Домашнее_задание_1_(03_07_2024).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OsXZ7TctudVa"
      },
      "source": [
        "#Домашнее задание № 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kbJt8TyMujxo"
      },
      "source": [
        "### Задание 1: Анализ данных о студентах с использованием NumPy\n",
        "\n",
        "\n",
        "Описание:\n",
        "У вас есть данные о студентах, включающие оценки по различным предметам. Используйте библиотеку NumPy для анализа этих данных."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oZgX9GMrugmO",
        "outputId": "71c5c052-c75b-4e89-ab28-56b6a29643cd"
      },
      "outputs": [],
      "source": [
        "students_data = {\n",
        "    'math': [85, 78, 92, 70, 88],\n",
        "    'physics': [90, 82, 76, 85, 89],\n",
        "    'informatics': [88, 92, 80, 87, 90]\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wM19w8jduxX8"
      },
      "source": [
        "Задание:\n",
        "\n",
        "Найдите среднюю оценку по каждому предмету.<br>\n",
        "Найдите медианную оценку по каждому предмету.<br>\n",
        "Вычислите стандартное отклонение по каждому предмету.<br>\n",
        "Определите предмет с самой высокой средней оценкой."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Средняя оценка по math: 82.6\n",
            "Средняя оценка по physics: 84.4\n",
            "Средняя оценка по informatics: 87.4\n",
            "Медианная оценка по math: 85.0\n",
            "Медианная оценка по physics: 85.0\n",
            "Медианная оценка по informatics: 88.0\n",
            "Стандартное отклонение по math: 7.79\n",
            "Стандартное отклонение по physics: 5.08\n",
            "Стандартное отклонение по informatics: 4.08\n",
            "Предмет с самой высокой оценкой - informatics\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "def calculate_mean_np(data):\n",
        "    '''Расчёт средней оценки'''\n",
        "    for key, data in data.items():\n",
        "        print(f'Средняя оценка по {key}: {np.mean(data)}')\n",
        "\n",
        "def calculate_median_np(data):\n",
        "    '''Расчёт медианной оценки'''\n",
        "    for key, data in data.items():\n",
        "        print(f'Медианная оценка по {key}: {np.median(data)}')\n",
        "\n",
        "def calculate_standard_deviation_np(data):\n",
        "    '''Расчёт стандартного отклонения'''\n",
        "    for key, data in data.items():\n",
        "        print(f'Стандартное отклонение по {key}: %.2f' % np.std(data))\n",
        "\n",
        "def calculate_max_mean(data):\n",
        "    '''Определение предмета с самой высокой средней оценкой'''\n",
        "    result_dct = data\n",
        "    for key, data in data.items():\n",
        "        result_dct.update({key: np.mean(data)})\n",
        "\n",
        "    max = sorted(result_dct, key=result_dct.get, reverse=True)\n",
        "    print(f'Предмет с самой высокой оценкой - {max[0]}')\n",
        "\n",
        "calculate_mean_np(students_data)\n",
        "calculate_median_np(students_data)\n",
        "calculate_standard_deviation_np(students_data)\n",
        "calculate_max_mean(students_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Решение без использования библиотеки numpy\n",
        "\n",
        "def calculate_mean(data):\n",
        "    '''Расчёт средней оценки'''\n",
        "    for key, data in data.items():\n",
        "        count = len(data)\n",
        "        total = sum(data)\n",
        "        mean = total/count\n",
        "        print(f'Средняя оценка по {key}: {mean}')\n",
        "\n",
        "calculate_mean(students_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Решение без использования библиотеки numpy\n",
        "\n",
        "def calculate_median(data):\n",
        "    '''Расчёт медианной оценки'''\n",
        "    for key, data in data.items():\n",
        "        data.sort()  # Cортируем данные по убыванию\n",
        "        n = len(data)\n",
        "        if n % 2 == 0:\n",
        "            median = (data[n//2 - 1] + data[n//2]) / 2\n",
        "        else:\n",
        "            median = data[n//2]\n",
        "        print(f'Медианная оценка по {key}: {median}')\n",
        "\n",
        "calculate_median(students_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Решение без использования библиотеки numpy\n",
        "\n",
        "def calculate_max_mean(data):\n",
        "    '''Определение предмета с самой высокой средней оценкой'''\n",
        "    result_dct = data\n",
        "    for key, data in data.items():\n",
        "        total = sum(data)\n",
        "        count = len(data)\n",
        "        mean = total/count\n",
        "        result_dct.update({key: mean})\n",
        "\n",
        "    max = sorted(result_dct, key=result_dct.get, reverse=True)\n",
        "    print(f'Предмет с самой высокой оценкой - {max[0]}')\n",
        "\n",
        "calculate_max_mean(students_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7OjM3A7Ou7HR"
      },
      "source": [
        "### Задание 2: Анализ данных о продажах с использованием Pandas\n",
        "\n",
        "Описание:\n",
        "Используйте библиотеку Pandas для анализа данных о продажах товаров. У вас есть данные о продажах товаров за последний год."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c9LrLZKBusJ1",
        "outputId": "1146abe4-8c04-4047-8366-b985c7c6eac3"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "sales_data = {\n",
        "    'Month': ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'],\n",
        "    'Sales': [15000, 17000, 16000, 18000, 14000, 19000, 22000, 21000, 23000, 25000, 24000, 26000]\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h8Uo_C36vEVh"
      },
      "source": [
        "Задание: <br>\n",
        "Найдите общий объем продаж за год.<br>\n",
        "Найдите средний объем продаж в месяц.<br>\n",
        "Найдите месяц с наибольшим и наименьшим объемом продаж.<br>\n",
        "Постройте линейный график изменения объема продаж по месяцам."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Общий объем продаж за год составляет 240000\n",
            "Cредний  объём продаж в месяц составляет 20000\n"
          ]
        }
      ],
      "source": [
        "sales_data_pd = pd.DataFrame(sales_data)\n",
        "\n",
        "print(f\"Общий объем продаж за год составляет {sum(list(sales_data_pd['Sales']))}\")  # Общий объем продаж за год\n",
        "\n",
        "print(f\"Cредний  объём продаж в месяц составляет {int(sales_data_pd['Sales'].mean())}\")  # Средний объём продаж в месяц"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Месяц с наименьшим объемом продаж - May\n",
            "Месяц с наибольшим объемом продаж - December\n"
          ]
        }
      ],
      "source": [
        "# Найдите месяц с наибольшим и наименьшим объемом продаж.\n",
        "\n",
        "sales_data_pd = pd.DataFrame(sales_data)\n",
        "\n",
        "sales_data_pd.sort_values(by='Sales', inplace=True)\n",
        "sales_data_pd.reset_index(drop=True, inplace=True)\n",
        "\n",
        "print(f\"Месяц с наименьшим объемом продаж - {sales_data_pd.loc[0, 'Month']}\\nМесяц с наибольшим объемом продаж - {sales_data_pd.iloc[-1, 0]}\")  # В одном принте\n",
        "\n",
        "# print(f\"Месяц с наибольшим объемом продаж - {sales_data_pd.iloc[-1, 0]}\")  # Либо можно отдельно вынести принт месяца с наибольшими продажами"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x245e323c710>"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Постройте линейный график изменения объема продаж по месяцам.\n",
        "\n",
        "import matplotlib.pyplot as plt  # Добавим пару настроек отображения графика\n",
        "\n",
        "sales_data = {\n",
        "    'Month': ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'],\n",
        "    'Sales': [15000, 17000, 16000, 18000, 14000, 19000, 22000, 21000, 23000, 25000, 24000, 26000]\n",
        "}\n",
        "\n",
        "sales_data_pd = pd.DataFrame(sales_data)\n",
        "\n",
        "sales_data_pd.plot( 'Month' , 'Sales' )\n",
        "plt.axis([0,12,10000,30000])  # Настройка начала и конца по осям\n",
        "plt.legend(loc=2)  # Настройка места отображения легенды"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I5FobQ6Tvur4"
      },
      "source": [
        "### Задание 3: Задание на визуализацию данных с использованием Matplotlib и Seaborn"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c-jTCYmJv1VX"
      },
      "source": [
        "Описание:\n",
        "Используйте готовый набор данных из библиотеки Seaborn для создания различных типов графиков. Ваша задача — загрузить один из встроенных наборов данных Seaborn и построить несколько базовых графиков для анализа данных."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "TGSP0t5DvzM1",
        "outputId": "dcd85260-a922-46bf-ba43-6910743989bc"
      },
      "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>mpg</th>\n",
              "      <th>cylinders</th>\n",
              "      <th>displacement</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>weight</th>\n",
              "      <th>acceleration</th>\n",
              "      <th>model_year</th>\n",
              "      <th>origin</th>\n",
              "      <th>name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>18.0</td>\n",
              "      <td>8</td>\n",
              "      <td>307.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>3504</td>\n",
              "      <td>12.0</td>\n",
              "      <td>70</td>\n",
              "      <td>usa</td>\n",
              "      <td>chevrolet chevelle malibu</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15.0</td>\n",
              "      <td>8</td>\n",
              "      <td>350.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>3693</td>\n",
              "      <td>11.5</td>\n",
              "      <td>70</td>\n",
              "      <td>usa</td>\n",
              "      <td>buick skylark 320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>18.0</td>\n",
              "      <td>8</td>\n",
              "      <td>318.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>3436</td>\n",
              "      <td>11.0</td>\n",
              "      <td>70</td>\n",
              "      <td>usa</td>\n",
              "      <td>plymouth satellite</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16.0</td>\n",
              "      <td>8</td>\n",
              "      <td>304.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>3433</td>\n",
              "      <td>12.0</td>\n",
              "      <td>70</td>\n",
              "      <td>usa</td>\n",
              "      <td>amc rebel sst</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17.0</td>\n",
              "      <td>8</td>\n",
              "      <td>302.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>3449</td>\n",
              "      <td>10.5</td>\n",
              "      <td>70</td>\n",
              "      <td>usa</td>\n",
              "      <td>ford torino</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
              "0  18.0          8         307.0       130.0    3504          12.0   \n",
              "1  15.0          8         350.0       165.0    3693          11.5   \n",
              "2  18.0          8         318.0       150.0    3436          11.0   \n",
              "3  16.0          8         304.0       150.0    3433          12.0   \n",
              "4  17.0          8         302.0       140.0    3449          10.5   \n",
              "\n",
              "   model_year origin                       name  \n",
              "0          70    usa  chevrolet chevelle malibu  \n",
              "1          70    usa          buick skylark 320  \n",
              "2          70    usa         plymouth satellite  \n",
              "3          70    usa              amc rebel sst  \n",
              "4          70    usa                ford torino  "
            ]
          },
          "execution_count": 93,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "df = sns.load_dataset('mpg')\n",
        "\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8X9QsFkjwMqd"
      },
      "source": [
        "Постройте следующие графики: <br>\n",
        "Линейный график <br>\n",
        "Столбчатый график <br>\n",
        "Гистограмма <br>\n",
        "Диаграмма рассеяния <br>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x245e20169f0>"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Линейный график\n",
        "\n",
        "df = sns.load_dataset('mpg')\n",
        "\n",
        "sns.relplot(x = \"horsepower\", y = \"acceleration\", kind = \"line\", data = df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='year', ylabel='passengers'>"
            ]
          },
          "execution_count": 62,
          "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": [
        "# Столбчатый график\n",
        "\n",
        "df = sns.load_dataset('flights')\n",
        "\n",
        "sns.barplot(data = df, x = \"year\", y = \"passengers\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='passengers', ylabel='Count'>"
            ]
          },
          "execution_count": 91,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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AAACYplGBqF56erq+/vprSVKfPn2UmJjYJEUBAAC0pEYFovz8fN1yyy1av369IiMjJUlFRUW68sortWzZMkVHRzdljQAAAM2qUWeZTZ8+XSUlJdq1a5cKCwtVWFionTt3yuVy6f7772/qGgEAAJpVo3qIVq1apY8//li9evXyzuvdu7cWLlzIoGoAAHDeaVQPkcfjUWBg4EnzAwMD5fF4zrkoAACAltSoQDRy5Eg98MADysnJ8c47dOiQHnzwQY0aNarJigMAAGgJjQpEL730klwulzp16qQuXbqoS5cu6ty5s1wul1588cWmrhEAAKBZNWoMUYcOHbRt2zZ9/PHH2rNnjySpV69eGj16dJMWBwAA0BLOqodo7dq16t27t1wul2w2m6666ipNnz5d06dP15AhQ9SnTx9t3LixuWoFAABoFmcViObPn697771XTqfzpGURERH65S9/qXnz5jVZcQAAAC3hrALR9u3bdfXVV592+ZgxY5Senn7ORQEAALSkswpEeXl5pzzdvl5AQIAKCgrOuSgAAICWdFaBqF27dtq5c+dpl3/11VeKj48/56IAAABa0lkFomuuuUaPPfaYKisrT1pWUVGhxx9/XNdee22TFQcAANASzuq0+0cffVT//ve/1b17d02bNk09evSQJO3Zs0cLFy6U2+3W7373u2YpFAAAoLmcVSCKjY3Vpk2bdN9992nWrFkyDEOSZLPZlJycrIULFyo2NrZZCgUAAGguZ31hxo4dO+rDDz/UsWPHdODAARmGoW7duqlVq1bNUR8AAECza9SVqiWpVatWGjJkSFPWAgAAYIpG3csMAADgQkIgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlmdqIJozZ46GDBmi8PBwxcTEaPz48dq7d2+DNpWVlUpJSVHr1q0VFhamCRMmKC8vr0GbrKwsjRs3TiEhIYqJidFDDz2k2traBm3Wr1+vgQMHyuFwqGvXrlqyZElzbx4AADhPmBqINmzYoJSUFG3evFmpqamqqanRmDFjVFZW5m3z4IMP6oMPPtBbb72lDRs2KCcnRzfeeKN3udvt1rhx41RdXa1Nmzbptdde05IlSzR79mxvm8zMTI0bN05XXnmlMjIyNGPGDN1zzz1avXp1i24vAADwTTaj/g6tPqCgoEAxMTHasGGDRowYoeLiYkVHR2vp0qWaOHGiJGnPnj3q1auX0tLSNHz4cK1cuVLXXnutcnJyvDeWXbRokR555BEVFBTIbrfrkUce0YoVK7Rz507ve91yyy0qKirSqlWrTqqjqqpKVVVV3ucul0sdOnRQcXGxnE5nM+8F/Lceffpp6vy3T7t80YyJ2rvrqxasCABwPnC5XIqIiDijz2+fGkNUXFwsSYqKipIkpaenq6amRqNHj/a26dmzpxISEpSWliZJSktLU9++fb1hSJKSk5Plcrm0a9cub5sT11Hfpn4d/23OnDmKiIjwTh06dGi6jQQAAD7HZwKRx+PRjBkzdOmll+riiy+WJOXm5sputysyMrJB29jYWOXm5nrbnBiG6pfXL/uhNi6XSxUVFSfVMmvWLBUXF3un7OzsJtlGAADgmxp9t/umlpKSop07d+rTTz81uxQ5HA45HA6zywAAAC3EJ3qIpk2bpuXLl2vdunVq3769d35cXJyqq6tVVFTUoH1eXp7i4uK8bf77rLP65z/Wxul0Kjg4uKk3BwAAnGdMDUSGYWjatGl65513tHbtWnXu3LnB8kGDBikwMFBr1qzxztu7d6+ysrKUlJQkSUpKStKOHTuUn5/vbZOamiqn06nevXt725y4jvo29esAAADWZuohs5SUFC1dulTvvfeewsPDvWN+IiIiFBwcrIiICE2ZMkUzZ85UVFSUnE6npk+frqSkJA0fPlySNGbMGPXu3Vu33367nnnmGeXm5urRRx9VSkqK97DX1KlT9dJLL+nhhx/W3XffrbVr1+rNN9/UihUrTNt2AADgO0ztIXr55ZdVXFysK664QvHx8d7pjTfe8LZ5/vnnde2112rChAkaMWKE4uLi9O9//9u73N/fX8uXL5e/v7+SkpJ022236Y477tCTTz7pbdO5c2etWLFCqamp6t+/v+bOnatXX31VycnJLbq9AADAN/nUdYh81dlcxwBNj+sQAQAa47y9DhEAAIAZCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyCEQAAMDyTA1En3zyia677jq1bdtWNptN7777boPlhmFo9uzZio+PV3BwsEaPHq39+/c3aFNYWKhJkybJ6XQqMjJSU6ZMUWlpaYM2X331lS677DIFBQWpQ4cOeuaZZ5p70wAAwHnE1EBUVlam/v37a+HChadc/swzz+iFF17QokWL9Pnnnys0NFTJycmqrKz0tpk0aZJ27dql1NRULV++XJ988ol+8YtfeJe7XC6NGTNGHTt2VHp6up599lk98cQTeuWVV5p9+wAAwPkhwMw3Hzt2rMaOHXvKZYZhaP78+Xr00Ud1/fXXS5L+/ve/KzY2Vu+++65uueUWff3111q1apW2bNmiwYMHS5JefPFFXXPNNXruuefUtm1bvf7666qurtbf/vY32e129enTRxkZGZo3b16D4HSiqqoqVVVVeZ+7XK4m3nIAAOBLfHYMUWZmpnJzczV69GjvvIiICA0bNkxpaWmSpLS0NEVGRnrDkCSNHj1afn5++vzzz71tRowYIbvd7m2TnJysvXv36tixY6d87zlz5igiIsI7dejQoTk2EQAA+AifDUS5ubmSpNjY2AbzY2Njvctyc3MVExPTYHlAQICioqIatDnVOk58j/82a9YsFRcXe6fs7Oxz3yAAAOCzTD1k5qscDoccDofZZeAEZVW1OpBfKkeAn+IighQRHCibzWZ2WQCAC4TPBqK4uDhJUl5enuLj473z8/LyNGDAAG+b/Pz8Bq+rra1VYWGh9/VxcXHKy8tr0Kb+eX0b+K48V6Vq+k/Q4s8Oym0Y3vnR4Q5d2zdezuBAE6sDAFwofPaQWefOnRUXF6c1a9Z457lcLn3++edKSkqSJCUlJamoqEjp6eneNmvXrpXH49GwYcO8bT755BPV1NR426SmpqpHjx5q1apVC20NGuNIaZVufWWzPAmD5TYMxTodinMGyd9mU0FJld7Ymq08V+WPrwgAgB9hag9RaWmpDhw44H2emZmpjIwMRUVFKSEhQTNmzNBTTz2lbt26qXPnznrsscfUtm1bjR8/XpLUq1cvXX311br33nu1aNEi1dTUaNq0abrlllvUtm1bSdLPf/5z/f73v9eUKVP0yCOPaOfOnVqwYIGef/55MzYZZ8hVWaPJf/tC3x4pk8qP6Wcj+io+IliSVFJZo/e35+hIabX+te17yUlPHwDg3JjaQ7R161YlJiYqMTFRkjRz5kwlJiZq9uzZkqSHH35Y06dP1y9+8QsNGTJEpaWlWrVqlYKCgrzreP3119WzZ0+NGjVK11xzjX7yk580uMZQRESEPvroI2VmZmrQoEH69a9/rdmzZ5/2lHuYzzAMTV/6pXbluNQ61K7AzX/zhiFJCg8K1MRB7dW+VbBq3IZqE3+mqlq3iRUDAM53NsM4YWAGTsnlcikiIkLFxcVyOp1ml3PBe397ju7/vy/lCPDTv+67RBPGXKap898+qV15da3+uTlLFTVu/eqKLnr46p4mVAsA8FVn8/nts2OIYE0llTV6avluSVLKlV11cbuI07YNsQdoVK+6yy4s2vCNtmWd+rpSAAD8GAIRfMrzqfuVX1Klzm1C9YsRF/1o+y7RYfLL3iaPIf3Piq9FhycAoDF89rR7WE/mkTK9lnZQkvT7n/ZRUKD/Gb0u4OtVsnUerPTvjmnj/iMa0T26Gav0TSOuHKW8/ILTLo+NidYn69acdjkAWB2BCD7jlU++kdtj6Ioe0WcVamxVJbpteEf99dNMzUvdp8u6tbHcRRvz8gtOOc6q3qIZE1uwGgA4/3DIDD4hz1Wpf6UfklQ3duhsTb28i4IC/ZSRXaT1e0/fUwIAwKkQiOATXt34rardHg3p1EpDOkWd9eujwx26I6mTJOmFtfubuDoAwIWOQATTFZVX6/XPsyRJv7ri7HuH6t172UWy+/vpy6wibc8uaqLqAABWQCCC6ZZtyVZ5tVs948J1RY/GD4iODnfo2n519737e9p3TVUeAMACCEQwlcdjaOnx3qG7L+18zoOh77ikkyTpg69ydLS06lzLAwBYBIEIpvrsmyPKKixXuCNA1/aPP+f1DegQqf7tI1Rd69EbW7OboEIAgBUQiGCq1zfX9Q7dOLCdQuxNcxWI+sHV/0z7TrVuT5OsEwBwYSMQwTT5rkqlfp0nSfr5sI5Ntt5x/eLVKiRQOcWV+vTAkSZbLwDgwkUggmne3Jott8fQ4I6t1CMuvMnWGxTor5/2bytJ+ve2Q022XgDAhYtABFMYhqG307+XJN06NKHJ1z9hUHtJ0upduXJV1jT5+gEAFxYCEUyxLatIB4+WK8Tur7F945p8/X3bRahrTJiqaj368KvDTb5+AMCFhUAEU7zzZV3v0NV94ppsMPWJbDabJgys6yX617bvm3z9AIALC4EILa6q1q0Pttf12twwsF2zvc8Nie3kZ5O2HDym746WNdv7AADOfwQitLh1ewpUXFGjWKdDl3Rp02zvExcRpEu71q2fwdUAgB9CIEKLqz9cNn5AO/n7nduVqX9M/WGzf3/5vTweo1nfCwBw/iIQoUUdK6vW2j35kpr3cFm95D5xCnMEKLuwQlsOFjb7+wEAzk8EIrSo5TsOq8ZtqHe8Uz3jnM3+fsF2f11z/Cw2BlcDAE6HQIQW9e/joeTGFugdqld/2OzDHbmqqHa32PsCAM4fBCK0mMwjZfoyq0h+NnmvJN0ShnSKUvtWwSqtqtVHu3Nb7H0BAOcPAhFazDtf1p3pdVm3aMU4g1rsff38bLrxeC9R/dWxAQA4EYEILcIwDO/ZZS15uKzejYl17/nZgSPKc1W2+PsDAHwbgQgtYsvBY8ourFCo3V9jejf9rTp+TKc2oRrUsZU8hvReBtckAgA0RCBCi/jX8UNVY/vGK9jub0oN9T1TXKQRAPDfCERodhXVbq3YUXerjvozvsxwbd+2svv7aU9uiXbnuEyrAwDgewhEaHYf7c5VaVWt2kUGa1jnKNPqiAgJ1OjeMZL+c/o/AAASgQgtoP7MrgkD28mvmW/V8WNuSKzroXo3I0e1bo+ptQAAfAeBCM0qt7hSnx04IkneU9/NdHn3aEWF2nWktEobj9cFAACBCM3qnS8PyWNIgzu2Uqc2oWaXI3uAn/eikO8wuBoAcByBCM3GMAzv/cMmDDK/d6he/dlmq3flqqSyxuRqAAC+gECEZvPV98U6kF8qR4CfxvWLN7scr77tItQ1JkxVtR6t3MGtPAAABCI0o/reoTF94uQMCjS5mv+w2Wy64fiVq//F2WYAABGI0Eyqat16f3uOpLqzy3zN+MR2stmkzzMLlV1YbnY5AACTEYjQLNbtyVdReY1inQ5d1i3a7HJO0i4yWD/p2kaStGxLlsnVAADMRiBCs6i/9tD4xHbyN/naQ6dz69AESdKbW79XDdckAgBLIxChyX1/rFxr9+RLkm4a1MHkak7vqt6xahPmUEFJlT7enWd2OQAAExGI0OT+74sseQzp0q6t1TUmzOxyTivQ308/G1x3OYClX3DYDACsjECEJlVd69EbW7IlSbcN62hyNT/u1qEJstmkjfuPKOsog6sBwKoIRGhSq3bl6khptWKdDo3uHWt2OT+qQ1SId9D339MOmlsMAMA0BCI0qX+mfSeprucl0P/8+PW665JOkqQ3tmRz5WoAsKjz4xML54Xt2UX64mChAvxsumVIgtnlnLHLu0era0yYSqpqvYf7AADWQiBCk/nzJ99Ikn46oK3iIoJMrubM+fnZNOUnnSVJiz87qFpOwQcAyyEQoUlkHinTyp119wX75YguJldz9m5IbKfWoXYdKqrQ6l2cgg8AVkMgQpN45ZNvZRjSyJ4x6hEXbnY5Zy0o0F+3Da87K27hugPyeAyTKwIAtKQAswvA+S/fVem9SerUy8+/3qF6d17SSX/9NFO7D7u0eleuxvaNN7skAOeJEVeOUl5+wWmXx8ZE65N1a1qwIpwtAhHO2YtrD6i61qOBCZEa0qmV2eU0WqtQu+6+tJNeWHtAz3+8T2P6xPnsbUcA+Ja8/AJNnf/2aZcvmjGxBatBYxCIcE4OHinT/x2/yvNDyT1ls53fAWLKZRdpyaaD2pdXquVf5ej6Ae3MLgktgG/3AAhEOCdzU/ep1mPo8u7RSurS2uxyzllEcKDuvewizU3dp+dT9+nqi+PkCPA3uyw0M77dA2BQNRpt56FifbA9R5L0UHIPk6tpOnf9pLPahDl08Gi5Xt2YaXY5AIAWQCBCo7g9hh57b6ck6af92+ridhEmV9R0whwB+t24npKkF9fuV3Yh9zgDgAsdh8zQKP/c/J2+zCpSmCNAs67paXY5TW78gHZa9kW2Ps8s1JPLd+svdww2u6TTqqxxyxPVSVu/K1RhabVKq2tVUe2WTTb5+dUFvNpeY/Xvbd8rqUtrxUcEm10yAPgcAhHOWk5RhZ5ZtUeS9MjVPS7ID1ibzaanxl+ssQs2KnV3nt798pDGJ/rOAOvKGrdW78rVhzsOa8O+AtVc+kt9duDoKdvmqUrqOkIz39wuSbooOlTX9o3X9Ynt1CU6rCXLBgCfRSDCWfF4DM369w6VVbs1qGMrTRrW0eySmk232HBNG9lV8z/er9+9s0P92kfoIpMDRHZhuZZsOqi3079XccUJN6KtdKlLh3jFhAcpPChAIfa6geC1HkOllbXauOo9XXz5NdrxfZG+LSjTC2sP6IW1BzS0c5TuvrSTRveKVcB5cjNeAGgOBCKclRfXHtCGfQVyBPjp6Rv7yu8Cv07P9JHdtPnbo9r8baFSln6pd351iYICW/6ss++OlulP677Rv7Z9r9rjV9FuFxmsGxLb6eqL43TjmMt07Q+cJZW26wO99+b/yFVZo3V78vVeRo427CvQF5mF+iKzUB1bh2j6yG4aP6AtwQhAs/D1y1sQiHDG1u/N1/w1+yRJ/3NDX3WLPf9u0XG2/P1sWnBLoq5ZsFFfH3Zp5psZeuGWxBYLDZlHyvTS2gN6N+OQ3MeD0KVdW2vKTzrr8u4x3gtHnmksdQYF6voB7XT9gHbKLa7UPzYf1NLPs/Td0XL95q3temntfk0f2U3XE4wANDFfv7wFgQhnZFdOsR5YliHDkH4+LEETB7U3u6QWE+sM0ou3JurOxVv04Y5chdh36JkJ/Zq1d+xAfqkWrjug9zIOqf62aiO6R+uBUV01qGNUk7xHXESQHkruqV9d0VX/2PydXvnkWx08Wq5fv7VdL607oAdGddN1/dtytW4AlkAgwo/am1ui2179XMUVNRrUsZUev6632SW1uEu6ttELtyYqZek2vZ3+vWySnrrh4ia/aOPOQ8V6ef03+nDnYRnHg9DInjGaPrKrEhOa57YooY4ATb28i24f3tEbjDKPlGnGGxlauO6AHryqu67uE3fBHx7FD/P1wx3AuSIQ+QBf/kOTkV2ke17bomPlNerXPkKL7xpi2Ss3X31xnJ67qZ9mvrldb6V/rwMFpXp50iDFRQSd03oNw9AXmYVauP4bfbLvP78Ho3vF6P5R3dSvfeQ5Vn5mTgxGr6Ud1J83fKv9+aX61evb1CveqQdGddNVvWPpMbIoXz/cAZwrApEP8NU/NMu+yNLs93ap2u1Rn7ZO/ePuYXIGBZpSi6+4IbG9WoXYdf//fakvs4qUPP8T3T+qm24f3lH2gLMbc1NWVasPtufo/77I0vbviyVJfjbpuv5tdd8VXdQzztkcm/CjQh0B+tUVXXXb8I7668ZM/fXTTH192KWp/0xXp9YhmvKTzpo4qIOC7dYMxgAuTAQinORQUYWeWr5bK3fmSpKS+8TquZv6K9ziYajeFT1i9MH0n+hXr2/TrhyX/rB8t5ZsytRNgzpo/IB2SmgdcsrXGYah749V6PPMQqXuztUn+46oosYtSbIH+OmmQe31yxFdTvv6luYMCtSDV3XXnZd00quffqt/bs7SwaPleuy9XZqbuk+3Deuom4d0UIco36i3MdweQ7Uejwx/u6pq3Qrw86MHDGesvLpWea4q5bkq5W7bX9uyjqnWbXh/r+rPCA3091Ntt5F6deO3CnUEKDrMofjIIMVHBKtVSOB5f1PsCwWBCF55rkr9Pe2g/vpppiprPPL3s2nmVd113+VdGD/yXzq2DtX7036it7Zm67mP9im7sELzUvdpXuo+RYc71DMuXK1D7bIH+Km82q08V6Uyj5TpSGl1g/Vc1CZUNw/poAmD2qtNmMOkrflhrULt3sHXb23N1t8+O6iswnK9tO6AXlp3QMMvitJ1/dvqqt6xigk/t8OHTcHjMVRQWqWswnJlHS1XVmG5vj9WocKyKhWW16iwrErHympUVl3rHaela36vRRu+lVR3ZqHd30/2AD85AvwU5ghQTd/r9dLa/YqLCFa7yGAltA5RnDOI8GQBbo+h74+V65uCUn2TX1b3WFCqbwrKVFh2wv/nQbdo4/4jp19Rz6v01IqvT5rtCPBTu1bB6hIdpq4xYeoaHaYuMWHqEh3Kl9AWZqlAtHDhQj377LPKzc1V//799eKLL2ro0KFml2WqovJqrd9boNW7cpW6O8/7jWZo5yj9/qd91CvenMM25wN/P5tuGZqg6/q31cqduXov45A+O3BEBSVVKiipOuVrAv1t6t02Qpd3j9aY3rHq09Z53nw7DHUE6M5LO+v2pE76aFeu/rH5O6Udv0bT5m8L9ei7O9WvfaQu7dJawy5qrf7tIxQZYm+WWooravT9sbqg8/2xCmUX1gWfrMJyZReWq6rW0+h1uz2GKjxub+9dfkmV1Gm4nvtoX4N2gf42tYsMVoeoECVEhXgfE6JC1KFViCJCLswPM8MwZEjyO09+b8+EYRgqLKvWwaNl+ragTAePlinzSJm+ya97rHaf/vcpxO6vOGeQMvd8pW59+svuX9fLeGJvY43bo+2frtZ119+g0spa5ZdU6XBxhY6UVquq1qNvC+reN3V3XoN1xzod6hoTpi7RdVPH1iHq1DpU7VoFK/A8uCyGx2PIVVmjY+U1Kiqvljumh74+7FJljVuVNZ66x1q3PEbdv0FN/xtNrddmGN7vSBe0N954Q3fccYcWLVqkYcOGaf78+Xrrrbe0d+9excTE/OBrXS6XIiIiVFxcLKez6QJCUXm1bv3L59rz9W7Fdewqm63uj4y/zSY/P5v8bHUfuge2rNNtN09UUKD/8clPQQF1Pwfb//OzI9BPwd42/nIE+Kmq1qOyqlqVV7tVVl2rwtJqfX+sQplHSrXjULG+PVKmE38DhnaK0t0/6azkPrE+80Hdo0+/Hx1jtXfXVy1Y0emVVdVqX16J9uWVqKSyVtVuj+z+foqLCFK7yGD1inc2y4UdzdpHh4oq9O6Xh/TRrlzvOKgTtW8VrM5tQtW+VbDatwpR+1bBahPmULDdXyF2f4UEBigo0E+1HkPVtR5Vuz2qPv47W1hWrcLyah0rq9aR0mrlFFUo+1iFvj9WrpLK2h+sy9+vLqzUB5X2rYIVHeZQVKhdrULtah1qV6gjQIH+Nvn72TRo6HDd+8zrDeqocXtUUe1WaVWtPvngDd146x06XFzpDWL1Xx5OxxkU0CAktY8KUYfj2x8ValdUqL1ZL/JpGIbKq90qqaxVSWWNXJU1clXWqqSyVq6KGu/80qpalVbW1j2eMJVV1arWbajG7ZHbY6isolI2/0DVb7Xt+H6un8qLjqhzQnsFBforPChAzqBAOYMC5AwO9D4PP8XzMEeAQhwBCgn0b7ae6Moat4rKa5TnqlSuq7LusbhSh4oqlHmkLvT80O+UI8BPF0XX9dp0iQ7TRccfO7YO8fbiNOb/YGWNW/muKn1XWKZv8kt14Hgv1IGC0tN+qZLq9nv7VsHq2DpUnVqHqF1ksGKdQYoJdyjG6VB0WN0V65tqf9a46/5PllTWqriiRkXlNSqqqK4LOmXHHyuqVVReo2PldY9F5dUqrqjRj/w3aajSpYPzb22Smuudzee3ZXqI5s2bp3vvvVd33XWXJGnRokVasWKF/va3v+m3v/2tKTVV1Xr09WGXFNleh4srT98wYYj+nvZds9XRIzZco3vH6Jq+8erT9sK5a70ZQh0BSkxo1WynyPuadpHBSrmyq1Ku7KrDxRX67MBRbTpwRNuyjung0f/04DSH1qF2b9A6MXgkRIUoPjLorL5B29w1CvD3U4C/ThlSNu1N1bM3zfU+d3sM5boqlXW0XNnHyr09VHWPFTpSWiVXZa125bi0K8d12vcNtfvLGRyo4EB/Bdv9T3o87SE5o+7vR1WtW1W1dd+06x8raupDUK33Yp5N4oQwdLwE1XqM/wTDkFbKPFJ2Tm8RYvdXiD1AoY66xzCHv4LtAQr0q/uSGHDCo7/NJtmkGreh6lr3f4JsraEqt0flVXUf3sUVNWfcY9guMlid2tT1wnRuE+rtnWkXGdwsYS0o0F8JrUOU0DpEl3WLbrCsuKJG3xaU6kB+3eG5bwtK9d3Rcn1XWKbKGk/dz0fL9clp1m2z1d3YuT54htj9FXj8UHCgv58C/GzyGJL7+Fgn9/F/S/fxLwVlVbUqqaoLzZU1je9xlep+zyND7Mo5eEAdLuqmoAC//3xxD/STv80mm03a+H9vSGraQHQ2LBGIqqurlZ6erlmzZnnn+fn5afTo0UpLSzupfVVVlaqq/pPOi4vrvvm6XKf/w9YYthq3Xv5ZL6U8MFOjJ8+UxzDkMSSPYchtGPJ4DLk90ucfvqF77/2FKmrdqqo54Y9fbV23Y9Xx7seq2rrux/+0MeQItCkksO6PS7DdXxHBgWobEax2rYLVq61TfeKdan3C2JWm3sam4Ha7VVlW+oPLfbHuluQL+yjUJo3p5tSYbnXfwoorarQ/t0TfF1Uop6hCh4oqdOhYhYoqqlVe7VZVjVvlNXW/r/7+frL7143dCfS3KdgeoFYhdrUKDVSr4LpenfgIh9pGhqhdZJDiI4MVYj/dn69aVZSV6mxiWGP2X7if1Cc6UH2iIyQ1/CJRXl2rnKKK44GwXNnH6rY9p6hCx8qqVVRRoxq3oZIqqaTkLAptBH8/m8Id/goLClRYUICcjkCFBfkr/PjzcHuAQoP8FeIIUJg9QKHHH0Mc/rIH+MnPz6ZAPz/99IYbdOusBXWHymw6/vep7u9UrcfQuy/M1qt//YsqajwqrTyhB6rSrZKq0z8vr3Z7exFKq6TT/yucGz+b1CbMrpjwIMU4HYp1BinWGeQ9DNUhKuQ0PXa1Ki398X+kpv4/aJPUJdJfXSIjpO7/+f3yeAwVlBwfI1dYpqzCCuUeP/xWUFqlgpJKlVTWHe4trpSKT+64bTRHoJ/CHQGKDAlURLBdkSEBigy2yxkSqMjguikixK7IoEBFhgYqIriuXf0ZuAOHTtfYq/9x2vVvzEpv8r9T9es7o4NhhgUcOnTIkGRs2rSpwfyHHnrIGDp06EntH3/8cUN1X4CYmJiYmJiYzvMpOzv7R7OCJXqIztasWbM0c+ZM73OPx6PCwkK1bt26ycfVuFwudejQQdnZ2U06PulCxL46c+yrM8e+OjvsrzPHvjpzzbWvDMNQSUmJ2rZt+6NtLRGI2rRpI39/f+XlNRzBn5eXp7i4uJPaOxwOORwNT4GOjIxszhLldDr5D3OG2Fdnjn115thXZ4f9debYV2euOfZVRETEGbXz/fP2moDdbtegQYO0Zs1/bn/h8Xi0Zs0aJSUlmVgZAADwBZboIZKkmTNnavLkyRo8eLCGDh2q+fPnq6yszHvWGQAAsC7LBKKbb75ZBQUFmj17tnJzczVgwACtWrVKsbGxptblcDj0+OOPn3SIDidjX5059tWZY1+dHfbXmWNfnTlf2FeWuTAjAADA6VhiDBEAAMAPIRABAADLIxABAADLIxABAADLIxCZaOHCherUqZOCgoI0bNgwffHFF2aX5JM++eQTXXfddWrbtq1sNpveffdds0vyWXPmzNGQIUMUHh6umJgYjR8/Xnv37jW7LJ/08ssvq1+/ft4LwSUlJWnlypVml3VeePrpp2Wz2TRjxgyzS/E5TzzxhGw2W4OpZ8+eZpflsw4dOqTbbrtNrVu3VnBwsPr27autW7eaUguByCRvvPGGZs6cqccff1zbtm1T//79lZycrPz8fLNL8zllZWXq37+/Fi5caHYpPm/Dhg1KSUnR5s2blZqaqpqaGo0ZM0ZlZed2F/ILUfv27fX0008rPT1dW7du1ciRI3X99ddr165dZpfm07Zs2aI///nP6tevn9ml+Kw+ffro8OHD3unTTz81uySfdOzYMV166aUKDAzUypUrtXv3bs2dO1etWrUypR5OuzfJsGHDNGTIEL300kuS6q6c3aFDB02fPl2//e1vTa7Od9lsNr3zzjsaP3682aWcFwoKChQTE6MNGzZoxIgRZpfj86KiovTss89qypQpZpfik0pLSzVw4ED96U9/0lNPPaUBAwZo/vz5ZpflU5544gm9++67ysjIMLsUn/fb3/5Wn332mTZu3Gh2KZLoITJFdXW10tPTNXr0aO88Pz8/jR49WmlpaSZWhgtNcXGxpLoPepye2+3WsmXLVFZWxu18fkBKSorGjRvX4G8XTrZ//361bdtWF110kSZNmqSsrCyzS/JJ77//vgYPHqybbrpJMTExSkxM1F/+8hfT6iEQmeDIkSNyu90nXSU7NjZWubm5JlWFC43H49GMGTN06aWX6uKLLza7HJ+0Y8cOhYWFyeFwaOrUqXrnnXfUu3dvs8vyScuWLdO2bds0Z84cs0vxacOGDdOSJUu0atUqvfzyy8rMzNRll12mkpISs0vzOd9++61efvlldevWTatXr9Z9992n+++/X6+99pop9Vjm1h2A1aSkpGjnzp2MX/gBPXr0UEZGhoqLi/X2229r8uTJ2rBhA6Hov2RnZ+uBBx5QamqqgoKCzC7Hp40dO9b7c79+/TRs2DB17NhRb775Jodi/4vH49HgwYP1xz/+UZKUmJionTt3atGiRZo8eXKL10MPkQnatGkjf39/5eXlNZifl5enuLg4k6rChWTatGlavny51q1bp/bt25tdjs+y2+3q2rWrBg0apDlz5qh///5asGCB2WX5nPT0dOXn52vgwIEKCAhQQECANmzYoBdeeEEBAQFyu91ml+izIiMj1b17dx04cMDsUnxOfHz8SV8+evXqZdohRgKRCex2uwYNGqQ1a9Z453k8Hq1Zs4bxCzgnhmFo2rRpeuedd7R27Vp17tzZ7JLOKx6PR1VVVWaX4XNGjRqlHTt2KCMjwzsNHjxYkyZNUkZGhvz9/c0u0WeVlpbqm2++UXx8vNml+JxLL730pMuC7Nu3Tx07djSlHg6ZmWTmzJmaPHmyBg8erKFDh2r+/PkqKyvTXXfdZXZpPqe0tLTBt6vMzExlZGQoKipKCQkJJlbme1JSUrR06VK99957Cg8P945Ji4iIUHBwsMnV+ZZZs2Zp7NixSkhIUElJiZYuXar169dr9erVZpfmc8LDw08ahxYaGqrWrVszPu2//OY3v9F1112njh07KicnR48//rj8/f116623ml2az3nwwQd1ySWX6I9//KN+9rOf6YsvvtArr7yiV155xZyCDJjmxRdfNBISEgy73W4MHTrU2Lx5s9kl+aR169YZkk6aJk+ebHZpPudU+0mSsXjxYrNL8zl333230bFjR8NutxvR0dHGqFGjjI8++sjsss4bl19+ufHAAw+YXYbPufnmm434+HjDbrcb7dq1M26++WbjwIEDZpflsz744APj4osvNhwOh9GzZ0/jlVdeMa0WrkMEAAAsjzFEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAGAit9stj8djdhmA5RGIALSYK664QtOmTdO0adMUERGhNm3a6LHHHlP9LRX/8Y9/aPDgwQoPD1dcXJx+/vOfKz8/3/v6Y8eOadKkSYqOjlZwcLC6deumxYsXS5Kqq6s1bdo0xcfHKygoSB07dtScOXO8ry0qKtI999yj6OhoOZ1OjRw5Utu3b/cuf+KJJzRgwAD94x//UKdOnRQREaFbbrlFJSUl3jYlJSWaNGmSQkNDFR8fr+eff15XXHGFZsyY4W1TVVWl3/zmN2rXrp1CQ0M1bNgwrV+/3rt8yZIlioyM1Pvvv6/evXvL4XAoKytL69ev19ChQxUaGqrIyEhdeuml+u6775r6nwDAaRCIALSo1157TQEBAfriiy+0YMECzZs3T6+++qokqaamRn/4wx+0fft2vfvuuzp48KDuvPNO72sfe+wx7d69WytXrtTXX3+tl19+WW3atJEkvfDCC3r//ff15ptvau/evXr99dfVqVMn72tvuukm5efna+XKlUpPT9fAgQM1atQoFRYWett88803evfdd7V8+XItX75cGzZs0NNPP+1dPnPmTH322Wd6//33lZqaqo0bN2rbtm0Ntm/atGlKS0vTsmXL9NVXX+mmm27S1Vdfrf3793vblJeX63//93/16quvateuXYqKitL48eN1+eWX66uvvlJaWpp+8YtfyGazNeWuB/BDDABoIZdffrnRq1cvw+PxeOc98sgjRq9evU7ZfsuWLYYko6SkxDAMw7juuuuMu+6665Rtp0+fbowcObLBuutt3LjRcDqdRmVlZYP5Xbp0Mf785z8bhmEYjz/+uBESEmK4XC7v8oceesgYNmyYYRiG4XK5jMDAQOOtt97yLi8qKjJCQkKMBx54wDAMw/juu+8Mf39/49ChQw3eZ9SoUcasWbMMwzCMxYsXG5KMjIwM7/KjR48akoz169efctsAND96iAC0qOHDhzfo+UhKStL+/fvldruVnp6u6667TgkJCQoPD9fll18uScrKypIk3XfffVq2bJkGDBighx9+WJs2bfKu584771RGRoZ69Oih+++/Xx999JF32fbt21VaWqrWrVsrLCzMO2VmZuqbb77xtuvUqZPCw8O9z+Pj472H7L799lvV1NRo6NCh3uURERHq0aOH9/mOHTvkdrvVvXv3Bu+zYcOGBu9jt9vVr18/7/OoqCjdeeedSk5O1nXXXacFCxbo8OHDjd/JAM5agNkFAIAkVVZWKjk5WcnJyXr99dcVHR2trKwsJScnq7q6WpI0duxYfffdd/rwww+VmpqqUaNGKSUlRc8995wGDhyozMxMrVy5Uh9//LF+9rOfafTo0Xr77bdVWlqq+Pj4BmN56kVGRnp/DgwMbLDMZrOd1YDn0tJS+fv7Kz09Xf7+/g2WhYWFeX8ODg4+6XDY4sWLdf/992vVqlV644039Oijjyo1NVXDhw8/4/cH0HgEIgAt6vPPP2/wfPPmzerWrZv27Nmjo0eP6umnn1aHDh0kSVu3bj3p9dHR0Zo8ebImT56syy67TA899JCee+45SZLT6dTNN9+sm2++WRMnTtTVV1+twsJCDRw4ULm5uQoICGgwruhsXHTRRQoMDNSWLVuUkJAgSSouLta+ffs0YsQISVJiYqLcbrfy8/N12WWXnfV7JCYmKjExUbNmzVJSUpKWLl1KIAJaCIEIQIvKysrSzJkz9ctf/lLbtm3Tiy++qLlz5yohIUF2u10vvviipk6dqp07d+oPf/hDg9fOnj1bgwYNUp8+fVRVVaXly5erV69ekqR58+YpPj5eiYmJ8vPz01tvvaW4uDhFRkZq9OjRSkpK0vjx4/XMM8+oe/fuysnJ0YoVK3TDDTdo8ODBP1p3eHi4Jk+erIceekhRUVGKiYnR448/Lj8/P29vT/fu3TVp0iTdcccdmjt3rhITE1VQUKA1a9aoX79+Gjdu3CnXnZmZqVdeeUU//elP1bZtW+3du1f79+/XHXfccY57G8CZIhABaFF33HGHKioqNHToUPn7++uBBx7wnlG1ZMkS/b//9//0wgsvaODAgXruuef005/+1Ptau92uWbNm6eDBgwoODtZll12mZcuWSaoLLM8884z2798vf39/DRkyRB9++KH8/OqGSn744Yf63e9+p7vuuksFBQWKi4vTiBEjFBsbe8a1z5s3T1OnTtW1114rp9Ophx9+WNnZ2QoKCvK2Wbx4sZ566in9+te/1qFDh9SmTRsNHz5c11577WnXGxISoj179ui1117T0aNHFR8fr5SUFP3yl788290LoJFshnH8AiAA0MyuuOIKDRgwQPPnzze7lCZRVlamdu3aae7cuZoyZYrZ5QA4B/QQAcAZ+vLLL7Vnzx4NHTpUxcXFevLJJyVJ119/vcmVAThXBCIAOAvPPfec9u7dK7vdrkGDBmnjxo3ei0MCOH9xyAwAAFgeF2YEAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACW9/8BP88ncc6Lt5QAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Гистограмма\n",
        "\n",
        "df = sns.load_dataset('taxis')\n",
        "\n",
        "sns.histplot(data = df, x = \"passengers\", kde = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='displacement', ylabel='horsepower'>"
            ]
          },
          "execution_count": 96,
          "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": [
        "# Диаграмма рассеяния\n",
        "\n",
        "df = sns.load_dataset(\"mpg\")\n",
        " \n",
        "sns.scatterplot(x = \"displacement\", y = \"horsepower\", data = df)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.12.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}