commit 82763149c2e623dd628132277be44ae2096243f7
Author: Илья Семёновых <stud203821@vyatsu.ru>
Date:   Wed May 6 17:38:17 2026 +0300

    LABA_2

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..21d0b89
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+.venv/
diff --git a/.idea/.gitignore b/.idea/.gitignore
new file mode 100644
index 0000000..26d3352
--- /dev/null
+++ b/.idea/.gitignore
@@ -0,0 +1,3 @@
+# Default ignored files
+/shelf/
+/workspace.xml
diff --git a/.idea/AC.iml b/.idea/AC.iml
new file mode 100644
index 0000000..2e31e26
--- /dev/null
+++ b/.idea/AC.iml
@@ -0,0 +1,10 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/.venv" />
+    </content>
+    <orderEntry type="jdk" jdkName="Python 3.13 (AC)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>
\ No newline at end of file
diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
new file mode 100644
index 0000000..105ce2d
--- /dev/null
+++ b/.idea/inspectionProfiles/profiles_settings.xml
@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
new file mode 100644
index 0000000..603042d
--- /dev/null
+++ b/.idea/misc.xml
@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.13" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.13 (AC)" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
new file mode 100644
index 0000000..fb7aa02
--- /dev/null
+++ b/.idea/modules.xml
@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/AC.iml" filepath="$PROJECT_DIR$/.idea/AC.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 0000000..35eb1dd
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>
\ No newline at end of file
diff --git a/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/.ipynb_checkpoints/Untitled-checkpoint.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/.ipynb_checkpoints/Untitled-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.ipynb_checkpoints/matplotlib-checkpoint.ipynb b/.ipynb_checkpoints/matplotlib-checkpoint.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/.ipynb_checkpoints/matplotlib-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.ipynb_checkpoints/numpy-checkpoint.ipynb b/.ipynb_checkpoints/numpy-checkpoint.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/.ipynb_checkpoints/numpy-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.ipynb_checkpoints/pandas-checkpoint.ipynb b/.ipynb_checkpoints/pandas-checkpoint.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/.ipynb_checkpoints/pandas-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.ipynb_checkpoints/seaborn-checkpoint.ipynb b/.ipynb_checkpoints/seaborn-checkpoint.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/.ipynb_checkpoints/seaborn-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.ipynb_checkpoints/tqdm-checkpoint.ipynb b/.ipynb_checkpoints/tqdm-checkpoint.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/.ipynb_checkpoints/tqdm-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..e69de29
diff --git a/Untitled.ipynb b/Untitled.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/Untitled.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/main.py b/main.py
new file mode 100644
index 0000000..5596b44
--- /dev/null
+++ b/main.py
@@ -0,0 +1,16 @@
+# This is a sample Python script.
+
+# Press Shift+F10 to execute it or replace it with your code.
+# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.
+
+
+def print_hi(name):
+    # Use a breakpoint in the code line below to debug your script.
+    print(f'Hi, {name}')  # Press Ctrl+F8 to toggle the breakpoint.
+
+
+# Press the green button in the gutter to run the script.
+if __name__ == '__main__':
+    print_hi('PyCharm')
+
+# See PyCharm help at https://www.jetbrains.com/help/pycharm/
diff --git a/matplotlib.ipynb b/matplotlib.ipynb
new file mode 100644
index 0000000..7bde446
--- /dev/null
+++ b/matplotlib.ipynb
@@ -0,0 +1,95 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1369b34f-eee7-4c7a-bf2d-b1e61fd633b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# Подготовка данных\n",
+    "x = np.linspace(0, 10, 100)\n",
+    "y1 = np.sin(x)\n",
+    "y2 = np.cos(x)\n",
+    "\n",
+    "# Создаем фигуру с несколькими подобластями (2 строки, 2 столбца)\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "\n",
+    "# 1. Линейный график: несколько линий и изменение цвета\n",
+    "plt.subplot(2, 2, 1)\n",
+    "plt.plot(x, y1, color='red', label='sin(x)') # Цвет изменен на красный\n",
+    "plt.plot(x, y2, color='blue', linestyle='--', label='cos(x)') # Добавлен второй график\n",
+    "plt.title(\"Линейные графики (sin и cos)\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "\n",
+    "# 2. Точечный график (scatter)\n",
+    "plt.subplot(2, 2, 2)\n",
+    "x_scatter = np.random.rand(50) * 10\n",
+    "y_scatter = np.random.rand(50)\n",
+    "plt.scatter(x_scatter, y_scatter, color='green', alpha=0.6)\n",
+    "plt.title(\"Точечный график (scatter)\")\n",
+    "\n",
+    "# 3. Столбчатая диаграмма (bar)\n",
+    "plt.subplot(2, 2, 3)\n",
+    "categories = ['A', 'B', 'C', 'D']\n",
+    "values = [15, 30, 45, 10]\n",
+    "plt.bar(categories, values, color='orange')\n",
+    "plt.title(\"Столбчатая диаграмма (bar)\")\n",
+    "\n",
+    "# 4. Гистограмма (hist)\n",
+    "plt.subplot(2, 2, 4)\n",
+    "data = np.random.randn(1000)\n",
+    "plt.hist(data, bins=30, color='purple', edgecolor='black')\n",
+    "plt.title(\"Гистограмма распределения (hist)\")\n",
+    "\n",
+    "# Автоматическое выравнивание и отображение\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39b178cd-f956-4bb0-89e3-8da6a7e0b07a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/numpy.ipynb b/numpy.ipynb
new file mode 100644
index 0000000..ac7d70d
--- /dev/null
+++ b/numpy.ipynb
@@ -0,0 +1,107 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "dee9612a-be34-4974-83a7-19463c8731cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- 1. Двумерный массив ---\n",
+      "[[1 2]\n",
+      " [3 4]]\n",
+      "\n",
+      "--- 2. Равномерные интервалы (linspace) ---\n",
+      "[0.         0.55555556 1.11111111 1.66666667 2.22222222 2.77777778\n",
+      " 3.33333333 3.88888889 4.44444444 5.        ]\n",
+      "\n",
+      "--- 3. Случайные числа (randn) ---\n",
+      "[[-0.21233837 -0.06728663]\n",
+      " [-0.00379929  1.34918302]]\n",
+      "\n",
+      "--- 4. Результат np.dot() ---\n",
+      "[[-0.21993694  2.63107942]\n",
+      " [-0.65221224  5.19487221]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.21993694,  2.63107942],\n",
+       "       [-0.65221224,  5.19487221]])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# 1. Создание двумерного массива (матрицы 2x2)\n",
+    "matrix_2x2 = np.array([[1, 2], [3, 4]])\n",
+    "\n",
+    "# 2. Использование np.linspace() \n",
+    "# Создаем 10 равномерно распределенных чисел от 0 до 5\n",
+    "lin_points = np.linspace(0, 5, 10)\n",
+    "\n",
+    "# 3. Использование np.random.randn()\n",
+    "# Генерируем случайную матрицу 2x2 из нормального распределения\n",
+    "random_matrix = np.random.randn(2, 2)\n",
+    "\n",
+    "# 4. Использование np.dot()\n",
+    "# Выполняем умножение двух матриц (нашей первой матрицы и случайной)\n",
+    "matrix_product = np.dot(matrix_2x2, random_matrix)\n",
+    "\n",
+    "# Вывод всех результатов\n",
+    "print(\"--- 1. Двумерный массив ---\")\n",
+    "print(matrix_2x2)\n",
+    "\n",
+    "print(\"\\n--- 2. Равномерные интервалы (linspace) ---\")\n",
+    "print(lin_points)\n",
+    "\n",
+    "print(\"\\n--- 3. Случайные числа (randn) ---\")\n",
+    "print(random_matrix)\n",
+    "\n",
+    "print(\"\\n--- 4. Результат np.dot() ---\")\n",
+    "print(matrix_product)\n",
+    "\n",
+    "# Последняя строчка для отображения в интерактивной среде\n",
+    "matrix_product"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e65894d4-712a-4dab-90ff-15e3b17cfcc1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pandas.ipynb b/pandas.ipynb
new file mode 100644
index 0000000..f1312ea
--- /dev/null
+++ b/pandas.ipynb
@@ -0,0 +1,160 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "42a5aacc-5e39-4262-99dc-f4fc3a2c58a4",
+   "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>Категория</th>\n",
+       "      <th>Прогноз_балла</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>A</td>\n",
+       "      <td>106.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Борис</td>\n",
+       "      <td>22</td>\n",
+       "      <td>76</td>\n",
+       "      <td>B</td>\n",
+       "      <td>91.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Виктор</td>\n",
+       "      <td>23</td>\n",
+       "      <td>95</td>\n",
+       "      <td>A</td>\n",
+       "      <td>114.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Галина</td>\n",
+       "      <td>24</td>\n",
+       "      <td>82</td>\n",
+       "      <td>B</td>\n",
+       "      <td>98.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Дмитрий</td>\n",
+       "      <td>21</td>\n",
+       "      <td>91</td>\n",
+       "      <td>A</td>\n",
+       "      <td>109.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Елена</td>\n",
+       "      <td>25</td>\n",
+       "      <td>88</td>\n",
+       "      <td>C</td>\n",
+       "      <td>105.6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Имя  Возраст  Баллы Категория  Прогноз_балла\n",
+       "0     Анна       21     89         A          106.8\n",
+       "1    Борис       22     76         B           91.2\n",
+       "2   Виктор       23     95         A          114.0\n",
+       "3   Галина       24     82         B           98.4\n",
+       "4  Дмитрий       21     91         A          109.2\n",
+       "5    Елена       25     88         C          105.6"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Создаем расширенный DataFrame (с добавлением категории)\n",
+    "data = {\n",
+    "    \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\", \"Дмитрий\", \"Елена\"],\n",
+    "    \"Возраст\": [21, 22, 23, 24, 21, 25],\n",
+    "    \"Баллы\": [89, 76, 95, 82, 91, 88],\n",
+    "    \"Категория\": [\"A\", \"B\", \"A\", \"B\", \"A\", \"C\"]\n",
+    "}\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "# 1. Добавляем новый столбец с коэффициентом 1.2 (отличие от примера)\n",
+    "df[\"Прогноз_балла\"] = df[\"Баллы\"] * 1.2\n",
+    "\n",
+    "# 2. Группировка данных по категории\n",
+    "grouped = df.groupby(\"Категория\").agg({\"Баллы\": [\"mean\", \"max\"]})\n",
+    "\n",
+    "# 3. Фильтрация\n",
+    "filtered_df = df[df[\"Баллы\"] > 85]\n",
+    "\n",
+    "# Вывод результата в последней строке\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5d0af1f0-3cd7-4720-b1e3-361b88b300d6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/seaborn.ipynb b/seaborn.ipynb
new file mode 100644
index 0000000..ae262dc
--- /dev/null
+++ b/seaborn.ipynb
@@ -0,0 +1,131 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "edf5661e-5b5d-4d56-bfce-8e3c3f8110b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_9176\\2582512541.py:40: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.boxplot(x=\"Категория\", y=\"Баллы\", data=df, palette=\"Set2\")\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1000 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Генерация pairplot...\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 589.847x500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# 1. Подготовка данных (воссоздаем df из первого шага)\n",
+    "data = {\n",
+    "    \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\", \"Дмитрий\", \"Елена\"],\n",
+    "    \"Возраст\": [21, 22, 23, 24, 21, 23],\n",
+    "    \"Баллы\": [89, 76, 95, 82, 70, 88]\n",
+    "}\n",
+    "df = pd.DataFrame(data)\n",
+    "df[\"Категория\"] = [\"A\", \"B\", \"A\", \"B\", \"A\", \"B\"]\n",
+    "\n",
+    "# Настройка стиля Seaborn для красивых графиков\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "\n",
+    "# Создаем большую область для графиков\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "\n",
+    "# --- Попробуйте: histplot ---\n",
+    "plt.subplot(2, 2, 1)\n",
+    "sns.histplot(data=df, x=\"Баллы\", kde=True, color=\"skyblue\")\n",
+    "plt.title(\"Распределение баллов (histplot)\")\n",
+    "\n",
+    "# --- Попробуйте: scatterplot ---\n",
+    "plt.subplot(2, 2, 2)\n",
+    "sns.scatterplot(data=df, x=\"Возраст\", y=\"Баллы\", hue=\"Категория\", s=100)\n",
+    "plt.title(\"Взаимосвязь возраста и баллов (scatterplot)\")\n",
+    "\n",
+    "# --- Попробуйте: heatmap (тепловая карта корреляции) ---\n",
+    "# Для корреляции берем только числовые столбцы\n",
+    "plt.subplot(2, 2, 3)\n",
+    "numeric_df = df.select_dtypes(include=[np.number])\n",
+    "sns.heatmap(numeric_df.corr(), annot=True, cmap=\"coolwarm\", fmt=\".2f\")\n",
+    "plt.title(\"Корреляция признаков (heatmap)\")\n",
+    "\n",
+    "# --- Попробуйте: boxplot (из примера на скриншоте) ---\n",
+    "plt.subplot(2, 2, 4)\n",
+    "sns.boxplot(x=\"Категория\", y=\"Баллы\", data=df, palette=\"Set2\")\n",
+    "plt.title(\"Разброс баллов по категориям (boxplot)\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# --- Попробуйте: pairplot ---\n",
+    "# Этот метод создает отдельное окно с матрицей графиков для всех пар признаков\n",
+    "print(\"Генерация pairplot...\")\n",
+    "sns.pairplot(df, hue=\"Категория\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bcef3b6f-ba23-458d-ab1f-c43d228c83b5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/top_movies.csv b/top_movies.csv
new file mode 100644
index 0000000..7c8b932
--- /dev/null
+++ b/top_movies.csv
@@ -0,0 +1,26 @@
+title,popularity,vote_average,vote_count,release_year
+The Shawshank Redemption,85.5,8.7,21000,1994
+The Godfather,70.2,8.7,16000,1972
+The Dark Knight,92.1,8.5,27000,2008
+Inception,120.4,8.3,31000,2010
+Pulp Fiction,65.8,8.5,23000,1994
+Interstellar,150.2,8.3,28000,2014
+The Matrix,75.4,8.2,24000,1999
+Forrest Gump,55.9,8.2,22000,1994
+Avengers: Endgame,250.7,8.3,20000,2019
+Spider-Man: No Way Home,310.5,8.1,15000,2021
+Parasite,45.3,8.5,12000,2019
+The Lion King,35.2,8.2,14000,1994
+Fight Club,60.1,8.4,24000,1999
+Spirited Away,38.7,8.5,11000,2001
+Gladiator,42.5,8.2,15000,2000
+Joker,180.3,8.2,19000,2019
+The Green Mile,30.2,8.5,13000,1999
+Titanic,110.1,7.9,21000,1997
+Avatar,140.8,7.5,25000,2009
+The Wolf of Wall Street,95.4,8.0,18000,2013
+Star Wars: A New Hope,50.2,8.2,17000,1977
+Mad Max: Fury Road,88.1,8.1,19000,2015
+La La Land,40.5,7.9,14000,2016
+The Silence of the Lambs,33.2,8.3,13000,1991
+Goodfellas,28.4,8.5,10000,1990
\ No newline at end of file
diff --git a/tqdm.ipynb b/tqdm.ipynb
new file mode 100644
index 0000000..b3c525d
--- /dev/null
+++ b/tqdm.ipynb
@@ -0,0 +1,166 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6ac02338-6e18-4fbc-874a-67d98f58cd38",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Запуск обработки данных с tqdm...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Обработка строк: 100%|\u001b[32m███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\u001b[0m| 1000/1000 [00:05<00:00, 174.99row/s]\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Обработка завершена!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Общий прогресс:   0%|                                                                                                                                                                                        | 0/5 [00:00<?, ?it/s]\n",
+      "Этап 1:   0%|                                                                                                                                                                                              | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Этап 1:  10%|██████████████████                                                                                                                                                                   | 10/100 [00:00<00:00, 94.51it/s]\u001b[A\n",
+      "Этап 1:  20%|████████████████████████████████████▏                                                                                                                                                | 20/100 [00:00<00:00, 93.67it/s]\u001b[A\n",
+      "Этап 1:  30%|██████████████████████████████████████████████████████▎                                                                                                                              | 30/100 [00:00<00:00, 93.27it/s]\u001b[A\n",
+      "Этап 1:  40%|████████████████████████████████████████████████████████████████████████▍                                                                                                            | 40/100 [00:00<00:00, 93.07it/s]\u001b[A\n",
+      "Этап 1:  50%|██████████████████████████████████████████████████████████████████████████████████████████▌                                                                                          | 50/100 [00:00<00:00, 92.95it/s]\u001b[A\n",
+      "Этап 1:  60%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                        | 60/100 [00:00<00:00, 92.65it/s]\u001b[A\n",
+      "Этап 1:  70%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                      | 70/100 [00:00<00:00, 91.36it/s]\u001b[A\n",
+      "Этап 1:  80%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                    | 80/100 [00:00<00:00, 91.22it/s]\u001b[A\n",
+      "Этап 1:  90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                  | 90/100 [00:00<00:00, 91.55it/s]\u001b[A\n",
+      "Этап 1: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 92.34it/s]\u001b[A\n",
+      "Общий прогресс:  20%|███████████████████████████████████▏                                                                                                                                            | 1/5 [00:01<00:04,  1.09s/it]\u001b[A\n",
+      "Этап 2:   0%|                                                                                                                                                                                              | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Этап 2:  10%|██████████████████                                                                                                                                                                   | 10/100 [00:00<00:00, 93.73it/s]\u001b[A\n",
+      "Этап 2:  20%|████████████████████████████████████▏                                                                                                                                                | 20/100 [00:00<00:00, 94.35it/s]\u001b[A\n",
+      "Этап 2:  30%|██████████████████████████████████████████████████████▎                                                                                                                              | 30/100 [00:00<00:00, 95.35it/s]\u001b[A\n",
+      "Этап 2:  40%|████████████████████████████████████████████████████████████████████████▍                                                                                                            | 40/100 [00:00<00:00, 94.90it/s]\u001b[A\n",
+      "Этап 2:  50%|██████████████████████████████████████████████████████████████████████████████████████████▌                                                                                          | 50/100 [00:00<00:00, 94.21it/s]\u001b[A\n",
+      "Этап 2:  60%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                        | 60/100 [00:00<00:00, 93.83it/s]\u001b[A\n",
+      "Этап 2:  70%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                      | 70/100 [00:00<00:00, 93.05it/s]\u001b[A\n",
+      "Этап 2:  80%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                    | 80/100 [00:00<00:00, 93.72it/s]\u001b[A\n",
+      "Этап 2:  90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                  | 90/100 [00:00<00:00, 92.32it/s]\u001b[A\n",
+      "Этап 2: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 92.27it/s]\u001b[A\n",
+      "Общий прогресс:  40%|██████████████████████████████████████████████████████████████████████▍                                                                                                         | 2/5 [00:02<00:03,  1.08s/it]\u001b[A\n",
+      "Этап 3:   0%|                                                                                                                                                                                              | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Этап 3:  10%|██████████████████                                                                                                                                                                   | 10/100 [00:00<00:00, 94.59it/s]\u001b[A\n",
+      "Этап 3:  20%|████████████████████████████████████▏                                                                                                                                                | 20/100 [00:00<00:00, 92.75it/s]\u001b[A\n",
+      "Этап 3:  30%|██████████████████████████████████████████████████████▎                                                                                                                              | 30/100 [00:00<00:00, 93.11it/s]\u001b[A\n",
+      "Этап 3:  40%|████████████████████████████████████████████████████████████████████████▍                                                                                                            | 40/100 [00:00<00:00, 93.59it/s]\u001b[A\n",
+      "Этап 3:  50%|██████████████████████████████████████████████████████████████████████████████████████████▌                                                                                          | 50/100 [00:00<00:00, 93.34it/s]\u001b[A\n",
+      "Этап 3:  60%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                        | 60/100 [00:00<00:00, 92.72it/s]\u001b[A\n",
+      "Этап 3:  70%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                      | 70/100 [00:00<00:00, 92.58it/s]\u001b[A\n",
+      "Этап 3:  80%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                    | 80/100 [00:00<00:00, 93.01it/s]\u001b[A\n",
+      "Этап 3:  90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                  | 90/100 [00:00<00:00, 93.08it/s]\u001b[A\n",
+      "Этап 3: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 91.88it/s]\u001b[A\n",
+      "Общий прогресс:  60%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                      | 3/5 [00:03<00:02,  1.08s/it]\u001b[A\n",
+      "Этап 4:   0%|                                                                                                                                                                                              | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Этап 4:  10%|██████████████████                                                                                                                                                                   | 10/100 [00:00<00:00, 94.63it/s]\u001b[A\n",
+      "Этап 4:  20%|████████████████████████████████████▏                                                                                                                                                | 20/100 [00:00<00:00, 93.68it/s]\u001b[A\n",
+      "Этап 4:  30%|██████████████████████████████████████████████████████▎                                                                                                                              | 30/100 [00:00<00:00, 93.20it/s]\u001b[A\n",
+      "Этап 4:  40%|████████████████████████████████████████████████████████████████████████▍                                                                                                            | 40/100 [00:00<00:00, 92.15it/s]\u001b[A\n",
+      "Этап 4:  50%|██████████████████████████████████████████████████████████████████████████████████████████▌                                                                                          | 50/100 [00:00<00:00, 92.53it/s]\u001b[A\n",
+      "Этап 4:  60%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                        | 60/100 [00:00<00:00, 92.56it/s]\u001b[A\n",
+      "Этап 4:  70%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                      | 70/100 [00:00<00:00, 92.67it/s]\u001b[A\n",
+      "Этап 4:  80%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                    | 80/100 [00:00<00:00, 92.54it/s]\u001b[A\n",
+      "Этап 4:  90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                  | 90/100 [00:00<00:00, 92.48it/s]\u001b[A\n",
+      "Этап 4: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 92.38it/s]\u001b[A\n",
+      "Общий прогресс:  80%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                   | 4/5 [00:04<00:01,  1.08s/it]\u001b[A\n",
+      "Этап 5:   0%|                                                                                                                                                                                              | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Этап 5:  10%|██████████████████                                                                                                                                                                   | 10/100 [00:00<00:00, 93.30it/s]\u001b[A\n",
+      "Этап 5:  20%|████████████████████████████████████▏                                                                                                                                                | 20/100 [00:00<00:00, 92.38it/s]\u001b[A\n",
+      "Этап 5:  30%|██████████████████████████████████████████████████████▎                                                                                                                              | 30/100 [00:00<00:00, 92.08it/s]\u001b[A\n",
+      "Этап 5:  40%|████████████████████████████████████████████████████████████████████████▍                                                                                                            | 40/100 [00:00<00:00, 92.83it/s]\u001b[A\n",
+      "Этап 5:  50%|██████████████████████████████████████████████████████████████████████████████████████████▌                                                                                          | 50/100 [00:00<00:00, 92.51it/s]\u001b[A\n",
+      "Этап 5:  60%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                        | 60/100 [00:00<00:00, 92.76it/s]\u001b[A\n",
+      "Этап 5:  70%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                      | 70/100 [00:00<00:00, 92.60it/s]\u001b[A\n",
+      "Этап 5:  80%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                    | 80/100 [00:00<00:00, 92.75it/s]\u001b[A\n",
+      "Этап 5:  90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                  | 90/100 [00:00<00:00, 93.10it/s]\u001b[A\n",
+      "Этап 5: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 92.48it/s]\u001b[A\n",
+      "Общий прогресс: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:05<00:00,  1.08s/it]\u001b[A\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from tqdm import tqdm\n",
+    "import time\n",
+    "\n",
+    "# 1. Подготовка данных (создаем DataFrame на 1000 строк)\n",
+    "df = pd.DataFrame({\n",
+    "    'ID': range(1000),\n",
+    "    'Data': np.random.randn(1000)\n",
+    "})\n",
+    "\n",
+    "print(\"Запуск обработки данных с tqdm...\")\n",
+    "\n",
+    "# 2. Использование tqdm для итерации по DataFrame (iterrows)\n",
+    "# Добавляем кастомную стилизацию через аргументы:\n",
+    "# desc — описание процесса\n",
+    "# unit — единица измерения\n",
+    "# colour — цвет бара (поддерживается в современных терминалах/ноутбуках)\n",
+    "for index, row in tqdm(df.iterrows(), \n",
+    "                       total=df.shape[0], \n",
+    "                       desc=\"Обработка строк\", \n",
+    "                       unit=\"row\", \n",
+    "                       colour=\"green\"):\n",
+    "    \n",
+    "    # Симуляция сложной обработки данных\n",
+    "    time.sleep(0.005) \n",
+    "    _ = row['Data'] ** 2\n",
+    "\n",
+    "print(\"\\nОбработка завершена!\")\n",
+    "\n",
+    "# 3. Пример с вложенным циклом и кастомным описанием\n",
+    "for i in tqdm(range(5), desc=\"Общий прогресс\", position=0):\n",
+    "    for j in tqdm(range(100), desc=f\"Этап {i+1}\", position=1, leave=False):\n",
+    "        time.sleep(0.01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "991a6680-77de-40b0-a078-c595653cc3ed",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}