Super Bowl EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Super Bowl EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. TV, devre arası gösterileri, ve Büyük Oyun\n",
    "<p>Super Bowl, ABD'de her yıl düzenlenen Amerikan futbolu ligi National Football League'in şampiyonluk maçıdır. Ancak sadece futbol maçı ile sınırlı değildir bu bir gösteridir. Sporseverler için patlama, geri dönüş ve tartışma şeklinde dramalar ve bazıları komik, diğerleri yürek burkan, düşündürücü ve tuhaf, gülünç derecede pahalı reklamlar var. Dünyanın en büyük müzisyenleri ile devre arası gösterileri, bazen <a href=\"https://youtu.be/ZD1QrIe--_Y?t=14\">dev mekanik kaplanlara binmek</a> ya da <a href=\"https://youtu.be/mjrdywp5nyE?t=62\">stadyumun çatısından sıçramak</a>. Bu notebook'da, bu gösterinin bazı öğelerinin birbirleriyle nasıl etkileşime girdiğini analiz edeceğiz. Verilerimizi biraz araştırıp temizledikten sonra, aşağıdaki gibi sorulara cevap vereceğiz:</p>\n",
    "<ul>\n",
    "<li>En olağanüstü oyun sonuçları nelerdir?</li>\n",
    "<li>Oyun, televizyon izleyiciliğini nasıl etkiliyor?</li>\n",
    "<li>İzleyici sayısı, TV derecelendirmeleri ve reklam maliyeti zaman içinde nasıl değişti?</li>\n",
    "<li>Devre arası gösteri performansları açısından en üretken müzisyenler kimler?</li>\n",
    "</ul>\n",
    "<p><img src=\"https://assets.datacamp.com/production/project_684/img/left_shark.jpg\" alt=\"Soldaki Köpekbalığı Rol Çaldı\">\n",
    "<em><a href=\"https://www.flickr.com/photos/huntleypaton/16464994135/in/photostream/\">Soldaki Köpekbalığı Rol Çaldı</a>. Katy Perry, Super Bowl XLIX'in devre arasında performans sergiliyor.</em></p>\n",
    "<p>Kullanacağımız veri seti Wikipedia'dan <a href=\"https://en.wikipedia.org/wiki/Web_scraping\">kazınmış</a> ve cilalanmıştır. 3 Üç adet CSV dosyasından oluşur; birincisi <a href=\"https://en.wikipedia.org/wiki/List_of_Super_Bowl_champions\">oyun verisi</a>, ikincisi <a href=\"https://en.wikipedia.org/wiki/Super_Bowl_television_ratings\">TV verisi</a>, ve üçüncüsü <a href=\"https://en.wikipedia.org/wiki/List_of_Super_Bowl_halftime_shows\">devre arası müzisyen verisi.</a> 2018'e kadar gerçekleşen 52 adet Super Bowl'dan oluşur. Jupyter Notebook'larda çıktısı çok daha güzel olduğu için <code>print()</code> yerine <code>display()</code> kullanarak bi göz atalım.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>date</th>\n",
       "      <th>super_bowl</th>\n",
       "      <th>venue</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>attendance</th>\n",
       "      <th>team_winner</th>\n",
       "      <th>winning_pts</th>\n",
       "      <th>qb_winner_1</th>\n",
       "      <th>qb_winner_2</th>\n",
       "      <th>coach_winner</th>\n",
       "      <th>team_loser</th>\n",
       "      <th>losing_pts</th>\n",
       "      <th>qb_loser_1</th>\n",
       "      <th>qb_loser_2</th>\n",
       "      <th>coach_loser</th>\n",
       "      <th>combined_pts</th>\n",
       "      <th>difference_pts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-02-04</td>\n",
       "      <td>52</td>\n",
       "      <td>U.S. Bank Stadium</td>\n",
       "      <td>Minneapolis</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>67612</td>\n",
       "      <td>Philadelphia Eagles</td>\n",
       "      <td>41</td>\n",
       "      <td>Nick Foles</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Doug Pederson</td>\n",
       "      <td>New England Patriots</td>\n",
       "      <td>33</td>\n",
       "      <td>Tom Brady</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bill Belichick</td>\n",
       "      <td>74</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-02-05</td>\n",
       "      <td>51</td>\n",
       "      <td>NRG Stadium</td>\n",
       "      <td>Houston</td>\n",
       "      <td>Texas</td>\n",
       "      <td>70807</td>\n",
       "      <td>New England Patriots</td>\n",
       "      <td>34</td>\n",
       "      <td>Tom Brady</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bill Belichick</td>\n",
       "      <td>Atlanta Falcons</td>\n",
       "      <td>28</td>\n",
       "      <td>Matt Ryan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dan Quinn</td>\n",
       "      <td>62</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-02-07</td>\n",
       "      <td>50</td>\n",
       "      <td>Levi's Stadium</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>California</td>\n",
       "      <td>71088</td>\n",
       "      <td>Denver Broncos</td>\n",
       "      <td>24</td>\n",
       "      <td>Peyton Manning</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gary Kubiak</td>\n",
       "      <td>Carolina Panthers</td>\n",
       "      <td>10</td>\n",
       "      <td>Cam Newton</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ron Rivera</td>\n",
       "      <td>34</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-02-01</td>\n",
       "      <td>49</td>\n",
       "      <td>University of Phoenix Stadium</td>\n",
       "      <td>Glendale</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>70288</td>\n",
       "      <td>New England Patriots</td>\n",
       "      <td>28</td>\n",
       "      <td>Tom Brady</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bill Belichick</td>\n",
       "      <td>Seattle Seahawks</td>\n",
       "      <td>24</td>\n",
       "      <td>Russell Wilson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pete Carroll</td>\n",
       "      <td>52</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-02-02</td>\n",
       "      <td>48</td>\n",
       "      <td>MetLife Stadium</td>\n",
       "      <td>East Rutherford</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>82529</td>\n",
       "      <td>Seattle Seahawks</td>\n",
       "      <td>43</td>\n",
       "      <td>Russell Wilson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pete Carroll</td>\n",
       "      <td>Denver Broncos</td>\n",
       "      <td>8</td>\n",
       "      <td>Peyton Manning</td>\n",
       "      <td>NaN</td>\n",
       "      <td>John Fox</td>\n",
       "      <td>51</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  super_bowl                          venue             city  \\\n",
       "0  2018-02-04          52              U.S. Bank Stadium      Minneapolis   \n",
       "1  2017-02-05          51                    NRG Stadium          Houston   \n",
       "2  2016-02-07          50                 Levi's Stadium      Santa Clara   \n",
       "3  2015-02-01          49  University of Phoenix Stadium         Glendale   \n",
       "4  2014-02-02          48                MetLife Stadium  East Rutherford   \n",
       "\n",
       "        state  attendance           team_winner  winning_pts     qb_winner_1  \\\n",
       "0   Minnesota       67612   Philadelphia Eagles           41      Nick Foles   \n",
       "1       Texas       70807  New England Patriots           34       Tom Brady   \n",
       "2  California       71088        Denver Broncos           24  Peyton Manning   \n",
       "3     Arizona       70288  New England Patriots           28       Tom Brady   \n",
       "4  New Jersey       82529      Seattle Seahawks           43  Russell Wilson   \n",
       "\n",
       "  qb_winner_2    coach_winner            team_loser  losing_pts  \\\n",
       "0         NaN   Doug Pederson  New England Patriots          33   \n",
       "1         NaN  Bill Belichick       Atlanta Falcons          28   \n",
       "2         NaN     Gary Kubiak     Carolina Panthers          10   \n",
       "3         NaN  Bill Belichick      Seattle Seahawks          24   \n",
       "4         NaN    Pete Carroll        Denver Broncos           8   \n",
       "\n",
       "       qb_loser_1 qb_loser_2     coach_loser  combined_pts  difference_pts  \n",
       "0       Tom Brady        NaN  Bill Belichick            74               8  \n",
       "1       Matt Ryan        NaN       Dan Quinn            62               6  \n",
       "2      Cam Newton        NaN      Ron Rivera            34              14  \n",
       "3  Russell Wilson        NaN    Pete Carroll            52               4  \n",
       "4  Peyton Manning        NaN        John Fox            51              35  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>super_bowl</th>\n",
       "      <th>network</th>\n",
       "      <th>avg_us_viewers</th>\n",
       "      <th>total_us_viewers</th>\n",
       "      <th>rating_household</th>\n",
       "      <th>share_household</th>\n",
       "      <th>rating_18_49</th>\n",
       "      <th>share_18_49</th>\n",
       "      <th>ad_cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>NBC</td>\n",
       "      <td>103390000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.1</td>\n",
       "      <td>68</td>\n",
       "      <td>33.4</td>\n",
       "      <td>78.0</td>\n",
       "      <td>5000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51</td>\n",
       "      <td>Fox</td>\n",
       "      <td>111319000</td>\n",
       "      <td>172000000.0</td>\n",
       "      <td>45.3</td>\n",
       "      <td>73</td>\n",
       "      <td>37.1</td>\n",
       "      <td>79.0</td>\n",
       "      <td>5000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50</td>\n",
       "      <td>CBS</td>\n",
       "      <td>111864000</td>\n",
       "      <td>167000000.0</td>\n",
       "      <td>46.6</td>\n",
       "      <td>72</td>\n",
       "      <td>37.7</td>\n",
       "      <td>79.0</td>\n",
       "      <td>5000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49</td>\n",
       "      <td>NBC</td>\n",
       "      <td>114442000</td>\n",
       "      <td>168000000.0</td>\n",
       "      <td>47.5</td>\n",
       "      <td>71</td>\n",
       "      <td>39.1</td>\n",
       "      <td>79.0</td>\n",
       "      <td>4500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48</td>\n",
       "      <td>Fox</td>\n",
       "      <td>112191000</td>\n",
       "      <td>167000000.0</td>\n",
       "      <td>46.7</td>\n",
       "      <td>69</td>\n",
       "      <td>39.3</td>\n",
       "      <td>77.0</td>\n",
       "      <td>4000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   super_bowl network  avg_us_viewers  total_us_viewers  rating_household  \\\n",
       "0          52     NBC       103390000               NaN              43.1   \n",
       "1          51     Fox       111319000       172000000.0              45.3   \n",
       "2          50     CBS       111864000       167000000.0              46.6   \n",
       "3          49     NBC       114442000       168000000.0              47.5   \n",
       "4          48     Fox       112191000       167000000.0              46.7   \n",
       "\n",
       "   share_household  rating_18_49  share_18_49  ad_cost  \n",
       "0               68          33.4         78.0  5000000  \n",
       "1               73          37.1         79.0  5000000  \n",
       "2               72          37.7         79.0  5000000  \n",
       "3               71          39.1         79.0  4500000  \n",
       "4               69          39.3         77.0  4000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>super_bowl</th>\n",
       "      <th>musician</th>\n",
       "      <th>num_songs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>Justin Timberlake</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52</td>\n",
       "      <td>University of Minnesota Marching Band</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51</td>\n",
       "      <td>Lady Gaga</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>Coldplay</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>Beyoncé</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   super_bowl                               musician  num_songs\n",
       "0          52                      Justin Timberlake       11.0\n",
       "1          52  University of Minnesota Marching Band        1.0\n",
       "2          51                              Lady Gaga        7.0\n",
       "3          50                               Coldplay        6.0\n",
       "4          50                                Beyoncé        3.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pandas'ı import etmek\n",
    "import pandas as pd\n",
    "\n",
    "# CSV verilerini DataFrame olarak yüklemek\n",
    "super_bowls = pd.read_csv(\"datasets/super_bowls.csv\")\n",
    "tv = pd.read_csv(\"datasets/tv.csv\")\n",
    "halftime_musicians = pd.read_csv(\"datasets/halftime_musicians.csv\")\n",
    "\n",
    "# Her bir DataFrame'in ilk beş satırını görüntülemek\n",
    "display(super_bowls.head())\n",
    "display(tv.head())\n",
    "display(halftime_musicians.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Veri seti sorunlarını not almak\n",
    "<p>Super Bowl oyun verileri için, veri setinin yedek oyun kurucu sütunlarındaki (<code>qb_winner_2</code> ve <code>qb_loser_2</code>) eksik değerler dışında bir bütün olarak göründüğünü görebiliriz; bu, Super Bowl oyunundaki çoğu başlangıç QB'sine (<code>qb_winner_1</code> ve <code>qb_loser_1</code>) bakıldığında mantıklıdır. \n",
    "<p>TV ve devre arası müzisyen verilerinin görsel incelemesinden, görüntülenen yalnızca bir eksik değer var, ancak daha fazlası olduğuna dair bir önsezim var. Super Bowl 1967'ye kadar uzanıyor ve daha ayrıntılı sütunlar (ör. Devre arası müzisyenleri için şarkı sayısı) muhtemelen zaman içinde güvenilir bir şekilde izlenemedi. Wikipedia harika ama mükemmel değil</p>\n",
    "<p><code>tv</code> ve <code>halftime_musicians</code> için <code>.info()</code> çıktısının incelenmesi, bize boş değerli birden çok sütun olduğunu gösterir.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 53 entries, 0 to 52\n",
      "Data columns (total 9 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   super_bowl        53 non-null     int64  \n",
      " 1   network           53 non-null     object \n",
      " 2   avg_us_viewers    53 non-null     int64  \n",
      " 3   total_us_viewers  15 non-null     float64\n",
      " 4   rating_household  53 non-null     float64\n",
      " 5   share_household   53 non-null     int64  \n",
      " 6   rating_18_49      15 non-null     float64\n",
      " 7   share_18_49       6 non-null      float64\n",
      " 8   ad_cost           53 non-null     int64  \n",
      "dtypes: float64(4), int64(4), object(1)\n",
      "memory usage: 3.9+ KB\n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 134 entries, 0 to 133\n",
      "Data columns (total 3 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   super_bowl  134 non-null    int64  \n",
      " 1   musician    134 non-null    object \n",
      " 2   num_songs   88 non-null     float64\n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 3.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# İncelenecek TV verilerinin özeti\n",
    "tv.info()\n",
    "\n",
    "print('\\n')\n",
    "\n",
    "# İncelenecek devre arası müzisyen verilerinin özeti\n",
    "halftime_musicians.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>TV verileri için, aşağıdaki sütunlarda eksik değerler var ve bunların çoğu:</p>\n",
    "<ul>\n",
    "<li><code>total_us_viewers</code> (Yayının en azından bir kısmını izleyen ABD'li izleyici sayısı)</li>\n",
    "<li><code>rating_18_49</code> (Televizyonu olan ve tüm yayını izleyen bir evde yaşayan 18-49 yaş arasındaki ABD'li yetişkinlerin ortalama yüzdesi)</li>\n",
    "<li><code>share_18_49</code> (\n",
    "Televizyonun kullanıldığı bir evde yaşayan 18-49 yaş arasındaki ABD'li yetişkinlerin ortalama yüzdesi yayının tamamını izliyor)</li>\n",
    "</ul>\n",
    "<p>Devre arası müzisyen verileri için performansların yaklaşık üçte biri için eksik çalınan şarkı sayısı var (<code>num_songs</code>) .</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Oyunların Toplam Skor Histogramı\n",
    "<p>Super Bowl tarihi boyunca en yüksek ve en düşük skorlu oyunlar.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>super_bowl</th>\n",
       "      <th>venue</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>attendance</th>\n",
       "      <th>team_winner</th>\n",
       "      <th>winning_pts</th>\n",
       "      <th>qb_winner_1</th>\n",
       "      <th>qb_winner_2</th>\n",
       "      <th>coach_winner</th>\n",
       "      <th>team_loser</th>\n",
       "      <th>losing_pts</th>\n",
       "      <th>qb_loser_1</th>\n",
       "      <th>qb_loser_2</th>\n",
       "      <th>coach_loser</th>\n",
       "      <th>combined_pts</th>\n",
       "      <th>difference_pts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-02-04</td>\n",
       "      <td>52</td>\n",
       "      <td>U.S. Bank Stadium</td>\n",
       "      <td>Minneapolis</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>67612</td>\n",
       "      <td>Philadelphia Eagles</td>\n",
       "      <td>41</td>\n",
       "      <td>Nick Foles</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Doug Pederson</td>\n",
       "      <td>New England Patriots</td>\n",
       "      <td>33</td>\n",
       "      <td>Tom Brady</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bill Belichick</td>\n",
       "      <td>74</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1995-01-29</td>\n",
       "      <td>29</td>\n",
       "      <td>Joe Robbie Stadium</td>\n",
       "      <td>Miami Gardens</td>\n",
       "      <td>Florida</td>\n",
       "      <td>74107</td>\n",
       "      <td>San Francisco 49ers</td>\n",
       "      <td>49</td>\n",
       "      <td>Steve Young</td>\n",
       "      <td>NaN</td>\n",
       "      <td>George Seifert</td>\n",
       "      <td>San Diego Chargers</td>\n",
       "      <td>26</td>\n",
       "      <td>Stan Humphreys</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bobby Ross</td>\n",
       "      <td>75</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  super_bowl               venue           city      state  \\\n",
       "0   2018-02-04          52   U.S. Bank Stadium    Minneapolis  Minnesota   \n",
       "23  1995-01-29          29  Joe Robbie Stadium  Miami Gardens    Florida   \n",
       "\n",
       "    attendance          team_winner  winning_pts  qb_winner_1 qb_winner_2  \\\n",
       "0        67612  Philadelphia Eagles           41   Nick Foles         NaN   \n",
       "23       74107  San Francisco 49ers           49  Steve Young         NaN   \n",
       "\n",
       "      coach_winner            team_loser  losing_pts      qb_loser_1  \\\n",
       "0    Doug Pederson  New England Patriots          33       Tom Brady   \n",
       "23  George Seifert    San Diego Chargers          26  Stan Humphreys   \n",
       "\n",
       "   qb_loser_2     coach_loser  combined_pts  difference_pts  \n",
       "0         NaN  Bill Belichick            74               8  \n",
       "23        NaN      Bobby Ross            75              23  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>super_bowl</th>\n",
       "      <th>venue</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>attendance</th>\n",
       "      <th>team_winner</th>\n",
       "      <th>winning_pts</th>\n",
       "      <th>qb_winner_1</th>\n",
       "      <th>qb_winner_2</th>\n",
       "      <th>coach_winner</th>\n",
       "      <th>team_loser</th>\n",
       "      <th>losing_pts</th>\n",
       "      <th>qb_loser_1</th>\n",
       "      <th>qb_loser_2</th>\n",
       "      <th>coach_loser</th>\n",
       "      <th>combined_pts</th>\n",
       "      <th>difference_pts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1975-01-12</td>\n",
       "      <td>9</td>\n",
       "      <td>Tulane Stadium</td>\n",
       "      <td>New Orleans</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>80997</td>\n",
       "      <td>Pittsburgh Steelers</td>\n",
       "      <td>16</td>\n",
       "      <td>Terry Bradshaw</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chuck Noll</td>\n",
       "      <td>Minnesota Vikings</td>\n",
       "      <td>6</td>\n",
       "      <td>Fran Tarkenton</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bud Grant</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1973-01-14</td>\n",
       "      <td>7</td>\n",
       "      <td>Memorial Coliseum</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90182</td>\n",
       "      <td>Miami Dolphins</td>\n",
       "      <td>14</td>\n",
       "      <td>Bob Griese</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Don Shula</td>\n",
       "      <td>Washington Redskins</td>\n",
       "      <td>7</td>\n",
       "      <td>Bill Kilmer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>George Allen</td>\n",
       "      <td>21</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1969-01-12</td>\n",
       "      <td>3</td>\n",
       "      <td>Orange Bowl</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Florida</td>\n",
       "      <td>75389</td>\n",
       "      <td>New York Jets</td>\n",
       "      <td>16</td>\n",
       "      <td>Joe Namath</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Weeb Ewbank</td>\n",
       "      <td>Baltimore Colts</td>\n",
       "      <td>7</td>\n",
       "      <td>Earl Morrall</td>\n",
       "      <td>Johnny Unitas</td>\n",
       "      <td>Don Shula</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  super_bowl              venue         city       state  \\\n",
       "43  1975-01-12           9     Tulane Stadium  New Orleans   Louisiana   \n",
       "45  1973-01-14           7  Memorial Coliseum  Los Angeles  California   \n",
       "49  1969-01-12           3        Orange Bowl        Miami     Florida   \n",
       "\n",
       "    attendance          team_winner  winning_pts     qb_winner_1 qb_winner_2  \\\n",
       "43       80997  Pittsburgh Steelers           16  Terry Bradshaw         NaN   \n",
       "45       90182       Miami Dolphins           14      Bob Griese         NaN   \n",
       "49       75389        New York Jets           16      Joe Namath         NaN   \n",
       "\n",
       "   coach_winner           team_loser  losing_pts      qb_loser_1  \\\n",
       "43   Chuck Noll    Minnesota Vikings           6  Fran Tarkenton   \n",
       "45    Don Shula  Washington Redskins           7     Bill Kilmer   \n",
       "49  Weeb Ewbank      Baltimore Colts           7    Earl Morrall   \n",
       "\n",
       "       qb_loser_2   coach_loser  combined_pts  difference_pts  \n",
       "43            NaN     Bud Grant            22              10  \n",
       "45            NaN  George Allen            21               7  \n",
       "49  Johnny Unitas     Don Shula            23               9  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# matplotlib import etme ve plot çizmini belirleme\n",
    "from matplotlib import pyplot as plt\n",
    "# plt.show()'a gerek yok\n",
    "%matplotlib inline \n",
    "plt.style.use('seaborn')\n",
    "\n",
    "# Toplam puanların histogramı\n",
    "plt.hist(super_bowls[\"combined_pts\"])\n",
    "plt.xlabel('Toplam Puan')\n",
    "plt.ylabel('Super Bowl Sayısı')\n",
    "plt.show() # Grafiği çıktılardan önce görmek için\n",
    "\n",
    "# Super Bowl tarihi boyunca en yüksek ve en düşük skorlu oyunlar\n",
    "display(super_bowls[super_bowls['combined_pts'] > 70])\n",
    "display(super_bowls[super_bowls['combined_pts'] < 25])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Super Bowl tarihinin **en yüksek skor**lu oyunu 1995 yılında 75 skorla San Francisco 49ers - San Diego Chargers arasında gerçekleşmiş. Mücadeleyi 49 - 26'lık skorla San Francisco 49ers kazanmış.\n",
    "* Super Bowl tarihinin **en düşük skor**lu oyunu 1973 yılında 21 skorla Miami Dolphins - Washington Redskins arasında gerçekleşmiş. Mücadeleyi 14 - 7'lik skorla Miami Dolphins kazanmış."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Oyunlardaki Skor Farkı Histogramı\n",
    "<p>Super Bowl tarihindeki ortalama skor farkları ve en yakın geçen maçlar</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(super_bowls.difference_pts)\n",
    "plt.xlabel(\"Skor Farkı\")\n",
    "plt.ylabel(\"Super Bowl Sayısı\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>date</th>\n",
       "      <th>super_bowl</th>\n",
       "      <th>venue</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>attendance</th>\n",
       "      <th>team_winner</th>\n",
       "      <th>winning_pts</th>\n",
       "      <th>qb_winner_1</th>\n",
       "      <th>qb_winner_2</th>\n",
       "      <th>coach_winner</th>\n",
       "      <th>team_loser</th>\n",
       "      <th>losing_pts</th>\n",
       "      <th>qb_loser_1</th>\n",
       "      <th>qb_loser_2</th>\n",
       "      <th>coach_loser</th>\n",
       "      <th>combined_pts</th>\n",
       "      <th>difference_pts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1991-01-27</td>\n",
       "      <td>25</td>\n",
       "      <td>Tampa Stadium</td>\n",
       "      <td>Tampa</td>\n",
       "      <td>Florida</td>\n",
       "      <td>73813</td>\n",
       "      <td>New York Giants</td>\n",
       "      <td>20</td>\n",
       "      <td>Jeff Hostetler</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bill Parcells</td>\n",
       "      <td>Buffalo Bills</td>\n",
       "      <td>19</td>\n",
       "      <td>Jim Kelly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marv Levy</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  super_bowl          venue   city    state  attendance  \\\n",
       "27  1991-01-27          25  Tampa Stadium  Tampa  Florida       73813   \n",
       "\n",
       "        team_winner  winning_pts     qb_winner_1 qb_winner_2   coach_winner  \\\n",
       "27  New York Giants           20  Jeff Hostetler         NaN  Bill Parcells   \n",
       "\n",
       "       team_loser  losing_pts qb_loser_1 qb_loser_2 coach_loser  combined_pts  \\\n",
       "27  Buffalo Bills          19  Jim Kelly        NaN   Marv Levy            39   \n",
       "\n",
       "    difference_pts  \n",
       "27               1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>super_bowl</th>\n",
       "      <th>venue</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>attendance</th>\n",
       "      <th>team_winner</th>\n",
       "      <th>winning_pts</th>\n",
       "      <th>qb_winner_1</th>\n",
       "      <th>qb_winner_2</th>\n",
       "      <th>coach_winner</th>\n",
       "      <th>team_loser</th>\n",
       "      <th>losing_pts</th>\n",
       "      <th>qb_loser_1</th>\n",
       "      <th>qb_loser_2</th>\n",
       "      <th>coach_loser</th>\n",
       "      <th>combined_pts</th>\n",
       "      <th>difference_pts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1990-01-28</td>\n",
       "      <td>24</td>\n",
       "      <td>Louisiana Superdome</td>\n",
       "      <td>New Orleans</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>72919</td>\n",
       "      <td>San Francisco 49ers</td>\n",
       "      <td>55</td>\n",
       "      <td>Joe Montana</td>\n",
       "      <td>NaN</td>\n",
       "      <td>George Seifert</td>\n",
       "      <td>Denver Broncos</td>\n",
       "      <td>10</td>\n",
       "      <td>John Elway</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dan Reeves</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  super_bowl                venue         city      state  \\\n",
       "28  1990-01-28          24  Louisiana Superdome  New Orleans  Louisiana   \n",
       "\n",
       "    attendance          team_winner  winning_pts  qb_winner_1 qb_winner_2  \\\n",
       "28       72919  San Francisco 49ers           55  Joe Montana         NaN   \n",
       "\n",
       "      coach_winner      team_loser  losing_pts  qb_loser_1 qb_loser_2  \\\n",
       "28  George Seifert  Denver Broncos          10  John Elway        NaN   \n",
       "\n",
       "   coach_loser  combined_pts  difference_pts  \n",
       "28  Dan Reeves            65              45  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(super_bowls[super_bowls[\"difference_pts\"] == 1])\n",
    "display(super_bowls[super_bowls[\"difference_pts\"] >= 40])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* En düşük skor farkı 1991 yılında New York Giants takımının Buffalo Bills takımını 20 - 19 yenmesi ile **1** skor farkla oluşmuştur.\n",
    "* En yüksek skor farkı ise en düşük skor farkının yaşanmasından 1 yıl önce 1990 yılında San Francisco 49ers'ın Denver Broncos'u 55 - 10 yenmesiyle **45** skor farkla oluşmuştur.\n",
    "* Genel olarak skor farklılıkları düşüktür. Sonuçta bu bir final ve bu beklenen bir durumdur."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Skor Farkı ve TV İzlenme İlişkisi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Oyunların tv verilerine etkisini görmek için iki farklı veri setinden verileri birleştirme\n",
    "games_tv = pd.merge(tv[tv['super_bowl'] > 1], super_bowls, on='super_bowl')\n",
    "\n",
    "# seaborn import etme\n",
    "import seaborn as sns\n",
    "\n",
    "# Doğrusal regresyon modeliyle scatter oluşturma\n",
    "sns.regplot(x=\"difference_pts\", y=\"share_household\", data=games_tv); # gelişmiş scatter plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Güven aralığının yüksek olması ve aşağı doğru bir eğim olması sebebiyle yüksek skor farklılıklarının yaşanması izleyici kaybına neden olur diyebiliriz. Ama çok net yorumlar yapmak için de daha fazla veriye ihtiyacımız var."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Zaman İçinde Görüntüleme ve Reklam Sektörü\n",
    "<p>Devre arası gösterileri TV kanalları ve reklamcılar için önemlidir. 30 saniyelik bir reklam şu anda 5 milyon dolara mal oluyor, ama her zaman böyle miydi? Ve reklam maliyetinin yanında izleyici sayısı ve hane halkı puanları nasıl bir eğilim gösterdi? Bir 'Super Bowl' x eksenini paylaşan çizgi grafiklerini kullanarak bulabiliriz.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAGECAYAAAA7lVplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdd3hUZdr48e+ZlkmDJBAgEAIhEAjNCIoiSHMRKYouKOUVVBBx3UURC+CioCJFWVHZ17LA+1sXG4gV1oqCBSQiEkoEAhhCSQgppCdTznl+fwyMxiQEQsIk4f5c11yTOfU5N8PMPU85j6aUUgghhBBCNCAmXxdACCGEEKKmSYIjhBBCiAZHEhwhhBBCNDiS4AghhBCiwZEERwghhBANjiQ4QgghhGhwJMERQlwSRo4cye7du31dDCHERaLJfXCEEEII0dBIDY4QNcjlctG3b1/uvvtu77Jjx44RFxfHyJEjGTlyJDfeeCO33nor27dvr3T92LFj+eSTT7zHGDRoEO+//36583322WdMmDChynINGjSowtqLKVOmcPDgwQr3+eWXX5g6dSqDBw9m5MiRjBs3jg0bNlR6jqNHjzJt2rQqy7Js2TKeeuqpKreraZXF4Pc6duxITk5OtY7/4osv8uGHH551m5EjR5Kfn19u+cmTJ5k+fTo33nij9/1xtlgLIapm8XUBhGhIvvzySzp16sSePXs4dOgQMTExANjtdj766CPvdp988gmzZ8/miy++qHD98ePHufPOOzGbzQwZMgS73Y6/v3+Nl3f58uUVLt+1axd/+9vfeOqppxgwYAAAhw4d4oEHHiArK4uxY8eW2yctLY2UlJQaL2N98cADD1S5ze//jX9vzpw5XHPNNbzwwgsAHDx4kHHjxhEdHe19Dwkhzo8kOELUoLfffpthw4YRFRXF66+/XmlNRW5uLuHh4ZUep1WrVtx///2sXLmSIUOGcOWVV9K5c2fAU1Owbt06QkJCaNOmjXcfp9PJkiVL2LZtG7qu07lzZ+bMmUNQUJB3m6KiIu655x7i4+N55JFHGDRoEC+++CLdunUrc/4XX3yRe++915vcAMTExPDss89y55138uc//5nXXnuNxMRETp48SYcOHdi9ezcZGRlMnjyZlStX8uqrr/LVV19RWlpKSUkJM2fOZPDgwWXOs3HjRl577TWcTic5OTncfPPNTJ8+nYSEBJ5//nkiIiJISUnB39+fe+65h1WrVpGSksL111/PY489hmEYLFiwgJ07d1JUVIRSivnz59OzZ89KY7tz506eeOIJ7+v09HTat2/PW2+9VWa7d999l7fffhvDMAgJCeHxxx8nIiKC/v37s2bNGqKjowG48847uf3229mwYQMdOnRg8uTJ7Ny5k/nz51NSUoLVauXRRx+ld+/edOzYkR9++IGwsLAy58rMzKS0tBTDMDCZTLRv355XXnmFRo0aAbB27VpWr16Ny+UiLy+PKVOmMH78eO666y6GDh3KbbfdBsDLL79Mbm4ujz32WJlzvfTSS3z55ZdYrVZCQ0NZuHAhzZo1Y9asWd4yC9HgKCFEjThw4IDq0qWLysnJUTt37lTdu3dXOTk56ujRo6pTp07qpptuUjfddJMaMGCA6tKli9q0aZNSSqmjR4+q+Pj4csdLTk5Wl112WZllX375pRo2bJgqKChQLpdL3XPPPer2229XSim1bNkytWjRImUYhlJKqX/84x9q7ty5SimlBg4cqLZs2aLGjBmjXnvtNe/xBg4cqHbt2lXu3PHx8So5ObnC6+zVq5dKSkpSL730khoyZIhyuVxKKaW2bt2qhg8frpRS6tixY2rChAmqpKREKaXU+vXr1YgRI5RSSr300kvqySefVIZhqNtvv12lpKQopZQ6ceKEiouLU9nZ2Wrr1q0qLi5OJSUlKaWUmjx5shozZoxyOBwqOztbdenSRZ04cUL9/PPPatq0aUrXdaWUUq+99pqaOnVqheWu6Fp37typ+vfvrw4ePKiUUio2NlZlZ2erhIQENX78eFVcXKyUUuq7775TN9xwg1JKqfnz56vFixcrpZRKTU1V/fv3V263W82cOVOtWLFCOZ1O1adPH7Vx40allFK7d+9WI0aMULque4//R1u2bFF9+vRRvXr1Uvfee69avny5OnHihFJKqcLCQnXbbbepnJwcpZRSO3bs8L5fvvzySzVq1CillFK6rquBAweqQ4cOlbmWtLQ01aNHD+VwOJRSSq1cuVJ9+eWXSinlLbMQDVGdq8HZuXMnS5YsYdWqVZVus3DhQrZv347JZGLmzJln/bUmxMXy9ttvM3DgQEJDQwkNDSUyMpI1a9YwfPjwck1QW7Zs4a9//Ssff/wxmqZVeDxN07Db7WWW/fDDDwwePNhbKzNq1Cjv/5VNmzZRUFDAli1bAE9/oCZNmnj3feSRR7BYLEycOPGcrsftdle43Ol0esscHx+PxVL+Y6RVq1Y8++yzrFu3jtTUVG8Nyx+v79VXX2XTpk2sX7+eQ4cOoZSipKQEgMjISG+tVVRUFMHBwdhsNsLCwggMDCQvL4/LL7+cxo0b884773D06FESEhIIDAw8p+tLTU1l2rRpPPfcc+WagTZt2kRqamqZprj8/Hxyc3MZP348t99+Ow8++CCrV69m9OjRmM1m73bJycmYTCZv7VfXrl1Zt27dWcvSu3dvNm3aRGJiIj/99BMbN27kf//3f3n99dfp3r07r776Kt988w2HDx9m3759FBcXAzBw4ECeeeYZ9u3bR0ZGBpGRkbRr167MsZs3b06nTp245ZZb6NevH/369aN3797nFCMh6rM61cl4+fLlzJkzB4fDUek2+/btY8eOHbz77rs8++yzPPPMMxexhEJUrLi4mI8++ojt27czaNAgBg0aRGZmJm+88UaFicI111xDVFTUWTu97t69m9jY2HLL1e8GPv7+i9UwDB577DE++ugjPvroI959911efPFF7/q//OUvdO/eneeee67K6+nRowcJCQnllu/atQur1er9Eg0ICKhw/6SkJMaMGUNhYSF9+vQp0+n6jOLiYm655RaSkpLo3Lkzjz76KBaLxXt9NputzPYVJVKbNm1i6tSpAFx33XWMGzeuymsDyM7OZsqUKTz00EP06tWr3HrDMBg5cqQ3lh988AHvvfcejRs3Jjo6mo4dO/LVV1+xfv16br311jL7ms3mcklrcnJypQljdnY28+bNQ9M0rrjiCu69917efPNNhg0bxocffsiJEye4+eabOX78OD179mT69OllzjVmzBjWrl3Le++9V2HfKJPJxBtvvMHChQsJCQlhwYIFPPvss+cUJyHqszqV4ERFRbFs2TLv6/379zNhwgQmTJjAtGnTKCgooFmzZtjtdpxOJ4WFhRV+6AlxsZ3pE/Pdd9/x9ddf8/XXX7NhwwaKi4v57LPPym2fkpLC8ePHiYuLq/B4KSkpvPzyy0yaNKnM8n79+vHZZ5+Rn5+PYRhlaoX69u3Lm2++idPpxDAMHn/8cZ5//nnv+u7duzNv3jw+++wzvv/++7Nez0MPPcSKFSv45ptvvMsOHTrE7NmzeeCBB/Dz8yu3j9lsxuVyAbBt2za6du3KXXfdRa9evfjqq6/Qdb3M9qmpqRQWFjJ9+nQGDRpEQkKCt+znavPmzQwcOJDx48fTtWtXNmzYUO48f3SmH9Lo0aO56aabKtymb9++/Pe//+XkyZOAp3bujjvu8K4fP348zz77LN27d6d58+Zl9m3Xrh2aprF582bAk+zdcccdlV5X48aN2bJlC//5z3+8yV1JSQlHjhyhc+fO7Nmzh7CwMO677z769u3Lxo0bAbzXeWbEVVJSUrk+TuD5UThixAhiYmKYOnUqd955p9wPSFwS6lR2MGTIEI4dO+Z9/fjjj7NgwQLat2/Pu+++y4oVK5g8eTImk4mhQ4dSUFDA008/7cMSC+Hx9ttvc9ddd5WpUWnUqBETJkzg3//+N6WlpYwcOdK7zjAMnnrqKaKjozl27FiZ9SaTCT8/P2bMmFGmky9A//792b9/P6NGjaJRo0Z06tSJU6dOAXDfffexePFibrnlFnRdJy4ujlmzZpXZPywsjLlz5/LYY4+dtdmkc+fOrFy5khdffJEFCxZgNptp1KgR06ZN44Ybbqhwn/bt2+Pn58fo0aN59dVX+eKLLxg6dCiGYTBw4EDy8vIoLCz0bt+xY0cGDBjA0KFDsdlsxMbG0r59e1JTU8vV3lRm7NixPPTQQ9x444243W769OnDF1984e2sW5E33niD/fv3YzKZ+OSTT7xJxR+TxSlTpjBp0iQ0TSMoKIh//vOf3pqZgQMHMmfOnAprTGw2G8uWLfPWlFitVpYtW1bpNVksFlauXMlzzz3HqlWrCAgIQNM0brnlFkaPHk1JSQlr167lhhtuQNM0evXqRVhYGKmpqbRr144mTZrQtWtXYmJisFqt5Y7fqVMnhg4dyqhRowgICMButzNnzpxziq8Q9Vmdu9HfsWPHmDFjBmvWrKFnz57eNniXy0V0dDRxcXHs2rWLxYsXU1RUxPjx41m5cmW5X1FCCFFbduzYwZw5c1i/fn2lfagulpycHEaPHs2bb75JRESET8siRF1Sp2pw/ig6OprFixfTsmVLtm/f7h1KGRAQgNlsJjAwEJvNVq7zohBC1JaZM2fy448/snTpUp8nN2vWrOH5559n2rRpktwI8Qd1ugZnz549LF682NvW/MwzzxAVFcWTTz5JcnIyuq4zbNgw7rrrLh+XWgghhBB1SZ1LcIQQQgghLlSdGkUlhBBCCFET6kwfnMzMgmrtFxoawKlTxTVcGlEVibvvSOx9Q+LuGxJ336hPcQ8PD65web2vwbFYzFVvJGqcxN13JPa+IXH3DYm7bzSEuNf7BEcIIYQQ4o/qTBOVEEIIIWpWUanG8RwTaTlmjueYyCs2ERGqE9VUp024TpNghY/vdlBrJMERQgghGoDCUo20HBPHTyczadlmcovLNtSYNMWxbDPbDnpeB/gZtAnXiWrqeW4VpmNtIJlBA7kMIYQQou5QCnQD3Aa4dc3ztw5uQ0PXf1vuZ1W0CDEwV6PDiGFAapaZvccs7D1mIbug7EEC7QaxLd20CtNpFWbQMkwn2F9xItdEaqaZI6cfe49Z2Xt6liSzSdEy1KBFE1C6HzYL+FkVNgvYLMr7t59VERZk0CS47t5pRhIcIYQQooZk5Jr48aCVxBQrJc5za/uxWRStTzcZtQ3Xad1Ux6/8tGIAON1wMN3CL8cs7Dtupthh8h6jY0s3LU8nM62a6DTyr7j5qVWYQaswg2s6eibHzSvWPMlOlpnUTE/tz9FsgLPPCadpin6dnVzXzUld7JMsCY4QQghxAVxu2HPEwo8HraRmer5Wg+0G7VvoWMyeWhGLGSwmz9/m039bzIrCUo3UTDOHTlg4dMKzr0lTRIR6mozaNNNpEaKTmumpqTmQbsGta95zXNneSedIN+1a6FirmWQ0DlB0a+OmWxu353p0CAgOJv1EIQ63htOl4XSDw6XhdGs4Tv+9/Vcr3yT5cSDdwm3XlNKssXHhwaxBkuAIIYQQ1XAyz8S2g1Z+/vW32poOEW6ubO8iLtJ9Xs1OxQ44kmnmcKanFuVYtpnjOWa27C+7XbPGOnGRbjpHumnVxMBUCx2ErWYICQRXIwVU3gR1TScn63+y8/OvVv75aQBDL3dwdayrznRalgRHCCGEOEe6cbq25oCVlJOer9BAP4N+nZ1c2d5V7T4pAX7QKVKnU6Rn7kWXDsezPclO+ikTrcI8iU3TRnWnz4vdCqN7l9KplZsPEuys+8nO/uMW/ty7lEb+vi+nJDhCCCHEOcjINfHuD3bScjxtQe2au+nVwUXnSHeN90GxmqFtM522zfSaPXAt6BrlJqppEe9ttZOcbuGl/wZwcy8HXaPcPi2XJDhCCCHEWRgGbN5n5cudfrgNjcujXQzs6qhTtSm+1ihAcefAErYmW/l0hx9vfedPj3YuRlxRir2SDtO1TRIcIYQQohI5BRprf7BzONNCoN1g3FUlxEXW/VoVX9A06N3RRUwLnTVbPH1zUk6amTSo2CfDyc+pC9TOnTuZMGFCueVff/01o0aNYsyYMaxZswYAwzB44oknGDNmDBMmTCA1NbVmSyyEEELUMqUg4YCVlz4J5HCmhS6tXTwwvFiSm3PQrLHBvdcXM6CLg7wizdukd7FVWYOzfPlyPv74Y/z9/cssd7lcLFy4kLVr1+Lv78+4ceMYOHAgO3bswOl0snr1ahITE1m0aBGvvPJKrV2AEEIIUZPyijU+ON2fxG5T3HZNCZe1ddeZ0UH1gcUM18c7GeTDe+RUWYMTFRXFsmXLyi0/dOgQUVFRNG7cGJvNRs+ePfnpp5/Yvn071157LQDx8fHs2bOn5ksthBBC1DClYOdhCy/9N5DkdAsdItw8MLyI+GhJbqrLlzcArLIGZ8iQIRw7dqzc8sLCQoKDg72vAwMDKSwspLCwkKCgIO9ys9mM2+3GYjn7qUJDA6o9PXt4eHDVG4kaJ3H3HYm9b0jcfaOm417igJxCOFUAOQVwqtDznHEKDqWDzQL/Mwj6d7OgaUFVH7CBqu/v92p3Mg4KCqKoqMj7uqioiODg4HLLDcOoMrkBOHWquFrlCA8PJjOzoFr7iuqTuPuOxN43JO6+caFxd7hg9xELvxy1klOokVdswuGqvDomurmbP19VSpNgRVZWtU9b79Wn93tliVi1E5yYmBhSU1PJzc0lICCAn376icmTJ6NpGhs3bmTYsGEkJiYSGxtb7UILIYQQ58tQkJJh5udfrew5YsF1emoDu1UREmDQOFDROMCgcYAiJNDzfOZ1Q5lJW1QjwVm3bh3FxcWMGTOGWbNmMXnyZJRSjBo1iubNmzN48GA2b97M2LFjUUqxYMGC2ii3EEIIUUZOocbPv1rZ8auVU0WeLqZhQQY92jm5PNpFaJDct+ZSoiml6sS/eHWrwupTNVpDInH3HYn9xXUky8SHCXa6tDVzdUwhgfY68ZF5yajq/V7ihL3HLGz/1UpKhuc3u82i6Bblpkc7F22b6dJBuBrq0+dMjTdRCSEurhO5Jnb8aqV7e4gIplYm2RNlHTphZtU3/jjdGicS4fukQAZ0cXJNR6c0ZfiAbnj+HxzN8kxGeTTLRGb+b4NTopu76dnORZfWbvx8dPdcUXfIf1Eh6jinG77ebeP7vTYMpfHdXmjWOIABXZx0a3N+MxY3BC4dfj1hxmKGILsiyK7w91M1nvDtPWbm7e/8UcC4viUosz8fb1V8nujH1mQrgy9zEN/WjekSi//FlFeskZoMSSl+HM0ykZZj9vanAfCzKNo1dxPdXOfyti7CfHC3XFF3SYIjRB22/7iZj7fZOVVkIjTQ4LrupRzL9efHfSbWbPHny10G/eKc9IhxYfXh/SYuFqcbXt/o753F+QxNUwT6KW/CE3j6uVMrNzEtzv/Os4kpFtb+YMdsgon9S2gfoRMeDh2aFfFNko0t+2ys/cGfzft0brjcQYeIs59DNyAz30T6KRMFJRqXtXXTOKDhfBnrBhSUaKcfJhoFGLQKM6rVNGQYsD/NTEKyjQPpZjxRsqFpihYhBpFNdFo31WndxCC8kSEJpqiU9MER1SJxr135xRrrt/ux54gVk6a4Ns7JwG5ObBZP7PenFPLdLza2H7LiNjSC7QZ945z06uBqsFXzTje8vsmflAwLHVu6adVEp7BUo6hUo7BUo7DURFGpRukfhgDHRboY1sNxznPhbE22sm6bH342uHNAMVHhBlD2PZ9bpLFhlx87frWg0OgQ4eaGyx1EhBo4XHDilJm0U56EJv2UmYxcE27jt3JZzYq+cU76dXZW+9/L4YK8YhPhjaqXSJyPwlKNzDwTWQUm8oo9iUx+sYn8Eo38Eo3iUg1F2UKEBBp0jXLTNcpF6yZVl7GgRGP7ISs/HrCSW+zJWqKa6lzZyUyovZhWYXqDfW/XRfXpM76yPjiS4IhqkbjXDsOArQesfJnoh8OtEdVU5+arSmkRYni3+X3sC0o0Nu+zkpBsw+HW8Lcpenf09BEJ8PPVVdQ8pxv+s8mfXzM8cwKN7VtaadOcS4eiUo3sAhNf7bJxONOC2eRJKAZ0OXtC8U2Sjc8T/Qi0G0waVEJEaMVxPyMtx8RnO/w4eMKChiIkSJFbWPbL3mxSNA8xiAjVaXn6eN8k2cgvMRFoN/hTdydXxLjOuakxI8/E1v1WdqRYcbo1mgQbxEe7LriJRinIL9E4mWciM8/EyTOPfBPFjooLZzUrGgUoGvkbBPt7/g6yG5w4ZWbvMQsOtycOjQNOJzut3bQO173NiUrB4UwzCclWko5a0A0Nm0UR39bFVbEuIkIN+azxkfoUd0lwRI2SuNe84zme0TrHc8zYbYqhlzvoGeMq17ekotiXOOCHZBtb9lspdpjwtyluqGT/+sbphlXf+HPohIXOrV2MO0ty80dKwe5UC5/u8COv2ESwv8EN8Q4ui3aXiYtS8EWijW9+8aNxgMGk64oJb1T2o/Fs7/kD6Wa+SPTjVJFGixCDlqGehCYi1CC8sVGuvE43fL/Xxre/2HC6NcIb6Qzt4aBjy4pH/OiGZ6TQ1mQrv54eKdQ4wKBVE50Dab/d56VNuJse7Ty1Jv62s8cmr1jjaJb59MNEeq653A3wNBShQYpmjQ2aNdYJb+S5h0wjf09S42el0poZlw4H083sOWJl7zGLt2atkb9B59ZuwoIMth+ykpHnaVtt1ljnqg4uLo92Yf9d2eWzxjfqU9wlwRE1qqHHPTHFQrFDo1Okm7BavneGS4cNO/34fp8VpTTi27oY1tNBUCXDkc8We6fbMwPy17s8NUBtwt3c3MtB89/VANUnrtPJzcETFuIiPclNdWZ0cbrh2188CYVb12jdVOfGK0qJbGJgKFi3zY+EAzaaBBtMGlRc4f1SauM9X1CisWGXjZ8Oef7t2zV3M7SHg1Zhhnf9toOeZpv8Ek+W1K65m6tjXcRFejqYl7og6YiFHSlWUjLMKDQsJkWnSDeXR7uIbaljGHA8x5PIHM32JDV5xb9lXZqmCG9knE5kDO/fTYONGhkt5tY9I9L2HPXcUbjE6Ul2TJqiS2s3V8W6iK5kOHdD/6ypq+pT3CXBETWqIcf90AkzK78K8L5uEaLTqZWbzq3dtAwzarRGJP2UiXe32DmRa6ZJsMHIK0tpX0WH1XOJfV6xxvqf/Eg6eroPT2cnA7t6+vDUFzWV3PzeqUKNT3d4+jYB9GjnwjAg8bCVFiE6dw0qIdj//BPLC5WR62nq2p/m+QeKb+tCAXuOeJpt/CyKy9t5mm2aN648Wc0t0th52MqOFAsnT9eM2K0KpxsM9dsbN8hu0LqpTlRTz/PF7N+iG/BrhpnsAhNdWrsrjfcZDfmzpi6rT3GXBEfUqIYad4cLXvxvIPnFGtd1d3Ik08yhE2ZvB9FG/gadWrmJi3TTroVe7ZFLhoLNe618sdMP3dDo1cHJsB6Oc0pAzif2+455RmHlFpsIDfIkULEtz39U0cXm0uGNb/w5kG6hUys3468tqdFZiX/NMLP+Jz9O5HoO2rqJzh0Di8/ab+livOcPppv5dIcf6ad+a7a5OtbTbHM+CYhSnv5BO1Ks7D1uIdiuTo888oxACglU9ebmdw31s6auq09xlwRH1KiGGvcPf/TjxwM2BnRxcH28E/AkPQdPWNh7zMK+42Zvh0ubRdEhwk18tJuOLd3n/AWcW6Tx7g92UjIsBNkN/nx1KZ1anXvScb6xd7rhq11+bN5nxVAa3dq4GN7TQaMKfjnrhmcEV06hidwiz8SEMS3ctAm/eE1cLh3e/Maf5HTPaKn/6Vezyc0ZugE/HbJyMs/E9Zc5qkwgLtZ73jAgOd2MnwW5Cy8N97OmrqtPcZc7GQtRhYMnzPx4wEbzxjqDujm9y/2s0KW1my6t3RgGHMnyjBD55ZiFpKNWko5a8bcpurdxcXm7yofEKgU7D1v4eJudUpdG50gXN19VeV+bmmKzwNAeDuKjXXyYYGd3qpXkNAv9OjtRCk4VaZwqNHGqyERekVamKQPgq102+nRyMTjeUev32nHp8Oa3tZ/cAJhNcFUHV+0c/AKYTJxXwiuEqJgkOMKnHC44lm2mTbhea19k51qO97faMWmK0b0r7+thMnl+Vbdt5hn1kn7K0wywM8VCwgGbt6Pq5dGuMpP7FTvgo22e5MJmUfz56hJ6tnNf1F/nEaEGU4cUs+2Alc8T/fhyZ9n2mGB/g8gmBqFBBqGBBmFBCptV8eVOP77fZ2N/mplbr/F0zD0fugG7Dlv48aBnWLNSniY6pUABSnmWATjcUFRqIjbCzfhaTG6EEA2fJDjCZ5SCt77z9LPwtykua+uiRztXte+AeiE+/dmP3CITA7o4aHUeX+ARoQYRoQ6GxDs4dMLMz79a+eWYhQ27/Niwy4+2zdx0bKnzw37PKJiopjq3XVPis1vKmzS4KtZF59ZuDqSbCfTzDAMODax8tEynVm4+2+HH1mQbr34ewICung7LVQ3VPpPYfL3Hj+wCE5qmPMOK8Yza8Tzz27MGFpOng+0tV5deEndmFkLUnioTHMMwmDdvHvv378dmszF//nzatGkDQGZmJjNmzPBuu3fvXh566CHGjRvHzTffTHCwp10sMjKShQsX1tIliPpq20ErB9ItNGusU+zQ2JpsY2uyjfBGOj3auYmPdp3X7ezduudL8nznZjqYbubHg+Wbps6H2QSxLXViW+reYbs/p3hmNz580oJJU1x/mYN+nZ114tbywf6KHu3c57StzQI3Xemgc2s37/1g5+vdfuw/bmH0NaUVjuj5Y2JjNil6tXfSv4uzwuHXQghRG6pMcDZs2IDT6WT16tUkJiayaNEiXnnlFQDCw8NZtWoVADt27GDp0qXcdtttOBwOAO86If7oVKHGJz/7Ybcq7hpUQpBdcSDdUwOy95iFzxP9+GKnjfYtdC6P9tQ4WM2eW8afKvR0gv3t4elDkl+sEeCnuPkqB11an9uXd+k5Nk2dD7sVesa46Rnj5lShRnKahahwvcxdceuj9i10HhhexPrtdn7+1cr/fhLA4Htn4BgAACAASURBVHgHfTq6MJkksRFC1C1VJjjbt2/n2muvBSA+Pp49e/aU20YpxdNPP82SJUswm83s2bOHkpISJk2ahNvtZsaMGcTHx9d86UW9ZCh4b6sdp1tjdO8Sby1Np1Y6nVrplDhgV6rnVvQH0i0cSLdgsyiUosxMwmdoeG4R3yZc51iOmTe/9eeKGCfDe1Y9MubTn/3ILTYxsOv5NU2dq9AgxVWxda8ja3XZbTC6dymdI9188KMfn/5sZ+9RC5e1dfP9PpskNkKIOqPKBKewsJCgoCDva7PZjNvtxmL5bdevv/6aDh060K5dOwDsdjuTJ0/m1ltv5fDhw0yZMoXPPvuszD5/FBoagKWaP58rGyImald1475pJ/yaAd2j4fpe/hX2t4mKhBF94MQp+GEv/HxAw2qB8MbQtDGENzr93BjCgjWsFg0wkZYNKz6Dnw7ZSM2yMXkIxLSsuBy/pMK2g9CqKdw20A+Luf5M3uTr93z/cOjRCd74Gn4+aDk93xP06wbDrtRo0sgGVDFXQD3k67hfqiTuvlHf415lghMUFERRUZH3tWEY5RKVjz/+mIkTJ3pfR0dH06ZNGzRNIzo6mpCQEDIzM4mIiKj0PKdOFVen/PVqrH5DUt245xRqvPtdIP42GBZfRFbW2X/hm4G+HTyPCrkh99RvL63AlOtgwy4b3/1iY/G7MLCCTrGlLvi/zwMxaRo3X1nMqZz603xUl97zo3pBh+YW0nLMXB3rqbExHJCZ6euS1by6FPdLicTdN+pT3CtLxKrs7tijRw++/fZbABITE4mNjS23TVJSEj169PC+Xrt2LYsWLQIgIyODwsJCwsPDq1Vw0XD8vmlqxBWlNDqPDsTnw2KGGy53MvlPJTT2V3y924/XvgggK/+3qqJPf/ZMvti/i9M77484f5oGl7X1zJ8kzVFCiLqkyhqcwYMHs3nzZsaOHYtSigULFrBu3TqKi4sZM2YMOTk5BAYGov2unWH06NHMnj2bcePGoWkaCxYsOGvzlLg0JCR7RhXFRbqIb3tunYAvRLvmOvcPL+LjbXYSD1tZ9kkgw3s6CA0y2HbQRosQnYFdqzdqSgghRN0mUzWIajnfuGcXaLz030AsZpg+oqjKCfZq2s7DFj7aZqfUqWE2eTos33dDMS3rYe2NvOd9Q+LuGxJ336hPca92E5UQF+pM05RL17jpytKLntyApxnl/mFFtGvuRjc0BnR11svkRgghxLmRdiNR67but3L4pIUurV10b1P7TVOVCQlUTLquhMw8E80quEGdEEKIhkMSHFGrsvI1Pk/0I8DPYGQvh89nRjZp0DxEkhshhGjopIlK1BrD+H3TVO3Pmi2EEEKcIQmOqBVKwfd7baRmWuga5dumKSGEEJceaaISNaqwVCMxxcJPh6yczDMT6Gdw05UOXxdLCCHEJUYSHHHBdAMOpJn56Vcr+45ZMJRnKHbXKBcDujilaUoIIcRFJwmOqLasfI3th6z8nGKloMTT2tkiROeKGBeXtXUTKImNEEIIH5EER1TJUJBXpJFdYCKrwER2gYkTeXAo3TMJq92muDrWSc8YFy1DDZ+PlBJCCCEkwanDSp3wwY92NKBjSzexLfVarxXJzNdIPWkhq0Ajq8BEVr6JnEITbr1s1qIB7Vu46RnjonNrN9bqTQQvhBBC1ApJcOqowlKNf3/tT9opT+awK9WKhiKyqUHHlm46tXITUUO1JQ4X7E618tMhK0eyymYqfhZF88YGTYINmjYyaBrs+btTdCBFBSUXfnIhhBCiFkiCUwflFmn831cBZBWYuLK9k96xLvanWdh/3MyRLDNHs/zYsMuPRv4GHVu56dhSJ6aFGz/ruZ9DKTicaWb7ISu7Uy24dA0NRYcIN12j3N5kJsiuKkyiAuxQVD+mKRFCCHEJkgSnjsnK9yQ3ucUm+nV2MCTeiaZBi1An/btAiQOS0y3sP24hOd3MtoM2th0EDUXjAEVY8OlalkYGTYIUTRoZhAUZ3iak/GKNHSme2prsAk/H4NAgg57tnPRo5yIkUDoGCyGEqP+qTHAMw2DevHns378fm83G/PnzadOmjXf9//t//4+1a9cSFhYGwJNPPknbtm3Puo+oWFqOif+30Z+iUhND4h307+Ist42/n2fiyMvaujEMOJZtYt9xC6lZZrLzTfyaYeHXjLL7nEl+gvwVx3NMKKVhMSvi27roGeMiurmOSToGCyGEaECqTHA2bNiA0+lk9erVJCYmsmjRIl555RXv+qSkJBYvXkzXrl29y7744ouz7iPKSz1p5vVN/jhccNOVpVwd66pyH5MJosINosJ/S4Scbsj53Win7ALt9LOJY9lmWoXp9IxxcVlbF/622rwiIYQQwneqTHC2b9/OtddeC0B8fDx79uwpsz4pKYl//etfZGZmMmDAAKZOnVrlPqKs5DQzb37rj27ArdeUEh9d/WkNbBZoEWrQIrT8hJK6AWaZnEMIIcQloMoEp7CwkKCgIO9rs9mM2+3GYvHsOnz4cMaPH09QUBB/+9vf2LhxY5X7VCQ0NACLpXpjjcPDg6u1X12w/QCs+sYzy/V9N8Jl7fx9XaRzVp/jXt9J7H1D4u4bEnffqO9xrzLBCQoKoqioyPvaMAxvoqKU4o477iA42BOE/v3788svv5x1n8qcOlVcrQsIDw8mM7N+Duf56ZCFDxLsWM0wcUAJLYN1MjN9XapzU5/jXt9J7H1D4u4bEnffqE9xrywRq7LBokePHnz77bcAJCYmEhsb611XWFjIiBEjKCoqQilFQkICXbt2Pes+lxqlPPe0STlpZttBK59s9+P1jf4s+SiQ97f6Y7fC3X8qpl1z3ddFFUIIIRqMKmtwBg8ezObNmxk7dixKKRYsWMC6desoLi5mzJgxPPjgg0ycOBGbzUbv3r3p378/hmGU2+dSoBtw4pSJ1Ewz6bkmMvPMZOabKHGWH6IUaDdo38LN8J4OmoeU7y8jhBBCiOrTlFJ14sYn1a0K82U1WqkTjmSZSc30PI5lm3G6f0tmTJoiLEgR3lgnvJHhfTRtZBDg55Mi15j6VH3Z0EjsfUPi7hsSd9+oT3GvrIlKbvR3jpSCU0UaRzJ/S2gyck0ofktomjXWaRPuebRqYtAkyKCa/aaFEEIIcQEkwamEyw3Hc8wcyTJxJMvMkUwzhaW/dVmymBVtmv2W0EQ11et9rYwQQgjRUEiCc5rTDfuOWzh6uskp/ZQJ3fitdqaRv0HXKBdRTT0JTUSo1M4IIYQQdZUkOHhm016xIYDjOZ6MxaQpWoYZRDX11MxEhes0Dqh40kkhhBBC1D2XfILj1mHVN/4czzHTvY2Lq2JdRIbpWC/5yAghhBD11yX9NW4YsHqznV8zLHSOdHHrNaUylYEQQgjRAFyyX+dKwcfb/Eg6aiW6mZsxfSW5EUIIIRqKS/YrfcMuGz8etBERqjOhfwlW6TAshBBCNBiXZIKzZZ+VjXv8CAsyuHNgCXabr0skhBBCiJp0ySU4iSkW1m+3E2w3mDSomGD/OnEjZyGEEELUoEsqwUlOM7P2Bzt2q+LOQSWEBUtyI4QQQjREl0yCcyTLxJvf+mMywYQBJUSEygSXQgghREN1SSQ4GXkmXt8YgG7AuL4lRDfTfV0kIYQQQtSiKu+DYxgG8+bNY//+/dhsNubPn0+bNm2869evX8/rr7+O2WwmNjaWefPmYTKZuPnmmwkO9szwGRkZycKFC2vvKs5afvjPJn9KnBqjri4hLlKSGyGEEKKhqzLB2bBhA06nk9WrV5OYmMiiRYt45ZVXACgtLeWFF15g3bp1+Pv7M2PGDDZu3Ejfvn0BWLVqVe2W/hw43XCq0ESHCDc9Y9y+Lo4QQgghLoIqE5zt27dz7bXXAhAfH8+ePXu862w2G++88w7+/v4AuN1u/Pz82LdvHyUlJUyaNAm3282MGTOIj48/63lCQwOwVHP2yvDw4ErXlTg8z4H+lrNuJ86fxNN3JPa+IXH3DYm7b9T3uFeZ4BQWFhIUFOR9bTabcbvdWCwWTCYTTZs2BTy1NcXFxfTp04fk5GQmT57MrbfeyuHDh5kyZQqfffYZFkvlpzt1qrhaFxAeHkxmZsFZt+nf2UZ0c53MTGmeqinnEndROyT2viFx9w2Ju2/Up7hXlohVmeAEBQVRVFTkfW0YRplExTAMnnvuOVJSUli2bBmaphEdHU2bNm28f4eEhJCZmUlEREQNXMr5G3K50yfnFUIIIYRvVDmKqkePHnz77bcAJCYmEhsbW2b9E088gcPh4OWXX/Y2Va1du5ZFixYBkJGRQWFhIeHh4TVddiGEEEKIClVZgzN48GA2b97M2LFjUUqxYMEC1q1bR3FxMV27dmXt2rVcccUV3HHHHQBMnDiR0aNHM3v2bMaNG4emaSxYsOCszVNCCCGEEDVJU0rJ7XyFEEII0aBcEjf6E0IIIcSlRRIcIYQQQjQ4kuAIIYQQosGRBEcIIYQQDY4kOEIIIYRocCTBEUIIIUSDU29vTlPVLOei5u3cuZMlS5awatUqUlNTmTVrFpqm0aFDB+bOnYvJJPlyTXK5XDz22GMcP34cp9PJX/7yF9q3by9xr2W6rjNnzhxSUlIwm80sXLgQpZTE/SLJzs7mz3/+M//3f/+HxWKRuF8kN998M8HBnikPIiMjuffee+t97OtXaX/n97OcP/TQQ947J4vasXz5cubMmYPD4Zm9dOHChUyfPp233noLpRRfffWVj0vY8Hz88ceEhITw1ltvsXz5cp5++mmJ+0WwceNGAN555x3uv/9+Fi5cKHG/SFwuF0888QR2ux2Qz5mL5czn+qpVq1i1alWDec/X2wTnbLOci5oXFRXFsmXLvK+TkpLo1asXAP369WPLli2+KlqDdcMNN/DAAw94X5vNZon7RfCnP/2Jp59+GoC0tDSaNm0qcb9IFi9ezNixY2nWrBkgnzMXy759+ygpKWHSpElMnDiRxMTEBhH7epvgVDbLuagdQ4YMKTPdhlIKTdMACAwMpKCgfsw6W58EBgYSFBREYWEh999/P9OnT5e4XyQWi4WZM2fy9NNPM2TIEIn7RfD+++8TFhbm/eEK8jlzsdjtdiZPnszKlSt58sknefjhhxtE7OttglPVLOeidv2+LbaoqIhGjRr5sDQNV3p6OhMnTmTkyJHceOONEveLaPHixXz++ec8/vjj3ip8kLjXlvfee48tW7YwYcIE9u7dy8yZM8nJyfGul7jXnujoaG666SY0TSM6OpqQkBCys7O96+tr7OttglPVLOeidnXu3JmEhAQAvv32W6644gofl6jhycrKYtKkSTzyyCOMHj0akLhfDB9++CGvvfYaAP7+/miaRteuXSXutezNN9/kjTfeYNWqVcTFxbF48WL69esncb8I1q5d6+3HmpGRQWFhIX369Kn3sa+3k22eGUWVnJzsneU8JibG18Vq0I4dO8aMGTNYs2YNKSkpPP7447hcLtq1a8f8+fMxm82+LmKDMn/+fD799FPatWvnXfb3v/+d+fPnS9xrUXFxMbNnzyYrKwu3282UKVOIiYmR9/tFNGHCBObNm4fJZJK4XwROp5PZs2eTlpaGpmk8/PDDhIaG1vvY19sERwghhBCiMvW2iUoIIYQQojKS4AghhBCiwZEERwghhBANjiQ4QgghhGhwJMERQgghRIMjCY4QQgghGhxJcIQQQgjR4EiCI4QQQogGRxIcIYQQQjQ4kuAIIYQQosGRBEcIIYQQDY4kOEIIIYRocCTBEcIHOnbsSE5OTpll77//PlOnTvVRiapWUlLC0qVLGT58OMOHD2fQoEE88sgjnDx50rvNwYMHmTJlCgAjR44kPz//gs6ZkJBA9+7dGTlyJCNHjuTGG29k4sSJbNmy5Zz2dzqdPPjgg4waNYp77rmHrKys8y7DrFmzWLly5XnvV5mpU6fy/vvvAzUTIyFExSy+LoAQou7TdZ27776b9u3b8+677xIQEIBhGKxYsYIpU6bw4Ycfomka7du3Z/ny5QB89NFHNXLuqKioMsfat28fkydP5uWXX+ayyy476742m42lS5fWSDlqQ03FSAhRniQ4QtRBKSkpPPXUUxQVFZGZmUmnTp144YUX8PPzo1u3btxzzz1s3ryZkydPcvfddzN+/HgA3n33Xd5++20MwyAkJITHH3+ciIgI+vfvz5o1a4iOjgbgzjvv5Pbbb6dfv34sWbKEbdu2oes6nTt3Zs6cOQQFBZUpz4YNG8jPz2fu3LmYTJ6KX5PJxD333ANAUVERQUFBbNiwgX/+858YhkFgYCCzZ8+me/fuLFu2jMTERE6ePEnHjh1ZsmQJr7zyCl988QWGYdCqVSvmzp1L8+bNq4xNp06dmDBhAv/+979ZunQpBQUFPPPMMyQnJ+NyuejduzePPvooFouFb775hiVLlmAymYiLi2PLli289dZbREZGVhirmJgYZs2aRW5uLkePHmXAgAFlzn3o0CGeeeYZcnNz0XWdCRMmMHr0aBISEli6dCmtW7fmwIEDuN1unnzySXr27ElGRgazZs3i5MmTtGzZkuzsbO/xOnbsyA8//MCmTZtYu3YtJSUlBAUFccstt3hjk5aWRvPmzbntttt44403OHz4MHfddReTJk2q9N88Jiamem88IRoSJYS46GJjY9WIESPUTTfd5H30799f3XPPPUoppRYtWqQ+/PBDpZRSTqdTjRgxQn322WfefVetWqWUUmr37t2qa9euqrS0VCUkJKjx48er4uJipZRS3333nbrhhhuUUkrNnz9fLV68WCmlVGpqqurfv79yu91q2bJlatGiRcowDKWUUv/4xz/U3Llzy5V3/vz5auHChWe9poMHD6prrrlGHTlyRCml1JYtW1SfPn1UQUGBeumll9SQIUOUy+VSSin1wQcfqOnTp3tfv/POO+ruu+8ud8ytW7eq4cOHl1u+ceNGNWzYMKWUUrNmzVL/+c9/lFJKud1u9fDDD6t//etfKicnR/Xq1Uvt3btXKaXU+++/r2JjY9XRo0fPGquZM2eqO+64w3uumTNnqhUrViiXy6WGDRum9uzZo5RSKj8/Xw0dOlTt2LFDbd26VcXFxalffvlFKaXUypUr1f/8z/8opZS677771NKlS5VSSh0+fFjFx8er9957Tynl+bfMzs5W7733nrryyitVQUGBUkqp9957T/Xs2VOlpaUpXdfVsGHD1LRp05Su62rv3r2qW7duStf1s16HEJc6qcERwkdef/11wsLCvK/ff/99Pv/8cwAeeeQRNm/ezPLlyzl8+DAnT56kuLjYu+11110HQJcuXXA6nRQXF7Np0yZSU1MZO3asd7v8/Hxyc3MZP348t99+Ow8++CCrV69m9OjRmM1mNm3aREFBgbdPi8vlokmTJuXKqpRC0zTv661bt7Jw4UIA8vLymDt3LmlpaVx99dW0bt0agN69exMWFsaePXsAiI+Px2LxfORs3LiR3bt3M2rUKAAMw6CkpOScY6dpGna7HYBNmzaxe/du1q5dC0BpaSkAP/30EzExMXTq1AmAW265hfnz53v3qSxWAD179ix3zsOHD3PkyBEee+wx77LS0lJ++eUXYmJiaNmyJXFxcQB07tyZDz74AIAtW7Ywc+ZMANq0acNVV11V4TV17NixTM1Zt27diIiIACAyMpK+fftiMplo3bo1DoeDkpKSs15HSEjIuQVTiAZKEhwh6qAZM2ag6zpDhw5lwIABpKeno5Tyrvfz8wPwJh1KKQzDYOTIkTzyyCOAJ2k4efIkjRs3JiQkhI4dO/LVV1+xfv161qxZ493mscceo3///oCnqcnhcJQrT48ePcp0tL366qu9/UcmTJiAw+HAMIwySdCZcrndbgACAgK8yw3DKNO05nQ6ycvLO+f47N69m9jYWO+xXnzxRW+zTH5+PpqmsW3btjIx+328zharP5b1DF3XCQ4OLtNvJisri+DgYBITE70J15nznDn37/8GvEneH/3xnDabrczrivar6jqEuJTJKCoh6qDvv/+ev/71rwwbNgyAnTt3ouv6Wffp27cv//3vf72jmt5++23uuOMO7/rx48fz7LPP0r17d29fl759+/Lmm2/idDoxDIPHH3+c559/vtyxr7/+egICAnjmmWcoKiryLt+5cydHjx7FbDbTu3dvvv/+e44ePQrADz/8QHp6eoUdgfv27cvatWspLCwE4MUXX+TRRx89p9js2rWrzLX17duXf//73yilcDqd/OUvf+GNN96gR48eHD58mL179wLwySefUFBQgKZpVcaqItHR0djtdm+Ck56ezogRI7w1VJW59tprWb16NQBpaWkkJCSc03Wei+pchxCXCqnBEaIOevDBB/nrX/9KQEAAQUFBXHnllRw5cuSs+/Tt25cpU6YwadIkNE0jKCiIf/7zn95ai4EDBzJnzpwyzRn33Xcfixcv5pZbbkHXdeLi4pg1a1a5Y1ssFlasWMGKFSu4/fbbMQyDvLw8oqOjefTRR/nTn/6EpmnMnTuXv/3tb+i6jt1u59VXXyU4OLjc8W699VYyMjK47bbb0DSNiIgIFi1aVOF1HTlyhJEjRwKejs1BQUEsWbLE2/T097//nWeeeYYbb7wRl8vFNddcw913343VauX5559n5syZKKW47LLLsFgs+Pv7VxmrithsNl5++WWeeeYZVqxYgdvt5oEHHqBnz55nTVrmzp3L7NmzGTp0KC1atPCWuyZU5zqEuFRo6o91uEKIBmnHjh3MmTOH9evXXxJfgIWFhbz88stMmzYNf39/kpKSmDp1Kt99990lcf1CXOqkBkeIS8DMmTP58ccfWbp06SXz5R4UFITVamX06NFYLBYsFgsvvPDCJXP9QlzqpAZHCCGEEA2OdDIWQgghRINTI01UN998s7cjYWRkJPfeey+zZs1C0zQ6dOhQ5u6nQgghhBC17YITnDP3zFi1apV32b333sv06dO56qqreOKJJ/jqq68YPHjwWY+TmVlQrfOHhgZw6lRx1RuKGiVx9x2JvW9I3H1D4u4b9Snu4eHlR2pCDTRR7du3j5KSEiZNmsTEiRNJTEwkKSmJXr16AdCvX79znvm3OiwWc60dW1RO4u47EnvfkLj7hsTdNxpC3C+4BsdutzN58mRuvfVWDh8+zJQpU8rc1j0wMJCCgqprZ0JDA6od0MqyN1G7JO6+I7H3DYm7b0jcfaO+x/2CE5zo6GjatGmDpmlER0cTEhJCUlKSd31RURGNGjWq8jjVrQoLDw+udvOWqL6GHHelFI7lmzGO52Jq2djziPA8tObBaD7+ZdOQY1+XSdx9Q+LuG/Up7pUlYhec4Kxdu5bk5GTmzZtHRkYGhYWF9OnTh4SEBK666iq+/fZbrr766gs9jRAXjfM/CTj/U8mdac0aWnjw6YSnEabopthu6oYW6HdxCymEEOKsLvg+OE6nk9mzZ5OWloamaTz88MOEhoby+OOP43K5aNeuHfPnz8dsPvuv3upmivUpy2xIGmrcXd8dpGT2R2jNgwlYfAuqoBQjPQ8jPQ+Vlo+RlouRno/KKvTuo4UG4DelD9bhXdHMtTNaUOkG7k3JuL7YS5MxV1DSo3WtnEdUrqG+5+s6ibtv1Ke4V1aDU2du9CcJTv3SEOOu/5pF0dS3QCkCXxmHuUOzSrdVDjdGRj7ur5NxvPkjlLgwtQ/Hfv9ALDWYfCi3gfvr/The34qRmuNdbhnQAfuD12FqElhj5xJn1xDf8/WBxN036lPca62JSogLpYqdoGlgNXuagHxwK30jr4TimR9CiQv/p0acNbkB0PwsmKPCMN95NdYRXXG89j2uT5Movn8Nln7tsf+1P6ZWIdUuj3IbuL7ci/P1BIxjp8CsYR3eFeufOmG88SOlmw5QuP0o9mn9sQ7tItMPCCEqpBwu9OST6HszMPadwDiRjzm2GeZurTBf1gpT0yBfF7HWSA2OqJaaiLuRW0zpoi9wf3/ot4UaYLWA1YRmNYPNDBYzms2CqXUo5rgWmOOaY+7YAq2R/cIu4jTl1ime8R76z0ex3XU19sl9qnUcfd8JSl/ciL47DaxmbLf2wO+Oq86rf45y67g+34vjPwmo47lgNmEd1gW/CVdhatkYgKZNAkn71/eUvvodlLgw92qD/yODMUU0rla5xbmRzxrfaIhxV4bCOJSJ++ejqFPFmKKbYG4fjqlN2AUNYlBuHSMlG33vCe/DSMkCvfKveS2iMZburTB3a4m5eytMbZugmbR6FXdpohI16kLj7t5+hJKnPkFlF2GKbYapSSDKqYNLR7k8z7h0zzK3jipxQaGjzDFMkSGY4lp4kp5OLTDHNkOzW8+7LCXPf4Xr/UQs/drjP/8mNFP1a0OUUri/3k/py9+iMgq8/XMsvdpWua/7x8M4ViWg0vPBYsI6oht+t/fC1KLsKMQzsTdO5FPy3JfoCYfB34p9al+sf778gsovKiefNb7REOKulMI4lou+/Qju7UfQdxxF5ZaU39BiwtT2dLITE+55bh+OKTQApRQqrwSVWYiRVYjKKjz9dxEqqxAjswAj9RQ43b8dz2bB3LGZ5/MxrgWmuBaYmgWjJ2eg7zrueexOQ+WX/rZPsB1Lt5aE/KkTpVdEYQqr+83gkuCIGlXduCu3jmPlDzjfSACTCb97+mAbd2WVX8pKKVRmoedXyb7Tv072ZZRNeswa5i4tsd3eC0vv6HNqtnF+tIvS577EFNOUwFfGoQXYzvuaKiyvw4Xz7e043kiAUnfVO5xhM2O9sRt+46/E1Lzi2yv8PvZKKU+Nz0sbUfmlmLu1xD7resxtmtTEZYjTVJGD8DZNyPpd53JxcdTXz3gjswD39qPepEad/O0atGZBWHq2wdyjNabmwRi/ZqEfykI/mInxaxY4yn5maI3tqGKX54dfZfytmFqFnK7lPp3QRDepskZIGQojNed0snMc967j0c8HngAAIABJREFUqLQ8z0qThjk+Euugjlj6d8AUGlDteNQmSXBEjapO3I20PEqe/C96UjpaRGP85w3H0iWi2mVQhsI4notxJun5JR19TzoApo7N8bvraix9YipNdNyJxyh+4F20IBuBy2/3NgHVJCOrEOc7P6FOVfBr7Q+0ZkHYRl1eZZt4RbE3coooXfo17o3JYDXj95d++N3W44LK/v/bu/P4qKr7/+OvO3fWbCQhAdkigbCKFFFZVEBBjVoQRLRYxVrU/sQFUaGAsimpkNa2Wuq+1BpBoIKI/bojLSoWBQQBAWWRLSEkIWSZmczMXX5/TBiIrIFMJjP5PB8PHsnM3Ln3zMdx5p1zzz1HBAU+34Z3ynu4+rRFnXBVTI9ZaIii5TPeKPOif7sHbfVu9LW7MXaXhh5TmjhRe2ZgvTAD9cIMLK2TT/i5ZOpG8HNtW1Ew8GwvwthdipLoREmLR0lPwJKWgJJW/TM9AUt6AsTZ62wsnlFYjmPtHg4t/S54yh2Cf0Be0AbboE5Y+3fA0sRVJ8eqCxJwRJ2qbd0Dy7bg/eMn4PZjvbIzrvFXoiTU/dwx+vYifP/8X/CL3gRLh3Qcd/TF2i+rRi+Rsb8c911vYlb4iPvriDq98incTlb7wH9/pOovyzBL3Dj+32U4RvWu59bVD9M0we3HKKrELK4IdtMXBbvpzWJ3sAu/1IPtqs44fnfZGX/w69uLcN/zFngDAChJTpwTrsJ2Rce6fDniJBrqZ7zp8aOt34e+Zhfa2j0YPx6Aw9+mLhvWHq1RLwyGGkv79Kg7dRw6FV5YTmD5D2if/YD+ffAPSFQL6oUZ2K/pinVQp7BNj1Gbth6PBBxxRk637qY3QNUznxH498bgOJGHBtbLVT/6jmJ8b6xCW7YlGHTap+O4ow/WAR3Ap+Ee8xbGtiKcjwzCfkOPsLalrp2q9kZBGe77F2AWVuAY0w/Hrb3qsXXhF1i2Fe9Tn0JF1Yk3sqnBfx4/9jv64Lyr9gPHjUMe3HfPwywow/XEYOJ1k+LZH4FPw3ZNV5wPDZQJHutBQ/uM11b9hO/1r9C/3w+6EbzTpqKe3xJrzwzUizJQOzeP+IznZ+u4PcUFZQSW/0Bg2VaMrYUAYZkeo7Yk4Ig6dTp11388gHfG/2HsOoilYzNcM36JmpFaTy2sbsOuEvxvfE3gk81gmMFz0qnx6Gt2Yxv2C1zjr6zX9tSF06m9se8Q7gcWYB6oxHH/ABwjL6qn1oWXvqUQ973zQVWwXtCmups+2G1/dJe90sSFedCD+975mPsO4Rx3BfYRp3/KztR0PA8tQv/2yJV16emJ7F+zC+8T72NsKURpkYRryrVYf9H69PZpmOjfF2BsK8J2TdczGhDfGDWUz3jTMPG//hW+f3wFihIcuHthm+Bpp/Nbojhi67/nKf+Q2luK75+rCHwQXJqpLqbHOFMScKKQGdCpmvMfFJuK7ZfdUNulRbpJISeru1Fcie8fXxH49wbQTew398RxTz8Ue+SmXdL3lOLPW0Xgo+9BN1F7tCburyOCl6JHmdN9zxt7S3E/sBCzqBLH2Mtx3HxhPbQufIxSD+4738QsqsCVewO2S9qd+jn7DgVDTokb1/TrsF3V5bSOdbwr6w7X3dR0fP/4H/68VWCa2G/rhWP0Jcd9L5l+DW3NHrTPt6F9uR2zxA2A9ZJ2uJ4cimKNbNd+NGgIn/FmuRfvzA/QvtqJck4ScTnXo3ZuHtE2hdvp1r0upsc4WxJwooxpmHhnvo/2yZbQfWq3FtgGn49tYKc6u9rnTB2v7qbHj++tb/C/tRqqNCwZKcGuyz6ZEWrlsYx9hwh8sR37tV1RkhrOILnaqM17Xt9Tiuf+BZglbpwPDcR+4wW1OpZpmg1iEkFT0/GMext93V4cd12K447TX99O//EA7gcWgjdAXO6wU74fT3Rl3c/rrm3Yh/eJDzALyoI9lNOuQ23bFLOiCu1/Owl8vg3tq51Hxu8ku7Be0g5jfzn62j3YftkN56SrG0R9G7JIf8brPxTieWwpZkE5aq+2uKZf16AG2IZLbep+oukxwrl8zdEk4ESZqr//B//8NajdWmC/6UL8729E//qn4CA2lw3blZ2xDT4ftes5EfmArHGpsqYTeG8Dvte+wiz1oDSNx/HbvtgGny9/oYZBbd/z+u6DwZBz0HPaY470XSUE3lmP/8PvsV3eAefEyH4RVz39Gf63v8U6oAOumUNqPWBTW78Xz0OLwAJxT9+EtVvL4293kivrThTqq57+jMD7m4JzjpzXAv27faGxGUrLJtj6ZWHtn4XarSWKasH0+HE/sBBja+EZjw+CYHDT1u3Ffn13FEfsTkpfV5/xpqbXelyM//82UvXnZeDXsP+2D447+kZ8QG19OZO6m74A/vlr8L0ZvuVrjkcCThTxLViDb85/sJybStxzI0N/LRj7ywm8vxH//23ELAy+Zku7tGCvTnaXev2rIj09kQMHytFWbMP3wucYe0rBZcPx64ux/+rCiPcwxbIzec/rP5XgeWAhZqkH54SrsA/tfsw2pmagfbEN/zvr0dfsDt6pKqCbOEb3xTH6krpofq35399I1ZMfYWnblPiXfn3G763AF9vxPvYuSryDuGdHombWnCvoVFfWnfLqtT9+jFlWhaVTc2z92mPtl4WlXdpxg6Fx0I17TPX4oPFXYh/2i1q9Fv8Hm6j60yfg17F0bEbcE4OxtE6p1T6ixWlf0ODXMPaXYxaUBxfIza/+V1CGWVCGWV6FpX061r6ZWPtkonZrccLAY/o0qp5ZTmDpd5DgwDXtutM6JRpLzua71SiuDC1fA+CaNRRbv6y6bF4NEnCiRODTLXhn/B9KWgLxL9xyzCy2EJwnQV+zG/97G9A+3waaEZyj4PxWWHu3xdonE0tWeq3/4jY9fvSthZilHrCpP1sqQQ1dmaLYVJK8AQr/+HFw3hlVwXZ9dxy/7RsVs15GuzN9z+s7ivGMXYh5yItz0tXYB58PBD+MAu9twL/0O8yi4ER2as822G/ogXp+y+AXcUFZ8EP+6tMbw1JX9M37cd83H+xW4l++FbXN2X2JHw5LSnoC8c8f+f/L9AZOeWXdqepuegOYHv9pL4Bq7C3Ffc9bmOVVuHKGYOvf4ZTPMTUd37Mr8P9rLSQ4sF6UgfafHyHOjmvS1dgGdjqtY0eT4/acGSbGjmK0tbvRV+9G33Yg+N493reZ3YqlRRJKkjM4OejhyfLi7VgvPhdrn0ysvdtiqf6SNPaX45myFGNLIZasdOL+cH1EBs5GWl18t+pb9uN/9zvsIy8M6+SjEnCigLZmN55HFoHDSvyzI1Gz0k/5HKPUQ+Cj79GWV89RUP1fU0lLCIUd68XnHjPnjBnQMbYXoX9/ZGZgY9dBMGr3drBe3gHH7y6r96ujGrOzec/r24uCIae8Csedl6DvKEb777bgKZV4O/Zrz8M29Bc1ejf0n0qCc8H4NOKeHnHaVw39nOn21WoyMuOgO9ijUlSJ64/DsfWtm7Fcvnnf4HtuBZY2KcQ9NxKliQvvtPfQ/vMjtqHdcU246rjPC8dnjb55P+4HFoBBsLbdW51wW+OQB++0f6Ov3YOlbSquWcNQ26Tg/+j7YG9OlYZteA+c9w+I6ID+una4t9jcdwht9e5gqFlbc6kDpVkClpbJWFo2wdKyCUqLJlhaVP+eGh86pWl6A2hrd6P9byfa/37CLCgL7cPSPh1rzzYEPv4es6wK27Xn4XxkUKO92i2avlsl4DRw+o8HcN+3AAI6cX8ejrVnRq33YRzyoH+9C23VTrRVPx35AFAV1G4tsV50LkapJxhmthXVnPbbZUPt1By1c3MsLZrUXA/q6J9+HTQDZ5wd/arOJxzLIMLnbN/z+o8H8Dz4r9D6M5b2adhv6IHt6i4nPP2jrd6F55HFwbEpL/66VqdDTN3A99pX+N/4H5Z26dhv+MVJjwXVg4of/Bf6+n1hmbCw6rkV+Od9g6Vzc6wXZuCf+80pr6wL12dN4KudeCe9A3EO4p879tQZVP83m/wu5v5yrP2ycE29tkb99J9K8E59D2NnCZZOzYOnrKK018E0zODaSgVlGHsPYdtaSOWX20On5QGU9ITQrMDWnm1OuKzJSY9jmhh7SqvDzk70b/cGP/NsKs4Hr8A2tHujHgAeTd+tEnAaMKOgLNhVfdCNa8ZgbIPOvpvZNEyMrYVoX+1A+99P6JuP9O5gtWDJSg8twKZ2OSe4im0tBs/FQt2jVZ10HW8vIvB/G7EO6IDavdXprdv13ndU5X6CpU0K8S/eclpXoRnFlXgffx/92z0oKXHBUKUbEGfHdm3X4Gmwtsd+oYcu0768I66Zg+v8i8Y0TapmfRQcHAwo5yQR//KtJ11rJ5zv+dCps2aJxL94S+h0CVSftp4VnGDQcecl2H/T57iDrM2qAFV/WRZ8TQkOXJOzsQ049Wmvs2GaJuaBiuolBYrRtx0IrqWkGShNXMF/ya7j/25TMfaXY+Qfwigox8gPjpUx9pcfs+aS0sSJekEG1gvbBJc6aJNS9+8JbwB9wz6UFk3O+lRoLIimz3gJOA2UUebFM+YtjN2lOMZeEbb1g4xDHvQN+ViaxgfH55xlF3a01z2aRbL2h3s+TmceIW31LryPv49Z6gnOJzM5G9OnEVj6Hf6lGzCLjxrvM7wH1svao1jV4JUrsz7C0i6N+BfqbgHUnzM1I7g22prdxD1zE2qHZifdPtx19+WtwvfiF8HL0//+K4iz43vxC/zzvgmOsZl2LbbLTj1Q0//BJqqe+hR8GvYRF+C4b8BZz/dk+gKYh7yYJW70HcWhdZL07cXHzigdb0dx2jDLvMHxgbWgJLuCp5daVp9iatGEtEvbUXbUaSZRP6LpMz7sAaekpIThw4fz2muvYbVamTRpEoqi0KFDB6ZPn47FcvLegcYYcMyqAJ5x/0LfWID91xfhvHdApJt02qK57tEukrU3DfPIeJVruuJ87Jpj/pI2dQPf6//D//pXoFpw3DsA+00X1NjO1HS0z7fjf2cd+to9QHDcmG1QJ/yL14HTRsIrt9bLaRYzoJ9WAAh33U3TpOqvnxFYvA61R2uwq+hf78LSOgXX7KHH7ek6EX1HMd5p72H8dBBLl3NOb+0sn4ZZ5sUo82KWeYOBpvp3qrRjt1fA0joFS/t01Kx0LFlpqFnNUJonoihKcL0wjz+0H+Pn+/TrKM0TQ2NlLC2aHDfMymdNZERT3U8UcOpkJFogEGDatGk4nU4AZs2axbhx4+jduzfTpk1j2bJlXHXV8QfuNUambmAWVVL19GfoGwuwXd0Fxz39I90sIU5JsSi4plyLu7CCwIffY2mdUmPSPaPEjfeJ99HX7A7O+PrEYNSux64Yr1hVbFd0xHZFR/SdJfiXrCPwwff4F6wBi0LcjF/W2xiShjKbtaIoOB+8AvOgO3hlFGDtm4lr2nUoic5a7Uttl0b8S7dS9edlBD76Ht/m/bVvkMuG0sSFJSM1dGrJkhKHpW3TYKhpl4biOvEAXEVRIN4RnNG2VTINo8qiMamTHpycnBwGDBjASy+9xIwZM7jjjjtYsWIFiqLw6aef8uWXXzJ9+vST7kPTdKxRvjjZYaZpBq9u2nsIbW8pgb2laHsPHfmZfwgCwa5b16XtaPHCr2PqqgcR+7SiCvbd/ApafhnN/nwjiYPPx/u/nRSOX4ReVEncwE40mzUUNfnEY1p+znD7qHx/E2pqHPGDOoex9Q2b4QtQ/MT72FqnkPy7y85qYjnTNPGt34te6jnltordipoah5oShyU5DksjvXpIxI6z/lZdvHgxqamp9OvXj5deegmoOb17fHw8FRWn7uYqPY3/AY+noXWjGaUevI++G1yX4ziUlDjUjs2DA9kym2K9qSfFZd7jbtuQNbS6NyYNpfaO2cPQxrzFgclLKP18W3BSNEXBcf8A1F9dyMGADrVt5+XBQbGeBvD6fq4+666MG4gGFB90n/3OWiUH/9VGRdXJV2uvRw3l/d7YRFPdw3aKatGiRSiKwldffcXmzZuZOHEiBw8eDD3udrtJSqr9JXzRSN9TiueRRZj5Zag9WqN2bHbMgLmTdekKEU3UdmnEzRyCZ8JiAkvWozRLxPXEYJk6QAjRIJx1wJk7d27o91GjRjFjxgz+9Kc/sWrVKnr37s2KFSvo0+f0F8aLVtrGfLwT38Esq8J+Rx8cd17SqOdQEI2DtVdbXDMGo63djeOuSxvFIoRCiOgQllXDJk6cyJw5c/jVr35FIBAgOzs7HIdpMAKfb8Mz9l+YlT6cv78K512XSrgRjYbtio64HrlSwo0QokGp05GteXl5od/ffPPNutx1g+VfvI6qpz8Du4orZ1ijW5BNCCGEaIjk0p0zZBomvhc/xz/3G5SUOOL+dANq53Mi3SwhhBBCIAEnRPtuH4GPN6O2TwsuXdA+/YTzY5h+De+sj9A+2YKldQpxfx4eteu+CCGEELFIAk413wufo3+3j8DhO2xqcL2m6rWa1M7nYMlIAY8fz2NL0dfuQe3WAtfsYVhqMdeHEEIIIcJPAg5gun3omwqwZKVjv6kn+ub9wRW3fzyAsXn/kdDjsqHE2TFL3MEVfWdch+KQy76FEEKIhkYCDqB9uwd0A+ul7bH/shv8shsApk/D2F4UDDxbCoOhJ/8Q9pt64rh/wFnNMCqEEEKI8JGAA2jf7ALA2uvcGvcrDitq1xY11tIxDVNWtRVCCCEaOOmCAPSvd4HLhnresYsC/pyEGyGEEKLha/QBx9hfjrGnFOsFbVBiZLFPIYQQorFr9AFHW33801NCCCGEiF4ScL7+CQD1Ygk4QgghRKxo1AHH1A301btRmiViyUiNdHOEEEIIUUcadcAxfjyAWV6F9eJzZXFMIYQQIoY06oATujxcTk8JIYQQMaVxB5yvd4EC6kUZkW6KEEIIIepQow04pjeAvmEflg7NZC0pIYQQIsY02oCjrdsLmiGnp4QQQogYdNZLNei6zpQpU9i5cyeqqjJr1ixM02TSpEkoikKHDh2YPn06FkvDylL6Nz8BYO3VNqLtEEIIIUTdO+uAs3z5cgDmz5/PqlWrQgFn3Lhx9O7dm2nTprFs2TKuuuqqs25sXdK+2QUOK+r5LSPdFCGEEELUsbPuVrnyyiuZOXMmAPn5+aSlpbFp0yZ69eoFQP/+/Vm5cuXZHqZOGUUVGDtLUHu0RrHLeqNCCCFErKmTb3er1crEiRP55JNP+Nvf/sby5ctD88rEx8dTUVFxyn2kpMRhPcO1oNLTE2u1ffkX26kEkq/oSHItnyuOqG3dRd2R2keG1D0ypO6REe11r7Pui9zcXMaPH8/NN9+Mz+cL3e92u0lKSjrl80tLPWd03PT0RIqKTh2gjub9bCsAVV3OqfVzRdCZ1F3UDal9ZEjdI0PqHhnRVPcTBbGzPkW1ZMkSXnzxRQBcLheKotCtWzdWrVoFwIoVK7jooovO9jB1xjRMtG92oTSNx9IuLdLNEUIIIUQYnHUPztVXX83kyZO59dZb0TSNRx99lPbt2zN16lT+8pe/0K5dO7Kzs+uirXXC2F6EWerBlt1VlmcQQgghYtRZB5y4uDieeeaZY+5/8803z3bXYXF4eQa1l8x/I4QQQsSqhjU5TT0IrT91kQQcIYQQIlY1qoBj+gLo6/dhaZ+GpWl8pJsjhBBCiDBpVAFH/24f+DVZnkEIIYSIcY0q4IROT13cNrINEUIIIURYNa6A8/UusKmov2gV6aYIIYQQIowaTcAxDroxthWhdm+F4rRFujlCCCGECKNGE3D01bsBZPyNEEII0Qg0moATGn8j898IIYQQMa9RBBzTrF6eIdmFJatZpJsjhBBCiDBrFAHH2FmCWVyJemEGikWWZxBCCCFiXaMIONrqw6en2ka2IUIIIYSoF40i4OhfH57/RsbfCCGEEI1BzAccU9PR1u3B0jYVS7PESDdHCCGEEPUg5gMOVRpUaVhaJUe6JUIIIYSoJ7EfcA5TZHCxEEII0Vg0noAjhBBCiEZDAo4QQgghYo71bHcQCAR49NFH2bdvH36/nzFjxpCVlcWkSZNQFIUOHTowffp0LBbJUkIIIYSoH2cdcJYuXUpycjJ/+tOfKC0t5YYbbqBz586MGzeO3r17M23aNJYtW8ZVV11VF+0VQgghhDilsw4411xzDdnZ2aHbqqqyadMmevXqBUD//v358ssvTxlwUlLisFrVM2pDevqJL//WnTYqALvdetLtRO1JPSNHah8ZUvfIkLpHRrTX/awDTnx8PACVlZWMHTuWcePGkZubi1J91VJ8fDwVFRWn3E9pqeeMjp+enkhR0Yn3b1b6APD7tZNuJ2rnVHUX4SO1jwype2RI3SMjmup+oiBWJwNjCgoKuP322xk6dChDhgypMd7G7XaTlJRUF4cRQgghhDgtZx1wiouLGT16NBMmTGDEiBEAdO3alVWrVgGwYsUKLrroorM9jBBCCCHEaTvrU1QvvPAC5eXlPPfcczz33HMAPPbYY+Tk5PCXv/yFdu3a1RijU+/i7diu7oK1T2bk2iCEEEKIeqWYpmlGuhHAGZ/ri6bzhLFE6h45UvvIkLpHhtQ9MqKp7mEdgyOEEEII0ZBIwBFCCCFEzGkwp6iEEEIIIeqK9OAIIYQQIuZIwBFCCCFEzJGAI4QQQoiYIwFHCCGEEDFHAo4QQgghYo4EHCGEEELEnLNeqiFSDMNgxowZbN26FbvdTk5ODueee26kmxXT1q9fz1NPPUVeXh67du1i0qRJKIpChw4dmD59eo1FVsXZCwQCPProo+zbtw+/38+YMWPIysqSuoeZrutMmTKFnTt3oqoqs2bNwjRNqXs9KSkpYfjw4bz22mtYrVapez0ZNmwYiYnBGYFbt27NPffcE/W1j67WHuXTTz/F7/ezYMECHnnkEWbPnh3pJsW0l19+mSlTpuDz+QCYNWsW48aNY968eZimybJlyyLcwtizdOlSkpOTmTdvHi+//DIzZ86UuteD5cuXAzB//nzGjh3LrFmzpO71JBAIMG3aNJxOJyCfM/Xl8Od6Xl4eeXl5MfOej9qAs2bNGvr16wdAjx492LhxY4RbFNsyMjKYM2dO6PamTZvo1asXAP3792flypWRalrMuuaaa3jwwQdDt1VVlbrXgyuvvJKZM2cCkJ+fT1pamtS9nuTm5jJy5EiaNWsGyOdMfdmyZQter5fRo0dz++23s27dupiofdQGnMrKShISEkK3VVVF07QItii2ZWdnY7UeOaNpmiaKogAQHx9PRUV0LMoWTeLj40lISKCyspKxY8cybtw4qXs9sVqtTJw4kZkzZ5KdnS11rweLFy8mNTU19IcryOdMfXE6ndx55528+uqrPP7444wfPz4mah+1ASchIQG32x26bRhGjS9gEV5Hn4t1u90kJSVFsDWxq6CggNtvv52hQ4cyZMgQqXs9ys3N5aOPPmLq1KmhLnyQuofLokWLWLlyJaNGjWLz5s1MnDiRgwcPhh6XuodPZmYm119/PYqikJmZSXJyMiUlJaHHo7X2URtwevbsyYoVKwBYt24dHTt2jHCLGpeuXbuyatUqAFasWMFFF10U4RbFnuLiYkaPHs2ECRMYMWIEIHWvD0uWLOHFF18EwOVyoSgK3bp1k7qH2dy5c3nzzTfJy8ujS5cu5Obm0r9/f6l7PXj77bdD41gLCwuprKzk0ksvjfraR+1im4evovrhhx8wTZMnn3yS9u3bR7pZMW3v3r08/PDDLFy4kJ07dzJ16lQCgQDt2rUjJycHVVUj3cSYkpOTwwcffEC7du1C9z322GPk5ORI3cPI4/EwefJkiouL0TSNu+++m/bt28v7vR6NGjWKGTNmYLFYpO71wO/3M3nyZPLz81EUhfHjx5OSkhL1tY/agCOEEEIIcSJRe4pKCCGEEOJEJOAIIYQQIuZIwBFCCCFEzJGAI4QQQoiYIwFHCCGEEDFHAo4QQgghYo4EHCGEEELEHAk4QgghhIg5EnCEEEIIEXMk4AghhBAi5kjAEUIIIUTMkYAjhBBCiJgjAUeIRqxTp04MGTKEoUOHMmzYMLKzs7nxxhvZsGHDKZ87atQoPvzww9O+vy506tSJgQMH8vM1gufMmUOnTp1O2e45c+bwxBNPAHD33Xezbdu2M2rHnj17eOCBBwAoLCxk5MiRZ7QfIUT4WCPdACFEZP3zn/8kNTU1dPvVV18lJyeHBQsWRLBVJ2aaJqtXr+biiy8O3f7ggw9o0qRJrfbz8ssvn3Eb8vPz2blzJwDNmzdn/vz5Z7wvIUR4SMARQoRomkZBQUGNsPD888/z8ccfYxgGrVq1Yvr06TRv3rzGcx555BGsViu5ubk19vfCCy+wbNkyqqqq8Hq9TJw4kauuuoo5c+awe/duCgsLKSoq4rzzzqN3794sWbKEvXv3MmHCBAYPHnzcNl5//fUsXbo0FHDWrFlDVlYWVVVVpzzu0QYOHMgzzzzDggULaNq0KQ899BAA7777Lh9//DHPPvssn332Gc8//zyBQACn08nEiRPp3r07U6ZMobCwkDvvvJPHH3+cIUOG8O23355d8YUQdUpOUQnRyP3mN79hyJAhXHbZZWRnZwMwa9YsAJYsWcIPP/zAv/71L959910GDBjAlClTQs8NBAI8+OCDNG3alKeeegqr9cjfTPv27WPlypXk5eXx3nvv8dBDD/G3v/0t9PiaNWt49tlneeedd1ixYgXbt29n7ty5TJ06lTlz5pywvYMHD+aTTz7B7/cD8M4773DDDTec9nF/7tZbb2XRokVomgbAwoULGTlyJD/99BN//etfeemll1iyZAkzZ87kgQcewOfzkZOTQ0ZGBq+++mptSi2EqEcNrgdn/fr1PPXUU+Tl5Z1wm8WLF/PWW2+h6zqDBg3ivvvuq8cWChFbDp+i2rRpE7/73e/o3bs3TZs2BWD58uXE14fPAAAetElEQVRs2LCBG2+8EQDDMPB6vaHn5ubm4na7+eSTT1AUpcZ+W7VqxR//+Efee+89du3axfr163G73aHHL7nkEhITEwFo1qwZ/fr1AyAjI4NDhw6dsL1Nmzale/fuLF++nAEDBrB69Woef/zx0z7uz3Xp0oXWrVvzn//8h8zMTA4cOMBll13GvHnzOHDgAHfccUdoW0VR2L1796lKKoRoABpUwHn55ZdZunQpLpfrhNvs3r2bt956i7y8POx2O3/7298IBALYbLZ6bKkQsee8885j8uTJTJo0KfSlbxgGd911F7/+9a8B8Pv9lJWVhZ5z/fXXY5omU6ZM4YUXXqixv02bNnHvvfdyxx13cOmll3LxxRfXCCJ2u73G9kf3/pzKsGHDWLp0KX6/n4EDB9Z47qmOezyHe3Hatm3LzTffjKIoGIZB3759efrpp0PbFRQU0KxZM1avXn3abRVCREaDOkWVkZFRo2t669atjBo1ilGjRvHAAw9QUVHBypUr6datGxMnTuS2226jZ8+eEm6EqCODBw+me/fuoVNUl112GW+//TaVlZUAPPPMM/z+978Pbd+9e3fGjRvH7t27WbhwYY19ffPNN3Tr1o3f/va39OrVi2XLlqHrep20c9CgQXz77bfMnTu3xumpMz1udnY2mzdv5qOPPgr1VvXt25cvv/yS7du3A/Df//6X66+/nqqqKlRVJRAI1MlrEUKER4PqwcnOzmbv3r2h21OnTuXJJ58kKyuLf/3rX7zyyis4nU5Wr17NW2+9hc/n45ZbbuHtt98mKSkpgi0XInZMnTqV66+/ns8//5ybbrqJwsLCUK9GixYtmD17do3tHQ4Hs2fPZvTo0fTp0yd0/+DBg/n444+59tprMQyDK664grKyslBYOhsOh4OBAwfy/fff07FjxxqPnclx7XY72dnZFBcXh64oy8rK4oknnuDhhx/GNE2sVivPP/888fHxZGVl4XA4GDFiBH/961/P+vUIIeqeYv58QokI27t3Lw8//DALFy7kwgsvpGvXrkBwMGNmZibdu3dn27ZtTJ06FYAxY8YwZswYunfvHslmCyGimMfj4bbbbmPatGn06NEj0s0RQtSBBtWD83OZmZnk5ubSsmVL1qxZQ1FREZmZmcybNw+fz4eu62zfvp2MjIxIN1UIEaU+//xzHnnkEW655RYJN0LEkAYdcGbMmMHEiRND58//8Ic/kJmZyY033sgtt9yCaZrce++9JCcnR7ilQoho1a9fP77++utIN0MIUcca3CkqIYQQQoiz1aCuohJCCCGEqAsScIQQQggRcxrMGJyiooozel5KShylpZ46bo04Fal75EjtI0PqHhlS98iIprqnpyce9/6o78GxWtVIN6FRkrpHjtQ+MqTukSF1j4xYqHuD6cERQgghRIQZVcTlz4F9+0moqoPZuhU7nlYPYjjanP2+akkCjhBCCCGw+PaRtPVWbJVrATjxqpC1E0jshS9dAo4QQggh6pmtfCVJW0dhCRRRlX4LzvOncbAOxuCYFmdEem9AAo4QQgjReJkmzv2vkPDTRDBNKjNz8Z5zD87EJPSqM7v4p6GQgCOEEEI0RkYVCTsewXUgD8PalPJObxBo0i/SraozEnCEEEKIRsbiy68eb7OGQHwPyjvPjdippHCRgCOEEEI0Itbyr2iydRSWwAGq0n9FRbu/gVpXQ4obDgk4QgghRGNgmjgLXyVh5++D423azsbbYgwoSqRbFhYScIQQQohYZ/hI2DEe14F/Vo+3+SeBJv0j3aqwkoAjhBBCxLDgeJvbsFWuJhD/C8o7zcVwZkS6WWEnAUcIIYSIUdby/5G0dRRqoDCmx9scjwQcIYQQIgY5979Gws4JYBpUtn0Sb4v7Yna8zfFIwBFCCCFiieEjYecEXIWvY1hTq8fbDIh0q+qdBBwhhBAiRlj8BSRtuQ1b5TcE4rtXj7c5N9LNighLOHdeUlLCgAED2L59ezgPI4QQQjR61vJVJK/vj63yG6rSbuJQt48bbbiBMPbgBAIBpk2bhtPpDNchhBBCiJimaKW49r+Kop98XShFr8RZ+DqYeqMcb3M8YQs4ubm5jBw5kpdeeum0tk9JicNqVc/oWOnpiWf0PHF2pO6RI7WPDKl7ZDTaupdvhPXDwH2aZ0HsTeGiBSSkDyKhDg4f7XUPS8BZvHgxqamp9OvX77QDTukZLsuenp5IUVF0r3gajaTukSO1jwype2Q01rrbi5eQtG0MiuHG0+ohfKm/POVzdFcHTFKgDuoVTXU/URALS8BZtGgRiqLw1VdfsXnzZiZOnMjzzz9Penp6OA4nhBBCxAZTJ353DnH7/oxpiaes4xv404ZFulVRKSwBZ+7cuaHfR40axYwZMyTcCCGEECehaKUk/nAXjkOfoDszKev0Fnp810g3K2rJZeJCCCFEhKmezSRtuQVr1Q78yYMo7/gapjUl0s2KamEPOHl5eeE+hBBCCBG17CVLSfrx/1WPt3kEd8YUUM7sohtxhPTgCCGEEJFg6sTt+QPxe5/CtMRR1vGf+NNuiHSrYoYEHCGEEKKeWap2kLj9Qexl/0V3tKWs81vo8edFulkxRQKOEEIIUV9MDVf+s8TveRLF8OJLuZaKrOcxbamRblnMkYAjhBBC1ANr5XoStt+Pzb0ew5pGRdaz+Jre2OhnHA4XCThCCCFEOOke4vfMxpU/BwWdqvRfU9n2D5i2ppFuWUyTgCOEEEKEia3svyRuH4tatRPd0ZaK9k8TSB4Y6WY1ChJwhBBCiDqmaKXE/zQF14E8TCx4Wo7F3WYyqPGRblqjIQFHCCGEqCOKVo5z/6vE5c/BohWjxZ1PRdYctISekW5aoyMBRwghhDB17KUfgmngTx5Y654WJVCCq+AFXAUvYtEPYahJVGY8jrfl/WCxhanR4mQk4AghhGi8DD/Oovm49v0Fa9UOAEyLE3/ylfhSh+BPveakSyYo/kLi8ufg2v8qiuHGsKbizpiK95y7Ma3J9fUqxHFIwBFCCNH46F6cB/5J3L6/ofr3Yip2vM1/i2FriqPk3zgOBv+ZipVAk/74UofgSx2MaW8OgKVqN3H5T+MszEMxfej2FnhbPoa3+W9lnE0DIQFHCCFEoxEaI1PwdyyBIkyLC0+Le/G2HIvhaAmAJ2MaqucH7Affw1GyFPuhz7Af+oyEHQ+jJfZGd7TCUfIuiqmhO9riaTWOqma3gsUR4VcnjiYBRwghRMw73hgZd6vxeFvei2lLO2Z7Pa4j3rhH8LZ+BEvVbhwH/4394HvYyldiqzDRXJ3wtHoYX/pNoMhXaUMk/1WEEELELCVQQlz+HJwFL2ExKs9ojIzhzMDb8l68Le9F8R9A9e1CS7gQFEuYWy/OhgQcIYQQMUcJHMSV/3dcBS9gMSrRbc2pzHj0rMfImPZmaPZmddhSES4ScIQQQsQMJXAQV8Hfq09FVWDYmlGZ8Rje5qNBdUW6eaIeScARQggRWaaJxV+A6t2K6t2K1bMV1fsDatUOcDUnwdEVPa4bWvz5aPHdjnvZtqIdwpX/LK6C57Ho5Ri2dCrbTK4ONnEReFEi0iTgCCGEqB+mhlq1E9X7I6r3h+ogswXV+yMWvbzmpigY9lZQsQlX2doaj+n21mjx3YL/4s7H6t2CK/85LHoZhjWNynP/gPecOyXYNHIScIQQQtQpRSsPhRjV+yNW7w/VPTLbUcxAjW1NxYbubE/AdQVaXCd0V6fgT2cWqHGkN3VxcM+3WN3fYXVvxOrZgOreiKP0QxylH4b2Y1ibUnnuTLzn3CXz0AhAAo4QQogzpARKsHq3onq2oHq3HOmR8Rccs62hNkGL/0UwwLg6ors6oMd1Qndmnvwya4sVPa4zelxnfOk3Hzm2vwirZwNW90ZMi5OqZr8GNSEcL1NEKQk4QggRg5TAweoelG0opq9u9mlUoXq3BcfJeLdiCRQds41ub4M/eSCaqxO6qyO6qyOaqyOmrRkoSp20A8C0pxOwDySQPLDO9iliiwQcIYSIVqaJxZ8fChyq54eTho86OywKhuNcfCkXors6V59a6owe1xFTTQzbcYWojbAEnEAgwKOPPsq+ffvw+/2MGTOGQYMGheNQQgjR+Jg6cXv+ELoUusZDofDRE93VCd3VAbOOxqSYWNFd7dCdHeSSa9HghSXgLF26lOTkZP70pz9RWlrKDTfcIAFHCCHqgBI4SNKPd2I/tAzd3gJf8qDgmJa4zsHTQs4sCR9CAIppmmZd79TtdmOaJgkJCZSWljJixAiWLVt20udomo7VqtZ1U4QQInaUb4BVw8CzA5pfBxfOBdvpLTcgRGMTlh6c+Phgd2hlZSVjx45l3Lhxp3xOaannjI6Vnp5IUVHFqTcUdUrqHjlS+8iIdN3txe+QtO1eFMONu/UEPG0eg0MWILbfC5Gue2MVTXVPTz/+uK+wrRRWUFDA7bffztChQxkyZEi4DiOEELHN1InfNYMmP/wGgLJOb+LJmCoLPQpxCmHpwSkuLmb06NFMmzaNvn37huMQQggR8xStlKQfRmM/tAzN2Y7yzm+hx3WJdLOEiAph+RPghRdeoLy8nOeee45Ro0YxatQoqqqqwnEoIYSISar7e1K+uxz7oWX4kq/iUPflEm6EqIWw9OBMmTKFKVOmhGPXQggR20wTR8kiErc9EBxv02o8nozHQJGLMISoDZnoTwghGgLTxF76IXF7ZmNzf4tpiaes4xv404ZFumVCRCUJOEIIEUmmib30feL25GJzrwPA13QY7jZT0OM6RrhxQkQvCThCCBEJpoH94L+J35OL1bMBE4WqpsPxtP49enzXSLdOiKgnAUcIIeqTaWAvWUr83j9i9WwMBpu0EcFgE9c50q0TImZIwBFCiHAxNSxVu7BWbUP1bket2o6t7HOs3i2YWKhK/xWeVhPkVJQQYSABRwghzpISOIjVsxHVsxW1ajuqd1vwp28XiqnV2NZEpSr9Fjytx6O7OkSoxULEPgk4QojGSffiOPguVvcGDFszDHsLDHtLDPs56PaWoMYd+xzTCAYX90asng1Y3Ruxujei+vces6lhTUWLvwDdlYXubI/uao/uzEJztQc1oR5eoBCNmwQcIUSjonq24iz8B86ieVi0QyfczlCTMeznBIOPLQ027yGtbAOK4a6xnW47B3/ylWjx56O5OocCjWlLDfdLEUKchAQcIUTsM6pwlCzBWfg69vKVwbts6XhaPYwvJRuLVorFn1/9bz+qPx+LrwBLoACrd0twH4oV3dUJLb4bWtz5oZ+mPT2CL0wIcSIScIQQMetIb81bWLRSAPxNrsDb/Lf4U68Di/3UO9E9WALFNG2ZRelBf5hbLISoKxJwhBANh+FHMbyn3s7UsGgHUQIlWALFWLQSlEBx6HdLoBiLfz9Wz8bgbm3peFo9hLf5bzCc7WrXJjUOQ80A1QFIwBEiWkjAEUKEl6mhVv2E6t2GJVAUDCLVIUQ5HEYCJcHf9Yq6O6zFib/J5dW9Nb88vd4aIUTMkIAjhKgbuhe16kesnq2o3q1YvT+ELptWzBP3fJiKDcOWhuFoi2Zrimk5ztVLP6eoGNZUTFvT4HOtqZi2NAxr9W1bGljiQFHq8AUKIaKJBBwhGivTBNOPoldW/3OjGEf9fvh+w4tiuFH04E8ML4ruQTE8wcd0D5ZAERbfLhTMGocwLAlo8d2Cg3NdHTHs52Bam2JUBxPT2hRTTZQgIoSocxJwhIgFhr96TMrh0z8lKFpp9X3VP7WjfgYOouiHjpmE7kyYih3DmkIg6TJ0V0e0uE7oro7ork4Y9pYSXoQQESEBR4iGQK9ErdoV7CkJ9ZB44eieEsODorthbwVJFQVYAiVYtGKUwEEsetlpHcZExbSmYNhSMJ2ZmGoCppqIqcYH/1kSqu+LP/LTEo+pxmFaXNW/u8ASh2mJw1TjQJGPESFEwyOfTEJEgl6JrWIV9rLPg2sTVa5FQT/tpzuoHrtibYrhaI1m64FhbRock2JtimFLxbSmBsemWFOqb6dgqkmgWML3uoQQooGQgCPEGVC0Q1h8e7AEiqt7NxKrezyCPSJYbDWfoLuPBJry6kBTfXrIREVLuAAt/hfVvSmuYO/I4R4Siyv0u2lxkdIsg+IKZ3VYkdM/QghxPBJwhPg5oyp4+sefj+rbg8W3B9W3u/pn8LZFLz/pLkzFceT0j8VZfSVRIPgYKlpCDwJJ/fA36YeW1CcYik5XQiKmt+4upxZCiFgkAUfENtNE0cur518pCV7tEyiqnhiu5Mh8LIGSIxPHGZUn3J2hJmI42hBwtMFwtMGwpQevLtIrfnYFUkXoKiSL/xBa/PkEkvoRaNKPQGIfTGtSPRZBCCEaHwk4ouEzTTB9KFo5Fr0MRStH0ctRtDIs+pHfFb08uKbQ4cnkAsXB308yB0voEIoDw5aG7mqPZk2tvoz5HAxnG3RHBnp1oDHVZDktJIQQUUACjqgbphGcU8XwgxkIhgrDV92bUY5FK0M5KpxYqgNJ8L4KFKOq+mqhquA8KzVuVx0zv8opm2OJx7Clo8Wfj2FLx7ClY9rSQ5PAGda0owbkNpVJ4YQQIsaELeAYhsGMGTPYunUrdrudnJwczj333HAd7syYJpiB6i9kDU5rThCz+svXWz3hmbf6Et7gZbwYVcFLfNEwUauvWLGAogIWzNBtC6AcObbhD47RMAPBn4YfTC0YFEwDMGv+M82jvvTN6oARQDF9YBwVMMzAz4KHfmR7TMCocVsxjep9GsH6HHNcI3hcxaSpXn0c019H86nYqgfXOsHiwrCdg2lxVl+WHBxUa6hNMK1JmGoSpjUJQ03CVJtgWpsEH7cmB2exVePPuj1CCCGiV9gCzqefforf72fBggWsW7eO2bNn8/zzz4frcCdmGjTZfCNW94bqMKNVB4hArS7LjXam4sBUbMGgpSiAAlSHLMWCWf0zeH/1Y8pR20B1OFMwUbBYrRiGNbhPix1TsYPFFvyp2DEtdlBs1fOpNAkGEeuRcGJYmwSDiZqEaU0MTs+vqJEpjhBCiJgTtoCzZs0a+vXrB0CPHj3YuHFjuA51SopeiSVwAM3VBdNiC05Mptiqv/BtmIq1+j4rh7/Mj3r2MfszLY7qS3ad1ROeOUM9D6YlDlQnJlYUDII9IXp1D4lR3UNihG6H2hEKB7aaoUGxHvXFHwwfZiigKEfap1iqQ4YdU3HUCBvBfdTt6Zf09ERKi+RKHiGEEA1T2AJOZWUlCQkJoduqqqJpGlbr8Q+ZkhKH1Xpmf8Gnp5/iEtuB/wNkwFFdO2XdRdhI7SND6h4ZUvfIiPa6h+07PyEhAbfbHbptGMYJww1AaannjI6Tnp5IkfQk1Dupe+RI7SND6h4ZUvfIiKa6nyiIhW3O9p49e7JixQoA1q1bR8eOHcN1KCGEEEKIGhTTNGt3/e1pOnwV1Q8//IBpmjz55JO0b98+HIcSQgghhKghbAFHCCGEECJSZFlhIYQQQsQcCThCCCGEiDkScIQQQggRcyTgCCGEECLmSMARQgghRMyRgCOEEEKImBO1qxdExWrlMWb9+vU89dRT5OXlsWvXLiZNmoSiKHTo0IHp06djsUherkuBQIBHH32Uffv24ff7GTNmDFlZWVL3MNN1nSlTprBz505UVWXWrFmYpil1ryclJSUMHz6c1157DavVKnWvJ8OGDSMxMTgjcOvWrbnnnnuivvbR1dqjHL1a+SOPPMLs2bMj3aSY9vLLLzNlyhR8Ph8As2bNYty4ccybNw/TNFm2bFmEWxh7li5dSnJyMvPmzePll19m5syZUvd6sHz5cgDmz5/P2LFjmTVrltS9ngQCAaZNm4bT6QTkc6a+HP5cz8vLIy8vL2be81EbcBrSauWNQUZGBnPmzAnd3rRpE7169QKgf//+rFy5MlJNi1nXXHMNDz74YOi2qqpS93pw5ZVXMnPmTADy8/NJS0uTuteT3NxcRo4cSbNmzQD5nKkvW7Zswev1Mnr0aG6//XbWrVsXE7WP2oBzotXKRXhkZ2fXWCzVNE0URQEgPj6eioroWJQtmsTHx5OQkEBlZSVjx45l3LhxUvd6YrVamThxIjNnziQ7O1vqXg8WL15Mampq6A9XkM+Z+uJ0Ornzzjt59dVXefzxxxk/fnxM1D5qA05tVysXdevoc7Fut5ukpKQItiZ2FRQUcPvttzN06FCGDBkida9Hubm5fPTRR0ydOjXUhQ9S93BZtGgRK1euZNSoUWzevJmJEydy8ODB0ONS9/DJzMzk+uuvR1EUMjMzSU5OpqSkJPR4tNY+agOOrFYeWV27dmXVqlUArFixgosuuijCLYo9xcXFjB49mgkTJjBixAhA6l4flixZwosvvgiAy+VCURS6desmdQ+zuXPn8uabb5KXl0eXLl3Izc2lf//+Uvd68Pbbb4fGsRYWFlJZWcmll14a9bWP2sU2ZbXy+rd3714efvhhFi5cyM6dO5k6dSqBQIB27dqRk5ODqqqRbmJMycnJ4YMPPqBdu3ah+x577DFycnKk7mHk8XiYPHkyxcXFaJrG3XffTfv27eX9Xo9GjRrFjBkzsFgsUvd64Pf7mTx5Mvn5+SiKwvjx40lJSYn62kdtwBFCCCGEOJGoPUUlhBBCCHEiEnCEEEIIEXMk4AghhBAi5kjAEUIIIUTMkYAjhBBCiJgjM+MJIY7x4Ycf8tJLL6FpGqZpMnToUO666y4geAnv/fffT+/evUPbT5o0iV69ejF8+HAGDhyI0+nEZrNhmiZWq5Xf//739OnThzlz5jB//nzS0tJqHO+FF15g9+7d3HPPPWRkZADBqSDcbjd33303t9xyS43tFy9ezOzZs2nRogWmaaJpGvfffz/XXXddaJvXX3+dBQsWoKoqqqpy8803c+utt+L3++nduzfLly8nOTkZgOHDh9OkSRP+8Y9/ALBjxw7uuusu3njjDW6//XY+++yzui+yECKsJOAIIWooLCwkNzeXxYsXk5KSgtvtZtSoUWRmZjJo0KDT2sdLL71E69atAfjss88YP348X3zxBQAjR47kgQceOOY5u3fvplu3buTl5YXu27x5MyNGjGDIkCE1lmYBGDhwYGhysqKiIrKzs+nXrx+JiYnMmTOHb775hry8PNLS0jh48CD33nsvhw4d4r777qNnz56sW7eOyy+/PDRb7s6dO/F6vbhcLtasWcMll1xS++IJIRoMOUUlhKihtLSUQCBAVVUVEFyHZvbs2WRlZZ3R/nr37k1RURGlpaW1fu6+fftwuVzY7faTbud2u4mLi8PhcOD1enn11Vd58sknQz1Fqamp5OTk8Morr+D1eunTpw9r164F4Msvv6RPnz707NmTr7/+GoDVq1dz6aWX1rq9QoiGQ3pwhBA1dO7cmUGDBnHllVfSpUsXevfuzZAhQzj33HPPaH///ve/adu2LSkpKQDMnz+fTz/9NPR469atefbZZwHYuHEjQ4cOxev1UlZWRu/evXnttdeOG3A+++wzhg4diq7r/PTTT9x9993Y7Xa+++47XC5XqAfpsKysLOx2Ozt27KBPnz7k5uYC8MUXXzBs2DDy8/P54osvGDBgAGvXrmXy5MlUVlae0WsWQkSeBBwhxDEef/xx7r33Xr744gu++OILbr75Zp566imuvvrq0ArDRzNNs8ZCoL/73e+w2WwEAgFatGjB008/HXrsRKeogNApKr/fz4QJE0hISKB79+7H3fboU1QHDhzgtttuo3379px77rnoun7c52iahqIonHfeeezatQu/38+aNWuYOXMmmZmZvPHGG+zfv58mTZqQnJwsAUeIKCYBRwhRw3/+8x88Hg/XXXcdN954IzfeeCMLFy7k7bff5uqrr6ZJkyZUVFTUeE5JSUmN1YaPHoNzJux2Ozk5OWRnZ/P+++/XGDx8PM2aNePyyy9n7dq1DBo0iEAgwI4dO2qs4/Xjjz9iGAaZmZlYLBa6d+/Ou+++S9u2bbHb7ZxzzjkYhsHnn38up6eEiAEyBkcIUYPT6eTPf/4ze/fuBYK9M5s3b6ZLly4A9OnThyVLlqBpGhC84mjTpk306NGjTtuRmJjIAw88wB//+MfQeKAT8fv9rF27lq5du+JyuRgzZgyPPfYYJSUlQDCATZ06lbvuuguXywVA3759ef3117nssstC++nduzdvvPGGBBwhYoD04AghaujTpw/3338/99xzD4FAAIB+/fpx3333AfCrX/2KPXv2MHToUCwWCw6Hg6eeeorU1NTT2v/Px+AATJw48bgrFd90003k5eXxj3/8gzFjxtR47PAYHEVR8Pv9XHLJJQwfPhwIniJLTEzkjjvuwDRNFEVh5MiR3HrrraHn9+3bl5kzZ9YIM5dddhmLFi2qEdby8/O54IILQrcvvPBCXnnlldN6rUKIyJHVxIUQQggRc+QUlRBCCCFijgQcIYQQQsQcCThCCCGEiDkScIQQQggRcyTgCCGEECLmSMARQgghRMyRgCOEEEKImPP/Abw4fX/Rig0nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3x1'lik subplot figürü oluşturma ve üst subplotu aktif etme\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.plot(tv.super_bowl, tv.avg_us_viewers, color='#648FFF')\n",
    "plt.title(\"ABD'deki Ortalama İzleyici Sayısı\")\n",
    "\n",
    "# Ortadaki subplotu aktif etme\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.plot(tv.super_bowl, tv.rating_household, color='#DC267F')\n",
    "plt.title('Haneye Göre Değerlendirme')\n",
    "\n",
    "# Alttaki subplotu aktif etme\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.plot(tv.super_bowl, tv.ad_cost, color='#FFB000')\n",
    "plt.title('Reklam Maliyeti')\n",
    "plt.xlabel('SUPER BOWL')\n",
    "\n",
    "# Subplotlar arasındaki aralığı iyileştirme\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* İzleyici sayıları reklam maliyetlerine göre daha önce artış göstermiş. Ancak muhtemelen network veriler hakkında şu anki kadar bilgili olmadığı için erken reaksiyon gösteremedi."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Devre Arası Müzisyenleri\n",
    "<p>Geç artışın bir sebebi de devre arası gösterilerinin önceki yıllarda bu kadar iyi olmaması olabilir. Michael Jackson'ın Super Bowl performansına kadar insanlar devre arası gösterilerinin bu denli önemli olduğunun farkında değillerdi.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "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>super_bowl</th>\n",
       "      <th>musician</th>\n",
       "      <th>num_songs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>27</td>\n",
       "      <td>Michael Jackson</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>26</td>\n",
       "      <td>Gloria Estefan</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>26</td>\n",
       "      <td>University of Minnesota Marching Band</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>25</td>\n",
       "      <td>New Kids on the Block</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>24</td>\n",
       "      <td>Pete Fountain</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>24</td>\n",
       "      <td>Doug Kershaw</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>24</td>\n",
       "      <td>Irma Thomas</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>24</td>\n",
       "      <td>Pride of Nicholls Marching Band</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>24</td>\n",
       "      <td>The Human Jukebox</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>24</td>\n",
       "      <td>Pride of Acadiana</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>23</td>\n",
       "      <td>Elvis Presto</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>22</td>\n",
       "      <td>Chubby Checker</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>22</td>\n",
       "      <td>San Diego State University Marching Aztecs</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>22</td>\n",
       "      <td>Spirit of Troy</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>21</td>\n",
       "      <td>Grambling State University Tiger Marching Band</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>21</td>\n",
       "      <td>Spirit of Troy</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>20</td>\n",
       "      <td>Up with People</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>19</td>\n",
       "      <td>Tops In Blue</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>18</td>\n",
       "      <td>The University of Florida Fightin' Gator March...</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>18</td>\n",
       "      <td>The Florida State University Marching Chiefs</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>17</td>\n",
       "      <td>Los Angeles Unified School District All City H...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>16</td>\n",
       "      <td>Up with People</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>15</td>\n",
       "      <td>The Human Jukebox</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>15</td>\n",
       "      <td>Helen O'Connell</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>14</td>\n",
       "      <td>Up with People</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>14</td>\n",
       "      <td>Grambling State University Tiger Marching Band</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>13</td>\n",
       "      <td>Ken Hamilton</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>13</td>\n",
       "      <td>Gramacks</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>12</td>\n",
       "      <td>Tyler Junior College Apache Band</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>12</td>\n",
       "      <td>Pete Fountain</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>12</td>\n",
       "      <td>Al Hirt</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>11</td>\n",
       "      <td>Los Angeles Unified School District All City H...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>10</td>\n",
       "      <td>Up with People</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>9</td>\n",
       "      <td>Mercer Ellington</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>9</td>\n",
       "      <td>Grambling State University Tiger Marching Band</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>8</td>\n",
       "      <td>University of Texas Longhorn Band</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>8</td>\n",
       "      <td>Judy Mallett</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>7</td>\n",
       "      <td>University of Michigan Marching Band</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>7</td>\n",
       "      <td>Woody Herman</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>7</td>\n",
       "      <td>Andy Williams</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6</td>\n",
       "      <td>Ella Fitzgerald</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>6</td>\n",
       "      <td>Carol Channing</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>6</td>\n",
       "      <td>Al Hirt</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6</td>\n",
       "      <td>United States Air Force Academy Cadet Chorale</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>5</td>\n",
       "      <td>Southeast Missouri State Marching Band</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>4</td>\n",
       "      <td>Marguerite Piazza</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>4</td>\n",
       "      <td>Doc Severinsen</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>4</td>\n",
       "      <td>Al Hirt</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>4</td>\n",
       "      <td>The Human Jukebox</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>3</td>\n",
       "      <td>Florida A&amp;M University Marching 100 Band</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2</td>\n",
       "      <td>Grambling State University Tiger Marching Band</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>1</td>\n",
       "      <td>University of Arizona Symphonic Marching Band</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>1</td>\n",
       "      <td>Grambling State University Tiger Marching Band</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>1</td>\n",
       "      <td>Al Hirt</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     super_bowl                                           musician  num_songs\n",
       "80           27                                    Michael Jackson        5.0\n",
       "81           26                                     Gloria Estefan        2.0\n",
       "82           26              University of Minnesota Marching Band        NaN\n",
       "83           25                              New Kids on the Block        2.0\n",
       "84           24                                      Pete Fountain        1.0\n",
       "85           24                                       Doug Kershaw        1.0\n",
       "86           24                                        Irma Thomas        1.0\n",
       "87           24                    Pride of Nicholls Marching Band        NaN\n",
       "88           24                                  The Human Jukebox        NaN\n",
       "89           24                                  Pride of Acadiana        NaN\n",
       "90           23                                       Elvis Presto        7.0\n",
       "91           22                                     Chubby Checker        2.0\n",
       "92           22         San Diego State University Marching Aztecs        NaN\n",
       "93           22                                     Spirit of Troy        NaN\n",
       "94           21     Grambling State University Tiger Marching Band        8.0\n",
       "95           21                                     Spirit of Troy        8.0\n",
       "96           20                                     Up with People        NaN\n",
       "97           19                                       Tops In Blue        NaN\n",
       "98           18  The University of Florida Fightin' Gator March...        7.0\n",
       "99           18       The Florida State University Marching Chiefs        7.0\n",
       "100          17  Los Angeles Unified School District All City H...        NaN\n",
       "101          16                                     Up with People        NaN\n",
       "102          15                                  The Human Jukebox        NaN\n",
       "103          15                                    Helen O'Connell        NaN\n",
       "104          14                                     Up with People        NaN\n",
       "105          14     Grambling State University Tiger Marching Band        NaN\n",
       "106          13                                       Ken Hamilton        NaN\n",
       "107          13                                           Gramacks        NaN\n",
       "108          12                   Tyler Junior College Apache Band        NaN\n",
       "109          12                                      Pete Fountain        NaN\n",
       "110          12                                            Al Hirt        NaN\n",
       "111          11  Los Angeles Unified School District All City H...        NaN\n",
       "112          10                                     Up with People        NaN\n",
       "113           9                                   Mercer Ellington        NaN\n",
       "114           9     Grambling State University Tiger Marching Band        NaN\n",
       "115           8                  University of Texas Longhorn Band        NaN\n",
       "116           8                                       Judy Mallett        NaN\n",
       "117           7               University of Michigan Marching Band        NaN\n",
       "118           7                                       Woody Herman        NaN\n",
       "119           7                                      Andy Williams        NaN\n",
       "120           6                                    Ella Fitzgerald        NaN\n",
       "121           6                                     Carol Channing        NaN\n",
       "122           6                                            Al Hirt        NaN\n",
       "123           6      United States Air Force Academy Cadet Chorale        NaN\n",
       "124           5             Southeast Missouri State Marching Band        NaN\n",
       "125           4                                  Marguerite Piazza        NaN\n",
       "126           4                                     Doc Severinsen        NaN\n",
       "127           4                                            Al Hirt        NaN\n",
       "128           4                                  The Human Jukebox        NaN\n",
       "129           3           Florida A&M University Marching 100 Band        NaN\n",
       "130           2     Grambling State University Tiger Marching Band        NaN\n",
       "131           1      University of Arizona Symphonic Marching Band        NaN\n",
       "132           1     Grambling State University Tiger Marching Band        NaN\n",
       "133           1                                            Al Hirt        NaN"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Super Bowl XXVII'e kadar devre arası gösterileri vermiş müzisyenler\n",
    "halftime_musicians[halftime_musicians.super_bowl <= 27]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Liste göze alındığında Michael Jackson'a kadar tanınmış kişilerin olmaması devre arası gösterilerinin yetersiz olduğunun kanıtı olabilir."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Müzisyenlerin Devre Arası Gösteri Sayıları\n",
    "<p>Performans sayıları da dahil olmak üzere birden fazla devre arası gösterisi yapmış tüm müzisyenler</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>musician</th>\n",
       "      <th>super_bowl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Grambling State University Tiger Marching Band</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Up with People</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Al Hirt</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>The Human Jukebox</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Spirit of Troy</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Florida A&amp;M University Marching 100 Band</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Gloria Estefan</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>University of Minnesota Marching Band</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bruno Mars</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Pete Fountain</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Beyoncé</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Justin Timberlake</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Nelly</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Los Angeles Unified School District All City H...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              musician  super_bowl\n",
       "28      Grambling State University Tiger Marching Band           6\n",
       "104                                     Up with People           4\n",
       "1                                              Al Hirt           4\n",
       "83                                   The Human Jukebox           3\n",
       "76                                      Spirit of Troy           2\n",
       "25            Florida A&M University Marching 100 Band           2\n",
       "26                                      Gloria Estefan           2\n",
       "102              University of Minnesota Marching Band           2\n",
       "10                                          Bruno Mars           2\n",
       "64                                       Pete Fountain           2\n",
       "5                                              Beyoncé           2\n",
       "36                                   Justin Timberlake           2\n",
       "57                                               Nelly           2\n",
       "44   Los Angeles Unified School District All City H...           2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Her müzisyen için devre arası gösterilerinin sayılması ve sıralanması\n",
    "halftime_appearances = halftime_musicians.groupby('musician').count()['super_bowl'].reset_index()\n",
    "halftime_appearances = halftime_appearances.sort_values('super_bowl', ascending=False)\n",
    "\n",
    "# Birden fazla devre arası gösterisi veren müzisyenler\n",
    "display(halftime_appearances[halftime_appearances.super_bowl > 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Grambling State University Tiger Marching Band, en fazla devre arası gösterisi veren müzisyen grubudur.\n",
    "* Tekil olarak en fazla gösteri veren müzisyenler ikişer kez ile Bruno Mars, Beyonce, Justin Timberlake ve Nelly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. En Çok Şarkı Seslendiren Müzisyenler\n",
    "<p> Önceki incelemelere göre, num_songs sütununda birçok eksik değer var:</p>\n",
    "\n",
    "* Pek çok grupta num_songs girişi yok.\n",
    "* Eksik veriler Super Bowl XX'de oluşmaya başlar.\n",
    "\n",
    "<p> Müzisyenlerin içinde 'Marching' kelimesi ve 'Spirit' kelimesi (bandolar için yaygın bir adlandırma kuralı 'Spirit of [bir şey]' dir) olan müzisyenleri filtreleyip dışarıda bırakalım. Ardından, eksik veri sorununu gidermek için Super Bowl XX'den sonra Super Bowls için filtre uygulayacağız, sonra kimin en çok şarkıya sahip olduğunu görelim.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAFXCAYAAACP5RboAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3de3zP9f//8ft7J6dZI0MsQge0iMoIE8WQmWkZY2I+Ki2HYjZrRs5yKFYafUJIUyIj6TBqPo4lowh9Sn6Wmvls2Ex2eL9/f/h6Z59t3vPh/Z4Xt+vl4pLX6/1+vZ6PPffK/fV8HU0Wi8UiAABww3Mq7wIAAEDZENoAABgEoQ0AgEEQ2gAAGAShDQCAQRDaAAAYhEt5F3AlGRnZ5V2Cw1WrVllZWbnlXYah0YfXB/147ejDa3cr9qGXV9VSP7NbaBcWFio2NlZHjx6Vs7Ozpk+fLovFoujoaJlMJt1zzz2aMGGCnJwY7F/OxcW5vEswPPrw+qAfrx19eO3ow6LsFtpbtmyRJCUmJmrXrl3W0B41apR8fX0VFxen5ORkde7c2V4lAABwU7HbMPeJJ57Q5MmTJUknTpxQjRo1dODAAbVq1UqS5Ofnp+3bt9ureQAAbjp2Paft4uKiqKgoffnll5o/f762bNkik8kkSapSpYqys698zrpatcq35KGRK53PQNnQh9cH/Xjt6MNrRx/+ze4Xos2cOVNjxoxRnz59dOHCBev8c+fOycPD44rL3moXH0gXN85b8QK864k+vD7ox2tHH167W7EPr7STYrfD45988okWLlwoSapUqZJMJpN8fHy0a9cuSVJKSooefvhhezUPAMBNx24j7S5dumjcuHHq37+/CgoKFBMTo0aNGmn8+PGaO3euGjZsKH9/f3s1DwDATcduoV25cmXNmzev2PwVK1bYq0kAAG5q3CQNAIBBENoAABgEoQ0AgEEQ2gAAGAShDQCAQdzQb/myh/AZm8u7hGIWR3cq7xIAAAbASBsAAIMgtAEAMAhCGwAAgyC0AQAwCEIbAACDILQBADAIQhsAAIMgtAEAMAhCGwAAgyC0AQAwCEIbAACDILQBADAIQhsAAIMgtAEAMAhCGwAAgyC0AQAwCEIbAACDILQBADAIQhsAAIMgtAEAMAhCGwAAgyC0AQAwCEIbAACDILQBADAIQhsAAIMgtAEAMAhCGwAAgyC0AQAwCEIbAACDILQBADAIQhsAAIMgtAEAMAhCGwAAgyC0AQAwCEIbAACDcLHHSvPz8xUTE6Pff/9deXl5GjZsmGrXrq3nn39ed911lySpX79+6t69uz2aBwDgpmSX0E5KSpKnp6dmzZqlrKwsBQUFKSIiQoMHD1Z4eLg9mgQA4KZnl9Du2rWr/P39rdPOzs768ccfdfToUSUnJ6t+/fqKiYmRu7u7PZoHAOCmZLJYLBZ7rTwnJ0fDhg1Tnz59lJeXp/vuu08+Pj56++23dfbsWUVFRV1x+YKCQrm4OF/XmgJGr7uu67se1s8JLO8SAAAGYJeRtiT98ccfioiIUGhoqAICAnT27Fl5eHhIkjp37qzJkyfbXEdWVq69yruhZGRkW//u5VW1yDSuHn14fdCP144+vHa3Yh96eVUt9TO7XD1+6tQphYeHKzIyUsHBwZKkIUOGaP/+/ZKkHTt26P7777dH0wAA3LTsMtJOSEjQ2bNntWDBAi1YsECSFB0drWnTpsnV1VU1atQo00gbAAD8zS6hHRsbq9jY2GLzExMT7dEcAAC3BB6uAgCAQRDaAAAYBKENAIBBENoAABgEoQ0AgEEQ2gAAGAShDQCAQRDaAAAYBKENAIBBENoAABgEoQ0AgEEQ2gAAGAShDQCAQRDaAAAYBKENAIBBENoAABgEoQ0AgEEQ2gAAGAShDQCAQRDaAAAYBKENAIBBENoAABgEoQ0AgEEQ2gAAGAShDQCAQRDaAAAYBKENAIBBENoAABgEoQ0AgEEQ2gAAGAShDQCAQRDaAAAYBKENAIBBENoAABgEoQ0AgEEQ2gAAGAShDQCAQRDaAAAYBKENAIBBENoAABgEoQ0AgEG42GOl+fn5iomJ0e+//668vDwNGzZMd999t6Kjo2UymXTPPfdowoQJcnJinwEAgLKyS2gnJSXJ09NTs2bNUlZWloKCgtS4cWONGjVKvr6+iouLU3Jysjp37myP5gEAuCnZZajbtWtXjRw50jrt7OysAwcOqFWrVpIkPz8/bd++3R5NAwBw07LLSLtKlSqSpJycHI0YMUKjRo3SzJkzZTKZrJ9nZ2fbXE+1apXl4uJsjxJvKF5eVa84jatHH14f9OO1ow+vHX34N7uEtiT98ccfioiIUGhoqAICAjRr1izrZ+fOnZOHh4fNdWRl5dqrvBtKRsbfOzBeXlWLTOPq0YfXB/147ejDa3cr9uGVdlLscnj81KlTCg8PV2RkpIKDgyVJTZs21a5duyRJKSkpevjhh+3RNAAANy27hHZCQoLOnj2rBQsWKCwsTGFhYRo1apTi4+MVEhKi/Px8+fv726NpAABuWnY5PB4bG6vY2Nhi81esWGGP5gAAuCVwozQAAAZBaAMAYBCENgAABkFoAwBgEDYvRDtx4kSJ8+vUqXPdiwEAAKWzGdoDBgyQyWSSxWKxzjOZTEpOTrZrYQAAoCibob1582ZH1AEAAGyweU47Ly9Phw4dkiStX79eM2fOVGZmpt0LAwAARdkM7cjISK1fv1779u1TfHy83N3dNW7cOEfUBgAALmMztNPS0hQZGakvvvhCwcHBioiI0KlTpxxRGwAAuIzN0C4sLFRmZqa++uorPfbYY8rIyNCFCxccURsAALiMzQvRhgwZoj59+qhTp06699575e/vr5EjRzqiNgAAcBmboR0QEKCAgADr9MaNG+Xs7GzXogAAQHE2Q7u0i86mT59+3YsBAAClsxnarVq1ckQdAADABpuh3ahRIzVr1swRteAGEj7jxnqozuLoTuVdAgCUO5uhPWvWLJ0+fVqBgYEKDAyUl5eXI+oCAAD/xWZoL1++XL///rvWrVun8PBw1alTR0FBQXr88cfl6urqiBoBAIDK+GrOunXrqlevXgoICNDPP/+s5cuXq0ePHvryyy/tXR8AAPg/NkfaH330kdatW6eMjAz16tVLK1euVO3atZWenq6goCB17tzZEXUCAHDLsxna3377rYYPHy5fX98i82vVqqUJEybYrTAAAFCUzdB+7bXXdPDgQX333XeyWCwqLCxUWlqagoOD5e/v74gaAQCAyhDasbGx2r17t86cOaOGDRvq0KFDatmypYKDgx1RHwAA+D82L0Tbvn27Pv30U/n7+2vy5MlatmyZ/vrrL0fUBgAALmMztGvWrClXV1c1atRIhw8f1gMPPKDs7GxH1AYAAC5j8/B4rVq1tHDhQrVp00azZs2SJOXl5dm9MAAAUJTNkfbUqVPl7e2tZs2aqUuXLtqwYYMmTpzogNIAAMDlbI603d3d9eSTT8psNqtFixYKDAyUh4eHI2oDAACXKXWkfezYMfXu3Vtff/218vLyFBISohEjRqhnz57as2ePI2sEAAC6QmhPmTJFQ4YMUYcOHbRu3Trl5ubqiy++0JIlS6zntgEAgOOUeng8PT1dTz75pKSLt335+/vLxcVFDRo0UE5OjsMKBAAAF5U60rZYLNb/7tq1S48++qh1Ojc31zHVAQAAq1JH2vfdd58WLVqkvLw8ubm5qWXLlsrLy9PixYv14IMPOrJGAACgK4y0J0yYoBMnTujQoUN666235OTkpGnTpmnbtm2KiYlxZI0AAEBXGGlXrVq12P3Y3J8NAED5sXmfNuwvfMbm8i4BAGAANp+IBgAAbgyENgAABmHz8PjBgweVkJCgM2fOWG8Dk6Rly5bZtTAAAFCUzdCOiopSSEiI7rnnHplMJkfUBAAASmAztCtWrKgBAwY4ohYAAHAFNs9pt2vXTsuXL9fRo0d14sQJ65+y2Ldvn8LCwiRJBw4cUPv27RUWFqawsDBt3Ljx2ioHAOAWY3OkvW7dOknSkiVLrPNMJpOSk5OvuNw777yjpKQkVapUSdLFc+ODBw9WeHj4tdQLAMAty2Zob978v91DXK9ePcXHx2vs2LGSpB9//FFHjx5VcnKy6tevr5iYGLm7u/9P6wYA4FZkM7TPnDmjWbNm6f/9v/+n+fPna+bMmRo3bpw8PDyuuJy/v7/S0tKs082aNdPTTz8tHx8fvf3223rrrbcUFRV1xXVUq1ZZLi7OZfxRcDPz8qpq1++jZPTjtaMPrx19+DeboT1+/Hi1bdtW+/fvV+XKlVWzZk2NGTNGixYtuqqGOnfubA36zp07a/LkyTaXycribWK4KCMju8zf9fKqelXfR8nox2tHH167W7EPr7STYvNCtLS0NIWEhMjJyUlubm566aWX9Oeff151EUOGDNH+/fslSTt27ND9999/1esAAOBWZnOk7ezsrOzsbOs92r/99pucnK7+QWoTJ07U5MmT5erqqho1apRppA0AAP5mM7SHDx+usLAw/fHHH3rhhReUmpqqadOmlWnl3t7e+vDDDyVJ999/vxITE6+tWgAAbmE2Q7t169ZavHix9u/fr8LCQk2aNEk1atRwRG0AAOAyNkO7S5cu6tixo4KCgtSsWTNH1AQAAEpg8+T0Z599pubNm2vu3LkKCAjQu+++q4yMDEfUBgAALmMztCtVqqRevXpp6dKlGjFihJYtW6YuXbrohRde0LFjxxxRIwAAUBkOjx87dkxJSUnasGGD6tSpozFjxqhLly7auXOnhg4dqi+++MIRdQIAcMuzGdqDBw9W7969tXjxYtWtW9c6v0OHDtq2bZtdiwMAAH+zGdrJycnKz8+Xm5ubjh07pqNHj8rPz09OTk6KiYlxRI0AAEBlOKe9YMECRUdH68SJE+rfv7+WLl1a5vu0AQDA9WMztJOTkzVt2jRt2LBBPXv21NKlS/X99987ojYAAHAZm6FtNptVsWJFbdmyRR06dJDZbNb58+cdURsAALiMzdBu06aNevToofz8fD3yyCMaMGCAOnbs6IjaAADAZWxeiBYVFaWwsDDVrl1bTk5OGj9+vJo0aeKI2gAAwGVKDe34+HgNHz5c48aNK/Hz6dOn260oAABQXKmhfel9161atSr22aXXdAIAAMcpNbQ7deokSTp9+rT69+8vNzc362ezZs2yf2UAAKAImxeivfHGG+rfv7/S09Ot87Zv327XogAAQHE2Q7tBgwZ67rnn1L9/f3333XeOqAkAAJTA5tXjJpNJTzzxhLy9vTVy5EgNHDhQrq6ujqgNAABcxuZI22KxSJIaN26sDz74QJs2bdJPP/1k98IAAEBRNkfab731lvXv1atX15IlS7Rp0ya7FgUAAIqzGdpvvvlmifN79Ohx3YsBAAClK/Xw+HPPPSfp4n3al/60bNlSWVlZ8vLycliBAADgolJH2pMnT5YkBQUFFZkfHByskJAQ+1YFAACKKXWkHR8fX+L8X375Rf/5z3/sVhAAAChZqSPtS6Ppxo0by2QyWa8ir169uiIjIx1THQAAsCo1tH18fCRJhw4dclgxAACgdKWG9ieffHLFBXv16nXdiwEAAKUrNbSjo6N1++23q02bNiU+AY3QBgDAsUoN7bVr12rjxo3atm2bGjdurO7du+vRRx+Vk5PNh6gBAAA7KDW0mzRpoiZNmmj06NH64YcftHHjRs2dO1c+Pj568skn5evr68g6AQC45dl8IpokPfDAA3rggQf03Xffafbs2Vq/fr327t1r79oAAMBlrhjaFotF3377rTZt2qSUlBQ1adJEYWFh6tixo6PqAwAA/6fU0J4wYYK2bt2qpk2bqlu3boqMjFSlSpUcWRsAALhMqaG9atUqeXp66uDBgzp48KDmzp1b5PPk5GS7FwcAAP5WamgTygAA3FhKDe26des6sg4AAGADN10DAGAQhDYAAAZBaAMAYBCENgAABkFoAwBgEHYN7X379iksLEySdOzYMfXr10+hoaGaMGGCzGazPZsGAOCmY7fQfueddxQbG6sLFy5IkqZPn65Ro0Zp5cqVslgs3AcOAMBVslto16tXT/Hx8dbpAwcOqFWrVpIkPz8/bd++3V5NAwBwU7JbaPv7+8vF5e9nt1gsFplMJklSlSpVlJ2dba+mAQC4KZXp1ZzXg5PT3/sH586dk4eHh81lqlWrLBcXZ3uWBYPw8qpq1++jZPTjtaMPrx19+DeHhXbTpk21a9cu+fr6KiUlRa1bt7a5TFZWrgMqgxFkZJT9yIyXV9Wr+j5KRj9eO/rw2t2KfXilnRSH3fIVFRWl+Ph4hYSEKD8/X/7+/o5qGgCAm4JdR9re3t768MMPJUkNGjTQihUr7NkcAAA3NR6uAgCAQRDaAAAYBKENAIBBENoAABgEoQ0AgEEQ2gAAGAShDQCAQRDaAAAYBKENAIBBENoAABgEoQ0AgEEQ2gAAGAShDQCAQRDaAAAYBKENAIBBENoAABgEoQ0AgEEQ2gAAGAShDQCAQRDaAAAYBKENAIBBENoAABgEoQ0AgEEQ2gAAGAShDQCAQRDaAAAYBKENAIBBENoAABgEoQ0AgEEQ2gAAGAShDQCAQRDaAAAYBKENAIBBENoAABgEoQ0AgEEQ2gAAGAShDQCAQRDaAAAYBKENAIBBENoAABgEoQ0AgEEQ2gAAGISLoxvs1auXqlatKkny9vbW9OnTHV0CAACG5NDQvnDhgiRp+fLljmwWAICbgkMPjx86dEjnz59XeHi4Bg4cqNTUVEc2DwCAoZksFovFUY0dPnxY+/bt09NPP63ffvtNQ4cO1aZNm+TiUvKAv6CgUC4uzte1hoDR667r+nDrWj8nsLxLAHCLcejh8QYNGqh+/foymUxq0KCBPD09lZGRoTvuuKPE72dl5TqyPOCqZGRkl3cJduXlVfWm/xntjT68drdiH3p5VS31M4ceHl+9erVmzJghSUpPT1dOTo68vLwcWQIAAIbl0JF2cHCwxo0bp379+slkMmnatGmlHhoHAABFOTQx3dzcNGfOHEc2CQDATYOHqwAAYBCENgAABkFoAwBgEIQ2AAAGwaXbAOwqfMbm8i6hmMXRncq7BOB/wkgbAACDILQBADAIQhsAAIMgtAEAMAhCGwAAgyC0AQAwCEIbAACDILQBADAIQhsAAIPgiWjA/+hGfNIXgJsbI20AAAyC0AYAwCAIbQAADILQBgDAIAhtAAAMgtAGAMAgCG0AAAyC0AYAwCAIbQAADILQBgDAIAhtAAAMgtAGAMAgCG0AAAyC0AYAwCAIbQAADILQBgDAIAhtAAAMgtAGAMAgCG0AAAzCpbwLAABHC5+xubxLKGZxdKfyLqGYG7GfbkSO/N0x0gYAwCAIbQAADILQBgDAIAhtAAAMgtAGAMAgCG0AAAzCobd8mc1mTZw4UYcPH5abm5umTJmi+vXrO7IEAAAMy6Ej7a+++kp5eXlatWqVRo8erRkzZjiyeQAADM2hob1nzx61b99ekvTggw/qxx9/dGTzAAAYmkMPj+fk5Mjd3d067ezsrIKCArm4lFyGl1fV617D+jmB132dAHAz4t/LG49DR9ru7u46d+6cddpsNpca2AAAoCiHhnbLli2VkpIiSUpNTdW9997ryOYBADA0k8VisTiqsUtXjx85ckQWi0XTpk1To0aNHNU8AACG5tDQBgAA/zsergIAgEEQ2gAAGAShfYPIz89XZGSkQkNDFRwcrOTk5PIuybD+85//qEOHDvrll1/KuxRDWrhwoUJCQtS7d2999NFH5V2O4eTn52v06NHq27evQkND2Q6v0r59+xQWFiZJOnbsmPr166fQ0FBNmDBBZrO5nKsrf4T2DSIpKUmenp5auXKl3nnnHU2ePLm8SzKk/Px8xcXFqWLFiuVdiiHt2rVLe/fu1QcffKDly5frzz//LO+SDOebb75RQUGBEhMTFRERoTfeeKO8SzKMd955R7Gxsbpw4YIkafr06Ro1apRWrlwpi8XCYEaE9g2ja9euGjlypHXa2dm5HKsxrpkzZ6pv376qWbNmeZdiSP/617907733KiIiQs8//7wee+yx8i7JcBo0aKDCwkKZzWbl5OTwLIqrUK9ePcXHx1unDxw4oFatWkmS/Pz8tH379vIq7YbB1nSDqFKliqSLT40bMWKERo0aVc4VGc+aNWtUvXp1tW/fXosWLSrvcgwpKytLJ06cUEJCgtLS0jRs2DBt2rRJJpOpvEszjMqVK+v3339Xt27dlJWVpYSEhPIuyTD8/f2VlpZmnbZYLNZtr0qVKsrOzi6v0m4YjLRvIH/88YcGDhyowMBABQQElHc5hvPxxx9r+/btCgsL008//aSoqChlZGSUd1mG4unpqXbt2snNzU0NGzZUhQoVlJmZWd5lGcrSpUvVrl07ff7551q3bp2io6Oth3txdZyc/o6oc+fOycPDoxyruTEQ2jeIU6dOKTw8XJGRkQoODi7vcgzp/fff14oVK7R8+XI1adJEM2fOlJeXV3mXZSgPPfSQtm7dKovFovT0dJ0/f16enp7lXZaheHh4qGrVi+9NuO2221RQUKDCwsJyrsqYmjZtql27dkmSUlJS9PDDD5dzReWPw+M3iISEBJ09e1YLFizQggULJF28KIMLquBIHTt21Lfffqvg4GBZLBbFxcVxfcVVGjRokGJiYhQaGqr8/Hy99NJLqly5cnmXZUhRUVEaP3685s6dq4YNG8rf37+8Syp3PBENAACD4PA4AAAGQWgDAGAQhDYAAAZBaAMAYBCENgAABsEtXzeZtLQ0de3aVY0aNZIk/fXXX2rZsqVGjx6tGjVqOKyOzZs3a9iwYfr444/l4+Nj9/aOHz+u2bNn68CBA3J2dlb16tU1ZswYPfTQQ5KkH374QYmJierbt68SExM1derUUte1f/9+ff7554qMjCxz+x988IEkqV+/fkXmd+rUSRUrVpSrq6ssFotcXFw0duxYtW7duszrPnDggCIiIlS3bl29//77ZV6uvIWFhenPP/9U5cqVZTabVa1aNc2YMUN16tQpt5rWrFmjGTNm6I477pDFYlFeXp569OihYcOGWW9te+WVV9S3b19t3rxZPj4+evzxx+1Sy9mzZ/Xqq6/qyJEjkqSaNWtq/Pjxuuuuu8q8jkuP/Bw+fHiZvn/ixAlNmjRJv//+uywWixo1aqS4uDjdfvvtV10/yokFN5Xjx49bOnbsaJ02m82W2bNnW/r16+fQOiIiIiwjRoywxMbG2r2tzMxMS7t27Swffvihdd73339vadu2rSUjI+Oq1/fxxx9boqKirkttHTt2tBw/ftw6nZycbGnbtu1VrSM+Pt4yZ86c61KPIw0YMMCyc+dO6/SSJUssI0aMKMeKiv9uz507Z3nmmWcsr7/+usNriYuLsyQkJFin169fb+nVq9dVrWP+/PmW+fPnl/n7Q4YMsaxfv946nZCQYImIiLiqNlG+GGnf5Ewmk4YPH662bdvq0KFDaty4sRYtWqTPPvtMhYWFateunSIjIzVjxgzVqlVL4eHhki7uuffs2VPJyck6ffq0jh07psjISNWoUUPTp0/XX3/9pWrVqunVV1/VnXfeWaTNzMxM7dy5U5988ol69eqlqKgoubu7S5Jat24tHx8fZWRkaPXq1Xr11Vf1888/69SpU7rvvvs0d+5cFRQU6OWXX9apU6ckSREREXr88ce1Zs0a7d69WzNmzCjS3qpVq9SyZUs9/fTT1nktWrRQdHS0zp8/L0nasmWL3njjDZnNZt15552aNGmSatSooZkzZ2rbtm1ycnLSE088oYEDB2r+/PnKzc3V22+/rWeffVavvfaadu/ercLCQvXu3VuDBg3Srl27NGvWLJnNZt1zzz3y9va29tuV+Pr6KiMjQ1lZWSosLFRcXJz+/PNPmUwmjR49Wo8++qji4+OVmpqqP/74Q/3797eO4t3c3NS3b1+98sorOnHihFxcXPTSSy/Jz8+vyDIDBgzQZ599pqZNm2rPnj26cOGCxowZo2XLlumXX37RoEGDNGjQIKWnpysmJkbZ2dk6efKkgoKCNHLkSK1Zs0Zbt27VmTNndPz4cbVt21YTJ07Un3/+qTFjxig3N1dOTk6KjY3Vgw8+WObRXk5OjvVoT05OjmJiYpSenq6TJ0+qTZs2mjp1qtLT0222ERYWpgceeEB79uxRZmamYmNj1aFDBx05ckSTJ09Wbm6uMjMz9eyzzxY78vHfKleurJdffllDhw7VyJEj9eabbxb5WTp16qRly5YpJydHcXFxKigoUIUKFTR9+nTdddddSklJ0fz581VQUCBvb29NnjxZ1apVU6dOndSzZ0/961//0vnz5zVz5sxiR5xOnTql22+/XWazWU5OTurevbv1ISyl9c/u3btL3O4KCwv10ksvydvbW2PHjlV0dLRatWql3r17F2vz0v8TktS/f3/98MMPklTq9hAaGqqIiAi1bdtWFotF/v7+Wr58uT788ENrXy1ZskRr166Vk5OTmjVrpkmTJkmS7rvvPh0+fPiKvwNcHUL7FuDm5qb69evr119/1cmTJ/Xjjz9q9erVMplMioyMVFJSkgIDAxUbG6vw8HDl5ORo7969mjNnjpKTk+Xp6amEhATl5eUpODhYCQkJqlOnjrZu3arx48dr6dKlRdpLSkpS27Zt5e3tLR8fHyUlJSk0NFTSxRdSDB06VL6+vvr222/l6uqqVatWyWw265lnntE333yj3Nxc1a1bV4sWLdJPP/2kpKSkKx6iTE1NVbt27YrN79Gjh6SL79eOi4vTBx98IG9vb/3zn//UpEmTFBUVpZSUFH366ac6f/68xo0bpwoVKmjEiBHavXu3hg0bZg3MtWvXKi8vT0OGDLH+4/vbb79py5Ytqlq1apE3E13Jhg0bdNddd6latWp66aWX9NRTT+nxxx/XyZMnFRoaqk8++USSlJeXp40bN0qS9dnfL774okaOHKnWrVtr8ODBOn78uPr161fiMp999pksFotWr16tN998U1OmTFFSUpIyMzPVq1cvDRo0SBs2bFCPHj0UFBSk7OxsdejQwfoe471792rDhtX7rZAAAAs4SURBVA1ydnZW165d1a9fP3355Zd67LHH9I9//EMpKSnas2ePHnzwwSv+vLGxsapcubKys7N15swZLV++XJL09ddfq0mTJpo/f77y8vL05JNP6sCBA/r666/L1EZ+fr5WrVqlzZs3a968eerQoYM++ugjvfDCC2rTpo2OHz+unj172gxtSbrnnnt0+vTpKz5j/b333tPgwYPVrVs3rV27VqmpqfLw8NCcOXO0bNky3XbbbUpMTNTs2bOtp148PT21evVqLV++XAsXLiy2jQwbNkwRERFauXKlWrdurbZt26pnz55X7B+p+HZnsVgUGxur2rVra+zYsVf8WV9++WVFRkYqPj5ebdq0kZ+fn7p27SpJpW4PTz31lNatW6e2bdvqu+++U7169VSrVi3rOgsLC7Vw4UJt3bpVzs7OeuWVV5Senl7kO7h+CO1bhMlkUsWKFbVjxw7t37/fugf+119/qU6dOgoMDFReXp6OHTumvXv3qlOnTnJzc5MkNWvWTNLFfyyOHz+uYcOGWdebk5NTrK21a9fqxRdflCR1795dK1assIa2JDVv3lyS9Mgjj8jT01Pvv/++fv31V/3222/Kzc1VixYtNHfuXKWnp+uxxx5TREREmX6+S8aOHavDhw8rNzdXffv2VcOGDdWsWTPrqCQkJESLFi1SrVq1VKFCBfXt21cdO3bUmDFjVKFChSLr3bFjh3766Sft3LlTkpSbm6vDhw/r7rvvVoMGDazPmL6SZ599Vq6ursrPz9cdd9xhfb/y9u3b9euvv2r+/PmSpIKCAh0/flzS333+33bu3KkpU6ZIku688041b95c+/btK3EZPz8/SVKdOnXUvHlzVapUSXXr1tXZs2clSUOGDNHOnTv17rvv6ueff1Z+fr51FNaiRQvr0ZE777xTZ86cUZs2bTR8+HD99NNP6tChgwYMGGDzZ58yZYp8fX0lSZs2bdLgwYOVnJysHj16aP/+/Vq6dKl+/fVXnT59Wrm5uWVuo3379pL+DlxJio6O1tatW7Vw4UIdOXJEubm5NuuT/t52/vt3f7kOHTpo0qRJ2rp1qzp16qSOHTsqJSXF+pIfSTKbzbrttttKrPGLL74otk4fHx8lJyfr+++/1/bt27V48WIlJiZq1apVpfaPpGLbXWJiorKzs8v0rmk/Pz+lpKRo165d2rFjh2bNmqVPP/1UCxYsKHV76Natm15//XXl5uZq7dq1xUbvzs7OatGihYKDg/X4449r8ODBBLYdEdq3gLy8PB09elR33323du7cqWeeeUaDBw+WdPFimEsX4PTs2VMbN27U3r179eyzz1qXv/T8c7PZLG9vb61bt07SxT3sS4ewLzlw4ICOHDmiqVOnavr06SosLNTJkyeVmppqHTFdWl9ycrLmz5+vgQMHqnfv3srKypLFYtFdd92lzz77TFu3btWWLVu0ePFi6wiyJA888IC+//579e/fX5L02muvSbp4kU5ubq7MZnOR71ssFhUUFMjFxUUfffSRdu/erZSUFPXt29c6EryksLBQkZGR6tKli6SLo94qVaooNTW1zM+FX7RokXWH4XJms1nvvfee9YUcJ0+e1O23366vvvqq1HVb/uupwxaLxfoyiv9extXV1fr3kt7pPGPGDB0/flw9evTQE088oe3bt1vXf3mAmUwmWSwWPfTQQ/r000/19ddfa+PGjVq7dq2WLFlSli6QdPGd8ePHj9fRo0eVmpqqzz//XH369NGjjz6qI0eOXFUbl+q7fGdt1KhR8vDwUMeOHdW9e3dt2LChTHUdPnxYtWvXlru7u0wmU5HtJT8/31p7ixYttGXLFi1dutR6RKBly5bWV29euHBB586du2KNl1gsFk2cOFExMTFq1aqVWrVqpYiICPn7++vgwYPat29fif0jFf89t2jRQk2bNtWUKVOsO4AlOX36tBYsWKCYmBj5+fnJz89PL7zwgtq1a6fMzEwtWrSoxO2hcuXK8vPz0+eff66dO3dqwoQJxda9YMECpaamKiUlRf/4xz80e/Zs63uwcX1xy9dNzmw2Kz4+Xs2bN1e9evXUunVrrVu3TufOnVNBQYEiIiL0+eefS5ICAgK0ceNGHTt2zHrV9eUaNmyoM2fO6LvvvpN08VWYY8aMKfKdNWvWqE+fPvr666+1efNmffPNNwoMDFRiYmKx9e3YsUPdunXTU089JQ8PD+3atUuFhYVasWKF4uPj1a1bN02YMEGZmZkljugv6devn/bs2aM1a9ZY/2E7deqUUlNT5eTkZB2NXnpP76pVq+Tr66uDBw9qwIABeuSRRxQVFaVGjRrp6NGjcnZ2VkFBgaSL5+A//PBD5efn69y5cwoNDVVqaur/8JsornXr1lq5cqUk6d///rcCAgKKnG8sbZnVq1dLunjF/Pfff2/zEHVptm3bpiFDhqhbt246evSo0tPTi+3gXO61115TUlKSgoKCFBcXp4MHD15Vez/++KMKCgrUoEEDbdu2TSEhIerZs6cuXLigQ4cOyWw2X1Mb27Zt04gRI/TEE08oJSVFkmy+XSs7O1vz5s2z7vBVq1ZN//73vyVdvIvg0qtdR40apR9++EF9+/bVyJEjdfDgQTVv3lypqak6evSopIvBdWmH0RaTyaRffvlF7777rrXP09LSVFBQoHr16pXaPyVp3Lixhg4dqp9//lmbN28utc2qVatq8+bN1tMp0sXt7vbbb9dtt912xe3hqaee0uuvv6727dsXOyKRmZmp7t27695779XIkSPVtm1bzmPbESPtm9DJkycVGBgo6WJoN2nSRHPnzpV08cKaQ4cOqU+fPiosLFT79u0VFBQkSbrjjjtUrVo1tWjRosTRgZubm+bNm6epU6fqwoULcnd318yZM62f5+XlacOGDVq2bFmR5QYNGqSQkBCNGzeuyPynn35aY8aM0aeffipXV1e1bNlSaWlpGjp0qF5++WUFBATI2dlZkZGRV3yPbvXq1ZWYmKg5c+bo3XffVWFhoVxdXdWzZ08NHDhQFSpU0KRJk/Tiiy8qPz9fderU0dSpU1WzZk09+OCD6tGjhypVqqSWLVvKz89Px48f15tvvqnZs2dr5MiROnbsmIKCglRQUKDevXvL19fX+rrAaxEbG6u4uDjru9Nfe+016yHp0rzyyiuKi4vTmjVrJF08/FyzZs3/qf3nnntOY8eOVcWKFVW7dm35+PhYd2xKEhYWptGjR2vNmjVydnYu8rsvzaVz2pd2hGbPni13d3c988wzmjhxohYtWiR3d3e1aNFCaWlp1jamTZum+++/v0xtXDJ8+HCFhoaqQoUKaty4serWrau0tDTVr1+/yPc2b96swMBAmUwmFRYWqkuXLho6dKgk6cknn9SXX35pDaEWLVpIkp5//nm98sorioiIUPPmzTVx4kR5eXlp2rRpGjVqlMxms2rVqqVZs2aVud65c+dq+vTp8vX1lZubm7y9vTVnzhx5enqW2j/16tUrcV1ubm6aOHGioqOjracj/puzs7MWLVqkGTNmaN68eapYsaJq1qyphIQEOTs7l7o91KtXTw899JBMJpOeeuqpYuutXr26QkJCFBwcrEqVKqlBgwYlfg/XB2/5AnBDsVgsGj58uAIDA9W5c+fyLsfq7NmzioyM1NChQ6/re5137Nih1atXa8qUKapUqdJ1W+/1YrFYdOTIEUVFRRUZpaN8MNIGcEN55513dOTIEesFZjeK+fPn67fffity3vpanTp1yjo6z8/PvyFD+7333tM///lPzZs3r7xLgRhpAwBgGFyIBgCAQRDaAAAYBKENAIBBENoAABgEoQ0AgEEQ2gAAGMT/B617K7MoNbe5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>super_bowl</th>\n",
       "      <th>musician</th>\n",
       "      <th>num_songs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>Justin Timberlake</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>30</td>\n",
       "      <td>Diana Ross</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>49</td>\n",
       "      <td>Katy Perry</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51</td>\n",
       "      <td>Lady Gaga</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>23</td>\n",
       "      <td>Elvis Presto</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>41</td>\n",
       "      <td>Prince</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>47</td>\n",
       "      <td>Beyoncé</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>48</td>\n",
       "      <td>Bruno Mars</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>Coldplay</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>45</td>\n",
       "      <td>The Black Eyed Peas</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>46</td>\n",
       "      <td>Madonna</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>44</td>\n",
       "      <td>The Who</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>27</td>\n",
       "      <td>Michael Jackson</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>32</td>\n",
       "      <td>The Temptations</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>39</td>\n",
       "      <td>Paul McCartney</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    super_bowl             musician  num_songs\n",
       "0           52    Justin Timberlake       11.0\n",
       "70          30           Diana Ross       10.0\n",
       "10          49           Katy Perry        8.0\n",
       "2           51            Lady Gaga        7.0\n",
       "90          23         Elvis Presto        7.0\n",
       "33          41               Prince        7.0\n",
       "16          47              Beyoncé        7.0\n",
       "14          48           Bruno Mars        6.0\n",
       "3           50             Coldplay        6.0\n",
       "25          45  The Black Eyed Peas        6.0\n",
       "20          46              Madonna        5.0\n",
       "30          44              The Who        5.0\n",
       "80          27      Michael Jackson        5.0\n",
       "64          32      The Temptations        4.0\n",
       "36          39       Paul McCartney        4.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bando ekiplerini filtreleme\n",
    "no_bands = halftime_musicians[~halftime_musicians.musician.str.contains('Marching')]\n",
    "no_bands = no_bands[~no_bands.musician.str.contains('Spirit')]\n",
    "\n",
    "# Gösteri performansı başına düşen şarkı sayısı\n",
    "most_songs = int(max(no_bands['num_songs'].values))\n",
    "plt.hist(no_bands.num_songs.dropna(), bins=most_songs)\n",
    "plt.xlabel('Devre Arası Gösteri Performansı Başına Düşen Şarkı Sayısı')\n",
    "plt.ylabel('Müzisyen Sayısı')\n",
    "plt.show()\n",
    "\n",
    "# Grup olmayan müzisyenleri gösteri performansı başına düşen şarkı sayısına göre sıralama...\n",
    "no_bands = no_bands.sort_values('num_songs', ascending=False)\n",
    "# ...ve ilk 15i görüntüleme\n",
    "display(no_bands.head(15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Devre arasının süresi kısıtlı olduğu için genelde müzisyenler 1-3 şarkı arası söyler. Ancak bu şarkı hit sayınıza göre değişebilir.\n",
    "* Verilere göre en çok şarkıyı 11 şarkı ile Justin Timberlake söylemiştir."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. Sonuç\n",
    "<p>Bu notebook'da Super Bowl maç, televizyon ve devre arası gösterileri verilerini yükledik, temizledik ve araştırdık. Histogramları kullanarak birleşik noktaların, nokta farklılıklarının ve devre arası gösteri performanslarının dağılımlarını görselleştirdik. İzleyici artışlarının gerisinde kalan reklam maliyeti artışlarını görmek için çizgi grafikleri kullandık. Ve patlamaların izleyicilerin sayısında bir düşüşe yol açtığını keşfettik.</p>"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}