diff --git a/1111/Untitled.ipynb b/1111/Untitled.ipynb
new file mode 100644
index 0000000..88b2b29
--- /dev/null
+++ b/1111/Untitled.ipynb
@@ -0,0 +1,234 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "59d7d25c-52ab-456a-a1c7-0d10884c68dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      Имя  Возраст  Баллы\n",
+      "0    Анна       21     89\n",
+      "1   Борис       22     76\n",
+      "2  Виктор       23     95\n",
+      "3  Галина       24     82\n",
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 4 entries, 0 to 3\n",
+      "Data columns (total 3 columns):\n",
+      " #   Column   Non-Null Count  Dtype\n",
+      "---  ------   --------------  -----\n",
+      " 0   Имя      4 non-null      str  \n",
+      " 1   Возраст  4 non-null      int64\n",
+      " 2   Баллы    4 non-null      int64\n",
+      "dtypes: int64(2), str(1)\n",
+      "memory usage: 228.0 bytes\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "#Создаем таблицу из твоего задания\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",
+    "#Проверяем структуру (методы из задания)\n",
+    "print(df.head())\n",
+    "print(df.info())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "abc54aaf-52d6-454e-b11e-0d39a350eedf",
+   "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": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4f4d214a-1464-454e-a1bc-13033776437c",
+   "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": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Новый столбец\"] = df[\"Баллы\"] * 1.1\n",
+    "df\n",
+    "df.groupby(\"Имя\").agg({\"Баллы\": \"mean\"})\n",
+    "df[df[\"Возраст\"] > 21]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "43f172bb-235f-465a-8b87-14f6c734bef2",
+   "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": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/1111/Untitled1.ipynb b/1111/Untitled1.ipynb
new file mode 100644
index 0000000..9eb25bc
--- /dev/null
+++ b/1111/Untitled1.ipynb
@@ -0,0 +1,86 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8b3cf142-a97b-418f-be08-18310b19211b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Матрица 2x3:\n",
+      " [[1 2 3]\n",
+      " [4 5 6]]\n",
+      "Форма (shape): (2, 3)\n",
+      "------------------------------\n",
+      "Равномерная шкала (linspace):\n",
+      " [0.         0.55555556 1.11111111 1.66666667 2.22222222 2.77777778\n",
+      " 3.33333333 3.88888889 4.44444444 5.        ]\n",
+      "------------------------------\n",
+      "Случайная матрица 2x2:\n",
+      " [[ 0.19881164 -0.06385133]\n",
+      " [-0.15785181 -1.76408788]]\n",
+      "------------------------------\n",
+      "Результат матричного умножения A и B:\n",
+      " [[19 22]\n",
+      " [43 50]]\n",
+      "------------------------------\n",
+      "Сумма по столбцам матрицы: [5 7 9]\n",
+      "Среднее по строкам матрицы: [2. 5.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "matrix = np.array([[1, 2, 3], [4, 5, 6]])\n",
+    "print(\"Матрица 2x3:\\n\", matrix)\n",
+    "print(\"Форма (shape):\", matrix.shape)\n",
+    "print(\"-\" * 30)\n",
+    "linear_space = np.linspace(0, 5, 10)\n",
+    "print(\"Равномерная шкала (linspace):\\n\", linear_space)\n",
+    "print(\"-\" * 30)\n",
+    "random_matrix = np.random.randn(2, 2)\n",
+    "print(\"Случайная матрица 2x2:\\n\", random_matrix)\n",
+    "print(\"-\" * 30)\n",
+    "A = np.array([[1, 2], [3, 4]])\n",
+    "B = np.array([[5, 6], [7, 8]])\n",
+    "dot_product = np.dot(A, B)\n",
+    "print(\"Результат матричного умножения A и B:\\n\", dot_product)\n",
+    "print(\"-\" * 30)\n",
+    "print(\"Сумма по столбцам матрицы:\", matrix.sum(axis=0))\n",
+    "print(\"Среднее по строкам матрицы:\", matrix.mean(axis=1))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b2c508b-6345-4902-8e15-44fd3c36d9f5",
+   "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": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/1111/Untitled2.ipynb b/1111/Untitled2.ipynb
new file mode 100644
index 0000000..8b64129
--- /dev/null
+++ b/1111/Untitled2.ipynb
@@ -0,0 +1,79 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "621c3a78-2532-4b6f-99b0-56484ecc51f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "x = np.linspace(0, 10, 100)\n",
+    "y_sin = np.sin(x)\n",
+    "y_cos = np.cos(x)\n",
+    "fig, axs = plt.subplots(2, 2, figsize=(12, 10))\n",
+    "axs[0, 0].plot(x, y_sin, color='red', label='sin(x)', linewidth=2)\n",
+    "axs[0, 0].plot(x, y_cos, color='blue', linestyle='--', label='cos(x)')\n",
+    "axs[0, 0].set_title(\"Линейные графики\")\n",
+    "axs[0, 0].legend()\n",
+    "axs[0, 0].grid(True)\n",
+    "x_rand = np.random.rand(50) * 10\n",
+    "y_rand = np.random.rand(50)\n",
+    "axs[0, 1].scatter(x_rand, y_rand, color='green', alpha=0.6, label='Данные')\n",
+    "axs[0, 1].set_title(\"Точечный график (Scatter)\")\n",
+    "axs[0, 1].set_xlabel(\"Параметр X\")\n",
+    "axs[0, 1].legend()\n",
+    "categories = ['A', 'B', 'C', 'D', 'E']\n",
+    "values = [5, 12, 8, 20, 15]\n",
+    "axs[1, 0].bar(categories, values, color='purple', alpha=0.7)\n",
+    "axs[1, 0].set_title(\"Столбчатая диаграмма (Bar)\")\n",
+    "data = np.random.randn(1000)\n",
+    "axs[1, 1].hist(data, bins=30, color='orange', edgecolor='black')\n",
+    "axs[1, 1].set_title(\"Гистограмма распределения (Hist)\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08cd2ced-4eb7-462b-8e8a-31c6e0e32084",
+   "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": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/1111/Untitled3.ipynb b/1111/Untitled3.ipynb
new file mode 100644
index 0000000..8fb0fff
--- /dev/null
+++ b/1111/Untitled3.ipynb
@@ -0,0 +1,106 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "587966ad-7f56-4ae2-abf9-ced1629c5dbf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Генерация Pairplot (может занять время на больших данных)...\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 839.847x750 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "data = {\n",
+    "    \"Баллы\": [89, 76, 95, 82, 70, 88, 92, 78, 85, 90],\n",
+    "    \"Возраст\": [21, 22, 23, 24, 21, 22, 23, 24, 22, 23],\n",
+    "    \"Часы_учебы\": [5, 3, 7, 4, 2, 6, 8, 3, 5, 6],\n",
+    "    \"Категория\": [\"A\", \"B\", \"A\", \"B\", \"A\", \"B\", \"A\", \"B\", \"A\", \"B\"]\n",
+    "}\n",
+    "df = pd.DataFrame(data)\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n",
+    "sns.histplot(df[\"Баллы\"], kde=True, color=\"skyblue\", ax=ax[0])\n",
+    "ax[0].set_title(\"Распределение баллов (Histplot)\")\n",
+    "sns.scatterplot(x=\"Часы_учебы\", y=\"Баллы\", hue=\"Категория\", style=\"Категория\", s=100, data=df, ax=ax[1])\n",
+    "ax[1].set_title(\"Связь часов учебы и баллов (Scatterplot)\")\n",
+    "plt.show()\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "corr_matrix = df.drop(columns=[\"Категория\"]).corr()\n",
+    "sns.heatmap(corr_matrix, annot=True, cmap=\"coolwarm\", fmt=\".2f\")\n",
+    "plt.title(\"Матрица корреляции признаков (Heatmap)\")\n",
+    "plt.show()\n",
+    "print(\"Генерация Pairplot (может занять время на больших данных)...\")\n",
+    "sns.pairplot(df, hue=\"Категория\", palette=\"husl\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2af24440-0ea8-4a29-b1af-602bc83d673d",
+   "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": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/1111/Untitled6.ipynb b/1111/Untitled6.ipynb
new file mode 100644
index 0000000..d953a9d
--- /dev/null
+++ b/1111/Untitled6.ipynb
@@ -0,0 +1,118 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4abcde78-eedd-4527-979d-f00d7d06e238",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Начинаем обработку DataFrame...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "322566ded7d94e769f950ad4e5047e4b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Анализ строк:   0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "==============================\n",
+      "\n",
+      "Запуск кастомного процесса...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "27a3ec943a1845a48c1b164574a41950",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Загрузка нейросети:   0%|          | 0/100 [00:00<?, ? слоев/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Готово!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from tqdm.auto import tqdm \n",
+    "import time\n",
+    "df = pd.DataFrame({\n",
+    "    'ID': range(1, 101),\n",
+    "    'Data': np.random.randn(100)\n",
+    "})\n",
+    "print(\"Начинаем обработку DataFrame...\")\n",
+    "for index, row in tqdm(df.iterrows(), total=df.shape[0], desc=\"Анализ строк\"):\n",
+    "    # Симуляция сложной математики\n",
+    "    time.sleep(0.02) \n",
+    "    \n",
+    "print(\"\\n\" + \"=\"*30 + \"\\n\")\n",
+    "print(\"Запуск кастомного процесса...\")\n",
+    "\n",
+    "for i in tqdm(range(100), \n",
+    "              desc='Загрузка нейросети', \n",
+    "              unit=' слоев', \n",
+    "              colour='green'):\n",
+    "    time.sleep(0.03)\n",
+    "\n",
+    "print(\"Готово!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0dd78fa7-8710-4642-8a58-38b0913c9b25",
+   "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": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/1111/Untitled7.ipynb b/1111/Untitled7.ipynb
new file mode 100644
index 0000000..198ffb1
--- /dev/null
+++ b/1111/Untitled7.ipynb
@@ -0,0 +1,612 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d534805c-6e4b-4704-b25f-3a45babbab2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'top_movies.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mFileNotFoundError\u001b[39m                         Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 10\u001b[39m\n\u001b[32m      6\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m numpy \u001b[38;5;28;01mas\u001b[39;00m np\n\u001b[32m      7\u001b[39m \n\u001b[32m      8\u001b[39m \n\u001b[32m      9\u001b[39m file_path = \u001b[33m\"top_movies.csv\"\u001b[39m\n\u001b[32m---> \u001b[39m\u001b[32m10\u001b[39m df = pd.read_csv(file_path)\n\u001b[32m     11\u001b[39m \n\u001b[32m     12\u001b[39m \n\u001b[32m     13\u001b[39m print(\u001b[33m\"=== ПЕРВИЧНЫЙ АНАЛИЗ ДАННЫХ ===\"\u001b[39m)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~\\OneDrive\\Desktop\\1111\\.venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:873\u001b[39m, in \u001b[36mread_csv\u001b[39m\u001b[34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, skip_blank_lines, parse_dates, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[39m\n\u001b[32m    861\u001b[39m kwds_defaults = _refine_defaults_read(\n\u001b[32m    862\u001b[39m     dialect,\n\u001b[32m    863\u001b[39m     delimiter,\n\u001b[32m   (...)\u001b[39m\u001b[32m    869\u001b[39m     dtype_backend=dtype_backend,\n\u001b[32m    870\u001b[39m )\n\u001b[32m    871\u001b[39m kwds.update(kwds_defaults)\n\u001b[32m--> \u001b[39m\u001b[32m873\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43m_read\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mfilepath_or_buffer\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mkwds\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~\\OneDrive\\Desktop\\1111\\.venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:300\u001b[39m, in \u001b[36m_read\u001b[39m\u001b[34m(filepath_or_buffer, kwds)\u001b[39m\n\u001b[32m    297\u001b[39m _validate_names(kwds.get(\u001b[33m\"\u001b[39m\u001b[33mnames\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[32m    299\u001b[39m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m300\u001b[39m parser = \u001b[30;43mTextFileReader\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mfilepath_or_buffer\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwds\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    302\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[32m    303\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~\\OneDrive\\Desktop\\1111\\.venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1645\u001b[39m, in \u001b[36mTextFileReader.__init__\u001b[39m\u001b[34m(self, f, engine, **kwds)\u001b[39m\n\u001b[32m   1642\u001b[39m     \u001b[38;5;28mself\u001b[39m.options[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m] = kwds[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m   1644\u001b[39m \u001b[38;5;28mself\u001b[39m.handles: IOHandles | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1645\u001b[39m \u001b[38;5;28mself\u001b[39m._engine = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_make_engine\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mf\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mengine\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~\\OneDrive\\Desktop\\1111\\.venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1904\u001b[39m, in \u001b[36mTextFileReader._make_engine\u001b[39m\u001b[34m(self, f, engine)\u001b[39m\n\u001b[32m   1902\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[32m   1903\u001b[39m         mode += \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m1904\u001b[39m \u001b[38;5;28mself\u001b[39m.handles = \u001b[30;43mget_handle\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m   1905\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mf\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1906\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mmode\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1907\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mencoding\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43moptions\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mget\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43mencoding\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mNone\u001b[39;49;00m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1908\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mcompression\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43moptions\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mget\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43mcompression\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mNone\u001b[39;49;00m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1909\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mmemory_map\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43moptions\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mget\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43mmemory_map\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mFalse\u001b[39;49;00m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1910\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mis_text\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mis_text\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1911\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43merrors\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43moptions\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mget\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43mencoding_errors\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43mstrict\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1912\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mstorage_options\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43moptions\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mget\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43mstorage_options\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mNone\u001b[39;49;00m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1913\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m   1914\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m.handles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m   1915\u001b[39m f = \u001b[38;5;28mself\u001b[39m.handles.handle\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~\\OneDrive\\Desktop\\1111\\.venv\\Lib\\site-packages\\pandas\\io\\common.py:926\u001b[39m, in \u001b[36mget_handle\u001b[39m\u001b[34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[39m\n\u001b[32m    921\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[32m    922\u001b[39m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[32m    923\u001b[39m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[32m    924\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m ioargs.encoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs.mode:\n\u001b[32m    925\u001b[39m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m926\u001b[39m         handle = \u001b[30;43mopen\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    927\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mhandle\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    928\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mioargs\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mmode\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    929\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mencoding\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mioargs\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mencoding\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    930\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43merrors\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43merrors\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    931\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mnewline\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    932\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    933\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    934\u001b[39m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[32m    935\u001b[39m         handle = \u001b[38;5;28mopen\u001b[39m(handle, ioargs.mode)\n",
+      "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: 'top_movies.csv'"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "from tqdm.auto import tqdm\n",
+    "import time\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "file_path = \"top_movies.csv\"\n",
+    "df = pd.read_csv(file_path)\n",
+    "\n",
+    "\n",
+    "print(\"=== ПЕРВИЧНЫЙ АНАЛИЗ ДАННЫХ ===\")\n",
+    "print(f\"\\nРазмерность таблицы: {df.shape}\")\n",
+    "\n",
+    "print(\"\\nПервые 5 строк:\")\n",
+    "display(df.head())\n",
+    "\n",
+    "print(\"\\nИнформация о типах данных и пропусках:\")\n",
+    "df.info()\n",
+    "\n",
+    "print(\"\\nОписательная статистика числовых признаков:\")\n",
+    "display(df.describe())\n",
+    "\n",
+    "print(\"\\nПроверка на наличие пустых значений:\")\n",
+    "print(df.isnull().sum())\n",
+    "\n",
+    "\n",
+    "print(\"\\nЗапуск процесса анализа строк (имитация)...\")\n",
+    "for index, row in tqdm(df.iterrows(), total=df.shape[0], desc=\"Анализ базы фильмов\"):\n",
+    "    time.sleep(0.001)\n",
+    "\n",
+    "sns.set_style(\"whitegrid\")\n",
+    "plt.rcParams['figure.facecolor'] = 'white'\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.histplot(df[\"vote_average\"], bins=15, kde=True, color=\"teal\", alpha=0.6)\n",
+    "plt.title(\"Распределение средних рейтингов фильмов\", fontsize=14, fontweight='bold')\n",
+    "plt.xlabel(\"Средний рейтинг (0-10)\", fontsize=12)\n",
+    "plt.ylabel(\"Количество фильмов\", fontsize=12)\n",
+    "plt.grid(axis=\"y\", linestyle=\"--\", alpha=0.5)\n",
+    "plt.show()\n",
+    "\n",
+    "plt.figure(figsize=(12, 7))\n",
+    "scatter = sns.scatterplot(\n",
+    "    data=df, \n",
+    "    x=\"popularity\", \n",
+    "    y=\"vote_average\", \n",
+    "    hue=\"vote_count\", \n",
+    "    size=\"vote_count\", \n",
+    "    palette=\"viridis\", \n",
+    "    sizes=(20, 200), \n",
+    "    alpha=0.6\n",
+    ")\n",
+    "plt.title(\"Зависимость рейтинга от популярности и числа голосов\", fontsize=14, fontweight='bold')\n",
+    "plt.xlabel(\"Популярность\", fontsize=12)\n",
+    "plt.ylabel(\"Средний рейтинг\", fontsize=12)\n",
+    "plt.legend(title=\"Кол-во голосов\", bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+    "plt.show()\n",
+    "\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "df['vote_group'] = pd.qcut(df['vote_count'], q=4, labels=['Низкое', 'Среднее', 'Высокое', 'Топ'])\n",
+    "\n",
+    "sns.boxplot(x=\"vote_group\", y=\"vote_average\", data=df, palette=\"Set3\")\n",
+    "plt.title(\"Разброс рейтингов по категориям активности зрителей\", fontsize=14, fontweight='bold')\n",
+    "plt.xlabel(\"Количество проголосовавших (группы)\", fontsize=12)\n",
+    "plt.ylabel(\"Средний рейтинг\", fontsize=12)\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"\\n=== ЗАДАНИЕ ВЫПОЛНЕНО С УСПЕХОМ ===\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f3797675-7795-45bd-8b1a-c14620de32c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'top_movies.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mFileNotFoundError\u001b[39m                         Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 10\u001b[39m\n\u001b[32m      6\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m numpy \u001b[38;5;28;01mas\u001b[39;00m np\n\u001b[32m      7\u001b[39m \n\u001b[32m      8\u001b[39m \n\u001b[32m      9\u001b[39m file_path = \u001b[33m\"top_movies.csv\"\u001b[39m\n\u001b[32m---> \u001b[39m\u001b[32m10\u001b[39m df = pd.read_csv(file_path)\n\u001b[32m     11\u001b[39m \n\u001b[32m     12\u001b[39m \n\u001b[32m     13\u001b[39m print(\u001b[33m\"=== ПЕРВИЧНЫЙ АНАЛИЗ ДАННЫХ ===\"\u001b[39m)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~\\OneDrive\\Desktop\\1111\\.venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:873\u001b[39m, in \u001b[36mread_csv\u001b[39m\u001b[34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, skip_blank_lines, parse_dates, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[39m\n\u001b[32m    861\u001b[39m kwds_defaults = _refine_defaults_read(\n\u001b[32m    862\u001b[39m     dialect,\n\u001b[32m    863\u001b[39m     delimiter,\n\u001b[32m   (...)\u001b[39m\u001b[32m    869\u001b[39m     dtype_backend=dtype_backend,\n\u001b[32m    870\u001b[39m )\n\u001b[32m    871\u001b[39m kwds.update(kwds_defaults)\n\u001b[32m--> \u001b[39m\u001b[32m873\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43m_read\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mfilepath_or_buffer\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mkwds\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~\\OneDrive\\Desktop\\1111\\.venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:300\u001b[39m, in \u001b[36m_read\u001b[39m\u001b[34m(filepath_or_buffer, kwds)\u001b[39m\n\u001b[32m    297\u001b[39m _validate_names(kwds.get(\u001b[33m\"\u001b[39m\u001b[33mnames\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[32m    299\u001b[39m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m300\u001b[39m parser = \u001b[30;43mTextFileReader\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mfilepath_or_buffer\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwds\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    302\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[32m    303\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~\\OneDrive\\Desktop\\1111\\.venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1645\u001b[39m, in \u001b[36mTextFileReader.__init__\u001b[39m\u001b[34m(self, f, engine, **kwds)\u001b[39m\n\u001b[32m   1642\u001b[39m     \u001b[38;5;28mself\u001b[39m.options[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m] = kwds[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m   1644\u001b[39m \u001b[38;5;28mself\u001b[39m.handles: IOHandles | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1645\u001b[39m \u001b[38;5;28mself\u001b[39m._engine = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_make_engine\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mf\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mengine\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~\\OneDrive\\Desktop\\1111\\.venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1904\u001b[39m, in \u001b[36mTextFileReader._make_engine\u001b[39m\u001b[34m(self, f, engine)\u001b[39m\n\u001b[32m   1902\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[32m   1903\u001b[39m         mode += \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m1904\u001b[39m \u001b[38;5;28mself\u001b[39m.handles = \u001b[30;43mget_handle\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m   1905\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mf\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1906\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mmode\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1907\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mencoding\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43moptions\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mget\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43mencoding\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mNone\u001b[39;49;00m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1908\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mcompression\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43moptions\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mget\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43mcompression\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mNone\u001b[39;49;00m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1909\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mmemory_map\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43moptions\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mget\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43mmemory_map\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mFalse\u001b[39;49;00m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1910\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mis_text\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mis_text\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1911\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43merrors\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43moptions\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mget\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43mencoding_errors\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43mstrict\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1912\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mstorage_options\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43moptions\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mget\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43mstorage_options\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mNone\u001b[39;49;00m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1913\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m   1914\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m.handles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m   1915\u001b[39m f = \u001b[38;5;28mself\u001b[39m.handles.handle\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~\\OneDrive\\Desktop\\1111\\.venv\\Lib\\site-packages\\pandas\\io\\common.py:926\u001b[39m, in \u001b[36mget_handle\u001b[39m\u001b[34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[39m\n\u001b[32m    921\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[32m    922\u001b[39m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[32m    923\u001b[39m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[32m    924\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m ioargs.encoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs.mode:\n\u001b[32m    925\u001b[39m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m926\u001b[39m         handle = \u001b[30;43mopen\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    927\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mhandle\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    928\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mioargs\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mmode\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    929\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mencoding\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mioargs\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mencoding\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    930\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43merrors\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43merrors\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    931\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mnewline\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    932\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    933\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    934\u001b[39m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[32m    935\u001b[39m         handle = \u001b[38;5;28mopen\u001b[39m(handle, ioargs.mode)\n",
+      "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: 'top_movies.csv'"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "from tqdm.auto import tqdm\n",
+    "import time\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "file_path = \"top_movies.csv\"\n",
+    "df = pd.read_csv(file_path)\n",
+    "\n",
+    "\n",
+    "print(\"=== ПЕРВИЧНЫЙ АНАЛИЗ ДАННЫХ ===\")\n",
+    "print(f\"\\nРазмерность таблицы: {df.shape}\")\n",
+    "\n",
+    "print(\"\\nПервые 5 строк:\")\n",
+    "display(df.head())\n",
+    "\n",
+    "print(\"\\nИнформация о типах данных и пропусках:\")\n",
+    "df.info()\n",
+    "\n",
+    "print(\"\\nОписательная статистика числовых признаков:\")\n",
+    "display(df.describe())\n",
+    "\n",
+    "print(\"\\nПроверка на наличие пустых значений:\")\n",
+    "print(df.isnull().sum())\n",
+    "\n",
+    "\n",
+    "print(\"\\nЗапуск процесса анализа строк (имитация)...\")\n",
+    "for index, row in tqdm(df.iterrows(), total=df.shape[0], desc=\"Анализ базы фильмов\"):\n",
+    "    time.sleep(0.001)\n",
+    "\n",
+    "sns.set_style(\"whitegrid\")\n",
+    "plt.rcParams['figure.facecolor'] = 'white'\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.histplot(df[\"vote_average\"], bins=15, kde=True, color=\"teal\", alpha=0.6)\n",
+    "plt.title(\"Распределение средних рейтингов фильмов\", fontsize=14, fontweight='bold')\n",
+    "plt.xlabel(\"Средний рейтинг (0-10)\", fontsize=12)\n",
+    "plt.ylabel(\"Количество фильмов\", fontsize=12)\n",
+    "plt.grid(axis=\"y\", linestyle=\"--\", alpha=0.5)\n",
+    "plt.show()\n",
+    "\n",
+    "plt.figure(figsize=(12, 7))\n",
+    "scatter = sns.scatterplot(\n",
+    "    data=df, \n",
+    "    x=\"popularity\", \n",
+    "    y=\"vote_average\", \n",
+    "    hue=\"vote_count\", \n",
+    "    size=\"vote_count\", \n",
+    "    palette=\"viridis\", \n",
+    "    sizes=(20, 200), \n",
+    "    alpha=0.6\n",
+    ")\n",
+    "plt.title(\"Зависимость рейтинга от популярности и числа голосов\", fontsize=14, fontweight='bold')\n",
+    "plt.xlabel(\"Популярность\", fontsize=12)\n",
+    "plt.ylabel(\"Средний рейтинг\", fontsize=12)\n",
+    "plt.legend(title=\"Кол-во голосов\", bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+    "plt.show()\n",
+    "\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "df['vote_group'] = pd.qcut(df['vote_count'], q=4, labels=['Низкое', 'Среднее', 'Высокое', 'Топ'])\n",
+    "\n",
+    "sns.boxplot(x=\"vote_group\", y=\"vote_average\", data=df, palette=\"Set3\")\n",
+    "plt.title(\"Разброс рейтингов по категориям активности зрителей\", fontsize=14, fontweight='bold')\n",
+    "plt.xlabel(\"Количество проголосовавших (группы)\", fontsize=12)\n",
+    "plt.ylabel(\"Средний рейтинг\", fontsize=12)\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"\\n=== ЗАДАНИЕ ВЫПОЛНЕНО С УСПЕХОМ ===\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f723e696-5e99-4878-9e5b-040b1be69615",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== ПЕРВИЧНЫЙ АНАЛИЗ ДАННЫХ ===\n",
+      "\n",
+      "Размерность таблицы: (25, 5)\n",
+      "\n",
+      "Первые 5 строк:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>title</th>\n",
+       "      <th>popularity</th>\n",
+       "      <th>vote_average</th>\n",
+       "      <th>vote_count</th>\n",
+       "      <th>release_year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>The Shawshank Redemption</td>\n",
+       "      <td>85.5</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>21000</td>\n",
+       "      <td>1994</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>The Godfather</td>\n",
+       "      <td>70.2</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>16000</td>\n",
+       "      <td>1972</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>The Dark Knight</td>\n",
+       "      <td>92.1</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>27000</td>\n",
+       "      <td>2008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Inception</td>\n",
+       "      <td>120.4</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>31000</td>\n",
+       "      <td>2010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Pulp Fiction</td>\n",
+       "      <td>65.8</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>23000</td>\n",
+       "      <td>1994</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      title  popularity  vote_average  vote_count  \\\n",
+       "0  The Shawshank Redemption        85.5           8.7       21000   \n",
+       "1             The Godfather        70.2           8.7       16000   \n",
+       "2           The Dark Knight        92.1           8.5       27000   \n",
+       "3                 Inception       120.4           8.3       31000   \n",
+       "4              Pulp Fiction        65.8           8.5       23000   \n",
+       "\n",
+       "   release_year  \n",
+       "0          1994  \n",
+       "1          1972  \n",
+       "2          2008  \n",
+       "3          2010  \n",
+       "4          1994  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Информация о типах данных и пропусках:\n",
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 25 entries, 0 to 24\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column        Non-Null Count  Dtype  \n",
+      "---  ------        --------------  -----  \n",
+      " 0   title         25 non-null     str    \n",
+      " 1   popularity    25 non-null     float64\n",
+      " 2   vote_average  25 non-null     float64\n",
+      " 3   vote_count    25 non-null     int64  \n",
+      " 4   release_year  25 non-null     int64  \n",
+      "dtypes: float64(2), int64(2), str(1)\n",
+      "memory usage: 1.1 KB\n",
+      "\n",
+      "Описательная статистика числовых признаков:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>popularity</th>\n",
+       "      <th>vote_average</th>\n",
+       "      <th>vote_count</th>\n",
+       "      <th>release_year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>91.828000</td>\n",
+       "      <td>8.268000</td>\n",
+       "      <td>18880.000000</td>\n",
+       "      <td>2002.560000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>70.081331</td>\n",
+       "      <td>0.268825</td>\n",
+       "      <td>5666.568627</td>\n",
+       "      <td>13.073765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>28.400000</td>\n",
+       "      <td>7.500000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>1972.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>42.500000</td>\n",
+       "      <td>8.200000</td>\n",
+       "      <td>14000.000000</td>\n",
+       "      <td>1994.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>70.200000</td>\n",
+       "      <td>8.300000</td>\n",
+       "      <td>19000.000000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>110.100000</td>\n",
+       "      <td>8.500000</td>\n",
+       "      <td>23000.000000</td>\n",
+       "      <td>2014.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>310.500000</td>\n",
+       "      <td>8.700000</td>\n",
+       "      <td>31000.000000</td>\n",
+       "      <td>2021.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       popularity  vote_average    vote_count  release_year\n",
+       "count   25.000000     25.000000     25.000000     25.000000\n",
+       "mean    91.828000      8.268000  18880.000000   2002.560000\n",
+       "std     70.081331      0.268825   5666.568627     13.073765\n",
+       "min     28.400000      7.500000  10000.000000   1972.000000\n",
+       "25%     42.500000      8.200000  14000.000000   1994.000000\n",
+       "50%     70.200000      8.300000  19000.000000   2000.000000\n",
+       "75%    110.100000      8.500000  23000.000000   2014.000000\n",
+       "max    310.500000      8.700000  31000.000000   2021.000000"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Проверка на наличие пустых значений:\n",
+      "title           0\n",
+      "popularity      0\n",
+      "vote_average    0\n",
+      "vote_count      0\n",
+      "release_year    0\n",
+      "dtype: int64\n",
+      "\n",
+      "Запуск процесса анализа строк (имитация)...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3136e9472e7b4470b45c499dfa78f82b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Анализ базы фильмов:   0%|          | 0/25 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\timsh\\AppData\\Local\\Temp\\ipykernel_24936\\2834083930.py:64: 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=\"vote_group\", y=\"vote_average\", data=df, palette=\"Set3\")\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "=== ЗАДАНИЕ ВЫПОЛНЕНО С УСПЕХОМ ===\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "from tqdm.auto import tqdm\n",
+    "import time\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "file_path = \"top_movies.csv\"\n",
+    "df = pd.read_csv(file_path)\n",
+    "\n",
+    "\n",
+    "print(\"=== ПЕРВИЧНЫЙ АНАЛИЗ ДАННЫХ ===\")\n",
+    "print(f\"\\nРазмерность таблицы: {df.shape}\")\n",
+    "\n",
+    "print(\"\\nПервые 5 строк:\")\n",
+    "display(df.head())\n",
+    "\n",
+    "print(\"\\nИнформация о типах данных и пропусках:\")\n",
+    "df.info()\n",
+    "\n",
+    "print(\"\\nОписательная статистика числовых признаков:\")\n",
+    "display(df.describe())\n",
+    "\n",
+    "print(\"\\nПроверка на наличие пустых значений:\")\n",
+    "print(df.isnull().sum())\n",
+    "\n",
+    "\n",
+    "print(\"\\nЗапуск процесса анализа строк (имитация)...\")\n",
+    "for index, row in tqdm(df.iterrows(), total=df.shape[0], desc=\"Анализ базы фильмов\"):\n",
+    "    time.sleep(0.001)\n",
+    "\n",
+    "sns.set_style(\"whitegrid\")\n",
+    "plt.rcParams['figure.facecolor'] = 'white'\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.histplot(df[\"vote_average\"], bins=15, kde=True, color=\"teal\", alpha=0.6)\n",
+    "plt.title(\"Распределение средних рейтингов фильмов\", fontsize=14, fontweight='bold')\n",
+    "plt.xlabel(\"Средний рейтинг (0-10)\", fontsize=12)\n",
+    "plt.ylabel(\"Количество фильмов\", fontsize=12)\n",
+    "plt.grid(axis=\"y\", linestyle=\"--\", alpha=0.5)\n",
+    "plt.show()\n",
+    "\n",
+    "plt.figure(figsize=(12, 7))\n",
+    "scatter = sns.scatterplot(\n",
+    "    data=df, \n",
+    "    x=\"popularity\", \n",
+    "    y=\"vote_average\", \n",
+    "    hue=\"vote_count\", \n",
+    "    size=\"vote_count\", \n",
+    "    palette=\"viridis\", \n",
+    "    sizes=(20, 200), \n",
+    "    alpha=0.6\n",
+    ")\n",
+    "plt.title(\"Зависимость рейтинга от популярности и числа голосов\", fontsize=14, fontweight='bold')\n",
+    "plt.xlabel(\"Популярность\", fontsize=12)\n",
+    "plt.ylabel(\"Средний рейтинг\", fontsize=12)\n",
+    "plt.legend(title=\"Кол-во голосов\", bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+    "plt.show()\n",
+    "\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "df['vote_group'] = pd.qcut(df['vote_count'], q=4, labels=['Низкое', 'Среднее', 'Высокое', 'Топ'])\n",
+    "\n",
+    "sns.boxplot(x=\"vote_group\", y=\"vote_average\", data=df, palette=\"Set3\")\n",
+    "plt.title(\"Разброс рейтингов по категориям активности зрителей\", fontsize=14, fontweight='bold')\n",
+    "plt.xlabel(\"Количество проголосовавших (группы)\", fontsize=12)\n",
+    "plt.ylabel(\"Средний рейтинг\", fontsize=12)\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"\\n=== ЗАДАНИЕ ВЫПОЛНЕНО С УСПЕХОМ ===\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0c8eba63-9db6-411a-876a-357aab57e8d4",
+   "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": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/1111/top_movies.csv b/1111/top_movies.csv
new file mode 100644
index 0000000..234ce2e
--- /dev/null
+++ b/1111/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