From bec5fbd8765eb9869bc2f73c141a6bee3ab5d989 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=D0=9D=D0=B8=D0=BA=D0=B8=D1=82=D0=B0=20=D0=91=D1=80=D0=B8?=
 =?UTF-8?q?=D0=BB=D0=B8=D0=BD?= <stud203989@outlook.com>
Date: Thu, 16 Apr 2026 21:11:52 +0300
Subject: [PATCH] =?UTF-8?q?=D0=94=D0=BE=D0=B1=D0=B0=D0=B2=D0=BB=D0=B5?=
 =?UTF-8?q?=D0=BD=20=D0=B0=D0=BD=D0=B0=D0=BB=D0=B8=D0=B7=20=D0=B4=D0=B0?=
 =?UTF-8?q?=D1=82=D0=B0=D1=81=D0=B5=D1=82=D0=B0?=
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Content-Type: text/plain; charset=UTF-8
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---
 data/filtered_data.csv |   49 ++
 week2_analysis.ipynb   | 1036 ++++++++++++++++++++++++++++++++++++++++
 2 files changed, 1085 insertions(+)
 create mode 100644 data/filtered_data.csv
 create mode 100644 week2_analysis.ipynb

diff --git a/data/filtered_data.csv b/data/filtered_data.csv
new file mode 100644
index 0000000..645f23c
--- /dev/null
+++ b/data/filtered_data.csv
@@ -0,0 +1,49 @@
+YEAR,BUDGET_ON_EDUCATION,GDP,RATIO
+1976,9314.0,1873412.0,0.49699999999999994
+1977,10568.0,2081826.0,0.508
+1978,11625.0,2351599.0,0.494
+1979,13996.0,2627333.0,0.5329999999999999
+1980,15209.0,2857307.0,0.532
+1981,16752.0,3207041.0,0.522
+1982,15374.0,3343789.0,0.45999999999999996
+1983,15267.0,3634038.0,0.42
+1984,15336.0,4037613.0,0.38
+1985,18952.0,4338979.0,0.437
+1986,17750.0,4579631.0,0.388
+1987,19475.0,4855215.0,0.40099999999999997
+1988,20098.0,5236438.0,0.384
+1989,22819.0,5641580.0,0.404
+1990,24549.0,5963144.0,0.41200000000000003
+1991,27360.0,6158129.0,0.44400000000000006
+1992,28685.0,6520327.0,0.44
+1993,31324.0,6858559.0,0.457
+1994,26881.0,7287236.0,0.369
+1995,32245.0,7639749.0,0.422
+1996,29097.0,8073122.0,0.36
+1997,33519.0,8577552.0,0.391
+1998,35298.0,9062817.0,0.38899999999999996
+1999,33643.0,9631172.0,0.349
+2000,31816.0,10250952.0,0.31
+2001,39734.0,10581929.0,0.375
+2002,55838.0,10929108.0,0.511
+2003,63001.0,11456450.0,0.5499999999999999
+2004,67161.0,12217196.0,0.5499999999999999
+2005,74476.0,13039197.0,0.571
+2006,100019.0,13815583.0,0.724
+2007,68265.0,14474228.0,0.47200000000000003
+2008,65399.0,14769862.0,0.443
+2009,131891.0,14478067.0,0.911
+2010,62911.0,15048971.0,0.418
+2011,43628.0,15599732.0,0.27999999999999997
+2012,57458.0,16253970.0,0.35400000000000004
+2013,39495.0,16880683.0,0.234
+2014,55200.0,17608138.0,0.313
+2015,87258.0,18295019.0,0.477
+2016,76977.0,18804913.0,0.409
+2017,114844.0,19612102.0,0.586
+2018,72114.0,20656516.0,0.349
+2019,106293.0,21521395.0,0.494
+2020,230750.0,21322950.0,1.082
+2021,469465.0,23594031.0,1.9900000000000002
+2022,565388.0,25744108.0,2.196
+2023,-100326.0,27360935.0,-0.367
\ No newline at end of file
diff --git a/week2_analysis.ipynb b/week2_analysis.ipynb
new file mode 100644
index 0000000..c9b7a6a
--- /dev/null
+++ b/week2_analysis.ipynb
@@ -0,0 +1,1036 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "762b244e",
+   "metadata": {},
+   "source": [
+    "# Работа с JupyterLab, анализ и визуализация данных\n",
+    "\n",
+    "В данной работе изучаются основы использования JupyterLab, а также библиотеки pandas, numpy, matplotlib, seaborn и tqdm.\n",
+    "Кроме этого, выполняется анализ данных и построение графиков."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b0b698e0-ed1f-46ad-bdc5-ccab57426b90",
+   "metadata": {},
+   "outputs": [],
+   "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",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c5c68830",
+   "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": 3,
+     "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",
+    "\n",
+    "df = pd.DataFrame(data)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b138db68",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Первые строки:\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",
+      "\n",
+      "Информация о данных:\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: 224.0+ bytes\n",
+      "\n",
+      "Описание числовых данных:\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",
+      "\n",
+      "Количество пропусков:\n",
+      "Имя        0\n",
+      "Возраст    0\n",
+      "Баллы      0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Первые строки:\")\n",
+    "print(df.head())\n",
+    "\n",
+    "print(\"\\nИнформация о данных:\")\n",
+    "df.info()\n",
+    "\n",
+    "print(\"\\nОписание числовых данных:\")\n",
+    "print(df.describe())\n",
+    "\n",
+    "print(\"\\nКоличество пропусков:\")\n",
+    "print(df.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0649f1a1",
+   "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",
+       "      <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": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Новый столбец\"] = df[\"Баллы\"] * 1.1\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "21d0c5b2",
+   "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",
+       "      <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": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df[\"Возраст\"] > 21]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0cd65a77",
+   "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 tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Возраст</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Баллы</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>mean</th>\n",
+       "      <th>max</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Категория</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>23</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>24</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Возраст     Баллы    \n",
+       "             mean max  mean max\n",
+       "Категория                      \n",
+       "A            22.0  23  92.0  95\n",
+       "B            23.0  24  79.0  82"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Категория\"] = [\"A\", \"B\", \"A\", \"B\"]\n",
+    "\n",
+    "df.groupby(\"Категория\").agg({\n",
+    "    \"Возраст\": [\"mean\", \"max\"],\n",
+    "    \"Баллы\": [\"mean\", \"max\"]\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "22b060a5",
+   "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": [
+    "arr = np.array([1, 2, 3, 4, 5])\n",
+    "\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": 9,
+   "id": "156a450a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Двумерный массив:\n",
+      "[[1 2]\n",
+      " [3 4]]\n",
+      "\n",
+      "linspace:\n",
+      "[ 0.   2.5  5.   7.5 10. ]\n",
+      "\n",
+      "Случайные значения:\n",
+      "[ 0.3650632   0.34464613 -0.71271462  0.39542024  1.32176194]\n",
+      "\n",
+      "Скалярное произведение: 11\n"
+     ]
+    }
+   ],
+   "source": [
+    "matrix = np.array([[1, 2], [3, 4]])\n",
+    "print(\"Двумерный массив:\")\n",
+    "print(matrix)\n",
+    "\n",
+    "line = np.linspace(0, 10, 5)\n",
+    "print(\"\\nlinspace:\")\n",
+    "print(line)\n",
+    "\n",
+    "random_values = np.random.randn(5)\n",
+    "print(\"\\nСлучайные значения:\")\n",
+    "print(random_values)\n",
+    "\n",
+    "dot_result = np.dot([1, 2], [3, 4])\n",
+    "print(\"\\nСкалярное произведение:\", dot_result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "03b22aff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(0, 10, 100)\n",
+    "y1 = np.sin(x)\n",
+    "y2 = np.cos(x)\n",
+    "\n",
+    "plt.plot(x, y1, label=\"sin(x)\", color=\"red\")\n",
+    "plt.plot(x, y2, label=\"cos(x)\")\n",
+    "plt.xlabel(\"X\")\n",
+    "plt.ylabel(\"Y\")\n",
+    "plt.title(\"Графики sin(x) и cos(x)\")\n",
+    "plt.legend()\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "b8e5fb08",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(df[\"Имя\"], df[\"Баллы\"])\n",
+    "plt.title(\"Баллы студентов\")\n",
+    "plt.xlabel(\"Имя\")\n",
+    "plt.ylabel(\"Баллы\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9e41020e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(df[\"Возраст\"], df[\"Баллы\"])\n",
+    "plt.title(\"Зависимость баллов от возраста\")\n",
+    "plt.xlabel(\"Возраст\")\n",
+    "plt.ylabel(\"Баллы\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "6a014195",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x=\"Категория\", y=\"Баллы\", data=df)\n",
+    "plt.title(\"Распределение баллов по категориям\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b6f918d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(df[\"Баллы\"], bins=5, kde=True)\n",
+    "plt.title(\"Распределение баллов\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "55b585e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "numeric_df = df.select_dtypes(include=[np.number])\n",
+    "\n",
+    "sns.heatmap(numeric_df.corr(), annot=True, cmap=\"coolwarm\")\n",
+    "plt.title(\"Корреляция числовых признаков\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "d7b7108a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Обработка: 100%|██████████| 30/30 [00:00<00:00, 32.44it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in tqdm(range(30), desc=\"Обработка\"):\n",
+    "    time.sleep(0.02)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1633e226",
+   "metadata": {},
+   "source": [
+    "## Анализ внешнего датасета\n",
+    "\n",
+    "В данной части был использован внешний набор данных, содержащий информацию о бюджете на образование, ВВП и их соотношении по годам.\n",
+    "\n",
+    "Был проведён базовый анализ данных и построены графики, отражающие распределение значений и зависимости между показателями."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "a1d3bd7a",
+   "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>YEAR</th>\n",
+       "      <th>BUDGET_ON_EDUCATION</th>\n",
+       "      <th>GDP</th>\n",
+       "      <th>RATIO</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1976</td>\n",
+       "      <td>9314.0</td>\n",
+       "      <td>1873412.0</td>\n",
+       "      <td>0.497</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1977</td>\n",
+       "      <td>10568.0</td>\n",
+       "      <td>2081826.0</td>\n",
+       "      <td>0.508</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1978</td>\n",
+       "      <td>11625.0</td>\n",
+       "      <td>2351599.0</td>\n",
+       "      <td>0.494</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1979</td>\n",
+       "      <td>13996.0</td>\n",
+       "      <td>2627333.0</td>\n",
+       "      <td>0.533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1980</td>\n",
+       "      <td>15209.0</td>\n",
+       "      <td>2857307.0</td>\n",
+       "      <td>0.532</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   YEAR  BUDGET_ON_EDUCATION        GDP  RATIO\n",
+       "0  1976               9314.0  1873412.0  0.497\n",
+       "1  1977              10568.0  2081826.0  0.508\n",
+       "2  1978              11625.0  2351599.0  0.494\n",
+       "3  1979              13996.0  2627333.0  0.533\n",
+       "4  1980              15209.0  2857307.0  0.532"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2 = pd.read_csv(\"data/filtered_data.csv\")\n",
+    "df2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "895dcdd9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 48 entries, 0 to 47\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   YEAR                 48 non-null     int64  \n",
+      " 1   BUDGET_ON_EDUCATION  48 non-null     float64\n",
+      " 2   GDP                  48 non-null     float64\n",
+      " 3   RATIO                48 non-null     float64\n",
+      "dtypes: float64(3), int64(1)\n",
+      "memory usage: 1.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df2.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "ca3adf87",
+   "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>YEAR</th>\n",
+       "      <th>BUDGET_ON_EDUCATION</th>\n",
+       "      <th>GDP</th>\n",
+       "      <th>RATIO</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>48.00</td>\n",
+       "      <td>48.000000</td>\n",
+       "      <td>4.800000e+01</td>\n",
+       "      <td>48.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1999.50</td>\n",
+       "      <td>64670.645833</td>\n",
+       "      <td>1.105737e+07</td>\n",
+       "      <td>0.513021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>14.00</td>\n",
+       "      <td>106058.970498</td>\n",
+       "      <td>6.920819e+06</td>\n",
+       "      <td>0.382191</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1976.00</td>\n",
+       "      <td>-100326.000000</td>\n",
+       "      <td>1.873412e+06</td>\n",
+       "      <td>-0.367000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1987.75</td>\n",
+       "      <td>19344.250000</td>\n",
+       "      <td>5.141132e+06</td>\n",
+       "      <td>0.383000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1999.50</td>\n",
+       "      <td>33581.000000</td>\n",
+       "      <td>9.941062e+06</td>\n",
+       "      <td>0.438500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2011.25</td>\n",
+       "      <td>67437.000000</td>\n",
+       "      <td>1.576329e+07</td>\n",
+       "      <td>0.513750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2023.00</td>\n",
+       "      <td>565388.000000</td>\n",
+       "      <td>2.736094e+07</td>\n",
+       "      <td>2.196000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          YEAR  BUDGET_ON_EDUCATION           GDP      RATIO\n",
+       "count    48.00            48.000000  4.800000e+01  48.000000\n",
+       "mean   1999.50         64670.645833  1.105737e+07   0.513021\n",
+       "std      14.00        106058.970498  6.920819e+06   0.382191\n",
+       "min    1976.00       -100326.000000  1.873412e+06  -0.367000\n",
+       "25%    1987.75         19344.250000  5.141132e+06   0.383000\n",
+       "50%    1999.50         33581.000000  9.941062e+06   0.438500\n",
+       "75%    2011.25         67437.000000  1.576329e+07   0.513750\n",
+       "max    2023.00        565388.000000  2.736094e+07   2.196000"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "ff5c9207",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(df2[\"BUDGET_ON_EDUCATION\"], bins=15, kde=True)\n",
+    "plt.title(\"Распределение бюджета на образование\")\n",
+    "plt.xlabel(\"Бюджет\")\n",
+    "plt.ylabel(\"Количество\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "87e124d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(df2[\"GDP\"], df2[\"BUDGET_ON_EDUCATION\"])\n",
+    "plt.xlabel(\"GDP\")\n",
+    "plt.ylabel(\"Бюджет на образование\")\n",
+    "plt.title(\"Зависимость бюджета от ВВП\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "d8f3377a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(df2[\"YEAR\"], df2[\"BUDGET_ON_EDUCATION\"], label=\"Бюджет\")\n",
+    "plt.plot(df2[\"YEAR\"], df2[\"GDP\"], label=\"GDP\")\n",
+    "plt.xlabel(\"Год\")\n",
+    "plt.ylabel(\"Значения\")\n",
+    "plt.title(\"Динамика бюджета и ВВП по годам\")\n",
+    "plt.legend()\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "75a5ceaa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Обработка данных: 100%|██████████| 48/48 [00:00<?, ?it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in tqdm(range(len(df2)), desc=\"Обработка данных\"):\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d5dd8e8",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv (3.10.11)",
+   "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": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}