Data_visualization_final(1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "kobis = pd.read_csv(\"./datas/Rev1_kobis_movierev.csv\", index_col=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Movie</th>\n",
       "      <th>Screen</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Audience</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Rate</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>AgeRate</th>\n",
       "      <th>Genre_1</th>\n",
       "      <th>four_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>명량</td>\n",
       "      <td>1587</td>\n",
       "      <td>135748398910</td>\n",
       "      <td>17613.682</td>\n",
       "      <td>사극</td>\n",
       "      <td>15세이상관람가</td>\n",
       "      <td>7</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>극한직업</td>\n",
       "      <td>1978</td>\n",
       "      <td>139647979516</td>\n",
       "      <td>16264.944</td>\n",
       "      <td>코미디</td>\n",
       "      <td>15세이상관람가</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>신과함께-죄와 벌</td>\n",
       "      <td>1912</td>\n",
       "      <td>115698654137</td>\n",
       "      <td>14410.754</td>\n",
       "      <td>판타지</td>\n",
       "      <td>12세이상관람가</td>\n",
       "      <td>12</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>국제시장</td>\n",
       "      <td>966</td>\n",
       "      <td>110828014630</td>\n",
       "      <td>14245.998</td>\n",
       "      <td>드라마</td>\n",
       "      <td>12세이상관람가</td>\n",
       "      <td>12</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>베테랑</td>\n",
       "      <td>1064</td>\n",
       "      <td>105024756250</td>\n",
       "      <td>13395.400</td>\n",
       "      <td>액션</td>\n",
       "      <td>15세이상관람가</td>\n",
       "      <td>8</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>악녀의 시</td>\n",
       "      <td>5</td>\n",
       "      <td>123000</td>\n",
       "      <td>0.014</td>\n",
       "      <td>범죄</td>\n",
       "      <td>15세이상관람가</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>마음</td>\n",
       "      <td>1</td>\n",
       "      <td>12000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>드라마</td>\n",
       "      <td>15세이상관람가</td>\n",
       "      <td>7</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>독고다이</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>드라마</td>\n",
       "      <td>15세이상관람가</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>어쩌다 옥탑방</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>SF</td>\n",
       "      <td>15세이상관람가</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>이 사랑도 전해질까요</td>\n",
       "      <td>1</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>멜로/로맨스</td>\n",
       "      <td>전체관람가</td>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>549 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Movie  Screen         Sales   Audience   Genre      Rate  month  \\\n",
       "1             명량    1587  135748398910  17613.682      사극  15세이상관람가      7   \n",
       "2           극한직업    1978  139647979516  16264.944     코미디  15세이상관람가      1   \n",
       "3      신과함께-죄와 벌    1912  115698654137  14410.754     판타지  12세이상관람가     12   \n",
       "4           국제시장     966  110828014630  14245.998     드라마  12세이상관람가     12   \n",
       "5            베테랑    1064  105024756250  13395.400      액션  15세이상관람가      8   \n",
       "..           ...     ...           ...        ...     ...       ...    ...   \n",
       "545        악녀의 시       5        123000      0.014      범죄  15세이상관람가      2   \n",
       "546           마음       1         12000      0.002     드라마  15세이상관람가      7   \n",
       "547         독고다이       1          1000      0.001     드라마  15세이상관람가      6   \n",
       "548      어쩌다 옥탑방       1          1000      0.001      SF  15세이상관람가      4   \n",
       "549  이 사랑도 전해질까요       1          3000      0.001  멜로/로맨스     전체관람가      6   \n",
       "\n",
       "     year  AgeRate  Genre_1  four_month  \n",
       "1    2014        1       11           3  \n",
       "2    2019        1       17           1  \n",
       "3    2017        0       18           1  \n",
       "4    2014        0        6           1  \n",
       "5    2015        1       14           3  \n",
       "..    ...      ...      ...         ...  \n",
       "545  2016        1       10           1  \n",
       "546  2016        1        6           3  \n",
       "547  2019        1        6           3  \n",
       "548  2019        1        0           2  \n",
       "549  2017        2        7           3  \n",
       "\n",
       "[549 rows x 11 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kobis['four_month'] = [(month%12 + 3)//3 for month in kobis['month']]\n",
    "kobis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = {'total_movie' : kobis.groupby('year').size(),\n",
    "          'year_screen' : kobis.groupby('year')['Screen'].sum(),\n",
    "          'year_sales' : kobis.groupby('year')['Sales'].sum(),\n",
    "          'year_audience' : kobis.groupby('year')['Audience'].sum(),}\n",
    "genre = {'total_genre' : kobis.groupby('Genre').size(),\n",
    "            'genre_sales' : kobis.groupby('Genre')['Sales'].sum(),\n",
    "            'genre_audience' : kobis.groupby('Genre')['Audience'].sum(),\n",
    "            'genre_screen' : kobis.groupby('Genre')['Screen'].sum()}\n",
    "season = {'total_movie' : kobis.groupby('four_month').size(),\n",
    "          'season_screen' : kobis.groupby('four_month')['Screen'].sum(),\n",
    "          'season_sales' : kobis.groupby('four_month')['Sales'].sum(),\n",
    "          'season_audience' : kobis.groupby('four_month')['Audience'].sum(),}\n",
    "year_df = pd.DataFrame(year)\n",
    "genre_df = pd.DataFrame(genre)\n",
    "season_df = pd.DataFrame(season)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_movie</th>\n",
       "      <th>year_screen</th>\n",
       "      <th>year_sales</th>\n",
       "      <th>year_audience</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>65</td>\n",
       "      <td>32258</td>\n",
       "      <td>926590453723</td>\n",
       "      <td>129289.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>68</td>\n",
       "      <td>28992</td>\n",
       "      <td>794368812653</td>\n",
       "      <td>103544.236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>73</td>\n",
       "      <td>37453</td>\n",
       "      <td>825174086485</td>\n",
       "      <td>105774.503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>70</td>\n",
       "      <td>41997</td>\n",
       "      <td>870859554653</td>\n",
       "      <td>109256.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>66</td>\n",
       "      <td>48552</td>\n",
       "      <td>878972264283</td>\n",
       "      <td>110925.956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>79</td>\n",
       "      <td>54104</td>\n",
       "      <td>814247124184</td>\n",
       "      <td>97803.354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>80</td>\n",
       "      <td>60749</td>\n",
       "      <td>972651434584</td>\n",
       "      <td>115552.252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>48</td>\n",
       "      <td>30930</td>\n",
       "      <td>245899678880</td>\n",
       "      <td>28518.410</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      total_movie  year_screen    year_sales  year_audience\n",
       "year                                                       \n",
       "2013           65        32258  926590453723     129289.017\n",
       "2014           68        28992  794368812653     103544.236\n",
       "2015           73        37453  825174086485     105774.503\n",
       "2016           70        41997  870859554653     109256.167\n",
       "2017           66        48552  878972264283     110925.956\n",
       "2018           79        54104  814247124184      97803.354\n",
       "2019           80        60749  972651434584     115552.252\n",
       "2020           48        30930  245899678880      28518.410"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_movie</th>\n",
       "      <th>year_screen</th>\n",
       "      <th>year_sales</th>\n",
       "      <th>year_audience</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>0.8125</td>\n",
       "      <td>0.531005</td>\n",
       "      <td>0.952644</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>0.8500</td>\n",
       "      <td>0.477242</td>\n",
       "      <td>0.816705</td>\n",
       "      <td>0.800874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.9125</td>\n",
       "      <td>0.616520</td>\n",
       "      <td>0.848376</td>\n",
       "      <td>0.818124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.691320</td>\n",
       "      <td>0.895346</td>\n",
       "      <td>0.845054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0.8250</td>\n",
       "      <td>0.799223</td>\n",
       "      <td>0.903687</td>\n",
       "      <td>0.857969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>0.9875</td>\n",
       "      <td>0.890615</td>\n",
       "      <td>0.837142</td>\n",
       "      <td>0.756471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.893751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.6000</td>\n",
       "      <td>0.509144</td>\n",
       "      <td>0.252814</td>\n",
       "      <td>0.220579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      total_movie  year_screen  year_sales  year_audience\n",
       "year                                                     \n",
       "2013       0.8125     0.531005    0.952644       1.000000\n",
       "2014       0.8500     0.477242    0.816705       0.800874\n",
       "2015       0.9125     0.616520    0.848376       0.818124\n",
       "2016       0.8750     0.691320    0.895346       0.845054\n",
       "2017       0.8250     0.799223    0.903687       0.857969\n",
       "2018       0.9875     0.890615    0.837142       0.756471\n",
       "2019       1.0000     1.000000    1.000000       0.893751\n",
       "2020       0.6000     0.509144    0.252814       0.220579"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = ['total_movie', 'year_screen', 'year_sales', 'year_audience']\n",
    "year_df_norm = year_df[col] / year_df[col].max()\n",
    "year_df_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_genre</th>\n",
       "      <th>genre_sales</th>\n",
       "      <th>genre_audience</th>\n",
       "      <th>genre_screen</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Genre_1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>71770710173</td>\n",
       "      <td>9914.564</td>\n",
       "      <td>2675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>4850252998</td>\n",
       "      <td>645.240</td>\n",
       "      <td>1623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>5173403200</td>\n",
       "      <td>478.928</td>\n",
       "      <td>778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>55451431001</td>\n",
       "      <td>6922.513</td>\n",
       "      <td>6193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>5573892210</td>\n",
       "      <td>651.084</td>\n",
       "      <td>812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "      <td>16879996212</td>\n",
       "      <td>2141.734</td>\n",
       "      <td>1355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>118</td>\n",
       "      <td>1514908375330</td>\n",
       "      <td>193191.896</td>\n",
       "      <td>78405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>43</td>\n",
       "      <td>224447398326</td>\n",
       "      <td>28508.032</td>\n",
       "      <td>17796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>220397800</td>\n",
       "      <td>15.414</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>27</td>\n",
       "      <td>239330390421</td>\n",
       "      <td>29602.886</td>\n",
       "      <td>18415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>54</td>\n",
       "      <td>954889658206</td>\n",
       "      <td>117869.804</td>\n",
       "      <td>46314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>28</td>\n",
       "      <td>660561815603</td>\n",
       "      <td>83745.075</td>\n",
       "      <td>26957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>36</td>\n",
       "      <td>274061088131</td>\n",
       "      <td>35250.770</td>\n",
       "      <td>22036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>46</td>\n",
       "      <td>87554748930</td>\n",
       "      <td>11629.939</td>\n",
       "      <td>18158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>44</td>\n",
       "      <td>941566099197</td>\n",
       "      <td>121172.707</td>\n",
       "      <td>35898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>136065998006</td>\n",
       "      <td>16899.427</td>\n",
       "      <td>2809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4</td>\n",
       "      <td>69071043953</td>\n",
       "      <td>8826.652</td>\n",
       "      <td>2770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>71</td>\n",
       "      <td>799761197636</td>\n",
       "      <td>100542.684</td>\n",
       "      <td>44022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8</td>\n",
       "      <td>266625512112</td>\n",
       "      <td>32654.546</td>\n",
       "      <td>7925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         total_genre    genre_sales  genre_audience  genre_screen\n",
       "Genre_1                                                          \n",
       "0                  4    71770710173        9914.564          2675\n",
       "1                  6     4850252998         645.240          1623\n",
       "2                 18     5173403200         478.928           778\n",
       "3                 19    55451431001        6922.513          6193\n",
       "4                  2     5573892210         651.084           812\n",
       "5                 15    16879996212        2141.734          1355\n",
       "6                118  1514908375330      193191.896         78405\n",
       "7                 43   224447398326       28508.032         17796\n",
       "8                  4      220397800          15.414            94\n",
       "9                 27   239330390421       29602.886         18415\n",
       "10                54   954889658206      117869.804         46314\n",
       "11                28   660561815603       83745.075         26957\n",
       "12                36   274061088131       35250.770         22036\n",
       "13                46    87554748930       11629.939         18158\n",
       "14                44   941566099197      121172.707         35898\n",
       "15                 2   136065998006       16899.427          2809\n",
       "16                 4    69071043953        8826.652          2770\n",
       "17                71   799761197636      100542.684         44022\n",
       "18                 8   266625512112       32654.546          7925"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_genre</th>\n",
       "      <th>genre_sales</th>\n",
       "      <th>genre_audience</th>\n",
       "      <th>genre_screen</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Genre</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SF</th>\n",
       "      <td>0.033898</td>\n",
       "      <td>0.047376</td>\n",
       "      <td>0.051320</td>\n",
       "      <td>0.034118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>가족</th>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.003202</td>\n",
       "      <td>0.003340</td>\n",
       "      <td>0.020700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>공연</th>\n",
       "      <td>0.152542</td>\n",
       "      <td>0.003415</td>\n",
       "      <td>0.002479</td>\n",
       "      <td>0.009923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>공포(호러)</th>\n",
       "      <td>0.161017</td>\n",
       "      <td>0.036604</td>\n",
       "      <td>0.035832</td>\n",
       "      <td>0.078987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>기타</th>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.003679</td>\n",
       "      <td>0.003370</td>\n",
       "      <td>0.010356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다큐멘터리</th>\n",
       "      <td>0.127119</td>\n",
       "      <td>0.011143</td>\n",
       "      <td>0.011086</td>\n",
       "      <td>0.017282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>드라마</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>멜로/로맨스</th>\n",
       "      <td>0.364407</td>\n",
       "      <td>0.148159</td>\n",
       "      <td>0.147563</td>\n",
       "      <td>0.226975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>뮤지컬</th>\n",
       "      <td>0.033898</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.001199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>미스터리</th>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.157983</td>\n",
       "      <td>0.153230</td>\n",
       "      <td>0.234870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>범죄</th>\n",
       "      <td>0.457627</td>\n",
       "      <td>0.630328</td>\n",
       "      <td>0.610118</td>\n",
       "      <td>0.590702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>사극</th>\n",
       "      <td>0.237288</td>\n",
       "      <td>0.436041</td>\n",
       "      <td>0.433481</td>\n",
       "      <td>0.343817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>스릴러</th>\n",
       "      <td>0.305085</td>\n",
       "      <td>0.180909</td>\n",
       "      <td>0.182465</td>\n",
       "      <td>0.281054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>애니메이션</th>\n",
       "      <td>0.389831</td>\n",
       "      <td>0.057795</td>\n",
       "      <td>0.060199</td>\n",
       "      <td>0.231592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>액션</th>\n",
       "      <td>0.372881</td>\n",
       "      <td>0.621533</td>\n",
       "      <td>0.627214</td>\n",
       "      <td>0.457853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>어드벤처</th>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.089818</td>\n",
       "      <td>0.087475</td>\n",
       "      <td>0.035827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전쟁</th>\n",
       "      <td>0.033898</td>\n",
       "      <td>0.045594</td>\n",
       "      <td>0.045689</td>\n",
       "      <td>0.035329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>코미디</th>\n",
       "      <td>0.601695</td>\n",
       "      <td>0.527927</td>\n",
       "      <td>0.520429</td>\n",
       "      <td>0.561469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>판타지</th>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.176001</td>\n",
       "      <td>0.169026</td>\n",
       "      <td>0.101078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_genre  genre_sales  genre_audience  genre_screen\n",
       "Genre                                                         \n",
       "SF         0.033898     0.047376        0.051320      0.034118\n",
       "가족         0.050847     0.003202        0.003340      0.020700\n",
       "공연         0.152542     0.003415        0.002479      0.009923\n",
       "공포(호러)     0.161017     0.036604        0.035832      0.078987\n",
       "기타         0.016949     0.003679        0.003370      0.010356\n",
       "다큐멘터리      0.127119     0.011143        0.011086      0.017282\n",
       "드라마        1.000000     1.000000        1.000000      1.000000\n",
       "멜로/로맨스     0.364407     0.148159        0.147563      0.226975\n",
       "뮤지컬        0.033898     0.000145        0.000080      0.001199\n",
       "미스터리       0.228814     0.157983        0.153230      0.234870\n",
       "범죄         0.457627     0.630328        0.610118      0.590702\n",
       "사극         0.237288     0.436041        0.433481      0.343817\n",
       "스릴러        0.305085     0.180909        0.182465      0.281054\n",
       "애니메이션      0.389831     0.057795        0.060199      0.231592\n",
       "액션         0.372881     0.621533        0.627214      0.457853\n",
       "어드벤처       0.016949     0.089818        0.087475      0.035827\n",
       "전쟁         0.033898     0.045594        0.045689      0.035329\n",
       "코미디        0.601695     0.527927        0.520429      0.561469\n",
       "판타지        0.067797     0.176001        0.169026      0.101078"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col2 = ['total_genre', 'genre_sales', 'genre_audience', 'genre_screen']\n",
    "genre_df_norm = genre_df[col2] / genre_df[col2].max()\n",
    "genre_df_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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>total_movie</th>\n",
       "      <th>season_screen</th>\n",
       "      <th>season_sales</th>\n",
       "      <th>season_audience</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four_month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.913907</td>\n",
       "      <td>0.996117</td>\n",
       "      <td>0.939072</td>\n",
       "      <td>0.941200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.821192</td>\n",
       "      <td>0.729742</td>\n",
       "      <td>0.415495</td>\n",
       "      <td>0.410332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.900662</td>\n",
       "      <td>0.991589</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.670628</td>\n",
       "      <td>0.666828</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            total_movie  season_screen  season_sales  season_audience\n",
       "four_month                                                           \n",
       "1              0.913907       0.996117      0.939072         0.941200\n",
       "2              0.821192       0.729742      0.415495         0.410332\n",
       "3              0.900662       0.991589      1.000000         1.000000\n",
       "4              1.000000       1.000000      0.670628         0.666828"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col3 = ['total_movie', 'season_screen', 'season_sales', 'season_audience']\n",
    "season_df_norm = season_df[col3] / season_df[col3].max()\n",
    "season_df_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "from matplotlib import font_manager\n",
    "f_path = \"C:\\Windows\\Fonts\\malgun.ttf\"\n",
    "font_manager.FontProperties(fname=f_path).get_name()\n",
    "from matplotlib import rc\n",
    "rc('font', family='Malgun Gothic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 연도별 총 영화 개봉 수\n",
    "sns.set_style('darkgrid')\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.title('Total Korean Movies (Year)')\n",
    "plt.xlabel('Year')\n",
    "plt.plot(kobis.groupby('year').size());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "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>Movie</th>\n",
       "      <th>Screen</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Audience</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Rate</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>AgeRate</th>\n",
       "      <th>Genre_1</th>\n",
       "      <th>four_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>신과함께-죄와 벌</td>\n",
       "      <td>1912</td>\n",
       "      <td>115698654137</td>\n",
       "      <td>14410.754</td>\n",
       "      <td>판타지</td>\n",
       "      <td>12세이상관람가</td>\n",
       "      <td>12</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>택시운전사</td>\n",
       "      <td>1906</td>\n",
       "      <td>95284747049</td>\n",
       "      <td>12099.971</td>\n",
       "      <td>드라마</td>\n",
       "      <td>15세이상관람가</td>\n",
       "      <td>8</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1987</td>\n",
       "      <td>1299</td>\n",
       "      <td>58165581145</td>\n",
       "      <td>7231.638</td>\n",
       "      <td>드라마</td>\n",
       "      <td>15세이상관람가</td>\n",
       "      <td>12</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>범죄도시</td>\n",
       "      <td>1315</td>\n",
       "      <td>56077813249</td>\n",
       "      <td>6851.297</td>\n",
       "      <td>범죄</td>\n",
       "      <td>청소년관람불가</td>\n",
       "      <td>10</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>군함도</td>\n",
       "      <td>2027</td>\n",
       "      <td>50510565168</td>\n",
       "      <td>6592.151</td>\n",
       "      <td>액션</td>\n",
       "      <td>15세이상관람가</td>\n",
       "      <td>7</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Movie  Screen         Sales   Audience Genre      Rate  month  year  \\\n",
       "3   신과함께-죄와 벌    1912  115698654137  14410.754   판타지  12세이상관람가     12  2017   \n",
       "9       택시운전사    1906   95284747049  12099.971   드라마  15세이상관람가      8  2017   \n",
       "22       1987    1299   58165581145   7231.638   드라마  15세이상관람가     12  2017   \n",
       "30       범죄도시    1315   56077813249   6851.297    범죄   청소년관람불가     10  2017   \n",
       "31        군함도    2027   50510565168   6592.151    액션  15세이상관람가      7  2017   \n",
       "\n",
       "    AgeRate  Genre_1  four_month  \n",
       "3         0       18           1  \n",
       "9         1        6           3  \n",
       "22        1        6           1  \n",
       "30        3       10           4  \n",
       "31        1       14           3  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kobis[kobis['year'] == 2017].sort_values('Sales', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "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>Movie</th>\n",
       "      <th>Screen</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Audience</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Rate</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>AgeRate</th>\n",
       "      <th>Genre_1</th>\n",
       "      <th>four_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>남산의 부장들</td>\n",
       "      <td>1659</td>\n",
       "      <td>41223596650</td>\n",
       "      <td>4750.104</td>\n",
       "      <td>드라마</td>\n",
       "      <td>15세이상관람가</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>다만 악에서 구하소서</td>\n",
       "      <td>1997</td>\n",
       "      <td>38536886110</td>\n",
       "      <td>4350.041</td>\n",
       "      <td>범죄</td>\n",
       "      <td>15세이상관람가</td>\n",
       "      <td>8</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>반도</td>\n",
       "      <td>2575</td>\n",
       "      <td>33067818280</td>\n",
       "      <td>3811.722</td>\n",
       "      <td>액션</td>\n",
       "      <td>15세이상관람가</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>히트맨</td>\n",
       "      <td>1122</td>\n",
       "      <td>20614278000</td>\n",
       "      <td>2406.232</td>\n",
       "      <td>코미디</td>\n",
       "      <td>15세이상관람가</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>#살아있다</td>\n",
       "      <td>1882</td>\n",
       "      <td>15965582900</td>\n",
       "      <td>1903.727</td>\n",
       "      <td>드라마</td>\n",
       "      <td>15세이상관람가</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Movie  Screen        Sales  Audience Genre      Rate  month  year  \\\n",
       "47       남산의 부장들    1659  41223596650  4750.104   드라마  15세이상관람가      1  2020   \n",
       "51   다만 악에서 구하소서    1997  38536886110  4350.041    범죄  15세이상관람가      8  2020   \n",
       "61            반도    2575  33067818280  3811.722    액션  15세이상관람가      7  2020   \n",
       "101          히트맨    1122  20614278000  2406.232   코미디  15세이상관람가      1  2020   \n",
       "119        #살아있다    1882  15965582900  1903.727   드라마  15세이상관람가      6  2020   \n",
       "\n",
       "     AgeRate  Genre_1  four_month  \n",
       "47         1        6           1  \n",
       "51         1       10           3  \n",
       "61         1       14           3  \n",
       "101        1       17           1  \n",
       "119        1        6           3  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kobis[kobis['year'] == 2020].sort_values('Sales', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2013년과 2014년에 볼 수 있듯이 영화 개봉수가 증가하더라도 스크린수가 함께 증가하지는 않음\n",
    "# 2017년에도 영화 개봉수는 2014년보다 적지만 스크린수는 더 많음\n",
    "# 각 해의 총 매출과 총 관객수는 같은 현상으로 움직이는 것으로 보임 그러나 2018년에 보이듯이 영화 개봉수가 많더라도 총 매출과 관객수가 똑같이 증가하지 않음\n",
    "\n",
    "sns.set_style('darkgrid')\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.title('Yearly total movie, total audience, sales, screens')\n",
    "plt.xlabel('Year')\n",
    "plt.plot(year_df_norm.groupby('year')['total_movie'].sum(), '-b', label='total movie')\n",
    "plt.plot(year_df_norm.groupby('year')['year_audience'].sum(), '-g', label='audience')\n",
    "plt.plot(year_df_norm.groupby('year')['year_sales'].sum(), '-r', label='sales')\n",
    "plt.plot(year_df_norm.groupby('year')['year_screen'].sum(), '-y', label='screens')\n",
    "plt.legend(loc='best');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.title('Yearly total movie, total audience, screens')\n",
    "plt.xlabel('Year')\n",
    "plt.plot(year_df_norm.groupby('year')['year_screen'].sum(), '-y', label='screens')\n",
    "plt.plot(year_df_norm.groupby('year')['year_audience'].sum(), '-g', label='audience')\n",
    "plt.plot(year_df_norm.groupby('year')['year_sales'].sum(), '-r', label='sales')\n",
    "plt.legend(loc='best');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 장르별 개봉한 총 영화 수 그래프\n",
    "from matplotlib import font_manager\n",
    "f_path = \"C:\\Windows\\Fonts\\malgun.ttf\"\n",
    "font_manager.FontProperties(fname=f_path).get_name()\n",
    "from matplotlib import rc\n",
    "rc('font', family='Malgun Gothic')\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.title('Total number of movies by Genre')\n",
    "plt.xticks(fontsize=10, rotation=45)\n",
    "sns.countplot(x='Genre', data=kobis, palette='Spectral');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "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>total_genre</th>\n",
       "      <th>genre_sales</th>\n",
       "      <th>genre_audience</th>\n",
       "      <th>genre_screen</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Genre</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SF</th>\n",
       "      <td>0.033898</td>\n",
       "      <td>0.047376</td>\n",
       "      <td>0.051320</td>\n",
       "      <td>0.034118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>가족</th>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.003202</td>\n",
       "      <td>0.003340</td>\n",
       "      <td>0.020700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>공연</th>\n",
       "      <td>0.152542</td>\n",
       "      <td>0.003415</td>\n",
       "      <td>0.002479</td>\n",
       "      <td>0.009923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>공포(호러)</th>\n",
       "      <td>0.161017</td>\n",
       "      <td>0.036604</td>\n",
       "      <td>0.035832</td>\n",
       "      <td>0.078987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>기타</th>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.003679</td>\n",
       "      <td>0.003370</td>\n",
       "      <td>0.010356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>다큐멘터리</th>\n",
       "      <td>0.127119</td>\n",
       "      <td>0.011143</td>\n",
       "      <td>0.011086</td>\n",
       "      <td>0.017282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>드라마</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>멜로/로맨스</th>\n",
       "      <td>0.364407</td>\n",
       "      <td>0.148159</td>\n",
       "      <td>0.147563</td>\n",
       "      <td>0.226975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>뮤지컬</th>\n",
       "      <td>0.033898</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.001199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>미스터리</th>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.157983</td>\n",
       "      <td>0.153230</td>\n",
       "      <td>0.234870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>범죄</th>\n",
       "      <td>0.457627</td>\n",
       "      <td>0.630328</td>\n",
       "      <td>0.610118</td>\n",
       "      <td>0.590702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>사극</th>\n",
       "      <td>0.237288</td>\n",
       "      <td>0.436041</td>\n",
       "      <td>0.433481</td>\n",
       "      <td>0.343817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>스릴러</th>\n",
       "      <td>0.305085</td>\n",
       "      <td>0.180909</td>\n",
       "      <td>0.182465</td>\n",
       "      <td>0.281054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>애니메이션</th>\n",
       "      <td>0.389831</td>\n",
       "      <td>0.057795</td>\n",
       "      <td>0.060199</td>\n",
       "      <td>0.231592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>액션</th>\n",
       "      <td>0.372881</td>\n",
       "      <td>0.621533</td>\n",
       "      <td>0.627214</td>\n",
       "      <td>0.457853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>어드벤처</th>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.089818</td>\n",
       "      <td>0.087475</td>\n",
       "      <td>0.035827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전쟁</th>\n",
       "      <td>0.033898</td>\n",
       "      <td>0.045594</td>\n",
       "      <td>0.045689</td>\n",
       "      <td>0.035329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>코미디</th>\n",
       "      <td>0.601695</td>\n",
       "      <td>0.527927</td>\n",
       "      <td>0.520429</td>\n",
       "      <td>0.561469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>판타지</th>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.176001</td>\n",
       "      <td>0.169026</td>\n",
       "      <td>0.101078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_genre  genre_sales  genre_audience  genre_screen\n",
       "Genre                                                         \n",
       "SF         0.033898     0.047376        0.051320      0.034118\n",
       "가족         0.050847     0.003202        0.003340      0.020700\n",
       "공연         0.152542     0.003415        0.002479      0.009923\n",
       "공포(호러)     0.161017     0.036604        0.035832      0.078987\n",
       "기타         0.016949     0.003679        0.003370      0.010356\n",
       "다큐멘터리      0.127119     0.011143        0.011086      0.017282\n",
       "드라마        1.000000     1.000000        1.000000      1.000000\n",
       "멜로/로맨스     0.364407     0.148159        0.147563      0.226975\n",
       "뮤지컬        0.033898     0.000145        0.000080      0.001199\n",
       "미스터리       0.228814     0.157983        0.153230      0.234870\n",
       "범죄         0.457627     0.630328        0.610118      0.590702\n",
       "사극         0.237288     0.436041        0.433481      0.343817\n",
       "스릴러        0.305085     0.180909        0.182465      0.281054\n",
       "애니메이션      0.389831     0.057795        0.060199      0.231592\n",
       "액션         0.372881     0.621533        0.627214      0.457853\n",
       "어드벤처       0.016949     0.089818        0.087475      0.035827\n",
       "전쟁         0.033898     0.045594        0.045689      0.035329\n",
       "코미디        0.601695     0.527927        0.520429      0.561469\n",
       "판타지        0.067797     0.176001        0.169026      0.101078"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre_df_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import font_manager\n",
    "f_path = \"C:\\Windows\\Fonts\\malgun.ttf\"\n",
    "font_manager.FontProperties(fname=f_path).get_name()\n",
    "from matplotlib import rc\n",
    "rc('font', family='Malgun Gothic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.title('Total sales, audience by Genre')\n",
    "plt.xlabel('Genre')\n",
    "plt.plot(genre_df_norm['genre_sales'], '-b', label='sales')\n",
    "plt.plot(genre_df_norm['genre_audience'], '-g', label='audience')\n",
    "# plt.plot(genre_df_norm['genre_screen'], '-r', label='screen')\n",
    "plt.plot(genre_df_norm['total_genre'], '-y', label='total genre')\n",
    "plt.xticks(fontsize=10, rotation=45)\n",
    "plt.legend(loc='best');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.set_style('darkgrid')\n",
    "# plt.figure(figsize=(10,6))\n",
    "# plt.title('Yearly total movie, total audience, sales, screens')\n",
    "# plt.xlabel('Season')\n",
    "# plt.plot(season_df_norm.groupby('four_month')['total_movie'].sum(), '-b', label='total movie')\n",
    "# plt.plot(season_df_norm.groupby('four_month')['season_audience'].sum(), '-g', label='audience')\n",
    "# plt.plot(season_df_norm.groupby('four_month')['season_sales'].sum(), '-r', label='sales')\n",
    "# plt.plot(season_df_norm.groupby('four_month')['season_screen'].sum(), '-y', label='screens')\n",
    "# plt.legend(loc='best');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.countplot(x='year', hue='four_month', data=kobis, palette='Spectral');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [],
   "source": [
    "thirteen = kobis[kobis['year'] == 2013].groupby('Genre')[['Sales']].agg(['sum', 'count']).sort_values(by=('Sales', 'sum'), ascending=False)\n",
    "fourteen = kobis[kobis['year'] == 2014].groupby('Genre')[['Sales']].agg(['sum', 'count']).sort_values(by=('Sales', 'sum'), ascending=False)\n",
    "fifteen = kobis[kobis['year'] == 2015].groupby('Genre')[['Sales']].agg(['sum', 'count']).sort_values(by=('Sales', 'sum'), ascending=False)\n",
    "sixteen = kobis[kobis['year'] == 2016].groupby('Genre')[['Sales']].agg(['sum', 'count']).sort_values(by=('Sales', 'sum'), ascending=False)\n",
    "seventeen = kobis[kobis['year'] == 2017].groupby('Genre')[['Sales']].agg(['sum', 'count']).sort_values(by=('Sales', 'sum'), ascending=False)\n",
    "eighteen = kobis[kobis['year'] == 2018].groupby('Genre')[['Sales']].agg(['sum', 'count']).sort_values(by=('Sales', 'sum'), ascending=False)\n",
    "nineteen = kobis[kobis['year'] == 2019].groupby('Genre')[['Sales']].agg(['sum', 'count']).sort_values(by=('Sales', 'sum'), ascending=False)\n",
    "twenty = kobis[kobis['year'] == 2020].groupby('Genre')[['Sales']].agg(['sum', 'count']).sort_values(by=('Sales', 'sum'), ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Genre</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>코미디</th>\n",
       "      <td>258729770363</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>드라마</th>\n",
       "      <td>223410040560</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>범죄</th>\n",
       "      <td>179972348206</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>사극</th>\n",
       "      <td>70114836469</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>어드벤처</th>\n",
       "      <td>69825366300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>멜로/로맨스</th>\n",
       "      <td>35055771970</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>미스터리</th>\n",
       "      <td>33818274554</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>애니메이션</th>\n",
       "      <td>33369951880</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>공포(호러)</th>\n",
       "      <td>16985991347</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>판타지</th>\n",
       "      <td>16293580567</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>액션</th>\n",
       "      <td>11164437160</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전쟁</th>\n",
       "      <td>8997921050</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>스릴러</th>\n",
       "      <td>8131086348</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>공연</th>\n",
       "      <td>3607839600</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>기타</th>\n",
       "      <td>2902500310</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>가족</th>\n",
       "      <td>245549900</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>뮤지컬</th>\n",
       "      <td>26167000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SF</th>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sales      \n",
       "                 sum count\n",
       "Genre                     \n",
       "코미디     258729770363    10\n",
       "드라마     223410040560    17\n",
       "범죄      179972348206    10\n",
       "사극       70114836469     4\n",
       "어드벤처     69825366300     1\n",
       "멜로/로맨스   35055771970     2\n",
       "미스터리     33818274554     4\n",
       "애니메이션    33369951880    10\n",
       "공포(호러)   16985991347     6\n",
       "판타지      16293580567     1\n",
       "액션       11164437160     3\n",
       "전쟁        8997921050     1\n",
       "스릴러       8131086348     4\n",
       "공연        3607839600     3\n",
       "기타        2902500310     1\n",
       "가족         245549900     1\n",
       "뮤지컬         26167000     1\n",
       "SF              1000     1"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kobis[kobis['year'] == 2019].groupby('Genre')[['Sales']].agg(['sum', 'count']).sort_values(by=('Sales', 'sum'), ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Genre\n",
       "코미디       1.0000\n",
       "드라마       0.8635\n",
       "범죄        0.6956\n",
       "사극        0.2710\n",
       "어드벤처      0.2699\n",
       "멜로/로맨스    0.1355\n",
       "미스터리      0.1307\n",
       "애니메이션     0.1290\n",
       "공포(호러)    0.0657\n",
       "판타지       0.0630\n",
       "액션        0.0432\n",
       "전쟁        0.0348\n",
       "스릴러       0.0314\n",
       "공연        0.0139\n",
       "기타        0.0112\n",
       "가족        0.0009\n",
       "뮤지컬       0.0001\n",
       "SF        0.0000\n",
       "Name: (Sales, sum), dtype: float64"
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm = round(nineteen['Sales', 'sum'] / nineteen['Sales', 'sum'].max(), 4)\n",
    "norm2 = round(nineteen['Sales', 'count'] / nineteen['Sales', 'count'].max(), 4)\n",
    "norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Genre'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mc:\\users\\luvu1\\anaconda3\\envs\\dss15\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2894\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2895\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Genre'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-375-ab9cdb7483e1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlineplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0mnorm2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnineteen\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Genre'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmarker\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'o'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msort\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[0max2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtwinx\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\luvu1\\anaconda3\\envs\\dss15\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2899\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_single_key\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2900\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2901\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2902\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2903\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\luvu1\\anaconda3\\envs\\dss15\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_getitem_multilevel\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2949\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2950\u001b[0m         \u001b[1;31m# self.columns is a MultiIndex\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2951\u001b[1;33m         \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2952\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mslice\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2953\u001b[0m             \u001b[0mnew_columns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\luvu1\\anaconda3\\envs\\dss15\\lib\\site-packages\\pandas\\core\\indexes\\multi.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method)\u001b[0m\n\u001b[0;32m   2699\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2700\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2701\u001b[1;33m             \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_level_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2702\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0m_maybe_to_slice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2703\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\luvu1\\anaconda3\\envs\\dss15\\lib\\site-packages\\pandas\\core\\indexes\\multi.py\u001b[0m in \u001b[0;36m_get_level_indexer\u001b[1;34m(self, key, level, indexer)\u001b[0m\n\u001b[0;32m   2964\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2965\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2966\u001b[1;33m             \u001b[0mcode\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_loc_single_level_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlevel_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2967\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2968\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlevel\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlexsort_depth\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\luvu1\\anaconda3\\envs\\dss15\\lib\\site-packages\\pandas\\core\\indexes\\multi.py\u001b[0m in \u001b[0;36m_get_loc_single_level_index\u001b[1;34m(self, level_index, key)\u001b[0m\n\u001b[0;32m   2632\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2633\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2634\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mlevel_index\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2635\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2636\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\luvu1\\anaconda3\\envs\\dss15\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2895\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2899\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Genre'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rc_file_defaults()\n",
    "ax1 = sns.set_style(style=None, rc=None )\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(12,6))\n",
    "\n",
    "sns.lineplot(data =norm2, x=nineteen['Genre'], marker='o', sort = False, ax=ax1)\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "sns.barplot(data = norm, alpha=0.5, ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting squarify\n",
      "  Downloading squarify-0.4.3-py3-none-any.whl (4.3 kB)\n",
      "Installing collected packages: squarify\n",
      "Successfully installed squarify-0.4.3\n"
     ]
    }
   ],
   "source": [
    "!pip install squarify"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "import squarify"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqsAAAHDCAYAAAADVFNbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAflUlEQVR4nO3deZhkd13v8U9V9b7NPpPJvi8EAogkEEEIIYAEwgVENkFE5KqIouKC0eATLlcjV/FyZXUJiGHfZDNAgmxGEAMEiCELmSyTyex771V17h+ddKaZJdt0968nr9fz5Jk69Tun+tvJk5p3nzpVXauqqgoAABSoPt8DAADA/ohVAACKJVYBACiWWAUAoFhiFQCAYolVAACKdcBYbY1NzNUcB1XVas33CMySamJ8vkdggVqoz2cAD3W1e/uc1S+teuFczXLQnLfhwxm/9sr5HoNZ0H36udly/pPmewwWoGWf++qCfD4DeKg4b8OH93m/ywAAACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVACjUlvp4rujeuM+1Wxsj+WbXlgMe/5HetbMxFsypjvkeAABINtXH8+bBH2V1qycbG+M5rtmfF48eOb3+Z4P/ncVVZ5LkkROLc3yrf3ptZ20y/9h/S7bXJtNfdWRVuzsvHzlmzr8HmA1iFQAKsKLdnf8xenge3hzKNZ078lMTi9OuVbm5MZwbOnblYc2h/MIe8XprY2T69lDVmdftPilf7N6Qh08O5fB273x8CzArXAYAAAW4sz6arfWJXN25PVvqE/le1/Zsr03m+FZ/Tm4OZl1jNF/s3pAvdm/IHfXRJMm1HTvz/Y4d04+xrjGWmzqG5+tbgFnhzCoAFGB1uzdPHV+ZzfWJ6Zf4t9THp9cPb/XmaeOrprdvbYzk9OZQzmguSpJc17EzpzQHsrYxmm21iSypuub2G4BZIlYBoBC7a83c0RidjtXeqpHjm1O3a0m+1L0hSVJPbfr+ZOp61/WNsZwzvjKTaefzPevz7LHVcz4/zAaxCgAF+XL3plzdtW16+6TmQI5v9ecFe1yvmsy8ZnVFuzvnjK9MknSmnueMHT43w8IcEKsAUIjD2715467T7tO+i9odqeXAb6Q6dXLwYIwF80qsAsACtLjqyuLWga9Lvft6VljIfBoAAADFEqsAABRLrAIAUCyxCgBAscQqAADFEqsAABRLrAIAUCyxCgBAscQqAADFEqsAABRLrAIAUCyxCgBAscQqAADFEqsAABRLrEJBrhubzOd2juQvN+7Y7z5/tWnnAdcOdOy+jv+nbbuzsdnKcLudWyaaueRejgeAudQx3wPAQ93fbNqZ68cn87i+7pzZ1z1j7a827czvrRja77EXrNmYozobuX2ylU8ft3Kv9St3jebDO0bSVavlwpVDWd259//y6ydbuX2imaO7OnLrRDPN6sF/TwBwsDizCvNoR6udei1555HLcvtkM/e3E5800JO3H7ksTxro2ef6lbvH8q4jlubClUP54PaRvdbXTDSztFHPFbvHsrHZyq2Tzdz/KQBg9ohVmEe7Wu0cedfZzuUdjUxWVf5zZCJrJpr3+7E+vH04zeqe0JyoqhzV2ZF6rbbfM6pf2DWaX146kN9cPphrRifz8iUDGah7WgCgHP5Wgnm0srOR9ZOtJMl4u0pXrZYz+7pyXNdUXFZJRtrt7G61s7nZytZma8bxY+0qW5qtjLWrvHBxfzpqtem1rlotY3fF685WO4P12oxjD+ts5NeWDeZLu8fSX6/nJUv6Z/E7BYAHxjWrMI+6arU8sb87H9o+nPOHejP5E6/An9XXlc/uHE2jVktPLTm1u3PG+mP6uvLt0Yk8pq9rn49/7kBPLt26O81qKmb35dqxyTxjsHd6+/E/cd0sAMwnsQrz7BG9XXlE71RsXjc2OWPtif37uhZ1dPrWnpG5L2f0duWM3n2H7N2+OTKe16zdMr091Kjn7H7BCkAZxCoU5LSezpzW05mB+th+9zlzP2dRk+T3Vgzlq7v3f+y+jv/oMSvu35AAMIdcswoF2t+7+5P9nW29b8fel+MBoCRiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACK1THfAwDMhdbYRM7b8OH5HgOA+0msAg8JjZ6uTG64/j7t27nqlGx/6VNmeSIA9rT4si/v836XAQAAUCyxCgBAscQqAADFEqsAABRLrAIAUCyxCgBAscQqAADFEqsAABRLrAIAUCyxCgBAscQqAADFEqsAABRLrAIAUCyxCgBAsTrmewAemGtvujU33bYuz3nK4/Nf196Qd3zoc9Nrw6Njeen55+SCcx43jxMCADx4YnUBeceHPpsXP/PJWTI0MOP+nz795Pzjm06e3r7m+jW5Y+PmuR4PAOCgE6sLyPDoWIZHxmbE6sat27Np644Z+113822ZbDazcev2rFy6eI6nBAA4eMTqAnL7+s352tU/yEvOPydJ8rWrf5gtO3bl5GOOmLHfkauWJ0nGxifmfEYAgINJrC4Ql33u3/LK556Xr3/n2txw6x1Jkp99zMPznKc8Pp/5yrfyySuvmrH/b7zo/By9euV8jAoAcNCI1QXgBzfekqNWLc+jTj0hZ5x8XN77qSvy2Efcc43qs598Vp795LOmt7/9wxuydcfu+RgVAOCgEqsLwCNOOnb6dr1ezyuf97Rce9Ot8zcQAMAcEasL1PIli1Kr1fa5dtRhK7Jq2eK5HQgAYBaI1QVq1bLF+w3Sw5YvmdthAABmid9gBQBAscQqAADFEqsAABRLrAIAUCyxCgBAscQqAADFEqsAABRLrAIAUCyxCgBAscQqAADFEqsAABRLrAIAUCyxCgBAscQqAADFEqsAABRLrAIAUCyxCgBAscQqAADFEqsAABRLrAIAUCyxCgBAsTrmewC4P9rj41n2ua/O9xgAwBwRqywo9e7urFh0ynyPwQK0acf18z0CAA+AywAAACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFhiFQCAYolVAACKJVYBACiWWAUAoFgds/0FbmkM51O966a3m6nyyMlFOW98Vb7fsSNX9GxMLcn5Y4fl5OZgPtK7Nr8weuRsjwUAwAIw67F6bKs/r9t9UibTTmfq2VIfzzWdO5Ik3+7altftPjG1JO/svzm3NUYzXGvO9kgAACwQc3YZwOd71mc8rdSqWla0upMki6qO1FNLLbUMVh155OSidFeNuRoJAIDCzeqZ1TWN4dzSMZxk6uX/b3Rvma7jNdVwHjuxNJd3r0+SnNgcyLrGaJq19myOBADAAjKrsXpcqz9HtXpzRffGbGyMZ1PGc2JzIE8ZX5F6akmSHbXJnNFcNH3M9R27Z3MkAAAWkFm/DOA/u7ZloOrIq4aPzSuHj02jquW7ndun13/UuWvG/qdPDs32SAAALBCz/gar0yYH89G+tflu5/ZUSfqqRl64x7v91zSG8zcDN05vd1eNnN4UrAAAzEGsLqm68urh4/e7/oe7T5ntEQAAWKD8UgDgIWnb9p3zPQIA94FYBQ55fUPJy172svyfd1yaO+7ckEs/+Mm849IPTK///T9/LOs3bs7wyEhuvnVtLrroonmcFh68t9y6/UGtQ0lm/TIAgPnU6EjareT9739/LvmzN2Tj5q177XPH+o25be26HHv0EVlz29pMTk7Ow6Swfx/duDtf3jo6vT3WrvLM5X15wcqBPP8H67Oyc+ozyo/o6cifHLtkxrG/c+PmjLWqJMnSzkbefMLSuRscDgKxChzSqiqp7vr45qVLFqW7qysf/pd/zdBAf5Lkx7fclmVLF+dfr/x6nnv+U7Pm1rVpNv0mPcrygpUDecHKgbSrKiPtKtcPT+bmsakfqh431JPfP2Zxkn2fMT28q2N6HRYisQoc0tqtqWD9+Mc/nsMPW5lFQwN54XN+LuvWb8idGzblM1/8Sl77Ky/N6Nh4PvaZL+RVv/jz+fN3f3i+x4YZ/uCmLdky2UpXvZbBRj3tJE9Y1DPfY8GcEKvAIW9iLHn+85+fyQ3Xp91u58XPe2ba7Sp9vT153atfns988d/y7Kedk1e86LnzPSrs01+euCxJ8nd37MyvHjGU7+waz/am3/jIQ4NYBR4SXvayl6U9MTK9PTIymo/8/VuTJN+/9vo8+2nnTK896UlPSq7/ylyPCPdqS7OVJDmtrzPVXfet6GrkfXdO/YKdxw5173XMyq5G/unOXamSNGrJuUt652haODjEKvCQ8P73vz+TG66f3n7zW981ffsb//mdvOK3/nh6e8mK1XnknE4H+/fZzcP53OZ7ftD69R9tmr59/vK+vGL14AGP/6V7WYfSiVXgIe9fP/ieGdudq07J9pc+ZZ6mgZmetbw/z1ref7+OOWvR3mdY7886lMTnrAIPSb/5Ky+d7xFg1vzs4gO/1H9v61ASsQo8JC0a8tIowEIgVgEAKJZYBQCgWGIVAIBiiVUAAIolVgEAKJZYBQCgWGIVAIBiiVUAAIolVgEAKJZYBQCgWGIVAIBiiVUAAIolVgEAKFbHfA8APHC9A0lH19Ttqp3s3p709Cdjw/s/pm8oGdk5J+MBwIMmVmGB6OqZ+nNi7J77mhNJuzV1++5ore/xeknfUNLoSGpJJsYPHLEAUCKxCgtB7Z4Y3TNWO3uS0d133T++92F3n0GtN+6JXQBYSMQqFK6rJ+noTEZ3TW33DiatZjIxmrQmp172T5JUM0M2mbpMoGonqU392dM/Fa4AsFCIVShcqzkzQkd33ROc46NTMVurTW13dCbt9j371mrJ6MjMx+sTqwAsIGIVCtdq7n3f3depJkln98yzpdUesVpvJN1992xPjB78+QBgNolVKFhXb9Ldu++18dGp+Gx0JFW1x0Ltnpu7t8/mdAAw+8QqFGxi9N7Phu7ccv8ec3Ifb8QCgFL5pQDwECNWAVhIxCoAAMUSqwAAFEusAgBQLLEKAECxxCoAAMUSqwAAFEusAgBQLLEKAECxxCoAAMUSqwAAFEusAgBQLLEKAECxxCoAAMUSqwAAFEusAgBQLLEKAECxxCoAAMUSqwAAFEusAgBQLLEKAECxxCoAAMUSqwAAFEusAgBQLLEKAECxxCoAAMUSqwAAFEusAgBQLLEKAECxxCoAAMUSqwAAFEusAgBQLLEKAECxxCoAAMUSqwAAFEusAgBQLLEKAECxxCoAAMUSqwAAFEusAgBQLLEKAECxxCoAAMUSqwAAFEusAgBQLLEKAECxxCoAAMUSqwAAFEusAgBQLLEKAECxxCoAAMXqmO8BZkPVbqX79HPnewwAAB6kQzJWa/VGhi960XyPwSzov/hD8z0CADCHXAYAAECxxCoAAMUSqwvYzTvH5nsEAIBZJVYXiL+4+va9bn/kxk373f/N/3XbrM8EADDbDsk3WB2Ktow186tfvjFJMtps5wu3bcvuyVaS5E+/eUvW7p7IprHJrO7ryusffcR8jgoAcNCI1QViWU9H3vIzxyVJ3vAfa9LbUU+9VkuSvOlxx+bK27fni7dvy/9+3LFp1GvzOSoAwEEjVheIPc+s9nbU87OHL8pVd+7MZKudy27YlOU9HfntRx6Rf7hufR63amiepwUAODjE6gJx91nVPT1h9VA6G/W89JSV2T7ezOdu2ZpXn746SfKZW7bM9YgAAAedWF1AXvLFH6W/ozG93dtRz+lL+7Jm13i2jk1mzc6xfGPdjvR1Ng7wKAAAC4dYXeCqJM12lSXdHXnu8cvS01HPoi7/WQGAQ4OqWUA+8LRT93n/mT2de9139mGuWwUAFj6fs3qIOufIxfM9AgDAgyZWAQAollgFAKBYYhUAgGKJVQAAiiVWAQAoVq2qqupAO3xp1QvnapaD5qlr359aZ9d8j8EsGBsdS09vz3yPwQJUtdup1e/bz+fVxHhqXd2zPBEA98Uh+Tmrtc6u7P7D5833GMyS3fM9AAA8CAOXfCLDf/5L8z1Gcfrf8L593u8yAAAAiiVWAQAollgFAKBYYhUAgGKJVQAAiiVWAQAollgFAKBYYhUAgGKJVQAAiiVWAQAollgFAKBYYhUAgGKJVQAAiiVWAQAollgFAKBYYhUAgGKJVQAAiiVWAQAollgFAKBYYhUAgGKJVQAAiiVWAQAollgFAKBYYhUAgGKJVQAAiiVWAQAollgFAKBYYhUAgGKJVQAAiiVWAQAollgFAKBYYhUAgGKJVQAAiiVWAQAollgFAKBYYhUAgGKJVQAAiiVWAQAollgFAKBYYhUAgGKJVQAAiiVWAQAollgFAKBYYhUAgGKJVQAAiiVWAQAollgFAKBYYhUAgGKJVeCQ8ozLr8+rv74mz/rCDdk23swl19yZd123cU6+9iXX3JlLrrlzTr4WwIH8xTfXzNqxD+axH4iOOf1qALPsnNVD+cNHrs67r9uYqzbsTquqsmOiNb3+5XU786Efb5neHm2185xjluTnj1u612N9f+tI/vbaDdPbrSSPXNqX3zp9VZLkeVfcmOXdHdkw1sw7zz5mvzN9Z/NwvrdlJK88ZcVB+A4BZrrg49/Nqr6uJMmRQz1548+csNc+n71pUxr1Wn7u+OUz7n/l569NozZ1+6ihnlz0E8f+5peuy3iznWZV5azVi/Jrjz5qdr6JAxCrwCFrqKuRWm3mfe2qutfj/t+1G/KKk5andS/7nr1yIK8/Y3U+cNNU/G4cm9xrn2a7yhfu2JGqSraON7O029MucHCdfcTi/NHjjkuy77Oeo5OtfG/jrlRJnnz0kvR2NKbXTl7aN33svvzteaclSbaNTebD160/uIPfR541gUPKWKudt/5wfXZNtvKLy5bl9uGJ3L57Ynp942gzb3jU4TlusHufx480W2lXVQa7Gtk+3sorTl6es1cN7nPfKskX1u7I5vHJdDZqGey85y+Aqqry5XW7cvXm4bzsxOVZ3tORf7x+U5b3dOaZRy3KwB77AsyGyVY7X1izJddtGc5rfuqo1JK867trc8Li3vzc8cvT2ahn/fBELv3+HanVajnn6CX5j3U7MjJ5z6tRb/nWLVne25nJdpUVd529nWtiFTikvPGnjsjnbtueO0cn88GbtyZJNozec8ZzqKuR//W9dZlotZMkvY16Hr9qIL988tRL9J+9bUfOP3pxkqS/s553Xrcxf3f9pow1qwx21nPyop68/ozVSZLfP2N1PnXLtvzW6YdNP9bdarVajh3sylMOH0ztrtO7v/GwVbl993i6G94uAMy+zkY9py3rz7NOvOcSpN957DFZs300nXc9Dx3W35VfPuOIJFOvPB3W3501O0an929V1fT6fBGrwCHnuu2juWHH2PT2xrHm9O1nHb04zzp6cb62fleS5GcPm3nWdM2u8fzC8VPXr565YiBnrhjITTvHcvXm4bzw+GV7fa3r9/g6v/3wVblxj+0Thnr22v+ogX2f0QV4oFb2deXS79+RJDlz9aIZaycs6dtr/+MW907fnmxXee8P7khVJbVacv4JK7Ln1VO37BjNh65bn2a7Sruq8ozjlu/1eLNNrAKHnNuGJ2Zsr+zpyJ0jE/n2puF89rbtM9b++cbNSaYidlVvZx67oj9JctWGXXnvDZtn7HvlHTuTZMaZ2C3jzbz66zOvEfv9M1Zn09jkXsffbc/jAR6sV+7nzOenb9yYz9y0aZ9rzz5xRS44aWX+9OzjD/jYbz33lNRSS2e9lka9dsB9Z4tYBQ45b3v8vt+Zf8ExXbngmCX7Pe4t378zv/uIqZf0z141uN9rVff0l2fu+52xJy3quU/HA8yGxx++KE86emkuOGnlAzr2bnu+GWtf63PBhVMAmbou6wmHDabxkx8fALAAPenovT+O72Ad+2Ae+4EQqwBJGrVaHr9yYL7HAOAniFUAAIolVgEAKJZYBQCgWGIVAIBiiVUAAIolVgEAKJZYBQCgWGIVAIBiiVUAAIolVgEAKJZYBQCgWGIVAIBiiVUAAIolVgEAKJZYBQCgWGIVAIBiiVUAAIolVgEAKJZYBQCgWGIVAIBiiVUAAIolVgEAKJZYBQCgWGIVAIBiiVUAAIolVgEAKJZYBQCgWGIVAIBiiVUAAIolVgEAKJZYBQCgWGIVAIBiiVUAAIolVgEAKJZYBQCgWGIVAIBiiVUAAIolVgEAKJZYBQCgWGIVAIBiiVUAAIolVgEAKJZYBQCgWGIVAIBiiVUAAIolVgEAKNYhG6v/vW00n751W5Lk6+t35dVfXzP9z8u/enM++OMt+z32kmvuzCXX3HnAx3+w6wCzbc/nwQeyDlCCjvke4L66pTGcT/Wum95upsojJxflvPFVSZI/G/zvLK46kyTr3ve+nLbHsU88bDBPPGwwSdKqqty0Yyz/cMPmvPiEZdk23szLvnJzDu/rzG3DE7n8GafM+LqfvnVbPvDjLRnqbCRJjhzoykWPPmLGPq+96taMt9ppVlXOWjGQ/3nayoP97QMkSf5t3c68+0cbp5+TkuTXH7Yyj17Wn1d/fU2SZNdkO79yyvIc3tc149inX359jumfuu+EoZ48++jFczY3wAO1YGL12FZ/Xrf7pEymnc7Us6U+nms6dyRJPta7NiO11nSsfuyjH82JJ52aJz7jiRl43vPy9re/PVdddVWGh4czPDycqqpyxNEPy6VH/nRGR0dz3gvOyoUXXphXvepVecH378ju4Y60Wq286ZJPpOcTn8jxX/lK3va2t82Yp/PiizNw0UVJkkvvum/r1q257LLLMvDa185YBzhYeq+4In/c15ezzz57r7UP3PXnt771rWzbti29RxyRnhtvzMDznpckObf7olx88cVJkosvvji9z33ujHVgblSTE+l/w/vme4wFo1ZVVXWgHb606oVzNct98i896/KMsVUZrrVyR2M0j2guSpK8efBHuXDXqUmSb732EWm8++r8R9edObW5JJvrY3ncxGHpSiPf7LwzP2psy2StlcGqK7/7zjfnX37vD3JmV3e+Nj6WNw0tzf/dvT3fm5zIpUtW5vKxkXx+fCSPfsmL8u53XZpdO9vp7q6ns6uWHdtaSZKhRfW02kktSatdZXS4ytDienZub8/XvybgENXdU8vgUCPtPZ66R3a3MzZaZdGSRprNKvV6Mj5apaqS/sF6RkfaGR+b+bw0tLie0eEq/YP1jI1OHQ8wn3YO37zP+xfEmdU1jeHc0jGcZOrl/290b5m+2HZNNZzjWv1J7nmivbu/T2wuztbaWH7UsTXXdGzKkqo7m+tjaVS1LK16cmpzSS6//PJcOLgk/zExlqd29+b9I7tyY2syHbXkS+MjuWpyLEtq9Vx44YV5+99emlYzGWm2M9S5x+W+tWR4lzAFZt/4WJXxseY+16qqmvFc1NlZy/j4VKju//GEKlC2BRGrx7X6c1SrN1d0b8zGxng2ZTwnNgfylPEVqaeWf+q7NWO1dv5o6IdJkiVf3ZLOrh05rbksPenIL46cmq93rcvTJ47JNzrX5TudG9JOlR92bs2fXHBB8s3v5j8nxnJYoyPndffmRb0DubE5mYd1dqVVJd+YGM173vOeNBrJ4KL6XmHa0VFLX389tdrU9uiIcAXmXmdnLf0Dd/0gXUsmJ6o9f45PuzV1pjXJAQMWoCQL5jKAq7q2pJ0qZ00sTZXk37u2ZKjqyGMml0zv84OOqWtYf/eOy/PnRz03m+qj2V2byFmTh+WKrtvz9IljkiSXd9+SWlXL0yeOySvu+OesO/ucvHbH5mxpt9JKlY7UUkvyG31D2VlVObrRkad++9+zfOkJqaqpvwBmvMxfy4y/EJK4DAA46Hr7aunt3/eHuIwOtzM6MvOJqLOzlo7O7HX/fV0HmEv7uwzgXmO1FOvXr8/FF1+ckZGRVFWVRYsW5Y1vfGO++tWv5pOf/ORe+09MTOQJT3hCTjjhhJx44om59NJLs3bt2iTJmjVrctxxx03vu3z58rz1rW/d59f9xCc+keOPPz6PetSjctlll+VpT3taVqxYkSuvvDLnnnvufue9t3WA2bZhw4Zs2rQpD3/4wx/QOkAJFkysPlDDw8N573vfm9e85jUP6Pgf/vCHWbZsWVatWpW//uu/zutf//qDPCEAAPtzyMdqklx99dV52MMelt7e3gf8GDfffHO6urpy5JFHHsTJAAA4kIdErAIAsDAdsr9uFQCAhU+sAgBQLLEKAECxxCoAAMUSqwAAFOv/A/varuFsr53JAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import font_manager\n",
    "f_path = \"C:\\Windows\\Fonts\\malgun.ttf\"\n",
    "font_manager.FontProperties(fname=f_path).get_name()\n",
    "from matplotlib import rc\n",
    "rc('font', family='Malgun Gothic')\n",
    "\n",
    "name = list(kobis[kobis['year'] == 2020].groupby('Genre')['Sales'].sum().index.get_level_values(0))\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "squarify.plot(sizes=thirteen, label=name, text_kwargs={'fontsize':8})\n",
    "plt.axis('off');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Sales'>"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thirteen.plot.pie()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "# winter = kobis[kobis['four_month'] == 1].head()\n",
    "# spring = kobis[kobis['four_month'] == 2].head()\n",
    "# summer = kobis[kobis['four_month'] == 3].head()\n",
    "# fall = kobis[kobis['four_month'] == 4].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jaVtbS1pbSxWoiKKoeOjRv3//zJ07t/x4zpw5GTRo0DuOa21tz+zZC7usjsGD++f2Pffr9LiP/fqyNL4yr0vrWFlv3B6DB/fPCz/5eqeXsfE3L8hrXbg+wOq3Kn3lzbq693YXXdV76RnsL3SE3vvO/CzRGYMH988zL1/R6XFD1h+T117z+aanWJm+UvG7t/Tp0yfNzc156aWXkiR//OMfs8MOO1R6WgAAAKCHq/iRHkkybty4HH744WloaMguu+ySoUOHVmNaAAAAoAerWOgxcuTIjBw5MkkyfPjwXHXVVZWaCgAAAGA5FT+9BQAAAGB1EHoAAAAAhST0AAAAAApJ6AEAAAAUUodCjx/84AfLPD7nnHMqUgwAAABAV1nh3VueeeaZ3H333bn55pvz7ne/O0nS1NSUP/zhDzniiCOqUiAAAADAylhh6NG/f//06tUrpVIpDQ0NSZLevXvnggsuqEpxAAAAACtrhaHHeuutlz333DNbbrll3ve+91WrJgAAAIBVtsLQY6mmpqYcfvjhmTNnTtra2lIqlXLZZZdVujYAAACAldah0OOkk07KmWeemaFDh6ZUKlW6JgAAAIBV1qHQ413vele22WabStcCAAAA0GU6FHpsscUWmTx5ckaMGFE+0uOjH/1oRQsDAAAAWBUdCj369u2bJHnggQfKzwk9AAAAgO6sQ6HHt771rUrXAUDBDOzfkLrevTo9rmVxU2bNa65ARQAA9DQdCj3Gjh273AVM3b0FgBWp690rt++5X6fHfezXlyVCDwAAukCHQo9LLrmk/PXMmTNz2223VaoeAAAAgC5R05EXNTQ0lP/bcsst09TUVOm6AAAAAFZJh470uPPOO8tfv/TSS3n00UcrVhAAAABAV+hQ6HH//feXvx44cGAmTpxYqXoAAAAAukSH794ye/bsPP/889lss83Sr1+/StcFAAAAsEo6FHr85je/ybRp0zJs2LA8/vjjOfDAA7PzzjtXujYAAACAldah0OOKK67I1KlT09DQkObm5nzta18TegAAAADdWofu3tKrV680NDQkef1OLu3t7RUtCgAAAGBVdSj02GCDDXL99ddn7ty5+e1vf5tBgwZVui4AAACAVdKh01t233333Hffffnd736XIUOG5MADD6x0XQAAAACrpEOhx8UXX5wrrrii/PirX/1qpkyZUrGiAAAAAFZVh05vefM1PBYsWFCRYgAAAAC6SoeO9Bg9enS+853v5NOf/nRuv/32fOhDH6p0XQAAAACrpEOhx/777597770306dPz6hRo7LLLrus1GRTpkzJjTfemNra2owfPz7bbLPNSi0HAAAA4J10KPRIkhEjRmTEiBErPdGrr76aW265JVdffXX++c9/5vTTT88FF1yw0ssDAAAAWJEOXdOjK/Tu3TtJsmTJksyaNSsDBw6s1tQAAABAD9ThIz1WVb9+/TJixIjstttuWbhwYX7+85+v8PW1taUMGNC3OsW9g6LV0V3Wp+hq65KaUm2nx7W1t6a1pQIFQQd0l97bHWpYqjvVQvdnf2FldJfe253YHnSG/YUVqVrocdttt6WlpSU333xz5syZk8MOOyxTpkxJfX39W76+tbU9s2cv7LL5Bw/uv9Jju2Md3WV9eHuDB/fPq3Nu7PS49dbZLa/NnleBiiiqVekHb9aVvbc79anuVAvdn/2Fjuiuvbc78bNEZ9hf6IiV2U+qdnrLCy+8kPXWWy+lUin9+vXLggUL0tTUVK3pAQAAgB6makd67LXXXjn++OOz7777prm5OXvvvXf69etXrekBAACAHqZqoUefPn0yefLkak0HAAAA9HBVO70FAAAAoJqEHgAAAEAhCT0AAACAQhJ6AAAAAIUk9AAAAAAKSegBAAAAFJLQAwAAACgkoQcAAABQSEIPAAAAoJCEHgAAAEAhCT0AAACAQhJ6AAAAAIUk9AAAAAAKqW51FwAAACQD+jWkvk+vTo9bsqgps+c3V6Ai6JhBg/qktrbzHy1bW1vS2LioAhXBfwg9AACgG6jv0yvTttur0+P2+ce1idCD1ai2ti6vzrmx0+PWW2e3ClQDy3J6CwAAAFBIQg8AAACgkIQeAAAAQCEJPQAAAIBCEnoAAAAAhST0AAAAAApJ6AEAAAAUktADAAAAKCShBwAAAFBIQg8AAACgkIQeAAAAQCEJPQAAAIBCEnoAAAAAhVTV0GP69OkZM2ZM9t577/zsZz+r5tQAAABAD1NXrYmWLFmS8847Lz/96U+zzjrrVGtaAAAAoIeq2pEet99+ezbeeOMcddRR2X///fPwww9Xa2oAAACgB6rakR7PPvts5syZkwsvvDD//ve/c/TRR+eqq65629fX1pYyYEDfapW3QkWro7usD29v6XtUW9uemprO/5i2tbWktbXU1WXRA3SX3tsdaliqq2upLbWlpq6+0+PaWpaktd2luLq77rTvsuboit67dHxNe2tqGxo6Pb61uTltpdpVqqEr6b09R1e813ovK1K10KO2tjY77rhj6urqsskmm6RUKqW9vT2l0lt/MGttbc/s2Qu7bP7Bg/uv9NjuWEd3WR/eXle8R4MH988zL1/R6fFD1h+T116bt9Lzs2ZZlX3tzbqy93anPtXdann52u92etz6e52V117xc10N3Wl/ofuqRO/tqt8dbt9zv06P/9ivL0tjF/eY7vSzpPdWVlftu6u6DIpvZfaTqsWW2267bW6//fYkyauvvpr6+vq3DTwAAAAAVlXVjvQYPnx4hgwZkr333jt1dXUZN25ctaYGAAAAeqCqhR5JcuSRR1ZzOgAAAKAHc1UeAAAAoJCEHgAAAEAhCT0AAACAQhJ6AAAAAIUk9AAAAAAKSegBAAAAFJLQAwAAACgkoQcAAABQSEIPAAAAoJCEHgAAAEAhCT0AAACAQhJ6AAAAAIUk9AAAAAAKqW51FwAAa4pB6/RKbUNDp8e1NjencU5TBSqCjllnYO801NV3akxzy5LMmbW4QhVBx+m9rIlWpu8mem8lCD0AoINqGxrywk++3ulxG3/zgiR+8Wb1aairzzl3/aJTY47Y4StJ/OLN6qf3siZamb6b6L2V4PQWAAAAoJCEHgAAAEAhCT0AAACAQhJ6AAAAAIUk9AAAAAAKSegBAAAAFJLQAwAAACgkoQcAAABQSEIPAAAAoJCEHgAAAEAhCT0AAACAQhJ6AAAAAIUk9AAAAAAKSegBAAAAFNJqCT322muv3H777atjagAAAKCHqHro8fvf/z5z586t9rQAAABAD1NXzcnmz5+f66+/Pp/97Gff8bW1taUMGNC3ClW9s6LV0V3Wh7fXFe+R95mVsbT3ltpaU9erodPjW5qa015Tu8p1dKf9tzv13u60XXhrRXuPirY+3VVX/N5btB6j9/Yc3qPlFW19Vreqhh4TJ07MN77xjfz5z39+x9e2trZn9uyFXTb34MH9V3psd6yju6wPb68r3iPvMx2xKvvJmy3tvYMH98+07fbq9Ph9/nFtXnll3irX1dX7b3fqvd1pu/DWivgerew6ddf16Q4q1XtXVnfsMXpvz+E9Wl7R1qe7WJntWrXTW66//vpsuOGGGT58eLWmBAAAAHqwqh3pccMNN6R379759re/nSeffDL33HNPNtlkk2y++ebVKgEAAADoQaoWelx00UXlr88999x84AMfEHgAAAAAFVPVa3osddhhh62OaQEAAIAepOq3rAUAAACoBqEHAAAAUEhCDwAAAKCQhB4AAABAIQk9AAAAgEISegAAAACFJPQAAAAACknoAQAAABSS0AMAAAAoJKEHAAAAUEhCDwAAAKCQhB4AAABAIdWt7gJYfQYN6JXa+oZOj2td0pzG2U0VqAiqq/86vdO7ob7T4xY3L8m8OYsrUBFAsem70L0MGtQntbWd/0jY2tqSxsZFFaho1Qwc1Cd1K7E+La0tmdUN14euIfTowWrrG/Lytd/t9Lj19zoridCDNV/vhvp85eJpnR73i6/tk3nxyzdAZ+m70L3U1tZl8eK7Oj2ud+8dKlDNqqurrctfZ17Z6XEf2WzvClRDd+H0FgAAAKCQhB4AAABAIQk9AAAAgEISegAAAACFJPQAAAAACknoAQAAABSS0AMAAAAoJKEHAAAAUEhCDwAAAKCQhB4AAABAIQk9AAAAgEISegAAAACFJPQAAAAACknoAQAAABRSXbUmmjt3bk466aS8+uqraWtryxlnnJF3v/vd1ZoeAAAA6GGqdqTHokWLMm7cuEydOjUHHXRQpkyZUq2pAQAAgB6oakd6bLDBBuWv11577fTp02eFr6+tLWXAgL6VLqtDilZHVyynu2yTSqirS0ql2k6Pa29vTUtL19TQ1e9RTW17ams6/+Pe2taSttbSKtdSREX9GeiK3lu0HqP3VkdX9d7auqRmJZbT1t6a1i7o4V3de2tqk9qazq9Pa1tr2lo7PWw53Wmf6061dLXu2Htr21tT09DQ6WW0NTendSV+BldUy+peTpH3vVVRtPdo2f5drN7bXmpPQ13n/z1qbmlJqX3N/yxQtdBjqZdeeilTpkzJ9773vRW+rrW1PbNnL+yyeQcP7r/SY7tjHV2xnO6yTbqbwYP7Z/Hiuzo9rnfvHTJ79rxllrOyuvo9Gjy4f/4688pOL+Mjm+2dxtfmvfML11BF+RlYlfV4s6W9tzvtv11B7+3+urL3vjrnxk4vZ711dstr//9yulPvHTy4f6575JpOL2OPbb6wTP9e2XXqrj+L3UHRe+/gwf0z/eD9Or2M4Rddltde6fqfpVVdTpH2va5UtPeoK9dnVXtvd9rnBg/un69cPK3T437xtX3yyivd67PAymzXqoYet956a2699daccsopGThwYDWnBgAAAHqYqoUejz32WG699dZMmDChWlMCAAAAPVjVQo877rgj9957b8aOHZsk2XDDDTNp0qRqTQ8AAAD0MFULPQ466KAcdNBB1ZoOAAAA6OGqdstaAAAAgGoSegAAAACFJPQAAAAACknoAQAAABSS0AMAAAAoJKEHAAAAUEhCDwAAAKCQhB4AAABAIQk9AAAAgEISegAAAACFJPQAAAAACknoAQAAABSS0AMAAAAopLrVXcA7GdCvIfV9enV63JJFTZk9v7kCFfFmgwb2Tm1dfafHtbYsSeOsxRWoiO5snYG907AS+0tzy5LM+f/3l7UH9E6v+s4vo2nJksydbZ+DpfRverL+a/dO716d3/8XNy3JvLn2f1a/QYP6pLa28x/nWltb0ti4qAIVQffU7UOP+j69Mm27vTo9bp9/XJsIPaqitq4+jXee0elxgz46LolfGnqahrr6nHPXLzo97ogdvpKl+0uv+vocfeNlnV7GD3fbL/Y5+I/auvrMeeKiTo9bZ8uD42eJNV3vXvX58hFTOz3uqnPGZp79n26gtrYu81/6TafH9dvg/61ANdB9Ob0FAAAAKCShBwAAAFBIQg8AAACgkIQeAAAAQCEJPQAAAIBCEnoAAAAAhST0AAAAAApJ6AEAAAAUktADAAAAKCShBwAAAFBIQg8AAACgkIQeAAAAQCEJPQAAAIBCqmrocfbZZ2fffffN3nvvnSeffLKaUwMAAAA9TNVCj3vvvTevvfZaLr/88kyYMCGTJk2q1tQAAABAD1S10OPOO+/MZz7zmSTJlltumTlz5lRragAAAKAHKrW3t7dXY6Lx48dn3333zZZbbpkk+cpXvpIrrrgiNTUuKwIAAAB0vaolDv369cvcuXP/M3FNjcADAAAAqJiqpQ4jRozI73//+yTJU089lXe9613VmhoAAADogap2ektbW1tOPvnkPPnkk1lrrbUyYcKEbLjhhtWYGgAAAOiBqhZ6AAAAAFSTi2oAAAAAhST0AAAAAApJ6AEAAAAUktADAAAAKCShBwAAAFBIQg8AAACgkIQeAAAAQCEJPQAAAIBCEnoAAAAAhST0AAAAAApJ6AEAAAAUktADAAAAKKS61V0AFEFzc3MmTJiQZ599NosXL8748eMzbNiwvPLKKznxxBMzb968bLrppjnllFNSX1+f+fPn5+qrr87TTz+diRMnlpdzwQUX5E9/+lNqampyyimnZIsttliNawXQvXVF7z3kkEOycOHCJMlrr72WnXbaKccdd9zqXC2Abqsr+u68efPyne98J/Pnz8/666+fSZMmpb6+fjWvGUXmSA/oAkuWLMl///d/Z+rUqTnttNNy7rnnJkkmT56cQw45JNOmTcvAgQPzxz/+MUlyzjnnpKampvyLdpL8+c9/TlNTU66++upceeWVAg+Ad9AVvffCCy/M1KlTM3Xq1GyxxRY54IADVseqAKwRuqLvXnnlldl1111zxRVXZNNNN82f/vSn1bIu9BxCD9Z4hxxySJ577rkkycyZM3PUUUeltbU1J510Uvbbb7/sv//+5e9feOGF2W+//bLnnnuWG+y4ceNy1lln5Ytf/GJaW1tXqoa11lorQ4cOTZKsvfba6dOnT5LkmWeeyXbbbZckGT16dO67774kyQknnJBdd911mWVce+216d27d8aMGZMTTjghixcvXqlaAKqhKL13qX/84x9517velQ033HClagGotKL03UGDBmX27NlJktmzZ2fgwIErVQt0lNCDNd6YMWPyq1/9Kkly1VVXZcyYMbn22mszZMiQXHbZZfne976X888/P0nyxS9+MZdddlnOO++8TJ06tbyMTTfdNL/85S9TW1u7zLKfeuqpjB07dpn/rrvuuretZe7cuTnzzDPzzW9+M0nS3t5e/t6AAQMyd+7ctx37z3/+M0OHDs0VV1yRzTffPNOmTev0tgColqL03qV+/vOfO8oD6NaK0ndHjx6d3/zmN9l9990zc+bMclgCleKaHqzxdtppp0yZMiWLFi3KE088kWOPPTb/8z//kwcffDC33HJLkmTgwIFpa2vLpZdempaWltTV1WXBggXlZWy77bZvuez3vOc9y/xDsSL3339/pk2blqOPPjobb7xxkmX/AZg7d24GDRr0tuNLpVI+/vGPJ0lGjRqVSy65pEPzAqwORem9SfLSSy+lra3NUR5At1aUvjt+/PicddZZ2XrrrfOnP/0pP/rRj3LMMcd0aG5YGUIP1nilUim77rprTjrppHzmM59Jkmy22Wb54Ac/mD322CNJsmjRojzyyCOZNWtWJkyYkIceeih33XVXeRlvTruXeuqpp3LyyScv89znP//58nKXevnllzNlypRMnjx5mWVtsMEGefjhh/O+970vf/jDH7LDDju87XoMHz48d9xxR0aNGpW77747W221VWc2A0BVFaX3JsmNN974tqe9AHQXRem7L774YtZdd90kyeDBg/PCCy90eBvAyhB6UAh77LFHLrjggpx66qlJki9/+cs57rjjcs0116RPnz458MADM3z48MyYMSNjx47NTjvt1KHldjT1vvfee/Pwww+XD42ur6/PlClT8p3vfCfHH398SqVShg0btsJ5v/nNb2bcuHG55JJLssEGGyxzVxeA7qgIvTdJ7r777hx//PEdqg1gdSpC3z3mmGNy5JFHpqamJqVSKd///vc7VCOsrFL7G49FgjXU7373uzz11FM5/PDDV3cpAD2G3gtQXfoudJ4jPVjjnXrqqZk5c2bOPvvs1V0KQI+h9wJUl74LK8eRHgAAAEAhuWUtAAAAUEhCDwAAAKCQuu01PZqbWzJnzqLVXQZAtzd4cP8uW5beC9Axei9A9a1M7+22R3qUSqXVXQJAj6P3AlSf3gtQOd029AAAAABYFUIPAAAAoJCEHgAAAEAhCT0AAACAQhJ6AAAAAIUk9AAAAAAKSegBAAAAFJLQAwAAACgkoQcAAABQSEIPAAAAoJCEHgAAAEAhCT0AAACAQhJ6AAAAAIVUt7oLAAAAimfg2g2p69Wr0+Nampoya25zBSoCeiKhBwAA0OXqevXKM8fv0+lxQ06blkToAXQNp7cAAAAAhST0AAAAAApJ6AEAAAAUktADAAAAKCShBwAAAFBIQg8AAACgkIQeAAAAQCEJPQAAAIBCEnoAAAAAhST0AAAAAApJ6AEAAAAUktADAAAAKCShBwAAAFBIQg8AAACgkOoqsdDGxsZceumlKZVKOfLII9PW1pbTTz8906dPT6lUyvnnn5+BAwdWYmoAAACAJBUKPc4444xsuummWbx4cZLkyiuvzFZbbZUTTjihEtMBAAAALKciocekSZNy991354477kiS3H777Rk6dGjGjBmT4cOH55hjjkmpVFrhMmprSxkwoG8lylutatOamvqGTo9rW9Kc1tRWoCKA/yhq7wXozvTe5dkeQFepSOjxZtOnT8+hhx6a7373u5kwYUJuuummjB49eoVjWlvbM3v2wmqUV1WDB/fPM8fv0+lxQ06bltdemVeBioA13eDB/btsWUXtvQBdTe99Z6uyjYq4PYBVtzJ9pSoXMl1vvfUyfPjwJMnOO++cp556qhrTAgAAAD1YVUKPjTbaKI8//niS5J577slWW21VjWkBAACAHqwqp7ccc8wxGT9+fJJk6623zq677lqNaQEAAIAerGKhx8iRIzNy5Mgkyeabb57LL7+8UlMBAAAALKcqp7cAAAAAVJvQAwAAACgkoQcAAABQSEIPAAAAoJCEHgAAAEAhCT0AAACAQhJ6AAAAAIUk9AAAAAAKSegBAAAAFJLQAwAAACgkoQcAAABQSEIPAAAAoJCEHgAAAEAhCT0AAACAQhJ6AAAAAIUk9AAAAAAKSegBAAAAFJLQAwAAACgkoQcAAABQSEIPAAAAoJCEHgAAAEAhCT0AAACAQhJ6AAAAAIUk9AAAAAAKSegBAAAAFJLQAwAAACgkoQcAAABQSEIPAAAAoJAqEno0NjZm8uTJOfvss5d5/pZbbsmXvvSlSkwJAAAAsIyKhB5nnHFG6uvr09LSUn6utbU11113XSWmAwAAAFhORUKPSZMmZfvtt1/mucsvvzyf/exnKzEdAAAAwHLqqjHJE088kfvvvz/7779/Lr744g6Nqa0tZcCAvhWubM1iewCVpvcCVJ/euzzbA+gqFQ89mpqaMnHixPzwhz/s1LjW1vbMnr2wQlWtPoMH91/psUXcHsCqW5W+8mZF7b0AXU3vfWd+7wW62sr0lYqHHnfddVdaW1szceLEJMmzzz6b888/P9/4xjcqPTUAAADQg1U89Bg1alRGjRpVfvylL31J4AEAAABUXMVCj5EjR2bkyJHLPX/11VdXakoAAACAsorcvQUAAABgdRN6AAAAAIUk9AAAAAAKSegBAAAAFJLQAwAAACgkoQcAAABQSEIPAAAAoJCEHgAAAEAhCT0AAACAQhJ6AAAAAIUk9AAAAAAKSegBAAAAFJLQAwAAACikutVdAABU2sC1G1LXq1enx7U0NWXW3OYKVAQAQDUIPQAovLpevfLM8ft0etyQ06YlEXoAAKypnN4CAAAAFJLQAwAAACgkoQcAAABQSEIPAAAAoJCEHgAAAEAhCT0AAACAQhJ6AAAAAIUk9AAAAAAKSegBAAAAFJLQAwAAACgkoQcAAABQSEIPAAAAoJCEHgAAAEAh1VVioY2Njbn00ktTKpVy5JFH5oYbbsiVV16ZBQsWZPTo0TnkkEMqMS0AAABAWUWO9DjjjDNSX1+flpaWJMmmm26aqVOn5pprrsktt9ySxsbGSkwLAAAAUFaR0GPSpEnZfvvty4+HDRv2+mQ1NRkwYEDq6+srMS0AAABAWUVOb3k7V1xxRUaMGJH+/fu/42tra0sZMKBvFapac9geQKXpvcvr6u1RW2pLTV3nw/+2liVpbXcpLigivXd5tgfQVaoSesyfPz+TJk3Kf/3Xf2XMmDEdGtPa2p7ZsxdWuLLqGzz4nQOft1PE7QGsulXpK2+m9y6vq7fH4MH98/K13+30uPX3OiuvvTKvS2sBVp7e+866U+8FimFl+kpVQo9TTjklX//61zNkyJBqTAcAAABQndDjtttuy7/+9a/y40MPPTQ77LBDNaYGAAAAeqiKhR4jR47MyJEjkyR33313paYBAAAAeEuuiAYAAAAUktADAAAAKCShBwAAAFBIQg8AAACgkIQeAAAAQCEJPQAAAIBCEnoAAAAAhST0AAAAAApJ6AEAAAAUktADAAAAKCShBwAAAFBIQg8AAACgkIQeAAAAQCHVre4CACimgf0bUte7V6fHtSxuyqx5zRWoCACAnkboAUBF1PXuldv33K/T4z7268sSoQcAAF3A6S0AAABAIQk9AAAAgEISegAAAACFJPQAAAAACknoAQAAABSS0AMAAAAoJKEHAAAAUEhCDwAAAKCQhB4AAABAIQk9AAAAgEISegAAAACFJPQAAAAACknoAQAAABRSXSUW2tjYmEsvvTSlUilHHnlkZsyYkZNPPjlNTU3Zdtttc+yxx1ZiWgAAAICyihzpccYZZ6S+vj4tLS1JktNOOy0TJ07MlVdemRdeeCEPPPBAJaYFAAAAKKtI6DFp0qRsv/32SZIlS5akqakpm2yySZJk9OjRue+++yoxLQAAAEBZRU5veaNZs2Zl4MCB5ccDBgzI008//Y7jamtLGTCgbyVLW+PYHkCldZfe2x1qWEotQKV1l97bnXT19qgttaWmrr7T49palqS13WUQ6bxSTXvqajv/cbultSXtbaUKVNRzVTz06N+/f+bOnVt+PGfOnAwaNOgdx7W2tmf27IWVLG21GDy4/0qPLeL2AFbdqvSVN+vK3tud+p1agK7WXXtvd9Kd+t3gwf3z8rXf7fS49fc6K6+9Mq9La6FnGDy4f8656xedHnfEDl/JK432ubezMn2l4rFlnz590tzcnJdeeilJ8sc//jE77LBDpacFAAAAeriKH+mRJOPGjcvhhx+ehoaG7LLLLhk6dGg1pgUAAAB6sIqFHiNHjszIkSOTJMOHD89VV11VqakAAAAAluOqPAAAAEAhCT0AAACAQhJ6AAAAAIUk9AAAAAAKqUOhxw9+8INlHp9zzjkVKQYAAACgq6zw7i3PPPNM7r777tx8881597vfnSRpamrKH/7whxxxxBFVKRAAAABgZaww9Ojfv3969eqVUqmUhoaGJEnv3r1zwQUXVKU4AAAAgJW1wtBjvfXWy5577pktt9wy73vf+6pVEwAAAMAqW2HosVRTU1MOP/zwzJkzJ21tbSmVSrnssssqXRsAAADASutQ6HHSSSflzDPPzNChQ1MqlSpdEwAAAMAq61Do8a53vSvbbLNNpWsBAAAA6DIdCj222GKLTJ48OSNGjCgf6fHRj360ooUBAAAArIoOhR59+/ZNkjzwwAPl54QeAAAAQHfWodDjW9/6VqXrAAAAAOhSHQo9xo4du9wFTN29BQAAAOjOOhR6XHLJJeWvZ86cmdtuu61S9QAAAAB0iZqOvKihoaH835ZbbpmmpqZK1wUAAACwSjp0pMedd95Z/vqll17Ko48+WrGCAAAAALpCh0KP+++/v/z1wIEDM3HixErVAwAAANAlOnz3ltmzZ+f555/PZpttln79+lW6LgAAAIBV0qHQ4ze/+U2mTZuWYcOG5fHHH8+BBx6YnXfeudK1AQAAAKy0DoUeV1xxRaZOnZqGhoY0Nzfna1/7mtADAAAA6NY6dPeWXr16paGhIcnrd3Jpb2+vaFEAAAAAq6pDoccGG2yQ66+/PnPnzs1vf/vbDBo0qNJ1AQAAAKySDp3esvvuu+e+++7L7373uwwZMiQHHnhgpesCAAAAWCUdCj0uvvjiXHHFFeXHX/3qVzNlypSKFQUAAACwqjp0esubr+GxYMGCihQDAAAA0FU6dKTH6NGj853vfCef/vSnc/vtt+dDH/pQpesCAAAAWCUdCj3233//3HvvvZk+fXpGjRqVXXbZpdJ1AQAAAKySDoUeSTJixIiMGDFilSabMmVKbrzxxtTW1mb8+PHZZpttVml5AAAAAG+nQ9f06Aqvvvpqbrnlllx99dU588wz8+Mf/7haUwMAAAA9UIeP9FhVvXv3TpIsWbIks2bNysCBA1f4+traUgYM6FuN0tYYtgdQad2l93aHGpZSC1Bp3aX3difdaXt0p1roGexzXatqoUe/fv0yYsSI7Lbbblm4cGF+/vOfr/D1ra3tmT17YXWKq6LBg/uv9Ngibg9g1a1KX3mzruy93anfqQXoat2193Yn3anfdada6Bnsc5WxMtu1aqe33HbbbWlpacnNN9+cG2+8MaeeemqWLFlSrekBAACAHqZqoccLL7yQ9dZbL6VSKf369cuCBQvS1NRUrekBAACAHqZqp7fstddeOf7447Pvvvumubk5e++9d/r161et6QEAAIAepmqhR58+fTJ58uRqTQcAAAD0cFU7vQUAAACgmoQeAAAAQCEJPQAAAIBCEnoAAAAAhST0AAAAAApJ6AEAAAAUktADAAAAKCShBwAAAFBIQg8AAACgkIQeAAAAQCEJPQAAAIBCEnoAAAAAhVS3uguoloH9G1LXu1enx7Usbsqsec0VqAje2cBBvVNXW9/pcS2tSzKrcXEFKgIAujO/8wIsq8eEHnW9e+X2Pffr9LiP/fqyxD8ArCZ1tfV55uUrOj1uyPpjkgg9AKCn8TsvwLKc3gIAAAAUktADAAAAKCShBwAAAFBIQg8AAACgkIQeAAAAQCEJPQAAAIBCEnoAAAAAhST0AAAAAApJ6AEAAAAUktADAAAAKCShBwAAAFBIQg8AAACgkIQeAAAAQCEJPQAAAIBCqmroMX369IwZMyZ77713fvazn1VzagAAAKCHqavWREuWLMl5552Xn/70p1lnnXWqNS0AAADQQ1XtSI/bb789G2+8cY466qjsv//+efjhh6s1NQAAANADVe1Ij2effTZz5szJhRdemH//+985+uijc9VVV73t62trSxkwoG+1ylshdbAmsr+wMrpL7+0ONSz1xlpqS62pqWvo9DLaWprT2l7bpbVAZ5Rq2lNX27lf+1paW9LeVqpQRbyR3rs8tdCT2ee6VtVCj9ra2uy4446pq6vLJptsklKplPb29pRKb/2PaWtre2bPXthl8w8e3H+lxxaxDtYM9hc6YlX2kzfryt7bnfbfrqpl8OD+eeEnX+/0Mjb+5gV57ZV5XVoLdMbgwf1zzl2/6NSYI3b4Sl5pnFehitZ8eu87Uws9mX2uMlZmu1bt9JZtt902t99+e5Lk1VdfTX19/dsGHgAAAACrqmpHegwfPjxDhgzJ3nvvnbq6uowbN65aUwMAAAA9UNVCjyQ58sgjqzkdAAAA0INV7fQWAAAAgGoSegAAAACFJPQAAAAACknoAQAAABSS0AMAAAAoJKEHAAAAUEhCDwAAAKCQhB4AAABAIQk9AAAAgEISegAAAACFJPQAAAAACknoAQAAABSS0AMAAAAopLrVXQArZ9A6vVLb0NDpca3NzWmc0/T6Mgb0Sm39SixjSXMaZzd1etyaYtCgPqmt7fyPRmtrSxobF3X5copmnYG901BX3+lxzS1LMmfW4gpUBADdw4B+Danv06vT45Ysasrs+c0VqKh76Irfe4GeS+ixhqptaMgLP/l6p8dt/M0Lkrze/GvrG/Lytd/t9DLW3+us8jKKqLa2LosX39Xpcb1777Dccl6dc2Onl7PeOrt1esyapKGuPufc9YtOjztih68kEXoAUFz1fXpl2nZ7dXrcPv+4Nilw6NEVv/cCPZfTWwAAAIBCEnoAAAAAhST0AAAAAApJ6AEAAAAUktADAAAAKCShBwAAAFBIQg8AAACgkIQeAAAAQCEJPQAAAIBCEnoAAAAAhST0AAAAAApJ6AEAAAAUktADAAAAKKTVEnrstddeuf3221fH1AAAAEAPUfXQ4/e//33mzp1b7WkBAACAHqaqocf8+fNz/fXX57Of/Ww1pwUAAAB6oLpqTjZx4sR84xvfyJ///Od3fG1tbSkDBvRNqa01db0aOj1XS1Nz2mtqV6bM5QwY0LdLlrOquqqOrlhOd9km3Y33qLKKuE7dzdLeu7p1hxqW8nNdHXV1SanU+X+329tb09Lyn8e1dUnNSiynrb01rS3v/Lqepsj7XHfSFb23aD1G762O7tR7a2vbU1PT+Y+nbW0taW0tdXrcitTUJrUr8Vmyta01ba2rPn+R97nVoWqhx/XXX58NN9www4cP71Do0drantmzF2bw4P6Ztt1enZ5vn39cm1demVd+PHhw/04vY6nZsxeu9Ng366o6umI53WWbdDfeo8oq4jqtbquyTd9sae/tCt3pvfZz3f0NHtw/ixff1elxvXvvkNmzl/33/tU5N3Z6Oeuts1tee8NyimZl97si73OrqhK9t2g9Ru/t/rpT7x08uH+eefmKTi9jyPpj8tprXdu/Bw/un+seuabT4/bY5gtpfO0/67OyirzPraqV2a5VCz1uuOGG9O7dO9/+9rfz5JNP5p577skmm2ySzTffvFolAAAAAD1I1UKPiy66qPz1ueeemw984AMCDwAAAKBiqnpNj6UOO+yw1TEtAAAA0INU/Za1AAAAANUg9AAAAAAKSegBAAAAFJLQAwAAACgkoQcAAABQSEIPAAAAoJCEHgAAAEAhCT0AAACAQhJ6AAAAAIUk9AAAAAAKSegBAAAAFJLQAwAAACgkoQcAAABQSHWruwAAAN7agIF9Ul/X+V/XlrS0ZPasRRWoCKD7GjioT+pqO98zW1pbMqtRzywqoQcAQDdVX1eX6x65ptPj9tjmCxWoBqB7q6uty19nXtnpcR/ZbO8KVEN34fQWAAAAoJCEHgAAAEAhCT0AAACAQhJ6AAAAAIUk9AAAAAAKSegBAAAAFJLQAwAAACgkoQcAAABQSEIPAAAAoJCEHgAAAEAhCT0AAACAQhJ6AAAAAIUk9AAAAAAKqa5aE82dOzcnnXRSXn311bS1teWMM87Iu9/97mpNDwAAAPQwVTvSY9GiRRk3blymTp2agw46KFOmTKnW1AAAAEAPVLUjPTbYYIPy12uvvXb69OlTrakBAACAHqhqocdSL730UqZMmZLvfe97K3xdbW0pAwb0XaW5VnX8m5dT296amoaGTo9va25Oa6m2y+roDsvpqlq6Wl1tUqrp/LZub2tNS+uqz99d36Oa2vbU1nT+x721rSVtraX/fxlJ7Ups29a21rR1wbZN3rBONe2pr+38+ixpbUnaSl1TTEEt7b2lttbU9ep8v2tpak77Suwnb9adekx3/bnmP7r83/va9tSsRM9sa2tJa2v36TH2uTVHd/m9tzu933pv91e096jbro/fe1dZVUOPW2+9NbfeemtOOeWUDBw4cIWvbW1tz+zZCzN4cP+Vnm/27IXlr7tiOYMH98/0g/fr9PjhF12W116Z12V1dNVyuqqW7mTw4P6Z/9JvOj2u3wb/b2a/Vtz3aPDg/vnrzCs7vYyPbLZ3Gt+wXa575JpOL2OPbb5QXsbS5aysN26Xo2+8rNPjf7jbfnmlcd47v3ANsyrb9M3e2HunbbdXp8fv849r80oX97uuUMSf66Lpbu/RMy9f0enxQ9Yfk9de69oes7q3S5H3uVVVqd67srpjj1nd++8bl9Odtkt3UrT3qGjrs3Q5fu/9j5XZrlULPR577LHceuutmTBhQrWmBAAAAHqwqoUed9xxR+69996MHTs2SbLhhhtm0qRJ1ZoeAAAA6GGqFnocdNBBOeigg6o1HQAAANDDVe2WtQAAAADVJPQAAAAACknoAQAAABSS0AMAAAAoJKEHAAAAUEhCDwAAAKCQhB4AAABAIQk9AAAAgEISegAAAACFJPQAAAAACknoAQAAABSS0AMAAAAoJKEHAAAAUEh1q7sAgNWl/zq907uhvtPjFjcvybw5iytQEW9l4NoNqevVq9PjWpqaMmtucwUq6h4GDeyd2rrO77+tLUvSOMv+C/3X7p3evVbi34CmJZk3t/g/Q3rvW9N76cn6r9MnvRs6HyEsbm7JvDmLXl/Gaui9Qg+gx+rdUJ+vXDyt0+N+8bV9Mi9+camWul69Mv3g/To9bvhFlyUp7i/etXX1abzzjE6PG/TRcYn9F9K7V32+fMTUTo+76pyxPeLfAL33rem99GS9G+ryyVOv6fS4P574hcxbuozV0Hud3gIAAAAUktADAAAAKCShBwAAAFBIQg8AAACgkIQeAAAAQCEJPQAAAIBCEnoAAAAAhST0AAAAAApJ6AEAAAAUktADAAAAKCShBwAAAFBIQg8AAACgkIQeAAAAQCEJPQAAAIBCqmrocfbZZ2fffffN3nvvnSeffLKaUwMAAAA9TNVCj3vvvTevvfZaLr/88kyYMCGTJk2q1tQAAABAD1Rqb29vr8ZEZ599dj7ykY/kIx/5SJLkS1/6Uq6++upqTA0AAAD0QFU70qOxsTGDBg0qP66trU1bW1u1pgcAAAB6mKqFHv369cvcuXP/M3FNTWpqXEcVAAAAqIyqpQ4jRozI73//+yTJU089lXe9613VmhoAAADogap2TY+2tracfPLJefLJJ7PWWmtlwoQJ2XDDDasxNQAAANADVS30AAAAAKgmF9UAAIBu6Oabb67Icp9++uk888wz7/i6l19+OdOnT69IDUt1dB0rsS1eeeWVPPbYYyt8TTW2QTV09D3viJkzZ+bpp5/ustfReZXqDSujI+/zn/70pypV89bqVuvsAABAkuSAAw7Iz3/+8/Ljyy+/PLvuumv58RFHHJE5c+YkSRYtWpRnnnkmCxcuzIgRI8qvefzxx3PbbbelV69eOeCAA5ZZfltbWy677LI88MADqa2tzZAhQ5Ik1157bX72s59lvfXWK792nXXWyQEHHJC77rorw4cPf8t6//SnP2WXXXZJkowePXq5U9cHDRqUH/3oR5k5c2Yuuuii/Otf/0qSzJ07N7vuumsOPfTQ5dZxv/32W+ZmB7W1tbnkkkuWe11zc3NeeeWVbLzxxm+7PW+88cbU1dVl3rx56dWrVz7zmc8k+c92njFjRv7+979n6623zrXXXptf//rXmT17dpqbm7P++utn5513zgc/+MEVboMVefrpp1NTU1PezqvilVdeyfXXX5+vfe1ryzx/8803l7fLXXfdldNOOy29evVKv379kiz/nq+//vqZP39+NthggyTJtGnTkiT77LPPcnOee+65ueWWW9K/f//ycz//+c/z8MMPp6mpKUOHDk2SXHzxxbnzzjuXGXvWWWfljDPOyKc+9akMHTo0Dz/8cB5//PHstddeK1zPl156KS+//HKGDRvWmc3zll599dX89re/Lf8cvHF/XVkLFy7MAw88kC222GKZZb/RG9dh5syZaW1tLW+rN/vjH/+YT37yk2/7uokTJ2b69Olpbm5ObW1thg8fnvHjxy/z83DbbbflkksuyWuvvZbZs2end+/e5Z+LAw44IJ/4xCdWen3vu+++PPjgg9lvv/1y/vnn5y9/+UuSpLW1NX369Mkll1yyzP7w5u3xX//1Xzn44INzxRVXdGrbv9N2W2r69On529/+lgMPPHCFrxN6AABAN7BkyZIVfv+cc85Z7rmDDjooP/vZz8qPv/71ry/z/TeGKG/1Ae2AAw7Iyy+/nJqamtTW1pbnOeqoo9523Ft9kNlwww2z3nrr5dVXX82jjz6a9773vWlsbMz8+fPz8MMPZ8SIEeUPvHfffXf+/ve/v+U61tTULFPzXnvtlX333TePPfZYrrvuuuyxxx458MADc/LJJ+f888/PxIkTy6898MADc8kll5QfL1q0qLxOP/nJT3LllVcmSZ544onccMMNeeqpp1JfX1+eZ6+99soVV1yRf//73zn66KOTJPfee+9b1tna2pof/OAHeeSRR7JgwYIsWrSoHJi85z3vSUNDQz73uc8tEy6tKBh6q3VY+vXS9V16pMZXv/rVLFiwIDNmzEhbW1s+/OEP56STTspzzz2XBQsW5JOf/GT+9re/ZdasWXnuuecyduzY7Lrrrrn66quz8cYb56677sphhx2Wtra23HnnnSmVStl7773f8s6aJ5544jKh2lJ//etfs3jx4vzhD39IqVRK8vrNKo477rgcc8wxOfzww/Pkk0/msccey2c/+9mceOKJmTdvXn7zm98kSf7973/nueeey4gRIzJ//vyMGjUqhx12WGbOnJm///3vGTZsWJYsWZKDDjpoubkfffTR/O///m/q6l7/KPv5z3++HMw89thj2XrrrfPII49k6NChef7559Pc3Lzc/jpt2rRMmTIlAwYMSJ8+fcrLfvLJJ3PzzTfnlVdeyfe+97089NBD2WyzzdK/f//84x//yJVXXplvf/vbueaaa3LzzTdn7bXXzl577bVMQDR37tzMmzcvf/zjH/Pwww/nvPPOK4dM999/fz74wQ/mkUceyTbbbJOHH344n/zkJ/Pwww9n3rx5Of/88/Piiy9mnXXWyamnnpoTTjih/POyxRZb5KWXXlpuXxk1alQ+9rGP5fTTT8+DDz6YjTbaKJ/5zGfy8Y9/vPyeHnPMMXnxxReX2Y5PP/10fvvb32bQoEFZvHhxTj755Lz44otpamrKEUcckY985CNpbm7O/PnzkyTf+MY38o1vfCPJ62HD9ddfv9x7kyzbc3baaafccccdeeyxxzJ27NgkyYwZM/K///u/SZLvfOc7y/SMJDnvvPOWC9auu+66XHfddUmSefPmZdttt82JJ56Y5ubmLFy48C3reCOhBwAArGYtLS15+OGH09LSUv4w1xGlUimtra1ZuHBh5s+fn4ULF5Y/hHbEOeeck2OOOSa77757Ghsb8/GPfzzrrLPOcq974weZt/ODH/wgSTJmzJjlXr948eI0NjYmef1DS0c9//zzufPOO3PQQQflxhtvXOZojzd7+eWXkyTPPfdczjnnnDz//PP5yle+kiT55je/WT7SY//990/fvn3Tu3fvtLa2lse/+uqrufnmm9O7d+9lPoS9lTvuuCM1NTW59NJLM3ny5GyxxRa5884784EPfCD//Oc/c+yxx+baa69dZsyGG274jtuxra0t//73v5O8fjTLW7nooovywx/+MDNnzkz//v2z2Wab5dJLL83AgQOTJHV1dZk6dWquvvrqXHTRRZk6depytSxatChnnnlmvvCFL6Smpibf//73c8wxx5SPEHnjNnn++edTKpXKRw+0tbXlySefzIIFC1JXV1cOacaNG5eZM2empqYmO+20U1599dWUSqXU19dn3Lhx+fvf/55DDz20vOwDDzwwF154Ye6///7cddddSV7/Ofj1r3+dQw89NCeccMIy70/y+gf1rbbaKkly0003Zdq0aXn++efL71V7e3seeeSRbLjhhjnrrLNy/vnn5+CDDy6Pf+655/LKK6/khhtuSH19fXbccceMGjUqS5YsSf/+/TN58uTU1dXl6KOPTq9evVJfX5/nn38++++/f5544olyiPJWlgZES0OKpYFbqVQqv+9LjzJaGlhsv/325fF/+ctf8rGPfSxf+MIXctddd+Xss8/OhAkTyt//85//nEMOOaT8eOn+ftFFF+XXv/51kmTPPffMxz/+8cyYMSMnn3xy/s//+T/56le/mkmTJiVJHnzwwWy99dapr6/PUUcdVQ79Lr744uy44475f/6f/yeLFi3K2LFjs9FGG2XevHnL1LjUrbfe+rZHbjz66KPlrzfffPNceumly4V5S62oZ7zRJptsks9//vOZO3du/vKXv+TjH/94/v3vf2fWrFlvO+aNhB4AALCa/fnPf85GG22UP/3pT/nUpz6V5PUPl9/+9rczcuTI3HDDDcu8/sUXX8ysWbPS3Nycj3zkI9liiy3Kf9le+kGmI2pra8thxAsvvLDM926++eYsXrw4ybIfZDbccMMMGDDgbZf57LPP5rXXXsu6665bfu7yyy/PU089VX788Y9/vLyOhx56aHbccceMGTNmmeUsXrw49fX1aWhoSHt7e2bOnJlLL700jz32WI466qhsscUW5dc+9thjef755zN9+vRsvfXWOeyww3LTTTeVv3/BBRfkl7/8ZZLXTwH6+Mc/nr59+5aPOPntb3+b3/zmN5kwYUIGDhyYE044Ie95z3uyww47vOU6brLJJpk6dWoaGxvz8MMP58gjj8ydd96ZD3/4w7n55pvT1NSUlpaW8gffjpo3b175w+ELL7yQQw89NE8++eQyr6mrq8unP/3pXHvttenfv3822GCD3HPPPfnrX/+6zOvuueeeLFiwoPwh/V//+ldOP/30rLXWWpkxY0YOOuigbLPNNklef0/Hjx+fzTbbLIcffnh5GTfffHMeeuih1NbW5gtf+ELOOuus/P3vf8/uu++eHXfcMSeddFKOPvroLFy4MI8//njuu+++9O7dOzvvvHNuu+22fOYzn8mrr76a008/PfPnz88999yT5PUjZZ588snMmjWrfCRBkvztb3/Le97zniTJhAkTcvDBB2fhwoXp27dv+aiFxx9/PKVSKZ/61KfykY98JPvvv3/5A/Oee+6ZtdZaK4sXL87f/va3JK+fKnHVVVclef0onwsvvDDrrLNOnnnmmUydOjU33HBDGhoasu666+a5555LQ0ND1l9//VxwwQU58MADs3jx4iy998fFF1+cpqamJK//DL5xn14aEL366qsrfI8bGxvT0tKy3PPPPvts9txzzyTJDjvskIsvvrj8vZdffjmLFy/Ou9/97iTJ/Pnz8/TTT+eUU07Jpz/96Wy66aa5++67M3369CxcuDCjRo0qL6uxsTGDBg1KkkyZMiUnn3xy6uvr079//6y11lpJkocffrgcRvTp0ycf/vCHs8cee2TWrFnLHZX14osv5h//+Mcy+8kb3XrrreWvl26rxsbGHHroodljjz3ecsxb9Yw36tu3bzbddNP8z//8T7bYYousu+66ueGGG/LCCy+Uw74VEXoAAMBq1NzcnF/84heZMmVKxo0blx133DFrrbVWampqMnny5CTJ3nvv/bbjH3jggXzgAx9I8vqpD5050qOhoaF8Wk1TU1N69epV/t6gQYPyyiuvJFn2g8zHP/7xDBgwYJkPMmuvvXbGjh2bBQsWZM6cOdlnn32y/vrrL3Pqzfjx45ebv6amJj/96U/fsrbevXunra0td911VxYsWJBNNtkkX//61/OPf/yjfHpL8vppQeecc06mTZuWH/7wh/nud7+bm266KY8++mhGjx5dPnXlzQYPHlz+0D906NDyYfUzZ87MOeeckxkzZpQDoTd7z3vek2OOOSYnnXRSZs6cWd6Gxx57bGbMmJHPfe5zGThw4Arft7eyzjrr5IQTTkjy+mkAP/3pT5e7XkFTU1MuuOCCXHbZZdlrr70ydOjQjBw5Mt///vfL9f7f//t/s+OOO+bZZ5/NuHHjst1222WjjTbKcccdl9tvv32Z05eSZKuttsqPfvSjLFq0aJnn99577/LpLT/5yU9SU1OTsWPHZv3118+oUaMyatSo/Pa3v82mm26aD3/4w/nc5z6XE044Iddcc01eeuml/PWvf826666bhoaG7LXXXuUjPcaPH5+NNtqo/B7uvPPOOeqoo3LHHXdk3333TfKf60asu+66Ofnkk7P22mvnpJNOykknnVQOQFpaWpYJGV577bX06dMnTU1N5RDvoosuynHHHZfddtstJ598cnbaaae8/PLLee9735sFCxbk+eefz4c+9KGceeaZ+da3vpWampq0tramsbExS5YsSU1NTZYsWZKmpqZsvPHG5dOMNtxww9x+++3lU6GWBkT//ve/s+666+bb3/72cqeUPPnkk9lzzz0za9asfPvb317meimlUmmZkGzpOr766qv53//933Jot2TJkjz//PP51a9+lVNPPTXrrrtuHnroofzrX//KsGHDsummm2bGjBlJXg9Lbr311nIwMHPmzPziF79Ir169MmTIkFx22WX56Ec/mqFDh+axxx7Ltttum+T1YGnGjBl57rnnlql/zpw5GT9+fCZOnPi2feaNR/NMnz49BxxwQF544YWss846mTZt2nKh7EMPPZSGhobcfPPN+fKXv7zc8tra2jJz5sz8+te/zu67714Oc7beeuvce++95aOEVkToAQAAq0lLS0vGjx+fsWPH5l3veleOPPLIHH744eXDvpdqbGzMEUccsdz4pqam9O3bt/yX7qV/uV3qzDPPLH/d1ta2zPcee+yx/PjHP868efNy7rnnpq6uLoceemje//73Z/bs2dluu+3KHyje+EFmqUGDBpUDi6VHpxxxxBE57bTTMnHixJx//vnp3bv3W653e3v7W17DpK2tbZmat9xyy0yfPj2zZ8/OhRdeuNyRE/PmzcsxxxyTfffdN+9973tz/PHH57TTTsuPf/zj/P73vy+/7i9/+UvOP//8Zca3tbWV//K89PSIl156qXzI/Oabb/62oUfyelBw7rnn5stf/nKOPvrorLXWWpkwYUJ+9KMf5eKLL17ulJKlwdCb/exnPytvpzlz5pT/gv7EE08s99o5c+bku9/9bg455JD85S9/yT777JNjjjmm/IHxvPPOK5+i84EPfCB9+vTJ1772tfzjH/8oL2NFRwK98foWyesX7pw7d25aW1uz++67Z5111sldd91V/gv+ySefnKampnz961/PZpttliTZdddds9FGG2XkyJFpaWnJv/71rzzxxBNpa2srH+kxY8aMbLzxxjn++OPLFy1dejTCvHnz8vOf/zzNzc25++67U1NTk89//vPlEGC33XbLEUcckd133z19+vRJTU1N+bSPpqam8gVb99hjj/J+3b9//2yxxRa5+OKLs/vuu2fddddNqVTK7Nmzs+mmm+amm27K9OnTUyqVMnbs2Dz99NM588wz89RTT5Wv2bJ48eLl7lKyySab5L777kvyn4Bo6ekt3/3ud3PjjTeWg53k9WDjqquuygknnJDJkycvsz80NDRk9uzZGTBgQJYsWZK2trbcdNNNmTJlSjloXLq/v+td78p73/venHLKKfn+97+f9vb2NDc35x//+Ec5qOjbt29OP/308gWEk2TkyJHLvefrrbdeDj744Jx44om54YYb8vLLL2f77bdPU1NT5s+fXw5C77333vzkJz/JkUcemU022eRt96Fbb7017e3taWlpyejRo/Oxj30sxxxzzDLX23mjn/3sZ/npT3+aiRMn5jOf+cxyp1jV1NSkV69eOfbYY/PXv/41l19++TLf33nnnd+2lqWEHgAAsJr8+9//zs4771z+xf2DH/xgjjjiiOWu6zFo0KBMnTp1ufEHHnjgMtc9OPfcc8tf/+AHPyifnpJkmaM4ktf/UvrTn/40zz77bO6///6USqXU1NSkT58+yxy2nyz7QWbBggX52Mc+liT53//931xwwQVJUv5wNXHixMyfPz+77rprNt9882y33XZZf/31c9RRR6VUKpX/Qvz5z39+ufX54Q9/WP5Anbx+tMdNN92UuXPn5tvf/naS/3zw/9CHPpR+/frlhBNOyKBBg9Lc3JzNN988P/vZz5b7K/SsWbOy5557LnPEx7333pt77rkn8+bNy4033pjW1tY8/PDD5QtRNjc3v+3dI1555ZWss846aWhoyFe/+tV8//vfz4c+9KFMmTKl/JfoN/vxj3/8ls+/0TXXXLPC76+99tqZMGFC/vrXv+bvf/97zjvvvGy33XYZP358tt122+yyyy6pq6vLvvvumyVLlmTq1Kk57bTT8uqrr6alpSW/+tWv8sILL7zlRW2X+trXvpaPfvSjef/735+bb745t9xyS3m/eKsPmHV1dTn44INTW1tbvsZMnz59UiqVssMOO+T9739/Dj/88CxevDgbb7xxWltbc//99+fggw9e5sPztGnT8olPfCKPP/54+cP5TjvtlOT1O5S0traW70Sy4YYbZtq0abnxxhszaNCgvPjii2lra8tGG22Uxx57LHV1dbnyyivz4IMPZoMNNsi//vWv8sV6+/Xrl29961sZOnRoJkyYkDFjxuRHP/pR3v3ud+d73/teBg0alM985jP59Kc/naOOOipnnHFGJk+enAMPPDBf+MIXykfiJK+f6nH22Wdn8eLFue+++9KnT5/sueeemT9/fubOnVsOapYeKfV21wNJkg9/+MM588wz881vfjO/+MUvsttuu2XmzJk54ogj8tBDDyVJeX8/7rjjyvv7pZdemlKplGuvvTa1tbX53Oc+l+T164ess846WWeddXLfffflpz/9aTn4rK+vz+GHH14+0il5ff+cM2dO1lprrXL/eeNFh++777788Ic/LIdTb+WQQw7Jyy+/nKeeeqq835x++ulpb2/P2LFj8+STT2bIkCGpq6vLQQcdlEcffTTvf//7s/XWW+eYY44ph5Zv9olPfCKNjY3ZdNNNl3l+xowZ+fOf//yOd1cSegAAwGqyySabLPdX03f6Bf6NQcPSu1W8+QiCgw46qBxMvJOTTjppuYsIfuITnyjfuWTpB5mlHxoHDx5cDlB23HHH7Ljjjpk3b15+8YtfLHPRyPvvvz8bbrhh7r333my++eZveYrJhRdeuMzjwYMHL/eaffbZJzfddNMyNT7//PM5//zzUyqVsskmm+SnP/1pPvShD2XkyJHlwOOdbo+6VENDQzbccMOUSqVsttlmqa+vT69evdK3b9/yxSLf7KmnnsqZZ56Zfv36paamJgceeGCuv/76jBkzpnzB1KXe+H69nTe+X/fcc0/+8Y9/lO/E861vfSubbLJJ+U41a621Vi6//PLyKSq77bZbBg0alHXWWSdbbLFF/v73v2eTTTbJ5MmTM3To0Hzve9/Lr3/965RKpTzzzDP54Ac/+JanEbzZxz/+8fK1V+66665ceOGFeeSRR/Lqq6+mra2tfCeWhQsXZvbs2dloo40yYMCAzJkzp3y0QVNTUx555JHybYqX2nDDDXP66aend+/eaWhoSHNzc1577bWst956efbZZ/Pe9753mQ/qS/e/XXbZpfxBfZ999infavdHP/pRZsyYkfPOOy9PP/107rnnnmyzzTbZbLPN8vLLL+fpp5/Ogw8+mAMOOCANDQ057rjjynfbeeihh7LRRhuVT+VKkmHDhuW6667Lb37zm3zqU59a5jStwYMHZ5NNNslFF12Um266KU899VS22GKL/PWvf019fX2uvfbazJo1q3wtlh122KF8qs173/vejB8/Po8++mj5DjeXXXZZ1l133Wy++ebZaaed8otf/CLbbrtteXvdfffd5bmX7u8vvvhi7rvvvg7t721tbTn55JNzySWXlK+Z8fLLL+eb3/xm+ZSZJMuFJsnrR4YsDaDe6k46b/bGa+AcffTRWbBgQb773e+mqakpl1xySQ4//PCceuqpWXvttTNv3rw89thj5eUOGzasfGeit/LCCy9kypQpyzw3f/58R3oAAEDRLA0akuTII4/M2WefXdH53u5inm/Uv3//ZQKP5PWjVrqTSy65pPxBPXn9NIpPfOIT6dWrV/mIgjd7u7tD7LDDDuVbaC71ta997S1f+8b3qyPa29uXORVp6XUWlurfv3923nnnNDQ0lJ9b+sH0jR+QlyxZkoEDB5Y/FNfU1GTAgAErPNrg7eywww7l/eCGG25IU1NT+UP2/fffnzvuuCOHHXbYMt/v1atXHnrooTz00ENZa6213vLaC3Pnzs1BBx2U6dOn5ytf+Ur5qKU77rgjhx9+eLbaaqvU19eX6953333zvve9L8nbB3tDhw4tH6EzaNCgXHfdddlpp50ybNiwZU6xuPLKK/PLX/4yv/rVr5KkfCRRkvyf//N/8tBDD6V379751a9+lcbGxvJRG0tPAerbt2/a2toydOjQfO5znyuHBUuPjlh6AeL3ve995W219HolBx54YH784x9n7Nix2W+//coXKn5jyLCqloYB7e3tqampWea0ps5c7LhSOtszZs2alVGjRq3wKKW3I/QAAIBuqCO3iV2ZwOOt/iL8VteaOPbYY8sfZCulI+v4Vq+rra19y+uXrL322ss8t/POO+e///u/85nPfGa5IzA6YsSIEeULeVbTNddcs1xIMHDgwGVOkTn99NOXuRBmkmy22WZZf/31k7x+m94zzzwzV111VWpqatLS0lI+fWpVvNV2vO6668qnQbz66qt57rnnstZaay1zetD73ve+jBs3brmxS5YsyeWXX55vfetb5dDmv/7rvzJkyJBcdNFF5fd01qxZOfjggzN16tTy0TNLj8CYO3duZsyYscx7teGGG2bIkCHl67jMnj17mQ/MLS0teemll8rPLT1C5NOf/nS+8Y1vlF93991357jjjsv8+fNz7rnn5sUXX8zYsWMzcODA9O7de5mwpFr22muvFe7vScr/r62tzXHHHbdMnaVSKSeeeOJyy73ggguWO8Vq2LBhOeaYY962lo78XL3d9Tw66xe/+EVuu+22ZZ7bcsstc/zxx69wXKl96f13gJXW3NycCRMm5Nlnn83ixYszfvz4DBs2LK+88kpOPPHEzJs3L5tuumlOOeWU1NfXZ/78+bn66qvz9NNPlw9VfPHFFzN+/PgsWLCgfMjfGxN8AJbVmd7b3t7eqT4NwOr1t7/9bZnTgkqlUg477LDynYo66tJLL8173/vefPjDH17lmpYexXHooYfmn//8Zy655JKcfPLJq7xcKkvoAV1gwYIF+fe//52hQ4fmySefzFlnnZWLLrooxx9/fL7whS9ku+22y5lnnplhw4Zl9913z8SJE7PxxhvngQceKN+K7qijjspXv/rVvP/978/kyZPz7ne/O1/4whdW85oBdF+d6b0777xzp/o0AMXQ1taWP/zhD9ltt926dLm33HJLdthhh/Tt27dLl0vXq1ndBcCqOuSQQ8oXS5o5c2aOOuqotLa25qSTTsp+++2X/fffv/z9Cy+8MPvtt1/23HPP/OlPf0qSjBs3LmeddVa++MUvLnP188544+F7a6+9dvlcv2eeeSbbbbddkmT06NHlW1qdcMIJy1zMKXn94jzvf//7k7x+8bAHH3xwpWoBqIY1rfd2tk8DUAw1NTVdHngkr/++LvBYMwg9WOONGTOmfAGiq666KmPGjMm1116bIUOG5LLLLsv3vve98v2xv/jFL+ayyy7Leeedt8xt3zbddNP88pe/XO7e70899VTGjh27zH9vvmjVG82dO7d8q6nk9QsHLTVgwIDMnTv3bcduvPHG5XM377rrrpX+EABQDWtq712VPg0ArHlcyJQ13k477ZQpU6Zk0aJFeeKJJ3Lsscfmf/7nf/Lggw/mlltuSfL6hZ/a2tpy6aWXpqWlJXV1dcvcDunNV8Ve6j3vec8yv6CvyP33359p06bl6KOPzsYbb5xk2V+m586du8L7Wo8bNy6nnHJKLrnkkmyzzTblZQB0R2ti713VPg0ArHmEHqzxSqVSdt1115x00knlqwdvttlm+eAHP5g99tgjSbJo0aI88sgjmTVrViZMmJCHHnpomStiv/mvjEs99dRTy12c6POf/3x5uUu9/PLLmTJlSiZPnrzMsjbYYIM8/PDDed/73pc//OEPK7zl2/rrr59zzz03ra2tGTduXA4//PDObAaAqlrTem9X9GmANdns2bNz1lln5emnny7ffvXss88u3+0FikroQSHsscceueCCC3LqqacmSb785S/nuOOOyzXXXJM+ffrkwAMPzPDhwzNjxoyMHTv2be/F/mYd/Wvjvffem4cffrh8y6v6+vpMmTIl3/nOd3L88cenVCpl2LBhK5z3N7/5TaZNm5b29vZ85Stfybvf/e4O1QiwuqxJvffGG29c5T4NsKZasmRJvvGNb+TII4/MyJEjk7x+B6yV1d7enlKp1FXlQUW5ewuF8Lvf/S5PPfWUoyMAqkjvBVgz3HjjjXnqqady2GGHLfe9c889N/fcc0/a2tpy3HHH5f3vf385qL7jjjvS1NSUCy64IIMGDcqXvvSlfOADH8j8+fNz4oknZvz48XnllVfSp0+fnHnmmRkwYED1Vw7egSM9WOOdeuqpmTlzZs4+++zVXQpAj6H3Aqw5nnvuuWy11VZJXj/N5bDDDsvcuXPzyU9+MnPnzs3UqVMze/bsHHvssbnwwguTJO9973tz8MEH5//+3/+bG264IWPHjs2sWbPy5S9/Oe95z3syefLk7Lbbbtl1113z5z//OVdccUX5ItHQnQg9WOOdeOKJq7sEgB5H7wVYc2y88cZ59tlnk7x+p6qpU6fm3HPPTZL89a9/zdixY5NkmbsHbr/99kmSzTffPA8++GCSpH///nnPe96TJHnkkUfyt7/9rXyx6mHDhlVtfaAzhB4AAAAFNmrUqOy///7ZaaedsvXWWyd5PeDYYost8ulPf7p8hMaiRYvKY5Zes6NUKpXvdFVX95+Pj5tttllGjx6dESNGJEkWL15clXWBzhJ6AAAAFNhaa62VyZMnZ9KkSWlsbExDQ0P69euXL37xi7n77rvzla98JWuttVb22muv7L777h1a5te//vWMGzcu55xzTtZaa60cddRR2XLLLSu8JtB5LmQKAAAAFFK3PdKjubklc+YseucXAvRwgwf377Jl6b0AHdOVvReAyqlZ3QW8Hfd9Bqg+vRcAgCLptqEHAAAAwKoQegAAAACFJPQAAAAACknoAQAAABSS0AMAAAAoJKEHAAAAUEhCDwAAAKCQhB4AAABAIQk9AAAAgEISegAAAACFJPQAAAAACknoAQAAABSS0AMAAAAopLrVXUBPM3DthtT16tXpcS1NTZk1t7kCFQEAAEAxCT2qrK5Xrzxz/D6dHjfktGlJhB4AAADQUU5vAQAAAApJ6AEAAAAUktADAAAAKCShBwAAAFBIQg8AAACgkIQeAAAAQCEJPQAAAIBCEnoAAAAAhST0AAAAAApJ6AEAAAAUktADAAAAKCShBwAAAFBIQg8AAACgkOoqsdDGxsZceumlKZVKOfLII9PW1pbTTz8906dPT6lUyvnnn5+BAwdWYmoAAACAJBUKPc4444xsuummWbx4cZLkyiuvzFZbbZUTTjihEtMBAAAALKcip7dMmjQp22+/ffnx7bffnmeeeSZjxozJmWeemfb29kpMCwAAAFBWkSM93mz69Ok59NBD893vfjcTJkzITTfdlNGjR69wTG1tKQMG9K1GeWsM2wOoNL0XAIAiqUrosd5662X48OFJkp133jkPPfTQO4Yera3tmT17YTXKq6rBg/uv9Ngibg9g1a1KX3mzovZegK7Wlb0XgMqpyt1bNtpoozz++ONJknvuuSdbbbVVNaYFAAAAerCqHOlxzDHHZPz48UmSrbfeOrvuums1pgUAAAB6sIqFHiNHjszIkSOTJJtvvnkuv/zySk0FAAAAsJyqnN4CAAAAUG1CDwAAAKCQhB4AAABAIQk9AAAAgEISegAAAACFJPQAAAAACknoAQAAABSS0AMAAAAoJKEHAAAAUEhCDwAAAKCQhB4AAABAIQk9AAAAgEISegAAAACFJPQAAAAACknoAQAAABSS0AMAAAAoJKEHAAAAUEhCDwAAAKCQhB4AAABAIQk9AAAAgEISegAAAACFJPQAAAAACknoAQAAABSS0AMAAAAoJKEHAAAAUEhCDwAAAKCQhB4AAABAIQk9AAAAgEISegAAAACFVJHQo7GxMZMnT87ZZ5+9zPO33HJLvvSlL1ViSgAAAIBlVCT0OOOMM1JfX5+Wlpbyc62trbnuuusqMR0AAADAcuoqsdBJkybl7rvvzh133FF+7vLLL89nP/vZXHzxxR1aRm1tKQMG9K1EeWss2wOoNL0XAIAiqUjo8WZPPPFE7r///uy///4dDj1aW9sze/bCCldWfYMH91/psUXcHsCqW5W+8mZF7b0AXa0rey8AlVPx0KOpqSkTJ07MD3/4w0pPBQAAAFBW8dDjrrvuSmtrayZOnJgkefbZZ3P++efnG9/4RqWnBgAAAHqwioceo0aNyqhRo8qPv/SlLwk8AAAAgIqrWOgxcuTIjBw5crnnr7766kpNCQAAAFBWkVvWAgAAAKxuQg8AAACgkIQeAAAAQCEJPQAAAIBCEnoAAAA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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 모든 해에 드라마 장르의 영화 개봉 수가 가장 많음\n",
    "\n",
    "from matplotlib import font_manager\n",
    "f_path = \"C:\\Windows\\Fonts\\malgun.ttf\"\n",
    "font_manager.FontProperties(fname=f_path).get_name()\n",
    "from matplotlib import rc\n",
    "rc('font', family='Malgun Gothic')\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.catplot(data=kobis, x='Genre', col='year', kind='count', \n",
    "            col_wrap=3, palette='Spectral', legend=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}