kda.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kpop Data Analysis\n",
    "## -- by Charlie Chengrui Zheng\n",
    "Kpop refers to the music genre and its idol industry originated in South Korea. Unlike other music genres having blurry origin and developed gradually in today's form, Kpop has an actual birthday when **Seo Taiji and Boys** debuted on 1992-03-23. After less than 30 years of development, Kpop obtains worldwide popularity among youth today. I gathered data of Kpop from 1992-03-23 (the birthday of Kpop) to 2021-01-11 including information of all major Kpop idols from [K-Pop Database](https://dbkpop.com/). In this article, I will investigate Kpop by analyzing the 4 tables in this dataset:\n",
    "<ol>\n",
    "    <li>all Kpop idols</li>\n",
    "    <li>all Kpop boy groups</li>\n",
    "    <li>all Kpop girl groups</li>\n",
    "    <li>all Kpop music videos</li>\n",
    "</ol>\n",
    "Music videos are great metrics to analyze because they are usually made for Kpop singers' hit songs, also known as 'debut' and 'comeback'. Note that the information of Seo Taiji and Boys are not recorded in first 2 tables because whether they were too different from all other Kpop idols.\n",
    "\n",
    "## General\n",
    "By 2021-01-11, when the data was collected, 4146 music videos were produced. There are 1435 Kpop idols in total. It is quite stunning that Kpop has been developing for less than 30 years, but has already attracted such a great number of artists to join this industry. Kpop has always been famous for group activities. Among all idols, 1318 of them (92%) are or has been a member of a Kpop group, only 117 of them (8%) has always been soloists. 48% of them are female. 52% of them are male. There has been 176 male groups and also 176 female groups. 134 boy groups and 94 girl groups are active now. Here is a small peek of the table of all idols:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1318 artist are or have been in Kpop group\n",
      "134 boy groups are active\n",
      "94 girl groups are active\n"
     ]
    },
    {
     "data": {
      "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>Stage Name</th>\n",
       "      <th>Full Name</th>\n",
       "      <th>Date of Birth</th>\n",
       "      <th>Group</th>\n",
       "      <th>Country</th>\n",
       "      <th>Birthplace</th>\n",
       "      <th>Other Group</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A.M</td>\n",
       "      <td>Seong Hyunwoo</td>\n",
       "      <td>12/31/96</td>\n",
       "      <td>Limitless</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>1996</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ace</td>\n",
       "      <td>Jang Wooyoung</td>\n",
       "      <td>8/28/92</td>\n",
       "      <td>VAV</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>1992</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aeji</td>\n",
       "      <td>Kwon Aeji</td>\n",
       "      <td>10/25/99</td>\n",
       "      <td>Hashtag</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>Daegu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>1999</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AhIn</td>\n",
       "      <td>Lee Ahin</td>\n",
       "      <td>9/27/99</td>\n",
       "      <td>MOMOLAND</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>Wonju</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>1999</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ahra</td>\n",
       "      <td>Go Ahra</td>\n",
       "      <td>2/21/01</td>\n",
       "      <td>Favorite</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>Yeosu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>2001</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Stage Name      Full Name Date of Birth      Group      Country Birthplace  \\\n",
       "0        A.M  Seong Hyunwoo      12/31/96  Limitless  South Korea        NaN   \n",
       "1        Ace  Jang Wooyoung       8/28/92        VAV  South Korea        NaN   \n",
       "2       Aeji      Kwon Aeji      10/25/99    Hashtag  South Korea      Daegu   \n",
       "3       AhIn       Lee Ahin       9/27/99   MOMOLAND  South Korea      Wonju   \n",
       "4       Ahra        Go Ahra       2/21/01   Favorite  South Korea      Yeosu   \n",
       "\n",
       "  Other Group Gender  Birth Year  Age  \n",
       "0         NaN      M        1996   25  \n",
       "1         NaN      M        1992   29  \n",
       "2         NaN      F        1999   22  \n",
       "3         NaN      F        1999   22  \n",
       "4         NaN      F        2001   20  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "def year_clean(df, col1_name, col2_name):\n",
    "    year_cleaned = []\n",
    "    for _, row in df.iterrows():\n",
    "        if row[col1_name][-2] in ['0','1','2']:\n",
    "            year_cleaned.append(2000 + int(row[col1_name][-2:]))\n",
    "        else:\n",
    "            year_cleaned.append(1900 + int(row[col1_name][-2:]))\n",
    "    df[col2_name] = year_cleaned\n",
    "    return df\n",
    "\n",
    "# creating dataframes and data cleaning\n",
    "idols = year_clean(pd.read_csv('kpop_idols.csv'), 'Date of Birth', 'Birth Year')\n",
    "boys = pd.read_csv('kpop_idols_boy_groups.csv')\n",
    "girls = pd.read_csv('kpop_idols_girl_groups.csv')\n",
    "all_groups = year_clean(pd.concat([boys, girls]),'Debut','Debut Year')\n",
    "\n",
    "# calculating ages of idols\n",
    "age = [2021-row['Birth Year'] for _, row in idols.iterrows()]\n",
    "idols['Age'] = age\n",
    "print(f'{idols.count().Group} artist are or have been in Kpop group')\n",
    "boy_active = boys.groupby('Active').count()['Name'].loc['Yes']\n",
    "girl_active = girls.groupby('Active').count()['Name'].loc['Yes']\n",
    "print(f'{boy_active} boy groups are active')\n",
    "print(f'{girl_active} girl groups are active')\n",
    "idols.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## International Artists\n",
    "Kpop is well-known for its internationality, 124 idols (9%) are not from South Korea, but from 12 other countries and states. Notably, there are 42 artists from China (mainland) now. There are also 38 Japanese artists and 14 American artists (Korean-Americans are not included in this dataset)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "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>Country</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>China</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Japan</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USA</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Canada</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Australia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Germany</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country  count\n",
       "0         China     42\n",
       "1         Japan     38\n",
       "2           USA     14\n",
       "3        Taiwan      7\n",
       "4      Thailand      7\n",
       "5        Canada      6\n",
       "6     Hong Kong      4\n",
       "7     Indonesia      2\n",
       "8     Australia      1\n",
       "9       Germany      1\n",
       "10     Malaysia      1\n",
       "11  Philippines      1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foreign = idols.groupby('Country')['Stage Name'].count().reset_index(name='count')\\\n",
    ".sort_values(['count'],ascending=False).iloc[1:]\n",
    "foreign.plot.bar(x='Country',title='Foreign Kpop Artists', rot=45, figsize=(10,5))\n",
    "plt.show()\n",
    "foreign.reset_index(drop=True, inplace=True)\n",
    "foreign"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Age\n",
    "Kpop Artists are famous for showing their youthful charisma. Kpop idols are generally young. The average age of all Kpop idols is 25, as for 2021."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average age of Kpop idols is 25\n"
     ]
    },
    {
     "data": {
      "image/png": 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7VtUN/TY3AjsObVKSJGk5GBKsVgCPAN5SVQ8Hvs+M3X5VVUDNduckxyRZm2Tthg0bBrQhSZI0HYYEq+uA66rqov72GXRB66YkOwH0P2+e7c5VdWJVra6q1StXrhzQhiRJ0nSY+BirqroxybVJ7l9VVwEHAFf0lzXA8f3PM5t0KklbAI+PlJa3iYNV70+A05JsA3wJeDbdLNjpSY4GvgIcPvAxJEmSloVBwaqqLgVWz7LqgCF1JUmSliPPvC5JktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIwYrSZKkRgxWkiRJjRisJEmSGjFYSZIkNWKwkiRJasRgJUmS1IjBSpIkqRGDlSRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1MjhYJdkqyWeSnNXf3iPJRUnWJXlvkm2GtylJkjT9WsxY/Slw5cjtE4A3VtX9gG8CRzd4DEmSpKk3KFgl2RU4GHhrfzvA/sAZ/SanAocOeQxJkqTlYuiM1d8DLwN+3t++N/Ctqrq1v30dsMtsd0xyTJK1SdZu2LBhYBuSJElLb+JgleTJwM1Vdckk96+qE6tqdVWtXrly5aRtSJIkTY0VA+77OOApSQ4C7gzcA3gTsF2SFf2s1a7A9cPblCRNYtWxZ4+13frjD17kTqQtw8QzVlX1iqratapWAUcA/15VTwc+DhzWb7YGOHNwl5IkScvAYpzH6uXAS5Ksozvm6qRFeAxJkqSpM2RX4G2q6nzg/P76l4B9WtSVJElaTjzzuiRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaafIlzBpu1bFnj7Xd+uMPXuROJEnSpJyxkiRJasRgJUmS1IjBSpIkqRGDlSRJUiMGK0mSpEYMVpIkSY0YrCRJkhrxPFaSpLF5zj1pfs5YSZIkNWKwkiRJasRgJUmS1IjBSpIkqRGDlSRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDUy8ZnXk+wGvB3YESjgxKp6U5LtgfcCq4D1wOFV9c3hrU4Xzz4sSZJmGjJjdSvwZ1W1N/Bo4PlJ9gaOBc6rqj2B8/rbkiRJm72Jg1VV3VBVn+6vfxe4EtgFOAQ4td/sVODQgT1KkiQtC02OsUqyCng4cBGwY1Xd0K+6kW5XoSRJ0mZvcLBKcnfgfcCLquo7o+uqquiOv5rtfsckWZtk7YYNG4a2IUmStOQGBaskW9OFqtOq6l/7xTcl2alfvxNw82z3raoTq2p1Va1euXLlkDYkSZKmwsTBKkmAk4Arq+rvRlZ9EFjTX18DnDl5e5IkScvHxKdbAB4HPBP4XJJL+2V/ARwPnJ7kaOArwOGDOpQkSVomJg5WVfVJIHOsPmDSupIkScuVZ16XJElqxGAlSZLUyJBjrCRJGsSvB9PmxhkrSZKkRgxWkiRJjRisJEmSGjFYSZIkNWKwkiRJasRPBUqSNht+ylBLzRkrSZKkRgxWkiRJjRisJEmSGtlijrFyv7skSVpszlhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIwYrSZKkRgxWkiRJjRisJEmSGjFYSZIkNWKwkiRJasRgJUmS1IjBSpIkqRGDlSRJUiMrlroBSZKm1apjzx5ru/XHH7wk9TR9nLGSJElqxBkrSZKWqWmeAZvm3haTM1aSJEmNLFqwSnJgkquSrEty7GI9jiRJ0rRYlGCVZCvgH4EnAXsDRybZezEeS5IkaVos1jFW+wDrqupLAEneAxwCXDFugS1136wkSUth2v/fXS6f0FysXYG7ANeO3L6uXyZJkrTZSlW1L5ocBhxYVc/tbz8T+M2qesHINscAx/Q37w9cNUbpHYCvN2zVetbbXOpNc2/Ws571lm+9ae5tKevtXlUrZ1uxWLsCrwd2G7m9a7/sNlV1InDiQoomWVtVq4e3Zz3rbV71prk361nPesu33jT3Nq31FmtX4H8BeybZI8k2wBHABxfpsSRJkqbCosxYVdWtSV4AfATYCji5qj6/GI8lSZI0LRbtzOtVdQ5wTuOyC9p1aD3rbUH1prk361nPesu33jT3NpX1FuXgdUmSpC2RX2kjSZLUyNQGqyQnJ7k5yeUjy16d5Pokl/aXg4bU65f/SZIvJPl8ktcP7O+9I72tT3LpwHoPS3JhX29tkn0G1ntokk8l+VySf0tyjzFr7Zbk40mu6H9Pf9ov3z7JuUm+2P+818B6f9Df/nmSsT+VMU+9N/Sv7WeTvD/JdgPrvaavdWmSjybZeUi9kfV/lqSS7DCwv4nGx3z9TTI+5ulvovExT72Jxsc89SYdH3dOcnGSy/p6f9Uv3yPJRem+1uu96T7IM2mtF/R1xn6fbKLeaem+cuzydP9WbD2w3kn9ss8mOSPJ3YfUG1n/D0m+1+D5npLkyyPvv4cNrJckr01ydZIrk7xwYL1PjPT2tSQfGFjvgCSf7ut9Msn9Btbbv693eZJTkyzoMKIkWyX5TJKz+tsLHhvz1JpobMxVb2T5gt57v6SqpvICPAF4BHD5yLJXAy9tWO+3gI8B2/a37zOk3oz1fwu8amB/HwWe1F8/CDh/YL3/Ap7YX38O8Joxa+0EPKK//ivA1XRfVfR64Nh++bHACQPrPZDunGbnA6sX8Fznqve7wIp++QkN+rvHyDYvBP5pSL3+9m50H/L4CrDDwP4mGh/z1JtofMz3fCcZH/P0N9H4mKfepOMjwN3761sDFwGPBk4HjuiX/xPwvAG1Hg6sAtaP+z7ZRL2D+nUB3j1Ob5uoNzo2/o7+34VJ6/W3VwPvAL7X4PmeAhw2wdiYq96zgbcDd1rg2Jjz+Y5s8z7gqIH9XQ08sF/+x8ApA+o9lu6E33v1y/8aOHqBv8eXAO8CzupvL3hszFNrorExV71J33ujl6mdsaqqC4BbFrne84Djq+rH/TY3t+gvSYDD6f7BGlKvgI1/Nd8T+NrAensBF/TXzwV+f8xaN1TVp/vr3wWupDuT/iHAqf1mpwKHDqlXVVdW1Tgnih233ker6tZ+swvpzqc2pN53Rja7G93rM3G9fvUbgZeNW2uMegs2T72Jxsem+lvo+Jin3kTjY556k46PqqqNf9lu3V8K2B84o18+1viYq1ZVfaaq1o/Tz5j1zunXFXAx44+Nuep9B257be/C+GNj1nrpvm/2DXRjY2zzvBYTmafe84C/rqqf99uNOzbm7S/dLOn+wAcG1pt0bMxW72fAT6rq6n752GMDIMmuwMHAW/vbYYKxMVutvueJxsZc9SZ9742a2mA1jxf0080nZ8xdT/PYC3h8PyX5H0ke1aJB4PHATVX1xYF1XgS8Icm1wN8ArxhY7/N0YQjgD/jlk7iOJckqur8QLgJ2rKob+lU3AjsOrDfYPPWeA3xoaL1++v9a4OnAq4bUS3IIcH1VXbbQOnP1x8DxMaPe4PExx+sx8fiYUe9FDBwfM+pNPD763QmXAjfT/cdzDfCtkWA/9td6zaxVVYPGxnz10u0CfCbw4aH1kryN7t+BBwD/Z2C9FwAfHPn3ZWzzPN/X9mPjjUm2HVjv14GnpdsF/aEkezboD7qAcd6MP+Imqfdc4Jwk19G9vsdPWo8ueK/ILw7POIyF/d/x93Qh5ef97Xsz4diYpdZQs9Wb+L230XILVm+he0M/DLiBbnfCECuA7emmTv8cOL1P00MdyQJmq+bxPODFVbUb8GLgpIH1ngP8cZJL6HaB/GQhd0533MT7gBfNHPj9X74L+stwvnqTmKteklcCtwKnDa1XVa/sX4/T6AbgRPX6fv6CCcLZPP0NGh+z1Bs0PuZ5fScaH7PUGzQ+Zqk38fioqp9V1cPoZn72oQsXE5lZK8mDJq01Rr03AxdU1SeG1quqZwM7080APm1AvSfQBduxw9kY/b2C7jV5FN17+uUD620L/Ki6M3T/M3DywHobLXhszFHvxcBBVbUr8Da63bMT1QN+g+4k329McjHwXbpZrE1K8mTg5qq6ZPxntPi15qqX7rjZid97t6kJ9h/eURe6/aZzHcM057px69H9lfZbI7evAVYO6Y/uP6ObgF2HPl/g2/zilBgBvtPw97cXcPECam1NdyzQS0aWXQXs1F/fCbhqSL2RdeezgGOs5qsHPAv4FHDXFvVG1v/qQt5/M+sBD6b7i3B9f7kV+Cpw30b9LWh8zPH6Tjw+5nk9Jhofc/Q38fgY4/e3oPEx476voguiX+cXx/g9BvjIhLVeOnJ7PRMcRzJbPeA4ul1Od2pRb2TZExg5XmWCesfRzXxtHBs/B9Y17G+/gf29FPgCsMfIe+/bDV6PHYBvAHce+Hr8OXDNyLJfBa5o+Pv7XeD0Me//OroZqfX9a/oDuj9KFzw25qj1zpH1Cxobc9T7Zov33rKasUqy08jNpwKXz7XtmD5Ad4AuSfYCtmH4lzn+NvCFqrpuYB3o9os/sb++PzBo12KS+/Q/7wT8Jd1Bg+PcL3SzAVdW1ehfPh8E1vTX1wBnDqw3kbnqJTmQbpr3KVX1gwb1Rqf7D6H7x3WielX1uaq6T1WtqqpVdAP8EVV144D+Jhof87weH2CC8bGJ13fB42OeehONj3l+f5OOj5XpP3Ga5C7A79DN2nycbrcJjDk+5qg11vtsIfWSPBf4PeDI6o8TGlDvqvSfOut/t08Zt+c56l1SVfcdGRs/qKpxP9U21/PdaaS/Qxl/bMz1enyAfmzQvQevnu3+C6gH3XvlrKr60Ti15ql3JXDPfswysmzi/kbGxrZ0s31jjY2qekVV7dq/jkcA/15VT2eCsTFHrWeM08cC6t1r0vfezOJTeaGbDr0B+CndfzpH0x2l/zngs3T/qe80sN42wDvpBtmngf2H1OuXnwL8UaPnuy9wCXAZ3TEgjxxY70/p/gG4mm6fe8astS/dbr7PApf2l4Po9pWfR/cf2seA7QfWe2rf64/pZjXG+gt/nnrr6D7NsnHZuJ/im6ve+/r3ymeBf6M7oH3iejO2Wc/4nwqcq7+Jxsc89SYaH/M9XyYYH/P0N9H4mKfepOPjIcBn+nqX03/aEfg1uuNT1gH/Qv/pyglrvZBubNxKFyjfOrC3W+lmIDc+/3E/oXm7enSHlPxn/967nG5G4h5D+puxzUI+FTjX8/33kf7eSf/JtwH1tgPO7mt+Cnjo0OdLN1N/4ALHxlz9PbXv7bK+7q8NrPcGunB2Fd2u87F7HKm9H7/4JN+Cx8Y8tSYaG3PVm/S9N3rxzOuSJEmNLKtdgZIkSdPMYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSlrUkh6b7ZvuJz3YuSa0YrCQtd0cCn+x/StKSMlhJWrb67/vbl+4EuEf0y+6U5M1JvpDk3CTnJDmsX/fIdF8ofUmSj8w4W70kDWawkrScHQJ8uKquBr6R5JHAf6f7rsS9gWfSfRcZSbam+3LVw6rqkXRfnPvapWha0uZrxVI3IEkDHAm8qb/+nv72CuBfqvsOvBuTfLxff3/gQcC53VfGsRXd1z5JUjMGK0nLUpLt6b58+cFJii4oFfD+ue4CfL6qHnMHtShpC+SuQEnL1WHAO6pq9+q+jX434MvALcDv98da7Uj3BavQfYHsyiS37RpM8htL0bikzZfBStJydSS3n516H3Bfum+7vwJ4J/Bp4NtV9RO6MHZCksuAS4HH3mHdStoipKqWugdJairJ3avqe0nuDVwMPK6qblzqviRt/jzGStLm6Kwk2wHbAK8xVEm6ozhjJUmS1IjHWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRG/j/fF4FXvAmdoQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "age = idols.groupby('Age')['Stage Name'].count()\n",
    "print(f'The average age of Kpop idols is {int(np.mean(idols.Age))}')\n",
    "age.plot.bar(title='Age of Kpop Artists',rot=0, figsize=(10,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the other hand, Kpop idols usually debut in even younger age. The average age of a newly debuted Kpop group members is 19. The youngest idols, such as IZ\\*ONE's Wonyoung and Shinee's Taemin, debuted are only 14 years old."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average age of a newly debuted Kpop group is 19\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter, defaultdict\n",
    "def debut_age(df):\n",
    "    age = []\n",
    "    checked = []\n",
    "    for _, group in df.iterrows():\n",
    "        for index, idol in idols.iterrows():\n",
    "            if index not in checked and idol.Group == group.Name:\n",
    "                checked.append(index)\n",
    "                debut = group['Debut Year'] - idol['Birth Year']\n",
    "                # I calculate the debut age by minus group debut year from idols' birth year\n",
    "                # However, this may not work sometimes when young idols were added to the group\n",
    "                # after the group already debut\n",
    "                # Therefore, I set the minimum debut age as 14, which is already known\n",
    "                if debut > 13:\n",
    "                    age.append(debut)\n",
    "    return age\n",
    "\n",
    "debut_age_list = debut_age(all_groups)\n",
    "debut_age_list.sort()\n",
    "print(f'The average age of a newly debuted Kpop group is {int(np.mean(debut_age_list))}')\n",
    "age_count = pd.DataFrame({'age': debut_age_list}).groupby('age', as_index=False).size()\n",
    "age_count.plot.bar(x='age',y='size',title='The debut Age of Kpop idols',rot=0, figsize=(10,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Timeline\n",
    "Kpop's fast development is epitomized by the timeline of music videos by year, and the timeline of group debuts by year. Especially, Kpop groups can be divided into generations. There are lot of opinions about how to divide the generations. Here, I will be showing only one of those opinions:\n",
    "<ul>\n",
    "    <li>0th Generation 1992 - 1995: This was the time when <b>Seo Taiji and Boys</b> were building the fundamentals of Kpop industry. In 'Music Videos by Year', they were the only artists who were releasing music videos, about 1 or 2 every year. There were no other group debut in this era.\n",
    "    <li>1st Generation 1996 - 2003: This was the time when the agency <b>SM Entertainment</b> trained their own artists and gathered them to form the first Kpop group <b>H.O.T.</b>, and kept managing their group activities. This kind system was adopted by other agencies and became the major charaterstics of Kpop industry. Kpop became popular in South Korea domestically. We can see that during late 1990s, there were several group debuts. But this number decreased in early 2000s because the 1st Generation Kpop group fell apart. Soloist activities were more popular. The exemplar groups in the 1st Generation are <b>H.O.T.</b> and <b>S.E.S.</b>\n",
    "    <li>2nd Generation 2004 - 2011:This was the time when Kpop groups became popular again and expanded their markets to other Asian countries.<b>TVXQ!</b>'s debut and its great success in Korea and Japan marked the start of this generation. 2nd Generation groups also started to have foreign members onboard, starting with <b>Super Juniors</b>' Chinese member <b>Han Geng</b>. We can see from the barplots that both the numbers of group debuts and music videos began soaring from 2004. Kpop's development began skyrocketing since this generation. The exemplar groups in the 2nd Generation are <b>TVXQ!, Super Juniors, BIGBANG</b> and <b>Girls' Generation</b>.\n",
    "    <li>3rd Generation 2011 - :This was the time when Kpop groups became more popular internationally. In 2012, <b>PSY's Gangnam Style</b> became a global hit. It brought light to Kpop not just in Asia, but also in Western world. 3rd Generation groups began obtaining popularity globally. They also take advantage of social networks and survival shows to promote themselves. We can see from the barplots that both the numbers of group debuts and music videos keep soaring. The examplar groups in the 3rd Generation are <b>EXO, TWICE, BLACKPINK</b> and <b>BTS</b>.\n",
    "</ul>\n",
    "Now, whether the 4th generation started or not is still disputed. Some opinions state that the 4th generation started in recent years. Other opinions also argue that there are already 5 generations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kyAMAwIAEeQAAGJAgDwAAAxLkAQBgQII8AAAMaMdmLwAAwLpUzX+erc1/nrAgeuQBAGBAgjwAAAxIkAcAgAEJ8gAAMCBBHgAABiTIAwDAgAR5AAAYkCAPAAADEuQBAGBAgjwAAAxIkAcAgAEJ8gAAMCBBHgAABiTIAwDAgAR5AAAYkCAPAAADEuQBAGBAgjwAAAxIkAcAgAEJ8gAAMCBBHgAABiTIAwDAgAR5AAAY0F6DfFUdXVV/X1UfrKoPVNUjpscPrqrXVdW/Tf9ec/GLCwAAJGvrkf9Wkke11o5PcoskD6uq45M8NsnrW2vXS/L66T4AALAEew3yrbULWmvvnP7+UpIPJTkyyalJzpomOyvJvRa0jAAAwCr7NEa+qo5LcpMkb01yaGvtgumpTyU5dL6LBgAA7M6ag3xVXTXJXyV5ZGvti7PPtdZakrab/3dGVZ1XVeddeOGFG1pYAACgW1OQr6orpof457fWXjo9/OmqOnx6/vAkn9nV/22tPau1dlJr7aSdO3fOY5kBAOByby1Xrakkz07yodbaH8w89Yokp09/n57k5fNfPAAAYFd2rGGaWyV5YJL3VdW7p8cen+R3kry4qh6c5GNJfmQhSwgAAPwPew3yrbU3J6ndPH37+S4OAACwFn7ZFQAABiTIAwDAgAR5AAAYkCAPAAADEuQBAGBAgjwAAAxIkAcAgAEJ8gAAMCBBHgAABiTIAwDAgAR5AAAYkCAPAAADEuQBAGBAgjwAAAxIkAcAgAEJ8gAAMKAdm70AALDlvKAWM9/T2mLmC1wu6ZEHAIABCfIAADAgQR4AAAYkyAMAwIAEeQAAGJAgDwAAAxLkAQBgQII8AAAMSJAHAIABCfIAADAgQR4AAAYkyAMAwIAEeQAAGJAgDwAAAxLkAQBgQII8AAAMSJAHAIABCfIAADAgQR4AAAYkyAMAwIAEeQAAGJAgDwAAAxLkAQBgQII8AAAMSJAHAIABCfIAADAgQR4AAAa0Y7MXAADW7AU1/3me1uY/T4Al0CMPAAADEuQBAGBAgjwAAAxIkAcAgAEJ8gAAMCBBHgAABuTykwBweVALuHRnc+lO2Ex65AEAYECCPAAADEiQBwCAAQnyAAAwIEEeAAAGJMgDAMCABHkAABiQIA8AAAMS5AEAYEB7DfJV9Zyq+kxVvX/msYOr6nVV9W/Tv9dc7GICAACz1tIjf2aSu6x67LFJXt9au16S10/3AQCAJdlrkG+tvSnJ51c9fGqSs6a/z0pyr/kuFgAAsCfrHSN/aGvtgunvTyU5dE7LAwAArMGGT3ZtrbUkbXfPV9UZVXVeVZ134YUXbrQcAACQ9Qf5T1fV4Uky/fuZ3U3YWntWa+2k1tpJO3fuXGc5AABg1nqD/CuSnD79fXqSl89ncQAAgLVYy+UnX5jkn5LcoKrOr6oHJ/mdJHesqn9LcofpPgAAsCQ79jZBa+3+u3nq9nNeFgAAYI38sisAAAxIkAcAgAEJ8gAAMCBBHgAABiTIAwDAgAR5AAAYkCAPAAADEuQBAGBAgjwAAAxor7/sCgB79YKa/zxPa/OfJ8A2okceAAAGJMgDAMCABHkAABiQIA8AAAMS5AEAYECCPAAADMjlJwGWzaUaAZgDPfIAADAgQR4AAAYkyAMAwIAEeQAAGJAgDwAAAxLkAQBgQII8AAAMSJAHAIABCfIAADAgQR4AAAYkyAMAwIAEeQAAGJAgDwAAA9qx2QsAwIK8oOY/z9Pa/OcJwLrokQcAgAEJ8gAAMCBBHgAABiTIAwDAgAR5AAAYkCAPAAADcvlJYGtzCUUA2CU98gAAMCBBHgAABiTIAwDAgAR5AAAYkCAPAAADEuQBAGBALj+5ILWAK+Y1V8wDAGCiRx4AAAYkyAMAwIAEeQAAGJAgDwAAAxLkAQBgQII8AAAMyOUnWTOX1OQyXrCADeK0TdwgFtGeZHPbBMC2pkceAAAGJMgDAMCABHkAABiQIA8AAAMS5AEAYECCPAAADOhyd/nJ7XYJxe3WnmXbbutvu7UHANg9PfIAADAgQR4AAAa0oSBfVXepqn+pqg9X1WPntVAAAMCerTvIV9V+Sf4kySlJjk9y/6o6fl4LBgAA7N5GeuRvnuTDrbWPtNa+keScJKfOZ7EAAIA92UiQPzLJx2funz89BgAALFi1dV5brqruk+QurbWfnO4/MMn3ttZ+dtV0ZyQ5Y7p7gyT/sv7FXapDknx2m9XabnWWWWu71VlmLW3a+nWWWUubtn6dZdbSpq1fZ5m1tmOb5uHY1trOXT2xkevIfyLJ0TP3j5oeu4zW2rOSPGsDdTZFVZ3XWjtpO9XabnWWWWu71VlmLW3a+nWWWUubtn6dZdbSpq1fZ5m1tmObFm0jQ2venuR6VXXtqrpSkvslecV8FgsAANiTdffIt9a+VVU/m+RvkuyX5DmttQ/MbckAAIDd2sjQmrTWXpXkVXNalq1mmcOBllVru9VZZq3tVmeZtbRp69dZZi1t2vp1lllLm7Z+nWXW2o5tWqh1n+wKAABsng39sisAALA5BHkAABiQIL9FVVVt9jJw+bOs7W6Z23dVbZv9XFVdecn1tt1+aLu1acnvpWXtH5bynt2Gda66jDoz9bbV58Wo+4Zt8wG3SFV1SFVdcUm1rl9VV29LPHlhu3wQVNWByww6i945V9X+06VdF66qrl5VByx6u6uq66xs34ve7qrq5lV1SGvtkgXXuV1V3WIJ7Tk5yUOmvxe97R1TVdfMBi+IsIY6ByxxG79WVV1lWfvWqtpvwfM/qKoOXEZ7quqwqrraEurcqKqu1Vq7ZJHbeFX9QFUdtuh9w1TrDknuWVX7L7jOyUl+uap2LGH/cIOqOjL9B5UWWefgZWzjy943zJsgvxdVda8kf5TkmCXUulOSlyX5/un+Qj4Iquo2VfULVXXfqrrGIjfeqjqxqk6pqqNW3pCL2MlU1b2TnJ3k1VV196q6zrxrTHV+oKp+IkkW+WFTVXdP8pwkr6mq206PLbLW2UleUVV3XESNqc5Nk/xjksdV1cGLDPPTe+nZSY5cxPxn6twlyV8k+fb7aBFtmtrzV0meUlVHLTKAVNU9k5yT5EVJfnR6bBFtOjX9NXphVd2pqo6dd42ZWvdO8sIkf11VD6mq711QnTtV1eOSpLV28QLfs/fIpfu70xYZsqvqbklekL5N/HhV7beg7eH4JH+f5OlVdeii9q/Te+msJAvb3mZq3TnJmUm+0lr72vTYItbdndNfo59Jct1p3S1q33qPJM9P8vQkPzV1oC3idTo1/XU6u6oeWFUL+eGmZe0bFqq15rabW5JbJPmXJLfdxXNXmHOtOyV5V/qO7LkLbNM9krw7ye+nfxDcYea5mnOteyZ5X5I/T/LkJE9Ncsi811+Sayf51yQ3S/KAJH+S5FeT3GQBr9EXkrwxyaMWuC3cZVpv35/kYUnekeQGC9oe7p7kndO2/pAk/5TkKguqdVD67078dpLfSbJzQXXuObXpptP9/RZQo5JcNcnrkpwyPXaVJFdOsv+CXqPjkzxuWndXWtC6OzHJ+5N891T3tUmutoA6J0zb+I2T/GB6CHlykuMXUOuIaT9+0+k9/Pgkf5bkjnOu8wNJPpPkn5M8eebxee8f7ji9Ricl+eH0S0B/74K2h7tNn0s3S3LXJH+X5JoLqrUj/cvC05K8OMlRC6hx5yTvSXKL6f6V5/36TPOtJPunf/n+oemxa0y3ue730j/T35nkO5M8PMlLklx1Qa/RDWb2DzdNcm6Say1gG7/+tH84fnpf/X562L71nOssZd+w6Jse+T37ziSvaK29oaqOrqr7V9W9q+rwNsfegqq6XZJnJnlQa+3kJN9VVQ+Yx7xX1TkwyY8leUhr7ReTfCzJjarqhjM9pPNq0wFJTktyemvtIUlemeRW6T2K39Hm26N49STnt9be3lo7O8lz0z8U7jHnXr7rJvm9JI9M8n1V9ahkvj3z0+HXOyf5jdbaP7TW/iTJa5KcMo/5r6p1xSS3TfL41tpbkrw6yTeS/FpV3bGqjphjrSukvybfSPIf6R90P1lVt6yqm8yrzuQ+SY5srb2zqq6e5Peq6i+q6gfn1abWPwW+luSCJH9TVQelh5DnJ3nidDh9w6bhLfdO8outtQ+mh8Rj03+EbxG9e8ck+WBr7X1J3pT+3vqjqvqZqjpxjnWOneq8t7V2bpI3JPne9PfszjnWSfq6+s/W2jtba69Nf53ek+TeVfU9c6xzRJInpO/nblJVT0m+vX+Yy9HVaT63Sv+icF5r7S/TX6cfnp6f9/bwPUl+ubX29vSweI0kv1tVP1pVN55XkWn/sDL05A1JPpD+PrpTVd1mXnWS3CHJAa21t0zb2dPTjwg9bJ7tad3X0j9j31J97PrL0q9b/odVdf951Jk+L+6Q5NGttX9P8uYkX05y6PT8vDPeIemfte9L7zz7zvSOsyfNa5+3qs4HW2tvSm/XwUnuOx25mZcdWc6+YaEE+V2Y2Rl+McnF098vTA89d0ny8qo6eo5h9FNJTmutvWeqfXaS661alnlo6d+eb1NVhye5b3qbHpvkT+ccsFuSw9K/uae19ob0IwFfTvLQee5gWmvvSXJRVT18un9eklckOTrJDedY5xlJnpHeI/H0JLeoql+cnruk5jAGctr5Pz19SM3Kh/8XM20Pyfy2idbaN5M8trX2munQ/F8leUv6juwB6R8Qc9Fau6S19tn0LwtvS9/5r/To7Ezm2q4fS/LmqvrnJP83yX8meXuSH0py8jxqTHW+lb5/eFaSP0zyl+m95R9LD6TXmEObLkrys6211081z01/X/3+dH/ew+LeluSQqnpxkg+lv49emv5eOmWOwyrel+SbVfVj0/3DpnonJJnrsLjW2seTfL6qnjzd/0j6kYZPZ9o/zaNNrbVzkryktfa5JA9OckJVPXV67uLpy95Ga1ycvg86tyZJPpnkO6bnW83xPKHW2hNba6+cOoHOTfLX6YH0u5LcvaquMKd1d0lr7cvp+4evt9Z+PT3Y/2Wmcdjz+MyYOrDeWFVvT/Ly9KMNL03/AnuXmXW6Iavm8X+S/EZ6B9PPp7fp3vPoZJo+Lx7dWvvb6aF3px8ZfOL0/LyH4L09yQFV9bdJ/j09q/xB+jZ4r+rnbcxj//D+JF+sql+a7t8kvef8a5njMOfW2n9OdRa6b1i4zT4ksNVuSa6Y5IrT30cn+Uj6DuynZ6Z5Snqv9jxqXXnm/soPdN00yUeTnDzHNl1p+vtm6R/Wr0vym9Njx6UfTrr7nGvdLX2o0OPTD5u/JD0cnj2HOrdN8iNJHjjdv9dU434z0/xE+s76inOo84BVj++fHgr/MsmPp/eIPTDrPMQ41blvkh/ZxXN3S/J709/3TXKnOay7+6Z/eZx9/IYzf98vPQTPY92dNvPY49KH8PxAko9P760nJbnWnLaH02cee2GS3565f//0YLpjjtvdselDxt6+8l5OPyx8bjYwBGHmNbr/zGNXmP49Pn0oyv/ayDrbRZvuP9Ome6aH0pVpfiB9CMeBc6hzv+n+T077hFcn+evpsZ9J8rQ5tOmo9PMWVu6fmB6Af2HmsbulH+1a91Co1XVWPXedJH+b5FfSjxI9Zr3vp9k6q/cx6Ucynt0u3cZ/LBsYTraqVs0+PvP3ydP+4cpzqrOybT9kWk+3Sj9y96JpGzl8nq9Rkj9O8oSZ+7dLP2q87vbsZrvbL73T4l+THDE9do30fdPRG6xz0KrHVtbhtab36oY+J/bQpv2T3DrJ82Yeu2H6vvXgOW0PO9KHkL1smu/L26Xb91nZwDDgadu6d6bP9PT96TPnvW9Y5k2P/IzqJ1ecmX6o7ZTWe3LumR6sbz0z6TfSx8fOo9bzp2EMV2vTFtRae2f6l4UHTUMD5lHnBVV119YPkd4yfSP9j6neR9MD+OFzrHXHJG9NP9R8cJL/aq3dp/Weg2tW1dEbqHNy+o7wmCSPrKo/SP+2/h9JbrYy5CXJV9OPAKzrG/Uu6jyj+pn6ab0n5P+lf0g/Jr3X5V1tHT0gM3WOTvKYqvrTVUNAvpHk4qo6Lb2n5WPrac8uav38Sq2qqtbaP6/qffivrPOo3ap19/NV9WfTMJGXpY/9f36Sn0s/l6FlGioyh1oPr6o/r6qDWmv3b609bqZNV0g/ujGPNj2yqp4+zevVSS5JDwdJ/2BY9xVfVr1Gj5q2uyNmtq3PTfO+9e7msY5axyT5xar6kyRfba29Ismnpm0u6eFjR5J1XWFmVZ1HTz1gf5v+Zfvn0r+IZ6rxX+trzbdr3Wua94Pr0mE6K0dnvrOqnjY9dtUk38w6t71VdVZ6jb/9/mm9d++uSX4q/TyhV7V+FGxDdXaxj7k4ySVV9aD0/dHbWu+532e7qNVmet0/MTPptdLft+u6mtse2vSy9N7Xl6b3Xj8o/Qjh3F6jJGmtPTz96NmKQ9LX47qvTrebWpekXyzjM0mePa3HO6d3nn1rg3V+YlWbVk5w/e/0nvkT1zP/3dT69nuptfa11tqbk3y5qh47TXrd9HOg5vE67Wytfau19rr0wH3G9G+SXC3JRStZaR117pp+BPW7kjy+qn6r9SGL52aO+4al2+xvElvllt5T/e700H739HGHv5H+gXzzJJ9P8ovpoW1DJx/uotYbk/xakmNmprlJ+olFx865TU9Mf8OdkN4r/5D0nr93pJ/tPs82PTGreiXTe63ftfrxfahT6ePU//d0f/9MPa/pJ+Kckv5h8PokH8w6T3jdTZ3np++UD5uZ7qfSh26s6yS9tdRJ3/F/Jf2Lw7p7YfdS69CZ6X4yyXlJvmvOdZ6a5EbpJ7OdMjPtuk+u3UOtp69q00OmNn33HOusnKB5VHov/PPSexDPS3LjBW93900fR3xA1tk7tZd1d+S0Hbw0/YvKe+fcphdObTpkZrpfSA9tN9rA9rBzeu8/N33//fBMJxdOdW80vUavSh/es979w+o6Pzvblpnp7pN+dHVdbdpTnWm9Vvq+/Evp+4cbrqfOPrbpZ9I/L9b7XtpTm66a/hl7m5k2ruvE7n1oz8OygX3DHmrtnHl+//QA+cz0K3fNfd2tmu4u6aMJrrKB/cMea6WfHPqK9KPuG9k/7PY9u2q6R077h/Wuu+tNr/Otp/vHpX+5v3r6fnQu+4bNuG36AmyVW/r42TNn7v90euB86HT/uulh4HHZ4JUVdlHroVOth2TmkGh64Ln2nOu8O/0b7oHpvWCvnTbedb0J17D+zkjvtbzCNM1b51DrfulDgQ6d7h+YfpWDp85Mc71sfLjGruqck+QZM9M8cr07lrXWST9x6e+ygYCzD7VukH50YRFtenEuO9RlLleTWUObjpieX0SbXpTkj2emuVaSqy96u5se3/DVQ/bQpt+f7l87/aoYxyygzur30hOTnLDBOldKH9t65Wl/87T0YHDYqukOzW6GxGywzndMz68Mczg9G/vyvcc6M+vyr+ewfe+tTTum99LTNlJrb69RLh2aue4hcGt9jdJD3FMWuO4OXTXd/tlYx8Watrvp73Vv32tp0/T81dOPDG5k6NPe2rQy5PhRWWfn0vT/r5dp6Gp6T/u10s8J+65V0x220XW37NumL8BWuaVfyus5mcaVpY/rfnb62dK3WWKtWy2zzvTm2dC4wH2otTPrHBeYPszgyunfnI9N7zm8Y/oVCJL+YfaOJD+4wXbsrc4BU51Tl1TnHumHe6+xhFp3Tf9wO2CBr9E7N7ru9rFNd0vv1VvXNr4Ft7t7LmHdHZj+JfxuI7yXpnkdkx4IDlz1+A+l9/w/fLp/0pLqbKg3b1/rrHf73sdaN57+XVfA3oLr7oTp33VfOnEfan3Pktp04kbqLLPWPtS56RzqfPvcx+mxlS8HZ2fqLN3oa7SZt8v1GPnqv/x4q6o6ofUrnXwoyUOq6lXp4fPB6T/2crsl1rrTEuo8b6VOa+3rrbWvL6NNrbULWz/vYF/r3C398P4fp39Z+Eb6oflHJPn+6pcD/e/0w3PfWE9b9qHOV6c66xrfuI46aa19s7W2rnHD+1jrCq1fPeKrC6rz3+njINe97tbRpmrdPm/jW3S7W9fY532s9d/pw+4WXWfD76WZWq9KHxL03Kr69pWqWmt/lT7Mb2dVvSzJ39c6L0G6j3XetHI+zYLr/MO0Lte7D9+XWv9vOldjn1+vday7ZbxGb1517skia71hSW36h/XWWWatfazzxjm8l56R/sNSK3VWzl86OMlVql/u+5ya/6Vvl2Ozv0ls1i29p+49SX4rvafoydPjV0s/SWTlyjWPSb+O7pavtd3alN6TenT6eLXbph8Of3T61U6OnJbhL6bbbyc5P8n1L+91tGmMNll3C2nTL6Rf2/9Gq6Y9O32s+j4PodhudbZjm6w7bVpDnUetrpPeyfCS9JEDGx66ulm3TV+ATWl0P3T86iS3n+4fk34liGevmu7n0q9nupExjkuptR3bNM1jv/SThI7MpYfDfj79yi2Hp4/ZPDX9ag0bOQF5W9XRpjHaZN0tpE2PSL/CyvWn+4enn/h+ojrbt03WnTbtQ50bTPefkuTD2cBJ4lvhtukLsCmN7mdyvzgzJzmk/8DKB5I8Zbp/QPqVZE4YodZ2a1P6ycU3Sz8h5UXpP3ox+/zj0r9Nb/S6v9uqjjaN0SbrbuFtenT6pXBXxuOv6yfrt1ud7dgm606b1lHnrPSgf0qS66x33W2V26YvwFIbO3MIN/361een/0DJM9PHal0n/Xq/B03TbORHB5ZSa5u26e7pl7N64zTfe6YfYnvczDTHpX/b3kh7tlUdbRqjTdbd0tr0zCW1aYg627FN1p02rbPOn29kvW2126YvwNIa2l/g/07yopnHHpHkl5L8bi4d0/3ybOBSSsustU3bdMv0k2ZXrsbwrPRf/jwi/Xrtv5T+jftB6deEXe/16LdVHW0ao03WnTZZd2PV0aYx2rSOOuv+Fdqtdtv0BVhKI/tQkNekX9P8zCQv3M10D0g/6eF//MDCVqu1Hds0zeOWSR40c39nLv359uukH5p/RjbwgyTbsY42jdEm606brLux6mjTGG1a5rrbardNX4ClNbR/K7tq+k8xvyTJC2ae25H+S2hvy3yuw7qUWtu0Tftl+kGd6e+j0q9pffj02LFTvWuoo02jtcm60ybrbqw62jRGm5a57rba7XJzHfnW2idba19urX02yU8luXJVnT09fcP0Hya4Z2vt3aPU2qZturi19sXpbiW5KMnnW2sXTNd6fXz6MJ51XVd9u9ZZZi1t2vp1lllLm7Z+nWXW2m51lllLm7Z+na1o5ZI8lztVdUj6lVZumf5rlrdprX1y5FrbsU1TrTPTr/96p/RDZ+9TZ2vV0qatX2eZtbRp69dZZq3tVmeZtbRp69fZbDs2ewE2S2vts1X13vTLD91xUSF0mbW2W5uqqtJ/Wvn7p39v31r7N3W2Ti1t2vp1lllLm7Z+nWXW2m51lllLm7Z+na3i8twjf830a6E/qrX23u1Qazu2aar1oCRvb619QJ2tWUubtn6dZdbSpq1fZ5m1tludZdbSpq1fZ7NdboN8klTV/q21r22nWtu0TdWWsKFutzrLrKVNW7/OMmtp09avs8xa263OMmtp09avs9ku10EeAABGdbm5ag0AAGwngjwAAAxIkAcAgAEJ8gAAMCBBHgAABiTIAwDAgP4/hqTq2yR6MHgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def timeline(df, title_name, column, include_zero):\n",
    "    zero_gen = list(range(1992,1996))\n",
    "    first_gen = list(range(1996,2004))\n",
    "    second_gen = list(range(2004,2012))\n",
    "    third_gen = list(range(2012,2021))\n",
    "    \n",
    "    generations = [zero_gen, first_gen, second_gen, third_gen]\n",
    "    colors = ['gray','blue','orange','red']\n",
    "\n",
    "    if include_zero == False:\n",
    "        generations = generations[1:]\n",
    "        colors = colors[1:]\n",
    "    counters = []\n",
    "    \n",
    "    for years in generations:\n",
    "        value_by_year = Counter()\n",
    "        for _, row in df.iterrows():    \n",
    "            for year in years:\n",
    "                if row[column] == year:\n",
    "                    value_by_year[year] += 1\n",
    "        counters.append(value_by_year)   \n",
    "                    \n",
    "    fig = plt.figure(figsize=(10,5))\n",
    "    ax = fig.add_axes([0,0,1,1])\n",
    "    all_year = []\n",
    "    for gen_color, years, counter in zip(colors, generations, counters):\n",
    "        ax.bar(years, [counter[year] for year in years], color=gen_color)\n",
    "        all_year += years\n",
    "    plt.xticks(all_year)\n",
    "    plt.xticks(rotation = 45)\n",
    "    plt.title(title_name)\n",
    "    plt.show()\n",
    "\n",
    "mv = year_clean(pd.read_csv('kpop_music_videos.csv'),'Date','Year')\n",
    "timeline(mv, \"Music Videos by Year\", 'Year', True)\n",
    "timeline(all_groups, \"group debuts by Year\", 'Debut Year', False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agency\n",
    "Kpop entertainment companies play significant role in this industry. These agencies recruit artists as trainees, select excellent trainees to form groups and manage their activities after they debut. Every Kpop agency manages 1.6 groups on average. Big companies like <b>SM Entertainment</b> can have up to 15 groups. Here are the 10 agencies which have most groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Every Kpop agency manages 1.6 groups on average\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 10 companies having most groups are:\n"
     ]
    },
    {
     "data": {
      "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>Company</th>\n",
       "      <th>Groups</th>\n",
       "      <th>Group count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SM</td>\n",
       "      <td>[EXO, Fly to the Sky, H.O.T, NCT, SHINee, Shin...</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JYP</td>\n",
       "      <td>[2AM, 2PM, DAY6, g.o.d., GOT7, Stray Kids, 15&amp;...</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FNC</td>\n",
       "      <td>[CNBLUE, FTISLAND, Honeyst, N.Flying, P1Harmon...</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YG</td>\n",
       "      <td>[BIGBANG, iKON, SECHKIES, Treasure, WINNER, 2N...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MBK</td>\n",
       "      <td>[1the9, Speed, Turbo, DIA, F-ve Dolls, T-ara]</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Big Hit</td>\n",
       "      <td>[2AM, 8Eight, BTS, Homme, TXT, GLAM]</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DSP</td>\n",
       "      <td>[A-JAX, SECHKIES, SS501, April, KARA, Rainbow]</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Starship</td>\n",
       "      <td>[Boyfriend, CRAVITY, MONSTA X, SISTAR, WJSN]</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Cube</td>\n",
       "      <td>[BtoB, PENTAGON, (G)I-DLE, 4Minute, CLC]</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Woollim</td>\n",
       "      <td>[DRIPPIN, Golden Child, Infinite, Lovelyz, Roc...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company                                             Groups  Group count\n",
       "0        SM  [EXO, Fly to the Sky, H.O.T, NCT, SHINee, Shin...           15\n",
       "1       JYP  [2AM, 2PM, DAY6, g.o.d., GOT7, Stray Kids, 15&...           11\n",
       "2       FNC  [CNBLUE, FTISLAND, Honeyst, N.Flying, P1Harmon...            8\n",
       "3        YG  [BIGBANG, iKON, SECHKIES, Treasure, WINNER, 2N...            7\n",
       "4       MBK      [1the9, Speed, Turbo, DIA, F-ve Dolls, T-ara]            6\n",
       "5   Big Hit               [2AM, 8Eight, BTS, Homme, TXT, GLAM]            6\n",
       "6       DSP     [A-JAX, SECHKIES, SS501, April, KARA, Rainbow]            6\n",
       "7  Starship       [Boyfriend, CRAVITY, MONSTA X, SISTAR, WJSN]            5\n",
       "8      Cube           [BtoB, PENTAGON, (G)I-DLE, 4Minute, CLC]            5\n",
       "9   Woollim  [DRIPPIN, Golden Child, Infinite, Lovelyz, Roc...            5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agency = defaultdict(list)\n",
    "for _, row in all_groups.iterrows():\n",
    "    if isinstance(row.Company, str):\n",
    "        for company in row.Company.split(','):\n",
    "            agency[company.strip()].append(row.Name)\n",
    "companies = list(agency.keys())\n",
    "group_counts = [len(groups) for groups in agency.values()]\n",
    "agency_df = pd.DataFrame({'Company':companies,\"Groups\":agency.values(),\"Group count\":group_counts})\n",
    "avg_group = round(agency_df['Group count'].mean(),1)\n",
    "print(f'Every Kpop agency manages {avg_group} groups on average')\n",
    "big10 = agency_df.sort_values(by='Group count',ascending=False).head(10)\n",
    "big10.plot.bar(x='Company',y='Group count',title='The 10 Companies Having Most Groups',rot=0, figsize=(10,5))\n",
    "plt.show()\n",
    "print(f'The 10 companies having most groups are:')\n",
    "big10.reset_index(drop=True, inplace=True)\n",
    "big10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group Members\n",
    "On average, Kpop groups have 5.5 members. 5-member group is the most common form. However, maintaining the team is not easy. 90 groups, about 25%, have lost group members."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kpop groups have 5.5 members on average\n",
      "90 groups, about 25%, have lost group members\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_groups.astype({'Orig. Memb.': 'int32','Members': 'int32'}).dtypes\n",
    "print(f'Kpop groups have {round(all_groups.Members.mean(),1)} members on average')\n",
    "\n",
    "loss = 0\n",
    "for _, row in all_groups.iterrows():\n",
    "    if row['Orig. Memb.'] - row['Members'] > 0:\n",
    "        loss += 1\n",
    "print(f'{loss} groups, about {int(100*loss/len(all_groups))}%, have lost group members')\n",
    "\n",
    "member_count = all_groups.groupby('Members', as_index=False).size().iloc[1:]\n",
    "member_count.plot.bar(x='Members',y='size',title='Number of Members in Kpop Groups',rot=0, figsize=(10,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fanclub\n",
    "Kpop's success largely depends on the devotion of their fans. Kpop groups are famous for making official fanclubs for their devoted fans and organize events within those fanclubs, such as <b>BTS</b>' fanclub <b>ARMY</b>. 41% of the boy groups have their fanclubs. 30% of the boy groups have their fanclubs. The data shows that boy groups gained more fans' devotion than girl groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36% of the boy groups have names for their fanclubs. 27% of the boy groups have names for their fanclubs.\n"
     ]
    }
   ],
   "source": [
    "print(f'{int(100*boys[\"Fanclub Name\"].count()/boys.shape[0])}% of the boy groups have names for their fanclubs. {int(100*girls[\"Fanclub Name\"].count()/girls.shape[0])}% of the boy groups have names for their fanclubs.')"
   ]
  }
 ],
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   "language": "python",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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