{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
   "display_name": "Python 3.7.0 64-bit ('3.7')"
  },
  "metadata": {
   "interpreter": {
    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "source": [
    "# Nigerian Music scraped from Spotify - an analysis"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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: 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: 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: 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: 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: 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: 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: 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": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '../../data/nigerian-songs.csv'",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-187d94548bba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"../../data/nigerian-songs.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    684\u001b[0m     )\n\u001b[1;32m    685\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 686\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    688\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    450\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    451\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 452\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    453\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    454\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    934\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    935\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 936\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    937\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    938\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1166\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"c\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1167\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1168\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1169\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1170\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"python\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1996\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"usecols\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1997\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1998\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1999\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2000\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../../data/nigerian-songs.csv'"
     ]
    }
   ],
   "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()"
   ]
  },
  {
   "source": [
    "Get information about the dataframe"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "df.info()"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 3,
   "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": [
    "There are no null values. Look at the general values of the data. Note that popularity can be '0'"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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  "
      ],
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     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "source": [
    "The song's genre is a good candidate for a cluster. 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": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top genres')"
      ]
     },
     "metadata": {},
     "execution_count": 5
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "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')"
   ]
  },
  {
   "source": [
    "Remove 'Missing' genres, as it's not classified in Spotify\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "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": {
      "text/plain": "<Figure size 720x504 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "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')"
   ]
  },
  {
   "source": [
    "The top three genres comprise the greatest part of the dataset, so let's focus on those"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-9d1c2502b665>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'artist_top_genre'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'afro dancehall'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'artist_top_genre'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'afropop'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'artist_top_genre'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'nigerian pop'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'popularity'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'artist_top_genre'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "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",
    "print(df)\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": "stream",
     "name": "stdout",
     "text": [
      "Object `energetic` not found.\n"
     ]
    }
   ],
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "corrmat = df.corr()\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "sns.heatmap(corrmat, vmax=.8, square=True);"
   ]
  },
  {
   "source": [
    "Examine our top three genres data distribution along a given x and y axis "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
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     },
     "metadata": {}
    }
   ],
   "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",
    ")"
   ]
  },
  {
   "source": [
    "A scatterplot of the same axes shows a similar pattern of convergence"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdad10a8b70>"
      ]
     },
     "metadata": {},
     "execution_count": 11
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {}
    }
   ],
   "source": [
    "df.plot(kind=\"scatter\", x=\"popularity\", y=\"danceability\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-12-515ac11340bc>, line 1)",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-12-515ac11340bc>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    You can visualize that scatterplot with hues aligning to genre\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "You can visualize that scatterplot with hues aligning to genre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/seaborn/axisgrid.py:316: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fe611ff3a20>"
      ]
     },
     "metadata": {},
     "execution_count": 138
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {}
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.FacetGrid(df, hue=\"artist_top_genre\", size=5) \\\n",
    "   .map(plt.scatter, \"popularity\", \"danceability\") \\\n",
    "   .add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "swarmplots show the general distribution as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/seaborn/categorical.py:1296: UserWarning: 17.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe5e2514588>"
      ]
     },
     "metadata": {},
     "execution_count": 146
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "sns.swarmplot(x=df['popularity'],\n",
    "              y=df['artist_top_genre'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduce the dataframe to only numeric values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'shuga rush'",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-148-d7a72f3235e1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# Calculate the linkage: mergings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mmergings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinkage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'complete'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/scipy/cluster/hierarchy.py\u001b[0m in \u001b[0;36mlinkage\u001b[0;34m(y, method, metric, optimal_ordering)\u001b[0m\n\u001b[1;32m   1040\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid method: {0}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1042\u001b[0;31m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_convert_to_double\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1043\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1044\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/scipy/cluster/hierarchy.py\u001b[0m in \u001b[0;36m_convert_to_double\u001b[0;34m(X)\u001b[0m\n\u001b[1;32m   1562\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_convert_to_double\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1563\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdouble\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1564\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdouble\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1565\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontiguous\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1566\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'shuga rush'"
     ]
    }
   ],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "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()"
   ]
  }
 ]
}