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{
"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": {
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"<div>\n",
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"<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": {},
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},
{
"name": "stdout",
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"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": {
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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
}
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