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231 lines
68 KiB
231 lines
68 KiB
{
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"metadata": {
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"display_name": "Python 3.7.0 64-bit ('3.7')"
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"metadata": {
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"interpreter": {
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"hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2,
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"cells": [
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{
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"source": [
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"# Nigerian Music scraped from Spotify - an analysis"
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],
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"cell_type": "markdown",
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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" name album \\\n",
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"0 Sparky Mandy & The Jungle \n",
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"1 shuga rush EVERYTHING YOU HEARD IS TRUE \n",
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"2 LITT! LITT! \n",
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"3 Confident / Feeling Cool Enjoy Your Life \n",
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"4 wanted you rare. \n",
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"\n",
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" artist artist_top_genre release_date length popularity \\\n",
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"0 Cruel Santino alternative r&b 2019 144000 48 \n",
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"1 Odunsi (The Engine) afropop 2020 89488 30 \n",
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"2 AYLØ indie r&b 2018 207758 40 \n",
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"3 Lady Donli nigerian pop 2019 175135 14 \n",
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"4 Odunsi (The Engine) afropop 2018 152049 25 \n",
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"\n",
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" danceability acousticness energy instrumentalness liveness loudness \\\n",
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"0 0.666 0.8510 0.420 0.534000 0.1100 -6.699 \n",
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"1 0.710 0.0822 0.683 0.000169 0.1010 -5.640 \n",
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"2 0.836 0.2720 0.564 0.000537 0.1100 -7.127 \n",
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"3 0.894 0.7980 0.611 0.000187 0.0964 -4.961 \n",
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"4 0.702 0.1160 0.833 0.910000 0.3480 -6.044 \n",
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"\n",
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" speechiness tempo time_signature \n",
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"0 0.0829 133.015 5 \n",
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"1 0.3600 129.993 3 \n",
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"2 0.0424 130.005 4 \n",
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"3 0.1130 111.087 4 \n",
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"4 0.0447 105.115 4 "
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],
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"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 & The Jungle</td>\n <td>Cruel Santino</td>\n <td>alternative r&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&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>"
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},
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"metadata": {},
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"execution_count": 33
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}
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],
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"source": [
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"\n",
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"df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
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"df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"<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"
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]
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}
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],
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"source": [
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"df.info()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"name 0\n",
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"album 0\n",
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"artist 0\n",
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"artist_top_genre 0\n",
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"release_date 0\n",
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"length 0\n",
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"popularity 0\n",
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"danceability 0\n",
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"acousticness 0\n",
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"energy 0\n",
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"instrumentalness 0\n",
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"liveness 0\n",
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"loudness 0\n",
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"speechiness 0\n",
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"tempo 0\n",
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"time_signature 0\n",
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"dtype: int64"
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]
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},
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"metadata": {},
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"execution_count": 34
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}
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],
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"source": [
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"df.isnull().sum()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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" release_date length popularity danceability acousticness \\\n",
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"count 530.000000 530.000000 530.000000 530.000000 530.000000 \n",
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"mean 2015.390566 222298.169811 17.507547 0.741619 0.265412 \n",
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"std 3.131688 39696.822259 18.992212 0.117522 0.208342 \n",
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"min 1998.000000 89488.000000 0.000000 0.255000 0.000665 \n",
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"25% 2014.000000 199305.000000 0.000000 0.681000 0.089525 \n",
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"50% 2016.000000 218509.000000 13.000000 0.761000 0.220500 \n",
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"75% 2017.000000 242098.500000 31.000000 0.829500 0.403000 \n",
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"max 2020.000000 511738.000000 73.000000 0.966000 0.954000 \n",
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"\n",
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" energy instrumentalness liveness loudness speechiness \\\n",
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"count 530.000000 530.000000 530.000000 530.000000 530.000000 \n",
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"mean 0.760623 0.016305 0.147308 -4.953011 0.130748 \n",
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"std 0.148533 0.090321 0.123588 2.464186 0.092939 \n",
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"min 0.111000 0.000000 0.028300 -19.362000 0.027800 \n",
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"25% 0.669000 0.000000 0.075650 -6.298750 0.059100 \n",
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"50% 0.784500 0.000004 0.103500 -4.558500 0.097950 \n",
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"75% 0.875750 0.000234 0.164000 -3.331000 0.177000 \n",
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"max 0.995000 0.910000 0.811000 0.582000 0.514000 \n",
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"\n",
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" tempo time_signature \n",
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"count 530.000000 530.000000 \n",
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"mean 116.487864 3.986792 \n",
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"std 23.518601 0.333701 \n",
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"min 61.695000 3.000000 \n",
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"25% 102.961250 4.000000 \n",
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"50% 112.714500 4.000000 \n",
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"75% 125.039250 4.000000 \n",
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"max 206.007000 5.000000 "
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],
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"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>"
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},
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"metadata": {},
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"execution_count": 35
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}
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],
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"source": [
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"df.describe()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 43,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"Text(0.5, 1.0, 'Top genres')"
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]
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},
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"metadata": {},
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"execution_count": 43
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},
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{
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"output_type": "display_data",
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"data": {
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"text/plain": "<Figure size 720x504 with 1 Axes>",
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"image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"496.8pt\" version=\"1.1\" viewBox=\"0 0 712.8 496.8\" width=\"712.8pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;white-space:pre;}\n </style>\n </defs>\n <g id=\"figure_1\">\n <g id=\"patch_1\">\n <path d=\"M 0 496.8 \nL 712.8 496.8 \nL 712.8 0 \nL 0 0 \nz\n\" style=\"fill:#ffffff;\"/>\n </g>\n <g id=\"axes_1\">\n <g id=\"patch_2\">\n <path d=\"M 27.382812 435.214828 \nL 705.6 435.214828 \nL 705.6 27.220937 \nL 27.382812 27.220937 \nz\n\" style=\"fill:#ffffff;\"/>\n </g>\n <g id=\"matplotlib.axis_1\">\n <g id=\"xtick_1\">\n <g id=\"line2d_1\"/>\n <g id=\"text_1\">\n <!-- afro dancehall -->\n <defs>\n <path 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f30W3y87OvmQg79u3ryTnRznEzxgAAFzLrhjIkpSamqqRI0fqgQceUI8ePTR79uyi7+Xm5srX11fe3t7Kzc296HIfH59L3l9oaGgxxtxWjNvAWYr3MwYAAChbSUlJl7z8ikexOHnypIYMGaLx48erT58+kqTmzZtr69atkqRNmzapbdu2atOmjRISEmS1WpWSkiKr1cryCgAAAFyTrrgFefHixcrKytKiRYu0aNEiSdLEiRM1Y8YMzZs3T40aNVJUVJRcXV3Vtm1b9e/fX1arVXFxcWUyPAAAAFDSLDabzVZWD5aUlKTw8PA/fbvw8W+VwjQoLUmzBzt7BAAAgKu6XJtyohAAAADAQCADAAAABgIZAAAAMBDIAAAAgIFABgAAAAwEMgAAAGAgkAEAAAADgQwAAAAYCGQAAADAQCADAAAABgIZAAAAMBDIAAAAgIFABgAAAAwEMgAAAGAgkAEAAAADgQwAAAAYCGQAAADAQCADAAAABgIZAAAAMBDIAAAAgIFABgAAAAwEMgAAAGAgkAEAAAADgQwAAAAYCGQAAADAQCADAAAABgIZAAAAMBDIAAAAgIFABgAAAAwEMgAAAGAgkAEAAAADgQwAAAAYCGQAAADAQCADAAAABgIZAAAAMBDIAAAAgIFABgAAAAwEMgAAAGAgkAEAAAADgQwAAAAYCGQAAADAQCADAAAABgIZAAAAMBDIAAAAgIFABgAAAAwEMgAAAGAgkAEAAAADgQwAAAAYCGQAAADAQCADAAAABgIZAAAAMPyhQN69e7cGDRokSdq7d686d+6sQYMGadCgQfr0008lSQsWLFCfPn00YMAA7dmzp/QmBgAAAEqR29WusHTpUq1du1bVqlWTJCUnJ+uRRx7RkCFDiq6TnJysxMRErV69WqmpqRo9erTi4+NLb2oAAACglFx1C3L9+vX18ssvF3393XffaePGjRo4cKBiY2OVk5OjpKQkRUREyGKxKCgoSIWFhcrIyCjVwQEAAIDScNUtyFFRUTp69GjR12FhYerbt69atmypV155RQsXLpSPj4/8/PyKruPl5aXs7GwFBAT87v727dtXQqOjvOJnDAAArmVXDeTf6tq1q3x9fYs+nz59um6//Xbl5uYWXSc3N1c+Pj6XvH1oaGgxxtxWjNvAWYr3MwYAAChbSUlJl7z8Tx/FYujQoUU74X399ddq0aKF2rRpo4SEBFmtVqWkpMhqtV5y6zEAAABQ3v3pLchTp07V9OnT5e7ursDAQE2fPl3e3t5q27at+vfvL6vVqri4uNKYFQAAACh1FpvNZiurB0tKSlJ4ePifvl34+LdKYRqUlqTZg509AgAAwFVdrk05UQgAAABgIJABAAAAA4EMAAAAGAhkAAAAwEAgAwAAAAYCGQAAADAQyAAAAICBQAYAAAAMBDIAAABgIJABAAAAA4EMAAAAGAhkAAAAwEAgAwAAAAYCGQAAADAQyAAAAICBQAYAAAAMBDIAAABgIJABAAAAA4EMAAAAGAhkAAAAwEAgAwAAAAYCGQAAADAQyAAAAICBQAYAAAAMBDIAAABgIJABAAAAA4EMAAAAGAhkAAAAwEAgAwAAAAYCGQAAADAQyAAAAICBQAYAAAAMBDIAAABgIJABAAAAA4EMAAAAGAhkAAAAwEAgAwAAAAYCGQAAADAQyAAAAICBQAYAAAAMBDIAAABgIJABAAAAA4EMAAAAGAhkAAAAwEAgAwAAAAYCGQAAADAQyAAAAICBQAYAAAAMBDIAAABgIJABAAAAA4EMAAAAGP5QIO/evVuDBg2SJP3888+6//779cADD2jKlCmyWq2SpAULFqhPnz4aMGCA9uzZU3oTAwAAAKXoqoG8dOlSTZo0SXl5eZKk5557TjExMXr33Xdls9m0YcMGJScnKzExUatXr9a8efM0bdq0Uh8cAAAAKA1XDeT69evr5ZdfLvo6OTlZ7du3lyRFRkZqy5YtSkpKUkREhCwWi4KCglRYWKiMjIzSmxoAAAAoJW5Xu0JUVJSOHj1a9LXNZpPFYpEkeXl5KTs7Wzk5OfLz8yu6juPygICA393fvn37SmJulGP8jAEAwLXsqoH8Wy4uv250zs3Nla+vr7y9vZWbm3vR5T4+Ppe8fWhoaDHG3FaM28BZivczBgAAKFtJSUmXvPxPH8WiefPm2rp1qyRp06ZNatu2rdq0aaOEhARZrValpKTIarVecusxAAAAUN796S3IEyZM0OTJkzVv3jw1atRIUVFRcnV1Vdu2bdW/f39ZrVbFxcWVxqwAAABAqbPYbDZbWT1YUlKSwsPD//Ttwse/VQrToLQkzR7s7BEAAACu6nJtyolCAAAAAAOBDAAAABgIZAAAAMBAIAMAAAAGAhkAAAAwEMgAAACAgUAGAAAADH/6RCFAeXPkmVbOHgF/Qv24b509AgAAV8QWZAAAAMBAIAMAAAAGAhkAAAAwEMgAAACAgUAGAAAADAQyAAAAYCCQAQAAAAOBDAAAABgIZAAAAMBAIAMAAAAGAhkAAAAwEMgAAACAgUAGAAAADAQyAAAAYCCQAQAAAAOBDAAAABgIZAAAAMBAIAMAAAAGAhkAAAAwEMgAAACAgUAGAAAADAQyAAAAYCCQAQAAAAOBDAAAABgIZAAAAMBAIAMAAAAGAhkAAAAwEMgAAACAgUAGAAAADAQyAAAAYCCQAQAAAAOBDAAAABgIZAAAAMBAIAMAAAAGAhkAAAAwEMgAAACAgUAGAAAADAQyAAAAYCCQAQAAAAOBDAAAABgIZAAAAMBAIAMAAAAGAhkAAAAwuBX3hvfdd5+8vb0lSddff7369++vf/3rX3J1dVVERIRGjRpVYkMCQHHc/PLNzh4Bf9Lm0ZudPQIAFC+Q8/LyZLPZtGLFiqLLevbsqZdffln16tXTP/7xD+3du1fNmzcvsUEBAACAslCsJRb79+/XuXPnNGTIEA0ePFjbtm1Tfn6+6tevL4vFooiICG3ZsqWkZwUAAABKXbG2IFetWlVDhw5V3759dfjwYT366KPy9fUt+r6Xl5d++eWXS9523759xZsU14yy/hl7lemj4a/iNQBXwvMDQHlQrEBu2LChgoODZbFY1LBhQ/n4+OjMmTNF38/Nzb0omE2hoaHFeMRtxRkTTlK8n3HxHSnTR8NfVabPj/Vl91AoGWX9+gGgcktKSrrk5cVaYvHBBx9o5syZkqS0tDSdO3dOnp6eOnLkiGw2mxISEtS2bdviTwsAAAA4SbG2IPfp00dPP/207r//flksFj377LNycXHRuHHjVFhYqIiICN14440lPSsAAABQ6ooVyB4eHpo7d+7vLl+1atVfHggAAABwJk4UAgAAABgIZAAAAMBAIAMAAAAGAhkAAAAwEMgAAACAgUAGAAAADAQyAAAAYCCQAQAAAAOBDAAAABgIZAAAAMBAIAMAAAAGAhkAAAAwEMgAAACAgUAGAAAADAQyAAAAYCCQAQAAAAOBDAAAABgIZAAAAMBAIAMAAAAGAhkAAAAwEMgAAACAgUAGAAAADAQyAAAAYCCQAQAAAAOBDAAAABgIZAAAAMDg5uwBAABwhi8jb3H2CPgTbtn0pbNHQCXCFmQAAADAQCADAAAABgIZAAAAMBDIAAAAgIFABgAAAAwEMgAAAGAgkAEAAAADgQwAAAAYCGQAAADAQCADAAAABgIZAAAAMBDIAAAAgIFABgAAAAwEMgAAAGAgkAEAAAADgQwAAAAYCGQAAADAQCADAAAABjdnDwAAAFDeLBj7sbNHwJ8wam6PEr0/tiADAAAABgIZAAAAMBDIAAAAgIFABgAAAAwEMgAAAGAgkAEAAABDiR7mzWq1aurUqTpw4IA8PDw0Y8YMBQcHl+RDAAAAAKWqRLcgr1+/Xvn5+Xr//fc1duxYzZw5syTvHgAAACh1FpvNZiupO3vuuecUFhamu+++W5LUuXNnffXVV0XfT0pKKqmHAgAAAP6y8PDw311WoksscnJy5O3tXfS1q6urLly4IDc3t8sOAAAAAJQnJbrEwtvbW7m5uUVfW63WojgGAAAArgUlGsht2rTRpk2bJEm7du3SDTfcUJJ3DwAAAJS6El2D7DiKxffffy+bzaZnn31WjRs3Lqm7BwAAAEpdiQYynOOdd95R9erVdc899zh7FAAAgGseJwq5xu3fv1+SlJycrPXr1zt5GpQFq9Va9Dl/3+K3zOfHpb4GLqewsNDZI6CcqoyvIwTyNez111/X+vXr1bRpU4WEhGj79u1EcgVntVrl4mL/z7awsFB5eXmSCGXYFRYWysXFRTabTc8995ySk5OLvgauxGq1ytXVVVarVS+88IJWrlypLVu2OHsslAOO15UTJ07ov//9b9HvHali/+5xnTp16lRnD4HiqVmzphISEnTu3Dk1bdpUbm5u+vbbb5WXl6dGjRo5ezyUMEccW61WjRw5Ups3b1ZCQoLq1q2rmjVrOns8OJnNZit6frz00kvas2ePEhMT1aRJE9WqVUs2m00Wi8XZY6Iccjx3bDabpk6dqrNnz6pOnTp68cUXFRQUpIYNGzp7RDiRi4uL0tLS9OSTT8rd3V1VqlRRrVq1ir5XURHI1yDHX3O+vr4KCwvThg0blJ2drWbNmsnDw0PffPONLBaLGjRo4OxRUYIsFotsNpuWLVumgIAADRo0SGfPntVHH32kDh06qFq1as4eEU7kiN8xY8bI399fMTExcnNz0/Lly9W8eXMFBgYSybgkx3Ni/vz5yszM1HPPPacWLVqoZs2aSkpKUkREhJMnhDMcPXpUvr6+slqtWrhwoRo0aKCePXvq5Zdf1sGDB3X+/PkK/cdTxU3/CsrxNtjx48f1/PPP68svv1RsbKzS0tK0a9cu1a5dW+3atVOrVq2cPSpKiPkW1tKlS/XZZ5/pjjvuUL169dSjRw8FBgbK1dXViRPCmX67NrBmzZoaOHCggoKCdN999ykwMFBz5szRDz/8QBzjIr9dc1y1alVduHBBe/fulSRVqVJFGRkZFfptdFxaVlaW3nrrLb399tv68ssvVb16dRUUFGjOnDkaPny4CgoKdObMGWePWaoI5GuMi4uLTp48qfHjx+u6667T/Pnz9eabb2rSpEn68ccf9eOPP6pr166qUaOGs0dFCSgsLCyKmvz8fA0aNEihoaFat26dzp8/ryNHjmjXrl3KyMhw8qRwBseyG5vNph9//FGS/TmzdOlSSfadeC9cuKCgoCB98803zhwV5Yy55vjVV1/V0qVLFRERoZo1a+rDDz/UggULtGLFCvXs2ZM/rCohX19ftW3bVnPnztXu3bs1YsQIDRkyRP369VNGRoYOHDhQ4c+OzGHeriE2m002m00vvPCCrr/+evXv31+TJk3SunXr1L9/fw0YMEBVq1ZlPWoFYa45jomJkSR5eXlpwIABWr58uXJyclS1alUNGDBAN910E2+fV1JWq1XR0dE6c+aMgoKCNGfOHD322GMKDAzUrl27NHv2bG3dulWpqakaN26cs8dFOWKz2RQdHa0bbrhBNptNBQUF6tixo3766Sdt2rRJd999t/r06ePsMVGGCgsLi96R/OGHH/T555/rm2++Ue/evdWrVy+9++67OnDggAYOHFjhTwbHGuRrgNVqlcViKXriBgYG6vjx41q8eLHi4uJUs2ZNrV27VgMHDlRAQICzx0UJccRubGysGjZsqClTpigpKUk7duxQXFycdu7cKQ8PDz388MMXXR8V37Fjx+Tr61t0QqYWLVpoxowZevvtt5WcnKxZs2YpMjJStWrVUkZGhuLj4zVixAheHyBJRX9Mb9q0Sfv379czzzyjTp066YcfftDWrVs1btw4nTp1SgcOHFBgYKBq1arF60sl4HhXIS0tTXPmzFGtWrXUs2dPNWjQQMuWLdNPP/2koKAgDR06tGgnvYqMJRblmM1m07Fjx+Ti4qL09HQ9+eSTevfdd5WVlaVbbrlFjRo10scff6yNGzdq4cKF8vX1dfbIKAHmmtL8/HwVFBQoMjJSkjRhwgSlpKTo1KlTmjhxoo4fP64XX3yR45dWIps2bdLhw4clSd9++6127Nghd3d3SdKKFSv03XffKTo6Wi4uLrJYLEpOTlZcXBxnNcA3X3EAABZ4SURBVEXR64Qjdps0aSIvLy8lJiZKkjp27KizZ8/qwoULGjp0qBo0aKC6desSx5WEozWeeOIJ+fj4aNmyZYqPj1erVq309NNP66efftKNN95Y9HpT0bHEohyLj4/Xxx9/rCeeeELLly9XUFCQqlWrppycHDVp0kSnT5/Wxo0bFRcXp5CQEGePixJgrik9evSovL29tXLlStWpU0dt27aV1WrV448/rtmzZ6tx48Y6e/assrOzVbt2bWePjjJy+vRp+fv76z//+Y88PT1VWFiotWvXqlu3burevbskadeuXWrdurWki98yReVlLtmaOHGigoODdf78efn5+SklJUVubm5KTEzUyJEj1aVLF2ePCycoLCzUG2+8oeuvv17dunXT8OHDdfToUd1111164IEHVL169Ur1WkIgl3MLFizQV199pTZt2mjChAn65ZdftHHjxqInbfPmzeXh4eHsMVECHCFjs9k0ZswYnT17Vi1atFB6eroCAwN16tQpHT16VMOGDdMtt9yiCxcuyM3Nzdljo4yYoXvu3Dm98MILCggIUFhYmAoKCrR69WrdeeeduvfeeyWJNen4Hcc+LOfPn1f79u21efNmFRQUqHfv3kpOTlbLli2L/rBC5fDbP6C3b9+uHTt2aP/+/Zo5c6YWLlyoQ4cOadq0afL393fipGWPNcjlkOM4x5LUvn175efnKzExUa1atVKDBg3k4+Oj7OxshYeHy8fHx8nToqQ4thw7jjc5ZswYWa1WZWZmqkuXLurevbtuvvnmol9gFfkA7biYecSB6dOnq3bt2urQoYP27dun06dPKyAgQKGhoQoODi7aSZc4xm8988wzSktL05QpUxQSEiJ/f3+lpqbq73//u8LCwlSnTh1nj4gyZB429s0339Tu3bvVvn17NWrUSNu2bVO1atX06aefKjY2VnXr1nX2uGWO37DljOMJm5qaqpkzZ2rOnDmKiIhQWFhY0cG5GzVqpH79+nEotwrCXHP8f//3f1qyZIkaN24sf39/3XzzzTp16pQ8PT0VGBhYoQ/Kjstz/PEUHR2tOnXq6Oeff1ZeXp7CwsKUlZWl5ORktW7dWqGhoc4eFeVYx44dderUKSUlJUmSzp49qz179igzM5P9GCohx2FjJ02apJCQEB0/flyPPfaY8vLyVKNGDS1ZskRTp06ttL932IJczlgsFp05c0Zjx45V7969tX//fq1Zs0aPP/64UlJStGHDBt12222VZpF8RWcuqzhx4oRatWqloKAgLV68WDfffLMyMzO1atUq3XbbbRy+rxJyHMFGknbv3q09e/ZoyJAhWrp0qX744QedO3dO9913n5o0aVIpt/Dg8swlNo53JR075b322ms6ePCgvvrqKz300EO64YYbeEeqknE8P7744gt5eXkpIiJCa9euVdeuXZWVlaVhw4YpKiqqUr+rwALGcsJ8Mfv+++/VpEkTNW7cWG+88YZuvfVWbdiwQWPGjNHp06eJ4wrE8bZ5dHS0fH19tX37di1atEj9+vXTgw8+qFatWmnq1KlsGayEzD+ejh8/rtq1a8vT01Mffvihhg0bpipVqmju3LkaNGiQvL29nT0uyhFzXWl+fr48PDyKdtKLioqSh4eHFi9erB49enAa6UrG8dwoKCiQh4eHateurW3btmncuHGaOnWqTp8+rTVr1uiuu+6q9K8rBHI54HjCZmVlKTMzU3Xr1lVmZqYGDx6shQsX6uzZs1q0aJHuvffeSrdIvqJy/LKS7Mc5vuGGG/TEE09o3bp1io6O1gcffCCbzaZ33nlH9erVk8ROV5WJ4zXBarVq/Pjx8vf31/DhwzVz5kz973//04kTJ/Taa6/p8ccfr/S/xHAx87kzadIknTp1StHR0brxxhuLXkO6dOkiq9WqhQsXqmHDhrr55pudPTZKmc1m04YNG3THHXcoLS1N8+fP1/XXX6+6desqKytLTZs21Y4dO/Tf//5XsbGx7Pwvllg4nc1mk4uLi9LS0jRs2DBlZGQoPz9fgYGBcnd31+HDhxUfH6+nnnpKQUFBzh4XJcRischms+n8+fM6fPiw2rdvr+uvv15NmjTR8ePH5ePjo7vuukspKSlasmSJevbsWakOr1OZHT58uOiEHpMnT1ZISIj69OmjlStX6syZM/Ly8tL58+fVtWtXderUycnTorxxrFefOXOmrrvuOjVs2FBvvPGGQkJCVKdOnaJIbtSokWrWrKkmTZpwDP1K4JdfftGgQYPk6empzz//XC1atFBhYaHOnj2r0NBQNW3aVOfOndODDz6oJk2aOHvccoHDvJUDeXl5mjx5stq1a6e+fftKkubPn69u3bopNTVVN9xwg6677jonT4mS8PXXX6tTp04qLCzUtGnT1LRpU506dUoXLlxQr169lJmZqSlTpmjGjBkKCwuT9Otxb1HxrVixQpmZmRo1apQkaejQobrvvvv0n//8R507d9a6des0bty4oucG4GC+w/Tee+9pyZIl+t///idJev/99/X+++9r0qRJatOmDe9GVTKOdxX279+v6OhoNWrUSMuWLZPNZtOyZct04cIFjRgxwtljljusyncSc4/hKlWqyNvbW1WqVCm67NSpU/L19VWXLl2I4wpi1qxZ+uCDDyTZtwympKRo4MCBGjp0qM6cOaP33ntP8+bN04QJExQWFlb0HPHz83Pm2Cgjr7/+ut544w1Vr15d48aN086dOzVmzBj5+/vrwQcfVN++feXi4sJbn/idwsLCouC1Wq2644471Lx5c40dO1aS1L9/f/Xu3btoWRdxXHk4joyVl5enZs2aafny5Tpw4IBWrVoli8WiOnXq6MiRI8rLyxPbSy/GFmQncKw/PXXqlHbv3q0WLVpo8+bNSklJUePGjeXh4aGFCxdqyZIlCgwMdPa4KCH//ve/dfLkSfn7+8vNzU2LFi1STEyM7r77buXl5cnFxUWnT5+uFOe4x+8dOnRIY8eOVYsWLbR7926dPn1aL7zwglq2bKkZM2bo559/1sMPP6zbb7/d2aOiHHGcMMhqtWr06NGqW7eumjZtqsjISC1dulTp6el66aWXnD0mnMDRGmlpaXr22WdVvXp1NW/eXHfeeaf69u1bdFSTkSNHcir6S2ANchlzvNVx+vRpxcTEaPv27Tpx4oRq1KghT09Pfffdd9qxY4cmT56s66+/3tnjogQ4DrGUl5enOXPm6NixY3ryySf1t7/9TTNmzJCfn5+aN28uV1dXeXp6snWnkvLy8lJmZqbq1aundu3a6dChQ/rwww/VsWNH9evXT506ddKNN97o7DFRzjjWHD/22GNq3bq16tWrp4SEBFWpUkU9evTQt99+q7p167KxpRKyWCzKyMjQuHHjdOutt6pXr16aMWOGqlatqjFjxuiTTz7RpEmTKu1xjq+GJRZlzNXVVRkZGZo/f74GDx6sd999V56enkpPT1etWrU0ceJEPfvss/w1V0E43t6y2Ww6dOiQGjVqpKioKK1atUqNGjXSv/71L82dO1cnT56UxFuflc3q1au1fft2SZKHh4dCQkK0fv16+fv7q2fPnvL19dWwYcOUk5PDTrq4yCuvvKKvv/5aknTkyBE1bdpUDz30kPbu3StPT0+tWbNG8fHxmjhxopo1a+bkaVGWdu7cqfz8fElSenq6goODdf/996t+/fp67bXXtHPnTgUHB2vFihUKDg528rTlF4FcRsyzpR09elSfffaZTp8+LUkaMGCAcnNztXv3buXl5cnLy8tZY6KEubi4yGq1atSoUdq3b58KCgq0YcMGnT17VitXrlRoaKjWrl3L1p1KaMuWLZo8ebImTpyoadOm6eeff1bnzp3Vv39/ubu7KzQ0VHfffbdeeeUVVa9e3dnjopx56KGH1KlTJ33xxRcKDg5WUFCQZs+erX79+umhhx6Su7u7brnllov2bUHFZ7PZdOrUKU2YMEGvvvqqsrOzdeTIkaKNMLt27VJmZqby8vLk5saRfq+EJRZlwLEO6OTJk0pMTFTDhg112223acaMGWrcuLGaNm2q5s2bq1WrVhzTtAJ6+eWXVbt2bY0dO1Y//PCDfvrpJx08eLDoEEycIa9yql27toKDg5WXl6eff/5Zbm5uevPNN5WamipJ6t69u5o1a8YWHlzEsWTLzc1NiYmJio6Olre3twYMGKA1a9boxIkTWrZsmf7xj3+oXbt2zh4XZcxisSg/P18LFy7U+fPnFR0drfT0dC1dulTff/+91q9fr7i4ONWsWZN3LK+CQC4DFotF6enpGjVqlM6fP6/ly5crKipKXbp0UXR0tFq2bKmQkBBVq1bN2aOiFHh4eMjd3V2LFy/WvffeqwYNGighIUHDhw9XSEiIs8eDk7i6uio4OFiurq5KT09XeHi4OnfurC+++ELbtm1TZGQkfzzhdxxrjkeMGKHw8HCNGTNG06ZNU5UqVdS3b1/t3LlTDzzwACf/qGQcp6UvLCxUrVq11KRJE3l6euqTTz7R2LFjVadOHYWFhenuu+9WgwYNnD3uNYGjWJQSm82mtLQ01alTR3l5eRo/frzuuecetWnTRlOnTlVmZqbGjx8vSapevTpbiSqwvLw8HT58WBs2bFB4eLiWL1+uIUOGqG3bts4eDeXAuXPn9Pnnn2vTpk0aNmyYmjZtqpMnT6p27drOHg3liHns4tWrV2vFihV68cUX1bBhQ6WkpKh3794aPny4Hn74YecOijJnHq1ixowZ8vb21uOPPy6bzaaVK1dq69atCg8PV0xMDCec+hPYglxK/v3vf+v5559Xhw4dVKNGDRUWFqpatWpatmyZBg0apGPHjmn16tX65z//qRo1ajh7XJQiNzc3WSwWrV+/Xh9++KEeeeQRtu6giLu7uxo0aKDc3FzFx8crIiKC1wRcxLGswiE/P19Vq1bVzp07VbduXQUHB6t79+7y8PDg6EeVkONoFbNnz1ZkZKROnDihdevWqXPnzurYsaNsNpvuvPPOojN04o9hC3Ipmjt3rr7//nvFxsaqSpUqSkpKkru7u7y8vLRmzRqNGTOGF7NK5Pz588rNzVWNGjU4kxV+5/z588rMzGTLMS7ieK2wWq2aOHGi/Pz8FBAQoBo1aignJ0fHjh1Tv379io58xGtL5VNYWKjnnntO3333nVauXCnJfmKqvXv36vnnn+c1pZg4ikUpycjIUGpqqjIzMzVhwgTl5eWpoKBAa9as0cyZM/Xoo48Sx5VM1apVi7YM8gsMv1W1alV+keF3HK8VMTExatWqlTp27KiNGzeqoKBAISEh8vHxuej1hNeWysFxZKyCggK5urrq/vvvl9Vq1YsvvihJmjBhglq3bn3REbTw57AFuRTk5+crLi5O7du319///nctXrxYiYmJiouLk8VikY+PD291AAAuy3FSKYdp06Zp9OjReuaZZ9SxY0dVrVpV7dq1k7+/vzw9PZ04KcqaY81xenq6nnnmmaKd7gYMGKB58+apTp06evLJJ507ZAXAFuRS4DhqQXZ2tiTpn//8p3Jzc7VkyRIFBQURxwCAyzJPMDR79mxduHBBBQUFioyM1D333KOoqCi99dZbysjIII4rIRcXF2VkZCgmJkYdO3ZU7969deTIEb355psaOXKkfvnlF2VkZIjtn38NO+mVMMehVqpVq6YdO3bo8OHDysjI0M8//6zHHnuMOAYAXJbNZis6wdCSJUu0Zs0abd26VRMnTtTJkyd18OBBvffee4qOjlaHDh2cPS7KkLmz5tGjR3XixAmNGjVK/v7+atWqlXbt2qVevXrpjjvukLe3N8tt/iJOo/IXOd7qcHB8HhoaKkn65JNPtHfvXj3++OMcyg0AcFkFBQVyd3eXJD322GMKDQ3VBx98oAULFmj8+PFatmyZsrKylJWVpfr16zt5WpQlx7sKqampWrdundq1a6eNGzdqwIABaty4sbZv364ff/xR586dU9WqVZ09boXAGuS/wBHHJ06c0Lfffqvz58+re/fuv7teXl4ep/sEAFzW+++/r/r166tTp06y2WyaPHmyHn300aINK/fff7+qV6+u+fPnq2rVqhytohJxtMapU6c0ZswYpaWlaf369Xrrrbf01ltv6e6779aWLVs0c+bMoqOZ4K9jicVf4DhD3tixY1WlShV98MEH+vHHHxUZGSnp1+UWnO8cAHAlwcHBaty4sT7++GPVrFlT33zzjQoKClSvXj398MMPOnLkiPLz83X69Gm1bt2aOK4kHEtu0tPT9dRTTyk8PFy1a9dWRESE/va3v6levXpq3bq1evbsybvUJYyd9IqhsLCw6PMFCxaoQ4cOGjVqlN577z1t375dq1evlqSLll4AAPBbjt8nPj4+ysrK0ttvv63PPvtMPXr0UEJCghYsWKBJkyZp6NChioyMvOj3Dyo2x0a2c+fO6YsvvlDfvn01ZswYnT59Wq6urvr000914MABNW7cWNddd52zx61w2LRZDK6urjpx4oQOHz6sVq1aycPDo+jMRj169JCHh4ezRwQAlHOOdaVWq1Vr1qxRvXr1tHz5co0bN06urq4aP368fHx8tHPnTh08eFCffPKJpk2b5uyxUQbM00c/9thjqlmzpho0aKBu3brJx8dHixcv1qZNmzRjxgyao5QQyH+Q1WrVa6+9pkcffVTp6emaO3euIiMj5e/vrzVr1sjd3V0nT57UunXrNGvWLGePCwAoxxxvndtsNj3++ONyc3PTTz/9pNtvv11z5szRyJEjlZaWpqFDh8rNzU1ff/21pkyZwhrTSsLFxUWnT5/W1KlTNWLECNWtW1dDhw5VXl6ejh07pqNHj2rWrFmqV6+es0etsAjkP2jRokU6cOCAJOnVV1/Vrl279Nxzz8nFxUWZmZk6fvy4Dh48qFmzZqlhw4ZOnhYAUJ451hC/+uqrCggI0JQpU5Sbm6thw4bpwoULWrRokQ4ePChvb2917txZN91000UnDkHFl5OTo7CwMPn5+WnVqlUaP368FixYoNzcXL322mvEcSnjKBZ/0LZt2/Tpp5/q7NmzGjZsmBYuXCibzVZ0Wkfp92c+AgDgcrKzs7V06VLt27dPMTExatGihbKysvTQQw9p/vz5Cg4O5mgVlVhBQYH27t2r+Ph49ezZUy4uLnrzzTc1btw44rgMEMhXYR7KLTo6WpL07LPPKiQkRKNHj1ZeXp6WLFni5CkBANeizMxMrV69WtnZ2brtttt04403srEFF1m4cKGOHTum5ORkzZ8/n3epywiBfAWOOM7JyVFaWpo8PDy0bds27d+/X927d1fr1q01duxYPfnkk6pdu7azxwUAXIMyMjL0/vvv68yZMxo9erQ8PT05ChKKHDt2TLt371bz5s3VoEEDZ49TaRDIl/HbPUh9fHzUokULxcTE6J133tG+ffvUv39/tWrVytmjAgCucRkZGcrNzeWtc6CcYCe9y7jcHqTVqlXT8OHDtWrVKtWtW9fZYwIAKoCAgAAFBAQ4ewwA/x+BfAWX2oN0+vTp8vT01KBBg5w9HgAAAEoBgXwFderU0U033VS0B6m7u7s6deqkm2++2dmjAQAAoJSwBvkPMPcgffHFF1kkDwAAUIERyH8Ae5ACAABUHgQyAAAAYOBAiwAAAICBQAYAAAAMBDIAAABgIJABAAAAA4EMAAAAGAhkAAAAwEAgAwAAAIb/B16emvO8yaJZAAAAAElFTkSuQmCC\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": []
|
|
}
|
|
]
|
|
} |