jupyter/week2_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c033731f-63ff-45a1-9466-15886f15ae09",
   "metadata": {},
   "source": [
    "Тест"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bf32d476-0486-4477-aab4-e86965882fdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "test1 = 3\n",
    "test2 = 2\n",
    "print(test1 + test2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8757d814-8118-4250-9fe6-8d4c847f4791",
   "metadata": {},
   "source": [
    "Часть 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6e2f0daf-10fe-4892-8438-7a10f5cc05bd",
   "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.core.frame.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      object\n",
      " 1   Возраст  4 non-null      int64 \n",
      " 2   Баллы    4 non-null      int64 \n",
      "dtypes: int64(2), object(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": "markdown",
   "id": "3b801627-8797-4b4d-8f3d-c3ea9911e1bf",
   "metadata": {},
   "source": [
    "Часть 3, изменено"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f9b40974-e401-451a-8dcb-3aa296931280",
   "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.core.frame.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      object\n",
      " 1   Возраст  4 non-null      int64 \n",
      " 2   Баллы    4 non-null      int64 \n",
      "dtypes: int64(2), object(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"
     ]
    },
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       "      <th></th>\n",
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       "      <td>Борис</td>\n",
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       "      <td>Виктор</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Галина</td>\n",
       "      <td>24</td>\n",
       "      <td>82</td>\n",
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      "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": 12,
     "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",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9ee10d5f-ba28-45bd-9766-89491bb8d9a1",
   "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.core.frame.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      object \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), object(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"
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       "      <td>Борис</td>\n",
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       "      <td>83.6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Виктор</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Галина</td>\n",
       "      <td>24</td>\n",
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       "      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"
      ]
     },
     "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",
    "df[\"Новый столбец\"] = df[\"Баллы\"] * 1.1\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",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "841cffd3-b0f4-4d76-918e-822044d8cda1",
   "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.core.frame.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      object \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), object(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"
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       "  <thead>\n",
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       "      <th>Возраст</th>\n",
       "    </tr>\n",
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       "      <th></th>\n",
       "      <th>mean</th>\n",
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       "      <th>76</th>\n",
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      "text/plain": [
       "      Баллы             Возраст\n",
       "       mean sum max min    mean\n",
       "Баллы                          \n",
       "76     76.0  76  76  76    22.0\n",
       "82     82.0  82  82  82    24.0\n",
       "89     89.0  89  89  89    21.0\n",
       "95     95.0  95  95  95    23.0"
      ]
     },
     "execution_count": 16,
     "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",
    "df[\"Новый столбец\"] = df[\"Баллы\"] * 1.1\n",
    "\n",
    "grouped_df = df.groupby(\"Баллы\").agg({\n",
    "    \"Баллы\": [\"mean\", \"sum\", \"max\", \"min\"],\n",
    "    \"Возраст\": \"mean\"\n",
    "})\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",
    "\n",
    "grouped_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c7b256e1-c871-4131-bd8d-a0608a18ec28",
   "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.core.frame.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      object \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), object(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"
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       "      <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>0</th>\n",
       "      <td>Анна</td>\n",
       "      <td>21</td>\n",
       "      <td>89</td>\n",
       "      <td>97.9</td>\n",
       "    </tr>\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",
       "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"
      ]
     },
     "execution_count": 17,
     "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",
    "df[\"Новый столбец\"] = df[\"Баллы\"] * 1.1\n",
    "df[df[\"Возраст\"] > 21]\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",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fabc986b-9e8a-4fad-82cf-7e0d22e2d485",
   "metadata": {},
   "source": [
    "Часть 3, часть 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5a137f99-a29a-4c32-8727-8fe71e93f82f",
   "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": 21,
   "id": "0bff7510-f22b-4335-9596-1ff30cb0f806",
   "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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([3.        , 3.02040816, 3.04081633, 3.06122449, 3.08163265,\n",
       "       3.10204082, 3.12244898, 3.14285714, 3.16326531, 3.18367347,\n",
       "       3.20408163, 3.2244898 , 3.24489796, 3.26530612, 3.28571429,\n",
       "       3.30612245, 3.32653061, 3.34693878, 3.36734694, 3.3877551 ,\n",
       "       3.40816327, 3.42857143, 3.44897959, 3.46938776, 3.48979592,\n",
       "       3.51020408, 3.53061224, 3.55102041, 3.57142857, 3.59183673,\n",
       "       3.6122449 , 3.63265306, 3.65306122, 3.67346939, 3.69387755,\n",
       "       3.71428571, 3.73469388, 3.75510204, 3.7755102 , 3.79591837,\n",
       "       3.81632653, 3.83673469, 3.85714286, 3.87755102, 3.89795918,\n",
       "       3.91836735, 3.93877551, 3.95918367, 3.97959184, 4.        ])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "\n",
    "arr2 = np.array([[1, 2], [3, 4]])\n",
    "np.linspace(3, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "01af748b-1dee-483d-be1d-39d9d39e2daf",
   "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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.063095687089255"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "\n",
    "arr2 = np.array([[1, 2], [3, 4]])\n",
    "np.random.randn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6120edc2-d49b-42a8-bf34-606f0b465c63",
   "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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "np.int64(11)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "\n",
    "arr2 = np.array([[1, 2], [3, 4]])\n",
    "np.dot(arr2[0], arr2[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39a7b707-93dd-4586-958f-b4bfa5e23d9c",
   "metadata": {},
   "source": [
    "Matplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "03295aee-25dd-48b0-a88e-2182a1213171",
   "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": 3,
   "id": "264e7d30-72b9-4b2f-8d49-79df44406e84",
   "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",
    "import numpy as np\n",
    "\n",
    "x = np.linspace(0, 10, 100)\n",
    "y = np.sin(x)\n",
    "\n",
    "plt.plot(x, y, label='sin(x)', color='green')\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": 4,
   "id": "f8a4b3e5-6281-4d26-9021-3336319097dd",
   "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, np.sin(x), label='sin(x)', color='yellow')\n",
    "plt.plot(x, np.cos(x), label='cos(x)', color='green') \n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Y\")\n",
    "plt.title(\"Два графика\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cbf8dfa9-b3f4-4c09-a268-712f18d62733",
   "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.bar(x, np.sin(x), label='sin(x)', color='purple')\n",
    "#plt.plot(x, np.cos(x), label='cos(x)', color='blue') \n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Y\")\n",
    "plt.title(\"График\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b80643f9-dba3-45d2-a3ba-d13579fda896",
   "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.scatter(x, np.sin(x), label='sin(x)', color='orange')\n",
    "#plt.plot(x, np.cos(x), label='cos(x)', color='blue') \n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Y\")\n",
    "plt.title(\"График\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bd42a32b-4bfd-4acc-9974-1b8bd4c55dcc",
   "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.hist(np.sin(x), bins=30, label='sin(x)', color='red', edgecolor='black')\n",
    "#plt.plot(x, np.cos(x), label='cos(x)', color='blue') \n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Y\")\n",
    "plt.title(\"График\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8f6279a5-850e-41ef-9920-75b3c9083302",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 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",
    "sns.boxplot(x=\"Категория\", y=\"Баллы\", data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fda61745-bc9b-4112-8ef3-9c4fac2f0751",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "df[\"Категория\"] = [1, 2, 6, 11] # догадайтесь откуда df и её содержимое взялось\n",
    "sns.scatterplot(x=\"Категория\", y=\"Баллы\", data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2eac5f2b-f14c-4f51-8de9-9808b7950e70",
   "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 1000x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "df[\"Категория\"] = [1, 2, 6, 11] # догадайтесь откуда df и её содержимое взялось\n",
    "sns.histplot(x=\"Категория\", y=\"Баллы\", data=df)\n",
    "sns.pairplot(df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9ee43629-88d4-4332-a459-b416539eeb8b",
   "metadata": {},
   "outputs": [
    {
     "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",
    "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",
    "sns.histplot(x=\"Возраст\", y=\"Баллы\", data=df)\n",
    "\n",
    "numeric_df = df.select_dtypes(include=['number'])\n",
    "sns.heatmap(numeric_df.corr(), annot=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3d37f39d-d5d8-4a2a-b2a0-621629ece250",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "4it [00:00, 88.64it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "import time\n",
    "\n",
    "for i in tqdm(range(100)):\n",
    "    time.sleep(0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f7ef7138-b839-478f-a077-a68f6da7f3b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "4it [00:00, 91.89it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "import time\n",
    "\n",
    "for i in tqdm(df.iterrows()):\n",
    "    time.sleep(0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ce9e095f-8033-4ebf-9719-1cf7f529e59a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Загрузка: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 92.20it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "import time\n",
    "\n",
    "for i in tqdm(range(100), desc='Загрузка'):\n",
    "    time.sleep(0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f32feeff-c86f-45d8-9049-1c93f319dde4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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