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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Лабораторная работа №3\n",
+    "\n",
+    "## Набор данных Students Performance in Exams (Успеваемость студентов на экзаменах)\n",
+    "\n",
+    "Выгрузка данных из CSV файла в датафрейм"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['gender', 'race/ethnicity', 'parental level of education', 'lunch',\n",
+      "       'test preparation course', 'math score', 'reading score',\n",
+      "       'writing score'],\n",
+      "      dtype='object')\n",
+      "\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1000 entries, 0 to 999\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column                       Non-Null Count  Dtype \n",
+      "---  ------                       --------------  ----- \n",
+      " 0   gender                       1000 non-null   object\n",
+      " 1   race/ethnicity               1000 non-null   object\n",
+      " 2   parental level of education  1000 non-null   object\n",
+      " 3   lunch                        1000 non-null   object\n",
+      " 4   test preparation course      1000 non-null   object\n",
+      " 5   math score                   1000 non-null   int64 \n",
+      " 6   reading score                1000 non-null   int64 \n",
+      " 7   writing score                1000 non-null   int64 \n",
+      "dtypes: int64(3), object(5)\n",
+      "memory usage: 62.6+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "\n",
+    "# Загрузка данных\n",
+    "df = pd.read_csv(\"..//..//static//csv//StudentsPerformance.csv\")\n",
+    "\n",
+    "# Вывод колонок\n",
+    "print(df.columns)\n",
+    "\n",
+    "print()\n",
+    "\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Описание набора  \n",
+    "\n",
+    "Контекст\n",
+    "Оценки, полученные студентами\n",
+    "\n",
+    "Содержание\n",
+    "Этот набор данных состоит из оценок, полученных учащимися по различным предметам.\n",
+    "\n",
+    "Вдохновение\n",
+    "Понять влияние происхождения родителей, подготовки к тестированию и т. д. на успеваемость учащихся."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gender</th>\n",
+       "      <th>race/ethnicity</th>\n",
+       "      <th>parental level of education</th>\n",
+       "      <th>lunch</th>\n",
+       "      <th>test preparation course</th>\n",
+       "      <th>math score</th>\n",
+       "      <th>reading score</th>\n",
+       "      <th>writing score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group B</td>\n",
+       "      <td>bachelor's degree</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>72</td>\n",
+       "      <td>72</td>\n",
+       "      <td>74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>69</td>\n",
+       "      <td>90</td>\n",
+       "      <td>88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group B</td>\n",
+       "      <td>master's degree</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>90</td>\n",
+       "      <td>95</td>\n",
+       "      <td>93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>male</td>\n",
+       "      <td>group A</td>\n",
+       "      <td>associate's degree</td>\n",
+       "      <td>free/reduced</td>\n",
+       "      <td>none</td>\n",
+       "      <td>47</td>\n",
+       "      <td>57</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>male</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>76</td>\n",
+       "      <td>78</td>\n",
+       "      <td>75</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   gender race/ethnicity parental level of education         lunch  \\\n",
+       "0  female        group B           bachelor's degree      standard   \n",
+       "1  female        group C                some college      standard   \n",
+       "2  female        group B             master's degree      standard   \n",
+       "3    male        group A          associate's degree  free/reduced   \n",
+       "4    male        group C                some college      standard   \n",
+       "\n",
+       "  test preparation course  math score  reading score  writing score  \n",
+       "0                    none          72             72             74  \n",
+       "1               completed          69             90             88  \n",
+       "2                    none          90             95             93  \n",
+       "3                    none          47             57             44  \n",
+       "4                    none          76             78             75  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Вывод столбцов\n",
+    "df.head()"
+   ]
+  },
+  {
+   "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>math score</th>\n",
+       "      <th>reading score</th>\n",
+       "      <th>writing score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1000.00000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>66.08900</td>\n",
+       "      <td>69.169000</td>\n",
+       "      <td>68.054000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>15.16308</td>\n",
+       "      <td>14.600192</td>\n",
+       "      <td>15.195657</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>57.00000</td>\n",
+       "      <td>59.000000</td>\n",
+       "      <td>57.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>66.00000</td>\n",
+       "      <td>70.000000</td>\n",
+       "      <td>69.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>77.00000</td>\n",
+       "      <td>79.000000</td>\n",
+       "      <td>79.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>100.00000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       math score  reading score  writing score\n",
+       "count  1000.00000    1000.000000    1000.000000\n",
+       "mean     66.08900      69.169000      68.054000\n",
+       "std      15.16308      14.600192      15.195657\n",
+       "min       0.00000      17.000000      10.000000\n",
+       "25%      57.00000      59.000000      57.750000\n",
+       "50%      66.00000      70.000000      69.000000\n",
+       "75%      77.00000      79.000000      79.000000\n",
+       "max     100.00000     100.000000     100.000000"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Краткая статистическая сводка для данных:\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Анализ содержимого\n",
+    "\n",
+    "*Объекты наблюдения:* студенты, участвующие в экзаменах.\n",
+    "\n",
+    "*Атрибуты объектов:* \n",
+    "\n",
+    "gender — пол: определяет гендерную принадлежность студента (мужской, женский).  \n",
+    "race/ethnicity — этническая принадлежность: группа, к которой относится студент (например, различные расовые/этнические категории).  \n",
+    "parental level of education — уровень образования родителей(например, среднее образование, высшее образование и т.д.).  \n",
+    "lunch — тип обеда: информация о том, получает ли студент бесплатный или платный обед.  \n",
+    "test preparation course — курс подготовки к тесту\n",
+    "math score  — результаты экзаменов по математике.\n",
+    "reading score — результаты экзаменов по чтению.\n",
+    "writing score — результаты экзаменов по письму.\n",
+    "\n",
+    "\n",
+    "### Бизнес-цель\n",
+    "1.  Анализ факторов, влияющих на успеваемость студентов:\n",
+    "\n",
+    "    **Цель:** Исследовать, как различные факторы, такие как пол, этническая принадлежность, уровень образования родителей, тип обеда и наличие курса подготовки к тесту, влияют на оценки студентов по математике, чтению и письму.\n",
+    "\n",
+    "    **Эффект:** Это поможет образовательным учреждениям и политикам лучше понять, какие аспекты могут быть улучшены для повышения успеваемости студентов, а также выявить возможные неравенства в образовательных возможностях.\n",
+    "\n",
+    "2.  Прогнозирование успеваемости студентов\n",
+    "\n",
+    "    **Цель:** Разработать модель прогнозирования успеваемости студентов на основе имеющихся данных, таких как пол, раса/этническая принадлежность, уровень образования родителей, тип обеда и участие в подготовительных курсах.\n",
+    "\n",
+    "    **Эффект:** Это позволит предсказать, какие студенты могут столкнуться с трудностями в обучении, и принять меры для их поддержки. Например, образовательные учреждения могут инициировать дополнительные занятия или индивидуальные консультации для студентов, у которых ожидаются низкие результаты на экзаменах, тем самым повышая их шансы на успешную сдачу экзаменов.\n",
+    "\n",
+    "### Техническая цель\n",
+    "1.  Разработка системы анализа факторов успеваемости студентов:\n",
+    "\n",
+    "    **Цель:** Создать аналитическую платформу, которая будет собирать, обрабатывать и визуализировать данные о студентах, включая их оценки и соответствующие факторы (пол, этническая принадлежность, уровень образования родителей, тип обеда, наличие подготовительных курсов).\n",
+    "\n",
+    "2.  Создание модели прогнозирования успеваемости студентов:\n",
+    "\n",
+    "    **Цель:** Разработать и внедрить предсказательную модель, которая будет оценивать вероятную успеваемость студентов на основании их характеристик и данных.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Анализ данных "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "gender                         0\n",
+       "race/ethnicity                 0\n",
+       "parental level of education    0\n",
+       "lunch                          0\n",
+       "test preparation course        0\n",
+       "math score                     0\n",
+       "reading score                  0\n",
+       "writing score                  0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Проверка на пропущенные данные\n",
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Нет пропущенных данных"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "gender — пол: определяет гендерную принадлежность студента (мужской, женский).  \n",
+    "race/ethnicity — этническая принадлежность: группа, к которой относится студент (например, различные расовые/этнические категории).  \n",
+    "parental level of education — уровень образования родителей(например, среднее образование, высшее образование и т.д.).  \n",
+    "lunch — тип обеда: информация о том, получает ли студент бесплатный или платный обед.  \n",
+    "test preparation course — курс подготовки к тесту\n",
+    "math score  — результаты экзаменов по математике.\n",
+    "reading score — результаты экзаменов по чтению.\n",
+    "writing score — результаты экзаменов по письму."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Построим графики boxplot для обнаружения выбросов по каждой характеристике\n",
+    "plt.figure(figsize=(10, 10))\n",
+    "\n",
+    "# Создание boxplot\n",
+    "for i, column in enumerate(['gender', 'race/ethnicity','parental level of education','lunch','test preparation course','math score','reading score','writing score'], 1):\n",
+    "    plt.subplot(8, 3, i)\n",
+    "    sns.boxplot(x=df[column])\n",
+    "    plt.title(f\"Boxplot для {column}\")\n",
+    "    \n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Попробуем решить устранить проблему выбросов для writing score и reading score и math score. Используется метод усреднения данных для устранения выбросов. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Связи между объектами:\n",
+    "1) Влияние атрибутов на успеваемость: Анализ данных покажет, как каждый из атрибутов (пол, этническая принадлежность, уровень образования родителей, тип обеда, курс подготовки) влияет на оценки студентов, что поможет выявить ключевые факторы, способствующие или препятствующие успеваемости."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2) Корреляция между результатами экзаменов: Можно исследовать взаимосвязь между оценками по математике, чтению и письму, чтобы понять, например, влияет ли высокая успеваемость в одном предмете на другие."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "3) Группировка по характеристикам: Студенты могут быть сгруппированы по различным признакам (например, пол, уровень образования родителей) для анализа различий в успеваемости и выявления возможных неравенств в образовательных возможностях."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "4) Предсказательные связи: Используя модель прогнозирования успеваемости, можно будет установить, какие комбинации атрибутов (например, пол + уровень образования родителей + участие в курсах подготовки) наиболее предрасполагают к высокому или низкому результату, что позволит образовательным учреждениям эффективно направлять ресурсы на поддержку студентов с высоким риском неуспеха."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Данная гистограмма в диапазоне с 10 по 51 строки отображает:\n",
+    "На оси X значения оценок по математике, разбитые на 100 интервалов.\n",
+    "На оси Y будет указано количество записей (частота) в каждом из этих интервалов. \n",
+    "Анализируя гистограмму \"math score\", можно сделать выводы о том, как распределяются оценки.\n",
+    "Например, оценку 70 имеет 4 человека, а оценку 18 всего 1 человек из этого диапазона."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: ylabel='Frequency'>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "df.iloc[10:51].plot.hist(column=[\"math score\"], bins=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Данная гистограмма отображает прцоентное соотношение мужчин и женщин.\n",
+    "Что позволяет сделать вывод о том, что женщин среди студентов больше, чем мужчин. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt  \n",
+    "\n",
+    "labels = 'Женщины', 'Мужчины'\n",
+    "sizes = [len(df[df['gender']== 'female']),\n",
+    "         len(df[df['gender']== 'male'])]\n",
+    "\n",
+    "plt.pie(sizes, labels=labels, autopct='%1.1f%%')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Данная диаграмма отображает соотношение студентов, которые прошли курс подготовки к тестированию по группам.\n",
+    "Что позволяет сделать вывод о том, что, например, больше всего неподготовленных студентов в группе С."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x20a9f46a900>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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KlSq5uxTcwQgoAIDr4rUDcBfu4gEAAMYhoAAAAOMQUAAAgHEIKAAAwDilDihbtmxR9+7dFR4eLpvNphUrVji022y2Eqe33nrL3qdOnTrF2qdPn37bXwYAAJQPpQ4oeXl5at68uWbPnl1ie0ZGhsM0f/582Ww29erVy6Hfq6++6tBv9OjRt/YNAABAuVPq24zj4+MVHx9/3fbQ0FCH+ZUrVyo2Nlb33HOPw3J/f/9ifQEAACQnX4OSmZmpv//97xoyZEixtunTpyskJETR0dF66623dPXq1euuJz8/Xzk5OQ4TAAAov5z6oLYFCxbI399fPXv2dFg+ZswYtWzZUsHBwdq+fbsSExOVkZGhWbNmlbiepKQkTZ061ZmlAgAAgzg1oMyfP1/9+vVT5cqVHZYnJCTYf27WrJm8vb01bNgwJSUlycfHp9h6EhMTHT6Tk5OjiIgI5xUOAADcymkB5euvv9aRI0e0dOnSX+3bpk0bXb16VSdOnFDDhg2Ltfv4+JQYXAAAQPnktGtQ5s2bp1atWql58+a/2jctLU0eHh6qUaOGs8oBAAB3kFIfQcnNzdWxY8fs88ePH1daWpqCg4NVq1YtST+fgvnss880c+bMYp/fsWOHdu3apdjYWPn7+2vHjh0aP368+vfvr7vuuus2vgoAACgvSh1Q9u7dq9jYWPv8tWtDBgwYoJSUFEnSkiVLZFmW+vbtW+zzPj4+WrJkiaZMmaL8/HxFRkZq/PjxDteYAACAis1mWdYd957rnJwcBQYGKjs7WwEBAe4uB0AZs021OX0b1uQ77q8+t2AsUJZK8+837+IBAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYpdUDZsmWLunfvrvDwcNlsNq1YscKhfeDAgbLZbA5TXFycQ5+LFy+qX79+CggIUFBQkIYMGaLc3Nzb+iIAAKD8KHVAycvLU/PmzTV79uzr9omLi1NGRoZ9+uSTTxza+/Xrp0OHDmndunVavXq1tmzZoueee6701QMAgHLJq7QfiI+PV3x8/A37+Pj4KDQ0tMS2w4cPa82aNdqzZ4/uu+8+SdL777+vbt26acaMGQoPDy9tSQAAoJxxyjUoX331lWrUqKGGDRtq+PDh+uGHH+xtO3bsUFBQkD2cSFLnzp3l4eGhXbt2lbi+/Px85eTkOEwAAKD8KvOAEhcXp4ULF2rDhg164403tHnzZsXHx6uwsFCSdPbsWdWoUcPhM15eXgoODtbZs2dLXGdSUpICAwPtU0RERFmXDQAADFLqUzy/pk+fPvafmzZtqmbNmqlu3br66quv1KlTp1taZ2JiohISEuzzOTk5hBQAAMoxp99mfM8996hatWo6duyYJCk0NFTnzp1z6HP16lVdvHjxutet+Pj4KCAgwGECAADll9MDyvfff68ffvhBYWFhkqSYmBhlZWUpNTXV3mfjxo0qKipSmzZtnF0OAAC4A5T6FE9ubq79aIgkHT9+XGlpaQoODlZwcLCmTp2qXr16KTQ0VOnp6XrppZdUr149de3aVZIUFRWluLg4DR06VHPnzlVBQYFGjRqlPn36cAcPAACQdAtHUPbu3avo6GhFR0dLkhISEhQdHa1JkybJ09NTBw4c0OOPP64GDRpoyJAhatWqlb7++mv5+PjY17Fo0SI1atRInTp1Urdu3fTQQw/pr3/9a9l9KwAAcEcr9RGUDh06yLKs67avXbv2V9cRHBysxYsXl3bTAACgguBdPAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcUodULZs2aLu3bsrPDxcNptNK1assLcVFBRo4sSJatq0qapUqaLw8HD9/ve/15kzZxzWUadOHdlsNodp+vTpt/1lAABA+VDqgJKXl6fmzZtr9uzZxdouXbqkffv26ZVXXtG+ffu0bNkyHTlyRI8//nixvq+++qoyMjLs0+jRo2/tGwAAgHLHq7QfiI+PV3x8fIltgYGBWrduncOyDz74QK1bt9bJkydVq1Yt+3J/f3+FhoaWdvMAAKACcPo1KNnZ2bLZbAoKCnJYPn36dIWEhCg6OlpvvfWWrl69et115OfnKycnx2ECAADlV6mPoJTG5cuXNXHiRPXt21cBAQH25WPGjFHLli0VHBys7du3KzExURkZGZo1a1aJ60lKStLUqVOdWSoAADCI0wJKQUGBnnzySVmWpTlz5ji0JSQk2H9u1qyZvL29NWzYMCUlJcnHx6fYuhITEx0+k5OTo4iICGeVDgAA3MwpAeVaOPnuu++0ceNGh6MnJWnTpo2uXr2qEydOqGHDhsXafXx8SgwuAACgfCrzgHItnBw9elSbNm1SSEjIr34mLS1NHh4eqlGjRlmXAwAA7kClDii5ubk6duyYff748eNKS0tTcHCwwsLC9Lvf/U779u3T6tWrVVhYqLNnz0qSgoOD5e3trR07dmjXrl2KjY2Vv7+/duzYofHjx6t///666667yu6bAQCAO1apA8revXsVGxtrn792bciAAQM0ZcoUrVq1SpLUokULh89t2rRJHTp0kI+Pj5YsWaIpU6YoPz9fkZGRGj9+vMM1JgAAoGIrdUDp0KGDLMu6bvuN2iSpZcuW2rlzZ2k3CzidbarN6duwJt/4zwcA4Ge8iwcAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBynvs0YQDm0ea+7KwBQAXAEBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGKfUAWXLli3q3r27wsPDZbPZtGLFCod2y7I0adIkhYWFydfXV507d9bRo0cd+ly8eFH9+vVTQECAgoKCNGTIEOXm5t7WFwEAAOVHqQNKXl6emjdvrtmzZ5fY/uabb+q9997T3LlztWvXLlWpUkVdu3bV5cuX7X369eunQ4cOad26dVq9erW2bNmi55577ta/BQAAKFe8SvuB+Ph4xcfHl9hmWZbeeecdvfzyy+rRo4ckaeHChapZs6ZWrFihPn366PDhw1qzZo327Nmj++67T5L0/vvvq1u3bpoxY4bCw8Nv4+sAAIDyoEyvQTl+/LjOnj2rzp0725cFBgaqTZs22rFjhyRpx44dCgoKsocTSercubM8PDy0a9euEtebn5+vnJwchwkAAJRfZRpQzp49K0mqWbOmw/KaNWva286ePasaNWo4tHt5eSk4ONje55eSkpIUGBhonyIiIsqybAAAYJg74i6exMREZWdn26dTp065uyQAAOBEZRpQQkNDJUmZmZkOyzMzM+1toaGhOnfunEP71atXdfHiRXufX/Lx8VFAQIDDBAAAyq8yDSiRkZEKDQ3Vhg0b7MtycnK0a9cuxcTESJJiYmKUlZWl1NRUe5+NGzeqqKhIbdq0KctyAADAHarUd/Hk5ubq2LFj9vnjx48rLS1NwcHBqlWrlsaNG6c//elPql+/viIjI/XKK68oPDxcTzzxhCQpKipKcXFxGjp0qObOnauCggKNGjVKffr04Q4eAAAg6RYCyt69exUbG2ufT0hIkCQNGDBAKSkpeumll5SXl6fnnntOWVlZeuihh7RmzRpVrlzZ/plFixZp1KhR6tSpkzw8PNSrVy+99957ZfB1AABAeWCzLMtydxGllZOTo8DAQGVnZ3M9CsqMbarN6duwJt9xf9yK27zX6ZuwfXW/07dRLsbCBfhzgbJUmn+/74i7eAAAQMVCQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABjHy90FAABu0ea97q4AcBqOoAAAAOMQUAAAgHEIKAAAwDhcgwIAwB3ANtXm9G1Yky2nb+NmcQQFAAAYh4ACAACMQ0ABAADGKfOAUqdOHdlstmLTyJEjJUkdOnQo1vb888+XdRkAAOAOVuYXye7Zs0eFhYX2+YMHD+qRRx5R79697cuGDh2qV1991T7v5+dX1mUAAIA7WJkHlOrVqzvMT58+XXXr1lX79u3ty/z8/BQaGlrWmwYAAOWEU69BuXLliv7nf/5HgwcPls32n9ujFi1apGrVqqlJkyZKTEzUpUuXbrie/Px85eTkOEwAAKD8cupzUFasWKGsrCwNHDjQvuzpp59W7dq1FR4ergMHDmjixIk6cuSIli1bdt31JCUlaerUqc4sFQAAGMSpAWXevHmKj49XeHi4fdlzzz1n/7lp06YKCwtTp06dlJ6errp165a4nsTERCUkJNjnc3JyFBER4bzCAQCAWzktoHz33Xdav379DY+MSFKbNm0kSceOHbtuQPHx8ZGPj0+Z1wgAAMzktGtQkpOTVaNGDT366KM37JeWliZJCgsLc1YpAADgDuOUIyhFRUVKTk7WgAED5OX1n02kp6dr8eLF6tatm0JCQnTgwAGNHz9e7dq1U7NmzZxRCgAAuAM5JaCsX79eJ0+e1ODBgx2We3t7a/369XrnnXeUl5eniIgI9erVSy+//LIzygAAAHcopwSULl26yLKKvxExIiJCmzdvdsYmAQBAOcK7eAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjOPUd/EAZWbzXndXAABwIY6gAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAObzMGAKAs8Nb1MsURFAAAYBwCCgAAMA6neNzMNtXm1PVbky2nrh8AAGfgCAoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAME6ZB5QpU6bIZrM5TI0aNbK3X758WSNHjlRISIiqVq2qXr16KTMzs6zLAAAAdzCnHEG59957lZGRYZ+2bt1qbxs/fry++OILffbZZ9q8ebPOnDmjnj17OqMMAABwh3LKu3i8vLwUGhpabHl2drbmzZunxYsXq2PHjpKk5ORkRUVFaefOnXrggQecUQ4AALjDOOUIytGjRxUeHq577rlH/fr108mTJyVJqampKigoUOfOne19GzVqpFq1amnHjh3XXV9+fr5ycnIcJgAAUH6VeUBp06aNUlJStGbNGs2ZM0fHjx/Xww8/rJ9++klnz56Vt7e3goKCHD5Ts2ZNnT179rrrTEpKUmBgoH2KiIgo67IBAIBByvwUT3x8vP3nZs2aqU2bNqpdu7Y+/fRT+fr63tI6ExMTlZCQYJ/PyckhpAAAUI45/TbjoKAgNWjQQMeOHVNoaKiuXLmirKwshz6ZmZklXrNyjY+PjwICAhwmAABQfjk9oOTm5io9PV1hYWFq1aqVKlWqpA0bNtjbjxw5opMnTyomJsbZpQAAgDtEmZ/ieeGFF9S9e3fVrl1bZ86c0eTJk+Xp6am+ffsqMDBQQ4YMUUJCgoKDgxUQEKDRo0crJiaGO3gAAIBdmQeU77//Xn379tUPP/yg6tWr66GHHtLOnTtVvXp1SdLbb78tDw8P9erVS/n5+eratav+/Oc/l3UZAADgDlbmAWXJkiU3bK9cubJmz56t2bNnl/WmAQBAOcG7eAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDhe7i7AaJv3ursCAAAqJI6gAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYp8wDSlJSku6//375+/urRo0aeuKJJ3TkyBGHPh06dJDNZnOYnn/++bIuBQAA3KHKPKBs3rxZI0eO1M6dO7Vu3ToVFBSoS5cuysvLc+g3dOhQZWRk2Kc333yzrEsBAAB3qDJ/UNuaNWsc5lNSUlSjRg2lpqaqXbt29uV+fn4KDQ0t680DAIBywOnXoGRnZ0uSgoODHZYvWrRI1apVU5MmTZSYmKhLly5ddx35+fnKyclxmAAAQPnl1EfdFxUVady4cWrbtq2aNGliX/7000+rdu3aCg8P14EDBzRx4kQdOXJEy5YtK3E9SUlJmjp1qjNLBQAABnFqQBk5cqQOHjyorVu3Oix/7rnn7D83bdpUYWFh6tSpk9LT01W3bt1i60lMTFRCQoJ9PicnRxEREc4rHAAAuJXTAsqoUaO0evVqbdmyRXffffcN+7Zp00aSdOzYsRIDio+Pj3x8fJxSJwAAME+ZBxTLsjR69GgtX75cX331lSIjI3/1M2lpaZKksLCwsi4HAADcgco8oIwcOVKLFy/WypUr5e/vr7Nnz0qSAgMD5evrq/T0dC1evFjdunVTSEiIDhw4oPHjx6tdu3Zq1qxZWZcDAADuQGUeUObMmSPp54ex/bfk5GQNHDhQ3t7eWr9+vd555x3l5eUpIiJCvXr10ssvv1zWpQAAgDuUU07x3EhERIQ2b95c1psFAADlCO/iAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACM49aAMnv2bNWpU0eVK1dWmzZttHv3bneWAwAADOG2gLJ06VIlJCRo8uTJ2rdvn5o3b66uXbvq3Llz7ioJAAAYwm0BZdasWRo6dKgGDRqkxo0ba+7cufLz89P8+fPdVRIAADCElzs2euXKFaWmpioxMdG+zMPDQ507d9aOHTuK9c/Pz1d+fr59Pjs7W5KUk5Pj3ELzcp27fkm67NzVO/135CrlYCykcjIejIU5GAuzOHs8ysFYXFu/ZVm/3tlyg9OnT1uSrO3btzssf/HFF63WrVsX6z958mRLEhMTExMTE1M5mE6dOvWrWcEtR1BKKzExUQkJCfb5oqIiXbx4USEhIbLZbG6s7Pbk5OQoIiJCp06dUkBAgLvLqdAYC3MwFuZgLMxRXsbCsiz99NNPCg8P/9W+bgko1apVk6enpzIzMx2WZ2ZmKjQ0tFh/Hx8f+fj4OCwLCgpyZokuFRAQcEfvcOUJY2EOxsIcjIU5ysNYBAYG3lQ/t1wk6+3trVatWmnDhg32ZUVFRdqwYYNiYmLcURIAADCI207xJCQkaMCAAbrvvvvUunVrvfPOO8rLy9OgQYPcVRIAADCE2wLKU089pfPnz2vSpEk6e/asWrRooTVr1qhmzZruKsnlfHx8NHny5GKnr+B6jIU5GAtzMBbmqIhjYbOsm7nXBwAAwHV4Fw8AADAOAQUAABiHgAIAAIxDQAEAAMYhoBji4sWL7i4BAABjEFDc7Msvv9STTz6p3/zmN+4upcLKy8vT/PnzNXv2bB09etTd5VQYqampio2NLfHlZNnZ2YqNjdX+/fvdUFnFtXHjRo0aNUqPPfaYunfvrjFjxmjLli3uLqtCGTFihHJz//PSwU8++UR5eXn2+aysLHXr1s0dpbkcAcUNvvvuO02ePFl16tRR79695eHhoYULF7q7rArh5MmTat++vfz9/fXII4/o5MmTatmypZ599lmNHj1aLVq04C9kF5k5c6Y6duxY4mO7AwMD9cgjj+itt95yQ2UV0/PPP6/OnTvrk08+0Q8//KDz589r0aJFio2N1ejRo91dXoXxl7/8RZcuXbLPDxs2zOG1MPn5+Vq7dq07SnM5AoqLXLlyRUuWLFHnzp3VqFEj7du3T99//722bt2qJUuWqHfv3u4usUJ44YUXdOXKFc2dO1d+fn7q2rWr6tevr4yMDGVmZio+Pl5Tpkxxd5kVwq5du9SjR4/rtnfv3l3bt293YUUV1/Lly5WcnKz58+frwoUL2rFjh3bu3Knz58/rww8/1F//+letWrXK3WVWCL98NFlFflTZHfE24zvd6NGj9cknn6h+/frq37+/li5dqpCQEFWqVEmenp7uLq9C2bJli1atWqXWrVsrPj5e1apV0/z58+1PMH7llVfUqVMnN1dZMZw+fVr+/v7Xba9ataoyMjJcWFHFlZycrISEBA0cONBhuYeHhwYPHqwjR45o3rx5evzxx91TICokjqC4wJw5czRs2DB9+eWXGjlypEJCQtxdUoV17tw51a5dW5IUHBwsPz8/h9crhIaG6scff3RXeRVK9erVdeTIkeu2f/PNN6pWrZoLK6q49u3bp9/+9rfXbe/Zs6dSU1NdWBHAERSX+PjjjzV//nyFhYXp0Ucf1TPPPKP4+Hh3l1Vh2Wy2En+Ga3Xu3FnTpk1TXFxcsTbLsjRt2jR17tzZDZVVPBcuXNDdd9993fa7775bP/zwgwsrqtgmTZokPz8/ST9fHjBt2jQFBgZKksP1KeUd7+JxoePHjyslJUUpKSm6dOmSLl68qKVLl+p3v/udu0urMDw8PPTcc8/Z//DPnj1b/fv3d/jD/+GHH6qwsNCdZVYI6enpatWqlRo2bKgJEyaoYcOGkn4+cjJz5kx9++232rt3r+rVq+fmSss/Dw8PZWZmqnr16iW2Z2ZmKjw8nD8XLtChQ4eb+o/Tpk2bXFCNexFQ3MCyLH355ZeaN2+eVq1apWrVqqlnz55677333F1auccffrPs3btXAwcO1L/+9S/7uFiWpcaNGys5OVn333+/myusGH4Z3H+J4A53IKC42cWLF7Vw4UIlJyfzzAdUWGlpaTp69Kgsy1KDBg3UokULd5dUoRDcYSICCgAAMA538QAAAOMQUAAAgHEIKAAAwDg8BwUAAAP9+OOPmjdvng4fPixJioqK0uDBgxUcHOzmylyDi2RdrKLvcCZhLMzBWACOtmzZoscff1wBAQG67777JP38BvCsrCx98cUXateunZsrdD4Ciguxw5mDsTAHY2EWwqIZmjZtqpiYGM2ZM8f+zrbCwkKNGDFC27dv1z//+U83V+h8BBQXYoczB2NhDsbCHIRFc/j6+iotLc3+hOVrjhw5ohYtWuj//u//3FSZ6xBQXIgdzhyMhTkYC3MQFs3Rtm1bvfjii3riiScclq9YsULTp0/Xzp073VOYC3GRrAu1bNlShw8fLvYX8eHDh9W8eXM3VVUxMRbmYCzMcezYMf3tb3+zhxNJ8vT0VEJCghYuXOjGyiqeMWPGaOzYsTp27JgeeOABSdLOnTs1e/ZsTZ8+XQcOHLD3bdasmbvKdCoCiguxw5mDsTAHY2EOwqI5+vbtK0l66aWXSmyz2WyyLEs2m63cviOJUzwu5OFx48fOVIQdzhSMhTkYC3MsXbpUL730kkaPHl1iWIyKirL3JSw613fffXfTfWvXru3EStyHgOJC7HDmYCzMwViYg7AIkxBQAACSCIsm+bVrfn7/+9+7qBL3IaC4EDucORgLczAWQHF33XWXw3xBQYEuXbokb29v+fn56eLFi26qzHUIKC7EDmcOxsIcjIU5CItmO3r0qIYPH64XX3xRXbt2dXc5TkdAcbOKtsOZjLEwB2PhHoRF8+3du1f9+/fXN9984+5SnI6AYoCKtMOZjrEwB2NhBsKiWdLS0tSuXTvl5OS4uxSn4zkoBvDy8tKZM2fcXQbEWJiEsTBD/fr1NX36dMKii61atcph3rIsZWRk6IMPPlDbtm3dVJVrEVBciB3OHIyFORgL8xEWXe+Xj7i32WyqXr26OnbsqJkzZ7qnKBfjFI8L/fIZA7/c4cLCwtxUWcXDWJiDsTDHjcJiRESE/t//+39uqgwVEQEFACCJsGiqa/9M22w2N1fiWjd+bCCcxrIskQ3NwFiYg7Fwr6KiIoepsLBQZ8+e1eLFiwknbrBw4UI1bdpUvr6+8vX1VbNmzfTxxx+7uyyXIaC4WEXf4UzCWJiDsTAPYdG9Zs2apeHDh6tbt2769NNP9emnnyouLk7PP/+83n77bXeX5xoWXGbmzJmWn5+f9dJLL1krV660Vq5cab344ouWn5+fNWvWLHeXV6EwFuZgLMyyYMECq0mTJpaPj4/l4+NjNW3a1Fq4cKG7y6pw6tSpYy1YsKDY8pSUFKtOnTpuqMj1CCguxA5nDsbCHIyFOQiL5vDx8bGOHj1abPm3335r+fj4uKEi1+M2YxfKyMjQgw8+WGz5gw8+qIyMDDdUVHExFuZgLMzx/vvva86cOQ6PtH/88cd17733asqUKRo/frwbq6tY6tWrp08//VR/+MMfHJYvXbpU9evXd1NVrsU1KC50bYf7pYq0w5mCsTAHY2EOwqI5pk6dqkmTJikuLk6vvfaaXnvtNcXFxWnq1Kl69dVX3V2eS3AExYWmTp2qp556Slu2bLE/gGrbtm3asGFDiX9Bw3kYC3MwFubgf+3m6NWrl3bv3q1Zs2ZpxYoVkqSoqCjt3r1b0dHR7i3ORXgOiovt27dPs2bN0uHDhyX9vMNNmDChwuxwJmEszMFYmOHzzz/XU089pc6dO5cYFn/729+6ucKKoaCgQMOGDdMrr7yiyMhId5fjNgQUF2GHMwdjYQ7GwjyERTMEBgYqLS2tQv+5IKC4EDucORgLczAWZiAsmmXAgAFq0aJFhb4wmYDiQuxw5mAszMFYmIOwaI4//elPmjlzpjp16qRWrVqpSpUqDu1jxoxxU2WuQ0BxIXY4czAW5mAszEFYNMeNQqLNZtO///1vF1bjHgQUF2KHMwdjYQ7GwhyERZiEgAIAkERYhFkIKAAAGCYhIaHE5TabTZUrV1a9evXUo0cPBQcHu7gy1yGguBA7nDkYC3MwFkBxsbGx2rdvnwoLC9WwYUNJ0rfffitPT081atRIR44ckc1m09atW9W4cWM3V+scBBQXYoczB2NhDsbCHIRFc7zzzjv6+uuvlZycrICAAElSdna2nn32WT300EMaOnSonn76af3f//2f1q5d6+ZqnYOA4kLscOZgLMzBWJiDsGiO3/zmN1q3bl2x3/OhQ4fUpUsXnT59Wvv27VOXLl104cIFN1XpZK58dXJFFx4ebh06dKjY8oMHD1rh4eGWZVlWamqqFRIS4urSKhzGwhyMhTnefvttq2fPnlZ2drZ9WVZWlvW73/3Oeuedd6y8vDyrR48eVpcuXdxYZcVQpUoVa9OmTcWWb9q0yapataplWZaVnp5u+fv7u7gy1+Ftxi6UnZ2tc+fOFVt+/vx55eTkSJKCgoJ05coVV5dW4TAW5mAszPHWW2/ptddesx/Jkn5+eNuUKVP05ptvys/PT5MmTVJqaqobq6wYevToocGDB2v58uX6/vvv9f3332v58uUaMmSInnjiCUnS7t271aBBA/cW6kQEFBdihzMHY2EOxsIchEVz/OUvf1GnTp3Up08f1a5dW7Vr11afPn3UqVMnzZ07V5LUqFEjffTRR26u1IncfQinIvnpp5+sZ5991vL29rY8PDwsDw8Py9vb2xo6dKiVm5trWZZl/eMf/7D+8Y9/uLfQCoCxMAdjYY6nn37aioyMtJYtW2adOnXKOnXqlLVs2TLrnnvusfr3729ZlmV98sknVqtWrdxcacXx008/Wfv377f2799v/fTTT+4ux6W4SNYNcnNz7Q88uueee1S1alU3V1RxMRbmYCzcLzc3V+PHj9fChQt19epVSZKXl5cGDBigt99+W1WqVFFaWpokqUWLFu4rFBUCAQUA4ICwCBMQUAAAgHG4SBYAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAYaeDAgfYHtd2qEydOyGaz2W+NdcU2AZQNAgqAMvfdd9/J19dXubm5v9q3tCGiNCIiIpSRkaEmTZrcVP93331XKSkp9vkOHTpo3LhxZV4XgF/n5e4CALjGlStX5O3t7ZJtrVy5UrGxsW5/foanp6dCQ0Nvun9gYKATqwFQGhxBAcqpDh06aNSoURo3bpyqVaumrl27atasWWratKmqVKmiiIgIjRgxothRjm3btqlDhw7y8/PTXXfdpa5du+rHH3+UJBUVFSkpKUmRkZHy9fVV8+bN9be//a3YtleuXKnHH3/cPv/RRx8pKipKlStXVqNGjfTnP//Z3hYZGSlJio6Ols1mU4cOHRzWNWPGDIWFhSkkJEQjR45UQUGBva1OnTp6/fXXNXjwYPn7+6tWrVr661//am8v6ejMoUOH9NhjjykgIED+/v56+OGHlZ6eLsnxFM/AgQO1efNmvfvuu7LZbLLZbDp+/Ljq1aunGTNmONSYlpYmm82mY8eO/dqwALhJBBSgHFuwYIG8vb21bds2zZ07Vx4eHnrvvfd06NAhLViwQBs3btRLL71k75+WlqZOnTqpcePG2rFjh7Zu3aru3bursLBQkpSUlKSFCxdq7ty5OnTokMaPH6/+/ftr8+bN9nVkZWVp69at9oCyaNEiTZo0SdOmTdPhw4f1+uuv65VXXtGCBQsk/fwiQElav369MjIytGzZMvu6Nm3apPT0dG3atEkLFixQSkqKwykYSZo5c6buu+8+/eMf/9CIESM0fPhwHTlypMTfx+nTp9WuXTv5+Pho48aNSk1N1eDBg+2Pdf9v7777rmJiYjR06FBlZGQoIyNDtWrV0uDBg5WcnOzQNzk5We3atVO9evVudmgA/Bp3vggIgPO0b9/eio6OvmGfzz77zAoJCbHP9+3b12rbtm2JfS9fvmz5+flZ27dvd1g+ZMgQq2/fvvb5RYsWWffdd599vm7dutbixYsdPvPaa69ZMTExlmVZ1vHjxy1JxV4GOGDAAKt27drW1atX7ct69+5tPfXUU/b52rVr219iZ1mWVVRUZNWoUcOaM2dOietOTEy0IiMjrStXrpT4HQcMGGD16NHDPt++fXtr7NixDn1Onz5teXp6Wrt27bIsy7KuXLliVatWzUpJSSlxnQBuDdegAOVYq1atHObXr1+vpKQkffPNN8rJydHVq1d1+fJlXbp0SX5+fkpLS1Pv3r1LXNexY8d06dIlPfLIIw7Lr1y5oujoaPv8f5/eycvLU3p6uoYMGaKhQ4fa+1y9evWmrve499575enpaZ8PCwvTP//5T4c+zZo1s/9ss9kUGhqqc+fOlbi+tLQ0Pfzww6pUqdKvbvt6wsPD9eijj2r+/Plq3bq1vvjiC+Xn51/39wbg1hBQgHKsSpUq9p9PnDihxx57TMOHD9e0adMUHBysrVu3asiQIbpy5Yr8/Pzk6+t73XVdu1bl73//u37zm984tPn4+Ej6OaysWbNGf/jDHxw+8+GHH6pNmzYOn/nv4HE9vwwSNptNRUVFpe5zzY2+X2k8++yzeuaZZ/T2228rOTlZTz31lPz8/Mpk3QB+RkABKojU1FQVFRVp5syZ8vD4+fKzTz/91KFPs2bNtGHDBk2dOrXY5xs3biwfHx+dPHlS7du3L3EbX331le666y41b95cklSzZk2Fh4fr3//+t/r161fiZ67dWXTtOhdnatasmRYsWKCCgoKbOori7e1dYl3dunVTlSpVNGfOHK1Zs0ZbtmxxRrlAhUZAASqIevXqqaCgQO+//766d+9uv3D2vyUmJqpp06YaMWKEnn/+eXl7e2vTpk3q3bu3qlWrphdeeEHjx49XUVGRHnroIWVnZ2vbtm0KCAjQgAEDtGrVKoe7dyRp6tSpGjNmjAIDAxUXF6f8/Hzt3btXP/74oxISElSjRg35+vpqzZo1uvvuu1W5cmWn3e47atQovf/+++rTp48SExMVGBionTt3qnXr1mrYsGGx/nXq1NGuXbt04sQJVa1aVcHBwfLw8JCnp6cGDhyoxMRE1a9fXzExMU6pF6jIuIsHqCCaN2+uWbNm6Y033lCTJk20aNEiJSUlOfRp0KCBvvzyS+3fv1+tW7dWTEyMVq5cKS+vn/8v89prr+mVV15RUlKSoqKiFBcXp7///e/2W4VLCijPPvusPvroIyUnJ6tp06Zq3769UlJS7J/x8vLSe++9p7/85S8KDw9Xjx49nPY7CAkJ0caNG5Wbm6v27durVatW+vDDD697NOWFF16Qp6enGjdurOrVq+vkyZP2tmunxgYNGuS0eoGKzGZZluXuIgDc+fbt26eOHTvq/Pnzt3UR6p3i66+/VqdOnXTq1CnVrFnT3eUA5Q6neACUiatXr+r9998v9+EkPz9f58+f15QpU9S7d2/CCeAkHEEBgFJISUnRkCFD1KJFC61atarYHU0AygYBBQAAGIeLZAEAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4/x/RxEW0eIcctkAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot = df.groupby([\"race/ethnicity\", \"test preparation course\"]).size().unstack().plot.bar(color=[\"pink\", \"green\"])\n",
+    "plot.legend([\"Прошёл\", \"Не прошёл\"])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "aimvenv",
+   "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": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}