{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to tell a story using data\n",
    "\n",
    "I’ve decided to open a small robot-run cafe in Los Ageles. The project is promising but expensive, so my partners and I decided to try to attract investors. They’re interested in the current market conditions — will we be able to maintain our success when the novelty of robot waiters wears off?\n",
    "\n",
    "I'm an analytics guru, so my partners have asked me to prepare some market research. I have open-source data on restaurants in LA.\n",
    "\n",
    "## Description of the data\n",
    "\n",
    "*/datasets/rest_data_us.csv*\n",
    "- **object_name** — establishment name\n",
    "- **chain** — chain establishment (True/False)\n",
    "- **object_type** — establishment type\n",
    "- **address** — physical address\n",
    "- **number** — number of seats\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Download the data & general overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "from plotly import graph_objects as go"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### General info & descriptive stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9651 entries, 0 to 9650\n",
      "Data columns (total 6 columns):\n",
      "id             9651 non-null int64\n",
      "object_name    9651 non-null object\n",
      "address        9651 non-null object\n",
      "chain          9648 non-null object\n",
      "object_type    9651 non-null object\n",
      "number         9651 non-null int64\n",
      "dtypes: int64(2), object(4)\n",
      "memory usage: 452.5+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\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>address</th>\n",
       "      <th>chain</th>\n",
       "      <th>object_type</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>11786</td>\n",
       "      <td>HABITAT COFFEE SHOP</td>\n",
       "      <td>3708 N EAGLE ROCK BLVD</td>\n",
       "      <td>False</td>\n",
       "      <td>Cafe</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>11787</td>\n",
       "      <td>REILLY'S</td>\n",
       "      <td>100 WORLD WAY # 120</td>\n",
       "      <td>False</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>11788</td>\n",
       "      <td>STREET CHURROS</td>\n",
       "      <td>6801 HOLLYWOOD BLVD # 253</td>\n",
       "      <td>False</td>\n",
       "      <td>Fast Food</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>11789</td>\n",
       "      <td>TRINITI ECHO PARK</td>\n",
       "      <td>1814 W SUNSET BLVD</td>\n",
       "      <td>False</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>11790</td>\n",
       "      <td>POLLEN</td>\n",
       "      <td>2100 ECHO PARK AVE</td>\n",
       "      <td>False</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id          object_name                    address  chain object_type  \\\n",
       "0  11786  HABITAT COFFEE SHOP     3708 N EAGLE ROCK BLVD  False        Cafe   \n",
       "1  11787             REILLY'S        100 WORLD WAY # 120  False  Restaurant   \n",
       "2  11788       STREET CHURROS  6801 HOLLYWOOD BLVD # 253  False   Fast Food   \n",
       "3  11789    TRINITI ECHO PARK         1814 W SUNSET BLVD  False  Restaurant   \n",
       "4  11790               POLLEN         2100 ECHO PARK AVE  False  Restaurant   \n",
       "\n",
       "   number  \n",
       "0      26  \n",
       "1       9  \n",
       "2      20  \n",
       "3      22  \n",
       "4      20  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# read .csv file & look at general info\n",
    "\n",
    "est_data = pd.read_csv('/datasets/rest_data_us.csv')\n",
    "\n",
    "display(est_data.info())\n",
    "display(est_data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>9651.000000</td>\n",
       "      <td>9651.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>16611.000000</td>\n",
       "      <td>43.695161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>2786.148058</td>\n",
       "      <td>47.622874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>11786.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>14198.500000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>16611.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>19023.500000</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>21436.000000</td>\n",
       "      <td>229.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id       number\n",
       "count   9651.000000  9651.000000\n",
       "mean   16611.000000    43.695161\n",
       "std     2786.148058    47.622874\n",
       "min    11786.000000     1.000000\n",
       "25%    14198.500000    14.000000\n",
       "50%    16611.000000    27.000000\n",
       "75%    19023.500000    46.000000\n",
       "max    21436.000000   229.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look at descriptive stats\n",
    "\n",
    "display(est_data.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check for duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of duplicates in 'est_data': 0\n"
     ]
    }
   ],
   "source": [
    "# checking for duplicates\n",
    "\n",
    "print(\"Number of duplicates in 'est_data':\", est_data.duplicated().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion\n",
    "There are a few issues we can immediately see from our initial overview. We'll need to address these in the next step.\n",
    " - We should change the column names to more descriptive labels\n",
    " - There are 3 missing values in the **'chain'** column\n",
    " - We should make all *object* dtype values lowercase\n",
    " - The **'id'** column dtype should be changed to *object*\n",
    " - The **'chain'** column dtype should be changed to *bool*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Data preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Change column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>est_name</th>\n",
       "      <th>address</th>\n",
       "      <th>is_chain</th>\n",
       "      <th>est_type</th>\n",
       "      <th>n_seats</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>11786</td>\n",
       "      <td>HABITAT COFFEE SHOP</td>\n",
       "      <td>3708 N EAGLE ROCK BLVD</td>\n",
       "      <td>False</td>\n",
       "      <td>Cafe</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>11787</td>\n",
       "      <td>REILLY'S</td>\n",
       "      <td>100 WORLD WAY # 120</td>\n",
       "      <td>False</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>11788</td>\n",
       "      <td>STREET CHURROS</td>\n",
       "      <td>6801 HOLLYWOOD BLVD # 253</td>\n",
       "      <td>False</td>\n",
       "      <td>Fast Food</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>11789</td>\n",
       "      <td>TRINITI ECHO PARK</td>\n",
       "      <td>1814 W SUNSET BLVD</td>\n",
       "      <td>False</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>11790</td>\n",
       "      <td>POLLEN</td>\n",
       "      <td>2100 ECHO PARK AVE</td>\n",
       "      <td>False</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id             est_name                    address is_chain    est_type  \\\n",
       "0  11786  HABITAT COFFEE SHOP     3708 N EAGLE ROCK BLVD    False        Cafe   \n",
       "1  11787             REILLY'S        100 WORLD WAY # 120    False  Restaurant   \n",
       "2  11788       STREET CHURROS  6801 HOLLYWOOD BLVD # 253    False   Fast Food   \n",
       "3  11789    TRINITI ECHO PARK         1814 W SUNSET BLVD    False  Restaurant   \n",
       "4  11790               POLLEN         2100 ECHO PARK AVE    False  Restaurant   \n",
       "\n",
       "   n_seats  \n",
       "0       26  \n",
       "1        9  \n",
       "2       20  \n",
       "3       22  \n",
       "4       20  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change column names\n",
    "\n",
    "est_data.columns = ['id', 'est_name', 'address', 'is_chain', 'est_type', 'n_seats']\n",
    "est_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Delete missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9648 entries, 0 to 9650\n",
      "Data columns (total 6 columns):\n",
      "id          9648 non-null int64\n",
      "est_name    9648 non-null object\n",
      "address     9648 non-null object\n",
      "is_chain    9648 non-null object\n",
      "est_type    9648 non-null object\n",
      "n_seats     9648 non-null int64\n",
      "dtypes: int64(2), object(4)\n",
      "memory usage: 527.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# drop missing values\n",
    "\n",
    "est_data.dropna(inplace=True)\n",
    "est_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lowercase values for *object* columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
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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>est_name</th>\n",
       "      <th>address</th>\n",
       "      <th>is_chain</th>\n",
       "      <th>est_type</th>\n",
       "      <th>n_seats</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>11786</td>\n",
       "      <td>habitat coffee shop</td>\n",
       "      <td>3708 n eagle rock blvd</td>\n",
       "      <td>False</td>\n",
       "      <td>cafe</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>11787</td>\n",
       "      <td>reilly's</td>\n",
       "      <td>100 world way # 120</td>\n",
       "      <td>False</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>11788</td>\n",
       "      <td>street churros</td>\n",
       "      <td>6801 hollywood blvd # 253</td>\n",
       "      <td>False</td>\n",
       "      <td>fast food</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>11789</td>\n",
       "      <td>triniti echo park</td>\n",
       "      <td>1814 w sunset blvd</td>\n",
       "      <td>False</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>11790</td>\n",
       "      <td>pollen</td>\n",
       "      <td>2100 echo park ave</td>\n",
       "      <td>False</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id             est_name                    address is_chain    est_type  \\\n",
       "0  11786  habitat coffee shop     3708 n eagle rock blvd    False        cafe   \n",
       "1  11787             reilly's        100 world way # 120    False  restaurant   \n",
       "2  11788       street churros  6801 hollywood blvd # 253    False   fast food   \n",
       "3  11789    triniti echo park         1814 w sunset blvd    False  restaurant   \n",
       "4  11790               pollen         2100 echo park ave    False  restaurant   \n",
       "\n",
       "   n_seats  \n",
       "0       26  \n",
       "1        9  \n",
       "2       20  \n",
       "3       22  \n",
       "4       20  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make column values lowercase\n",
    "\n",
    "lower_cols = ['est_name', 'address', 'est_type']\n",
    "\n",
    "est_data[lower_cols] = est_data[lower_cols].apply(lambda x: x.astype(str).str.lower())\n",
    "est_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Change data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9648 entries, 0 to 9650\n",
      "Data columns (total 6 columns):\n",
      "id          9648 non-null object\n",
      "est_name    9648 non-null object\n",
      "address     9648 non-null object\n",
      "is_chain    9648 non-null bool\n",
      "est_type    9648 non-null object\n",
      "n_seats     9648 non-null int64\n",
      "dtypes: bool(1), int64(1), object(4)\n",
      "memory usage: 461.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# change 'id' to object dtype with .astype() method\n",
    "\n",
    "est_data['id'] = est_data['id'].astype(str)\n",
    "est_data['is_chain'] = est_data['is_chain'].astype(bool)\n",
    "\n",
    "est_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Proportions of different restaurant types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "# bar graph of restaurant types\n",
    "\n",
    "est_type_proportions = est_data.groupby('est_type')['id'].count().reset_index()\n",
    "\n",
    "ax = sns.barplot(x='est_type', y='id', data=est_type_proportions)\n",
    "ax.set_title('No. of establishments by type')\n",
    "ax.set_xlabel('Establishment type')\n",
    "ax.set_ylabel('No. of Establishments')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Conclusion\n",
    "Restaurants are the most common establishment type by a long shot, with over 7000 establishments. The next most common is fast food. The remaining establishment types have relatively similar proportions in relation to the total number of establishments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Proportions of chain & non-chain restaurants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "# pie chart of chain type\n",
    "\n",
    "chain_type_proportions = est_data.groupby('is_chain')['id'].count().reset_index()\n",
    "\n",
    "ax = sns.barplot(x='is_chain', y='id', data=chain_type_proportions)\n",
    "ax.set_title('Establishments by chain type')\n",
    "ax.set_xlabel('Chain type')\n",
    "ax.set_ylabel('No. of Establishments')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Conclusion\n",
    "The majority of establishments are not chains. There are roughly 6000 establishments that are not chains and just under 4000 that are part of a chain. This is approximately a 60-40 percentage breakdown."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Establishments that are typically chains"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
    "# group & plot by restaurant type & chain type\n",
    "\n",
    "typ_chains = est_data.groupby(['est_type', 'is_chain'])['id'].count().reset_index()\n",
    "\n",
    "ax = sns.barplot(x='est_type', y='id', hue='is_chain', data=typ_chains)\n",
    "ax.set_title('No. of Establishments by type & chain type')\n",
    "ax.set_xlabel('Establishment type')\n",
    "ax.set_ylabel('No. of Establishments')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zoN9zCoe2n3P9vMKbkhwILAWOrKo7gU2B7/Ysc2Nb9muSHAYcBrDFFlvMcWiStLD1qz56db8Vq+qMldjfx4D30FxpvIfmKuT1s9lAVS0BlgAsXry4ZlhckjQL/aqPXtFnXtH0hTQrVXXrxHiSjwPntJM3AZv3LLpZWyZJGqF+1Ucz3mE0W0k2qaqb28k9gYk7k84GTknyYZqG5q2Bi+d6/5Kk/gZ5yc4GwDuBF9FcIXwLeHdV3THDev8G7AJsmOTGdhu7JNmh3c51wBsBqurKJKcDVwEPAId755Ekjd4g71M4FbgQ+KN2+rXAacBu/Vaqqv2mKP5kn+XfB7xvgHgkSUMySFLYpKre0zP93iT7DCsgSdL4DPJE81eT7JtktXbYGzhv2IFJkkav3y2p99DU/Qd4C/CZdtZqwL3A24YenSRppPrdfbTuKAORJI3fIG0KJHkSzW2ia02UVdWFwwpKkjQeg9yS+gbgCJoHyi4FXkjTJ5HvaJakeWaQhuYjgJ2A69t+kJ4L/GyoUUmSxmKQpPCLqvoFQJLHtt1pP2O4YUmSxmGQNoUbk6wHfAH4WpI7geuHG5YkaRxmTApVtWc7+q4kFwBPBL481KgkSWMxY/VRkk9PjFfVf1bV2cCJQ41KkjQWg7QpbN87kWR14HnDCUeSNE7TJoUkx7RPNT87yd3tcA9wG3DWyCKUJI3MtEmhqt7fPtV8bFU9oR3WraoNquqYEcYoSRqRQaqPzknyeIAk+yf5cJKnDjkuSdIYDJIUPgbcl+Q5wJHAD4GThxqVJGksBkkKD1RVAXsAH62qfwbsLE+S5qFBHl67J8kxwP7A7yZZDVhzuGFJksZhkCuFfYD7gUOq6haajvGOHWpUkqSx6PeSnW2r6vtVdUuSf66q+wGq6sdJ/nt0IUqSRqXflcIpPePfmTTv+CHEIkkas35JIdOMTzUtSZoH+iWFmmZ8qmlJ0jzQ7+6jzZIcR3NVMDFOO73p0COTJI1cv6RwVM/40knzJk9LkuaBaZNCVZ00ykAkSeM3yHMKkqQFwqQgSer0e5/CB9rP14wuHEnSOPW7Utg9SQDfnSBJC0S/pPAV4E5+9ea1e3o/Z9pwkhOT3Jbkip6y9ZN8LckP2s8nteVJclyS5UkuS7LjIz4ySdKs9Xvz2lFVtR7wpZ63rnWfA2z7U8AfTCo7Gji/qrYGzm+nAV4GbN0Oh9G8w0GSNGIzNjRX1R5JNk7y8nbYaJANV9WFwE8nFe8BTNzqehLwqp7yk6vxXWC9JJsMdgiSpLkyY1JoG5ovBl4D7A1cnGSvldzfxlV1czt+C7BxO74pcEPPcjcyzVPTSQ5LsjTJ0hUrVqxkGJKkqQzykp23AztV1W0A7ZXC14HPP5IdV1UlmXUfSlW1BFgCsHjxYvtgkqQ5NMhzCqtNJITWHQOuN5VbJ6qF2s+J7d4EbN6z3GZtmSRphAY5uX8lyXlJDk5yMPAl4NyV3N/ZwEHt+EHAWT3lB7Z3Ib0QuKunmkmSNCIzVh9V1VFJXg28qC1aUlVnzrRekn8DdgE2THIj8E7g74DTkxwCXE/TRgFNktkdWA7cB7xulschSZoDg7QpUFVnAGfMZsNVtd80s3adYtkCDp/N9iVJc8++jyRJHZOCJKljUpAkdVYqKSR51xzHIUlaBazslcKyOY1CkrRKWKmkUFVfnOtAJEnjN0jfR5slOTPJirYr7H9PstkogpMkjdYgVwr/SvPE8SbAU4AvtmWSpHlmkKSwUVX9a1U90A6fAgbqPluS9OgySFK4I8n+SVZvh/1pOsWTJM0zgySF19P0UXQLcDOwF/ZNJEnz0iAd4l0PvHIEsUiSxmzapJDkHX3Wq6p6zxDikSSNUb8rhZ9PUfZ44BBgA8CkIEnzzLRJoao+NDGeZF3gCJq2hFOBD023niTp0atvm0KS9YG3Aq8FTgJ2rKo7RxGYJGn0+rUpHAu8GlgCPKuq7h1ZVJKkseh3S+qRNE8wvx34SZK72+GeJHePJjxJ0ij1a1PwXQuStMB44pckdUwKkqSOSUGS1DEpSJI6JgVJUsekIEnqmBQkSR2TgiSpY1KQJHVMCpKkjklBktSZ8XWcw5DkOuAe4EHggapa3HbTfRqwCLgO2NtuuiVptMZ5pfDiqtqhqha300cD51fV1sD57bQkaYRWpeqjPWhe5EP7+aoxxiJJC9K4kkIBX02yLMlhbdnGVXVzO34LsPFUKyY5LMnSJEtXrFgxilglacEYS5sC8KKquinJk4GvJfl+78yqqiQ11YpVtYTmbXAsXrx4ymUkSStnLFcKVXVT+3kbcCbwfODWJJsAtJ+3jSM2SVrIRp4Ukjw+yboT48BLgCuAs4GD2sUOAs4adWyStNCNo/poY+DMJBP7P6WqvpLke8DpSQ4Brgf2HkNskrSgjTwpVNWPgOdMUX4HsOuo45Ek/cqqdEuqJGnMTAqSpI5JQZLUMSlIkjomBUlSx6QgSeqYFCRJHZOCJKljUpAkdUwKkqSOSUGS1DEpSJI6JgVJUsekIEnqmBQkSR2TgiSpY1KQJHVMCpKkjklBktQxKUiSOiYFSVLHpCBJ6pgUJEkdk4IkqWNSkCR1TAqSpI5JQZLUMSlIkjomBUlSx6QgSeqsckkhyR8kuSbJ8iRHjzseSVpIVqmkkGR14J+BlwHbAfsl2W68UUnSwrFKJQXg+cDyqvpRVf0vcCqwx5hjkqQFY41xBzDJpsANPdM3Ai/oXSDJYcBh7eS9Sa4ZUWwLwYbA7eMOYlWQDx407hD0cP5tTnhn5mIrT51uxqqWFGZUVUuAJeOOYz5KsrSqFo87Dmky/zZHZ1WrProJ2LxnerO2TJI0AqtaUvgesHWSpyV5DLAvcPaYY5KkBWOVqj6qqgeSvAk4D1gdOLGqrhxzWAuJ1XJaVfm3OSKpqnHHIElaRaxq1UeSpDEyKUiSOqtUm4LmVpIHgct7il5VVddNs+wi4JyqeubwI5MgyQbA+e3kbwAPAiva6ee3D7BqxEwK89v/VNUO4w5CmkpV3QHsAJDkXcC9VfXB3mWShKbt86HRR7gwWX20wCRZlOSbSS5ph9+eYpntk1yc5NIklyXZui3fv6f8X9q+qqQ5lWSrJFcl+SxwJbB5kp/1zN83ySfa8Y2TnJFkafu3+cJxxT1fmBTmt7XbE/ilSc5sy24Dfr+qdgT2AY6bYr0/Af6xvcpYDNyY5Dfb5Xduyx8EXjv8Q9ACtS3wkarajv4PsB4H/H37tPPewCdGEdx8ZvXR/DZV9dGawEeTTJzYt5live8Af51kM+CMqvpBkl2B5wHfa67oWZsmwUjD8MOqWjrAcrsBz2j/JgGelGTtqvqf4YU2v5kUFp4/B24FnkNzpfiLyQtU1SlJLgL+EDg3yRuBACdV1TGjDFYL1s97xh+i+fubsFbPeLBRek5ZfbTwPBG4uW24O4DmyfGHSfJ04EdVdRxwFvBsmrtE9kry5HaZ9ZNM29OiNFfav9U7k2ydZDVgz57ZXwcOn5hor4D1CJgUFp7jgYOS/BdNve3Pp1hmb+CKJJcCzwROrqqrgLcDX01yGfA1YJMRxSz9JU33N9+m6VJ/wuHAzu0NEVcBh44juPnEbi4kSR2vFCRJHZOCJKljUpAkdUwKkqSOSUGS1DEpaMFJ8htJTk3ywyTLkpybZJskuyQ5Z5bbeneS3Wax/A5Jdp991NJo+ESzFpS2180zaZ7O3rctew6w8cpsr6reMctVJvqTOndl9icNm1cKWmheDPyyqk6YKKiq/6qqb7aT6yT5fJLvJ/lsm0RI8o4k30tyRZIlPeWfSrJXO35dkr9te5+9PMm2vTtO8hjg3cA+bSeF+yT5QZKN2vmrJVmeZKN2uye0vX/+d5KXt8usnuTYNpbL2i5IpDljUtBC80xgWZ/5zwXeAmwHPB3YuS3/aFXt1L6EaG3g5dOsf3vbA+3HgLf1zmj753kHcFpV7VBVpwGf4Ve9ze4G/FdVTbxoZhHwfJo+qE5IshZwCHBXVe0E7AQcmuRpAx25NACTgvRwF1fVjW1/O5fSnJgBXpzkoiSXA78HbD/N+me0n8t61u3nRODAdvz1wL/2zDu9qh6qqh8AP6LpluQlwIFtFyQXARsAWw9yYNIgbFPQQnMlsFef+ff3jD8IrNH+Qj8eWFxVN7RvCVtrqpV71n+QAf5/tdu7Ncnv0VwV9L6jYnIfNEXTK+ifVdV5M21bWhleKWih+Q/gsUkOmyhI8uwkv9NnnYkEcHuSdeifVGZyD7DupLJP0FQjfa6qHuwpf03bzrAlTVXWNTSdwv1pkjXb2LdJ8vhHEI/0MCYFLSjV9AC5J7Bbe0vqlcD7gVv6rPMz4OPAFTQn5e89ghAuALabaGhuy84G1uHhVUcAPwYuBr4M/ElV/YImgVwFXJLkCuBf8Ipfc8heUqUxS7KY5tWTv9NT9ingnKr6/NgC04LkLwxpjJIcDfwpvu9aqwivFCRJHdsUJEkdk4IkqWNSkCR1TAqSpI5JQZLU+f8s6nYC7cZrFgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cafe chains vs non-chains\n",
    "\n",
    "ax = sns.barplot(x='is_chain', y='id', data=typ_chains.query('est_type == \"cafe\"'))\n",
    "ax.set_title('No. of Cafes by chain type')\n",
    "ax.set_xlabel('Chain type')\n",
    "ax.set_ylabel('No. of Establishments')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Conclusion\n",
    "Bakeries are most likely to be chains. In fact, there are no bakeries that are not part of a chain! On the contrary, most restaurants are not part of chains. As far as cafes go, most are chain cafes. Maybe it's worth analyzing data before and after the COVID-19 pandemic, because many small, family-owned restaurants have gone out of business!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chain establishment characteristics"
   ]
  },
  {
   "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": [
    "# filter & plot histogram of seat count\n",
    "\n",
    "chains_only = est_data.query('is_chain == True')\n",
    "\n",
    "ax = sns.distplot(chains_only['n_seats'], bins=20)\n",
    "ax.set_title('Number of seats for chain establishments')\n",
    "ax.set_xlabel('No. of seats')\n",
    "ax.set_ylabel('Frequency (normalized)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Conclusion\n",
    "The majority of chain establishments have less than 50 seats. It seems like their strategy is to have a high number of smaller locations as opposed to a small number of large locations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Avg number of seats per establishment type\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "# plot & lable chart\n",
    "\n",
    "ax = sns.barplot(x='n_seats', y='est_type', data=est_data)\n",
    "ax.set_title('Average no. of seats per establishment type')\n",
    "ax.set_xlabel('Avg no. of seats')\n",
    "ax.set_ylabel('Establishment type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Conclusion\n",
    "Bars and restaurants generally have the highest number of seats. Bakeries and cafes usually have less seating available to customers. This makes sense since cafes and bakeries are usually smaller establishments that cater to a niche audience, while bars and restaurants are attempting to draw large crowds of people."
   ]
  },
  {
   "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>id</th>\n",
       "      <th>est_name</th>\n",
       "      <th>address</th>\n",
       "      <th>is_chain</th>\n",
       "      <th>est_type</th>\n",
       "      <th>n_seats</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>11786</td>\n",
       "      <td>habitat coffee shop</td>\n",
       "      <td>3708 n eagle rock blvd</td>\n",
       "      <td>False</td>\n",
       "      <td>cafe</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>11787</td>\n",
       "      <td>reilly's</td>\n",
       "      <td>100 world way # 120</td>\n",
       "      <td>False</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>11788</td>\n",
       "      <td>street churros</td>\n",
       "      <td>6801 hollywood blvd # 253</td>\n",
       "      <td>False</td>\n",
       "      <td>fast food</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>11789</td>\n",
       "      <td>triniti echo park</td>\n",
       "      <td>1814 w sunset blvd</td>\n",
       "      <td>False</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>11790</td>\n",
       "      <td>pollen</td>\n",
       "      <td>2100 echo park ave</td>\n",
       "      <td>False</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9646</td>\n",
       "      <td>21432</td>\n",
       "      <td>hall of justice</td>\n",
       "      <td>217 w temple ave</td>\n",
       "      <td>False</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9647</td>\n",
       "      <td>21433</td>\n",
       "      <td>fin-melrose</td>\n",
       "      <td>5750 melrose ave</td>\n",
       "      <td>False</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9648</td>\n",
       "      <td>21434</td>\n",
       "      <td>juicy wingz</td>\n",
       "      <td>6741 hollywood blvd</td>\n",
       "      <td>True</td>\n",
       "      <td>fast food</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9649</td>\n",
       "      <td>21435</td>\n",
       "      <td>medidate coffee</td>\n",
       "      <td>548 s spring st ste 100</td>\n",
       "      <td>False</td>\n",
       "      <td>cafe</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9650</td>\n",
       "      <td>21436</td>\n",
       "      <td>cafe sprouts</td>\n",
       "      <td>1300 s san pedro st ste 111</td>\n",
       "      <td>True</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9648 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id             est_name                      address  is_chain  \\\n",
       "0     11786  habitat coffee shop       3708 n eagle rock blvd     False   \n",
       "1     11787             reilly's          100 world way # 120     False   \n",
       "2     11788       street churros    6801 hollywood blvd # 253     False   \n",
       "3     11789    triniti echo park           1814 w sunset blvd     False   \n",
       "4     11790               pollen           2100 echo park ave     False   \n",
       "...     ...                  ...                          ...       ...   \n",
       "9646  21432      hall of justice             217 w temple ave     False   \n",
       "9647  21433          fin-melrose             5750 melrose ave     False   \n",
       "9648  21434          juicy wingz          6741 hollywood blvd      True   \n",
       "9649  21435      medidate coffee      548 s spring st ste 100     False   \n",
       "9650  21436         cafe sprouts  1300 s san pedro st ste 111      True   \n",
       "\n",
       "        est_type  n_seats  \n",
       "0           cafe       26  \n",
       "1     restaurant        9  \n",
       "2      fast food       20  \n",
       "3     restaurant       22  \n",
       "4     restaurant       20  \n",
       "...          ...      ...  \n",
       "9646  restaurant      122  \n",
       "9647  restaurant       93  \n",
       "9648   fast food       15  \n",
       "9649        cafe        6  \n",
       "9650  restaurant       19  \n",
       "\n",
       "[9648 rows x 6 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extract street names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>id</th>\n",
       "      <th>est_name</th>\n",
       "      <th>address</th>\n",
       "      <th>is_chain</th>\n",
       "      <th>est_type</th>\n",
       "      <th>n_seats</th>\n",
       "      <th>street_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>11786</td>\n",
       "      <td>habitat coffee shop</td>\n",
       "      <td>3708 n eagle rock blvd</td>\n",
       "      <td>False</td>\n",
       "      <td>cafe</td>\n",
       "      <td>26</td>\n",
       "      <td>n eagle rock blvd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>11787</td>\n",
       "      <td>reilly's</td>\n",
       "      <td>100 world way # 120</td>\n",
       "      <td>False</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>9</td>\n",
       "      <td>world way</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>11788</td>\n",
       "      <td>street churros</td>\n",
       "      <td>6801 hollywood blvd # 253</td>\n",
       "      <td>False</td>\n",
       "      <td>fast food</td>\n",
       "      <td>20</td>\n",
       "      <td>hollywood blvd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>11789</td>\n",
       "      <td>triniti echo park</td>\n",
       "      <td>1814 w sunset blvd</td>\n",
       "      <td>False</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>22</td>\n",
       "      <td>w sunset blvd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>11790</td>\n",
       "      <td>pollen</td>\n",
       "      <td>2100 echo park ave</td>\n",
       "      <td>False</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>20</td>\n",
       "      <td>echo park ave</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id             est_name                    address  is_chain  \\\n",
       "0  11786  habitat coffee shop     3708 n eagle rock blvd     False   \n",
       "1  11787             reilly's        100 world way # 120     False   \n",
       "2  11788       street churros  6801 hollywood blvd # 253     False   \n",
       "3  11789    triniti echo park         1814 w sunset blvd     False   \n",
       "4  11790               pollen         2100 echo park ave     False   \n",
       "\n",
       "     est_type  n_seats        street_name  \n",
       "0        cafe       26  n eagle rock blvd  \n",
       "1  restaurant        9         world way   \n",
       "2   fast food       20    hollywood blvd   \n",
       "3  restaurant       22      w sunset blvd  \n",
       "4  restaurant       20      echo park ave  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract street names from 'address' column using regex\n",
    "\n",
    "est_data['street_name'] = est_data['address'].str.extract(r'(((?<= )[\\w]+) [a-z0-9 ]+)+').reset_index(drop=True)[0]\n",
    "est_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 10 steets by no. of restaurants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "# sort top 10 & plot\n",
    "\n",
    "rest_only = est_data.query('est_type == \"restaurant\"')\n",
    "n_rest_by_street = rest_only.groupby('street_name')['id'].count().reset_index()\n",
    "\n",
    "top_10_streets = n_rest_by_street.sort_values(by='id', ascending=False)[:10]\n",
    "\n",
    "ax = sns.barplot(x='street_name', y='id', data=top_10_streets)\n",
    "ax.set_title('Top 10 Streets by No. of Restaurants')\n",
    "ax.set_xlabel('Street name')\n",
    "ax.set_ylabel('No. of Restaurants')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Conclusion\n",
    "Pico Blvd and Sunset Blvd have the highest number of restaurants. Some other popular streets are Hollywood Blvd, Wilshire Blvd, and W. 3rd St. Pico and Sunset are by far the leaders in this metric."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Streets with only one restaurant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Streets with only one restaurant: 1107\n"
     ]
    }
   ],
   "source": [
    "# print # of streets with only one restaurant\n",
    "\n",
    "print('Streets with only one restaurant:', n_rest_by_street.query('id == 1')['id'].count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number of seats for restaurants on popular streets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": [
    "# filter for only top 10 street names\n",
    "\n",
    "ax = sns.distplot(rest_only[rest_only['street_name'].isin(top_10_streets['street_name'])]['n_seats'], bins=20)\n",
    "ax.set_title('Number of seats for restaurants on popular streets')\n",
    "ax.set_xlabel('No. of seats')\n",
    "ax.set_ylabel('Frequency (normalized)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Conclusion\n",
    "This distribution closely mirrors the one above for chain restaurants. It's likely that most of the restaurants on the most popular streets in LA are chain restaurants. Their strategy is to have a high number of establishments with less than 50-60 seats in each establishment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Preparing a presentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Presentation: https://drive.google.com/file/d/1l6TH9eXIfXRJJysAyS9a5zgzCPgSRbPZ/view?usp=sharing"
   ]
  }
 ],
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