283 lines
165 KiB
Plaintext
283 lines
165 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Начало лабораторной работы №1\n",
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"\n",
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"*Набор данных для анализа продуктов Jio Mart*\n",
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"\n",
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"Выгрузка данных из CSV файла в датафрейм"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>category</th>\n",
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" <th>sub_category</th>\n",
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" <th>href</th>\n",
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" <th>items</th>\n",
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" <th>price</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>Groceries</td>\n",
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" <td>Fruits & Vegetables</td>\n",
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" <td>https://www.jiomart.com/c/groceries/fruits-veg...</td>\n",
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" <td>Fresh Dates (Pack) (Approx 450 g - 500 g)</td>\n",
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" <td>109.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>Groceries</td>\n",
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" <td>Fruits & Vegetables</td>\n",
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" <td>https://www.jiomart.com/c/groceries/fruits-veg...</td>\n",
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" <td>Tender Coconut Cling Wrapped (1 pc) (Approx 90...</td>\n",
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" <td>49.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>Groceries</td>\n",
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" <td>Fruits & Vegetables</td>\n",
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" <td>https://www.jiomart.com/c/groceries/fruits-veg...</td>\n",
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" <td>Mosambi 1 kg</td>\n",
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" <td>69.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>Groceries</td>\n",
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" <td>Fruits & Vegetables</td>\n",
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" <td>https://www.jiomart.com/c/groceries/fruits-veg...</td>\n",
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" <td>Orange Imported 1 kg</td>\n",
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" <td>125.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>Groceries</td>\n",
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" <td>Fruits & Vegetables</td>\n",
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" <td>https://www.jiomart.com/c/groceries/fruits-veg...</td>\n",
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" <td>Banana Robusta 6 pcs (Box) (Approx 800 g - 110...</td>\n",
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" <td>44.0</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" category sub_category \\\n",
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"0 Groceries Fruits & Vegetables \n",
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"1 Groceries Fruits & Vegetables \n",
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"2 Groceries Fruits & Vegetables \n",
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"3 Groceries Fruits & Vegetables \n",
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"4 Groceries Fruits & Vegetables \n",
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"\n",
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" href \\\n",
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"0 https://www.jiomart.com/c/groceries/fruits-veg... \n",
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"1 https://www.jiomart.com/c/groceries/fruits-veg... \n",
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"2 https://www.jiomart.com/c/groceries/fruits-veg... \n",
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"3 https://www.jiomart.com/c/groceries/fruits-veg... \n",
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"4 https://www.jiomart.com/c/groceries/fruits-veg... \n",
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"\n",
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" items price \n",
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"0 Fresh Dates (Pack) (Approx 450 g - 500 g) 109.0 \n",
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"1 Tender Coconut Cling Wrapped (1 pc) (Approx 90... 49.0 \n",
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"2 Mosambi 1 kg 69.0 \n",
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"3 Orange Imported 1 kg 125.0 \n",
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"4 Banana Robusta 6 pcs (Box) (Approx 800 g - 110... 44.0 "
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]
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},
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"execution_count": 22,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# Загрузка данных\n",
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"df = pd.read_csv(\".//static//csv//jio_mart_items.csv\")\n",
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"\n",
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"# Срез данных, первые 15000 строк\n",
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"df = df.iloc[:15000]\n",
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"\n",
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"# Вывод столбцов\n",
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"df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Построение диаграмм\n",
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"\n",
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"## Соотношение количества подкатегорий"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
|
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"<Figure size 800x800 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"subcategory_counts = df['sub_category'].value_counts()\n",
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"plt.figure(figsize=(8, 8))\n",
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"subcategory_counts.plot(kind='pie', autopct='%1.1f%%', startangle=30)\n",
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"plt.title('Соотношение количества подкатегорий')\n",
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"plt.ylabel('')\n",
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"plt.axis('equal')\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Данная диаграмма отображает соотношение количества подкатегорий в датасете, представленный в виде круговой диаграммы. Каждый сектор диаграммы показывает процентное соотношение каждой подкатегории к общему количеству. Это позволяет сделать вывод о том, какие подкатегории являются наиболее распространёнными, а также выявить менее популярные подкатегории."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Количество продуктов по подкатегориям"
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]
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},
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{
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"cell_type": "code",
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|
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"execution_count": 25,
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"metadata": {},
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"outputs": [
|
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{
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"data": {
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|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"category_counts = df['sub_category'].value_counts()\n",
|
|||
|
|
"plt.figure(figsize=(12, 6))\n",
|
|||
|
|
"category_counts.plot(kind='bar', color='orange', edgecolor='black')\n",
|
|||
|
|
"plt.title('Количество продуктов по подкатегориям')\n",
|
|||
|
|
"plt.xlabel('Подкатегория')\n",
|
|||
|
|
"plt.ylabel('Количество продуктов')\n",
|
|||
|
|
"plt.xticks(rotation=45)\n",
|
|||
|
|
"plt.grid(axis='y')\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Данная диаграмма отображает количество продуктов по подкатегориям, представленное в виде столбчатой диаграммы. Из диаграммы можно сделать вывод о том, что распределение продуктов по подкатегориям неравномерно, что может указывать на предпочтения потребителей или ассортимент, представленный в магазине."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"## Цены товаров в подкатегориях"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 26,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1000x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"df_subset = df.iloc[:2000] \n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.figure(figsize=(10, 6))\n",
|
|||
|
|
"plt.scatter(df_subset['price'], df_subset['sub_category'], c='green', alpha=0.5)\n",
|
|||
|
|
"plt.title('Цены товаров в подкатегориях')\n",
|
|||
|
|
"plt.xlabel('Цена')\n",
|
|||
|
|
"plt.ylabel('Подкатегория')\n",
|
|||
|
|
"plt.tight_layout()\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Данная диаграмма отображает распределение цен товаров по подкатегориям, что позволяет сделать вывод о том, что существуют значительные различия в ценах среди различных подкатегорий. Данная диаграмма позволяет сделать вывод о том, что существует разнообразие цен в разных подкатегориях товаров, а также может помочь выявить тенденции, такие как наличие более дорогих или дешевых товаров в определенных подкатегориях."
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "aimenv",
|
|||
|
|
"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": "3.9.7"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 2
|
|||
|
|
}
|