practica2/week2_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2943daff-0aa6-4fd1-bfa3-d9ef696d5b2c",
   "metadata": {},
   "source": [
    "2. Основы работы с JupyterLab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fba773cc-adf0-4828-905e-6333c4952650",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello!!\n"
     ]
    }
   ],
   "source": [
    "print(\"hello!!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "718ec28e-eec6-49d9-90b0-e6fffd5d61f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "for i in range(1, 10):\n",
    "    print(i+2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b7a229a-53f5-4c47-9363-a5489c363480",
   "metadata": {},
   "source": [
    "Работа с JupyterLab, анализ и визуализация данных!!!!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1958e1cd-4c81-4f9c-90ae-37f4b20ba9db",
   "metadata": {},
   "source": [
    "3. Работа с pandas и numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "512ab845-b639-46c6-911b-4ca39d22af6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Первый взгляд на данные:\n",
      "      Имя  Возраст  Баллы\n",
      "0    Анна       21     89\n",
      "1   Борис       22     76\n",
      "2  Виктор       23     95\n",
      "3  Галина       24     82\n",
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype\n",
      "---  ------   --------------  -----\n",
      " 0   Имя      4 non-null      str  \n",
      " 1   Возраст  4 non-null      int64\n",
      " 2   Баллы    4 non-null      int64\n",
      "dtypes: int64(2), str(1)\n",
      "memory usage: 228.0 bytes\n",
      "None\n",
      "         Возраст      Баллы\n",
      "count   4.000000   4.000000\n",
      "mean   22.500000  85.500000\n",
      "std     1.290994   8.266398\n",
      "min    21.000000  76.000000\n",
      "25%    21.750000  80.500000\n",
      "50%    22.500000  85.500000\n",
      "75%    23.250000  90.500000\n",
      "max    24.000000  95.000000\n",
      "Имя        0\n",
      "Возраст    0\n",
      "Баллы      0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Создадим DataFrame\n",
    "data = {\n",
    "    \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\"],\n",
    "    \"Возраст\": [21, 22, 23, 24],\n",
    "    \"Баллы\": [89, 76, 95, 82]\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "print(\"Первый взгляд на данные:\")\n",
    "print(df.head())\n",
    "print(df.info())\n",
    "print(df.describe())\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "676f4702-6ea3-42ff-a3f8-46c5d9c1748e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Первый взгляд на данные:\n",
      "      Имя  Возраст  Баллы\n",
      "0    Анна       21     89\n",
      "1   Борис       22     76\n",
      "2  Виктор       23     95\n",
      "3  Галина       24     82\n",
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype\n",
      "---  ------   --------------  -----\n",
      " 0   Имя      4 non-null      str  \n",
      " 1   Возраст  4 non-null      int64\n",
      " 2   Баллы    4 non-null      int64\n",
      "dtypes: int64(2), str(1)\n",
      "memory usage: 228.0 bytes\n",
      "None\n",
      "         Возраст      Баллы\n",
      "count   4.000000   4.000000\n",
      "mean   22.500000  85.500000\n",
      "std     1.290994   8.266398\n",
      "min    21.000000  76.000000\n",
      "25%    21.750000  80.500000\n",
      "50%    22.500000  85.500000\n",
      "75%    23.250000  90.500000\n",
      "max    24.000000  95.000000\n",
      "Имя        0\n",
      "Возраст    0\n",
      "Баллы      0\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>Имя</th>\n",
       "      <th>Возраст</th>\n",
       "      <th>Баллы</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Анна</td>\n",
       "      <td>21</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Борис</td>\n",
       "      <td>22</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Виктор</td>\n",
       "      <td>23</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Галина</td>\n",
       "      <td>24</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Имя  Возраст  Баллы\n",
       "0    Анна       21     89\n",
       "1   Борис       22     76\n",
       "2  Виктор       23     95\n",
       "3  Галина       24     82"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Создадим DataFrame\n",
    "data = {\n",
    "    \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\"],\n",
    "    \"Возраст\": [21, 22, 23, 24],\n",
    "    \"Баллы\": [89, 76, 95, 82]\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "print(\"Первый взгляд на данные:\")\n",
    "print(df.head())\n",
    "print(df.info())\n",
    "print(df.describe())\n",
    "print(df.isnull().sum())\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a4a6f1ea-7570-465b-9623-7062f5ee2ad7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Первый взгляд на данные:\n",
      "      Имя  Возраст  Баллы  Новый столбец\n",
      "0    Анна       21     89           97.9\n",
      "1   Борис       22     76           83.6\n",
      "2  Виктор       23     95          104.5\n",
      "3  Галина       24     82           90.2\n",
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 4 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Имя            4 non-null      str    \n",
      " 1   Возраст        4 non-null      int64  \n",
      " 2   Баллы          4 non-null      int64  \n",
      " 3   Новый столбец  4 non-null      float64\n",
      "dtypes: float64(1), int64(2), str(1)\n",
      "memory usage: 260.0 bytes\n",
      "None\n",
      "         Возраст      Баллы  Новый столбец\n",
      "count   4.000000   4.000000       4.000000\n",
      "mean   22.500000  85.500000      94.050000\n",
      "std     1.290994   8.266398       9.093038\n",
      "min    21.000000  76.000000      83.600000\n",
      "25%    21.750000  80.500000      88.550000\n",
      "50%    22.500000  85.500000      94.050000\n",
      "75%    23.250000  90.500000      99.550000\n",
      "max    24.000000  95.000000     104.500000\n",
      "Имя              0\n",
      "Возраст          0\n",
      "Баллы            0\n",
      "Новый столбец    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Создадим DataFrame\n",
    "data = {\n",
    "    \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\"],\n",
    "    \"Возраст\": [21, 22, 23, 24],\n",
    "    \"Баллы\": [89, 76, 95, 82]\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "df[\"Новый столбец\"] = df[\"Баллы\"] * 1.1\n",
    "print(\"Первый взгляд на данные:\")\n",
    "print(df.head())\n",
    "print(df.info())\n",
    "print(df.describe())\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e27fe1cd-2ce2-4c48-899f-e1fd9458d76a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Баллы  Возраст\n",
      "Номер                \n",
      "1       92.0       23\n",
      "2       79.0       24\n",
      "Первый взгляд на данные:\n",
      "      Имя  Возраст  Баллы Номер\n",
      "0    Анна       21     89     1\n",
      "1   Борис       22     76     2\n",
      "2  Виктор       23     95     1\n",
      "3  Галина       24     82     2\n",
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 4 columns):\n",
      " #   Column   Non-Null Count  Dtype\n",
      "---  ------   --------------  -----\n",
      " 0   Имя      4 non-null      str  \n",
      " 1   Возраст  4 non-null      int64\n",
      " 2   Баллы    4 non-null      int64\n",
      " 3   Номер    4 non-null      str  \n",
      "dtypes: int64(2), str(2)\n",
      "memory usage: 260.0 bytes\n",
      "None\n",
      "         Возраст      Баллы\n",
      "count   4.000000   4.000000\n",
      "mean   22.500000  85.500000\n",
      "std     1.290994   8.266398\n",
      "min    21.000000  76.000000\n",
      "25%    21.750000  80.500000\n",
      "50%    22.500000  85.500000\n",
      "75%    23.250000  90.500000\n",
      "max    24.000000  95.000000\n",
      "Имя        0\n",
      "Возраст    0\n",
      "Баллы      0\n",
      "Номер      0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Создадим DataFrame\n",
    "data = {\n",
    "    \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\"],\n",
    "    \"Возраст\": [21, 22, 23, 24],\n",
    "    \"Баллы\": [89, 76, 95, 82]\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "df[\"Номер\"] = [\"1\", \"2\", \"1\", \"2\"]\n",
    "res = df.groupby(\"Номер\")\n",
    "res1 = res.agg({\"Баллы\": \"mean\", \"Возраст\": \"max\"})\n",
    "print(res1)\n",
    "print(\"Первый взгляд на данные:\")\n",
    "print(df.head())\n",
    "print(df.info())\n",
    "print(df.describe())\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a30b256b-93d4-4cac-9004-0ae0508158b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Баллы  Возраст\n",
      "Номер                \n",
      "1       92.0       23\n",
      "2       79.0       24\n",
      "Первый взгляд на данные:\n",
      "      Имя  Возраст  Баллы\n",
      "0    Анна       21     89\n",
      "1   Борис       22     76\n",
      "2  Виктор       23     95\n",
      "3  Галина       24     82\n",
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype\n",
      "---  ------   --------------  -----\n",
      " 0   Имя      4 non-null      str  \n",
      " 1   Возраст  4 non-null      int64\n",
      " 2   Баллы    4 non-null      int64\n",
      "dtypes: int64(2), str(1)\n",
      "memory usage: 228.0 bytes\n",
      "None\n",
      "         Возраст      Баллы\n",
      "count   4.000000   4.000000\n",
      "mean   22.500000  85.500000\n",
      "std     1.290994   8.266398\n",
      "min    21.000000  76.000000\n",
      "25%    21.750000  80.500000\n",
      "50%    22.500000  85.500000\n",
      "75%    23.250000  90.500000\n",
      "max    24.000000  95.000000\n",
      "Имя        0\n",
      "Возраст    0\n",
      "Баллы      0\n",
      "dtype: int64\n"
     ]
    },
    {
     "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>Имя</th>\n",
       "      <th>Возраст</th>\n",
       "      <th>Баллы</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Борис</td>\n",
       "      <td>22</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Виктор</td>\n",
       "      <td>23</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Галина</td>\n",
       "      <td>24</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Имя  Возраст  Баллы\n",
       "1   Борис       22     76\n",
       "2  Виктор       23     95\n",
       "3  Галина       24     82"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Создадим DataFrame\n",
    "data = {\n",
    "    \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\"],\n",
    "    \"Возраст\": [21, 22, 23, 24],\n",
    "    \"Баллы\": [89, 76, 95, 82]\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "df[df[\"Возраст\"] > 21]\n",
    "print(res1)\n",
    "print(\"Первый взгляд на данные:\")\n",
    "print(df.head())\n",
    "print(df.info())\n",
    "print(df.describe())\n",
    "print(df.isnull().sum())\n",
    "df[df[\"Возраст\"] > 21]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f3d39b10-d40e-4309-b142-f358e492b930",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Сумма элементов массива: 15\n",
      "Среднее значение: 3.0\n",
      "Медиана: 3.0\n",
      "Стандартное отклонение: 1.4142135623730951\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.array([1, 2, 3, 4, 5])\n",
    "print(\"Сумма элементов массива:\", np.sum(arr))\n",
    "print(\"Среднее значение:\", np.mean(arr))\n",
    "print(\"Медиана:\", np.median(arr))\n",
    "print(\"Стандартное отклонение:\", np.std(arr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3c306802-cc0f-4c7c-ac50-779060118ef2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.          2.85714286  5.71428571  8.57142857 11.42857143 14.28571429\n",
      " 17.14285714 20.        ]\n",
      "[-1.20018338 -2.49830982  0.32735039 -0.85043605  2.8325787 ]\n",
      "[[2 4]\n",
      " [6 8]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.array([[1, 2], [3, 4]])\n",
    "print(np.linspace(0, 20, 8))\n",
    "print(np.random.randn(5))\n",
    "print(np.dot(arr, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec738b1d-eac1-4121-a5b8-fee78231add1",
   "metadata": {},
   "source": [
    "4. Визуализация данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "774c03cc-94dd-4c94-b779-3237d9366656",
   "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",
    "x = np.linspace(0, 10, 100)\n",
    "y = np.sin(x)\n",
    "\n",
    "plt.plot(x, y, label='sin(x)')\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Y\")\n",
    "plt.title(\"График синуса\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "05e47602-460f-4fda-850f-e26de9bb5f44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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",
    "x = np.linspace(0, 10, 100)\n",
    "y = np.sin(x)\n",
    "\n",
    "plt.plot(x, y, label='sin(x)', color='red')\n",
    "plt.plot(x, np.cos(x), label='cos(x)')\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Y\")\n",
    "plt.title(\"График синуса\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()\n",
    "plt.bar([\"1\", \"2\", \"3\"], [1, 2, 3], color='blue')\n",
    "plt.show()\n",
    "x = [1, 2, 8, 5, 4]\n",
    "y = [11, 21, 4, 9, 2]\n",
    "plt.scatter(x, y)\n",
    "plt.show()\n",
    "a = [1, 2, 3, 4, 5, 6]\n",
    "plt.hist(a)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "829f217a-6cd6-48fb-a275-46f1b670b8d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "df[\"Категория\"] = [\"A\", \"B\", \"A\", \"B\"] # догадайтесь откуда df и её содержимое взялось\n",
    "#df содержимое взялось из первого задания \n",
    "sns.boxplot(x=\"Категория\", y=\"Баллы\", data=df)\n",
    "plt.show()\n",
    "sns.histplot(df[\"Баллы\"]) \n",
    "plt.show()\n",
    "sns.scatterplot(x=\"Возраст\", y=\"Баллы\",hue=\"Категория\", data=df)\n",
    "plt.show()\n",
    "sns.pairplot(df)\n",
    "plt.show()\n",
    "sns.heatmap(df[[\"Возраст\", \"Баллы\"]].corr(), annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ee2cc3d-ec1f-4e4e-8278-6462cacdd5bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "5. Прогресс-бар с tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "830c2649-f452-4f30-8177-4b69ef31155c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Загрузка: 100%|██████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 92.79it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "import time\n",
    "for i in tqdm(range(100), desc='Загрузка'):\n",
    "    time.sleep(0.01)  # Симуляция долгого процесса"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c5e30e4-15b0-4e8b-82a1-cfd2013d278b",
   "metadata": {},
   "outputs": [],
   "source": [
    "6. Самостоятельное задание"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "ea559f6a-9449-41ef-863e-820f808b021d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 178 entries, 0 to 177\n",
      "Data columns (total 13 columns):\n",
      " #   Column               Non-Null Count  Dtype\n",
      "---  ------               --------------  -----\n",
      " 0   track_name           178 non-null    str  \n",
      " 1   artist_name          178 non-null    str  \n",
      " 2   streams              178 non-null    int64\n",
      " 3   stream_change        178 non-null    int64\n",
      " 4   7day                 178 non-null    int64\n",
      " 5   genre                178 non-null    str  \n",
      " 6   country              178 non-null    str  \n",
      " 7   pos                  178 non-null    int64\n",
      " 8   days                 178 non-null    int64\n",
      " 9   viral_score          178 non-null    int64\n",
      " 10  trend                178 non-null    str  \n",
      " 11  popularity_category  178 non-null    str  \n",
      " 12  longevity            178 non-null    str  \n",
      "dtypes: int64(6), str(7)\n",
      "memory usage: 18.2 KB\n",
      "None\n",
      "            streams  stream_change          7day         pos         days  \\\n",
      "count  1.780000e+02   1.780000e+02  1.780000e+02  178.000000   178.000000   \n",
      "mean   2.093366e+06  -2.719877e+05  1.244096e+07   96.123596   536.275281   \n",
      "std    1.277789e+06   7.528285e+05  5.786175e+06   56.151997   617.572830   \n",
      "min    1.191777e+06  -4.369341e+06  2.523194e+06    1.000000     2.000000   \n",
      "25%    1.350050e+06  -1.267320e+05  9.277907e+06   48.250000    80.000000   \n",
      "50%    1.590738e+06  -7.408450e+04  1.080256e+07   96.500000   346.500000   \n",
      "75%    2.202585e+06  -5.174750e+03  1.354278e+07  141.750000   766.250000   \n",
      "max    1.127383e+07   1.899990e+05  3.659983e+07  200.000000  3070.000000   \n",
      "\n",
      "        viral_score  \n",
      "count  1.780000e+02  \n",
      "mean   1.453433e+07  \n",
      "std    6.699639e+06  \n",
      "min    3.774959e+06  \n",
      "25%    1.067544e+07  \n",
      "50%    1.257072e+07  \n",
      "75%    1.608216e+07  \n",
      "max    4.233369e+07  \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GCXptUzzVbAob2x5/U23Tm5WKFkyxXnQbqrn98bg0JEZf1+gXG60S6mruHv3bo5YtWiTUFO9Rcwqshoh/DyLA/PSnP13pyX18fnxv0dS4pmiuXFdXqB122CG7YBEjWdfczsZ+vxHASkcsjxG54/uOCw01WyTE9xBNj2u2EFuZmF4sykZcPInay9oUZxiIv6lUsaVBNDuuS7GJd32nr1uV1d2e+oggGs3Ro5936fcWF+GiGXZjPiOmzov3Ld5ivxe/z5r7Jj6z2DokgvaqRNeG6JddDNdxUSREM/loVRFdO6JsxAWcuIBUVGz9Uvo3xkXE+J2srlUdk2r77Pj/4lSFq1NmAfKgrGvS6xJ9naJJVNwXpz+K5u0xbU0sj+appWKgoWhe+aMf/aiFthhorSLMnnXWWdmUYXGCHLVEcTwprYULUWMUJ69xUh3TDcUFwQh9UXM8atSodOCBBzbq86N5/ec+97ns86NGMQJM9P8sNkWNE/W4CBknyHGSG+vFoHcRRmMgtKhh/5//+Z9sW6L/fYSTuGBZ2hy3GO7jgmecxA8dOjQL/hEmonY6+ppGs9mowY3axgiW0Tw2jrvx90V3gHhtfE5dohlw9BWP2uAY8C6aT0erg7i4GhcP4iQ+BqFqjOi/Hk1s66oJjYG7oq9rBN3YN1EjGRcNos9uLL/33ntX2sIg9nNM0xW1mRGiYz9HzXK8NqaPitc2VHwPxxxzTDb1VwTwa665JvuO49+x2v6+KHfFwcDqIwZ2i5C4sv7RUWsZNZZXXHFFduEhvp8ou1EDGt93tCCpWQMczahDlK+4IBXlq6kGj2vo9jRG1F5HmY5gGxf2o4931KrHmBPRHa60Rnx1RZ/xaGEQt/jNxkWN+O3ENG/x+4kLP3WJ6RxjTvL4bdR18SumMoxuHjHX+8MPP1x13Ipa9BiYMC48xO82WhLGBYWGztfe0GNS1OzH8SOOD1FOoozERbyafdMbU2YBcqGlh5fPi9gVd955Z9XjP/7xj9mymJaj9BbTAtU2rclvfvOb7Ll58+Y185YDeVWc2uqvf/1rnevFlFc/+MEPsqmxYkqg3XffvTB9+vRsaqLS6YmK0w6NGTMmm6prrbXWKvTs2bPw9a9/vfDKK680erqj3/72t4XRo0dnU4rF58dUVqVTGBU99dRTha9+9avZFHIxPVxMexXHw6lTp1b77FXdak6zFVNHfe5zn6s6zm611VbZ1FnFKZ9OOumkwrBhw7Jp1GqqOQVb0RVXXJFNjxZ/z/rrr1/YbrvtsmnT3njjjUZPwRZTO82YMaPa8tq+o2XLlhXOP//8bH/Hftpwww2zbRk/fnw2BdqqPProo9m0drHdsT8GDRpUuPTSSxs1BVv8fffee2/2HrEtsW9vu+22Wsvp4MGDq031Vvw+a5uCbWVqe/7jjz/O/vZime3du3dW3qLc19ze0nLSvXv3bBqz+C2sSkO+y/puz6rUte+LU67F/o7PiGnJRo4cucLUeqs7BdusWbMKBx98cGGzzTYrdO7cOZuuLspaTPm3smn7imL6tC233DLbx/X526Jcxm+gOAVivP+5556b7fsoWzGtX5w7RRmIZUVr6pj0j3/8o7DXXntlU8dFWTnuuOMKzzzzTJOUWYCW1i7+09IXCvIgmi6Wju4eV5aHDx+e1V7UHNAorjbXbCYYV/njSm5ps0KAPIua7qg5jFq0xtYul4ra8GiuHTVqNfs7F0W/1lgvauVYs+I7iLFUYpRuKMdjEkBrpbn7SgwZMiRrohjT5ayqj3mckEazxhgVFQAAABqrrEN69JmK0UJLw3b0YYx+StGPL2rSow9ozAEaoT0GR4r+WzG6aOnAL9HHL/oN1jVSPEBbF62M4rhZV9/WOH4Wx/kAAGBFZR3SY97V0kFiYjCmEIPKRFPMGFgnBjUqzhMaU/XEaKelIxbHVC6xbowSWts8vwDlIo6RxUG/6hrRHQCAldMnHQAAAHLCPOkAAACQE0I6AAAA5ETZ9UmPPuRvvPFGWn/99bNp1wAAAGBNipnP33///WwA3YqKuuvKyy6kR0Dv3bt3S28GAAAAZea1115Lm266aZ3rlF1Ijxr04s7p0qVLS28OAAAAbdyiRYuyyuJiHq1L2YX0YhP3COhCOgAAAM2lPl2uDRwHAAAAOSGkAwAAQE4I6QAAAJATQjoAAADkhJAOAAAAOSGkAwAAQE4I6QAAAJATQjoAAADkhJAOAAAAOSGkAwAAQE4I6QAAAJATQjoAAADkhJAOAAAAOdGhpTeAtm358uVp5syZacGCBalbt25p0KBBqX379i29WQAAALkkpLPGTJs2LU2aNCnNmzevalnPnj3TqFGj0rBhw+x5AACAGjR3Z40F9HHjxqX+/funiRMnpilTpmT38TiWx/MAAABU165QKBRSGVm0aFHq2rVrWrhwYerSpUtLb06bbeI+fPjwLJCfc845qaLiP9eCKisr0xlnnJFmz56dbrrpJk3fAQCANm9RA3KomnSaXPRBjybuEdRLA3pW4CoqsuVvvvlmth4AAAD/IaTT5GKQuNCvX79any8uL64HAADA/xLSaXIxinuIJu21KS4vrgcAAMD/EtJpcjHNWoziPnny5KwPeql4HMt79eqVrQcAAMB/COk0uZgHPaZZmz59ejZI3HPPPZcWL16c3cfjWD5y5EiDxgEAANRgdHeadZ70qEGPgG6edAAAoFwsasDo7kI6a3w6thjFPQaJiz7o0cQ9atoBAADKxaIGhPQOzbZVlKUI5EOGDGnpzQAAAGgV9EkHAACAnBDSAQAAICeEdAAAAMgJIR0AAAByQkgHAACAnBDSAQAAICeEdAAAAMgJIR0AAAByQkgHAACAnBDSAQAAICeEdAAAAMgJIR0AAAByQkgHAACAnBDSAQAAICeEdAAAAMgJIR0AAAByQkgHAACAnBDSAQAAICeEdAAAAMgJIR0AAAByQkgHAACAnBDSAQAAICeEdAAAAMgJIR0AAAByQkgHAACAnBDSAQAAICeEdAAAAMgJIR0AAAByQkgHAACAnBDSAQAAICeEdAAAAMgJIR0AAAByQkgHAACAnBDSAQAAICeEdAAAAMgJIR0AAAByQkgHAACAnBDSAQAAICeEdAAAAMgJIR0AAAByQkgHAACAnBDSAQAAICdaNKRPmzYtHXDAAWnjjTdO7dq1S3fdddcqX/PQQw+lHXbYIXXq1Cltvvnm6brrrmuWbQUAAIA2HdI//PDDtP3226eJEyfWa/3Zs2en/fffP33hC19ITz/9dPr+97+fjj322HTvvfeu8W0FAACANa1DakH77rtvdquvyy+/PPXr1y/94he/yB5vvfXW6dFHH02//OUv0z777LMGtxQAAADWvFbVJ3369Olpr732qrYswnksX5mlS5emRYsWVbsBAABAHrWqkD5v3rzUo0ePasvicQTvjz76qNbXnHfeealr165Vt969ezfT1gIAAEAbDumNMXr06LRw4cKq22uvvdbSmwQAAAD565PeUD179kzz58+vtiwed+nSJa299tq1viZGgY8bAAAA5F2rqkkfOnRomjp1arVl9913X7YcAAAAWrsWDekffPBBNpVa3IpTrMX/z507t6qp+hFHHFG1/vHHH59mzZqVTjvttPTCCy+kSZMmpVtvvTWdfPLJLfY3AAAAQJsI6X/729/SkCFDsls45ZRTsv8fO3Zs9vjNN9+sCuwhpl+7++67s9rzmF89pmK76qqrTL8GAABAm9CuUCgUUhmJkeBjlPcYRC76sgMAAEBecmir6pMOAAAAbZmQDgAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQEy0e0idOnJg222yz1Llz57TLLrukJ554os71L7roorTlllumtddeO/Xu3TudfPLJacmSJc22vQAAANAmQ/ott9ySTjnllDRu3Lj05JNPpu233z7ts88+6a233qp1/d/85jfpRz/6Ubb+888/n66++ursPX784x83+7YDAABAmwrpF154YTruuOPSUUcdlbbZZpt0+eWXp3XWWSddc801ta7/2GOPpd133z0ddthhWe373nvvnQ499NBV1r4DAABAa9BiIX3ZsmVpxowZaa+99vrPxlRUZI+nT59e62t222237DXFUD5r1qw0ZcqUtN9++630c5YuXZoWLVpU7QYAAAB51KGlPvidd95Jy5cvTz169Ki2PB6/8MILtb4matDjdZ/73OdSoVBIn3zySTr++OPrbO5+3nnnpfHjxzf59gMAAECbGziuIR566KF07rnnpkmTJmV92O+444509913p7PPPnulrxk9enRauHBh1e21115r1m0GAACA3Nekd+/ePbVv3z7Nnz+/2vJ43LNnz1pfc+aZZ6bDDz88HXvssdnj7bbbLn344YdpxIgRacyYMVlz+Zo6deqU3QAAACDvWqwmvWPHjmnHHXdMU6dOrVpWWVmZPR46dGitr1m8ePEKQTyCfojm7wAAANCatVhNeojp14488si00047pZ133jmbAz1qxmO093DEEUekTTbZJOtXHg444IBsRPghQ4Zkc6q//PLLWe16LC+GdQAAAGitWjSkH3LIIentt99OY8eOTfPmzUuDBw9O99xzT9VgcnPnzq1Wc37GGWekdu3aZfevv/56+vSnP50F9AkTJrTgXwEAAABNo12hzNqJxxRsXbt2zQaR69KlS0tvDgAAAG3cogbk0FY1ujsAAAC0ZUI6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQEx1aegNo25YvX55mzpyZFixYkLp165YGDRqU2rdv39KbBQAAkEtCOmvMtGnT0qRJk9K8efOqlvXs2TONGjUqDRs2zJ4HAACoQXN31lhAHzduXOrfv3+aOHFimjJlSnYfj2N5PA8AAEB17QqFQiGVkUWLFqWuXbumhQsXpi5durT05rTZJu7Dhw/PAvk555yTKir+cy2osrIynXHGGWn27Nnppptu0vQdAABo8xY1IIeqSafJRR/0aOIeQb00oGcFrqIiW/7mm29m6wEAALCafdIjaLVr167OmlTKVwwSF/r161fr88XlxfUAAABYjZB+5513NuZllIkYxT1Ek/aBAweu8HwsL10PAACA1WjufuCBB1a7Rc35jBkz0tKlS7PHlLeYZi1GcZ88eXLWB71UPI7lvXr1ytYDAACgCfukn3/++enQQw9N99xzTzrmmGPS+PHjV/ctaeViHvSYZm369OnZIHHPPfdcWrx4cXYfj2P5yJEjDRoHAADQ1KO7b7PNNmn06NHp8MMPTw888EA6+uij06uvvpryyujuLTtPetSgR0A3TzoAAFAuFjVgdPfVDukbbLBB+utf/5q22GKLrLY0Pvjjjz9OeSWkN6/oChGjuMcgcdEHPZq4R007AABAuVjUgJDeqIHjaoawDh3+920ifNXsg0x5izIxZMiQlt4MAACAVqFRIT1CV3EKto8++igdcMABqWPHjmk1K+UBAACgrDUqpB900EFV/19zNPfS5wAAAID6W+0+6a2NPukAAADkNYeu9hRsAAAAQAs2d1/V6NwxmBwAAADQDCE9RnPfaKON0jHHHGPkbgAAAGjJkP7666+na6+9Nl111VXp7rvvTiNGjEiHHXZYWnfddZtquwAAAKDsNKpPevfu3dMPf/jD9OKLL6YLLrggTZ06NW222Wbpd7/7XdNvIQAAAJSJJhk4rjhnekWFcegAAACgWZu7v/3221XN3TfYYIOsuXv8v+buAAAA0MwhvXfv3tnAcUcffXTaYYcdsmXR5L3oK1/5ympsEgAAAJSndoVCodDQF9XVrD2avud5CraGTCIPAAAAzZlDG1WTXllZ2dhtAwAAAFbCSG8AAACQE42qSb/kkkvqfP673/1uY7cHAAAAylaj+6Svs8462eBxNV8efdJnzZqV8kqfdAAAAPKaQxvV3H3MmDFZUN9rr73S448/nmbPnl11y3NABwAAgDxrVEg/++yz0/PPP5+WLVuWttxyyzRhwoS0dOnSpt86AAAAKCONHjhuk002Sdddd1164IEHsjnSN99883TDDTc07dYBAABAGWlUn/SZM2eusOz3v/99+tnPfpa22GKLNGPGjJRX+qQDAADQpuZJHzx4cDZAXDHfl/7/008/3Zi3BAAAgLLXqJAeA8QBAAAAOQjpffv2beLNAAAAABoV0sOLL76YLr300myU97D11lunk046KRvtHQAAAGim0d1vv/32tO2222YDxG2//fbZ7cknn8yWxXMNMXHixLTZZpulzp07p1122SU98cQTda7/3nvvpRNOOCH16tUrderUKQ0YMCBNmTKlMX8GAAAAtP7R3T/zmc+k4cOHp7POOqva8nHjxqWbbropvfLKK/V6n1tuuSUdccQR6fLLL88C+kUXXZRuu+22rJZ+o402WmH9mJd99913z5778Y9/nE0DN2fOnLTBBhtkFwrqw+juAAAANKeG5NBGhfR11lknm4Yt5kYv9c9//jMLy4sXL67X+0Qw/+xnP5t+9atfZY8rKytT7969s2bzP/rRj1ZYP8J8TPP2wgsvpLXWWqten7F06dLsVrpz4jPqs3MAAACgOUN6o5q7f/7zn0+PPPLICssfffTRtMcee9TrPaJWPJrL77XXXv/ZmIqK7PH06dNrfc0f/vCHNHTo0Ky5e48ePbLm9eeee25avnz5Sj/nvPPOy3ZG8RYBHQAAANrMwHFf+cpX0umnn56F7F133TVb9vjjj2dN1cePH5+F6dJ1a/POO+9k4TrCdql4HDXltZk1a1Z64IEHsqb20Q/95ZdfTqNGjUoff/xx1tS+NqNHj06nnHLKCjXpAAAAkDeNau4eNd71evN27VZay/3GG29kfcofe+yxrHa86LTTTksPP/xw+stf/rLCa2KQuCVLlmTztLdv3z5bduGFF2ZN4N988816bZM+6QAAADSnhuTQRtWkR9/x1dW9e/csaM+fP7/a8njcs2fPWl8TI7pHX/RiQC9O/TZv3rys+XzHjh1Xe7sAAACgpTSqT/oNN9xQbTC2xohAveOOO6apU6dWC//xuLRmvVSM7B5N3EsvErz00ktZeBfQAQAAKMuQftRRR2XV9Ksr+opfeeWV6frrr0/PP/98GjlyZPrwww+z9w8xPVv0KS+K5xcsWJC+973vZeH87rvvzgaOi4HkAAAAoLVrVHP3RnRjr9UhhxyS3n777TR27NisyfrgwYPTPffcUzWY3Ny5c6v1f48B3+6999508sknp0GDBmV92iOwxyB2AAAAULYDx11yySUr7fAeNeB5ZeA4AAAA8ppDGx3SN91002oDuFW9Ybt22VRpeSWkAwAA0KZGdw9/+9vf0kYbbdTYlwMAAABNMXAcAAAAkJOQ3rdv31qbugMAAADNFNIfeOCBtM0226RnnnkmfepTn6r2XLStHzhwYHrkkUdWY3MAAACgfDUopF900UXpuOOOq7Wje3SC/853vpMuvPDCptw+AAAAKBsNCulRg/6lL31ppc/vvffeacaMGU2xXQAAAFB2GhTS58+fn9Zaa62VPt+hQ4f09ttvN8V2AQAAQNlpUEjfZJNN0t///veVPj9z5szUq1evptguAAAAKDsNCun77bdfOvPMM9OSJUtWeO6jjz5K48aNS1/+8pebcvsAAACgbLQrFAqFhjR332GHHbLp10488cS05ZZbZstfeOGFNHHixLR8+fL05JNPph49eqS8WrRoUTbIXYxGX9sAeAAAANBSObRDQ944wvdjjz2WRo4cmUaPHp2K+b5du3Zpn332yYJ6ngM6AAAA5FmDQnro27dvmjJlSnr33XfTyy+/nAX1LbbYIm244YZrZgsBAACgTDQ4pBdFKP/sZz/btFsDAAAAZaxBA8cBAAAAa46QDgAAADkhpAMAAEBOCOkAAACQE0I6AAAAtPbR3cmnJUuWpLlz57b0ZrQaffr0SZ07d27pzQAAAMgI6W1MBPQRI0a09Ga0GldccUUaMGBAS28GAABARkhvgzXDETzzZM6cOWnChAlpzJgxqW/fvilv+wsAACAvhPQ2Jppu57VmOAJ6XrcNAAAgDwwcBwAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAAADkhpAMAAEBOCOkAAACQEx1aegNau/nz56eFCxe29Gbk2pw5c6rdU7euXbumHj162E0AAFCG2hUKhUIqI4sWLcpCUATrLl26rHZA/9bhR6SPly1tsu2DtTp2SjfdeIOgDgAAZZhD1aSvhtjBEdA/6r9nquzcdXXeCjIVSxamNOvhrGypTQcAgPIjpDeBCOiV63ZvircCAACgjBk4DgAAAHJCSAcAAICcENIBAAAgJ4R0AAAAyAkhHQAAAHJCSAcAAICcENIBAAAgJ4R0AAAAyAkhHQAAAHJCSAcAAICcENIBAAAgJ4R0AAAAyAkhHQAAAHKiQ0tvQFtQ8dF7Lb0JtBHKEgAAlDchvQmsPXtaU7wNAAAAZU5IbwIf9RuWKtfeoCneijIXNeku+gAAQPkS0ptABPTKdbs3xVsBAABQxgwcBwAAADkhpAMAAEBOCOkAAACQE/qkA6ym5cuXp5kzZ6YFCxakbt26pUGDBqX27dvbrwAANJiQDrAapk2bliZOnJjmz59ftaxHjx7phBNOSMOGDbNvAQBoEM3dAVYjoI8dOza999571ZbH41gezwMAQEMI6QCNbOJ+4YUXZv+/ww47ZLXpU6ZMye7jcYjnYz0AAKgvIR2gEZ5++umsxny77bZLEyZMSAMHDkzrrLNOdh+PY3k8H+sBAEB9CekAjVAM30cddVSqqKh+KI3H3/72t6utBwAA9SGkA6yGQqFg/wEA0GSEdIBGGDx4cHZ/3XXXpcrKymrPxeNYXroeAADUh5AO0AgRvjfYYIP07LPPpjFjxqTnnnsuLV68OLuPx7F8ww03FNIBAGgQ86QDNEL79u3TKaecksaNG5eefPLJNH369KrnOnXqlNq1a5dOPvnkbD0AAKgvNekAjTRs2LA0fvz4rMa8VLdu3bLl8TwAADSEmnSA1RBBfPfdd08zZ85MCxYsyAL6oEGD1KADANAoQjrAaoom7UOGDLEfAQBYbZq7AwAAQE7kIqRPnDgxbbbZZqlz585pl112SU888US9XnfzzTdngzMddNBBa3wbAQAAoM2H9FtuuaXaCMnbb7992meffdJbb71V5+teffXVdOqpp6Y99tij2bYVAAAA2nRIv/DCC9Nxxx2XjjrqqLTNNtukyy+/PK2zzjrpmmuuWelrli9fnoYPH56Nnty/f/9m3V6A2o5JTz31VJo6dWp2H48BAKDVDRy3bNmyNGPGjDR69OiqZRUVFWmvvfaqNudwTWeddVbaaKON0jHHHJMeeeSROj9j6dKl2a1o0aJFTbT1AClNmzYtTZo0Kc2bN69qd/Ts2TONGjXKFGwAALSumvR33nknq3Hq0aNHteXxuPSEt9Sjjz6arr766nTllVfW6zPOO++81LVr16pb7969m2TbASKgR1edaNETY2tMmTIlu4/HsTyeBwCANjsF2/vvv58OP/zwLKB37969Xq+JWvro815ak97UQb1iycImfT/Kl7LUesQFxqhBHzp0aDrnnHOyVkBh4MCB2eMzzjgjXXbZZdkc6jFFGwAA5D6kR9COk9f58+dXWx6Po7loTa+88ko2YNwBBxxQtayysjK779ChQ3rxxRfTZz7zmWqv6dSpU3ZbE6Jmfq2OnVKa9fAaeX/KU5SpKFvk28yZM7MWP2eeeWZVQC+KxzFuxgknnJCtZw51AABaRUjv2LFj2nHHHbPBlorTqEXojscnnnjiCutvtdVW6dlnn622LGqroob94osvbvam7NEs/6Ybb0gLF6pJr8ucOXPShAkT0pgxY1Lfvn2b7ftprSKg1+wCQv4sWLAgu+/Xr1+tzxeXF9cDAIBW0dw9mqIfeeSRaaeddko777xzuuiii9KHH36YjfYejjjiiLTJJptkfctjHvVtt9222us32GCD7L7m8uYSYUqgqp8I6AMGDFjD3wg0j27dumX3s2fPzpq41xTLS9cDAIBWEdIPOeSQ9Pbbb6exY8dmTUcHDx6c7rnnnqrgO3fu3BWakgK0tEGDBmXdciZPnlytT3qxRVAs79WrV7YeAADUV7tCoVBIZSQGjovmxNFEvUuXLi29OWXhpZdeSiNGjEhXXHGFmnTa5Ojuu+66a9YSKMa/iCkfn3jiifT444+n8ePHm4YNAIDUkBza4jXpAK3VsGHDstZAt912W5o+fXrV8hgQM5bH8wAA0BBCehuzZMmSrItA3gaOK73Pkz59+mRjHUBja9JvueWWqpr0KEvxG4ya9Fi+zTbbCOoAADSI5u5ttGk59aMJPqszT3pMs9a/f/9a+6THzBMxeNxNN91knnQAgDK3SHP38hU1wxE8qf/+gsYwTzoAAGuC5u5tTDS3Nc0ZrHnmSQcAYE0wtxnAas6TXhvzpAMA0BhCOsBqzpMefdBLmScdAIDGEtIBGiGmWRs1alQ29VoMEvfcc8+lxYsXZ/fxOJaPHDnSoHEAADSI0d0BVnMatkmTJqV58+ZVLevVq1cW0M2TDgBAQ0d3F9IBmmA6thjtPQaTi77q0RQ+atoBACCYgg2gGUUgHzJkiH0OAMBq0ycdAAAAckJIBwAAgJwQ0gEAACAnhHQAAADIiQ4tvQEArZ3R3QEAaCpCOkATz5Pes2fPNGrUKPOkAwDQYJq7A6xGQB83blzq379/mjhxYpoyZUp2H49jeTwPAAAN0a5QKBRSGWnIJPIAdTVxHz58eBbIzznnnFRR8Z9rnpWVlemMM85Is2fPTjfddFM2jzoAAOVrUQNyqObuAI0wc+bMrIn7mWeemeJa51NPPZUWLFiQunXrlgYNGpQF+BNOOCFbb8iQIfYxDbZkyZI0d+5ce66e+vTpkzp37mx/AdDqCekAjRCBPLzxxhvp7LPPXqFP+jHHHFNtPWioCOgjRoyw4+rpiiuuSAMGDLC/AGj1hHSARoga83DuueemoUOHZjXq/fr1y5q4T548OVteuh40VNQMR/DMkzlz5qQJEyakMWPGpL59+6a87S8AaAuEdIBGGDhwYNbXPPoUnXXWWalDhw5Vy+PxwQcfnPU9isfQGNF0O681wxHQ87ptANDaGd0doBGee+65bPC4d999N40dOzZ7vHjx4uw+HsfyeD4eAwBAfalJB2iEYl/zaPZ79dVXZ4PEFfXq1StbHs2C9UkHAKAhhHSARij2Nd94442zPugxinvp6O4vvPBCtfUAyL9oAVXzeG4aTaC5CekAjRAnbjGKewT0mCe9dJq1mCc9lkeNeqwHQP5NmzYtTZo0aYXZOkaNGpWGDRvWotsGlBd90gEaIWpW4sRt+vTp6YwzzqjWJz0ex/KRI0eqgQFoJQF93LhxqX///mnixIlpypQp2X08juXxPEBzaVcoFAqpjMRoy127dk0LFy7MRmUGaOqal6hBj4Cu5oW25qWXXsrmbjcnOW2tifvw4cOzQB4toyoqKqq1jIoLrzG95k033eTCK9AsOVRzd4DVEEF8991314cRoJWKPuhxofXMM8+sFtBDPI4AH4ODxnqlXZsA1hQhHaAJmr47cQNonYqzcPTr16/W54vLzdYBNBd90gEAKFvFWTiiSXttisvN1gE0FyEdAICyVTpbR/RBL2W2DqAlCOkAAJQts3UAeaNPOgAAqdwHAR0/fnw2W0cMElc6W0csN1sH0JyEdAAAyp7ZOoC8ENIBAMBsHUBO6JMOAAAAOSGkAwAAQE5o7g60OkuWLElz585t6c1oNfr06ZM6d+7c0psBAEA9COlAqxMBfcSIES29Ga3GFVdckQYMGNDSmwEAQD0I6UCrrBmO4Jknc+bMSRMmTEhjxoxJffv2TXnbXwAAtA5COtDqRNPtvNYMR0DP67ZRt/nz56eFCxfaTau4GFV6T926du2aevToYTcB0CBCOgBlLwL6tw4/In28bGnZ74v6iFYjrNpaHTulm268QVAHoEGEdADKXtSgR0D/qP+eqbJz17LfH6y+iiULU5r1cFa21KYD0BBCOgD8nwjolet2tz8AgBZjnnQAAADICSEdAAAAckJIBwAAgJwQ0gEAACAnhHQAAADICSEdAAAAckJIBwAAgJwQ0gEAACAnhHQAAADICSEdAAAAckJIBwAAgJwQ0gEAACAnhHQAAADICSEdAAAAckJIBwAAgJwQ0gEAACAnhHQAAADICSEdAAAAckJIBwAAgJwQ0gEAACAnOrT0BgAAUJ6WLFmS5s6d29Kb0Wr06dMnde7cuaU3A1jDhHQAAFpEBPQRI0bY+/V0xRVXpAEDBthf0MYJ6QAAtFjNcATPPJkzZ06aMGFCGjNmTOrbt2/K2/4C2j4hHQCAFhFNt/NaMxwBPa/bBrRtBo4DAACAnBDSAQAAICdyEdInTpyYNttss6zJ0y677JKeeOKJla575ZVXpj322CNtuOGG2W2vvfaqc30AAABoLVo8pN9yyy3plFNOSePGjUtPPvlk2n777dM+++yT3nrrrVrXf+ihh9Khhx6aHnzwwTR9+vTUu3fvtPfee6fXX3+92bcdAAAA2tTAcRdeeGE67rjj0lFHHZU9vvzyy9Pdd9+drrnmmvSjH/1ohfUnT55c7fFVV12Vbr/99jR16tR0xBFHNNt2AwC0NvPnz08LFy5s6c3ItRjdvfSeunXt2jX16NHDbqrDkiVLsukGqf8sBp07dy7r3dWiIX3ZsmVpxowZafTo0VXLKioqsibsUUteH4sXL04ff/xx6tatW63PL126NLsVLVq0qAm2HMqLk7pVc1LXME7qoGWO5d86/Ij08bL/nBexcjENG6u2VsdO6aYbbxDU6xABfcSIEYpTPV1xxRVlP7NCi4b0d955Jy1fvnyFH3U8fuGFF+r1HqeffnraeOONs2Bfm/POOy+NHz++SbYXypGTuoZxUlc/Tuqg+UUNegT0j/rvmSo7d/UVsNoqlixMadbDWdlSm153zXAEz7xVLsQ5y5gxY7LpBvO2v8pdizd3Xx0//elP080335z1U19Zk4iopY8+76U16dGPHagfJ3U0NSd10LIioFeu293XAM0kcsqAAQNyub8joOd128pZi4b07t27p/bt22c1daXicc+ePet87c9//vMspN9///1p0KBBK12vU6dO2Q1YPU7qAACgjY/u3rFjx7Tjjjtmg74VVVZWZo+HDh260tddcMEF6eyzz0733HNP2mmnnZppawEAAKCNN3ePpuhHHnlkFrZ33nnndNFFF6UPP/ywarT3GLF9k002yfqWh/PPPz+NHTs2/eY3v8nmVp83b162fL311stuAAAA0Fq1eEg/5JBD0ttvv50F7wjcgwcPzmrIi4NPxGiIMeJ70WWXXZaNCv/1r3+92vvEPOs/+clPmn37AQAAoM2E9HDiiSdmt9rEoHClXn311WbaKgAAACijPukAAADAfwjpAAAAkBNCOgAAAORELvqkA0AeVHz0XktvAm2EsgRAYwnpAPB/1p49zb4AAFqUkA4A/+ejfsNS5dob2B80SU16Xi/6qOVHWYJ8E9IB4P9EQK9ct7v9QZuW14sH0FTmz5+fFi5caIfWYc6cOdXuqVvXrl1Tjx49UnMR0gEAyogWI7TlFiMR0L91+BHp42VLW3pTWoUJEya09Ca0Cmt17JRuuvGGZgvqQjoA/J+KJWpeaPtlSYsR2rKoQY+A/lH/PVNl564tvTm0leP5rIezsiWkA0AzNmOLq+TxjzA0lShTUbaA5hcBXfclWis16QCUvbgyHs3Y9GGsW/RdjKaRY8aMSX379i37cpO3PowAtA1COgCklIUpgap+IqAPGDBAuQGANaBiTbwpAAAA0HBCOgAAAOSEkA4AAAA5IaQDAABAThg4DqiXio/es6doEsoSAMDKCelAvaw9e5o9BQAAa5iQDtTLR/2Gpcq1N7C3aJKadBd9AABqJ6QD9RIBvXLd7vYWQCtXsWRhS28CbYSyBGuGkA4AObRkyZI0d+7clCdz5sypdp8nffr0SZ07d27pzci1rl27prU6dkpp1sMtvSm0IVGmomwBTUdIB4AcioA+YsSIlEcTJkxIeXPFFVekAQMGtPRm5FqPHj3STTfekBYuVJNel7gIFWV8zJgxqW/fvs32/bRWEdCjbOWNQUppzWVJSGeNWr58eZo5c2ZasGBB6tatWxo0aFBq3769vd4KadKGstT8NcMRPKn//mLVIkzlMVDlUQR0F35aL2Of0JoJ6awx06ZNS5MmTUrz5s2rWtazZ880atSoNGzYMHu+ldA8kjVB88hVi6bbAgJA4yzZZIdU6Lie3cdqa7fsg9T59SdTcxLSWWMBfdy4cWno0KHpzDPPTP369UuzZ89OkydPzpaPHz9eUG8lNI+sH80j20bzSADaSOVCM4cq2ra1mnnsBSGdNdLEPWrQI6Cfc845qaKiIls+cODA7PEZZ5yRLrvssrT77rtr+t5KaB5Zf5pHAtSfARIbxgCJrbNyoXghn/oZk8PxIJq7ckFIp8lFH/Ro4h416MWAXhSPhw8fnk444YRsvSFDhvgGaDAndQ3jpA7IKwMkNowBEltn5YIxRhq+vzqX+WwdQjpNLgaJC9HEvTbF5cX1oKGc1DWMkzogr4SXhu8vWh9jjNBQQjpNLkZxD9EHPZq41xTLS9eDhnJS1/D9BZBHwgvAioR0mlxMsxajuMcgcaV90kNlZWW2vFevXtl60BhO6gAAaKuqdxiGJhDzoMc0a9OnT88GiXvuuefS4sWLs/t4HMtHjhxp0DgAAIAa2hUKhUIqI4sWLcpG54sRH7t06dLSm1N286RHDXoEdPOkAwAA5WJRA3KokM4an44tRnGPQeKiD3o0cY+adgAAgHKxqAEhXZ901qgI5KZZAwAAqB990gEAACAnhHQAAADICSEdAAAAckJIBwAAgJwQ0gEAACAnhHQAAADICSEdAAAAckJIBwAAgJwQ0gEAACAnhHQAAADICSEdAAAAckJIBwAAgJwQ0gEAACAnOqQyUygUsvtFixa19KYAAABQBhb9X/4s5tG6lF1If//997P73r17t/SmAAAAUGZ5tGvXrnWu065QnyjfhlRWVqY33ngjrb/++qldu3YtvTllc9UoLoq89tprqUuXLi29ObBGKOeUA+WccqCcUw6U8+YXsTsC+sYbb5wqKurudV52NemxQzbddNOW3oyyFAFdSKetU84pB8o55UA5pxwo581rVTXoRQaOAwAAgJwQ0gEAACAnhHTWuE6dOqVx48Zl99BWKeeUA+WccqCcUw6U83wru4HjAAAAIK/UpAMAAEBOCOkAAACQE0I6AAAA5ISQDgAADfTtb387HXTQQfYbLea6665LG2ywgW+gDRLSadQ/Qr/73e9S586d0y9+8Ytqyx966KHUrl27qluPHj3S1772tTRr1ix7mlZb/ovluWPHjmnzzTdPZ511Vvrkk09aetMowzK41lprpX79+qXTTjstLVmyJLU2TihpTm3pt0N5WdkFoOJ59nvvvZc9PuSQQ9JLL720Wp/16quvZu/59NNPr/Dc5z//+fT9739/td6fxunQyNdRxq666qp0wgknpMsvvzwdddRRta7z4osvpvXXXz/985//TCNGjEgHHHBAmjlzZmrfvn2zby+sri996Uvp2muvTUuXLk1TpkzJyn+c8I0ePdrOpVnL4Mcff5xmzJiRjjzyyOyk6vzzz/cNgN8OZWrttdfObrQ9atJpkAsuuCCddNJJ6eabb15pQA8bbbRR6tWrVxo2bFgaO3Zs+sc//pFefvnl7LnLLrssfeYzn8lqJbfccst04403VnttnHjGOvvuu2924Onfv39Wcw8tOZdoz549U9++fdPIkSPTXnvtlf7whz+kd999Nx1xxBFpww03TOuss05WZuPCVM1aw7vuuittscUWWeuTffbZJ7322mu+TBpVBnv37p3VrkQZvO+++7LnKisr03nnnZfVEsYxc/vtt1/hmBnltVgGv/CFL6Trr7++Wm1MuPLKK7P3j7L83//93+nCCy+s1ozylVdeSQceeGDWQmq99dZLn/3sZ9P9999f7XPiQtapp56aNtlkk7TuuuumXXbZJav5CXEf/24sXLiwqnbzJz/5SfZcfX9L9957b9p6662zz48LF2+++aaSRKN/O1Fev/vd72bnLPHb+NznPpf++te/Vnv9c889l7785S+nLl26ZJUPe+yxR/ZbqE289tOf/rSLZ7RY66Q4pg4ePDj9+te/rjqef+Mb38iOu03BeU/zEdKpt9NPPz2dffbZ6Y9//GN2AldfxSt8y5YtS3feeWf63ve+l37wgx+kv//97+k73/lOdtL24IMPVnvNmWeemTWTf+aZZ9Lw4cPTN7/5zfT888/7tsiFKNNRnqM52t/+9rcsAE2fPj0VCoW03377ZbWdRYsXL04TJkxIN9xwQ/rzn/+chaIoz9BYcex87LHHsgudIQJ6lK9o3RSB4uSTT07f+ta30sMPP5w9P3v27PT1r389CyhxTI3j7pgxY6q9Z5TN448/Pjs+R5PHL37xi1m5LfXBBx9k5Xvq1KnpqaeeykJytJKaO3du1Tonnnhi9luIC7nReurggw/O1ovAvdtuu6WLLrooCzsRruMWgT7U97f085//PLuwO23atOxzi6+Hxvx2oun77bffnl20evLJJ7PuTHEhdcGCBdnzr7/+elbZEEH/gQceyFqxHH300bV2d4rni7+bOF+ClhKVYrfeemv6n//5n3TPPfdkx+tRo0Y1yXs772lGBViFI488stCxY8dCFJepU6fWue6DDz6Yrffuu+9mj994443CbrvtVthkk00KS5cuzf7/uOOOq/aagw8+uLDffvtVPY7XH3/88dXW2WWXXQojR470XdEi5f/AAw/M/r+ysrJw3333FTp16lQ46KCDsrL65z//uWrdd955p7D22msXbr311uzxtddem63z+OOPV63z/PPPZ8v+8pe/+Dapdxls3759Yd11183KXpSfioqKwu9+97vCkiVLCuuss07hscceq/aaY445pnDooYdm/3/66acXtt1222rPjxkzptqx+pBDDinsv//+1dYZPnx4oWvXrnVu28CBAwuXXnpp9v9z5szJtvP111+vts5//dd/FUaPHl31m6j5ni+99FK9f0svv/xy1ToTJ04s9OjRY5X7j/JV12/ngw8+KKy11lqFyZMnV62/bNmywsYbb1y44IILssdRbvv165ctr+vfhzvuuKOw3nrrFW6++eZm+9son7JbeuvcuXO1Y3fNY+q4ceOy1/3rX/+qWvanP/0pK/dvvvlmrZ81e/bs7D3jmFvz8+J13/ve9xp8rHbes/r0SadeBg0alN555500bty4tPPOO2dNDQcOHJjmzJmTPR/Nv/70pz9Vrb/ppptmNSFR8xFNL+NKdVy5jtrw6KNeavfdd08XX3xxtWVDhw5d4XFtA1pAc4jWI1Hmo1YvmhYfdthh6atf/Wq2PJrzFn3qU5/KunCUtvro0KFD1iy4aKuttsqapsU68VuC+ogm6tEN6MMPP0y//OUvs3IVrY2i5jyOs1GDVypaegwZMqRqjJDSMhhqlr1Yp2YLqVgnynhpTXo0pbz77ruzWvCoTfzoo4+qatKfffbZtHz58jRgwIBq7xNNiuO3sTLxW4i/Z1W/pWi2GV2liqJL1VtvvbWKPUe5W9lvJ1p6xDE9zkGKYqyRKPfFchfnHXF+E8tX5i9/+Uv2O4kuJkZ6Z02U3ZrlLVpK1aVPnz5Zl6PSc+g4d4njfHT9WJlbbrkl605UKlqzNvRY7bynaQjp1Ev82OMfoDhgRNPFCOQxgFaxKWLNQSseeeSRrElj9POKPlzQFv6hjAtNG2+8cfYPUDTLheYS/bujKW645pprsoufV199ddp2222zZRGcS0/KQjTRbUrRtDz68kaT89iWOO5HM/q4IFAM8TE4aDQJrjlIaFzkWl01g1L0af/fxlfQ8N9OzQtXtanPgFxx4SiCSrz3/vvvX2egh8aW3aJ//etfa2wnRh/2mp9nULqWo0869RaDZkUfx3nz5mVBvVu3btmPOW41Tw5jAKP4h6tmQI8rdNH3sVQ83mabbaote/zxx1d4XPPqHjT3P5RxdToCeojyGDWJcVW76N///nd2pbq0PMc60de2KJ6PfunKM41VUVGRfvzjH6czzjgjK2sRxqM2u3g8Lt7ihCtELUdpGQw1B8eKdWouq/k4jtXRHzFq3LfbbrusRiam7imKmvuoSY/a7ZrbUqy9iQtdsU6p+v6WoCl/O8UBbEvPSaLiIcp9sdxFK8KodCgdG6Gm7t27Z/3Rox9wDNBV17rQHOLfgzfeeKPaOXSU/TjOrw7nPc1LSKdB4qQvRuiNk7AYXGXRokUNev0Pf/jDbCTKqJWMgYRi9OA77rhjhcF/brvttuyqdMz9GE3sn3jiiWxAIsiLGCk7Rro+7rjj0qOPPpoNyBVN0OKCVSwvilqVmBEhAkjUMEbI2XXXXTV1Z7XEgGxRWx0j+MbxMwaLi8GvYtTpGADr0ksvzR6HGCjuhRdeyAazimNqDCgUx+FibXSIMhqto+KYHMfmeN9oMVV8vljm43gdTYCjvEe3j2hCWRTN3KNpZIzSHuvFgHVx7I6B7aKmP2y22WZZjXsMPhddqKKpfn1/S9CUv504D4nZOuK8JAbXilloogxGmTzmmGOydeO8I85zYrDPuNAVv40YuDAuIJWKVoMR1ON3duihh9Y6sBw0l5ipIKbpjGNpXGSKGQziAlJdTd3rw3lP8xLSabDobx5BPU6wGhrUo79W9D+P5pLRpz1OBGPu389//vPV1hs/fnw2OnBcxY5Ri3/729+qUSF3ouzuuOOO2fQ80ecrmt5G0Clt7hj9aCMcRaCJvo/R7Df6fcHqiBYdESBiWszRo0dnM2JEGI6ajmjpFKE4WjSFuI/uShGc45ga4aQ4unuxSXyUzRgdPkJ6NAeO0BLBP072iuK5mCItRmmPUd3j+L/DDjus8JuIkB4zeEStTRzzo2YyWqGEeG2MIn/IIYdkU1XF9tf3twRN/duJkdijf/rhhx+eleWoDY9p/qKch2jGHuE7LiztueeeWRmNqQprK5cRgGLdGJshLlbVbDECzSVaL8W4OTFDxt57750d9ydNmtQk7+28p/m0i9HjmvHzYJWi5iamajMAC61d1FZ+//vfrzYXNeRBhJMI5a+99tpK14laxagZjJoYAPIvBve86667WmywZec9TcfAcQDQxkUtSgyUFTWD0Qf3Zz/72QpdiKKFU4wSH2MwRFP3aC7fVLUvAED9CekA0MZFX9pzzjknLViwIGt6Hs3Ro5l8qeg/Hk2A33///dS/f/90ySWXpGOPPbbFthkAypXm7gAAAJATBo4DAACAnBDSAQAAICeEdAAAAMgJIR0AAAByQkgHAACAnBDSAQAAICeEdABow1566aXUo0ePNHLkyGrLH3roodSuXbv03nvvVS1bsmRJ+sIXvpCGDBlSbTkA0Hw6NONnAQDNbMCAAemee+7JwvcGG2yQzjvvvFrX++STT9LBBx+c3njjjfTII49k6wIAzU9NOgC0cVEzfvfdd6dLLrkkXXDBBSs8X1lZmY488sj0zDPPpPvuuy9ttNFG2fIbb7wx7bTTTmn99ddPPXv2TIcddlh666236qyND7Hsrrvuyv7/1VdfzR7ffPPNabfddkudO3dO2267bXr44YfX+N8NAK2RkA4AZWD33XdPd9xxRzrzzDPTr3/962rPnXDCCVk4v//++1OfPn2qln/88cfp7LPPzsJ7hO4I3N/+9rcb9fk//OEP0w9+8IP01FNPpaFDh6YDDjgg/fvf/17tvwsA2hohHQDKRNeuXbNa85NOOimr2Q6jR49OV111VVq+fPkKTdyPPvrotO+++6b+/funXXfdNauJ/9Of/pQ++OCDBn/2iSeemL72ta+lrbfeOl122WXZtlx99dVN9rcBQFshpANAGVi2bFk69thj0/HHH58mTZpUVZt+/fXXp6lTp6aBAwdmQbrUjBkzshrvqF2PJu977rlntnzu3LnV1tt0003TeuutV3WrTdSeF3Xo0CFrRv/888+vgb8UAFo3A8cBQBmYMGFCVgMeA8dFkH7zzTfT2LFj0y233JKGDRuW1aZvv/326fe//3068MAD04cffpj22Wef7DZ58uT06U9/Ogvn8TgCf6kYaC5CfNEWW2zRAn8hALQNatIBoI37+9//nn76059mtefFmu499tij2n2MAj9u3Lg0atSotHDhwvTCCy9kfcbjdbHOVlttVW3QuFL9+vVLm2++edWtNo8//ni1keSjlj6avgMA1QnpANCGRR/0aOb+zW9+M6sFr8upp56azake99HEvWPHjunSSy9Ns2bNSn/4wx+yQeQaa+LEienOO+/Mwn8MVPfuu+9mfd4BgOqEdABowy6++OJsVPZf/vKXq1w3+opfc801WT/1qH2/7rrr0m233Za22WabrEb95z//eaO3I14ft2hS/+ijj2ahv3v37o1+PwBoq9oVCoVCS28EANA2xQWCaA4fU68NHjy4pTcHAHJPTToAAADkhJAOAAAAOaG5OwAAAOSEmnQAAADICSEdAAAAckJIBwAAgJwQ0gEAACAnhHQAAADICSEdAAAAckJIBwAAgJwQ0gEAACDlw/8P8TnYpboRUfYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Обработка треков: 100%|██████████████████████████████████████████████████████████████████████| 178/178 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                           track_name   streams pop_category\n",
      "0                                                SWIM  11273830          Хит\n",
      "1                                        Body to Body   6815694      Средний\n",
      "2                                            Babydoll   5733862      Средний\n",
      "3                                            Hooligan   5338608      Средний\n",
      "4                                                 FYA   5196767      Средний\n",
      "5                                              NORMAL   4749288      Средний\n",
      "6          Stateside + Zara Larsson (w/ Zara Larsson)   4738329      Средний\n",
      "7                                              Aliens   4718569      Средний\n",
      "8                                                   2   4573989      Средний\n",
      "9                                        Like Animals   4567309      Средний\n",
      "10                                     Merry Go Round   4490039      Средний\n",
      "11                           they don’t know ’bout us   4423675      Средний\n",
      "12                                         Man I Need   4300853      Средний\n",
      "13                                               DtMF   4274742      Средний\n",
      "14                                   End of Beginning   4108838      Средний\n",
      "15                                        Risk It All   4007478      Средний\n",
      "16                                     One More Night   3979999      Средний\n",
      "17                          So Easy (To Fall In Love)   3932082      Средний\n",
      "18                                             Please   3897341      Средний\n",
      "19                                Raindance (w/ Tems)   3876044      Средний\n",
      "20                                             No. 29   3769141      Средний\n",
      "21                                       Into the Sun   3766459      Средний\n",
      "22                                The Fate of Ophelia   3721332      Средний\n",
      "23                                    back to friends   3583316      Средний\n",
      "24                                  BAILE INoLVIDABLE   3344867      Средний\n",
      "25                                          Lush Life   3341957      Средний\n",
      "26                               WHERE IS MY HUSBAND!   3241010      Средний\n",
      "27                                           NUEVAYoL   3221298      Средний\n",
      "28                                     American Girls   3101460      Средний\n",
      "29                              iloveitiloveitiloveit   2978129      Средний\n",
      "30                                       I Just Might   2860193      Средний\n",
      "31                                               Iris   2776320      Средний\n",
      "32                   Die With A Smile (w/ Bruno Mars)   2691880      Средний\n",
      "33                                           Manchild   2649376      Средний\n",
      "34                                   Tití Me Preguntó   2649180      Средний\n",
      "35                                        Love Me Not   2547681      Средний\n",
      "36                                                EoO   2542905      Средний\n",
      "37                                         White Keys   2531236      Средний\n",
      "38                                 BIRDS OF A FEATHER   2529075      Средний\n",
      "39                                   Die On This Hill   2398309      Средний\n",
      "40                              Every Breath You Take   2367833      Средний\n",
      "41                 Dracula - JENNIE Remix (w/ JENNIE)   2332341      Средний\n",
      "42                                           12 to 12   2260402      Средний\n",
      "43                                     Mr. Brightside   2220784      Средний\n",
      "44                                           Espresso   2213750      Средний\n",
      "45                                     Choosin' Texas   2169091      Средний\n",
      "46                    I Thought I Saw Your Face Today   2155500      Средний\n",
      "47  (When You Gonna) Give It Up to Me (w/ Keyshia ...   2154338      Средний\n",
      "48                                        Homewrecker   2139700      Средний\n",
      "49                                        Sailor Song   2107410      Средний\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm\n",
    "\n",
    "df = pd.read_csv('spotify_global_trends.csv') \n",
    "\n",
    "print(df.info()) \n",
    "print(df.describe())\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.histplot(df['streams'], bins=50, kde=True, color='purple')\n",
    "plt.title('Распределение количества стримов треков')\n",
    "plt.xlabel('Количество стримов')\n",
    "plt.ylabel('Количество треков')\n",
    "plt.show()\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x='streams', y='viral_score', data=df, hue='trend', alpha=0.6)\n",
    "plt.title('Зависимость виральности от количества стримов')\n",
    "plt.xlabel('Стримы')\n",
    "plt.ylabel('Виральность')\n",
    "plt.legend(title='Тренд')\n",
    "plt.show()\n",
    "top_genres = df['genre'].value_counts().head(5).index\n",
    "df_top = df[df['genre'].isin(top_genres)]\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.boxplot(x='genre', y='streams', data=df_top)\n",
    "plt.xlabel('Жанр')\n",
    "plt.ylabel('Стримы')\n",
    "plt.title('Распределение стримов по топ-5 жанрам')\n",
    "plt.show()\n",
    "def popularity_level(streams):\n",
    "    if streams > 10_000_000:\n",
    "        return 'Хит'\n",
    "    elif streams > 1_000_000:\n",
    "        return 'Средний'\n",
    "    else:\n",
    "        return 'Низкий'\n",
    "\n",
    "categories = []\n",
    "for streams in tqdm(df['streams'], desc=\"Обработка треков\"):\n",
    "    categories.append(popularity_level(streams))\n",
    "df['pop_category'] = categories\n",
    "print(df[['track_name', 'streams', 'pop_category']].head(50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65b8dacd-da48-45ec-82d0-6139dbda399b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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",
   "version": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}