Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "63cfc092-7b86-45e4-8332-021df243b498",
   "metadata": {},
   "source": [
    "<h1>Анализ клиентских данных магазина</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48e87b47-5a87-4368-b6c1-0ce062393000",
   "metadata": {},
   "source": [
    "<h2>Первый проект на Jupiter Notebook</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0643080",
   "metadata": {},
   "source": [
    "### Задачи в рамках проекта\n",
    "* Для анализа дан магазин с 200 покупателями. Данные получены из банка открытых датасетов [Kaggle](https://www.kaggle.com/datasets)\n",
    "* Существует выгрузка в виде CSV, которую нужно распарсить с помощью Python и статистически проанализировать.\n",
    "* Посчитать средний возраст и доход клиентов магазина.\n",
    "* Построить точечный график распределения дохода в зависимости от покупательского балла.\n",
    "* Построить столбчатую диаграмму среднего дохода покупателей по возрастам."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e87ebdb-5035-4ca0-89a1-f62ba69e4076",
   "metadata": {},
   "source": [
    "В первую очередь, понимаем, какие библиотеки нам понадобятся в работе. Это Pandas для загрузки и обработки датасета и Matplotlib для отрисовки графиков."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "737d4699-e95d-4163-86a0-e05a5f23d5d7",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fa97b01",
   "metadata": {},
   "source": [
    "## Статистические расчеты"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2ff4dbf",
   "metadata": {},
   "source": [
    "Выгрузим таблицу с помощью библиотеки **Pandas.**\n",
    "\n",
    "Выведем содержимое таблицы с клиентскими данными в интерфейс *Jupiter Notebook.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "bc547f88-3f1d-4701-85bb-13fd1ede2cce",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual_Income</th>\n",
       "      <th>Spending_Score</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
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       "      <td>81</td>\n",
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       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>195</th>\n",
       "      <td>196</td>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>120</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>197</td>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>126</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>198</td>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>126</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>199</td>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>137</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>200</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>137</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CustomerID   Genre  Age  Annual_Income  Spending_Score\n",
       "0             1    Male   19             15              39\n",
       "1             2    Male   21             15              81\n",
       "2             3  Female   20             16               6\n",
       "3             4  Female   23             16              77\n",
       "4             5  Female   31             17              40\n",
       "..          ...     ...  ...            ...             ...\n",
       "195         196  Female   35            120              79\n",
       "196         197  Female   45            126              28\n",
       "197         198    Male   32            126              74\n",
       "198         199    Male   32            137              18\n",
       "199         200    Male   30            137              83\n",
       "\n",
       "[200 rows x 5 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('Mall_Customers.csv')\n",
    "data.head(200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6284b70f-c4de-481e-864d-b7e88c28b347",
   "metadata": {},
   "source": [
    "Считаем колонку **CustomerID** как индекс первого столбца и установим его ключом. Убедимся, что первая колонка-индекс пропала"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "bd4abe55-fbfa-49c6-98c4-703dd30edafd",
   "metadata": {
    "scrolled": true,
    "tags": []
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   "outputs": [
    {
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       "      <td>39</td>\n",
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       "      <td>79</td>\n",
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       "      <th>197</th>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>126</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>126</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>137</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>Male</td>\n",
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       "      <td>137</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Genre  Age  Annual_Income  Spending_Score\n",
       "CustomerID                                            \n",
       "1             Male   19             15              39\n",
       "2             Male   21             15              81\n",
       "3           Female   20             16               6\n",
       "4           Female   23             16              77\n",
       "5           Female   31             17              40\n",
       "...            ...  ...            ...             ...\n",
       "196         Female   35            120              79\n",
       "197         Female   45            126              28\n",
       "198           Male   32            126              74\n",
       "199           Male   32            137              18\n",
       "200           Male   30            137              83\n",
       "\n",
       "[200 rows x 4 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.set_index('CustomerID', inplace=True) \n",
    "data.head(200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "517eb8d0-dd48-4f0f-bb42-91eed5f25d0a",
   "metadata": {},
   "source": [
    "Считаем статистики по датасету: *средние, отклонения, экстремумы, процентили.*\n",
    "\n",
    "Воспользуемся стандартыми возможностями функции Python **desсribe()**\n",
    "\n",
    "Для расчета средней была использована следующая формула:\n",
    "\n",
    "\\begin{equation*}\n",
    "\\frac{\\sum x_i}{n}\n",
    "\\end{equation*}\n",
    "\n",
    "Для расчета отклонения была использована формула дисперсии:\n",
    "\n",
    "<img src='https://smart-lab.ru/uploads/images/00/00/16/2012/09/16/7273a8.png'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b7c0ffdc-af08-426b-a2a7-f783eb97f03c",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Age</th>\n",
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       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.850000</td>\n",
       "      <td>60.560000</td>\n",
       "      <td>50.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.969007</td>\n",
       "      <td>26.264721</td>\n",
       "      <td>25.823522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.750000</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>34.750000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>61.500000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>49.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>99.000000</td>\n",
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      ],
      "text/plain": [
       "              Age  Annual_Income  Spending_Score\n",
       "count  200.000000     200.000000      200.000000\n",
       "mean    38.850000      60.560000       50.200000\n",
       "std     13.969007      26.264721       25.823522\n",
       "min     18.000000      15.000000        1.000000\n",
       "25%     28.750000      41.500000       34.750000\n",
       "50%     36.000000      61.500000       50.000000\n",
       "75%     49.000000      78.000000       73.000000\n",
       "max     70.000000     137.000000       99.000000"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b6052d5",
   "metadata": {},
   "source": [
    "**Вывод:** средний возраст покупателей - *38.85 лет*, средний годовой доход покупателей - *60 560 $ в год*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62f11f68-4e90-42b3-aa24-847b6ff4ea60",
   "metadata": {},
   "source": [
    "Сделаем рассчет суммы и количества повторений годового дохода по возрастным группам.\n",
    "\n",
    "Соберем новую таблицу с тремя колонками: возраст (age), суммарный годовой доход (sum), число лиц в возрастной группе (count), средний годовой доход (mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d52b0829-0198-42a8-a1c5-7b01b79485b8",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
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       "      <th>27</th>\n",
       "      <td>379</td>\n",
       "      <td>6</td>\n",
       "      <td>63.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>341</td>\n",
       "      <td>4</td>\n",
       "      <td>85.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>318</td>\n",
       "      <td>5</td>\n",
       "      <td>63.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>533</td>\n",
       "      <td>7</td>\n",
       "      <td>76.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>387</td>\n",
       "      <td>8</td>\n",
       "      <td>48.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>959</td>\n",
       "      <td>11</td>\n",
       "      <td>87.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>241</td>\n",
       "      <td>3</td>\n",
       "      <td>80.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>395</td>\n",
       "      <td>5</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>420</td>\n",
       "      <td>9</td>\n",
       "      <td>46.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>486</td>\n",
       "      <td>6</td>\n",
       "      <td>81.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>195</td>\n",
       "      <td>3</td>\n",
       "      <td>65.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>447</td>\n",
       "      <td>6</td>\n",
       "      <td>74.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>218</td>\n",
       "      <td>3</td>\n",
       "      <td>72.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>370</td>\n",
       "      <td>6</td>\n",
       "      <td>61.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>202</td>\n",
       "      <td>2</td>\n",
       "      <td>101.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>120</td>\n",
       "      <td>2</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>197</td>\n",
       "      <td>3</td>\n",
       "      <td>65.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>151</td>\n",
       "      <td>2</td>\n",
       "      <td>75.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>208</td>\n",
       "      <td>3</td>\n",
       "      <td>69.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>177</td>\n",
       "      <td>3</td>\n",
       "      <td>59.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>421</td>\n",
       "      <td>6</td>\n",
       "      <td>70.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>291</td>\n",
       "      <td>5</td>\n",
       "      <td>58.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>357</td>\n",
       "      <td>7</td>\n",
       "      <td>51.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>293</td>\n",
       "      <td>5</td>\n",
       "      <td>58.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>111</td>\n",
       "      <td>2</td>\n",
       "      <td>55.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>111</td>\n",
       "      <td>2</td>\n",
       "      <td>55.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>79</td>\n",
       "      <td>2</td>\n",
       "      <td>39.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>239</td>\n",
       "      <td>4</td>\n",
       "      <td>59.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>57.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>129</td>\n",
       "      <td>2</td>\n",
       "      <td>64.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>108</td>\n",
       "      <td>2</td>\n",
       "      <td>54.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>261</td>\n",
       "      <td>4</td>\n",
       "      <td>65.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>130</td>\n",
       "      <td>3</td>\n",
       "      <td>43.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>113</td>\n",
       "      <td>2</td>\n",
       "      <td>56.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>101</td>\n",
       "      <td>2</td>\n",
       "      <td>50.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>126</td>\n",
       "      <td>2</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>182</td>\n",
       "      <td>4</td>\n",
       "      <td>45.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>170</td>\n",
       "      <td>3</td>\n",
       "      <td>56.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>95</td>\n",
       "      <td>2</td>\n",
       "      <td>47.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sum  count        mean\n",
       "Age                        \n",
       "18   205      4   51.250000\n",
       "19   456      8   57.000000\n",
       "20   208      5   41.600000\n",
       "21   194      5   38.800000\n",
       "22    94      3   31.333333\n",
       "23   249      6   41.500000\n",
       "24   157      4   39.250000\n",
       "25   173      3   57.666667\n",
       "26   116      2   58.000000\n",
       "27   379      6   63.166667\n",
       "28   341      4   85.250000\n",
       "29   318      5   63.600000\n",
       "30   533      7   76.142857\n",
       "31   387      8   48.375000\n",
       "32   959     11   87.181818\n",
       "33   241      3   80.333333\n",
       "34   395      5   79.000000\n",
       "35   420      9   46.666667\n",
       "36   486      6   81.000000\n",
       "37   195      3   65.000000\n",
       "38   447      6   74.500000\n",
       "39   218      3   72.666667\n",
       "40   370      6   61.666667\n",
       "41   202      2  101.000000\n",
       "42   120      2   60.000000\n",
       "43   197      3   65.666667\n",
       "44   151      2   75.500000\n",
       "45   208      3   69.333333\n",
       "46   177      3   59.000000\n",
       "47   421      6   70.166667\n",
       "48   291      5   58.200000\n",
       "49   357      7   51.000000\n",
       "50   293      5   58.600000\n",
       "51   111      2   55.500000\n",
       "52   111      2   55.500000\n",
       "53    79      2   39.500000\n",
       "54   239      4   59.750000\n",
       "55    57      1   57.000000\n",
       "56    79      1   79.000000\n",
       "57   129      2   64.500000\n",
       "58   108      2   54.000000\n",
       "59   261      4   65.250000\n",
       "60   130      3   43.333333\n",
       "63   113      2   56.500000\n",
       "64    19      1   19.000000\n",
       "65   101      2   50.500000\n",
       "66   126      2   63.000000\n",
       "67   182      4   45.500000\n",
       "68   170      3   56.666667\n",
       "69    44      1   44.000000\n",
       "70    95      2   47.500000"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data.groupby(['Age'])['Annual_Income'].agg(['sum', 'count', 'mean'])\n",
    "df.head(200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "487c2da7",
   "metadata": {},
   "source": [
    "## Визуализация результатов"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "085761c3-0027-4fa5-8822-f19fc74fc45e",
   "metadata": {},
   "source": [
    "Рисуем график распределения возраста по бонусной шкале"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f4b23f28-4804-4e21-8e1c-778aeced216f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0hg3LQsysfK1URO5zdexJK9Iycpeyh74HAPhFsnMDcqTgWE79MG+XSIiZ114rWZ3d5yqgtvtcjSsN+XqcZAN9DwBAPCZScCRCiHCsVO5z5bRJoNvR9wAAxKPYBBwnEkJ07Ae3+sZDmr6kWqu219nUMtiJ+1zZh74HACAeEyk4SmchRFJbCBGVwvyH+1zZh74HACAeEyk4SiohRHAWs2Wxk31e5D5XiTJwAmoL//TTfa6sQt8DABCPHCk4CiFE7mQ2py2V50XuczV9SbUCUsyqpdPuc+U19D0AAPFYkYKjEELkPmZz2sw87+JhhVr0nTMVCsbu/1Awj/LbWUbfAwAQi/tIyTn3kaLcd1sfjKpaq/rGQ+3mSQXU9sFtw5yx7fYNfWityP5KFI6ZaH+Zfd7Rz3f6fnZDG83w6vsCACCC+0i5DOW+26QTQkQfWs9sWex0y2k78T5XR/PyWHR63wMAYBVC+xyAct+xzIQQ0Yf2MJvT5uVcOMYiAAD+wIqUzTor9x1QW7nvcaUhX4XPXDysUONKQ0mFENGH9jGb0+bVXDjGIgAA/sFEymbphjh5WbIhRPShfSJlsTvLaTu2LLbZ5zmdXWPRbN4S+U4AAJjHRMpmXg5xsgp9aB+zOW1eLadtx1i0ovQ8AACIR46Uzbwa4mQl+tBeZstie7GcttVj0crS8wAAIBYrUjbzaoiTlehD+6WS05aJ55llJpQtledYORbN5mORxwUAQGYwkbKZV0OcrEQfOoPZsthWldM2E8qW6nOsHIt2lZ4HAABtCO1zAC+GOFmNPkRHzISymQ1/s2osUnoeAAB7sSLlEFaHOHkRfYj2mAllSzf8zYqxSOl5AADsxUTKQawKcfIyN/QhJaetZSaULRPhb9kei5SeBwDAXkykAAtRctp6ZkLZ3BD+Rul5AADsRY4UYBFKTtvDTCibW8LfKD0PAIB9WJECLEDJafuYCWVzU/ibW0rPAwDgNaxIARZIJecmk1oOh/Xw+p26Y/l2Pbx+p1oOhzP6990gEsomHQldi0gUymbmOXaK5GNNGt5fZYP7OqZdAAB4GStSgAXsyLmpXFmjxetrFT5qSeXulW9p6ugSlU8ozdjruEEklO3Y/LRQB/lpZp7jJuTrAQCQHiZSgAWszrmpXFmjB1+pjXs8bCj6uB8nU6mGsnk1/C2Sr3ds2GIkX488KQAAOsdECo7lpTLhVubctBwOa/H6+EnU0Ravr9Vt44eqe1f7o3ut3M9mSpK7oaR+KsjXAwAgM5hIwZG8FnZkZcnpJzZ9EBPO156w0bbdDaMHpf166fDafnaDTNwjCwAAUGwCDuTVMuFWlZzete+LjG6XLV7dz07nhntkAQDgBqxIwVG8HnZkRc7NgD49M7pdNnh9PzuZW+6RBQCA07EiBUexq0y4lbJdqvqasoHq7E/mBNq2S6Q1bGjTjgYt3/aRNu1oUGtnsYIp8sN+dqpIvl6iIRJQW3ilE+6RBQCAk7EiBUch7Ch93bvmaOroknar9kVMHV2SsNCEFXlL7Gf7WJmvBwCAl7EiBUch7CgzyieUatr5JXErUzkBadr5ie8jZVXeEvvZXlbl6wEA4GWsSMFRrCwT7nXlE0p12/ihemLTB9q17wsN6NNT15QNTLgSZWXe0lkDTlBOQB1WF8wJtG3nVk4v3+/Ve2QBAGAVJlJwFMKOMqt715ykS5xbWRZ76679SZVo37prvytLcLulrLvX7pEFAICVCO2D4xB2ZA8r85a8nCNFWXcAAPyBFSk4EmFH1rMyb8mrOVKUdQcAwD+YSMGxCDuylpX5aV7NhbMyPBIAANiL0D4Ako7kp0mKu8dQpvPTrHwtK3k5ZBEAAMRiIgUgysr8NC/mwnk1ZBEAAMQjtA/wMDMluC8eVqixQwuSLpueDq/lwnk1ZBEAAMRjIgV4lNkS3O0973cbarNWuttLuXCU7wcAwD8I7QM8yGwJbkp3p8+LIYsAACBewDCMTm6L6X1NTU0KBoNqbGxUfn6+3c0B0tIaNjSqam3C6nGR8LINc8bGrIyYfR7aZyasEgAA2C/ZuQGhfYDHmC3BTenuzPJSyCIAAIhHaB/gMWZLcFO6GwAAIHlMpACPMVuCm9LdAAAAyWMiBXjMWQNOUGepODmBtu2OFindneipAbVV/aN0NwAAABMppKjlcFgPr9+pO5Zv18Prd6rlcNjuJuEYW3ftV7iTEjJho227o0VKd0uKm0xRuhsAACAWxSaQtMqVNVq8vjbmQ/rdK9/S1NElKp9Qal/DECOdXKdI6e5j7yMVSuL+UwAAAH7CRApJqVxZowdfqY17PGwo+jiTKWdIN9fp4mGFGlcaonQ3AABABwjtQ6daDoe1eH38JOpoi9fXEubnEJnIdYqU7p40vL/KBvdlEgUAAHAMR0+kDh8+rNtvv10lJSXq0aOHBg0apLvuukvh8JEP7IZh6M4771RRUZF69OihCy64QG+++aaNrfaeJzZ9kFTOzRObPrCkPegYuU4AAADZ5+iJVFVVlX77299q4cKFeuuttzR//nz9x3/8h371q19Ft5k/f74WLFighQsXasuWLQqFQho3bpwOHDhgY8u9Zde+LzK6HbIvkusUCsaG74WCeVr0nTPJdQIAAEiTo3OkNm3apEmTJunrX/+6JGngwIFaunSpXnvtNUltq1H33XeffvrTn+ryyy+XJD322GMqKCjQU089pWnTptnWdi8Z0KdnRreDNch1AgAAyB5Hr0iNGjVKL7/8st59911J0v/8z/9ow4YNmjBhgiSptrZW9fX1Gj9+fPQ5ubm5GjNmjDZu3Jjw7zY3N6upqSnmB4ldUzYwqfsSXVM2sN3ftYYNbdrRoOXbPtKmHQ1q7SxOEBlDrlP6GL8AAKA9jl6RmjNnjhobGzV06FB16dJFra2tuvvuuzV58mRJUn19vSSpoKAg5nkFBQXatWtXwr9bWVmpioqK7DXcY7p3zdHU0SXtVu2LmDq6RN27xs/LV22viyulXUgpbbgE4xcAACTi6BWpp59+WkuWLNFTTz2l6upqPfbYY7r33nv12GOPxWwXCMR+y24YRtxjRysvL1djY2P0Z8+ePVlpv5eUTyjVtPNL4lamcgLStPPbv4/Uqu11mr6kOuZDqCTVNx7S9CXVWrW9LptNBtLC+AUAAB1x9IrUv/3bv+nHP/6xrrrqKknS6aefrl27dqmyslLXXnutQqGQpLaVqcLCI98O7927N26V6mi5ubnKzc3NbuM9qHxCqW4bP1RPbPpAu/Z9oQF9euqasoHtrkS1hg1VrKhRe0FQhtqqx1WsqNG40hDhZg7UGjZ8nVvF+AUAAJ1x9ETqiy++UE5O7If0Ll26RMufl5SUKBQKac2aNTrjjDMkSS0tLVq3bp2qqqosb68fdO+aoxtGD+p0u1dr98V9k380Q1Jd4yG9WrtPZYP7ZrCFSBfhbIxfAADQOUdPpCZOnKi7775bX/rSl3Taaafpr3/9qxYsWKDrr79eUltI36xZszRv3jwNGTJEQ4YM0bx589SzZ09NmTLF5tb7294DiT+EmtkO1oiEsx27EhMJZ/NL6XTGLwAA6IyjJ1K/+tWv9LOf/Uw33XST9u7dq6KiIk2bNk133HFHdJsf/ehHOnjwoG666Sbt379f55xzjlavXq3evXvb2HL0653X+UYpbIfsI5ztCMYvAADoTMAwDN/X8m1qalIwGFRjY6Py8/Ptbo4ntIYNjapaq/rGQ+1+MA+o7eawG+aMTfih3Eyejt9ze9KxaUeDJi/e3Ol2S6ee2244m5f6PhPjFwAAuFOycwNHr0jBvbrkBDR3YqmmL6lWQIr5MBr52Dl3YmnCD6Fm8nTI7UlPOuFsXuv7dMcvAADwPkeXP4e7XTysUIu+c6ZCwdjwp1Awr8NcGzNlpylVnT6z4Wxe7Xuz4xcAAPgDoX0itC/bWg6HkyqZLh0JqUpUMa29kCozz0E8M+Fsfuh7N4QsuqGNAAC4BaF9cIT2Qr5+t6E2YciXmbLTlKrODDPhbH7o+y45AUe33WthlQAAuAWhfcgaMyFfZvJ0KFWdOamGs9H39vJqWCUAAG7AihSywmwpbTN5OpSqzqyLhxVqXGkoqVAx+t4+lKsHAMBeTKR8Kts5FWZDvs4u6aPCYF6neTpnl/RJ6zluZGUeTLLhbH7pezOceoxZ2UYAALyMiZQPWZFTYTbky0yejh9KVTs1D8YPfW+Gk48xK9sIAICXkSPlM1blVKQT8mWm7LSXS1U7PQ/Gy31vhhuOMaePKQAA3IDy5/JP+XMrS1WbKaXd3t9INezIa6FKbiov7rW+N8PK/dVyOKyhP3tR4Q7O4DkB6e2fXxJzuwE3jSkAAOxA+XPEsbJUdSZCvsyUnXZ6qepUuam8uNm+T+U+Y05n5f7aumt/h5MoSQobbdsd/VpuGlMAADgZEykfsbpUdSTk69g8jBB5GEnzennxypU1Wry+NmZCcPfKtzR1dInKJ5Ta1zCTrNxfZl/L62MKAACrMJHyETtKVadSShvxvFxevHJljR58pTbu8bCh6ONum0xZub/MvpaXxxQAAFZiIuUjmShVbSYPxg3hdk7N77GrvLiZcLtU+rDlcFiL18dPoo62eH2tbhs/1FVhflbuL7OvRcl6AAAyg4mUj6Sbt+TVcslOfl92lBc3E26Xah8+semDpPJ7ntj0gW4YPcjU+7CDlfvL7GtRsh4AgMxwz1e9yAizpaq9Wi7ZDe/LyvLikXC7Yyc5kXC7ypU1cc8x04e79n2RVHuS3c5JrNxfZl+LkvUAAKSP8ufyT/nzo6UShuXVcslue1/ZDj80U07bbB8ufmWn7l75Vqdt+umEUzX1fPesSB3NynBRs6/l1JBWAADsRPlzdCiVvCWvlkt22/vKdq6ZmXA7s304tKB3Um1KdrtsMjvZsDI30OxruSF/EQAAp2IihU55tVyyV9+XWWbC7cz24b6DLUk9L9ntssXJ+XMAAMBe5EihU14tl+zV92XWgD49U97OyyW43ZA/BwAA7MNECp2KlEtOFMwUUNu39G4rl+zV92XWlHMGpLyd2T50et+3hg1VrKhptzx45LGKFTVq7SwWEgAAeBYTKXQqUi5ZUtwHXzeXS/bq+zJr257PUt4u0oeJphOGOi7BLTmz71PJ/QIAAP7ERApJ8Wq5ZK++LzOszhlzct+TPwcAADpDsQkk7eJhhRpXGvJcuWSvvq9UmclbioTAJRJQWwjcuNJQu/3p1L53Qw4XAACwFxMppMSr5ZK9+r5SEclbqm881G6oXuSeUEfnLWWihLwT+95MXwAAAH8htA+AJHN5S14NgXN6DhcAALAfEykAUanmLXk5BM7JOVwAAMB+hPYBFmsNG47LCTpaKnlLmQiBc3J/ODWHCwAA2I+JFGChVdvrVLGiJiavqDCYp7kTSx21wpFs3lIkBG76kmoFpJjJVDIhcG7oDyfmcAEAAPsR2gdYZNX2Ok1fUh1XnKG+8ZCmL6nWqu11NrUsPWZD4LzaHwAAwB8ChmEkupembzQ1NSkYDKqxsVH5+fl2Nwce1Bo2NKpqbcIKd5EQuA1zxro2bCyVED0/9AcAAHCnZOcGhPYBFshEmXAn5xJJqYXAZaI/AAAA7MRECrBAumXC3ZBLlAqvlk0HAAD+QY4UYIF0yoR7MZfIy2XTAQCAPzCRAiwQKROeKBAvoLYVpmPLhLeGDVWsqGm3tHjksYoVNWoNuyvV0Wx/AAAAOAUTKcACkTLhkuImDx2VCU8ll8hNzPYHAACAUzCRAixipky4l3OJzJZNBwAAcAKKTQAWunhYocaVhpKuvuf1XKJU+wMAAMApmEj5lNNLaXtZKmXCI7lE9Y2H2s2Titxvyc25RKn0BwAAgFMwkfIhr5XS9rJILtH0JdUKSDGTKXKJAAAA7EOOlM94sZS215FLBAAA4DysSPlIZ6W0A2orpT2uNMQKh8OQSwQAAOAsTKR8JJVS2m7NWfFy7he5RAAAAM7BRMpHvFxKWyL3CwAAANYhR8pHvFxKm9wvAAAAWImJlI9ESmknCnQLqG0Fx22ltDvL/ZLacr9aw+1tAQAAAKSOiZSPREppS4qbTLm5lHYquV8AAABAJjCR8hkvltL2eu4XAAAAnIdiEz7ktVLaXs79AgAAgDMxkfIpL5XSjuR+1TceajdPKqC2FTe35X75hZdL1gMAAO9iIgXXi+R+TV9SrYAUM5lyc+6XH1CyHgAAuBU5UvAEL+Z+eR0l6wEAgJuxIgXP8Frul5d1VrI+oLaS9eNKQ+w/AADgSEyk4Cleyv3yslRK1rM/AQCAExHaB8BylKwHAABux0QKgOUoWQ8AANyO0D4AGZFKGXM3laynPDsAAGgPEykAaUu1jLlbStZTnh0AACRCaB+AtJgtY+70kvWUZwcAAB0JGIbRXmSNrzQ1NSkYDKqxsVH5+fl2NwdwjdawoVFVaxNW4IuE6G2YMzbh6pITQ+cy8b4AAIA7JTs3ILQPgGmZKGNupmS92clXss+jPDsAJObEL8AAOzh+IvXRRx9pzpw5evHFF3Xw4EF9+ctf1sMPP6yzzjpLkmQYhioqKvTQQw9p//79Ouecc/TrX/9ap512ms0tB7zPjjLmZvOWUnke5dkBoH3kjgJHODpHav/+/TrvvPPUrVs3vfjii6qpqdEvfvELHX/88dFt5s+frwULFmjhwoXasmWLQqGQxo0bpwMHDtjXcMAnrC5jbjZvKdXnUZ4dAOKROwrEcvSKVFVVlYqLi/Xoo49GHxs4cGD034Zh6L777tNPf/pTXX755ZKkxx57TAUFBXrqqac0bdo0q5uclmyHKwGZZmUZ89awoYoVNe2+jvG/r1WxokbjSkMx49/M89xUnh3IBq4rsegP8+dg2I/xmz2Onki98MIL+trXvqYrrrhC69atU//+/XXTTTdp6tSpkqTa2lrV19dr/Pjx0efk5uZqzJgx2rhxY8KJVHNzs5qbm6P/b2pqyu4bSYIV4UpApllZxtxs3pKZ57mlPDuQDVxXYtEfbcgddSfGb3Y5OrRv586dWrRokYYMGaKXXnpJN954o2655RY9/vjjkqT6+npJUkFBQczzCgoKor9rT2VlpYLBYPSnuLg4e28iCVaFKwHZYFUZc7N5S2af5/Ty7EA2cF2JRX8cQe6o+zB+s8/RK1LhcFgjRozQvHnzJElnnHGG3nzzTS1atEjf/e53o9sFArHfChuGEffY0crLyzV79uzo/5uammybTFkZrgRky8XDCjWuNJTV0AGzeUvp5DtZ8b4Ap+C6Eov+iEXuqLswfq3h6BWpwsJClZaWxjx26qmnavfu3ZKkUCgkSXGrT3v37o1bpTpabm6u8vPzY37skspSeSaeB2RLpIz5pOH9VTa4b8ZPzJG8pUR/NaC2cIVj85bMPi8i2+/LbVrDhjbtaNDybR9p044GtYZ9fytCz+C6Eivd/vDasZLuuRTW4ni2hqNXpM477zy98847MY+9++67GjBggCSppKREoVBIa9as0RlnnCFJamlp0bp161RVVWV5e82wOlwJcCuzeUvkO2UOsfbexnUlVjr94cVjhXOpu3A8W8PRK1K33nqrNm/erHnz5un999/XU089pYceekgzZsyQ1BbSN2vWLM2bN0/PPfectm/fruuuu049e/bUlClTbG59cuwIVwLcymzeEvlO6SPW3vu4rsQy2x9ePlY4l7oHx7M1HL0i9c///M967rnnVF5errvuukslJSW67777dPXVV0e3+dGPfqSDBw/qpptuit6Qd/Xq1erdu7eNLU+e2TLLdpVnpoQm7GY2b4l8J/OItfeHTFxXvHSNMNMffjhWOJe6A7fxsEbAMAx3B+1mQFNTk4LBoBobG23Jl4p8eyW1v1Se6Fses89Lp51eC1UA0LlNOxo0efHmTrdbOvVcyh67XDrXFS9eI1LtD44VOInVnxO9JNm5gaND+/zCDeFKXg5VANAxYu39w+x1xavXiFT7g2MFTkIoZvY5OrTPT5wcruSHUAUAiRFr7y+pXle8fo1IpT84VuA0hGJmFxMpB4mUWbbqecnibuaZ5aUcAvgDsfboiB+uEcleZzlWEuPaZ59sf07MBLeODyZS6BShCpnjxRwCeB9lj/0l1fMU14gjOFbax7UPHXHz+CBHCp0iVCEzvJpDAH8g1t4fzJynuEbE4liJxbUPHXH7+GBFygOyvRyabqiCW5drk5Hse/N6DgHsZdUxRqy9t5k9TxHOFo9jpQ3XPnfL9rXFC+ODiZTLWbEcmk6ogpuXazuTynvzQw4B7GH1MeaGWHuYY/Y8FblG3Pi/ZZbbe54fw9k4Vrj2uZkV1xYvjA9C+1zMyuVQM6EKbl+u7Uiq740cAmSDl48xWI/zFDKNMeVOVl1bvDA+WJFyKTuWQ1MJVfDCcm0iZt4bOQTINC8fY7CH2fNUZCwmwlj0L6597mPlteXE43Izup0dWJFyqVSWQzMpEqowaXh/lQ3um/Agsqt9VjDz3iI5BIlOOQG1LZm7OYegNWxo044GLd/2kTbtaFBruL3TMDLFy8cY7GH2PMVYRCJ+uPZ5jaXHc7LzMAd//8JEyqWcvhzq9Palw8x7i+QQSPHnAy+UxF21vU6jqtZq8uLNmrlsmyYv3qxRVWsJLcsiLx9jsIfZ8xRjEYl4/drnRVYez5/+vTmj29mBiZRLOX253OntS4fZ9+bVkrjk6djDy8cY7GPmPMVYREe8eu3zKiuPZy+cO0znSH322Wd65plntGPHDv3bv/2b+vTpo+rqahUUFKh///6ZbCPacdaAE5QTkDqKnsoJtG1nBy+Xw02n771WEpc8Hft4+RiDvVI9TzEW0RmvXfu8zMrj2QvnDlMrUq+//rq+/OUvq6qqSvfee68+++wzSdJzzz2n8vLyTLYPCWzdtb/DD/JS2wf9rbv2W9OgY3h5OT/dvk82z8wNyI2wj5ePMTfxam5gKucpxiKS4aVrn5dZeTx74dxhaiI1e/ZsXXfddXrvvfeUl3dkue2SSy7RK6+8krHGITE3xKR7dTnfDX1vFfrCXl49xtyC3MAjGIuAd1h5PLv93GEqtG/Lli168MEH4x7v37+/6uvr024UOueWuFIvLue7pe+tQF/Yz4vHmBtEcgOPXX+K5Aa64QNApjEWAe+w8nh287nD1EQqLy9PTU1NcY+/8847Oumkk9JuFDrnprhSs3d3bw0bKR9UZp6TKjf1fbbRF85g9hiDOX7IDTR7LmUsAsmx4vNKuq9l5fHs1nOHqYnUpEmTdNddd+k///M/JUmBQEC7d+/Wj3/8Y/3rv/5rRhuI9kXiSqcvqVZAirmguyWutCOrttepYkVNTP5NYTBPcyeWJvyW18xzzPB636eCvoAfpZIb6MYPBladSwG/svIY43jOLlM5Uvfee68++eQT9evXTwcPHtSYMWN0yimnqHfv3rr77rsz3UYk4Pa40kTMlNO2ugS3V/veDPoCfuPl3EBuZwBkl5XHGMdz9gUMwzBdYmjt2rWqrq5WOBzWmWeeqYsuuiiTbbNMU1OTgsGgGhsblZ+fb3dzUmbl8nC2tYYNjapam/Db3kio2IY5Y6Pv0cxzMtler/R9uugL+MWmHQ2avHhzp9stnXquq1ak7DyXAn5g5THG8ZyeZOcGKYf2HT58WHl5edq2bZvGjh2rsWPHptVQpM+tcaXtMRMyY2eYjZm+9+qEw0vjEOiIV3MDvR6y6HVevbZYKdt9aOUxxvFsjZQnUl27dtWAAQPU2tqajfbA58yEzLgpzIZYZcD9vJob6KZzKWJxbUmfFX1o5THG8WwNUzlSt99+u8rLy7VvHzfZRGaZKaftlhLcxCoD3uHF3EC3nEsRi2tL+qzqQyuPMY5na5iq2vfAAw/o/fffV1FRkQYMGKDjjjsu5vfV1dUZaRyS46XlfDMhM24Is/FDuWTAKaw6J7r53iftsetc6qVrmNW4tqTPyj5M9xhL5Vhxw2cjLzA1kfrmN7+Z4WbALK8t55sJmXFDmA2xyoA1rD4neik30I5zqdeuYVbj2pI+K/swnWMs1WPFDZ+NvCCtqn1e4daqfZGl6GN3YOSQcGt4ieTs+0iZsXzbR5q5bFun291/1XBNGt4/+w0CPMjL50QrWXUuZX+lj2tL+uzow1SPsXSOFSd/NnKyrFXtO9rWrVv11ltvKRAIqLS0VGeccUY6fw4p8PpyvpmQGSeH2Zx4XG5GtwMQy+vnRCtZcS5lf2UGeTDps6MPUznG0j1WnPzZyAtMTaT27t2rq666Sn/+8591/PHHyzAMNTY26l/+5V+0bNkynXTSSZluJ46R7lK0G2LSzYTMODbMJtmuddYuAFyDEKfEzJzvs30uZX9lhpvyYJz6ucOuPkz2GMvEseLYz0YeYGoi9YMf/EBNTU168803deqpp0qSampqdO211+qWW27R0qVLM9pIxEunrCXLvNb79O/NGd0OQCxK/bbPqed79ldmuCUPxqnjUHJ+H3KsOJup8uerVq3SokWLopMoSSotLdWvf/1rvfjiixlrHBIzuxRNmVR7EH4BZBfHWDwnn+/ZX5nj9FL8Th6HEU7uQ44VZzO1IhUOh9WtW7e4x7t166ZwOJx2o/wq22UtiUm3j5vCL4COEJ7jDume77O9n9lfmeXUPBg3fe5wah/aeaw49XzvJKYmUmPHjtXMmTO1dOlSFRUVSZI++ugj3Xrrrbrwwgsz2kC/sKKsJTHp9nF66ACQDMJz3COd870V+5n9lXlOzINx2+cOJ/Zh5Fi5cUn792g1lJ1jxcnneycxFdq3cOFCHThwQAMHDtTgwYN1yimnqKSkRAcOHNCvfvWrTLfR88wue6e6FE2crb2cHDoAdIbwHHcxe763cj+zv7yPzx3u5IbzvVOYWpEqLi5WdXW11qxZo7fffluGYai0tFQXXXRRptvneVaWtSTO1n5ODR0AOkJ4jvuYOd/bsZ/ZX97G5470RY7LRDJ9XLrpfO8Ead1Haty4cRo3blym2uJLVpa1JCYdTkQMtvMRnuMM2c6jtWs/e3V/eVmyY5HPHemz+rh02/nebqYmUrfccotOOeUU3XLLLTGPL1y4UO+//77uu+++TLTNF6xc9iYm3X7EHMeiP9yB8Bz7WZFHy35GMlIZi3zuSJ/VxyXngdSYypH64x//qPPOOy/u8ZEjR+qZZ55Ju1F+YvWyNzHp9iHmOBb94R6E59jLqjxa9jM6Y2Ys8rkjPVYfl5wHUmNqRaqhoUHBYDDu8fz8fH366adpN8pP7Fj2JibdesQcx6I/3IXwHPtYmUfLfkZH0hmLfO4wz+rjkvNAakytSJ1yyilatWpV3OMvvviiBg0alHaj/CSy7C0dWeaOSHbZuzVsaNOOBi3f9pE27WhQa7i9oR//umWD+2rS8P4qG9yXk1mWpRJz7Af0h7tk4jwFc6w8Vuy6HlnJbPuc/r6skO5Y5HOHOVaff9N9Pb8dK6ZWpGbPnq2bb75Zn3zyicaOHStJevnll3Xvvffq/vvvz2gD/SCy7H1szHEoiVwRckzcgZjjWPSH+6RznoJ56R4rqV4jvHw9Mts+p78vq3Deto/V51+zr+fHYyVgGIapqeKiRYt099136+OPP5YklZSUaO7cufrud7+b0QZaoampScFgUI2NjcrPz7etHalWL4vEKh+7AyPPIPbYOTbtaNDkxZs73W7p1HN9UQWH/nAvqixaK51jJZ1rhNeuR2bb5/T3ZSXO2/az+vybyut57VhJdm5gakXq4MGDuvbaazV9+nR98skn+tvf/qY1a9aooKDAdIORWglYckzchZjjWPSHe1Gq2lpmj5V0rxFeuh6ZbZ/T35fVOG/bz+rzb7Kv5+djxVSO1KRJk/T4449Lkrp166aLLrpICxYs0De/+U0tWrQoow1E+8gxcRdyTGLRH0ByzB4rVl4jnH49Mts+p78vq3HeRiJ+PlZMTaSqq6s1evRoSdIzzzyjgoIC7dq1S48//rgeeOCBjDYQ7SNW2X0oARuL/gCSY+ZYsfIa4fTrkdn2Of192YHzNtrj52PFVGjfF198od69e0uSVq9ercsvv1w5OTk699xztWvXrow2EO2jzr87UQI2Fv0BJCfVY8XKa8SJvXIzul2mme0LrrPt47yNY/n5WDE1kTrllFP0/PPP67LLLtNLL72kW2+9VZK0d+9eW4s1+Amxyu5Fjkks+gN+ZCZpPJVjxdJrRLIlq2yqgmy2L7jOJsZ5G0fz87FiKrTvjjvu0A9/+EMNHDhQ55xzjsrKyiS1rU6dccYZGW0g2kesMgC406rtdRpVtVaTF2/WzGXbNHnxZo2qWqtV2+sy9hpWXiM+/bw5o9tlmtm+4DoLJMfPx4qpidS3vvUt7d69W6+99lrMjXkvvPBC/fKXv8xY49AxYpUBwF0iJYKPTcyubzyk6UuqMzqZsuoa4YawHrN9wXUWSI5fjxXT95HyEqfcR8os7usCAM7XGjY0qmptwupWkfCXDXPGZvQcnu1rROR9dRbWk+h9WXkNM/taXGf9gf0cy0x/eKUPs3ofKTgLscoA4HyplAjO5Dk929eISFjP9CXVCig2FaqzsJ5V2+tUsaImpl8Kg3maO7E0K99gm+0LrrPeZ/VYdDqz/eG3Y8VUaB8AAEiNl0sEmwnrsTLMEegIYzEW/ZE8VqQAALCAG3KJ0pFKWezWsKGKFTXthgIaalvJqlhRo3GlIVeGBcE9GIux6I/UMJFCSrwS++oX5B74A33oDumWCLbyGDP7WsmG9dgV5ggci7EYi/5IDRMpJI34YXexcn+ZfS3GVProQ/ewI5fIzPOsGFNeDnOEuzAWY9EfqSFHCkkhXtZdrNxfZl+LMZU++tB9rMwlMvM8q8aU18Mc4R6MxVj0R2qYSDlIa9jQph0NWr7tI23a0aDWsDMq03cWLyu1xctmur1O7Q+ns3J/mX0tu8aUl9CH7nXxsEJtmDNWS6eeq/uvGq6lU8/Vhjlj251EWXmMWTmmImGOiYIFA2pbBUsU5ghkih/GYiqfp/zQH5lEaJ9DODk8x454WSf3h9NZub/MvhYx2OmjD90t27lEZp5n5ZhKJ8wRyCSvj8VUP095vT8yjRUpB3B6eI7V8bJO7w+ns3J/mX0tYrDTRx/6g5XHmNVjykyYI5ANXh2LZj9PebU/soEVKZu5ocyklfGybugPp7Nyf5l9LWKw00cf+sOJvXJNbWdmfNgxplIpmQ5kk9fGYrqfp7zWH9nCRMpmbgjPSbdkbyrc0B92SbYcsZX7y+xrWdlGr7KrDym1brFk05GO2c7M+LBrTCUb5ghkm5fGYiY+T3mpP7LFVaF9lZWVCgQCmjVrVvQxwzB05513qqioSD169NAFF1ygN998075GpsgN4TmReFlJccmHmY6XdUN/2GHV9jqNqlqryYs3a+aybZq8eLNGVa1td1neyv0Vea1En/WMBK9lZRu9ymzfpyOVcYjM+PTzZlPbmTnGOC4B7+DzlDVcM5HasmWLHnroIX3lK1+JeXz+/PlasGCBFi5cqC1btigUCmncuHE6cOCATS1NjVvCc6yKl3VLf1jJTIyzG+Kb3dBGHEHuoj3SOSeaOcY4LgFv4POUNQKGYTi+Nu7f//53nXnmmfrNb36jf//3f9fw4cN13333yTAMFRUVadasWZozZ44kqbm5WQUFBaqqqtK0adOS+vtNTU0KBoNqbGxUfn5+Nt9KnNawoVFVazsNpdgwZ6wjvgXMdliP2/oj2yL9kWh5vrP+sGp/mW2fFW30qkz0vRNfC7EycU40c4xxXALuxuep9CQ7N3DFitSMGTP09a9/XRdddFHM47W1taqvr9f48eOjj+Xm5mrMmDHauHFjwr/X3NyspqammB+7ZCKUwsr7LUXiZScN76+ywX0zfvARWhIrlRjn9mR7f6XbPiva6FWZ6HsrX4v7wpmTiXOimWOM4xJwNz5PWcPxxSaWLVum6upqbdmyJe539fX1kqSCgoKYxwsKCrRr166Ef7OyslIVFRWZbWgaIqEUx9b5DyVx3yQv3m8pnf7wGqfHODu9fV7mhjL3EV48T1mJcyIAMzh3ZJ+jJ1J79uzRzJkztXr1auXlJY7hDARiZ9OGYcQ9drTy8nLNnj07+v+mpiYVFxen3+A0mCkzGclZOPZ73UjOgpvj2Sm72cbpMc5Ob5+XuaHMveTt85SVOCcCMINzR3Y5eiK1detW7d27V2eddVb0sdbWVr3yyitauHCh3nnnHUltK1OFhUcuxHv37o1bpTpabm6ucnOTuzeHlVIpM+mH+y1RdtP5ZcKd3j4vc0OZez+cp6zEORGAGZw7ssfROVIXXnih3njjDW3bti36M2LECF199dXatm2bBg0apFAopDVr1kSf09LSonXr1mnkyJE2tjz7rMyPgH2cHuPs9PZ5mR1l7lN9Lc5TAAAvc/REqnfv3ho2bFjMz3HHHae+fftq2LBh0XtKzZs3T88995y2b9+u6667Tj179tSUKVPsbn5WkZviH04vR+z09nmZlX1v5rU4TwEAvMzRoX3J+NGPfqSDBw/qpptu0v79+3XOOedo9erV6t27t91NyypyU/zF6THOTm+fl1nZ96m+FucpAICXueI+Utlm532kzOL+AACcjvMUAMCNPHUfKcQjNwWA03GeAgB4GRMpFyM3BYDTcZ4CAHgVoX1yZ2jf0VrDBrkpAByN85R9Wg6H9cSmD7Rr3xca0KenrikbqO5d+R4VABJJdm7ARErun0gBANCeypU1Wry+VuGjrvQ5AWnq6BKVTyi1r2EA4GDJzg1cX7UPAADEq1xZowdfqY17PGwo+jiTKQAwj7V9AAA8puVwWIvXx0+ijrZ4fa1aDoctahEAeA8rUgDQAfJL3Id8LOmJTR/EhPO1J2y0bXfD6EHWNApJYfz6A/vZG5hIAUAC7eWX3L3yLfJLHGzV9jpVrKhRXeOh6GOFwTzNnVjqqwqBu/Z9kdHtYA3Grz+wn72Dr1UBoB2R/JJjv9WP5JdUrqyxp2FIaNX2Ok1fUh3z4USS6hsPafqSaq3aXmdTy6w3oE/PjG6H7GP8+gP72VuYSAFqW2LftKNBy7d9pE07GtTaWUwMPI38EvdpDRuqWFGj9o7cyGMVK2p8c2xfUzZQnUUJ5QTatoP9GL/+wH72HiZS8L1V2+s0qmqtJi/erJnLtmny4s0aVbWWb4V8LJX8EjjDq7X74r7hPZohqa7xkF6t3Wddo2zUvWuOpo4u6XCbqaNLyPdzCMavP7CfvYczKHyNJXa0h/wS99l7IPGHEzPbeUH5hFJNO78kbmUqJyBNO588Pydh/PoD+9l7KDYB3+psiT2gtiX2caUhKun4DPkl7tOvd15Gt/OK8gmlum38UCpPOhzj1x/Yz97DmRS+xRI7EiG/xH3OLumjwmCeEu22gNqqYp1d0sfKZjlC9645umH0IN01aZhuGD2ISZQDMX79gf3sPZxN4VsssSMR8kvcp0tOQHMntoWqHfshJfL/uRNLWV2GIzF+/YH97D18CoBvscSOjpBf4j4XDyvUou+cqVAw9pgNBfO06Dtncn8WOBrj1x/Yz94SMAzD9zUWm5qaFAwG1djYqPz8fLubA4u0hg2Nqlqr+sZD7eZJBdR2YtswZyzfDvlYy+Ew+SUu0xo29GrtPu09cEj9ereFyXAMwy0Yv/7Afna2ZOcGTKTERMrPIlX7JMVMpiKnMr4dgtW4uLoL+wsAvCfZuQFV++BrkSX2ihU1MYUnQsE8zZ1YyiQKllq1vS5uLBYyFh2L/QUA/saKlFiRAt8qw36R1dFjT8isjjoT+wsAvIsVKSAFXXICKhvc1+5mwKfcdE8zvnRw1/4CAGQPEykAsFkq9zSzc8JPKFsbt+wvAEB2UXoKAGzmhnuaRULZjp1A1Dce0vQl1Vq1vc6mllnPDfsLAJB9TKQAwGZOv6dZZ6FsUlsoW2vYHym3Tt9fAABrMJECAJudXdJHhcG8uDvdRwTUFkJ3dkkfK5sVlUoomx84fX8BAKzBRAoAbNYlJ6C5E0slKe7DeeT/cyeW2la4gFC2WE7fXwAAazCRAgAHiNzTLBSMDQcLBfNsL6VNKFs8J+8vAIA1qNoHx6LMMvzm4mGFGlcacty4j4Sy1TceajdPKqC2CYTfQtmcur/8gmsEALsxkYIjUWYZfuXEe5pFQtmmL6lWQIqZTPk9lM2J+8sPuEYAcAJC++A4lFkGnIdQNjgF1wgAThEwDMMf9Wo70NTUpGAwqMbGRuXn59vdHF9rDRsaVbU2YYWwSAjRhjljffntN2A3wqlgJ64RAKyQ7NyA0D44SipllgmnAaxHKFssr04snfq+uEYAcBImUnAUyiwDcAuv5uk4+X1xjQDgJORIwVEoswzADbyap+P098U1AoCTMJGCo0TKLCcKIAmo7ZtRv5VZhvu0hg1t2tGg5ds+0qYdDWoN+z4d1TNaw4YqVtS0Wwo+8ljFihrX7XM3vC+uEQCchIkUHCVSZllS3IXS72WW4R6rttdpVNVaTV68WTOXbdPkxZs1qmqt7d/mIzNSydNxEze8L64RAJyEiRQchzLLcDOnh0YhfV7N03HL++IaAcApKDYBR7p4WKHGlYYcWTUKSKSz0KiA2kKjxpWGGMsu5tU8HTe9L64RAJyAiRQcizLLcBtKM/tDJE+nvvFQu5PmyL2M3Jank4n3ZWXZdK4RAOzGRAoAMsQtoVFITyRPZ/qSagWkmEmHm/N00n1fTi6bDgDZQI4UAGSIm0KjkB6v5umYfV/kBgLwI1akACBDzhpwgnICUkfVoXMCbdvB/byap5Pq+yI3EIBfMZECgAzZumt/h5MoqW2StXXXfnI7PMKreTqpvC9yAwH4FaF9AJAh5EjBjxj3APyKiRQAZAg5UvAjxj0Av2IiBQAZEikfnSgLJKC2KmZuK4sNdMQP4741bGjTjgYt3/aRNu1oUGtnMbwAfIEcKQDIEK+WxQY64vVxT1l3AImwIgUAGeTVsthAR7w67inrDqAjAcMwfL8+3dTUpGAwqMbGRuXn59vdHAAe0Bo2PFcWG+iMl8Z9a9jQqKq1CSsSBtQ2UdwwZ6xr3yOA9iU7NyC0D4BvWPkhz6tlsYGOeGncU9YdQGeYSAHwBfIcAKSCsu4AOkOOFADPI88BQKoo6w6gM0ykAHhaa9hQxYoatZcMGnmsYkUN5YwBxPBDWXcA6WEiBcDTUslzAICISFl3SXGTKS+UdQeQPiZSADyNPAcAZnm1rDuAzKDYBABPO7FXbka3A+AvFw8r1LjSkGfKugPIHCZSALwt2dQnUqQAJOClsu4AMofQPgCe9unnzRndDgAAQGIiBcDjKGEMAACygdA+AJ4WKWFc33io3ei9gNoSxylh7DytYcOyvBQrXwsA4A2OnkhVVlbq2Wef1dtvv60ePXpo5MiRqqqq0j/90z9FtzEMQxUVFXrooYe0f/9+nXPOOfr1r3+t0047zcaWA3CKSAnj6UuqFVBsKhQljJ1r1fY6VayoiSldXxjM09yJpRmvlGblawEAvMPRoX3r1q3TjBkztHnzZq1Zs0aHDx/W+PHj9fnnn0e3mT9/vhYsWKCFCxdqy5YtCoVCGjdunA4cOGBjywE4CSWM3WXV9jpNX1Idd/+v+sZDmr6kWqu217nytQAA3hIwDMM1tao++eQT9evXT+vWrdP5558vwzBUVFSkWbNmac6cOZKk5uZmFRQUqKqqStOmTUvq7zY1NSkYDKqxsVH5+fnZfAsAbET4lvO1hg2Nqlqb8CbKkVDMDXPGpr3vrHwtAIB7JDs3cPSK1LEaGxslSX36tOUy1NbWqr6+XuPHj49uk5ubqzFjxmjjxo0J/05zc7OamppifgB4X6SE8aTh/VU2uC8fjh3o1dp9CSc2UltoZl3jIb1au89VrwUA8B7XTKQMw9Ds2bM1atQoDRs2TJJUX18vSSooKIjZtqCgIPq79lRWVioYDEZ/iouLs9dwAEDS9h5IPLExs51TXgsA4D2umUjdfPPNev3117V06dK43wUCsd8qG4YR99jRysvL1djYGP3Zs2dPxtsLAEidleXqKY0PAEiHKyZSP/jBD/TCCy/oT3/6k04++eTo46FQSJLiVp/27t0bt0p1tNzcXOXn58f8AADsFylXn+irsIDaKuploly9la8FAPAeR0+kDMPQzTffrGeffVZr165VSUlJzO9LSkoUCoW0Zs2a6GMtLS1at26dRo4caXVzAZjQGja0aUeDlm/7SJt2NKg17Jr6N8iCSLl6SXETnEyXq7fytQAA3uPo+0jNmDFDTz31lJYvX67evXtHV56CwaB69OihQCCgWbNmad68eRoyZIiGDBmiefPmqWfPnpoyZYrNrQfQGe7fg/ZEytUfOzZCWRgbVr4WAMBbHF3+PFGe06OPPqrrrrtO0pEb8j744IMxN+SNFKRIBuXPAetF7t9z7AkoctRzfydYWa6e0vgAgIhk5waOnkhZhYkUYC3u3+M/ZiYqZic3TIoAAOlIdm7g6NA+AN6Uyv17ygb3ta5hyAozIZxmwz4JFwUAWMXRxSYAeBP37/GPSAjnsRPn+sZDmr6kWqu212XkOek8DwAAM5hIAbAc9+/xh9awoYoVNXF5cJKij1WsqImp1GjmOek8DwAAs5hIAbAc9+/xh1RCONN5TjrPc5OWw2E9vH6n7li+XQ+v36mWw2G7mwQAvkaOFADLRe7fM31JtQJSzCoC9+/xDjMhnGbDPr0eLlq5skaL19fq6AW1u1e+pamjS1Q+odS+hgGAj7EiBcAWkfv3hIKx4XuhYB6lzz3CTAin2bBPL4eLVq6s0YOvxE6iJClsSA++UqvKlTX2NAwAfI4VKQC2uXhYocaVhihV7VGREM76xkPt5i5FytwfHcJp5jnpPM8uyZZobzkc1uL1tR3+rcXra3Xb+KHq3pXvRpGalsNhPbHpA+3a94UG9Ompa8oGMo6AFDCRAmCrLjkBSpx7lJkQTrNhn24KF02lRPsTmz6IW4k6Vtho2+6G0YOy0Vx4FOGiQPr42gEAkDVmQjjNhn26IVw01RLtu/Z9kdTfTXY7QCJcFMgUVqQAAFllJoTTbNink8NFOyvRHlBbifZxpaFoewf06ZnU3052O4BwUSBzmEgBALLOTAin2bBPp4aLplKiPdL+a8oG6u6Vb3UY3pcTaNsuk5LN4fILL/VHJsJFvdQffsD+yh4mUgAAWMBMifbuXXM0dXSJHnwl8QrC1NElGV05SCWHyw+81h/phot6rT+8jv2VXazZAgBgAbMl2ssnlGra+SU69gvknIA07fzMFgZINYfL67zYH+mEi3qxP7yM/ZV9AcMwOlng9b6mpiYFg0E1NjYqPz/f7uYA8DGnh2A4vX1O1ho2NKpqbacl2jfMGZuwFHo2S1VH2pco/LCz9lkt22PRbf2RrJbDYQ392Yudhou+/fNLYsaXV/vDq9hf6Ul2bkBoHwA4hNNDMJzePqdLt0R79645WS1xbiaHyy5WjEU39UcqzIaLerU/vIr9ZQ1C+wDAAZweguH09rmFk0u0m8nhsoNVY9Et/WGGmXBRL/eHF7G/rMGKFADYzExZbCs5vX1u49QS7WZzuKxk5Vh0Q3+ko3xCqW4bPzTpcFGv94fXsL+swUQKEHkfsJfTQzCc3j43cmKJ9rNL+qgwmNdpDtfZJX2sblqUlWPRDf2RrlTCRf3QH17C/rIGoX3wvVXb6zSqaq0mL96smcu2afLizRpVtZZQJVjG6SEYTm8fMiOSwyUdydmKSCaHywpWjkU39IeV6A93YX9Zg4kUfI28DziB00MwnN4+ZI6Tc7gk68ei0/vDavSHu7C/so/y56L8uV9RGhROkW5Z7GxzevuQeU4Nd7ZrLDq1P+xCf7gL+yt1yc4NWJGCb6USaw9kk9NDMJzePmReJIdr0vD+Khvc1zH71q6x6NT+AJLB+M0eJlLwLfI+4CROD8FwevvgH4xFe5FXDBxBaJ8I7fOrTTsaNHnx5k63Wzr1XMdV14J3OT0Ew+ntg38wFq0XySs+9oNjpNeZyMIrkp0bUP7cA7iYmENpUGST2ePSiWWxj+b09sE/GIvW4n5yQDwmUi63anudKlbUxOT6FAbzNHdiKd8KdSISaz99SbUCUszFgbwPpIPjEoDXcD85IB45Ui5G6e70EWuPTOO4BOBF5BUD8ViRcimW2DPn4mGFGlcaIjwSaeO4BOBV3E8OiMdEyqVYYs8sYu2RCRyXyCbyYWEn8oqBeEykXIoldsB5OC6RLeTdwW7kFQPxyJFyKZbYAefhuEQ2kHcHpyCvGIjFipRLscQOP3NqiBPHJTKNvDs4DXnFwBFMpFyKJXb4lZNDnDgukWnk3cGJyCsG2hDa52IsscNv3BDixHGJTCLvDgCcixUpl2OJHX7hphAnjktkCnl3AOBcTKQ8gCV2+IHbQpw4Lt2FvDsAQKqYSAFwBUKckC3k3QEAzCBHCoArEOKEbCDvDgBgFitSPuXUMBYgEbtCnDhWvIu8OwBAOphI+ZCTw1iAROwIceJY8Tby7gAA6SC0z2fcEMYCJGJliBPHiveRdwcASAcrUj7ipjAWIBErQpw4VvyBvDsAQDqYSHlAsjkcbgtjARLJdogTx4o/2FlanNw7ZBpjCrAeEymXSyWHgzAWIDkcK/5gV2lxcu+QaYwpwB7kSLlYqjkchLEAyeFY8Q+rS4uTe4dMY0wB9mFFyqXM5HCcNeAE5QSkcHtP+l85gbbtAD+zM+QL1rOqtDi5d8g0xhRgL1akXCqVHI6Irbv2dziJktomWVt37c9QKwF3ioR8SUdCvCKyGfIF+0Ty7iYN76+ywX2zsm/NnLeRWa1hQ5t2NGj5to+0aUeDWju7KDqcm8aU1/oekFiRci0zORzkfQDJi4R8HZt3ECLvACZxDraXF/OI3DKmvNj3gMREyrXM5HCQ9wGkxqqQL/gD52D7RPKIjl0DieQRZSMfzgpuGFNe7XtAIrTPtSI5HIk+zgXU9m3P0TkcZp4D+J0VIV/wB87B9ugsj0hqyyNyY6iZ08eUl/s+gpBFf2Mi5VJmcjjI+wAA+3AOtoeb8ohS5fQx5eW+l9pW20ZVrdXkxZs1c9k2TV68WaOq1lIp0UeYSLmYmbK9Vpf6BQAcwTnYem7JIzLLyWPKy31P2XlI5Ei5npkcDvI+AMA+nIOt5YY8onQ5dUx5te8pO48IJlIeEMnhyPZzAACZwTnYOn65L5wTx5RX+z6VkEWn7RNkFqF9AADAs5yeR+RlXu17L4csIjVMpAAAgKc5OY/I67zY914NWUTqCO0D4ButYcNxOQTHckMbATdyah6RH3it770asojUMZEC4AurttepYkVNTFx7YTBPcyeWOuYbUTe0EXAzJ+YR+YWX+j4Ssjh9SbUCUsxkys0hi0gdoX0APM8NZWrd0EYAQBsvhiwidQHDMHx/C+ampiYFg0E1NjYqPz/f7uYAyKDWsKFRVWsTVliKhGBsmDPWtm8P3dBGAEA8wrG9Kdm5AaF9ADzNDWVq3dBGAEA8L4UsInWeCe37zW9+o5KSEuXl5emss87S+vXr7W4SAAdwQ5laN7QRAADE8sRE6umnn9asWbP005/+VH/96181evRoXXLJJdq9e7fdTQNgMzeUqXVDGwEAQCxPTKQWLFigG264Qd///vd16qmn6r777lNxcbEWLVpkd9MA2CxSpjZRxHpAbZXx7CxT64Y2AgCAWK6fSLW0tGjr1q0aP358zOPjx4/Xxo0b231Oc3OzmpqaYn4AeFOkTK2kuImKU8rUuqGNAAAglusnUp9++qlaW1tVUFAQ83hBQYHq6+vbfU5lZaWCwWD0p7i42IqmArCJG8rUuqGNAADgCM9U7QsEYr+pNQwj7rGI8vJyzZ49O/r/pqYmJlOAx108rFDjSkOOLlPrhjYCAIA2rp9InXjiierSpUvc6tPevXvjVqkicnNzlZuba0XzADiIG8rUuqGNAADAA6F93bt311lnnaU1a9bEPL5mzRqNHDnSplYBAAAA8DLXr0hJ0uzZs3XNNddoxIgRKisr00MPPaTdu3frxhtvtLtpAAAAADzIExOpK6+8Ug0NDbrrrrtUV1enYcOGaeXKlRowYIDdTQMAAADgQQHDMAy7G2G3pqYmBYNBNTY2Kj8/3+7mAAAAALBJsnMD1+dIAQAAAIDVmEgBAAAAQIqYSAEAAABAiphIAQAAAECKmEgBAAAAQIqYSAEAAABAijxxH6l0RSrANzU12dwSAAAAAHaKzAk6u0sUEylJBw4ckCQVFxfb3BIAAAAATnDgwAEFg8GEv+eGvJLC4bA+/vhj9e7dW4FAwO7mWKapqUnFxcXas2cPNyJGHMYHEmFsoCOMDyTC2EBHnDQ+DMPQgQMHVFRUpJycxJlQrEhJysnJ0cknn2x3M2yTn59v+4CFczE+kAhjAx1hfCARxgY64pTx0dFKVATFJgAAAAAgRUykAAAAACBFTKR8LDc3V3PnzlVubq7dTYEDMT6QCGMDHWF8IBHGBjrixvFBsQkAAAAASBErUgAAAACQIiZSAAAAAJAiJlIAAAAAkCImUgAAAACQIiZSHldZWal//ud/Vu/evdWvXz9985vf1DvvvBOzjWEYuvPOO1VUVKQePXroggsu0JtvvmlTi2GlRYsW6Stf+Ur05ndlZWV68cUXo79nbCCisrJSgUBAs2bNij7G+PCvO++8U4FAIOYnFApFf8/Y8LePPvpI3/nOd9S3b1/17NlTw4cP19atW6O/Z3z418CBA+POHYFAQDNmzJDkvrHBRMrj1q1bpxkzZmjz5s1as2aNDh8+rPHjx+vzzz+PbjN//nwtWLBACxcu1JYtWxQKhTRu3DgdOHDAxpbDCieffLLuuecevfbaa3rttdc0duxYTZo0KXrSYmxAkrZs2aKHHnpIX/nKV2IeZ3z422mnnaa6urrozxtvvBH9HWPDv/bv36/zzjtP3bp104svvqiamhr94he/0PHHHx/dhvHhX1u2bIk5b6xZs0aSdMUVV0hy4dgw4Ct79+41JBnr1q0zDMMwwuGwEQqFjHvuuSe6zaFDh4xgMGj89re/tauZsNEJJ5xg/O53v2NswDAMwzhw4IAxZMgQY82aNcaYMWOMmTNnGobBucPv5s6da3z1q19t93eMDX+bM2eOMWrUqIS/Z3zgaDNnzjQGDx5shMNhV44NVqR8prGxUZLUp08fSVJtba3q6+s1fvz46Da5ubkaM2aMNm7caEsbYY/W1lYtW7ZMn3/+ucrKyhgbkCTNmDFDX//613XRRRfFPM74wHvvvaeioiKVlJToqquu0s6dOyUxNvzuhRde0IgRI3TFFVeoX79+OuOMM7R48eLo7xkfiGhpadGSJUt0/fXXKxAIuHJsMJHyEcMwNHv2bI0aNUrDhg2TJNXX10uSCgoKYrYtKCiI/g7e9sYbb6hXr17Kzc3VjTfeqOeee06lpaWMDWjZsmWqrq5WZWVl3O8YH/52zjnn6PHHH9dLL72kxYsXq76+XiNHjlRDQwNjw+d27typRYsWaciQIXrppZd044036pZbbtHjjz8uiXMHjnj++ef12Wef6brrrpPkzrHR1e4GwDo333yzXn/9dW3YsCHud4FAIOb/hmHEPQZv+qd/+idt27ZNn332mf74xz/q2muv1bp166K/Z2z40549ezRz5kytXr1aeXl5CbdjfPjTJZdcEv336aefrrKyMg0ePFiPPfaYzj33XEmMDb8Kh8MaMWKE5s2bJ0k644wz9Oabb2rRokX67ne/G92O8YGHH35Yl1xyiYqKimIed9PYYEXKJ37wgx/ohRde0J/+9CedfPLJ0ccjVZaOnenv3bs37hsBeFP37t11yimnaMSIEaqsrNRXv/pV3X///YwNn9u6dav27t2rs846S127dlXXrl21bt06PfDAA+ratWt0DDA+IEnHHXecTj/9dL333nucO3yusLBQpaWlMY+deuqp2r17tyQ+d6DNrl279H//7//V97///ehjbhwbTKQ8zjAM3XzzzXr22We1du1alZSUxPy+pKREoVAoWjVFaotZXbdunUaOHGl1c+EAhmGoubmZseFzF154od544w1t27Yt+jNixAhdffXV2rZtmwYNGsT4QFRzc7PeeustFRYWcu7wufPOOy/uNivvvvuuBgwYIInPHWjz6KOPql+/fvr6178efcyVY8O2MhewxPTp041gMGj8+c9/Nurq6qI/X3zxRXSbe+65xwgGg8azzz5rvPHGG8bkyZONwsJCo6mpycaWwwrl5eXGK6+8YtTW1hqvv/668ZOf/MTIyckxVq9ebRgGYwOxjq7aZxiMDz+77bbbjD//+c/Gzp07jc2bNxuXXnqp0bt3b+ODDz4wDIOx4Wevvvqq0bVrV+Puu+823nvvPePJJ580evbsaSxZsiS6DePD31pbW40vfelLxpw5c+J+57axwUTK4yS1+/Poo49GtwmHw8bcuXONUChk5ObmGueff77xxhtv2NdoWOb66683BgwYYHTv3t046aSTjAsvvDA6iTIMxgZiHTuRYnz415VXXmkUFhYa3bp1M4qKiozLL7/cePPNN6O/Z2z424oVK4xhw4YZubm5xtChQ42HHnoo5veMD3976aWXDEnGO++8E/c7t42NgGEYho0LYgAAAADgOuRIAQAAAECKmEgBAAAAQIqYSAEAAABAiphIAQAAAECKmEgBAAAAQIqYSAEAAABAiphIAQAAAECKmEgBAAAAQIqYSAEAAABAiphIAQAAAECKmEgBAAAAQIqYSAEAPG3VqlUaNWqUjj/+ePXt21eXXnqpduzYEf39xo0bNXz4cOXl5WnEiBF6/vnnFQgEtG3btug2NTU1mjBhgnr16qWCggJdc801+vTTT214NwAAp2AiBQDwtM8//1yzZ8/Wli1b9PLLLysnJ0eXXXaZwuGwDhw4oIkTJ+r0009XdXW1fv7zn2vOnDkxz6+rq9OYMWM0fPhwvfbaa1q1apX+9re/6dvf/rZN7wgA4AQBwzAMuxsBAIBVPvnkE/Xr109vvPGGNmzYoNtvv10ffvih8vLyJEm/+93vNHXqVP31r3/V8OHDdccdd+gvf/mLXnrppejf+PDDD1VcXKx33nlHX/7yl+16KwAAG7EiBQDwtB07dmjKlCkaNGiQ8vPzVVJSIknavXu33nnnHX3lK1+JTqIk6eyzz455/tatW/WnP/1JvXr1iv4MHTo0+rcBAP7U1e4GAACQTRMnTlRxcbEWL16soqIihcNhDRs2TC0tLTIMQ4FAIGb7YwM1wuGwJk6cqKqqqri/XVhYmNW2AwCci4kUAMCzGhoa9NZbb+nBBx/U6NGjJUkbNmyI/n7o0KF68skn1dzcrNzcXEnSa6+9FvM3zjzzTP3xj3/UwIED1bUrl00AQBtC+wAAnnXCCSeob9++euihh/T+++9r7dq1mj17dvT3U6ZMUTgc1v/5P/9Hb731ll566SXde++9khRdqZoxY4b27dunyZMn69VXX9XOnTu1evVqXX/99WptbbXlfQEA7MdECgDgWTk5OVq2bJm2bt2qYcOG6dZbb9V//Md/RH+fn5+vFStWaNu2bRo+fLh++tOf6o477pCkaN5UUVGR/t//+39qbW3V1772NQ0bNkwzZ85UMBhUTg6XUQDwK6r2AQBwlCeffFLf+9731NjYqB49etjdHACAQxHsDQDwtccff1yDBg1S//799T//8z+aM2eOvv3tbzOJAgB0iIkUAMDX6uvrdccdd6i+vl6FhYW64oordPfdd9vdLACAwxHaBwAAAAApIksWAAAAAFLERAoAAAAAUsRECgAAAABSxEQKAAAAAFLERAoAAAAAUsRECgAAAABSxEQKAAAAAFLERAoAAAAAUvT/AVrXwmtFryz6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "x = data.Age\n",
    "y = data.Spending_Score\n",
    "plt.scatter(x,y)\n",
    "plt.xlabel(\"age\")\n",
    "plt.ylabel(\"score\")\n",
    "plt.title(\"Score Map\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d37e5af0",
   "metadata": {},
   "source": [
    "**Вывод:** чем старше по возрасту покупатель, тем ниже его покупательский балл. Это объясняет большую лояльность молодого поколения к различным промоакциям и стремление участвовать в программах скидок, кэшбэков, накопления бонусных валют для последующих покупок."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22124983",
   "metadata": {},
   "source": [
    "Теперь нарисуем столбчатую диаграмму.\n",
    "\n",
    "Назовем ее *Income Bar Chart*, добавим легенду, назовем горизонтальную ось *AGE*, вертикальную ось - *Average Annual Income*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ad9f6b16-f733-4b18-a007-550a15bb3ab4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qdu7cqUqVKmXYa2bvsZT9bJCve6SSkpJUs2ZNTZw4McP548eP14QJEzRx4kStW7dO4eHhatasmc6fP+9YZvDgwZo7d66++OIL/fLLL7pw4YLatm3ruJEZAAAAgFujRo0ajv/29vZWaGioqlev7pgWFhYmSUpMTNSGDRu0bNkyFSlSxPFICz5ph+/t27dPPXr00J133qmiRYsqOjpaknT48OFMtxsREeHYRl7K16v2tWrVSq1atcpwnjFG8fHxGjlypDp16iTp2q7CsLAwzZw5U/3799fZs2c1depUffrpp2ratKkk6bPPPlNUVJR++ukntWjR4pa9FgAAAOB25+vr6/TcZrM5TUu7Ea7dbpfdbtfDDz+st956K9160sLQww8/rKioKH300UeKjIyU3W5XtWrVlJKSkul2r99GXnLbc6QOHDighIQENW/e3DHNz89PjRo10urVqyVJGzZs0JUrV5yWiYyMVLVq1RzLZCQ5OVnnzp1zegAAAAC4df72t79p+/btKleunMqXL+/0KFy4sE6dOqWdO3fq1Vdf1UMPPaTKlSvrzJkz+d22g9sGqYSEBEn/2/2XJiwszDEvISFBhQoVUrFixTJdJiNjx45VcHCw4xEVFeXi7gEAAABk5dlnn9Xp06fVvXt3rV27Vvv379eiRYv0xBNPKDU1VcWKFVNoaKimTJmivXv3aunSpRoyZEh+t+3g9jfkTds1l8YYk27ajW62zIgRI5z+Ec6dO0eYAgAAgFsraBfeiYyM1KpVq/Tyyy+rRYsWSk5OVtmyZdWyZUt5eXnJZrPpiy++0MCBA1WtWjVVrFhR7733nho3bpzfrUty4yAVHh4u6X9X9kiTmJjo2EsVHh6ulJQUnTlzxmmvVGJioho2bJjpuv38/OTn55dHnQMAAAC3n59//jndtIMHD6abdv1Fw2NiYjRnzpxM19m0aVPt2LEj0/py5crpxouQh4SEpJuWF9z20L7o6GiFh4dr8eLFjmkpKSlavny5IyTVqVNHvr6+TsscP35cv//+e5ZBCgAAAAByI1/3SF24cEF79+51PD9w4IA2b96s4sWLq0yZMho8eLDi4uIUExOjmJgYxcXFKTAwUD169JAkBQcHq2/fvho6dKhCQ0NVvHhxDRs2TNWrV3dcxQ8AAAAAXC1fg9T69evVpEkTx/O085Z69+6t6dOn66WXXtKlS5c0YMAAxw15Fy1apKCgIEfNu+++Kx8fH3Xp0sVxQ97p06fL29v7lr8eAAAAALcHm7kVBxC6uezevRgAkDfKDf8+y/kF7QRrAMjK5cuXdeDAAUVHR8vf3z+/2ymQsnqPs5sN3PYcKQAAAOB2xv6OvOOK95YgBQAAALiRtFNUUlJS8rmTguvixYuSJF9f3xyvw20vfw4AAADcjnx8fBQYGKgTJ07I19dXXl7s+3AVY4wuXryoxMREhYSE5Oq6CgQpAAAAwI3YbDZFRETowIEDOnToUH63UyCFhIQ47lubUwQpAAAAwM0UKlRIMTExHN6XB3x9fV1yhW+CFAAAAOCGvLy8uGqfG+OASwAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsMgnvxsAUPCVG/59lvMPjmtzizoBAABwDfZIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIu4/DmQD7K6HDiXAgcAAHB/7JECAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABa5dZC6evWqXn31VUVHRysgIEB33nmnxowZI7vd7ljGGKPY2FhFRkYqICBAjRs31vbt2/OxawAAAAAFnU9+N5CVt956S5MnT9aMGTNUtWpVrV+/Xo8//riCg4M1aNAgSdL48eM1YcIETZ8+XRUqVNAbb7yhZs2aadeuXQoKCsrnVwDgdlVu+PdZzj84rs0t6gQAAOQFt94jtWbNGrVv315t2rRRuXLl1LlzZzVv3lzr16+XdG1vVHx8vEaOHKlOnTqpWrVqmjFjhi5evKiZM2fmc/cAAAAACiq33iN13333afLkydq9e7cqVKigLVu26JdfflF8fLwk6cCBA0pISFDz5s0dNX5+fmrUqJFWr16t/v3751PnAFwpq7077NkBAAD5wa2D1Msvv6yzZ8+qUqVK8vb2Vmpqqt588011795dkpSQkCBJCgsLc6oLCwvToUOHMl1vcnKykpOTHc/PnTuXB90DAAAAKKjc+tC+2bNn67PPPtPMmTO1ceNGzZgxQ++8845mzJjhtJzNZnN6boxJN+16Y8eOVXBwsOMRFRWVJ/0DAAAAKJjcOki9+OKLGj58uLp166bq1aurZ8+eeuGFFzR27FhJUnh4uKT/7ZlKk5iYmG4v1fVGjBihs2fPOh5HjhzJuxcBAAAAoMBx6yB18eJFeXk5t+jt7e24/Hl0dLTCw8O1ePFix/yUlBQtX75cDRs2zHS9fn5+Klq0qNMDAAAAALLLrc+Revjhh/Xmm2+qTJkyqlq1qjZt2qQJEyboiSeekHTtkL7BgwcrLi5OMTExiomJUVxcnAIDA9WjR4987h6AO+BCFQBgDbdvALLHrYPU+++/r7///e8aMGCAEhMTFRkZqf79++u1115zLPPSSy/p0qVLGjBggM6cOaP69etr0aJF3EMKAAAAQJ5x6yAVFBSk+Ph4x+XOM2Kz2RQbG6vY2Nhb1hcAAACA25tbnyMFAAAAAO6IIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEVufUNeAK5Vbvj3Wc4/OK7NLeoEAADAs7FHCgAAAAAsyvEeqb1792rfvn164IEHFBAQIGOMbDabK3sDAMCtZbWXlz28AFCwWd4jderUKTVt2lQVKlRQ69atdfz4cUlSv379NHToUJc3CAAAAADuxnKQeuGFF+Tj46PDhw8rMDDQMb1r165auHChS5sDAAAAAHdk+dC+RYsW6ccff1Tp0qWdpsfExOjQoUMuawwAAAAA3JXlIJWUlOS0JyrNyZMn5efn55KmAAA5x3k7AADkPcuH9j3wwAP65JNPHM9tNpvsdrvefvttNWnSxKXNAQAAAIA7srxH6u2331bjxo21fv16paSk6KWXXtL27dt1+vRprVq1Ki96BAAAAAC3YnmPVJUqVbR161bdfffdatasmZKSktSpUydt2rRJd911V170CAAAAABuJUf3kQoPD9fo0aNd3QsAAAAAeIQcBanLly9r69atSkxMlN1ud5rXrl07lzQGAAAAAO7KcpBauHChevXqpZMnT6abZ7PZlJqa6pLGAAC3Hlf8AwAgeyyfI/Xcc8/pkUce0fHjx2W3250ehCgAAAAAtwPLQSoxMVFDhgxRWFhYXvQDAAAAAG7PcpDq3Lmzfv755zxoBQAAAAA8g+VzpCZOnKhHHnlEK1euVPXq1eXr6+s0f+DAgS5rDgAAAADckeUgNXPmTP34448KCAjQzz//LJvN5phns9kIUgAAAAAKPMtB6tVXX9WYMWM0fPhweXlZPjIQAAAAADye5SSUkpKirl27EqIAAAAA3LYsp6HevXtr9uzZedELAAAAAHgEy4f2paamavz48frxxx9Vo0aNdBebmDBhgsuaAwAArpXVTZclbrwMANllOUht27ZNtWvXliT9/vvvTvOuv/AEAAAAABRUloPUsmXL8qIPAAAAAPAYloPU9Y4ePSqbzaZSpUq5qh8AAG4LHGIHAJ7N8sUm7Ha7xowZo+DgYJUtW1ZlypRRSEiIXn/9ddnt9rzoEQAAAADciuU9UiNHjtTUqVM1btw43XvvvTLGaNWqVYqNjdXly5f15ptv5kWfAAAAAOA2LAepGTNm6N///rfatWvnmFazZk2VKlVKAwYMIEgBAAAAKPAsH9p3+vRpVapUKd30SpUq6fTp0y5pCgAAAADcmeUgVbNmTU2cODHd9IkTJ6pmzZouaQoAAAAA3JnlQ/vGjx+vNm3a6KefflKDBg1ks9m0evVqHTlyRD/88ENe9AgAAAAAbsXyHqlGjRpp165d6tixo/773//q9OnT6tSpk3bt2qX7778/L3oEAAAAALeSo/tIlSpViotKAAAAALhtWd4jNW3aNH311Vfppn/11VeaMWOGS5oCAAAAAHdmOUiNGzdOJUqUSDe9ZMmSiouLc0lTAAAAAODOLAepQ4cOKTo6Ot30smXL6vDhwy5pCgAAAADcmeUgVbJkSW3dujXd9C1btig0NNQlTQEAAACAO7McpLp166aBAwdq2bJlSk1NVWpqqpYuXapBgwapW7duedEjAAAAALgVy1fte+ONN3To0CE99NBD8vG5Vm6329WrVy/OkQIAeJxyw7/PdN7BcW1uYScAAE9iOUgVKlRIs2fP1uuvv64tW7YoICBA1atXV9myZfOiPwAAAOCm+FEEt1qO7iMlSRUqVFCFChVc2QsAQFl/GZD4QgAAgDuwHKRSU1M1ffp0LVmyRImJibLb7U7zly5d6rLmAAAAAMAdWQ5SgwYN0vTp09WmTRtVq1ZNNpstL/oCAACAh2GPOm4nloPUF198oS+//FKtW7fOi34AAAAAwO1Zvvx5oUKFVL58+bzoBQAAAAA8guU9UkOHDtU///lPTZw4kcP6gHzAVYkAAADyn+Ug9csvv2jZsmVasGCBqlatKl9fX6f5c+bMcVlzAAAAAOCOLAepkJAQdezYMS96ATwGJ9MCAABPxhEuuWc5SE2bNi0v+gAAAAAAj2H5YhMAAAAAcLvL9h6p2rVrZ+viEhs3bsxVQwAAAADg7rIdpDp06JCHbQAAAACA58h2kBo1alRe9gEA8HBchAUAcDvhHCkAAAAAsMjtg9Sff/6pxx57TKGhoQoMDFStWrW0YcMGx3xjjGJjYxUZGamAgAA1btxY27dvz8eOAQAAABR0bh2kzpw5o3vvvVe+vr5asGCBduzYoX/84x8KCQlxLDN+/HhNmDBBEydO1Lp16xQeHq5mzZrp/Pnz+dc4AAAAgALN8n2kbqW33npLUVFRTveuKleunOO/jTGKj4/XyJEj1alTJ0nSjBkzFBYWppkzZ6p///63umUAAAAAtwG33iP13XffqW7dunrkkUdUsmRJ1a5dWx999JFj/oEDB5SQkKDmzZs7pvn5+alRo0ZavXp1putNTk7WuXPnnB4AAAAAkF3Z2iP13nvvZXuFAwcOzHEzN9q/f78mTZqkIUOG6JVXXtHatWs1cOBA+fn5qVevXkpISJAkhYWFOdWFhYXp0KFDma537NixGj16tMv6BADkH64WCADID9kKUu+++262Vmaz2VwapOx2u+rWrau4uDhJ124KvH37dk2aNEm9evVy2u71jDFZ3jx4xIgRGjJkiOP5uXPnFBUV5bK+AQAAciurHwn4gQDIf9kKUgcOHMjrPjIUERGhKlWqOE2rXLmyvv76a0lSeHi4JCkhIUERERGOZRITE9Ptpbqen5+f/Pz88qBjAAAAALcDtz5H6t5779WuXbucpu3evVtly5aVJEVHRys8PFyLFy92zE9JSdHy5cvVsGHDW9orAAAAgNtHjq7ad/ToUX333Xc6fPiwUlJSnOZNmDDBJY1J0gsvvKCGDRsqLi5OXbp00dq1azVlyhRNmTJF0rVD+gYPHqy4uDjFxMQoJiZGcXFxCgwMVI8ePVzWBwAA7oTzwgAg/1kOUkuWLFG7du0UHR2tXbt2qVq1ajp48KCMMfrb3/7m0ubq1aunuXPnasSIERozZoyio6MVHx+vRx991LHMSy+9pEuXLmnAgAE6c+aM6tevr0WLFikoKMilvQAAgPxDeATgbiwHqREjRmjo0KEaM2aMgoKC9PXXX6tkyZJ69NFH1bJlS5c32LZtW7Vt2zbT+TabTbGxsYqNjXX5tgEAAAAgI5aD1M6dOzVr1qxrxT4+unTpkooUKaIxY8aoffv2euaZZ1zeJJAX+HUTAKzjSnIAcI3li00ULlxYycnJkqTIyEjt27fPMe/kyZOu6wwAAAAA3JTlPVL33HOPVq1apSpVqqhNmzYaOnSotm3bpjlz5uiee+7Jix6BTLFXCQAAAPnBcpCaMGGCLly4IEmKjY3VhQsXNHv2bJUvXz7bN+4FAAAAAE9mOUjdeeedjv8ODAzUBx984NKGCgKOHwcAAPmJIzaAvJej+0gBAAAAgFUFKeRbDlJeXl6y2WyZzk9NTc1VQwAAAICnKEjBANZYDlJz5851en7lyhVt2rRJM2bM0OjRo13WGAAAAAC4K8tBqn379ummde7cWVWrVtXs2bPVt29flzQGAAAAAO7KZedI1a9fX08++aSrVgcAAOASuTn0isO2AGTG8g15M3Lp0iW9//77Kl26tCtWBwAAAABuzfIeqWLFijldbMIYo/PnzyswMFCfffaZS5sDAAAAAHdkOUjFx8c7Pffy8tIdd9yh+vXrq1ixYq7qCwAAAADcluUg1bt377zoAwAAAAA8Ro4uNvHf//5Xa9euVWJioux2u9O8Xr16uaQxAAAAAHBXloPUvHnz9OijjyopKUlBQUFO50vZbDaCFAAAAHIkq6skcoVEuBvLV+0bOnSonnjiCZ0/f17//e9/debMGcfj9OnTedEjAAAAALgVy0Hqzz//1MCBAxUYGJgX/QAAAACA27McpFq0aKH169fnRS8AAAAA4BEsnyPVpk0bvfjii9qxY4eqV68uX19fp/nt2rVzWXMAAAAA4I4sB6knn3xSkjRmzJh082w2m1JTU3PfFQAAAAC4MctB6sbLnQMAAADA7cbyOVIAAAAAcLvL0Q15lyxZoiVLlmR4Q96PP/7YJY0BAAAAgLuyHKRGjx6tMWPGqG7duoqIiHC6IS8AAAAA3A4sB6nJkydr+vTp6tmzZ1704za4szYAAACAzFgOUikpKWrYsGFe9AIAAAowfqQEUJBYvthEv379NHPmzLzoBQAAAAA8guU9UpcvX9aUKVP0008/qUaNGuluyDthwgSXNQcAAAAgY+zlzV+Wg9TWrVtVq1YtSdLvv//u6n4AAAAAwO1ZDlLLli3Liz4AAAAAwGO45Ia8drtd8+bNU4cOHVyxOgAAAABwa7kKUnv27NGIESNUunRpdenSxVU9AQAAAIBbs3xo36VLl/Tll19q6tSp+vXXX5Wamqp3331XTzzxhIoUKZIXPQIAAACAW8n2Hqm1a9fqqaeeUnh4uCZOnKj/+7//05EjR+Tl5aWmTZsSogAAAADcNrK9R6phw4Z6/vnntXbtWlWsWDEvewLghrK6xKrEZVYBAMDtJdtB6sEHH9TUqVOVmJionj17qkWLFrLZbHnZGwAAAAC4pWwf2rdo0SJt375dFStW1DPPPKOIiAgNGjRIkghUAAAAAG4rlq7aFxUVpddee00HDhzQp59+qsTERPn4+Kh9+/Z65ZVXtHHjxrzqEwAAAADchuWr9qVp1qyZmjVrpjNnzuizzz7Txx9/rLfeekupqamu7A8AAAAAci2r871zcq53rm/IW6xYMT3//PPatGmT1q1bl9vVAQAAAIDby3WQut7f/vY3V64OAAAAANxSjg/tA4CCjku+AwCAzLh0jxQAAAAA3A4IUgAAAABgUY6C1NWrV/XTTz/pww8/1Pnz5yVJx44d04ULF1zaHAAAAAC4I8vnSB06dEgtW7bU4cOHlZycrGbNmikoKEjjx4/X5cuXNXny5LzoEwAAAIAb4BziayzvkRo0aJDq1q2rM2fOKCAgwDG9Y8eOWrJkiUubAwAAAAB3ZHmP1C+//KJVq1apUKFCTtPLli2rP//802WNAQAAAIC7srxHym63KzU1Nd30o0ePKigoyCVNAQAAAIA7sxykmjVrpvj4eMdzm82mCxcuaNSoUWrdurUrewMAAAAAt2T50L53331XTZo0UZUqVXT58mX16NFDe/bsUYkSJTRr1qy86BEAAADAbc7dLnJhOUhFRkZq8+bNmjVrljZu3Ci73a6+ffvq0Ucfdbr4BAAAAAAUVJaDlCQFBAToiSee0BNPPOHqfgAAAADA7VkOUt99912G0202m/z9/VW+fHlFR0fnujEAAADgVnC3Q8bgGSwHqQ4dOshms8kY4zQ9bZrNZtN9992nb775RsWKFXNZowAAAADgLixftW/x4sWqV6+eFi9erLNnz+rs2bNavHix7r77bs2fP18rVqzQqVOnNGzYsLzoFwAAAADyneU9UoMGDdKUKVPUsGFDx7SHHnpI/v7+euqpp7R9+3bFx8dz/hQAAACAAsvyHql9+/apaNGi6aYXLVpU+/fvlyTFxMTo5MmTue8OAAAAANyQ5SBVp04dvfjiizpx4oRj2okTJ/TSSy+pXr16kqQ9e/aodOnSrusSAAAAANyI5UP7pk6dqvbt26t06dKKioqSzWbT4cOHdeedd+rbb7+VJF24cEF///vfXd7s7cBTrxqTVd/u2jMAAACQU5aDVMWKFbVz5079+OOP2r17t4wxqlSpkpo1ayYvr2s7uDp06ODqPgEAAADAbVg+tE+6dqnzli1bauDAgRo0aJBatGjhCFF5aezYsbLZbBo8eLBjmjFGsbGxioyMVEBAgBo3bqzt27fneS8AAAAAbl+W90hJUlJSkpYvX67Dhw8rJSXFad7AgQNd0tiN1q1bpylTpqhGjRpO08ePH68JEyZo+vTpqlChgt544w01a9ZMu3btUlBQUJ70AgAAAOD2ZjlIbdq0Sa1bt9bFixeVlJSk4sWL6+TJkwoMDFTJkiXzJEhduHBBjz76qD766CO98cYbjunGGMXHx2vkyJHq1KmTJGnGjBkKCwvTzJkz1b9/f5f3AgAAAACWj8d74YUX9PDDD+v06dMKCAjQr7/+qkOHDqlOnTp655138qJHPfvss2rTpo2aNm3qNP3AgQNKSEhQ8+bNHdP8/PzUqFEjrV69OtP1JScn69y5c04PAAAAAMguy0Fq8+bNGjp0qLy9veXt7a3k5GRFRUVp/PjxeuWVV1ze4BdffKGNGzdq7Nix6eYlJCRIksLCwpymh4WFOeZlZOzYsQoODnY8oqKiXNs0AAAAgALNcpDy9fWVzWaTdC2wHD58WJIUHBzs+G9XOXLkiAYNGqTPPvtM/v7+mS6X1k8aY0y6adcbMWKEzp4963gcOXLEZT0DAAAAKPgsnyNVu3ZtrV+/XhUqVFCTJk302muv6eTJk/r0009VvXp1lza3YcMGJSYmqk6dOo5pqampWrFihSZOnKhdu3ZJurZnKiIiwrFMYmJiur1U1/Pz85Ofn59Le0XOcQ8qAEBBxWccUHBZ3iMVFxfnCC2vv/66QkND9cwzzygxMVFTpkxxaXMPPfSQtm3bps2bNzsedevW1aOPPqrNmzfrzjvvVHh4uBYvXuyoSUlJ0fLly9WwYUOX9gIAAAAAaSztkTLG6I477lDVqlUlSXfccYd++OGHPGlMkoKCglStWjWnaYULF1ZoaKhj+uDBgxUXF6eYmBjFxMQoLi5OgYGB6tGjR5715a6y+tVL4pcvAAAAwFUsB6mYmBht375dMTExedWTJS+99JIuXbqkAQMG6MyZM6pfv74WLVrEPaQAAAAA5BlLQcrLy0sxMTE6depUvgWpn3/+2em5zWZTbGysYmNj86UfAAAAALcfy+dIjR8/Xi+++KJ+//33vOgHAAAAANye5av2PfbYY7p48aJq1qypQoUKKSAgwGn+6dOnXdYcAAAAALgjy0EqPj4+D9oAAAAAAM9hOUj17t07L/oAAAAAAI9h+RwpSdq3b59effVVde/eXYmJiZKkhQsXavv27S5tDgAAAADckeUgtXz5clWvXl2//fab5syZowsXLkiStm7dqlGjRrm8QQAAAABwN5aD1PDhw/XGG29o8eLFKlSokGN6kyZNtGbNGpc2BwAAAADuyHKQ2rZtmzp27Jhu+h133KFTp065pCkAAAAAcGeWg1RISIiOHz+ebvqmTZtUqlQplzQFAAAAAO7McpDq0aOHXn75ZSUkJMhms8lut2vVqlUaNmyYevXqlRc9AgAAAIBbsRyk3nzzTZUpU0alSpXShQsXVKVKFT3wwANq2LChXn311bzoEQAAAADciuX7SPn6+urzzz/XmDFjtGnTJtntdtWuXVsxMTF50R8AAAAAuB3LQWr58uVq1KiR7rrrLt1111150RMAAAAAuDXLh/Y1a9ZMZcqU0fDhw/X777/nRU8AAAAA4NYsB6ljx47ppZde0sqVK1WjRg3VqFFD48eP19GjR/OiPwAAAABwO5aDVIkSJfTcc89p1apV2rdvn7p27apPPvlE5cqV04MPPpgXPQIAAACAW7EcpK4XHR2t4cOHa9y4capevbqWL1/uqr4AAAAAwG3lOEitWrVKAwYMUEREhHr06KGqVatq/vz5ruwNAAAAANyS5av2vfLKK5o1a5aOHTumpk2bKj4+Xh06dFBgYGBe9AcAAAAAbsdykPr55581bNgwde3aVSVKlHCat3nzZtWqVctVvQEAAACAW7IcpFavXu30/OzZs/r888/173//W1u2bFFqaqrLmgMAAAAAd5Tjc6SWLl2qxx57TBEREXr//ffVunVrrV+/3pW9AQAAAIBbsrRH6ujRo5o+fbo+/vhjJSUlqUuXLrpy5Yq+/vprValSJa96BAAAAAC3ku09Uq1bt1aVKlW0Y8cOvf/++zp27Jjef//9vOwNAAAAANxStvdILVq0SAMHDtQzzzyjmJiYvOwJ+aDc8O+znH9wXJtb1AkAAADg/rK9R2rlypU6f/686tatq/r162vixIk6ceJEXvYGAAAAAG4p20GqQYMG+uijj3T8+HH1799fX3zxhUqVKiW73a7Fixfr/PnzedknAAAAALgNy1ftCwwM1BNPPKFffvlF27Zt09ChQzVu3DiVLFlS7dq1y4seAQAAAMCt5Pjy55JUsWJFjR8/XkePHtWsWbNc1RMAAAAAuLVcBak03t7e6tChg7777jtXrA4AAAAA3JpLghQAAAAA3E4IUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAitw5SY8eOVb169RQUFKSSJUuqQ4cO2rVrl9MyxhjFxsYqMjJSAQEBaty4sbZv355PHQMAAAC4Hbh1kFq+fLmeffZZ/frrr1q8eLGuXr2q5s2bKykpybHM+PHjNWHCBE2cOFHr1q1TeHi4mjVrpvPnz+dj5wAAAAAKMp/8biArCxcudHo+bdo0lSxZUhs2bNADDzwgY4zi4+M1cuRIderUSZI0Y8YMhYWFaebMmerfv39+tA0AAACggHPrPVI3Onv2rCSpePHikqQDBw4oISFBzZs3dyzj5+enRo0aafXq1ZmuJzk5WefOnXN6AAAAAEB2eUyQMsZoyJAhuu+++1StWjVJUkJCgiQpLCzMadmwsDDHvIyMHTtWwcHBjkdUVFTeNQ4AAACgwPGYIPXcc89p69atmjVrVrp5NpvN6bkxJt20640YMUJnz551PI4cOeLyfgEAAAAUXG59jlSa559/Xt99951WrFih0qVLO6aHh4dLurZnKiIiwjE9MTEx3V6q6/n5+cnPzy/vGgYAAABQoLn1HiljjJ577jnNmTNHS5cuVXR0tNP86OhohYeHa/HixY5pKSkpWr58uRo2bHir2wUAAABwm3DrPVLPPvusZs6cqW+//VZBQUGO856Cg4MVEBAgm82mwYMHKy4uTjExMYqJiVFcXJwCAwPVo0ePfO4eAAAAyFy54d9nOf/guDa3qBPkhFsHqUmTJkmSGjdu7DR92rRp6tOnjyTppZde0qVLlzRgwACdOXNG9evX16JFixQUFHSLuwUAAABwu3DrIGWMuekyNptNsbGxio2NzfuGAAAAAEBufo4UAAAAALgjghQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBSZIffDBB4qOjpa/v7/q1KmjlStX5ndLAAAAAAqoAhGkZs+ercGDB2vkyJHatGmT7r//frVq1UqHDx/O79YAAAAAFEAFIkhNmDBBffv2Vb9+/VS5cmXFx8crKipKkyZNyu/WAAAAABRAPvndQG6lpKRow4YNGj58uNP05s2ba/Xq1RnWJCcnKzk52fH87NmzkqRz5845ptmTL2a6zeuXy0he1d6sPr9qb1bP+2WtnvfLWj3vl7V63i9r9bxf1up5v6zV835Zq+f9slbP+2Wt/vratP82xmS5Ppu52RJu7tixYypVqpRWrVqlhg0bOqbHxcVpxowZ2rVrV7qa2NhYjR49+la2CQAAAMCDHDlyRKVLl850vsfvkUpjs9mcnhtj0k1LM2LECA0ZMsTx3G636/Tp0woNDc2w5ty5c4qKitKRI0dUtGhRS31Ra40n9s375Rm1+bltT6zNz217Ym1+btsTa/Nz255Ym5/b9sTa/Ny2J9bm57bdtdYYo/PnzysyMjLL9Xh8kCpRooS8vb2VkJDgND0xMVFhYWEZ1vj5+cnPz89pWkhIyE23VbRo0Rz9cVLrOdv2xNr83LYn1ubntj2xNj+37Ym1+bltT6zNz217Ym1+btsTa/Nz255Ym5/bdsfa4ODgm9Z7/MUmChUqpDp16mjx4sVO0xcvXux0qB8AAAAAuIrH75GSpCFDhqhnz56qW7euGjRooClTpujw4cN6+umn87s1AAAAAAVQgQhSXbt21alTpzRmzBgdP35c1apV0w8//KCyZcu6ZP1+fn4aNWpUusMBqXVtbX5u2xNr83Pbnlibn9v2xNr83LYn1ubntj2xNj+37Ym1+bltT6zNz217Ym1+btsTa6/n8VftAwAAAIBbzePPkQIAAACAW40gBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgUYG4j1RBkZSUpJkzZ2r16tVKSEiQzWZTWFiY7r33XnXv3l2FCxfO0Xr/+usvffjhh3rttdcyXebo0aMKCQlRkSJFnKZfuXJFa9as0QMPPJBh3alTp7R161bVrFlTxYsX18mTJzV16lQlJyfrkUceUeXKlS33e+edd+rHH39UTExMtmuuXLmi77//Xnv27FFERIQ6duyY6ft19OhR+fv7q0SJEpKklStXavLkyTp8+LDKli2rZ599Vg0aNMiw9h//+Ic6d+6c43uUzZs3T+vXr1fLli3VoEEDLV26VO+8847sdrs6deqkp556Ksv6S5cuadasWfrll190/PhxeXt7Kzo6Wh06dNBDDz2Uo54AV2D8yvuxS/Lc8YuxC+4sN+MX44g1BW4cMUjnu+++M6+99ppZvXq1McaYJUuWmFatWpkWLVqYDz/8MMvaixcvmqlTp5rHH3/ctGzZ0rRp08Y899xz5qeffsqybvv27SYyMtKEhISY9u3bm6eeeso8+eSTpn379iYkJMSUKlXKbN++PUevZ/PmzcbLyyvDeceOHTP16tUzXl5extvb2/Tq1cucP3/eMT8hISHT2t9++80EBwcbm81mihUrZtavX2+io6NNTEyMKV++vAkICDAbNmzItK9//vOfGT68vb3NiBEjHM8z0qBBA3PmzBljjDGJiYmmevXqplChQiYmJsb4+/ubMmXKmKNHj2Za+8MPPxhjjPnmm2+Ml5eXadeunXn55ZdNx44dja+vr5k3b16GtTabzXh7e5umTZuaL774wiQnJ2f6+m40adIk4+PjY+rUqWOKFi1qPvvsMxMUFGT69etn+vfvbwICAkx8fHym9Xv27DFly5Y1oaGhJiIiwthsNtOmTRtTv3594+3tbR555BFz5cqVTOsvXLhgpkyZYvr06WNatmxpWrVqZfr06WM++ugjc+HChWy/jhslJCSY0aNHZ7nMkSNHnP6u0qSkpJjly5dnWXvy5EmzdOlSc+rUKWOMMSdOnDDjxo0zo0ePNjt27LDcb3R0tNm9e7elmpSUFDN37lwzfvx48+mnn2b5fh05csScOHHC8XzFihWmR48e5r777jOPPvqoY1zJyDvvvGMOHjxoqbfr5WbsMobxK7vjV36NXWn1njZ+5XbsMsYzxy9Xj13GMH5lJT/GL8YRzxlH8moMIUjdIL/+kRs3bmy6deuW4R9kcnKy6d69u2ncuHGGtVu2bMnyMXv27Ey/TPTq1cvcc889Zt26dWbx4sWmbt26pk6dOub06dPGmGsfMjabLcPapk2bmn79+plz586Zt99+25QuXdr069fPMb9v376mQ4cOGdYac+1/xtKlS5ty5co5PWw2mylVqpQpV66ciY6OzrT2r7/+MsYY8+STT5patWqZ48ePG2OufXg1bNjQPPHEExnWBgUFmQMHDhhjjKlfv74ZN26c0/z333/f1K5dO9PtTps2zbRv3974+vqa0NBQM2jQILNt27ZMX2eaypUrmylTphhjjFm6dKnx9/c3//rXvxzzp02bZipXrpxpfatWrUz//v1NamqqMcaYsWPHmlatWhljjNm9e7cpV66cGTVqVIa1nvhF1xjP/JDyxA8oYxi/rIxf+TV2GeOZ41duxi5jPHP84ofG22P8YhzxjHEkL8cQgtQN8usfOSAgIMt/xG3btpmAgIAM59lsNuPl5WVsNlu6R9r0zAb7yMhI89tvvzmeX7582bRv397UqlXLnDp1KssPimLFijl+VUtJSTFeXl5O69q4caMpVapUpq/pqaeeMrVq1Ur3y5yPj89N/6CvH0QqVKhg5s+f7zR/2bJlply5chnWBgcHmy1bthhjjClZsqTjv9Ps3bvXBAYG3nS7f/31l3nrrbdMpUqVjJeXl6lXr56ZMmWKOXfuXIa1AQEB5tChQ47nvr6+TgPPgQMHMt2uMcYEBgY6/RKZnJxsfH19zcmTJ40x1z70MnvNnvhF1xjP/JDyxA8oYxi/rIxf+TV2GeOZ41duxi5jPHP84ofG22P8YhzxjHEkN2PIzRCkbpBf/8iRkZHmm2++ybSvuXPnmsjIyAznlShRwkydOtUcPHgww8f333+f6QdF4cKF0x0mcOXKFdOhQwdTo0YNs3Xr1ixr0wZcY4wpUqSI2bdvn+P5oUOHjL+/f6avKe11RUVFmffff98xLbuDSGJiojHm2iBw4/IHDx40fn5+Gda2a9fODB8+3BhjTIsWLdL9qvfRRx+ZmJiYTLebNoBcb8WKFaZ3796mcOHCpnDhwhnWli5d2qxYscIYY8yff/5pbDab+f777x3zf/75Z1O6dOkMa4259jdy/S+YZ86cMTabzTFg7d+/P9PX7IlfdI3xzA8pT/yAMobxy+r4lR9jlzGeOX7lZuwyxjPHL35ovD3GL8aR/3HncSQ3Y8jNEKRukF//yKNGjTLBwcHm7bffNps3bzbHjx83CQkJZvPmzebtt982xYoVy/Q47hYtWpjXX38909e0efPmTH81q169uvnPf/6Tbnral5EyZcpk+kFRqVIls2TJEsfz+fPnm4sXLzqe//rrr1kGgzRHjx41Dz74oGnZsqU5fvx4tgeR1q1bm44dO5pixYo5DkVIs2bNGhMWFpZh7Y4dO0xoaKjp1auXef31102RIkXMY489Zt58803Tq1cv4+fnZ6ZNm5ZhrZeXV4YDSJqzZ886flW70bPPPmtiYmLMG2+8Ye6++27Tu3dvU6lSJbNgwQKzcOFCU7169Sx3xffu3ds0atTI7Ny50+zfv9907drV6ZfBn3/+2URFRWVY64lfdNPqPe1DyhM/oIxh/MrJ+HWrxy5jPHP8ys3YZYxnjl/80Pg/BXn8YhxJzx3HkdyMITdDkLpBfn5YjBs3znFsr5eXl+PXsoiICPPWW29lWjdnzhzz6aefZjr/9OnTZvr06RnOe+mll0zz5s0znHflyhXTrl27TD8oYmNjzaxZszLd7iuvvGI6deqU6fzr2e12ExcXZ8LDw423t/dNB5E+ffo4Pb788kun+cOGDTMtWrTItH7v3r2mW7duJigoyPHrpK+vr2nYsKGZO3dupnWZfVBkx4ULF0y/fv1MtWrVzNNPP21SUlLM22+/bQoVKmRsNptp3Lhxluv+66+/zD333OP4+yhXrpzZuHGjY/5XX31l3nvvvQxrPfGLrjGe+SHliR9QxjB+3Si749etHruM8bzxKzdjlzGeOX7xQ2N6BXH8YhyxJr/GkdyMITdDkLpBfn5YpNm/f79ZvXq1Wb16tdm/f7/LXltGrly5Ys6ePZvp/KtXr+b4KjxJSUnm8uXLlmrWr19v4uPjHceg59SFCxfMpUuXbrqc3W43CQkJ5tixYyYlJSVX28ypS5cuZXq4Q0Z2795ttm3bdtOrXN3I077oGuO5H1J79+41Xbt29ZgPKGPyZvy6/hd4q+x2+02XyWz8SqvNyfiVVmt1/Fq/fr2ZMGFCjseutO1md+xKq0kbv6yc2J/ZtnMiKSkp2+PXjWOXle162vjFD43W5MX4df0eqvz6/pWTceRWfgcyxvO+B93q70A3YzPGmPy58LpnuXz5sq5cuaKgoKCbLrtnzx4lJyerUqVK8vHhVl1wLwcOHFBCQoIkKTw8XNHR0Xm2ratXr+rixYsqWrRohvNTU1N19OjRHN+P4uLFi/L29pafn1+2lt+wYYN++eUX9erVS8WKFcvRNqVr9xzx9vaWv79/lssZY5SYmCi73a4SJUrI19c3x9vMKStjl+Ta8atQoULasmVLju4nR61nbPtW1xaU8cvq2CVJGzdu1MqVKxm/ssD3L9yMq8cQ/sqyyd/fX/7+/jpy5IhGjRqljz/+ONNlM7uJ2s1qL126pA0bNqh48eKqUqWK07zLly/ryy+/VK9evQpMraf27anv186dO/Xrr7+qYcOGatCggf744w+NHz9eycnJeuyxx/Tggw9mut202gYNGqhSpUr6448/9M9//vOmtT4+Pvrzzz/19ddfW67N7bYzqi1cuLD++OMPDR06NNu1DRs2VMWKFXO03etrx4wZk6fvdW57vrHex8cn2/VDhgzJcHpqaqrGjRun0NBQSdKECROozUWtp/ad29d8vejoaIWEhGjGjBnas2ePIiMj1atXL0VFRd20VpLOnDnjqI2IiFDv3r0zrfXx8XEKUTfW9unTJ9shysp2M6tfsWKF9uzZo3/961+W6nO6bZvNpkKFCt2S9zqr2sjISPXu3VulS5e+aV3a9y8r2960aZNCQkIcX6g/++wzTZo0yXFz2ueee07dunUrMLWe2nduap9//nl16dJF999/v6Kjo137A0yO92XdprK650Ruanft2mXKli3r2OXYqFEjc+zYMcf8rK4O5Im1ntq3p75fCxYsMIUKFTLFixc3/v7+ZsGCBeaOO+4wTZs2NQ899JDx8fFxOp7fHWo9tW9PrM1tvc1mM7Vq1TKNGzd2ethsNlOvXj3TuHFj06RJE2pzWeupfef2NUdERDiuvrZ//34TERFhwsPDTbNmzUzp0qVNcHCw2blzZ7Zqw8PD3b7WU/v21Perdu3aZunSpcaYaxfTCAgIMAMHDjSTJk0ygwcPNkWKFDFTp04tMLWe2nduatO+N8XExJhx48Y5bgfgCgSpG3z77bdZPt59991Mv6zmprZDhw6mbdu25sSJE2bPnj3m4YcfNtHR0Y7LgWb1JdkTaz21b099vxo0aGBGjhxpjDFm1qxZplixYuaVV15xzH/llVdMs2bN3KrWU/v2xNrc1sfFxZno6Oh0QSs7J8ZTm/1aT+07t6/5+nNvunXrZho3bmySkpKMMdcuSd62bVvTuXPnAlPrqX176vsVGBjo+BytXbu2+fDDD53mf/7556ZKlSoFptZT+85Nrc1mMz/99JMZNGiQKVGihPH19TXt2rUz8+bNc9x7LKcIUjfI6p4T1997wtW1JUuWNFu3bnWaNmDAAFOmTBmzb9++LL8ke2Ktp/btqe9X0aJFzZ49e4wxxqSmphofHx+nE3G3bduW6VWc8qvWU/v2xFpX1K9du9ZUqFDBDB061HHCcna/KFOb/VpP7Ts3tdd/Sc4okGV1BTxPrPXUvj31/QoNDTXr1683xlz7nN28ebPT/L1792Z6jyFPrPXUvnNTe/3fR0pKipk9e7Zp0aKF8fb2NpGRkeaVV15xfP5Z5eW6gwQLhoiICH399dey2+0ZPjZu3JgntZcuXUp3YuS//vUvtWvXTo0aNdLu3bsLVK2n9u2p79f1vLy85O/vr5CQEMe0oKAgnT171m1rPbVvT6zNaX29evW0YcMGnThxQnXr1tW2bdtks9mytT1qs1/rqX3n9jWnLZucnKywsDCneWFhYTpx4kSBqvXUvj3x/WrVqpUmTZokSWrUqJH+85//OM3/8ssvVb58+QJT66l95/Y1p/H19VWXLl20cOFC7d+/X08++aQ+//xzVaxY8aa1GcpR/CrAHn74YfP3v/890/lZ3XMiN7X16tUzn3zySYbznn32WRMSEpLp3gZPrPXUvj31/apRo4ZZsGCB4/mNlw5duXKliY6OdqtaT+3bE2tdUX+9WbNmmbCwMOPl5ZXtvSzUWq/Nz23fylqbzWaqV69uateubYoUKWLmzJnjNH/58uWmVKlSBabWU/v21Pfrzz//NOXKlTMPPPCAGTJkiAkICDD33XefefLJJ80DDzxgChUq5HRzYE+v9dS+c1N7s0vz2+12s2jRokznZ4Wr9t3gxRdfVFJSUqbzy5cvr2XLlrm8tmPHjpo1a5Z69uyZbt7EiRNlt9s1efLkAlPrqX176vv1zDPPKDU11fG8WrVqTvMXLFiQ6RXZ8qvWU/v2xFpX1F+vW7duuu+++7RhwwbLl4am1hpP7Ntq7ahRo5yeBwYGOj2fN2+e7r///gJT66l9e+r7FRkZqU2bNmncuHGaN2+ejDFau3atjhw5onvvvVerVq1S3bp1C0ytp/adm9qyZcvK29s70/fDZrOpWbNmmc7PCveRAgAAAACLOEcKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAF2urVq+Xt7a2WLVumm5eSkqK3335bf/vb31S4cGEFBwerZs2aevXVV3Xs2DHHcn369JHNZkv3yGidAIDbA1ftAwAUaP369VORIkX073//Wzt27FCZMmUkXbtxZ/PmzbV161aNHj1a9957r4KDg7Vv3z598803CgkJ0dixYyVdC1J//fWXpk2b5rRuPz8/FStW7Ja/JgBA/uM+UgCAAispKUlffvml1q1bp4SEBE2fPl2vvfaaJOndd9/VL7/8ovXr16t27dqOmvLly6tFixa68XdGPz8/hYeH39L+AQDui0P7AAAF1uzZs1WxYkVVrFhRjz32mKZNm+YISLNmzVKzZs2cQtT1bDbbrWwVAOBhCFIAgAJr6tSpeuyxxyRJLVu21IULF7RkyRJJ0u7du1WxYkWn5Tt27KgiRYqoSJEiatiwodO8+fPnO+alPV5//fVb80IAAG6HQ/sAAAXSrl27tHbtWs2ZM0eS5OPjo65du+rjjz9W06ZNJaXf6/TBBx8oKSlJ7733nlasWOE0r0mTJpo0aZLTtOLFi+fhKwAAuDOCFACgQJo6daquXr2qUqVKOaYZY+Tr66szZ84oJiZGf/zxh1NNRESEpIwDUuHChVW+fPm8bRoA4DE4tA8AUOBcvXpVn3zyif7xj39o8+bNjseWLVtUtmxZff755+revbsWL16sTZs25Xe7AAAPxB4pAECBM3/+fJ05c0Z9+/ZVcHCw07zOnTtr6tSpWrNmjb7//ns9+OCDio2N1f33369ixYpp9+7dWrBggby9vZ3qkpOTlZCQ4DTNx8dHJUqUyPPXAwBwP9xHCgBQ4Dz88MOy2+36/vvv083buHGj6tSpow0bNqhq1aqKj4/XrFmztHv3btntdkVHR6tVq1Z64YUXFBUVJenafaRmzJiRbl0VK1ZMd3ggAOD2QJACAAAAAIs4RwoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFv0/F4avtsIV7EwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby(['Age'])['Annual_Income'].agg(['mean']).plot(kind='bar', figsize=(10,5))\n",
    "plt.xlabel(\"AGE\")\n",
    "plt.ylabel(\"Average Annual Income\")\n",
    "plt.title(\"Income Bar Chart\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3620d10b",
   "metadata": {},
   "source": [
    "**Вывод**: покупатели среднего возраста имеют доход выше, чем более молодые или пристарелые покупатели."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfede26a",
   "metadata": {},
   "source": [
    "### Конец первого проекта с использованием Jupiter Notebook\n",
    "### Поздравляю!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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",
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}