{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "12aca7b3-741c-4277-92bb-b395feaf9c87",
   "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": 2,
   "id": "6d19cb5a-9032-49f9-a330-39eaf53d9edb",
   "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>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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1c3845dc-933a-4047-a4bf-6b2f4b751342",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (1064560805.py, line 8)",
     "output_type": "error",
     "traceback": [
      "  \u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 8\u001b[39m\n\u001b[31m    \u001b[39m\u001b[31mПроверяем структуру (методы из задания)\u001b[39m\n              ^\n\u001b[31mSyntaxError\u001b[39m\u001b[31m:\u001b[39m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "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",
    "Проверяем структуру (методы из задания)\n",
    "print(df.head())\n",
    "print(df.info())\n",
    "df[\"Новый столбец\"] = df[\"Баллы\"] * 1.1\n",
    "df\n",
    "df.groupby(\"Имя\").agg({\"Баллы\": \"mean\"})\n",
    "df[df[\"Возраст\"] > 21]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9e5aa4d2-fce7-4afb-872a-ff95f18a02ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      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"
     ]
    },
    {
     "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",
       "      <th>Новый столбец</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",
       "      <td>83.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Виктор</td>\n",
       "      <td>23</td>\n",
       "      <td>95</td>\n",
       "      <td>104.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Галина</td>\n",
       "      <td>24</td>\n",
       "      <td>82</td>\n",
       "      <td>90.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Имя  Возраст  Баллы  Новый столбец\n",
       "1   Борис       22     76           83.6\n",
       "2  Виктор       23     95          104.5\n",
       "3  Галина       24     82           90.2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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(df.head())\n",
    "print(df.info())\n",
    "df[\"Новый столбец\"] = df[\"Баллы\"] * 1.1\n",
    "df\n",
    "df.groupby(\"Имя\").agg({\"Баллы\": \"mean\"})\n",
    "df[df[\"Возраст\"] > 21]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d09f7958-5eae-4992-800d-50599d3e5a55",
   "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))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f47f39b3-5f23-4a65-bae3-8b021e4e8ef5",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "closing parenthesis ')' does not match opening parenthesis '[' (3156140434.py, line 2)",
     "output_type": "error",
     "traceback": [
      "  \u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[31m    \u001b[39m\u001b[31mmatrix = np. array([[2, 2, 3), [4, 5, 61])\u001b[39m\n                                ^\n\u001b[31mSyntaxError\u001b[39m\u001b[31m:\u001b[39m closing parenthesis ')' does not match opening parenthesis '['\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "matrix = np. array([[2, 2, 3), [4, 5, 61])\n",
    "print (\"Матрица 2х3: \\n\", matrix)\n",
    "print(\"форма (shape):\", matrix. shape)\n",
    "print(\"-\" * 30)\n",
    "linear_space = np. linspace(0, 5,\n",
    "10)\n",
    "print (\"Равномерная шкала (linspace): \\n\", linear_space)\n",
    "print(\"-\" * 38)\n",
    "random_matrix = np.random.randn(2, 2)\n",
    "print (\"Случайная матрица 2x2: \\n\", random_matrix)\n",
    "print(\"-\" * 30)\n",
    "A = пр.array([[1, 21, [3, 411)\n",
    "в = пр.array([[5, 6], (7, 811)\n",
    "dot_product = np.dot(A, B)\n",
    "print(\"Результат матричного умножения\n",
    "A B: \\n\", dot_product)\n",
    "print(\"-\" * 30)\n",
    "print( \"Сумма по столбцам матрицы:\"\n",
    ", matrix.sum(axis=0))\n",
    "print(\"Среднее по строкам матрицы:\n",
    ",matrix.mean(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2d017e52-ea6e-454d-9613-74dc3af19ee2",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "closing parenthesis ')' does not match opening parenthesis '[' (4271847362.py, line 2)",
     "output_type": "error",
     "traceback": [
      "  \u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[31m    \u001b[39m\u001b[31mmatrix = np. array([[1, 2, 3), [4, 5, 61])\u001b[39m\n                                ^\n\u001b[31mSyntaxError\u001b[39m\u001b[31m:\u001b[39m closing parenthesis ')' does not match opening parenthesis '['\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "matrix = np. array([[1, 2, 3), [4, 5, 61])\n",
    "print (\"Матрица 2х3: \\n\", matrix)\n",
    "print(\"форма (shape):\", matrix. shape)\n",
    "print(\"-\" * 30)\n",
    "linear_space = np. linspace(0, 5,\n",
    "10)\n",
    "print (\"Равномерная шкала (linspace): \\n\", linear_space)\n",
    "print(\"-\" * 38)\n",
    "random_matrix = np.random.randn(2, 2)\n",
    "print (\"Случайная матрица 2x2: \\n\", random_matrix)\n",
    "print(\"-\" * 30)\n",
    "A = пр.array([[1, 21, [3, 411)\n",
    "в = пр.array([[5, 6], (7, 811)\n",
    "dot_product = np.dot(A, B)\n",
    "print(\"Результат матричного умножения\n",
    "A B: \\n\", dot_product)\n",
    "print(\"-\" * 30)\n",
    "print( \"Сумма по столбцам матрицы:\"\n",
    ", matrix.sum(axis=0))\n",
    "print(\"Среднее по строкам матрицы:\n",
    ",matrix.mean(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9ff207bf-f559-41d8-b5d5-bdd2f5be6ca8",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "closing parenthesis ')' does not match opening parenthesis '[' (173234514.py, line 13)",
     "output_type": "error",
     "traceback": [
      "  \u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 13\u001b[39m\n\u001b[31m    \u001b[39m\u001b[31mA = пр.array([[1, 21, [3, 411)\u001b[39m\n                                 ^\n\u001b[31mSyntaxError\u001b[39m\u001b[31m:\u001b[39m closing parenthesis ')' does not match opening parenthesis '['\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "matrix = np. array([[2, 2, 3], [4, 5, 61]])\n",
    "print (\"Матрица 2х3: \\n\", matrix)\n",
    "print(\"форма (shape):\", matrix. shape)\n",
    "print(\"-\" * 30)\n",
    "linear_space = np. linspace(0, 5,\n",
    "10)\n",
    "print (\"Равномерная шкала (linspace): \\n\", linear_space)\n",
    "print(\"-\" * 38)\n",
    "random_matrix = np.random.randn(2, 2)\n",
    "print (\"Случайная матрица 2x2: \\n\", random_matrix)\n",
    "print(\"-\" * 30)\n",
    "A = пр.array([[1, 21, [3, 411)\n",
    "в = пр.array([[5, 6], (7, 811)\n",
    "dot_product = np.dot(A, B)\n",
    "print(\"Результат матричного умножения\n",
    "A B: \\n\", dot_product)\n",
    "print(\"-\" * 30)\n",
    "print( \"Сумма по столбцам матрицы:\"\n",
    ", matrix.sum(axis=0))\n",
    "print(\"Среднее по строкам матрицы:\n",
    ",matrix.mean(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "884d13e8-0098-48df-aa16-5933f8583fb7",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "closing parenthesis ')' does not match opening parenthesis '[' (3832752131.py, line 2)",
     "output_type": "error",
     "traceback": [
      "  \u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[31m    \u001b[39m\u001b[31mmatrix = np. array([[2, 2, 3), [4, 5, 61])\u001b[39m\n                                ^\n\u001b[31mSyntaxError\u001b[39m\u001b[31m:\u001b[39m closing parenthesis ')' does not match opening parenthesis '['\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "matrix = np. array([[2, 2, 3), [4, 5, 61])\n",
    "print (\"Матрица 2х3: \\n\", matrix)\n",
    "print(\"форма (shape):\", matrix. shape)\n",
    "print(\"-\" * 30)\n",
    "linear_space = np. linspace(0, 5,\n",
    "10)\n",
    "print (\"Равномерная шкала (linspace): \\n\", linear_space)\n",
    "print(\"-\" * 38)\n",
    "random_matrix = np.random.randn(2, 2)\n",
    "print (\"Случайная матрица 2x2: \\n\", random_matrix)\n",
    "print(\"-\" * 30)\n",
    "A = пр.array([[1, 21], [3, 411]])\n",
    "в = пр.array([[5, 6], [7, 811]])\n",
    "dot_product = np.dot(A, B)\n",
    "print(\"Результат матричного умножения\n",
    "A B: \\n\", dot_product)\n",
    "print(\"-\" * 30)\n",
    "print( \"Сумма по столбцам матрицы:\"\n",
    ", matrix.sum(axis=0))\n",
    "print(\"Среднее по строкам матрицы:\n",
    ",matrix.mean(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2625458b-9343-48d8-8e1d-e8be7704a5cd",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "closing parenthesis ')' does not match opening parenthesis '[' (1711020430.py, line 2)",
     "output_type": "error",
     "traceback": [
      "  \u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[31m    \u001b[39m\u001b[31mmatrix = np. array([[2, 2, 3), [4, 5, 6])\u001b[39m\n                                ^\n\u001b[31mSyntaxError\u001b[39m\u001b[31m:\u001b[39m closing parenthesis ')' does not match opening parenthesis '['\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "matrix = np. array([[2, 2, 3), [4, 5, 6])\n",
    "print (\"Матрица 2х3: \\n\", matrix)\n",
    "print(\"форма (shape):\", matrix. shape)\n",
    "print(\"-\" * 30)\n",
    "linear_space = np. linspace(0, 5,\n",
    "10)\n",
    "print (\"Равномерная шкала (linspace): \\n\", linear_space)\n",
    "print(\"-\" * 38)\n",
    "random_matrix = np.random.randn(2, 2)\n",
    "print (\"Случайная матрица 2x2: \\n\", random_matrix)\n",
    "print(\"-\" * 30)\n",
    "A = пр.array([[1, 2], [3, 4]])\n",
    "в = пр.array([[5, 6], [7, 8]])\n",
    "dot_product = np.dot(A, B)\n",
    "print(\"Результат матричного умножения\n",
    "A B: \\n\", dot_product)\n",
    "print(\"-\" * 30)\n",
    "print( \"Сумма по столбцам матрицы:\"\n",
    ", matrix.sum(axis=0))\n",
    "print(\"Среднее по строкам матрицы:\n",
    ",matrix.mean(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "07cc02ed-e387-4896-9930-9fb230b5dbf4",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "unterminated string literal (detected at line 16) (3477622439.py, line 16)",
     "output_type": "error",
     "traceback": [
      "  \u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 16\u001b[39m\n\u001b[31m    \u001b[39m\u001b[31mprint(\"Результат матричного умножения\u001b[39m\n          ^\n\u001b[31mSyntaxError\u001b[39m\u001b[31m:\u001b[39m unterminated string literal (detected at line 16)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "matrix = np. array([[2, 2, 3], [4, 5, 61]])\n",
    "print (\"Матрица 2х3: \\n\", matrix)\n",
    "print(\"форма (shape):\", matrix. shape)\n",
    "print(\"-\" * 30)\n",
    "linear_space = np. linspace(0, 5,\n",
    "10)\n",
    "print (\"Равномерная шкала (linspace): \\n\", linear_space)\n",
    "print(\"-\" * 38)\n",
    "random_matrix = np.random.randn(2, 2)\n",
    "print (\"Случайная матрица 2x2: \\n\", random_matrix)\n",
    "print(\"-\" * 30)\n",
    "A = пр.array([[1, 2], [3, 4]]\n",
    "в = пр.array([[5, 6], [7, 8]]\n",
    "dot_product = np.dot(A, B)\n",
    "print(\"Результат матричного умножения\n",
    "A B: \\n\", dot_product)\n",
    "print(\"-\" * 30)\n",
    "print( \"Сумма по столбцам матрицы:\"\n",
    ", matrix.sum(axis=0))\n",
    "print(\"Среднее по строкам матрицы:\n",
    ",matrix.mean(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ce623218-2a74-4b2f-9a5b-35d30bd6f12b",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "unterminated string literal (detected at line 20) (1502413043.py, line 20)",
     "output_type": "error",
     "traceback": [
      "  \u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[12]\u001b[39m\u001b[32m, line 20\u001b[39m\n\u001b[31m    \u001b[39m\u001b[31mprint(\"Среднее по строкам матрицы:\u001b[39m\n          ^\n\u001b[31mSyntaxError\u001b[39m\u001b[31m:\u001b[39m unterminated string literal (detected at line 20)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "matrix = np. array([[2, 2, 3], [4, 5, 61]])\n",
    "print (\"Матрица 2х3: \\n\", matrix)\n",
    "print(\"форма (shape):\", matrix. shape)\n",
    "print(\"-\" * 30)\n",
    "linear_space = np. linspace(0, 5,\n",
    "10)\n",
    "print (\"Равномерная шкала (linspace): \\n\", linear_space)\n",
    "print(\"-\" * 38)\n",
    "random_matrix = np.random.randn(2, 2)\n",
    "print (\"Случайная матрица 2x2: \\n\", random_matrix)\n",
    "print(\"-\" * 30)\n",
    "A = пр.array([[1, 2], [3, 4]]\n",
    "в = пр.array([[5, 6], [7, 8]]\n",
    "dot_product = np.dot(A, B)\n",
    "print(\"Результат матричного умножения A B: \\n\", dot_product)\n",
    "print(\"-\" * 30)\n",
    "print( \"Сумма по столбцам матрицы:\"\n",
    ", matrix.sum(axis=0))\n",
    "print(\"Среднее по строкам матрицы:\n",
    ",matrix.mean(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b5a5335a-e8a0-4500-b3d1-31616f641c93",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax. Perhaps you forgot a comma? (2265371125.py, line 13)",
     "output_type": "error",
     "traceback": [
      "  \u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[13]\u001b[39m\u001b[32m, line 13\u001b[39m\n\u001b[31m    \u001b[39m\u001b[31mA = пр.array([[1, 2], [3, 4]]\u001b[39m\n                 ^\n\u001b[31mSyntaxError\u001b[39m\u001b[31m:\u001b[39m invalid syntax. Perhaps you forgot a comma?\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "matrix = np. array([[2, 2, 3], [4, 5, 61]])\n",
    "print (\"Матрица 2х3: \\n\", matrix)\n",
    "print(\"форма (shape):\", matrix. shape)\n",
    "print(\"-\" * 30)\n",
    "linear_space = np. linspace(0, 5,\n",
    "10)\n",
    "print (\"Равномерная шкала (linspace): \\n\", linear_space)\n",
    "print(\"-\" * 38)\n",
    "random_matrix = np.random.randn(2, 2)\n",
    "print (\"Случайная матрица 2x2: \\n\", random_matrix)\n",
    "print(\"-\" * 30)\n",
    "A = пр.array([[1, 2], [3, 4]]\n",
    "в = пр.array([[5, 6], [7, 8]]\n",
    "dot_product = np.dot(A, B)\n",
    "print(\"Результат матричного умножения A B: \\n\", dot_product)\n",
    "print(\"-\" * 30)\n",
    "print( \"Сумма по столбцам матрицы:\", matrix.sum(axis=0))\n",
    "print(\"Среднее по строкам матрицы:\",matrix.mean(axis=1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b2bda042-9048-46e4-930a-3e55a7e6d30c",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax. Perhaps you forgot a comma? (1180920976.py, line 12)",
     "output_type": "error",
     "traceback": [
      "  \u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[14]\u001b[39m\u001b[32m, line 12\u001b[39m\n\u001b[31m    \u001b[39m\u001b[31mA = пр.array([[1, 2], [3, 4]]\u001b[39m\n                 ^\n\u001b[31mSyntaxError\u001b[39m\u001b[31m:\u001b[39m invalid syntax. Perhaps you forgot a comma?\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "matrix = np. array([[2, 2, 3], [4, 5, 61]])\n",
    "print (\"Матрица 2х3: \\n\", matrix)\n",
    "print(\"форма (shape):\", matrix. shape)\n",
    "print(\"-\" * 30)\n",
    "linear_space = np. linspace(0, 5,10)\n",
    "print (\"Равномерная шкала (linspace): \\n\", linear_space)\n",
    "print(\"-\" * 38)\n",
    "random_matrix = np.random.randn(2, 2)\n",
    "print (\"Случайная матрица 2x2: \\n\", random_matrix)\n",
    "print(\"-\" * 30)\n",
    "A = пр.array([[1, 2], [3, 4]]\n",
    "B = пр.array([[5, 6], [7, 8]]\n",
    "dot_product = np.dot(A, B)\n",
    "print(\"Результат матричного умножения A B: \\n\", dot_product)\n",
    "print(\"-\" * 30)\n",
    "print( \"Сумма по столбцам матрицы:\", matrix.sum(axis=0))\n",
    "print(\"Среднее по строкам матрицы:\",matrix.mean(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "87bb561a-3c7e-46a5-8d0e-de764f149807",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax. Perhaps you forgot a comma? (2845897726.py, line 12)",
     "output_type": "error",
     "traceback": [
      "  \u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[15]\u001b[39m\u001b[32m, line 12\u001b[39m\n\u001b[31m    \u001b[39m\u001b[31mA = np.array([[1, 2], [3, 4]]\u001b[39m\n                 ^\n\u001b[31mSyntaxError\u001b[39m\u001b[31m:\u001b[39m invalid syntax. Perhaps you forgot a comma?\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "matrix = np. array([[2, 2, 3], [4, 5, 61]])\n",
    "print (\"Матрица 2х3: \\n\", matrix)\n",
    "print(\"форма (shape):\", matrix. shape)\n",
    "print(\"-\" * 30)\n",
    "linear_space = np. linspace(0, 5,10)\n",
    "print (\"Равномерная шкала (linspace): \\n\", linear_space)\n",
    "print(\"-\" * 38)\n",
    "random_matrix = np.random.randn(2, 2)\n",
    "print (\"Случайная матрица 2x2: \\n\", random_matrix)\n",
    "print(\"-\" * 30)\n",
    "A = np.array([[1, 2], [3, 4]]\n",
    "B = np.array([[5, 6], [7, 8]]\n",
    "dot_product = np.dot(A, B)\n",
    "print(\"Результат матричного умножения A B: \\n\", dot_product)\n",
    "print(\"-\" * 30)\n",
    "print( \"Сумма по столбцам матрицы:\", matrix.sum(axis=0))\n",
    "print(\"Среднее по строкам матрицы:\",matrix.mean(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed616189-08dd-4064-8517-79a117f4062d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "matrix = np. array([[2, 2, 3], [4, 5, 61]])\n",
    "print (\"Матрица 2х3: \\n\", matrix)\n",
    "print(\"форма (shape):\", matrix. shape)\n",
    "print(\"-\" * 30)\n",
    "linear_space = np. linspace(0, 5,10)\n",
    "print (\"Равномерная шкала (linspace): \\n\", linear_space)\n",
    "print(\"-\" * 38)\n",
    "random_matrix = np.random.randn(2, 2)\n",
    "print (\"Случайная матрица 2x2: \\n\", random_matrix)\n",
    "print(\"-\" * 30)\n",
    "A = np.array([[1, 2], [3, 4]]\n",
    "B = np.array([[5, 6], [7, 8]]\n",
    "dot_product = np.dot(A, B)\n",
    "print(\"Результат матричного умножения A B: \\n\", dot_product)\n",
    "print(\"-\" * 30)\n",
    "print( \"Сумма по столбцам матрицы:\", matrix.sum(axis=0))\n",
    "print(\"Среднее по строкам матрицы:\",matrix.mean(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "df6be0fa-ec9b-41d0-9d51-bea88fdf2678",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Матрица 2x3:\n",
      " [[1 2 3]\n",
      " [4 5 6]]\n",
      "Форма (shape): (2, 3)\n",
      "------------------------------\n",
      "Равномерная шкала (linspace):\n",
      " [0.         0.55555556 1.11111111 1.66666667 2.22222222 2.77777778\n",
      " 3.33333333 3.88888889 4.44444444 5.        ]\n",
      "------------------------------\n",
      "Случайная матрица 2x2:\n",
      " [[-1.08208599  1.68003717]\n",
      " [ 0.95230412  1.07440619]]\n",
      "------------------------------\n",
      "Результат матричного умножения A и B:\n",
      " [[19 22]\n",
      " [43 50]]\n",
      "------------------------------\n",
      "Сумма по столбцам матрицы: [5 7 9]\n",
      "Среднее по строкам матрицы: [2. 5.]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "matrix = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "print(\"Матрица 2x3:\\n\", matrix)\n",
    "print(\"Форма (shape):\", matrix.shape)\n",
    "print(\"-\" * 30)\n",
    "linear_space = np.linspace(0, 5, 10)\n",
    "print(\"Равномерная шкала (linspace):\\n\", linear_space)\n",
    "print(\"-\" * 30)\n",
    "random_matrix = np.random.randn(2, 2)\n",
    "print(\"Случайная матрица 2x2:\\n\", random_matrix)\n",
    "print(\"-\" * 30)\n",
    "A = np.array([[1, 2], [3, 4]])\n",
    "B = np.array([[5, 6], [7, 8]])\n",
    "dot_product = np.dot(A, B)\n",
    "print(\"Результат матричного умножения A и B:\\n\", dot_product)\n",
    "print(\"-\" * 30)\n",
    "print(\"Сумма по столбцам матрицы:\", matrix.sum(axis=0))\n",
    "print(\"Среднее по строкам матрицы:\", matrix.mean(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b1d7a738-61de-4368-a6ac-bd41d697ebc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "x = np.linspace(0, 10, 100)\n",
    "y_sin = np.sin(x)\n",
    "y_cos = np.cos(x)\n",
    "fig, axs = plt.subplots(2, 2, figsize=(12, 10))\n",
    "axs[0, 0].plot(x, y_sin, color='red', label='sin(x)', linewidth=2)\n",
    "axs[0, 0].plot(x, y_cos, color='blue', linestyle='--', label='cos(x)')\n",
    "axs[0, 0].set_title(\"Линейные графики\")\n",
    "axs[0, 0].legend()\n",
    "axs[0, 0].grid(True)\n",
    "x_rand = np.random.rand(50) * 10\n",
    "y_rand = np.random.rand(50)\n",
    "axs[0, 1].scatter(x_rand, y_rand, color='green', alpha=0.6, label='Данные')\n",
    "axs[0, 1].set_title(\"Точечный график (Scatter)\")\n",
    "axs[0, 1].set_xlabel(\"Параметр X\")\n",
    "axs[0, 1].legend()\n",
    "categories = ['A', 'B', 'C', 'D', 'E']\n",
    "values = [5, 12, 8, 20, 15]\n",
    "axs[1, 0].bar(categories, values, color='purple', alpha=0.7)\n",
    "axs[1, 0].set_title(\"Столбчатая диаграмма (Bar)\")\n",
    "data = np.random.randn(1000)\n",
    "axs[1, 1].hist(data, bins=30, color='orange', edgecolor='black')\n",
    "axs[1, 1].set_title(\"Гистограмма распределения (Hist)\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d1b28bbc-e3dd-4b96-b106-c29a412bf9e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WBRfPP/98V8uvZ8+etM9G6Ddbv92qdfKzn/3M1f165513aLNawSHRSbzf//737vv361//2t544w3aqZHHb2j4sUFLIjjUjB5//HHXET7ssMOqbysvL6/R2ZHMzEz3t6KiojlfPm3b2DuYzs7OdoW81EH64Ycf7JhjjnHtj4ZToE3t+5e//MVFrFWwVwVmleXGttwy7azt2e/3uwNvnWW58MIL7b333rNTTz3VYrEYm3MDaKeqTEIVSlaBTW27OtOuwE84HK5zW/auE7hovnbWtqz93DbbbGP333+//eEPf7CnnnrKHaQBG/PZZ5/ZRRddZOPHj3cnfFC33/3ud/bMM8/YgQceaH/84x/t22+/palqueKKK1xx19qZHtiwGK76zmvXrrXTTz/dZfJp8hEVO9cEDqiikR+i7Bd97x544AH7+c9/7vpotFPjjt/Q8GODlvTT+BA0S+X6CRMmWFZWVvVt+n/tgw0vKMQZnuZpY13XWelOnTpV3zZkyBB3myL5mk0Am6az/+eee66L7HvDRHTwpx+ru+66i225hdr57rvvtiOPPNINsxANHejatav95je/sa+//trNroWNU6BH7aizxfod0JkYHRhoBggF3hSgr/277F1XkBnN087KGFIH2htWrW1Zy5x99tnuMV26dKGpsYHXXnvNdYg1Y9nNN99MC22EN4xMJ8W+/PJLe/TRR+26666jzRL6jBpG/Pzzz9Mmm6CSAZpdKhAIVPext956a/v+++/dkKnaQ7HTlfZhoqyhiRMnuv9vueWW9t1339k//vEP2qkRx29o+LHBX//6V2spZA41k+nTp9v8+fM3ODOhoQu1hzd517t3795cL5/WbSyJgSFR1oXSFjW8AQ2jjqXO9ifWfBAFI5Taz7bcMu2sg2ovMJQY7BS254ZT3QSdUVdn96OPPnIHTGo/pedqaEFdv8sK2OusDZqnnXWwkVhvj20Zm6LghrIWFGBU5qSXaY2awzM0jEyZHh7tNxQoYjh9TfptWrlypcs+U/aQLqJsxxNOOIHNqhbVjKt98K7+hzJDYTWO3byaXx59/1QrBo07fkPDjg1aEsGhZqIzEyokVbto1Lhx41w02UtDFHWg9QPc0gWm2msba0ymag0lFpLVD7SGNlCcseG8GiwqSJhIQ0P69+/vtmUV7FVNkcRtWcVm9bmgedpZGRXHHntsjfuUMSRszw2j39ujjjrKdUgUJM7Ly3O/CfotVvq3zsqoYHIibcvKVNBBFpqnnY8++mg3NKj2tqwzr9rWgdrDDq6++mpX7+TWW2/dYOgnqmgCiHPOOafGEBYdVOh7RzHhmpR59tJLL7msBe8iZ5xxhsu2wk+UIaR9oAL9ib755hv6Hgk0yZCO4XQwX7sP19K1Ydr68RsafmzQkugFNxPtlIcNG7bB7SomqyEhZ511lutAK11anZ7jjz+ejk8ztfE+++zjZg7QuHIVitOsFDrzqJ0chSwbTnVBxowZ44aB6EBZMxGpeKw6oBpzfsghh7iDQRVMU5rjs88+69IfmZ2hedtZgU79X8N1NHPZ22+/bRdffLEb207Hv2EUpFCwWJ1/dXgVkFC9G81IptR4BS2++uord+Awe/ZsVzPgv//9L2eSm7mdtS0/99xz9s9//tOdNdRB2o033ujS8fVYwKN997XXXuv259qnKACyfPlyd6EYf03KWNAwzv/7v/9z/R0dPKg2nQov1z6xkO6U5aHZExMvogNVsvdrUv9CNeQ0HFgH89o3KhP0iy++cL/rqKLMKmWdqS7MCy+84PppkyZNsvfff9+OO+44mqkRx29o+LFBS/JpPvsWfcV26sQTT3SdXWWx1KZ0sCuvvNL92CrF/tBDD3XBC85QN18b68ujYtSKuOpsoyq9J9a6QMOoEKF+jN566y33f3VCdYZSU9iLDqh1IKgfegU9FeRU5gCat51ffvllVwxSxSE1zEmpuAowM8Si4ZQGrywE7WT1m6DCtprFUGf8RDOw3HTTTW4H3Lt3b/ebTH2y5m/nxx57zF0UHPJqZ6mjw/4PiTSErK59u6iuB1OQ16SAmYq/64Sj/q9sSAWIvCHIqJ8OUhX0ULFX1KSgrLard9991wUbR4wY4ep/eTVQ8BPVF9IwWO0DFVhTH0IJAWjc8RsafmzQUggOAQAAAAAApDGGlQEAAAAAAKQxgkMAAAAAAABpjOAQAAAAAABAGiM4BAAAAAAAkMYIDgEAAAAAAKQxgkMAAAAAAABpjOAQAAAAAABAGiM4BCAlLrzwQhs2bFi9l+XLl/PJAACANuXjjz92/Rj9rcvRRx/tLvjJyy+/7Npk7NixNnz4cNd++vv000/TTEALCrbkiwFAoq5du9rdd99dZ6MUFhbSWAAAAO3Ygw8+aJMmTbKTTz7ZTjnlFMvJybFgMGjdunWz7t27p3r1gLRCcAhAymRkZNi2227LJwAAAJBmwuGwCwzddNNNtuuuu6Z6dYC0x7AyAK1aeXm53XLLLTZ+/HjbeuutbfTo0XbcccfZtGnTaixX3/C0xNTtZ599tt7ldF/iMgsWLGhSujgAAEhfPp+v2fs4b7/9th1++OHuhNrOO+9sl112ma1bty5p/ZZDDjnEvV5txx57rFu/+l5zzz33dGUDPBUVFXbjjTfabrvt5t7fQQcdZC+99FL1/d9//71rgz59+rjMoe2228522GEHu+iii2zVqlXVy911113u9eqzqTYA0DBkDgFIqUgkUt2ZCgQCG9x//vnn29SpU+2cc86xvn372rx58+yOO+6wc88911588cUanbBDDz3Ufv3rX1dfv/LKK+t8TQ1l05A2UW2j0047LQnvDAAApBsNi5KSkpJNLtuQPs6bb75pf/jDH2yvvfay22+/3dasWeMCLgsXLrS///3vSXkP6k9dccUVbn369evnblu8eLELMOm1lfGzKfF43P74xz/aZ599ZmeccYYNGjTIXn31VTv77LOtsrLSJkyY4N5DKBSy3/3udy4wdtttt7mgkP5+88039tRTT1lWVlZS3iOADREcApAy6hRstdVW1dfVQdD4cqUWqyORm5vrOleXXnqp7b///m6Z7bff3oqLi+3666+3FStWVAd5pEePHjWGqeXl5dX5ultuuaX17t3b/Z+zTAAAoLkoC0ZBna+//tpl0tRHAZKG9HGUNaN+i05seSfENCxfQSQtkwwHHnigW4fnnnvO9cdE/1e/bJ999rH//ve/7rZoNFrvc3zwwQf27rvvukCP9/522WUXKysrs5tvvtm9RmlpqRUVFbni0wp8eYYOHeqyl5QRdOSRRyblPQLYEMEhACmjTo/GmnsdDKVIT5kyxe6//36XUv3EE09UnxVbunSpzZkzx+bOnevOonkdq2SJxWJunerKZgIAAKhLhw4dbI899rCHHnrIBg4c6IZUKSCiPszrr79u3333nY0YMcIFeDbVx9GQKy1/+umn18iUVrDFC7gko9+Sn5/vhrr95z//qQ4O/fvf/3avqUyezp07u9uWLFlSnVlU24cffujWWe/fyxIXBcz0vBpS5r2nX/3qVzUeqyFoyqRSplJicEjPU1+mOYDNR3AIQMqoYzRy5MgatylrSGeR/vnPf9oXX3zh/n/ttdfaDz/84M5Y6eySl7KtlOVk0Zkxyc7OdmcBdQZLY+0BAAA2RgWWVRfoggsuqM6uUR9GmUEKvHiUWbOxPs7atWvdXy8Y05L9Fg0tUxBHw94UjFHg6oYbbnD3qTaQgmDKXtIwM62zho+pz+bR8Detu4aL1WXZsmXuPUtds5J17NjRZVEl8rLN9bgBAwbYMcccs0FgCUDTERwC0OoonVg++eQTl0a9995729/+9rfqVO3HHnvMdaiaWgSyIcspo0mZTSqm+Omnn1Z3iJTaDQAAUB8Na7/11ltd3R5l1yiQ0qVLFxdk8SbK+PHHH11Nno31cfQ8ui2xOLOob/LRRx/ZqFGjktZvUSBL2TsaQub3+10WlDd0X+ul4WKqmaRaSFJYWOgypDwKgilo9PDDD9f5/Mo4WrRokft/7fcnuu9nP/tZjduefvpp91ev88Ybb7jX94JpADYfs5UBaHWmT59e/X91ck466STXQfGCOl6nycscUiq1qPOyMd5yDUlHVoBKWU1jx451M2gwQxkAAGiMgoIC159QZkztvocKLm+qj6MMGQV3vKFmnnfeecc9Ttk3yeq3aH0OPvhge+2111wgZuLEiTXu//nPf+7W64UXXnDD5RSs6tatW43gkmoK6X1ovbzLzJkz7S9/+YsbIjZkyBDXNs8///wG708ThtSe3t57Dj23ZkVT+zJ7LNB8yBwCkDIaT6+hYx6lD6tzoTNDmsp03333dSnLSs8+/vjj3fIqTvjWW2+55dXpmD9/vn311VfuujoJ9fnyyy9dJpI6O/UVqk6kmkcq9Kh1Uh0kdWZ++ctfNsv7BgAA6U1DpILB4Eb7OKKaP5qtTDOaaYYv9U2UlaSMIwWEFGRKVr9FwSEVxJa6hm+pPIACPHVRraFx48bZqaee6i6arUz9tTvvvNMVpu7UqZNbTkEezWCm96fX0Kxoen8auvaLX/yixnOqz6hgk2pUKjClv3qNhswMB2DTCA4BSBmdFTrssMOqr6uTpHH1GievcfoK4txyyy1uaJk6RkrLVkrzI4884tKyNQ5eWUbPPPOMbbPNNhsUZ0z029/+1j2/zrQ1JDjkTW+vjk+vXr1cMUiN3VeHCwAAYHNoWNWm+jjK/lFx63vuucctp2FoCqocdNBBrl+S7H6LsnpUB0lD4uqqC7Qxyua+99573Uk+DZtbuXKle47jjjvOvQ+P13fTsgqEqWaShqpdfPHFG2RbeX1GFcXWMLwrr7zSnUhUUA3A5vPFk1nRFQAAAADQ5mgWNQWnlO2jTCUA7RvBIQAAAABo5zRz2qbyAjT8XkPSVEfolVdecY9RXaFN1XUE0PYxrAwAAAAA2rn/b+eOiQCGYSAIhofAi5mwZGQATp/frYzhxvo9M9v9xZuqOps/3X3OwPYtDEEGP4cAAAB+bmY+x5t3s2i3joA84hAAAABAMMejAAAAAMHEIQAAAIBg4hAAAABAMHEIAAAAIJg4BAAAABBMHAIAAAAIJg4BAAAABBOHAAAAAJ5cL2u1CjVL5rzLAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Генерация Pairplot (может занять время на больших данных)...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 839.847x750 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = {\n",
    "    \"Баллы\": [89, 76, 95, 82, 70, 88, 92, 78, 85, 90],\n",
    "    \"Возраст\": [21, 22, 23, 24, 21, 22, 23, 24, 22, 23],\n",
    "    \"Часы_учебы\": [5, 3, 7, 4, 2, 6, 8, 3, 5, 6],\n",
    "    \"Категория\": [\"A\", \"B\", \"A\", \"B\", \"A\", \"B\", \"A\", \"B\", \"A\", \"B\"]\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n",
    "sns.histplot(df[\"Баллы\"], kde=True, color=\"skyblue\", ax=ax[0])\n",
    "ax[0].set_title(\"Распределение баллов (Histplot)\")\n",
    "sns.scatterplot(x=\"Часы_учебы\", y=\"Баллы\", hue=\"Категория\", style=\"Категория\", s=100, data=df, ax=ax[1])\n",
    "ax[1].set_title(\"Связь часов учебы и баллов (Scatterplot)\")\n",
    "plt.show()\n",
    "plt.figure(figsize=(8, 6))\n",
    "corr_matrix = df.drop(columns=[\"Категория\"]).corr()\n",
    "sns.heatmap(corr_matrix, annot=True, cmap=\"coolwarm\", fmt=\".2f\")\n",
    "plt.title(\"Матрица корреляции признаков (Heatmap)\")\n",
    "plt.show()\n",
    "print(\"Генерация Pairplot (может занять время на больших данных)...\")\n",
    "sns.pairplot(df, hue=\"Категория\", palette=\"husl\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dafe6977-c829-4cf1-8aec-82ad9f8e0be6",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'tqdm'",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[19]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m pandas \u001b[38;5;28;01mas\u001b[39;00m pd\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m numpy \u001b[38;5;28;01mas\u001b[39;00m np\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m tqdm.auto \u001b[38;5;28;01mimport\u001b[39;00m tqdm\n\u001b[32m      4\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m time\n\u001b[32m      5\u001b[39m df = pd.DataFrame({\n\u001b[32m      6\u001b[39m     \u001b[33m'ID'\u001b[39m: range(\u001b[32m1\u001b[39m, \u001b[32m101\u001b[39m),\n",
      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'tqdm'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm.auto import tqdm \n",
    "import time\n",
    "df = pd.DataFrame({\n",
    "    'ID': range(1, 101),\n",
    "    'Data': np.random.randn(100)\n",
    "})\n",
    "print(\"Начинаем обработку DataFrame...\")\n",
    "for index, row in tqdm(df.iterrows(), total=df.shape[0], desc=\"Анализ строк\"):\n",
    "    # Симуляция сложной математики\n",
    "    time.sleep(0.02) \n",
    "    \n",
    "print(\"\\n\" + \"=\"*30 + \"\\n\")\n",
    "print(\"Запуск кастомного процесса...\")\n",
    "\n",
    "for i in tqdm(range(100), \n",
    "              desc='Загрузка нейросети', \n",
    "              unit=' слоев', \n",
    "              colour='green'):\n",
    "    time.sleep(0.03)\n",
    "\n",
    "print(\"Готово!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d1964d7a-de1d-4de1-b297-020929056117",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'tqdm'",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[20]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m pandas \u001b[38;5;28;01mas\u001b[39;00m pd\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m numpy \u001b[38;5;28;01mas\u001b[39;00m np\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m tqdm.auto \u001b[38;5;28;01mimport\u001b[39;00m tqdm\n\u001b[32m      4\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m time\n\u001b[32m      5\u001b[39m df = pd.DataFrame({\n\u001b[32m      6\u001b[39m     \u001b[33m'ID'\u001b[39m: range(\u001b[32m1\u001b[39m, \u001b[32m101\u001b[39m),\n",
      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'tqdm'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm.auto import tqdm \n",
    "import time\n",
    "df = pd.DataFrame({\n",
    "    'ID': range(1, 101),\n",
    "    'Data': np.random.randn(100)\n",
    "})\n",
    "print(\"Начинаем обработку DataFrame...\")\n",
    "for index, row in tqdm(df.iterrows(), total=df.shape[0], desc=\"Анализ строк\"):\n",
    "   \n",
    "    time.sleep(0.02) \n",
    "    \n",
    "print(\"\\n\" + \"=\"*30 + \"\\n\")\n",
    "print(\"Запуск кастомного процесса...\")\n",
    "\n",
    "for i in tqdm(range(100), \n",
    "              desc='Загрузка нейросети', \n",
    "              unit=' слоев', \n",
    "              colour='green'):\n",
    "    time.sleep(0.03)\n",
    "\n",
    "print(\"Готово!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88ba1116-7518-490f-ab34-b50fc7d67872",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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