rest/rest_mow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Исследование рынка заведений общественного питания Москвы"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вы решили открыть небольшое кафе в Москве. Оно оригинальное — гостей должны обслуживать роботы. Проект многообещающий, но дорогой. Вместе с партнёрами вы решились обратиться к инвесторам. Их интересует текущее положение дел на рынке — сможете ли вы снискать популярность на долгое время, когда все зеваки насмотрятся на роботов-официантов?\n",
    "Вы — гуру аналитики, и партнёры просят вас подготовить исследование рынка. У вас есть открытые данные о заведениях общественного питания в Москве."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Загрузка данных и подготовка к анализу"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats as st\n",
    "import math\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>id</th>\n",
       "      <th>object_name</th>\n",
       "      <th>chain</th>\n",
       "      <th>object_type</th>\n",
       "      <th>address</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>151635</td>\n",
       "      <td>СМЕТАНА</td>\n",
       "      <td>нет</td>\n",
       "      <td>кафе</td>\n",
       "      <td>город Москва, улица Егора Абакумова, дом 9</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>77874</td>\n",
       "      <td>Родник</td>\n",
       "      <td>нет</td>\n",
       "      <td>кафе</td>\n",
       "      <td>город Москва, улица Талалихина, дом 2/1, корпус 1</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24309</td>\n",
       "      <td>Кафе «Академия»</td>\n",
       "      <td>нет</td>\n",
       "      <td>кафе</td>\n",
       "      <td>город Москва, Абельмановская улица, дом 6</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21894</td>\n",
       "      <td>ПИЦЦЕТОРИЯ</td>\n",
       "      <td>да</td>\n",
       "      <td>кафе</td>\n",
       "      <td>город Москва, Абрамцевская улица, дом 1</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>119365</td>\n",
       "      <td>Кафе «Вишневая метель»</td>\n",
       "      <td>нет</td>\n",
       "      <td>кафе</td>\n",
       "      <td>город Москва, Абрамцевская улица, дом 9, корпус 1</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id             object_name chain object_type  \\\n",
       "0  151635                 СМЕТАНА   нет        кафе   \n",
       "1   77874                  Родник   нет        кафе   \n",
       "2   24309         Кафе «Академия»   нет        кафе   \n",
       "3   21894              ПИЦЦЕТОРИЯ    да        кафе   \n",
       "4  119365  Кафе «Вишневая метель»   нет        кафе   \n",
       "\n",
       "                                             address  number  \n",
       "0         город Москва, улица Егора Абакумова, дом 9      48  \n",
       "1  город Москва, улица Талалихина, дом 2/1, корпус 1      35  \n",
       "2          город Москва, Абельмановская улица, дом 6      95  \n",
       "3            город Москва, Абрамцевская улица, дом 1      40  \n",
       "4  город Москва, Абрамцевская улица, дом 9, корпус 1      50  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/datasets/rest_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15366 entries, 0 to 15365\n",
      "Data columns (total 6 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   id           15366 non-null  int64 \n",
      " 1   object_name  15366 non-null  object\n",
      " 2   chain        15366 non-null  object\n",
      " 3   object_type  15366 non-null  object\n",
      " 4   address      15366 non-null  object\n",
      " 5   number       15366 non-null  int64 \n",
      "dtypes: int64(2), object(4)\n",
      "memory usage: 720.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15366.000000</td>\n",
       "      <td>15366.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>119720.066901</td>\n",
       "      <td>59.547182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>73036.130732</td>\n",
       "      <td>74.736833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>838.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28524.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>144974.500000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>184262.250000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>223439.000000</td>\n",
       "      <td>1700.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id        number\n",
       "count   15366.000000  15366.000000\n",
       "mean   119720.066901     59.547182\n",
       "std     73036.130732     74.736833\n",
       "min       838.000000      0.000000\n",
       "25%     28524.000000     12.000000\n",
       "50%    144974.500000     40.000000\n",
       "75%    184262.250000     80.000000\n",
       "max    223439.000000   1700.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# сэкономим память откорректировав тип данных\n",
    "df['number'] = df['number'].astype('int16')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# изменим название столбца  \n",
    "df = df.rename(columns={'number':'number_of_seats'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# приведем все записи к нижнему регистру для избежания дубликатов\n",
    "df['object_name'] = df['object_name'].str.lower()\n",
    "df['address'] = df['address'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                 0\n",
       "object_name        0\n",
       "chain              0\n",
       "object_type        0\n",
       "address            0\n",
       "number_of_seats    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# проверка на пропуски\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "183"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# проверка на явные дубликаты (полное повторение в четырех столбцах)\n",
    "df.duplicated(subset=['object_name', 'address', 'chain', 'object_type']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>object_name</th>\n",
       "      <th>chain</th>\n",
       "      <th>object_type</th>\n",
       "      <th>address</th>\n",
       "      <th>number_of_seats</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>151635</td>\n",
       "      <td>сметана</td>\n",
       "      <td>нет</td>\n",
       "      <td>кафе</td>\n",
       "      <td>город москва, улица егора абакумова, дом 9</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>77874</td>\n",
       "      <td>родник</td>\n",
       "      <td>нет</td>\n",
       "      <td>кафе</td>\n",
       "      <td>город москва, улица талалихина, дом 2/1, корпус 1</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24309</td>\n",
       "      <td>кафе «академия»</td>\n",
       "      <td>нет</td>\n",
       "      <td>кафе</td>\n",
       "      <td>город москва, абельмановская улица, дом 6</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21894</td>\n",
       "      <td>пиццетория</td>\n",
       "      <td>да</td>\n",
       "      <td>кафе</td>\n",
       "      <td>город москва, абрамцевская улица, дом 1</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>119365</td>\n",
       "      <td>кафе «вишневая метель»</td>\n",
       "      <td>нет</td>\n",
       "      <td>кафе</td>\n",
       "      <td>город москва, абрамцевская улица, дом 9, корпус 1</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>27429</td>\n",
       "      <td>стол. при гоу сош № 1051</td>\n",
       "      <td>нет</td>\n",
       "      <td>столовая</td>\n",
       "      <td>город москва, абрамцевская улица, дом 15, корп...</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>148815</td>\n",
       "      <td>брусника</td>\n",
       "      <td>да</td>\n",
       "      <td>кафе</td>\n",
       "      <td>город москва, переулок сивцев вражек, дом 6/2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20957</td>\n",
       "      <td>буфет мтуси</td>\n",
       "      <td>нет</td>\n",
       "      <td>столовая</td>\n",
       "      <td>город москва, авиамоторная улица, дом 8, строе...</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20958</td>\n",
       "      <td>кпф семья-1</td>\n",
       "      <td>нет</td>\n",
       "      <td>столовая</td>\n",
       "      <td>город москва, авиамоторная улица, дом 8, строе...</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>28858</td>\n",
       "      <td>столовая мтуси</td>\n",
       "      <td>нет</td>\n",
       "      <td>столовая</td>\n",
       "      <td>город москва, авиамоторная улица, дом 8, строе...</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id               object_name chain object_type  \\\n",
       "0  151635                   сметана   нет        кафе   \n",
       "1   77874                    родник   нет        кафе   \n",
       "2   24309           кафе «академия»   нет        кафе   \n",
       "3   21894                пиццетория    да        кафе   \n",
       "4  119365    кафе «вишневая метель»   нет        кафе   \n",
       "5   27429  стол. при гоу сош № 1051   нет    столовая   \n",
       "6  148815                  брусника    да        кафе   \n",
       "7   20957               буфет мтуси   нет    столовая   \n",
       "8   20958               кпф семья-1   нет    столовая   \n",
       "9   28858            столовая мтуси   нет    столовая   \n",
       "\n",
       "                                             address  number_of_seats  \n",
       "0         город москва, улица егора абакумова, дом 9               48  \n",
       "1  город москва, улица талалихина, дом 2/1, корпус 1               35  \n",
       "2          город москва, абельмановская улица, дом 6               95  \n",
       "3            город москва, абрамцевская улица, дом 1               40  \n",
       "4  город москва, абрамцевская улица, дом 9, корпус 1               50  \n",
       "5  город москва, абрамцевская улица, дом 15, корп...              240  \n",
       "6      город москва, переулок сивцев вражек, дом 6/2               10  \n",
       "7  город москва, авиамоторная улица, дом 8, строе...               90  \n",
       "8  город москва, авиамоторная улица, дом 8, строе...              150  \n",
       "9  город москва, авиамоторная улица, дом 8, строе...              120  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# удаляем явные дубликаты\n",
    "df = df.drop_duplicates(subset=['object_name', 'address', 'chain', 'object_type'], ignore_index=True)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Вывод**\n",
    "\n",
    "В процессе предобработки привел к нижнему регистру текстовые данные в столбцах, переименовал столбец с количеством посадочных мест, проверил на дубли и отсутствующие значения."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Анализ данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cоотношение видов объектов общественного питания по количеству"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# построим график с количеством объектов для каждого вида заведений\n",
    "df['object_type'].value_counts().sort_values(ascending=True).plot(kind='barh', figsize=(15,5), grid=True)\n",
    "plt.title('Объекты общественного питания в Москве')\n",
    "plt.xlabel('Количество объектов')\n",
    "plt.ylabel('Тип объекта')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Вывод**\n",
    "\n",
    "Самый распространненый вид заведений общественного питания - кафе, затем идут столовая и ресторан."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Соотношение сетевых и несетевых заведений по количеству"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['chain'] = df['chain'].map({'нет':'несетевой','да':'сетевой'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.groupby('chain')['object_name'].count().sort_values(ascending=False).plot(kind='bar', grid=True)\n",
    "ax.set_title('Сетевые и несетевые объекты общественного питания в Москве')\n",
    "ax.set_xlabel('Тип объекта')\n",
    "ax.set_ylabel('Количество объектов')\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Вывод**\n",
    "\n",
    "Несетевых объектов общественного питания в несколько раз больше, чем сетевых."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cетевое распространение некоторых видов объектов общественного питания "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "type_grouped = (df.groupby(['object_type', 'chain'])\n",
    "                  .agg({'id':'count'}).reset_index().sort_values(by='id', ascending=False)\n",
    "               )\n",
    "plt.figure(figsize=(15,6))\n",
    "sns.set_style(\"darkgrid\")\n",
    "sns.barplot(x='id',\n",
    "            y='object_type',\n",
    "            hue='chain',\n",
    "            data=type_grouped,\n",
    "            palette=\"Set2\")\n",
    "plt.title('Количество заведений по типам')\n",
    "plt.xlabel('Количество объектов')\n",
    "plt.ylabel('Тип объекта')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Вывод**\n",
    "\n",
    "Как видим, наиболее характерным сетевое распространение является для предприятий быстрого обслуживания."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Характерные черты сетевых заведений"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>index</th>\n",
       "      <th>id</th>\n",
       "      <th>object_name</th>\n",
       "      <th>chain</th>\n",
       "      <th>object_type</th>\n",
       "      <th>address</th>\n",
       "      <th>number_of_seats</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>21894</td>\n",
       "      <td>пиццетория</td>\n",
       "      <td>сетевой</td>\n",
       "      <td>кафе</td>\n",
       "      <td>город москва, абрамцевская улица, дом 1</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>148815</td>\n",
       "      <td>брусника</td>\n",
       "      <td>сетевой</td>\n",
       "      <td>кафе</td>\n",
       "      <td>город москва, переулок сивцев вражек, дом 6/2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>22579</td>\n",
       "      <td>алло пицца</td>\n",
       "      <td>сетевой</td>\n",
       "      <td>кафе</td>\n",
       "      <td>город москва, улица авиаторов, дом 14</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>144107</td>\n",
       "      <td>суши wok</td>\n",
       "      <td>сетевой</td>\n",
       "      <td>предприятие быстрого обслуживания</td>\n",
       "      <td>город москва, азовская улица, дом 3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>58565</td>\n",
       "      <td>тануки</td>\n",
       "      <td>сетевой</td>\n",
       "      <td>ресторан</td>\n",
       "      <td>город москва, большая академическая улица, дом 65</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index      id object_name    chain                        object_type  \\\n",
       "0      3   21894  пиццетория  сетевой                               кафе   \n",
       "1      6  148815    брусника  сетевой                               кафе   \n",
       "2     13   22579  алло пицца  сетевой                               кафе   \n",
       "3     16  144107    суши wok  сетевой  предприятие быстрого обслуживания   \n",
       "4     18   58565      тануки  сетевой                           ресторан   \n",
       "\n",
       "                                             address  number_of_seats  \n",
       "0            город москва, абрамцевская улица, дом 1               40  \n",
       "1      город москва, переулок сивцев вражек, дом 6/2               10  \n",
       "2              город москва, улица авиаторов, дом 14               32  \n",
       "3                город москва, азовская улица, дом 3                7  \n",
       "4  город москва, большая академическая улица, дом 65              160  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# выделим сетевые заведения в отдельный датафрейм\n",
    "df_chain = df[df['chain'] == 'сетевой'].reset_index()\n",
    "df_chain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# группируем данные по заведениям и найдем среднее количество посадочных мест и количество заведений\n",
    "chain_grouped = (df_chain.groupby('object_name')\n",
    "                 .agg({'id': 'count', 'number_of_seats' : 'mean'})\n",
    "                 .sort_values(by = 'id', ascending = False)\n",
    "                )\n",
    "sns.set_style(\"darkgrid\")\n",
    "ax = sns.jointplot(x=\"number_of_seats\", y=\"id\", data=chain_grouped)\n",
    "ax.ax_joint.set_xlabel('Среднее количество посадочных мест')\n",
    "ax.ax_joint.set_ylabel('Количество заведений в сети')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Вывод**\n",
    "\n",
    "Для сетевых заведений характерно большое количество точек с небольшим количеством посадочных мест. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Среднее количество посадочных мест для каждого вида объекта общественного питания"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# построим столбчатую диаграмму с медианными значениями\n",
    "plt.figure(figsize=(15,6))\n",
    "sns.set_style(\"darkgrid\")\n",
    "sns.barplot(x=\"number_of_seats\", y=\"object_type\", data=df, estimator=np.median)\n",
    "plt.title('Медианное количество посадочных мест в объектах общественного питания в Москве')\n",
    "plt.xlabel('Количество мест')\n",
    "plt.ylabel('Тип объекта')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Вывод**\n",
    "\n",
    "Как видим, в среднем больше всего мест предоставляет столовая. Затем идет ресторан, буфет, бар и кафе."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Топ-10 улиц по количеству объектов общественного питания"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>id</th>\n",
       "      <th>object_name</th>\n",
       "      <th>chain</th>\n",
       "      <th>object_type</th>\n",
       "      <th>address</th>\n",
       "      <th>number_of_seats</th>\n",
       "      <th>streets_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>151635</td>\n",
       "      <td>сметана</td>\n",
       "      <td>несетевой</td>\n",
       "      <td>кафе</td>\n",
       "      <td>город москва, улица егора абакумова, дом 9</td>\n",
       "      <td>48</td>\n",
       "      <td>улица егора абакумова</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>77874</td>\n",
       "      <td>родник</td>\n",
       "      <td>несетевой</td>\n",
       "      <td>кафе</td>\n",
       "      <td>город москва, улица талалихина, дом 2/1, корпус 1</td>\n",
       "      <td>35</td>\n",
       "      <td>улица талалихина</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24309</td>\n",
       "      <td>кафе «академия»</td>\n",
       "      <td>несетевой</td>\n",
       "      <td>кафе</td>\n",
       "      <td>город москва, абельмановская улица, дом 6</td>\n",
       "      <td>95</td>\n",
       "      <td>абельмановская улица</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21894</td>\n",
       "      <td>пиццетория</td>\n",
       "      <td>сетевой</td>\n",
       "      <td>кафе</td>\n",
       "      <td>город москва, абрамцевская улица, дом 1</td>\n",
       "      <td>40</td>\n",
       "      <td>абрамцевская улица</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>119365</td>\n",
       "      <td>кафе «вишневая метель»</td>\n",
       "      <td>несетевой</td>\n",
       "      <td>кафе</td>\n",
       "      <td>город москва, абрамцевская улица, дом 9, корпус 1</td>\n",
       "      <td>50</td>\n",
       "      <td>абрамцевская улица</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id             object_name      chain object_type  \\\n",
       "0  151635                 сметана  несетевой        кафе   \n",
       "1   77874                  родник  несетевой        кафе   \n",
       "2   24309         кафе «академия»  несетевой        кафе   \n",
       "3   21894              пиццетория    сетевой        кафе   \n",
       "4  119365  кафе «вишневая метель»  несетевой        кафе   \n",
       "\n",
       "                                             address  number_of_seats  \\\n",
       "0         город москва, улица егора абакумова, дом 9               48   \n",
       "1  город москва, улица талалихина, дом 2/1, корпус 1               35   \n",
       "2          город москва, абельмановская улица, дом 6               95   \n",
       "3            город москва, абрамцевская улица, дом 1               40   \n",
       "4  город москва, абрамцевская улица, дом 9, корпус 1               50   \n",
       "\n",
       "             streets_name  \n",
       "0   улица егора абакумова  \n",
       "1        улица талалихина  \n",
       "2    абельмановская улица  \n",
       "3      абрамцевская улица  \n",
       "4      абрамцевская улица  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# выделим информацию об улице из столбца address и создадим отдельный столбец, куда передадим их названия\n",
    "text = df['address'].str.split(\",\", expand=True)\n",
    "df['streets_name']=text[1]\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# посчитаем количество объектов на каждой улице\n",
    "df = df.query('streets_name != \" город зеленоград\"')\n",
    "df = df.query('streets_name != \" поселение сосенское\"')\n",
    "top10 = df.groupby('streets_name').count().sort_values(by='object_type', ascending=False).head(10).reset_index()\n",
    "plt.figure(figsize=(15,6))\n",
    "sns.set_style(\"darkgrid\")\n",
    "sns.barplot(x=\"object_type\", y=\"streets_name\", data=top10)\n",
    "plt.title('Топ-10 улиц по количеству объектов общественного питания')\n",
    "plt.xlabel('Количество объектов')\n",
    "plt.ylabel('Название улицы')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# посмотрим в каких районах находятся эти улицы\n",
    "# для этого загрузим внешнюю информацию о районах и улицах Москвы\n",
    "streets = pd.read_csv('https://frs.noosphere.ru/xmlui/bitstream/handle/20.500.11925/714058/mosgaz-streets.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "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>streets_name</th>\n",
       "      <th>areaid</th>\n",
       "      <th>okrug</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>выставочный переулок</td>\n",
       "      <td>17</td>\n",
       "      <td>ЦАО</td>\n",
       "      <td>Пресненский район</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>улица гашека</td>\n",
       "      <td>17</td>\n",
       "      <td>ЦАО</td>\n",
       "      <td>Пресненский район</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>большая никитская улица</td>\n",
       "      <td>17</td>\n",
       "      <td>ЦАО</td>\n",
       "      <td>Пресненский район</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>глубокий переулок</td>\n",
       "      <td>17</td>\n",
       "      <td>ЦАО</td>\n",
       "      <td>Пресненский район</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>большой гнездниковский переулок</td>\n",
       "      <td>17</td>\n",
       "      <td>ЦАО</td>\n",
       "      <td>Пресненский район</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      streets_name  areaid okrug               area\n",
       "0             выставочный переулок      17   ЦАО  Пресненский район\n",
       "1                     улица гашека      17   ЦАО  Пресненский район\n",
       "2          большая никитская улица      17   ЦАО  Пресненский район\n",
       "3                глубокий переулок      17   ЦАО  Пресненский район\n",
       "4  большой гнездниковский переулок      17   ЦАО  Пресненский район"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# приведем к нижнему регистру и переименуем столбец для дальнейшего жойна таблиц\n",
    "streets['streetname'] = streets['streetname'].str.lower()\n",
    "streets = streets.rename(columns={'streetname':'streets_name'})\n",
    "streets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>streets_name</th>\n",
       "      <th>okrug</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>проспект мира</td>\n",
       "      <td>СВАО</td>\n",
       "      <td>Алексеевский район</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>196</td>\n",
       "      <td>проспект мира</td>\n",
       "      <td>СВАО</td>\n",
       "      <td>Ярославский Район</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>196</td>\n",
       "      <td>проспект мира</td>\n",
       "      <td>СВАО</td>\n",
       "      <td>Район Марьина роща</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>196</td>\n",
       "      <td>проспект мира</td>\n",
       "      <td>СВАО</td>\n",
       "      <td>Останкинский район</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>196</td>\n",
       "      <td>проспект мира</td>\n",
       "      <td>СВАО</td>\n",
       "      <td>Район Ростокино</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>196</td>\n",
       "      <td>проспект мира</td>\n",
       "      <td>СВАО</td>\n",
       "      <td>Район Свиблово</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>196</td>\n",
       "      <td>проспект мира</td>\n",
       "      <td>ЦАО</td>\n",
       "      <td>Мещанский район</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>180</td>\n",
       "      <td>профсоюзная улица</td>\n",
       "      <td>ЮЗАО</td>\n",
       "      <td>Академический район</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>180</td>\n",
       "      <td>профсоюзная улица</td>\n",
       "      <td>ЮЗАО</td>\n",
       "      <td>Район Черемушки</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>180</td>\n",
       "      <td>профсоюзная улица</td>\n",
       "      <td>ЮЗАО</td>\n",
       "      <td>Район Ясенево</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>180</td>\n",
       "      <td>профсоюзная улица</td>\n",
       "      <td>ЮЗАО</td>\n",
       "      <td>Район Коньково</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>180</td>\n",
       "      <td>профсоюзная улица</td>\n",
       "      <td>ЮЗАО</td>\n",
       "      <td>Обручевский район</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>180</td>\n",
       "      <td>профсоюзная улица</td>\n",
       "      <td>ЮЗАО</td>\n",
       "      <td>Район Теплый Стан</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>170</td>\n",
       "      <td>ленинградский проспект</td>\n",
       "      <td>САО</td>\n",
       "      <td>Район Аэропорт</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>170</td>\n",
       "      <td>ленинградский проспект</td>\n",
       "      <td>САО</td>\n",
       "      <td>Район Беговой</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>170</td>\n",
       "      <td>ленинградский проспект</td>\n",
       "      <td>САО</td>\n",
       "      <td>Хорошевский район</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>170</td>\n",
       "      <td>ленинградский проспект</td>\n",
       "      <td>САО</td>\n",
       "      <td>Район Сокол</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>165</td>\n",
       "      <td>пресненская набережная</td>\n",
       "      <td>ЦАО</td>\n",
       "      <td>Пресненский район</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>159</td>\n",
       "      <td>варшавское шоссе</td>\n",
       "      <td>ЮАО</td>\n",
       "      <td>Район Чертаново Центральное</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>159</td>\n",
       "      <td>варшавское шоссе</td>\n",
       "      <td>ЮАО</td>\n",
       "      <td>Район Чертаново Северное</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id            streets_name okrug                         area\n",
       "0   196           проспект мира  СВАО           Алексеевский район\n",
       "1   196           проспект мира  СВАО            Ярославский Район\n",
       "2   196           проспект мира  СВАО           Район Марьина роща\n",
       "3   196           проспект мира  СВАО           Останкинский район\n",
       "4   196           проспект мира  СВАО              Район Ростокино\n",
       "5   196           проспект мира  СВАО               Район Свиблово\n",
       "6   196           проспект мира   ЦАО              Мещанский район\n",
       "7   180       профсоюзная улица  ЮЗАО          Академический район\n",
       "8   180       профсоюзная улица  ЮЗАО              Район Черемушки\n",
       "9   180       профсоюзная улица  ЮЗАО                Район Ясенево\n",
       "10  180       профсоюзная улица  ЮЗАО               Район Коньково\n",
       "11  180       профсоюзная улица  ЮЗАО            Обручевский район\n",
       "12  180       профсоюзная улица  ЮЗАО            Район Теплый Стан\n",
       "13  170  ленинградский проспект   САО               Район Аэропорт\n",
       "14  170  ленинградский проспект   САО                Район Беговой\n",
       "15  170  ленинградский проспект   САО            Хорошевский район\n",
       "16  170  ленинградский проспект   САО                  Район Сокол\n",
       "17  165  пресненская набережная   ЦАО            Пресненский район\n",
       "18  159        варшавское шоссе   ЮАО  Район Чертаново Центральное\n",
       "19  159        варшавское шоссе   ЮАО     Район Чертаново Северное"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# уберем лишние пробелы в столбце с названиями улиц и соединим таблицы\n",
    "top10['streets_name'] = top10['streets_name'].str.strip()\n",
    "top10 = top10.merge(streets[['streets_name', 'okrug','area']], on='streets_name', how='left')\n",
    "top10[['id','streets_name', 'okrug','area']].head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Вывод**\n",
    "\n",
    "Некоторые улицы находятся не только в разных районах, но и округах. Выделяется Пресненская набережная, где наблюдается высокая концентрация заведений."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Количество улиц с одним объектом общественного питания"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 579 entries, 0 to 578\n",
      "Data columns (total 1 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   streets_name  579 non-null    object\n",
      "dtypes: object(1)\n",
      "memory usage: 4.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# сгруппируем данные в датафрейм с 1-м объектом общественного питания\n",
    "one = df.groupby('streets_name').agg({'object_name': 'count'}).sort_values(by = 'object_name', ascending = False)\n",
    "one = one.query('object_name == 1')\n",
    "one = one.drop('object_name',1)\n",
    "one = one.reset_index()\n",
    "one.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Один объект общественного питания находится примерно на 600 улицах."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>streets_name</th>\n",
       "      <th>okrug</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1-й ботанический проезд</td>\n",
       "      <td>СВАО</td>\n",
       "      <td>Район Свиблово</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>чуксин тупик</td>\n",
       "      <td>САО</td>\n",
       "      <td>Тимирязевский Район</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>улица красных зорь</td>\n",
       "      <td>ЗАО</td>\n",
       "      <td>Можайский Район</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>бабаевская улица</td>\n",
       "      <td>ВАО</td>\n",
       "      <td>Район Сокольники</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8-й проезд марьиной рощи</td>\n",
       "      <td>СВАО</td>\n",
       "      <td>Район Марьина роща</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               streets_name okrug                 area\n",
       "0   1-й ботанический проезд  СВАО       Район Свиблово\n",
       "1              чуксин тупик   САО  Тимирязевский Район\n",
       "2        улица красных зорь   ЗАО      Можайский Район\n",
       "3          бабаевская улица   ВАО     Район Сокольники\n",
       "4  8-й проезд марьиной рощи  СВАО   Район Марьина роща"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# уберем лишние пробелы в столбце с названиями улиц и соединим таблицы\n",
    "one['streets_name'] = one['streets_name'].str.strip()\n",
    "one = one.merge(streets[['streets_name', 'okrug','area']], on='streets_name', how='left')\n",
    "one.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# посчитаем районы, в которых находятся улицы с одним объектом общественного питания\n",
    "len(one['area'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Вывод**\n",
    "\n",
    "Исходя из полученных данных, в 100 районах имеется 579 улиц, на которых находится один объект общественного питания."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Распределение количества посадочных мест для улиц с большим количеством объектов общественного питания"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>150</td>\n",
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       "      <td>сетевой</td>\n",
       "      <td>кафе</td>\n",
       "      <td>город москва, варшавское шоссе, дом 2</td>\n",
       "      <td>150</td>\n",
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       "      <td>город москва, варшавское шоссе, дом 7, корпус 1</td>\n",
       "      <td>36</td>\n",
       "      <td>варшавское шоссе</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>152</td>\n",
       "      <td>155852</td>\n",
       "      <td>кафе «ламаджо»</td>\n",
       "      <td>несетевой</td>\n",
       "      <td>кафе</td>\n",
       "      <td>город москва, варшавское шоссе, дом 29</td>\n",
       "      <td>30</td>\n",
       "      <td>варшавское шоссе</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>153</td>\n",
       "      <td>152556</td>\n",
       "      <td>шаурма в пите</td>\n",
       "      <td>несетевой</td>\n",
       "      <td>предприятие быстрого обслуживания</td>\n",
       "      <td>город москва, варшавское шоссе, дом 72, корпус 2</td>\n",
       "      <td>0</td>\n",
       "      <td>варшавское шоссе</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>154</td>\n",
       "      <td>120658</td>\n",
       "      <td>выпечка</td>\n",
       "      <td>несетевой</td>\n",
       "      <td>кафетерий</td>\n",
       "      <td>город москва, варшавское шоссе, дом 100</td>\n",
       "      <td>2</td>\n",
       "      <td>варшавское шоссе</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   index      id      object_name      chain  \\\n",
       "0    150  155973  кафе «андерсон»    сетевой   \n",
       "1    151   23618    кафе «subway»    сетевой   \n",
       "2    152  155852   кафе «ламаджо»  несетевой   \n",
       "3    153  152556    шаурма в пите  несетевой   \n",
       "4    154  120658          выпечка  несетевой   \n",
       "\n",
       "                         object_type  \\\n",
       "0                               кафе   \n",
       "1                               кафе   \n",
       "2                               кафе   \n",
       "3  предприятие быстрого обслуживания   \n",
       "4                          кафетерий   \n",
       "\n",
       "                                            address  number_of_seats  \\\n",
       "0             город москва, варшавское шоссе, дом 2              150   \n",
       "1   город москва, варшавское шоссе, дом 7, корпус 1               36   \n",
       "2            город москва, варшавское шоссе, дом 29               30   \n",
       "3  город москва, варшавское шоссе, дом 72, корпус 2                0   \n",
       "4           город москва, варшавское шоссе, дом 100                2   \n",
       "\n",
       "       streets_name  \n",
       "0  варшавское шоссе  \n",
       "1  варшавское шоссе  \n",
       "2  варшавское шоссе  \n",
       "3  варшавское шоссе  \n",
       "4  варшавское шоссе  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['streets_name'] = df['streets_name'].str.strip()\n",
    "top_10 = list(top10['streets_name'])\n",
    "temp = df[df['streets_name'].isin(top_10)].reset_index()\n",
    "temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x=temp['number_of_seats'])\n",
    "plt.title('Распределение количества посадочных мест')\n",
    "plt.xlabel('Количество мест')\n",
    "ax.set_xlim(-10,200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "ax = sns.boxplot(x=\"streets_name\", y=\"number_of_seats\", data=temp, orient='v', showfliers=False)\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Распределение количества посадочных мест')\n",
    "plt.xlabel('Улица')\n",
    "plt.ylabel('Количество мест')\n",
    "ax.set_ylim(-10,300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Вывод**\n",
    "\n",
    "На наиболее популярных улицах в среднем количество посадочных мест составляет 50 единиц."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Общие выводы"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "По результатам исследования нам стало известно, что:\n",
    "\n",
    "\n",
    "- самый популярный вид заведения общественного питания - кафе\n",
    "- в Москве преобладают несетевые заведения\n",
    "- по сетевым программа чаще открывают предприятия быстрого обслуживания и рестораны\n",
    "- в среднем больше всего посадочных мест в ресторанах и столовых\n",
    "- наибольшее количество объектов питания расположено на самых протяженных улицах Москвы\n",
    "- наименьшее количество объектов питания расположено на окраинах города"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Рекомендации:\n",
    "\n",
    "Небольшое кафе с количеством посадочных мест в диапазоне 30-50 штук следует самостоятельно открыть в центральной части города, с одной стороны с высокой проходимостью, с другой стороны, чтобы по соседству было не много заведений подобного рода. То есть обратить внимание на улицы в центре, на которых расположено не много заведений общественного питания. "
   ]
  }
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