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ML-For-Beginners/5-Clustering/2-K-Means/notebook.ipynb

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adding a note on constrained data
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{
"metadata": {
"language_info": {
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"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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"version": "3.7.0"
adding a note on constrained data
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},
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"orig_nbformat": 2,
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"name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
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}
}
adding a note on constrained data
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},
"nbformat": 4,
"nbformat_minor": 2,
"cells": [
{
"source": [
"# Nigerian Music scraped from Spotify - an analysis"
],
"cell_type": "markdown",
"metadata": {}
spelling issue
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},
{
"cell_type": "code",
"execution_count": 5,
"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: 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: 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: 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: 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: 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: 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: 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"
]
},
{
"source": [
"Start where we finished in the last lesson, with data imported and filtered."
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 6,
"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 "
],
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},
"metadata": {},
"execution_count": 6
}
],
"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()"
]
},
{
"source": [
"We will focus only on 3 genres. Maybe we can get 3 clusters built!"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Top genres')"
]
},
"metadata": {},
"execution_count": 7
},
{
"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": 8,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" name album \\\n",
"1 shuga rush EVERYTHING YOU HEARD IS TRUE \n",
"3 Confident / Feeling Cool Enjoy Your Life \n",
"4 wanted you rare. \n",
"5 Kasala Pioneers \n",
"6 Pull Up Everything Pretty \n",
"\n",
" artist artist_top_genre release_date length popularity \\\n",
"1 Odunsi (The Engine) afropop 2020 89488 30 \n",
"3 Lady Donli nigerian pop 2019 175135 14 \n",
"4 Odunsi (The Engine) afropop 2018 152049 25 \n",
"5 DRB Lasgidi nigerian pop 2020 184800 26 \n",
"6 prettyboydo nigerian pop 2018 202648 29 \n",
"\n",
" danceability acousticness energy instrumentalness liveness loudness \\\n",
"1 0.710 0.0822 0.683 0.000169 0.1010 -5.640 \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",
"5 0.803 0.1270 0.525 0.000007 0.1290 -10.034 \n",
"6 0.818 0.4520 0.587 0.004490 0.5900 -9.840 \n",
"\n",
" speechiness tempo time_signature \n",
"1 0.3600 129.993 3 \n",
"3 0.1130 111.087 4 \n",
"4 0.0447 105.115 4 \n",
"5 0.1970 100.103 4 \n",
"6 0.1990 95.842 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>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>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 <tr>\n <th>5</th>\n <td>Kasala</td>\n <td>Pioneers</td>\n <td>DRB Lasgidi</td>\n <td>nigerian pop</td>\n <td>2020</td>\n <td>184800</td>\n <td>26</td>\n <td>0.803</td>\n <td>0.1270</td>\n <td>0.525</td>\n <td>0.000007</td>\n <td>0.1290</td>\n <td>-10.034</td>\n <td>0.1970</td>\n <td>100.103</td>\n <td>4</td>\n </tr>\n <tr>\n <th>6</th>\n <td>Pull Up</td>\n <td>Everything Pretty</td>\n <td>prettyboydo</td>\n <td>nigerian pop</td>\n <td>2018</td>\n <td>202648</td>\n <td>29</td>\n <td>0.818</td>\n <td>0.4520</td>\n <td>0.587</td>\n <td>0.004490</td>\n <td>0.5900</td>\n <td>-9.840</td>\n <td>0.1990</td>\n <td>95.842</td>\n <td>4</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 8
}
],
"source": [
"df.head()"
]
adding a note on constrained data
4 years ago
}
]
}