AIM-PIbd-31-Medvedkov-A-D/Lab_1/lab1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Импорт библиотек и загрузка датасета"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:4: SyntaxWarning: invalid escape sequence '\\j'\n",
      "<>:4: SyntaxWarning: invalid escape sequence '\\j'\n",
      "C:\\Users\\MaD\\AppData\\Local\\Temp\\ipykernel_13404\\161500005.py:4: SyntaxWarning: invalid escape sequence '\\j'\n",
      "  df = pd.read_csv(\"../data\\jio_mart_items.csv\")\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv(\"../data\\jio_mart_items.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Изучение полученных данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 162313 entries, 0 to 162312\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   category      162313 non-null  object \n",
      " 1   sub_category  162313 non-null  object \n",
      " 2   href          162313 non-null  object \n",
      " 3   items         162280 non-null  object \n",
      " 4   price         162282 non-null  float64\n",
      "dtypes: float64(1), object(4)\n",
      "memory usage: 6.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()\n",
    "\n",
    "# print(df.describe().transpose())\n",
    "\n",
    "# cleared_df = df.drop([\"Name\", \"Ticket\", \"Embarked\"], axis=1)\n",
    "# print(cleared_df.head())\n",
    "# print(cleared_df.tail())\n",
    "\n",
    "# sorted_df = cleared_df.sort_values(by=\"Age\")\n",
    "# print(sorted_df.head())\n",
    "# print(sorted_df.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Захотел посмотреть кол-во подкатегорий."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "72\n"
     ]
    }
   ],
   "source": [
    "print(len(set(df[\"sub_category\"])))\n",
    "\n",
    "# print(df.sort_values(by=\"category\")[[\"category\", \"sub_category\"]])\n",
    "\n",
    "# print(df.loc[100])\n",
    "\n",
    "# print(df.loc[100, \"Name\"])\n",
    "\n",
    "# print(df.loc[100:200, [\"Age\", \"Name\"]])\n",
    "\n",
    "# print(df[0:3])\n",
    "\n",
    "# print(df.iloc[0])\n",
    "\n",
    "# print(df.iloc[3:5, 0:2])\n",
    "\n",
    "# print(df.iloc[[3, 4], [0, 1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "avg_price_by_category = df.groupby('category')['price'].mean()\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "avg_price_by_category.plot(kind='bar', color='skyblue')\n",
    "plt.title('Средняя цена по категории')\n",
    "plt.xlabel('Категория')\n",
    "plt.ylabel('Средняя цена')\n",
    "plt.xticks(rotation=0)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данная диаграмма отображает среднюю цену по категории и иллюстрирует очевидный факт: электроника и ювелирные украшения стоят больше всего."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "category_counts = df['category'].value_counts()\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(category_counts, labels=category_counts.index, autopct='%1.1f%%', colors=plt.cm.Paired.colors)\n",
    "plt.title('Распространенность каждой категории')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данная диаграмма отображает распространённость каждой категории"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categories = df['category'].unique()\n",
    "data = [df[df['category'] == cat]['price'].dropna() for cat in categories]\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.boxplot(data, vert=True, patch_artist=True, boxprops=dict(facecolor=\"lightblue\"))\n",
    "\n",
    "plt.title('Разброс цен по категориям')\n",
    "plt.xlabel('Категория')\n",
    "plt.ylabel('Цена')\n",
    "\n",
    "plt.ylim(0, 10000) \n",
    "\n",
    "plt.xticks(ticks=range(1, len(categories) + 1), labels=categories)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данная диаграмма отображает разброс цен по категориям"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}