neznayhz/drygoe.ipynb
2026-05-06 21:19:10 +03:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"id": "55e5277f-ec2d-46ff-aef5-124414265326",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Первые 5 строк:\n",
" id height weight age chest_size waist_size hip_size size brand \\\n",
"0 1 165 55 25 85 68 92 S Zara \n",
"1 2 170 65 30 95 78 100 M H&M \n",
"2 3 175 75 28 100 85 105 L Adidas \n",
"3 4 160 50 22 80 65 88 XS Mango \n",
"4 5 180 85 35 110 92 110 XL Nike \n",
"\n",
" category price rating purchase_count \n",
"0 Dress 45.99 4.5 120 \n",
"1 Jeans 59.99 4.2 85 \n",
"2 T-shirt 29.99 4.7 200 \n",
"3 Blouse 39.99 4.3 95 \n",
"4 Jacket 89.99 4.6 150 \n",
"\n",
"Информация о данных:\n",
"<class 'pandas.DataFrame'>\n",
"RangeIndex: 20 entries, 0 to 19\n",
"Data columns (total 13 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 id 20 non-null int64 \n",
" 1 height 20 non-null int64 \n",
" 2 weight 20 non-null int64 \n",
" 3 age 20 non-null int64 \n",
" 4 chest_size 20 non-null int64 \n",
" 5 waist_size 20 non-null int64 \n",
" 6 hip_size 20 non-null int64 \n",
" 7 size 20 non-null str \n",
" 8 brand 20 non-null str \n",
" 9 category 20 non-null str \n",
" 10 price 20 non-null float64\n",
" 11 rating 20 non-null float64\n",
" 12 purchase_count 20 non-null int64 \n",
"dtypes: float64(2), int64(8), str(3)\n",
"memory usage: 2.2 KB\n",
"None\n",
"\n",
"Описательная статистика:\n",
" id height weight age chest_size waist_size \\\n",
"count 20.00000 20.000000 20.000000 20.000000 20.000000 20.000000 \n",
"mean 10.50000 170.250000 65.850000 28.400000 93.800000 77.600000 \n",
"std 5.91608 6.835472 12.227127 4.321306 10.262861 9.981035 \n",
"min 1.00000 158.000000 48.000000 21.000000 78.000000 63.000000 \n",
"25% 5.75000 164.750000 54.750000 25.000000 84.750000 68.000000 \n",
"50% 10.50000 170.500000 65.500000 28.500000 94.500000 77.500000 \n",
"75% 15.25000 175.250000 75.250000 31.250000 102.250000 86.250000 \n",
"max 20.00000 182.000000 90.000000 36.000000 112.000000 95.000000 \n",
"\n",
" hip_size price rating purchase_count \n",
"count 20.000000 20.000000 20.000000 20.000000 \n",
"mean 98.950000 57.640000 4.455000 113.500000 \n",
"std 8.172322 27.265314 0.208945 42.835671 \n",
"min 86.000000 24.990000 4.100000 65.000000 \n",
"25% 91.750000 39.490000 4.300000 87.250000 \n",
"50% 99.500000 47.990000 4.450000 97.500000 \n",
"75% 106.250000 68.740000 4.600000 122.500000 \n",
"max 112.000000 129.990000 4.800000 220.000000 \n",
"\n",
"Проверка пропущенных значений:\n",
"id 0\n",
"height 0\n",
"weight 0\n",
"age 0\n",
"chest_size 0\n",
"waist_size 0\n",
"hip_size 0\n",
"size 0\n",
"brand 0\n",
"category 0\n",
"price 0\n",
"rating 0\n",
"purchase_count 0\n",
"dtype: int64\n",
"\n",
"Обработка данных...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Обработка: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 522.23it/s]\n"
]
},
{
"data": {
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",
"text/plain": [
"<Figure size 1400x1000 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"==================================================\n",
"СТАТИСТИКА ПО РАЗМЕРАМ\n",
"==================================================\n",
" S: 5 товаров\n",
" L: 5 товаров\n",
" M: 4 товаров\n",
" XS: 3 товаров\n",
" XL: 1 товаров\n",
" XXL: 1 товаров\n",
" XXS: 1 товаров\n",
"\n",
"==================================================\n",
"СРЕДНИЕ ПАРАМЕТРЫ ПО РАЗМЕРАМ\n",
"==================================================\n",
" height weight chest_size waist_size\n",
"size \n",
"L 176.2 76.2 102.8 87.0\n",
"M 171.5 67.2 95.8 79.2\n",
"S 166.6 57.8 86.8 70.2\n",
"XL 180.0 85.0 110.0 92.0\n",
"XS 161.7 51.7 81.7 66.3\n",
"XXL 182.0 90.0 112.0 95.0\n",
"XXS 158.0 48.0 78.0 63.0\n",
"\n",
"==================================================\n",
"ТОП-3 БРЕНДА ПО РЕЙТИНГУ\n",
"==================================================\n",
" Adidas: 4.60\n",
" Mango: 4.53\n",
" Nike: 4.53\n",
"\n",
"==================================================\n",
"СРЕДНЯЯ ЦЕНА ПО КАТЕГОРИЯМ\n",
"==================================================\n",
" Coat: $129.99\n",
" Jacket: $94.99\n",
" Tracksuit: $89.99\n",
" Sneakers: $79.99\n",
" Jeans: $57.99\n",
" Dress: $55.49\n",
" Pants: $54.99\n",
" Sweater: $49.99\n",
" Hoodie: $44.99\n",
" Blouse: $41.49\n",
" Leggings: $39.99\n",
" Skirt: $37.99\n",
" Polo: $34.99\n",
" Shorts: $34.99\n",
" T-shirt: $27.49\n",
"\n",
"==================================================\n",
"КЛЮЧЕВЫЕ ВЫВОДЫ\n",
"==================================================\n",
" Самый популярный размер: S\n",
" Товар с наивысшим рейтингом: Mango (рейтинг: 4.8)\n",
" Самая дорогая категория: Coat ($129.99)\n",
" Самая дешевая категория: T-shirt ($27.49)\n",
"\n",
"Анализ завершен\n"
]
}
],
"source": [
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"from tqdm import tqdm\n",
"import time\n",
"import numpy as np\n",
"\n",
"# Загрузка данных из файла\n",
"file_path = \"clothing_sizes.csv\"\n",
"df = pd.read_csv(file_path)\n",
"\n",
"# Вывод информации о данных\n",
"print(\"\\nПервые 5 строк:\")\n",
"print(df.head())\n",
"\n",
"print(\"\\nИнформация о данных:\")\n",
"print(df.info())\n",
"\n",
"print(\"\\nОписательная статистика:\")\n",
"print(df.describe())\n",
"\n",
"print(\"\\nПроверка пропущенных значений:\")\n",
"print(df.isnull().sum())\n",
"\n",
"# Имитация загрузки и обработки данных\n",
"print(\"\\nОбработка данных...\")\n",
"for index, row in tqdm(df.iterrows(), total=df.shape[0], desc=\"Обработка\"):\n",
" time.sleep(0.001) \n",
"\n",
"# Настройка стиля графиков\n",
"sns.set_style(\"whitegrid\")\n",
"\n",
"# Создание сетки из 4 графиков (2 строки, 2 столбца)\n",
"fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
"fig.suptitle('Анализ данных об одежде', fontsize=16, fontweight='bold')\n",
"\n",
"# ГРАФИК 1: Распределение размеров одежды\n",
"# Преобразуем текстовые размеры в числа для правильного порядка на графике\n",
"size_mapping = {'XXS': 0, 'XS': 1, 'S': 2, 'M': 3, 'L': 4, 'XL': 5, 'XXL': 6}\n",
"df['size_numeric'] = df['size'].map(size_mapping)\n",
"\n",
"# Строим гистограмму распределения размеров\n",
"sns.histplot(data=df, x='size_numeric', bins=7, kde=True, \n",
" color='steelblue', alpha=0.7, ax=axes[0, 0])\n",
"axes[0, 0].set_title('Распределение размеров одежды', fontsize=12, fontweight='bold')\n",
"axes[0, 0].set_xlabel('Размер (XXS=0, XS=1, S=2, M=3, L=4, XL=5, XXL=6)', fontsize=10)\n",
"axes[0, 0].set_ylabel('Количество товаров', fontsize=10)\n",
"axes[0, 0].grid(axis='y', linestyle='--', alpha=0.7)\n",
"\n",
"# Добавляем числовые подписи над столбцами\n",
"counts = df['size_numeric'].value_counts().sort_index()\n",
"for i, count in enumerate(counts):\n",
" axes[0, 0].text(i, count + 1, str(count), ha='center', fontsize=9)\n",
"\n",
"# ГРАФИК 2: Зависимость роста от веса с группировкой по размерам\n",
"# Этот график показывает, как соотносятся рост и вес для разных размеров одежды\n",
"sns.scatterplot(data=df, x='height', y='weight', hue='size',\n",
" palette='coolwarm', s=80, alpha=0.7, ax=axes[0, 1])\n",
"axes[0, 1].set_title('Рост и вес покупателей по размерам', fontsize=12, fontweight='bold')\n",
"axes[0, 1].set_xlabel('Рост (см)', fontsize=10)\n",
"axes[0, 1].set_ylabel('Вес (кг)', fontsize=10)\n",
"axes[0, 1].legend(title='Размер', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
"axes[0, 1].grid(alpha=0.3)\n",
"\n",
"# ГРАФИК 3: Распределение цен по категориям одежды\n",
"# Используем boxplot для отображения медианы, квартилей и выбросов\n",
"# Исправлено: убран palette, используется стандартная цветовая схема\n",
"sns.boxplot(data=df, x='category', y='price', ax=axes[1, 0])\n",
"axes[1, 0].set_title('Цены на одежду по категориям', fontsize=12, fontweight='bold')\n",
"axes[1, 0].set_xlabel('Категория одежды', fontsize=10)\n",
"axes[1, 0].set_ylabel('Цена ($)', fontsize=10)\n",
"axes[1, 0].tick_params(axis='x', rotation=45)\n",
"axes[1, 0].grid(axis='y', linestyle='--', alpha=0.7)\n",
"\n",
"# ГРАФИК 4: Распределение рейтингов товаров\n",
"# Гистограмма с ядерной оценкой плотности показывает популярность товаров\n",
"sns.histplot(data=df, x='rating', bins=10, kde=True, \n",
" color='seagreen', alpha=0.7, ax=axes[1, 1])\n",
"axes[1, 1].set_title('Распределение рейтингов товаров', fontsize=12, fontweight='bold')\n",
"axes[1, 1].set_xlabel('Рейтинг', fontsize=10)\n",
"axes[1, 1].set_ylabel('Количество товаров', fontsize=10)\n",
"axes[1, 1].grid(axis='y', linestyle='--', alpha=0.7)\n",
"\n",
"# Автоматическая настройка отступов, чтобы графики не наезжали друг на друга\n",
"plt.tight_layout()\n",
"plt.show()\n",
"\n",
"# ВЫВОД СТАТИСТИКИ\n",
"\n",
"print(\"\\n\" + \"=\"*50)\n",
"print(\"СТАТИСТИКА ПО РАЗМЕРАМ\")\n",
"print(\"=\"*50)\n",
"size_stats = df['size'].value_counts()\n",
"for size, count in size_stats.items():\n",
" print(f\" {size}: {count} товаров\")\n",
"\n",
"print(\"\\n\" + \"=\"*50)\n",
"print(\"СРЕДНИЕ ПАРАМЕТРЫ ПО РАЗМЕРАМ\")\n",
"print(\"=\"*50)\n",
"print(df.groupby('size')[['height', 'weight', 'chest_size', 'waist_size']].mean().round(1))\n",
"\n",
"print(\"\\n\" + \"=\"*50)\n",
"print(\"ТОП-3 БРЕНДА ПО РЕЙТИНГУ\")\n",
"print(\"=\"*50)\n",
"top_brands = df.groupby('brand')['rating'].mean().sort_values(ascending=False).head(3)\n",
"for brand, rating in top_brands.items():\n",
" print(f\" {brand}: {rating:.2f}\")\n",
"\n",
"print(\"\\n\" + \"=\"*50)\n",
"print(\"СРЕДНЯЯ ЦЕНА ПО КАТЕГОРИЯМ\")\n",
"print(\"=\"*50)\n",
"avg_prices = df.groupby('category')['price'].mean().sort_values(ascending=False)\n",
"for category, price in avg_prices.items():\n",
" print(f\" {category}: ${price:.2f}\")\n",
"\n",
"print(\"\\n\" + \"=\"*50)\n",
"print(\"КЛЮЧЕВЫЕ ВЫВОДЫ\")\n",
"print(\"=\"*50)\n",
"\n",
"# Автоматический анализ данных\n",
"most_common_size = size_stats.index[0]\n",
"print(f\" Самый популярный размер: {most_common_size}\")\n",
"\n",
"best_rating_row = df.loc[df['rating'].idxmax()]\n",
"print(f\" Товар с наивысшим рейтингом: {best_rating_row.get('brand', 'Неизвестно')} (рейтинг: {best_rating_row['rating']:.1f})\")\n",
"\n",
"most_expensive_cat = avg_prices.index[0]\n",
"print(f\" Самая дорогая категория: {most_expensive_cat} (${avg_prices.iloc[0]:.2f})\")\n",
"\n",
"cheapest_cat = avg_prices.index[-1]\n",
"print(f\" Самая дешевая категория: {cheapest_cat} (${avg_prices.iloc[-1]:.2f})\")\n",
"\n",
"print(\"\\nАнализ завершен\")"
]
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