{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d451aecb-932b-45cf-84e0-4affdaa7c81d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>race/ethnicity</th>\n",
       "      <th>parental level of education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test preparation course</th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>69</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>master's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>group A</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>47</td>\n",
       "      <td>57</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender race/ethnicity parental level of education         lunch  \\\n",
       "0  female        group B           bachelor's degree      standard   \n",
       "1  female        group C                some college      standard   \n",
       "2  female        group B             master's degree      standard   \n",
       "3    male        group A          associate's degree  free/reduced   \n",
       "4    male        group C                some college      standard   \n",
       "\n",
       "  test preparation course  math score  reading score  writing score  \n",
       "0                    none          72             72             74  \n",
       "1               completed          69             90             88  \n",
       "2                    none          90             95             93  \n",
       "3                    none          47             57             44  \n",
       "4                    none          76             78             75  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Загрузка данных (файл должен быть в папке проекта)\n",
    "df = pd.read_csv('StudentsPerformance.csv')  \n",
    "\n",
    "# Первые 5 строк\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c93720f7-ab0e-40d1-bb9c-0b5ddb77f452",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 8 columns):\n",
      " #   Column                       Non-Null Count  Dtype \n",
      "---  ------                       --------------  ----- \n",
      " 0   gender                       1000 non-null   object\n",
      " 1   race/ethnicity               1000 non-null   object\n",
      " 2   parental level of education  1000 non-null   object\n",
      " 3   lunch                        1000 non-null   object\n",
      " 4   test preparation course      1000 non-null   object\n",
      " 5   math score                   1000 non-null   int64 \n",
      " 6   reading score                1000 non-null   int64 \n",
      " 7   writing score                1000 non-null   int64 \n",
      "dtypes: int64(3), object(5)\n",
      "memory usage: 62.6+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "gender                         0\n",
       "race/ethnicity                 0\n",
       "parental level of education    0\n",
       "lunch                          0\n",
       "test preparation course        0\n",
       "math score                     0\n",
       "reading score                  0\n",
       "writing score                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Общая информация\n",
    "df.info()\n",
    "\n",
    "# 2. Статистика\n",
    "df.describe()\n",
    "\n",
    "# 3. Проверка на пропуски\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aa630a90-7d6d-460e-a7af-848c282b2406",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.histplot(data=df, x='math score', bins=20, kde=True)\n",
    "plt.title('Распределение оценок по математике')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a6be28c4-7e4b-49cd-8082-9be7999acd61",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Обработка: 100%|█████████████████████████████| 100/100 [00:01<00:00, 96.62it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "import time\n",
    "\n",
    "# Пример: медленная обработка данных\n",
    "for i in tqdm(range(100), desc='Обработка'):\n",
    "    time.sleep(0.01)  # Имитация долгой операции"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc47ad07-5dad-4f31-a355-c2241eb2a237",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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