "
      ],
      "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>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>"
     },
     "metadata": {},
     "execution_count": 35
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top genres')"
      ]
     },
     "metadata": {},
     "execution_count": 43
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}