Battle of Neighborhoods Week 2-Final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Battle of Neighborhoods W2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This is the second portion Week 2 of Capstone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import requests\n",
    "import csv\n",
    "import json\n",
    "from pandas.io.json import json_normalize\n",
    "from geopy.geocoders import Nominatim\n",
    "import requests\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "from matplotlib.colors import rgb2hex\n",
    "from sklearn.cluster import KMeans\n",
    "import folium\n",
    "import math\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.width', None)\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "\n",
    "#import matplotlib \n",
    "#import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reload Data set  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(495, 14)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://github.com/casalf724/Capstone_Project/blob/main/week1Check.csv?raw=true'\n",
    "df = pd.read_csv(url, index_col=0).reset_index(drop=False)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>index</th>\n",
       "      <th>Name</th>\n",
       "      <th>City</th>\n",
       "      <th>Miles to Ft Laud</th>\n",
       "      <th>Miles to Palm Beach</th>\n",
       "      <th>DB Hoovers Industry</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Ownership Type</th>\n",
       "      <th>Parent Country_Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>494</td>\n",
       "      <td>Kemet Corporation</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>0.04</td>\n",
       "      <td>41.34</td>\n",
       "      <td>Semiconductor and Other Electronic Component Manufacturing</td>\n",
       "      <td>InfoTech</td>\n",
       "      <td>Private</td>\n",
       "      <td>Taiwan Region</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>493</td>\n",
       "      <td>Ri/Bbnm Acquisition Corp.</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>0.16</td>\n",
       "      <td>41.53</td>\n",
       "      <td>Investment Services</td>\n",
       "      <td>Financial</td>\n",
       "      <td>Private</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>492</td>\n",
       "      <td>Advanced Recovery Systems, LLC</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>0.22</td>\n",
       "      <td>41.40</td>\n",
       "      <td>Outpatient Care</td>\n",
       "      <td>LifeScience</td>\n",
       "      <td>Private</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>489</td>\n",
       "      <td>Templeton International, Inc.</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>0.24</td>\n",
       "      <td>41.48</td>\n",
       "      <td>Investment Services</td>\n",
       "      <td>Financial</td>\n",
       "      <td>Private</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>490</td>\n",
       "      <td>Templeton/Franklin Investment Services, Inc</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>0.24</td>\n",
       "      <td>41.48</td>\n",
       "      <td>Investment Services</td>\n",
       "      <td>Financial</td>\n",
       "      <td>Private</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                                         Name             City  \\\n",
       "0  494    Kemet Corporation                            Fort Lauderdale   \n",
       "1  493    Ri/Bbnm Acquisition Corp.                    Fort Lauderdale   \n",
       "2  492    Advanced Recovery Systems, LLC               Fort Lauderdale   \n",
       "3  489    Templeton International, Inc.                Fort Lauderdale   \n",
       "4  490    Templeton/Franklin Investment Services, Inc  Fort Lauderdale   \n",
       "\n",
       "   Miles to Ft Laud  Miles to Palm Beach  \\\n",
       "0  0.04              41.34                 \n",
       "1  0.16              41.53                 \n",
       "2  0.22              41.40                 \n",
       "3  0.24              41.48                 \n",
       "4  0.24              41.48                 \n",
       "\n",
       "                                          DB Hoovers Industry     Industry  \\\n",
       "0  Semiconductor and Other Electronic Component Manufacturing  InfoTech      \n",
       "1  Investment Services                                         Financial     \n",
       "2  Outpatient Care                                             LifeScience   \n",
       "3  Investment Services                                         Financial     \n",
       "4  Investment Services                                         Financial     \n",
       "\n",
       "  Ownership Type Parent Country_Region  \n",
       "0  Private        Taiwan Region         \n",
       "1  Private        United States         \n",
       "2  Private        United States         \n",
       "3  Private        United States         \n",
       "4  Private        United States         "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['Postal Code', 'Latitude', 'Longitude', 'Revenue', 'Employees'], axis=1).reset_index(drop=True) # should we remove postal code?\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Mining the Data (Data Visualization) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Which cities have the most businesses?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(38, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>City</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Palm Beach Gardens</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Sunrise</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Boca Raton</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  City  Name\n",
       "33  Palm Beach Gardens  24  \n",
       "34  Sunrise             27  \n",
       "35  West Palm Beach     57  \n",
       "36  Boca Raton          89  \n",
       "37  Fort Lauderdale     98  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Chart of Cities based on number of businesses meeting criteria\n",
    "city_group = df.groupby('City').count()\n",
    "bar = city_group['Name'].reset_index(drop=False)\n",
    "bar = bar.sort_values(by='Name', ascending=True).reset_index(drop=True)\n",
    "print(bar.shape)\n",
    "bar.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The data is for 38 cities\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bus_count = bar['Name']\n",
    "city = bar['City']\n",
    "\n",
    "colors = cm.rainbow(np.linspace(0, 1, len(bus_count.index)))\n",
    "plt.figure(figsize = (16, 12))\n",
    "plt.xticks(rotation = 90)\n",
    "plt.xlabel(\"Count\", fontsize = 16)\n",
    "plt.ylabel(\"City\", fontsize = 16)\n",
    "plt.title(\"Number of businesses in each City\", fontsize = 16)\n",
    "plt.barh(city, bus_count.values, color = colors)\n",
    "print('The data is for {} cities'.format(len(bar['City'].unique())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many businesses per DB Hoover Industry Category?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of businesses per DBH database Industry categories \n",
    "df_cat = df.groupby(['DB Hoovers Industry']).count()\n",
    "df_cat = df_cat['Name'].reset_index(drop=False)\n",
    "df_cat = df_cat.sort_values(by='Name', ascending=False).reset_index(drop=True)\n",
    "#print(df_cat.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As a percent of total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25, 2)\n"
     ]
    },
    {
     "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>DBH Category</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Physicians and Health Practitioners</td>\n",
       "      <td>0.179798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Computer Programming</td>\n",
       "      <td>0.092929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Banking</td>\n",
       "      <td>0.088889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Computer System Design Services</td>\n",
       "      <td>0.080808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hospitals</td>\n",
       "      <td>0.074747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Investment Services</td>\n",
       "      <td>0.072727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Health and Personal Care Wholesale</td>\n",
       "      <td>0.050505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Pharmaceutical Manufacturing</td>\n",
       "      <td>0.046465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Medical Equipment and Supplies</td>\n",
       "      <td>0.042424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Electricity Generation and Distribution</td>\n",
       "      <td>0.036364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              DBH Category     Count\n",
       "0  Physicians and Health Practitioners      0.179798\n",
       "1  Computer Programming                     0.092929\n",
       "2  Banking                                  0.088889\n",
       "3  Computer System Design Services          0.080808\n",
       "4  Hospitals                                0.074747\n",
       "5  Investment Services                      0.072727\n",
       "6  Health and Personal Care Wholesale       0.050505\n",
       "7  Pharmaceutical Manufacturing             0.046465\n",
       "8  Medical Equipment and Supplies           0.042424\n",
       "9  Electricity Generation and Distribution  0.036364"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfcat = df['DB Hoovers Industry'].value_counts(normalize=True).reset_index(drop=False)\n",
    "dfcat.columns = ['DBH Category', 'Count']\n",
    "print(df_cat.shape)\n",
    "dfcat.head(10) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "\n",
    "cat_count = df_cat['Name']\n",
    "dbh_cat = df_cat['DB Hoovers Industry']\n",
    "dfcat[\"cumpercentage\"] = dfcat[\"Count\"].cumsum()/dfcat[\"Count\"].sum()*100\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (16,12), sharex='all')\n",
    "colors = cm.rainbow(np.linspace(0, 1, len(cat_count.index)))\n",
    "\n",
    "plt.xticks(rotation = 90) \n",
    "plt.xlabel(\"Business Category\", fontsize = 16)\n",
    "\n",
    "ax.bar(dbh_cat, cat_count.values, color = colors) # change back to plt\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(dfcat.index, dfcat[\"cumpercentage\"], color=\"C1\", marker=\"D\", ms=7)\n",
    "ax2.yaxis.set_major_formatter(PercentFormatter())\n",
    "ax.set_xlabel(\"DBH Category\", fontsize = 16)\n",
    "\n",
    "\n",
    "plt.ylabel(\"Percent\", fontsize = 16)\n",
    "plt.title(\"No of Businesses in each DB Hoover Industry Category\", fontsize = 16)\n",
    "ax.tick_params(axis=\"y\", colors=\"C0\")\n",
    "ax2.tick_params(axis=\"y\", colors=\"C1\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#city_df.to_csv('week2.csv') # explort file for seeding geocooridnates for city data only."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the distribution of Ownership Type across the businesses?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Ownership Type</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Private</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Partnership</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nonprofit</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Public</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Ownership Type  Name\n",
       "0  Private        432 \n",
       "1  Partnership    26  \n",
       "2  Nonprofit      23  \n",
       "3  Public         14  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Chart of data set based on number of businesses ownership type \n",
    "ownertype = df.groupby('Ownership Type').count()\n",
    "ownertype = ownertype['Name'].reset_index(drop=False)\n",
    "ownertype = ownertype.sort_values(by='Name', ascending=False).reset_index(drop=True)\n",
    "ownertype.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Ownership Type</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Private</td>\n",
       "      <td>87.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Partnership</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nonprofit</td>\n",
       "      <td>4.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Public</td>\n",
       "      <td>2.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Ownership Type  Percent\n",
       "0  Private        87.27  \n",
       "1  Partnership    5.25   \n",
       "2  Nonprofit      4.65   \n",
       "3  Public         2.83   "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "owntype = df['Ownership Type'].value_counts(normalize=True).mul(100).reset_index(drop=False)\n",
    "owntype.columns = ['Ownership Type', 'Percent'] #owntype.style.hide_index()\n",
    "rounded_df = owntype.round(decimals=2)\n",
    "rounded_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the distribution with regards to Parent Country / Region?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Parent Country_Region</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>93.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Canada</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Israel</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Australia</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Spain</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>France</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Italy</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Taiwan Region</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bermuda</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Hong Kong SAR</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Japan</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Parent Country_Region  Percent\n",
       "0   United States         93.13  \n",
       "1   United Kingdom        1.21   \n",
       "2   Canada                0.81   \n",
       "3   Netherlands           0.61   \n",
       "4   Ireland               0.61   \n",
       "5   Israel                0.61   \n",
       "6   Sweden                0.40   \n",
       "7   Australia             0.40   \n",
       "8   Spain                 0.40   \n",
       "9   France                0.20   \n",
       "10  Italy                 0.20   \n",
       "11  Mexico                0.20   \n",
       "12  Taiwan Region         0.20   \n",
       "13  Bermuda               0.20   \n",
       "14  Hong Kong SAR         0.20   \n",
       "15  South Africa          0.20   \n",
       "16  Switzerland           0.20   \n",
       "17  Japan                 0.20   "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region = df['Parent Country_Region'].value_counts(normalize=True).mul(100).reset_index(drop=False)\n",
    "region.columns = ['Parent Country_Region', 'Percent']\n",
    "decimal_df = region.round(decimals=2)\n",
    "decimal_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregating counts by City"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the top ten cities based on number of businesses?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(38, 9)\n"
     ]
    },
    {
     "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>City</th>\n",
       "      <th>index</th>\n",
       "      <th>Name</th>\n",
       "      <th>Miles to Ft Laud</th>\n",
       "      <th>Miles to Palm Beach</th>\n",
       "      <th>DB Hoovers Industry</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Ownership Type</th>\n",
       "      <th>Parent Country_Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Belle Glade</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boca Raton</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Boynton Beach</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Coconut Creek</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Coral Springs</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            City  index  Name  Miles to Ft Laud  Miles to Palm Beach  \\\n",
       "0  Belle Glade    1      1     1                 1                     \n",
       "1  Boca Raton     89     89    89                89                    \n",
       "2  Boynton Beach  7      7     7                 7                     \n",
       "3  Coconut Creek  1      1     1                 1                     \n",
       "4  Coral Springs  14     14    14                14                    \n",
       "\n",
       "   DB Hoovers Industry  Industry  Ownership Type  Parent Country_Region  \n",
       "0  1                    1         1               1                      \n",
       "1  89                   89        89              89                     \n",
       "2  7                    7         7               7                      \n",
       "3  1                    1         1               1                      \n",
       "4  14                   14        14              14                     "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_df = df.groupby('City').count().reset_index(drop=False)\n",
    "print(city_df.shape)\n",
    "city_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_df.to_csv('week2.csv') # Extract file for seeding with geo cooridnates for cities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add geo coordinates for each city to map cities. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(38, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>City</th>\n",
       "      <th>Businesses</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Belle Glade</td>\n",
       "      <td>1</td>\n",
       "      <td>26.684510</td>\n",
       "      <td>-80.667558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boca Raton</td>\n",
       "      <td>89</td>\n",
       "      <td>26.368306</td>\n",
       "      <td>-80.128932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Boynton Beach</td>\n",
       "      <td>7</td>\n",
       "      <td>26.531787</td>\n",
       "      <td>-80.090547</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            City  Businesses   Latitude  Longitude\n",
       "0  Belle Glade    1           26.684510 -80.667558\n",
       "1  Boca Raton     89          26.368306 -80.128932\n",
       "2  Boynton Beach  7           26.531787 -80.090547"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "urlg = 'https://github.com/casalf724/Capstone_Project/blob/main/week2seeded.csv?raw=true'\n",
    "cityloc = pd.read_csv(urlg, index_col=0).reset_index(drop=True) # prior was False\n",
    "print(cityloc.shape)\n",
    "cityloc.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregate counts for businesses and get median distance to Ft Laudedate and Palm Beach for each city. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(38, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>City</th>\n",
       "      <th>Count</th>\n",
       "      <th>MedD_FL</th>\n",
       "      <th>MedD_PB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Belle Glade</td>\n",
       "      <td>1</td>\n",
       "      <td>52.88</td>\n",
       "      <td>38.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boca Raton</td>\n",
       "      <td>89</td>\n",
       "      <td>18.76</td>\n",
       "      <td>22.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Boynton Beach</td>\n",
       "      <td>7</td>\n",
       "      <td>28.04</td>\n",
       "      <td>14.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            City  Count  MedD_FL  MedD_PB\n",
       "0  Belle Glade    1      52.88    38.13  \n",
       "1  Boca Raton     89     18.76    22.61  \n",
       "2  Boynton Beach  7      28.04    14.73  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cty = df.groupby('City').agg(\n",
    "    Count=pd.NamedAgg(column=\"Name\", aggfunc=\"count\"),\n",
    "    MedD_FL=pd.NamedAgg(column=\"Miles to Ft Laud\", aggfunc=\"median\"),\n",
    "    MedD_PB=pd.NamedAgg(column=\"Miles to Palm Beach\", aggfunc=\"median\")).reset_index(drop=False)\n",
    "print(cty.shape)\n",
    "cty.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We noticed that median distance to Fort Lauderdale conflicts with Median distance to Palm Beach. This comfounds the k-means alogrithm since both positions will pull against each other. Palm Beach was set as additional criteria over Fort Lauderdale see report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>City</th>\n",
       "      <th>Count</th>\n",
       "      <th>MedD_PB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Belle Glade</td>\n",
       "      <td>1</td>\n",
       "      <td>38.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boca Raton</td>\n",
       "      <td>89</td>\n",
       "      <td>22.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Boynton Beach</td>\n",
       "      <td>7</td>\n",
       "      <td>14.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Coconut Creek</td>\n",
       "      <td>1</td>\n",
       "      <td>29.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            City  Count  MedD_PB\n",
       "0  Belle Glade    1      38.13  \n",
       "1  Boca Raton     89     22.61  \n",
       "2  Boynton Beach  7      14.73  \n",
       "3  Coconut Creek  1      29.20  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb_cty = cty.drop(['MedD_FL'], axis=1).reset_index(drop=True)\n",
    "pb_cty.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze Business type count for each City."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create dummy variables to analyze across Key Industry Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(495, 5)\n"
     ]
    },
    {
     "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>City</th>\n",
       "      <th>Clean Tech</th>\n",
       "      <th>Financial</th>\n",
       "      <th>InfoTech</th>\n",
       "      <th>LifeScience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              City  Clean Tech  Financial  InfoTech  LifeScience\n",
       "0  Fort Lauderdale  0           0          1         0          \n",
       "1  Fort Lauderdale  0           1          0         0          \n",
       "2  Fort Lauderdale  0           0          0         1          "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "industry_onehot = pd.get_dummies(df[['Industry']], prefix=\"\", prefix_sep=\"\") # one hot encoding\n",
    "industry_onehot['City'] = df['City'] # add neighborhood column back to dataframe\n",
    "\n",
    "# move neighborhood column to the first column\n",
    "fixed_columns = [industry_onehot.columns[-1]] + list(industry_onehot.columns[:-1])\n",
    "industry_onehot = industry_onehot[fixed_columns]\n",
    "print(industry_onehot.shape)\n",
    "industry_onehot.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(38, 5)\n"
     ]
    },
    {
     "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>City</th>\n",
       "      <th>Clean Tech</th>\n",
       "      <th>Financial</th>\n",
       "      <th>InfoTech</th>\n",
       "      <th>LifeScience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Belle Glade</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boca Raton</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>34</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Boynton Beach</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Coconut Creek</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Coral Springs</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            City  Clean Tech  Financial  InfoTech  LifeScience\n",
       "0  Belle Glade    0           0          0         1          \n",
       "1  Boca Raton     0           16         34        39         \n",
       "2  Boynton Beach  0           1          0         6          \n",
       "3  Coconut Creek  0           0          0         1          \n",
       "4  Coral Springs  0           3          4         7          "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group sum for each industry by city\n",
    "city_ind_group = industry_onehot.groupby('City').sum().reset_index()\n",
    "print(city_ind_group.shape)\n",
    "city_ind_group.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Put combine attributes into a pandas dataframe for cluster algorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract columns for k-means clustering into a data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>City</th>\n",
       "      <th>Count</th>\n",
       "      <th>MedD_PB</th>\n",
       "      <th>Clean Tech</th>\n",
       "      <th>Financial</th>\n",
       "      <th>InfoTech</th>\n",
       "      <th>LifeScience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Belle Glade</td>\n",
       "      <td>1</td>\n",
       "      <td>38.13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boca Raton</td>\n",
       "      <td>89</td>\n",
       "      <td>22.61</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>34</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Boynton Beach</td>\n",
       "      <td>7</td>\n",
       "      <td>14.73</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Coconut Creek</td>\n",
       "      <td>1</td>\n",
       "      <td>29.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Coral Springs</td>\n",
       "      <td>14</td>\n",
       "      <td>33.27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            City  Count  MedD_PB  Clean Tech  Financial  InfoTech  LifeScience\n",
       "0  Belle Glade    1      38.13    0           0          0         1          \n",
       "1  Boca Raton     89     22.61    0           16         34        39         \n",
       "2  Boynton Beach  7      14.73    0           1          0         6          \n",
       "3  Coconut Creek  1      29.20    0           0          0         1          \n",
       "4  Coral Springs  14     33.27    0           3          4         7          "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb_cty = pb_cty.join(city_ind_group.set_index('City'), on='City') # add city_ind_group (Key Indystries)\n",
    "pb_cty.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis - k means "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the optimal k value for clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": [
    "city_ind_group_clustering = pb_cty.drop('City', 1) # changed to cty dataframe\n",
    "\n",
    "sse = {}\n",
    "for k in range(1,7): # ran to 8\n",
    "    kmeans = KMeans(n_clusters=k, random_state=0) \n",
    "    kmeans.fit(pb_cty.drop('City',axis=1))\n",
    "    city_ind_group['Cluster'] = kmeans.labels_\n",
    "    sse[k] = kmeans.inertia_\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(list(sse.keys()), list(sse.values()))\n",
    "plt.xlabel(\"Number of clusters\")\n",
    "plt.ylabel(\"SSE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Perform Clustering with k=3 clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 0, 2, 2, 2, 2, 2, 2, 0])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kclusters = 3 # set number of clusters based on elbow\n",
    "ind_group_clustering = pb_cty.drop('City', 1) # changed to cty dataframe\n",
    "kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(ind_group_clustering) # run k-means clustering\n",
    "kmeans.labels_[0:10]  # check cluster labels generated for each row in the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>Businesses</th>\n",
       "      <th>LifeScience</th>\n",
       "      <th>InfoTech</th>\n",
       "      <th>Financial</th>\n",
       "      <th>Clean Tech</th>\n",
       "      <th>MedD_PB</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Belle Glade</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38.13</td>\n",
       "      <td>26.684510</td>\n",
       "      <td>-80.667558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boca Raton</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>39</td>\n",
       "      <td>34</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>22.61</td>\n",
       "      <td>26.368306</td>\n",
       "      <td>-80.128932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Boynton Beach</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14.73</td>\n",
       "      <td>26.531787</td>\n",
       "      <td>-80.090547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Coconut Creek</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.20</td>\n",
       "      <td>26.251748</td>\n",
       "      <td>-80.178935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Coral Springs</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>33.27</td>\n",
       "      <td>26.271192</td>\n",
       "      <td>-80.270604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            City  Cluster Labels  Businesses  LifeScience  InfoTech  \\\n",
       "0  Belle Glade    2               1           1            0          \n",
       "1  Boca Raton     1               89          39           34         \n",
       "2  Boynton Beach  0               7           6            0          \n",
       "3  Coconut Creek  2               1           1            0          \n",
       "4  Coral Springs  2               14          7            4          \n",
       "\n",
       "   Financial  Clean Tech  MedD_PB   Latitude  Longitude  \n",
       "0  0          0           38.13    26.684510 -80.667558  \n",
       "1  16         0           22.61    26.368306 -80.128932  \n",
       "2  1          0           14.73    26.531787 -80.090547  \n",
       "3  0          0           29.20    26.251748 -80.178935  \n",
       "4  3          0           33.27    26.271192 -80.270604  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge data frames add cluster labels and latitude/longitude then reorder\n",
    "pb_cty.insert(0, 'Cluster Labels', kmeans.labels_) # add clustering labels\n",
    "pb_cty_merged = pb_cty.join(cityloc.set_index('City'), on='City') # add (city geo coordinates)\n",
    "pb_cty_merged = pb_cty_merged[['City', 'Cluster Labels', 'Businesses', 'LifeScience', 'InfoTech', 'Financial', 'Clean Tech', 'MedD_PB', 'Latitude', 'Longitude']]\n",
    "pb_cty_merged.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The geograpical coordinate of Fort Lauderdale are 26.1223084, -80.1433786.\n"
     ]
    }
   ],
   "source": [
    "address = 'Fort Lauderdale, FL'\n",
    "geolocator = Nominatim(user_agent=\"ny_explorer\")\n",
    "location = geolocator.geocode(address)\n",
    "latitude = location.latitude\n",
    "longitude = location.longitude\n",
    "print('The geograpical coordinate of Fort Lauderdale are {}, {}.'.format(latitude, longitude))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
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            popup_0d1ec6378919409aadd283dd3f9d80f8.setContent(html_f73106b2dd3e46fbb04d5552097a0fc7);
        

        circle_marker_fcdfdcbfba0c401b9ec9aa944f005279.bindPopup(popup_0d1ec6378919409aadd283dd3f9d80f8)
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            var circle_marker_55846978364a4551b3f24745add9d2c3 = L.circleMarker(
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        var popup_21f08bc830d744a6ba6e81450039f1f7 = L.popup({"maxWidth": "100%"});

        
            var html_babb2f5130a540688185cc48eb0bc7b9 = $(`<div id="html_babb2f5130a540688185cc48eb0bc7b9" style="width: 100.0%; height: 100.0%;">Hallandale Cluster2</div>`)[0];
            popup_21f08bc830d744a6ba6e81450039f1f7.setContent(html_babb2f5130a540688185cc48eb0bc7b9);
        

        circle_marker_55846978364a4551b3f24745add9d2c3.bindPopup(popup_21f08bc830d744a6ba6e81450039f1f7)
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            var circle_marker_0ecae959c2a3417faed8460e8a82fdbe = L.circleMarker(
                [25.981202399999997, -80.148379],
                {"bubblingMouseEvents": true, "color": "#80ffb4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#80ffb4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
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        var popup_708770b0a619464d86c5da341e49863e = L.popup({"maxWidth": "100%"});

        
            var html_8e2887ceb3d1410e86a427d939895ac6 = $(`<div id="html_8e2887ceb3d1410e86a427d939895ac6" style="width: 100.0%; height: 100.0%;">Hallandale Beach Cluster2</div>`)[0];
            popup_708770b0a619464d86c5da341e49863e.setContent(html_8e2887ceb3d1410e86a427d939895ac6);
        

        circle_marker_0ecae959c2a3417faed8460e8a82fdbe.bindPopup(popup_708770b0a619464d86c5da341e49863e)
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            var circle_marker_8e70e7f96dc14456b7c8ad72d5995cc1 = L.circleMarker(
                [26.0112014, -80.1494901],
                {"bubblingMouseEvents": true, "color": "#80ffb4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#80ffb4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_29dd5ab6631c4bd3831f95e7f09eb1ee);
        
    
        var popup_7da3054dfbcd41f197cde21c4ea19257 = L.popup({"maxWidth": "100%"});

        
            var html_6d95f863a74542f6850fcf37485d1e9e = $(`<div id="html_6d95f863a74542f6850fcf37485d1e9e" style="width: 100.0%; height: 100.0%;">Hollywood Cluster2</div>`)[0];
            popup_7da3054dfbcd41f197cde21c4ea19257.setContent(html_6d95f863a74542f6850fcf37485d1e9e);
        

        circle_marker_8e70e7f96dc14456b7c8ad72d5995cc1.bindPopup(popup_7da3054dfbcd41f197cde21c4ea19257)
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                [26.879781899999998, -80.0533743],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_29dd5ab6631c4bd3831f95e7f09eb1ee);
        
    
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            popup_940daacafc1d49e5a7f6df6e67c75dcb.setContent(html_83b00f6dbb9d427b9ab7bf0117bf80f1);
        

        circle_marker_65dc9ab661994ce1a03d53e50edb0a81.bindPopup(popup_940daacafc1d49e5a7f6df6e67c75dcb)
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            var circle_marker_f4290780b1ff48968253521a1fe02978 = L.circleMarker(
                [26.934224600000004, -80.09420870000001],
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            ).addTo(map_29dd5ab6631c4bd3831f95e7f09eb1ee);
        
    
        var popup_1a8175aea2234816afea771682579ae2 = L.popup({"maxWidth": "100%"});

        
            var html_bd89a9ca66e34458ad4fffab9f169e3d = $(`<div id="html_bd89a9ca66e34458ad4fffab9f169e3d" style="width: 100.0%; height: 100.0%;">Jupiter Cluster0</div>`)[0];
            popup_1a8175aea2234816afea771682579ae2.setContent(html_bd89a9ca66e34458ad4fffab9f169e3d);
        

        circle_marker_f4290780b1ff48968253521a1fe02978.bindPopup(popup_1a8175aea2234816afea771682579ae2)
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            var circle_marker_d46d91f8697a4ccab0bac67cb81df9db = L.circleMarker(
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                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_29dd5ab6631c4bd3831f95e7f09eb1ee);
        
    
        var popup_fcffdf7aa3a246e4b21b889b96400fee = L.popup({"maxWidth": "100%"});

        
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            popup_fcffdf7aa3a246e4b21b889b96400fee.setContent(html_cc5516df10c64cccb4e677fa148e71f8);
        

        circle_marker_d46d91f8697a4ccab0bac67cb81df9db.bindPopup(popup_fcffdf7aa3a246e4b21b889b96400fee)
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            var circle_marker_24455310f5f142edbad700455a1f2a00 = L.circleMarker(
                [26.1664736, -80.2083806],
                {"bubblingMouseEvents": true, "color": "#80ffb4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#80ffb4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_29dd5ab6631c4bd3831f95e7f09eb1ee);
        
    
        var popup_ce656fe01e6748f89c76fbbb790529c2 = L.popup({"maxWidth": "100%"});

        
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            popup_ce656fe01e6748f89c76fbbb790529c2.setContent(html_2bbbdb3b338f42509e222163bc54c49c);
        

        circle_marker_24455310f5f142edbad700455a1f2a00.bindPopup(popup_ce656fe01e6748f89c76fbbb790529c2)
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            var circle_marker_2ef27255906a4efca51def1ce1c10310 = L.circleMarker(
                [26.275636, -80.0872654],
                {"bubblingMouseEvents": true, "color": "#80ffb4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#80ffb4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_29dd5ab6631c4bd3831f95e7f09eb1ee);
        
    
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            var html_aaacf7409aaf4943b97c7a2f1555461c = $(`<div id="html_aaacf7409aaf4943b97c7a2f1555461c" style="width: 100.0%; height: 100.0%;">Lighthouse Point Cluster2</div>`)[0];
            popup_159f732de4d0481a9ac37d581e799d47.setContent(html_aaacf7409aaf4943b97c7a2f1555461c);
        

        circle_marker_2ef27255906a4efca51def1ce1c10310.bindPopup(popup_159f732de4d0481a9ac37d581e799d47)
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            var circle_marker_d647c06a37124fc3a5bffe1b316d4f7a = L.circleMarker(
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                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_29dd5ab6631c4bd3831f95e7f09eb1ee);
        
    
        var popup_9fb676d406a34d47b542f531f0901f28 = L.popup({"maxWidth": "100%"});

        
            var html_4f1717970de04d49ad25616daa0cf3fd = $(`<div id="html_4f1717970de04d49ad25616daa0cf3fd" style="width: 100.0%; height: 100.0%;">Loxahatchee Cluster0</div>`)[0];
            popup_9fb676d406a34d47b542f531f0901f28.setContent(html_4f1717970de04d49ad25616daa0cf3fd);
        

        circle_marker_d647c06a37124fc3a5bffe1b316d4f7a.bindPopup(popup_9fb676d406a34d47b542f531f0901f28)
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            var circle_marker_ab29478310554f41a293d775a556cf88 = L.circleMarker(
                [26.244526300000004, -80.206436],
                {"bubblingMouseEvents": true, "color": "#80ffb4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#80ffb4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_29dd5ab6631c4bd3831f95e7f09eb1ee);
        
    
        var popup_db114711c7434df1863aec4745490a1e = L.popup({"maxWidth": "100%"});

        
            var html_e7b762fc73ef468994dfacd84125d200 = $(`<div id="html_e7b762fc73ef468994dfacd84125d200" style="width: 100.0%; height: 100.0%;">Margate Cluster2</div>`)[0];
            popup_db114711c7434df1863aec4745490a1e.setContent(html_e7b762fc73ef468994dfacd84125d200);
        

        circle_marker_ab29478310554f41a293d775a556cf88.bindPopup(popup_db114711c7434df1863aec4745490a1e)
        ;

        
    
    
            var circle_marker_8a739a33015c4edcbcf45a83e1502a27 = L.circleMarker(
                [25.9860762, -80.3035602],
                {"bubblingMouseEvents": true, "color": "#80ffb4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#80ffb4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_29dd5ab6631c4bd3831f95e7f09eb1ee);
        
    
        var popup_cab0c51c54cc486799eb205752cb4fd3 = L.popup({"maxWidth": "100%"});

        
            var html_8b26673f478e43e4a882749c52ac096f = $(`<div id="html_8b26673f478e43e4a882749c52ac096f" style="width: 100.0%; height: 100.0%;">Miramar Cluster2</div>`)[0];
            popup_cab0c51c54cc486799eb205752cb4fd3.setContent(html_8b26673f478e43e4a882749c52ac096f);
        

        circle_marker_8a739a33015c4edcbcf45a83e1502a27.bindPopup(popup_cab0c51c54cc486799eb205752cb4fd3)
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            var circle_marker_81b5b830ab5a43318ec5e8e69a5897d6 = L.circleMarker(
                [26.817116499999997, -80.0590804],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_29dd5ab6631c4bd3831f95e7f09eb1ee);
        
    
        var popup_3208e4e020464f82ab1cd9e1fa36fb73 = L.popup({"maxWidth": "100%"});

        
            var html_1ad0a29760b34f3ab43b847510e1fcdc = $(`<div id="html_1ad0a29760b34f3ab43b847510e1fcdc" style="width: 100.0%; height: 100.0%;">North Palm Beach Cluster0</div>`)[0];
            popup_3208e4e020464f82ab1cd9e1fa36fb73.setContent(html_1ad0a29760b34f3ab43b847510e1fcdc);
        

        circle_marker_81b5b830ab5a43318ec5e8e69a5897d6.bindPopup(popup_3208e4e020464f82ab1cd9e1fa36fb73)
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            var circle_marker_9f77ca42cf5c439ca2c9f161648fb469 = L.circleMarker(
                [26.1723065, -80.13198929999999],
                {"bubblingMouseEvents": true, "color": "#80ffb4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#80ffb4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_29dd5ab6631c4bd3831f95e7f09eb1ee);
        
    
        var popup_41f27f8846cd45699b220295bf2c1623 = L.popup({"maxWidth": "100%"});

        
            var html_2f13abded2cb451c9520dfe59309a197 = $(`<div id="html_2f13abded2cb451c9520dfe59309a197" style="width: 100.0%; height: 100.0%;">Oakland Park Cluster2</div>`)[0];
            popup_41f27f8846cd45699b220295bf2c1623.setContent(html_2f13abded2cb451c9520dfe59309a197);
        

        circle_marker_9f77ca42cf5c439ca2c9f161648fb469.bindPopup(popup_41f27f8846cd45699b220295bf2c1623)
        ;

        
    
    
            var circle_marker_a845c402dd8f4e6ea182bff7eb28a18a = L.circleMarker(
                [26.7056206, -80.0364297],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_29dd5ab6631c4bd3831f95e7f09eb1ee);
        
    
        var popup_cb44b56d11ea4b12b3b63f0c27d7e1db = L.popup({"maxWidth": "100%"});

        
            var html_8cbf28717f7d44b3aaa18e6684bae7e3 = $(`<div id="html_8cbf28717f7d44b3aaa18e6684bae7e3" style="width: 100.0%; height: 100.0%;">Palm Beach Cluster0</div>`)[0];
            popup_cb44b56d11ea4b12b3b63f0c27d7e1db.setContent(html_8cbf28717f7d44b3aaa18e6684bae7e3);
        

        circle_marker_a845c402dd8f4e6ea182bff7eb28a18a.bindPopup(popup_cb44b56d11ea4b12b3b63f0c27d7e1db)
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            var circle_marker_b9ab9554c124474a9869d4b258ac56f8 = L.circleMarker(
                [26.839609600000003, -80.10191440000001],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_29dd5ab6631c4bd3831f95e7f09eb1ee);
        
    
        var popup_b4581f77d4a24410aad01943dc8e8761 = L.popup({"maxWidth": "100%"});

        
            var html_8732a2ad1c30411c90d27a870b689dd4 = $(`<div id="html_8732a2ad1c30411c90d27a870b689dd4" style="width: 100.0%; height: 100.0%;">Palm Beach Gardens Cluster0</div>`)[0];
            popup_b4581f77d4a24410aad01943dc8e8761.setContent(html_8732a2ad1c30411c90d27a870b689dd4);
        

        circle_marker_b9ab9554c124474a9869d4b258ac56f8.bindPopup(popup_b4581f77d4a24410aad01943dc8e8761)
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            var circle_marker_02c79766079841aeb0d7312ff5373c83 = L.circleMarker(
                [26.635900899999996, -80.0961538],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_29dd5ab6631c4bd3831f95e7f09eb1ee);
        
    
        var popup_5ff361164ba4490ab0f41ad9ee97de38 = L.popup({"maxWidth": "100%"});

        
            var html_65bad6f8786346b49abffb5727ae1fe4 = $(`<div id="html_65bad6f8786346b49abffb5727ae1fe4" style="width: 100.0%; height: 100.0%;">Palm Springs Cluster0</div>`)[0];
            popup_5ff361164ba4490ab0f41ad9ee97de38.setContent(html_65bad6f8786346b49abffb5727ae1fe4);
        

        circle_marker_02c79766079841aeb0d7312ff5373c83.bindPopup(popup_5ff361164ba4490ab0f41ad9ee97de38)
        ;

        
    
    
            var circle_marker_235309adcdda4392ba92144853935900 = L.circleMarker(
                [26.007765, -80.2962555],
                {"bubblingMouseEvents": true, "color": "#80ffb4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#80ffb4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_29dd5ab6631c4bd3831f95e7f09eb1ee);
        
    
        var popup_c0f92897d4df4f579079ea047bf8f552 = L.popup({"maxWidth": "100%"});

        
            var html_86d2d8984cdc4716abbb2bd5ebb63e1d = $(`<div id="html_86d2d8984cdc4716abbb2bd5ebb63e1d" style="width: 100.0%; height: 100.0%;">Pembroke Pines Cluster2</div>`)[0];
            popup_c0f92897d4df4f579079ea047bf8f552.setContent(html_86d2d8984cdc4716abbb2bd5ebb63e1d);
        

        circle_marker_235309adcdda4392ba92144853935900.bindPopup(popup_c0f92897d4df4f579079ea047bf8f552)
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            var circle_marker_28eb6942995944459ca30dc4c4d6f9aa = L.circleMarker(
                [26.1275862, -80.23310359999999],
                {"bubblingMouseEvents": true, "color": "#80ffb4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#80ffb4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_29dd5ab6631c4bd3831f95e7f09eb1ee);
        
    
        var popup_91223673e01d467eb69bec99d283bbde = L.popup({"maxWidth": "100%"});

        
            var html_b9014911b1cb45dd82adbf6c8f3190c0 = $(`<div id="html_b9014911b1cb45dd82adbf6c8f3190c0" style="width: 100.0%; height: 100.0%;">Plantation Cluster2</div>`)[0];
            popup_91223673e01d467eb69bec99d283bbde.setContent(html_b9014911b1cb45dd82adbf6c8f3190c0);
        

        circle_marker_28eb6942995944459ca30dc4c4d6f9aa.bindPopup(popup_91223673e01d467eb69bec99d283bbde)
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            var circle_marker_7049a5b507624eaf9c1b803583c3e9d1 = L.circleMarker(
                [26.2378597, -80.1247667],
                {"bubblingMouseEvents": true, "color": "#80ffb4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#80ffb4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_29dd5ab6631c4bd3831f95e7f09eb1ee);
        
    
        var popup_57a922da07b9482bb0ca24e96f72d4ea = L.popup({"maxWidth": "100%"});

        
            var html_7d55dbdcc5e542de9bd2a1d09fecf1b9 = $(`<div id="html_7d55dbdcc5e542de9bd2a1d09fecf1b9" style="width: 100.0%; height: 100.0%;">Pompano Beach Cluster2</div>`)[0];
            popup_57a922da07b9482bb0ca24e96f72d4ea.setContent(html_7d55dbdcc5e542de9bd2a1d09fecf1b9);
        

        circle_marker_7049a5b507624eaf9c1b803583c3e9d1.bindPopup(popup_57a922da07b9482bb0ca24e96f72d4ea)
        ;

        
    
    
            var circle_marker_c7de1a9a1159416b9ac2f7b21718001e = L.circleMarker(
                [26.775340500000002, -80.05809690000001],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_29dd5ab6631c4bd3831f95e7f09eb1ee);
        
    
        var popup_5cf8affc5b474704aa8278bdbc93ce9e = L.popup({"maxWidth": "100%"});

        
            var html_4844b5ff4c1c46caa72f796f8e2f9e6f = $(`<div id="html_4844b5ff4c1c46caa72f796f8e2f9e6f" style="width: 100.0%; height: 100.0%;">Riviera Beach Cluster0</div>`)[0];
            popup_5cf8affc5b474704aa8278bdbc93ce9e.setContent(html_4844b5ff4c1c46caa72f796f8e2f9e6f);
        

        circle_marker_c7de1a9a1159416b9ac2f7b21718001e.bindPopup(popup_5cf8affc5b474704aa8278bdbc93ce9e)
        ;

        
    
    
            var circle_marker_3f9a1842b2af414ab0103b42b7940879 = L.circleMarker(
                [26.7083984, -80.23060190000001],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_29dd5ab6631c4bd3831f95e7f09eb1ee);
        
    
        var popup_b47ea350255d4f45ac32baf535bf8fb5 = L.popup({"maxWidth": "100%"});

        
            var html_16f5907f30dc46bea0f1ed0566599b65 = $(`<div id="html_16f5907f30dc46bea0f1ed0566599b65" style="width: 100.0%; height: 100.0%;">Royal Palm Beach Cluster0</div>`)[0];
            popup_b47ea350255d4f45ac32baf535bf8fb5.setContent(html_16f5907f30dc46bea0f1ed0566599b65);
        

        circle_marker_3f9a1842b2af414ab0103b42b7940879.bindPopup(popup_b47ea350255d4f45ac32baf535bf8fb5)
        ;

        
    
    
            var circle_marker_334c9f247acc4e5fbac6ec27951e32b6 = L.circleMarker(
                [26.1669711, -80.25659499999999],
                {"bubblingMouseEvents": true, "color": "#80ffb4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#80ffb4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_29dd5ab6631c4bd3831f95e7f09eb1ee);
        
    
        var popup_837bb41eb13d41989b6fca1e08c25668 = L.popup({"maxWidth": "100%"});

        
            var html_096b561cabc041f49df65286b9743e59 = $(`<div id="html_096b561cabc041f49df65286b9743e59" style="width: 100.0%; height: 100.0%;">Sunrise Cluster2</div>`)[0];
            popup_837bb41eb13d41989b6fca1e08c25668.setContent(html_096b561cabc041f49df65286b9743e59);
        

        circle_marker_334c9f247acc4e5fbac6ec27951e32b6.bindPopup(popup_837bb41eb13d41989b6fca1e08c25668)
        ;

        
    
    
            var circle_marker_a070636569c942b8a79d57c3699bf990 = L.circleMarker(
                [26.2128609, -80.2497707],
                {"bubblingMouseEvents": true, "color": "#80ffb4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#80ffb4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_29dd5ab6631c4bd3831f95e7f09eb1ee);
        
    
        var popup_1129ef7c906644f5a62e18da9111663a = L.popup({"maxWidth": "100%"});

        
            var html_b7ae410e60bc4d7c9e51548e2b2664a2 = $(`<div id="html_b7ae410e60bc4d7c9e51548e2b2664a2" style="width: 100.0%; height: 100.0%;">Tamarac Cluster2</div>`)[0];
            popup_1129ef7c906644f5a62e18da9111663a.setContent(html_b7ae410e60bc4d7c9e51548e2b2664a2);
        

        circle_marker_a070636569c942b8a79d57c3699bf990.bindPopup(popup_1129ef7c906644f5a62e18da9111663a)
        ;

        
    
    
            var circle_marker_b7439748a8274d3897a7cfca5ae24c9f = L.circleMarker(
                [26.6617635, -80.2683571],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_29dd5ab6631c4bd3831f95e7f09eb1ee);
        
    
        var popup_5fec0c705df94438bcf7ca9e9788d802 = L.popup({"maxWidth": "100%"});

        
            var html_34692c28ba0445f38958b328f0b958d8 = $(`<div id="html_34692c28ba0445f38958b328f0b958d8" style="width: 100.0%; height: 100.0%;">Wellington Cluster0</div>`)[0];
            popup_5fec0c705df94438bcf7ca9e9788d802.setContent(html_34692c28ba0445f38958b328f0b958d8);
        

        circle_marker_b7439748a8274d3897a7cfca5ae24c9f.bindPopup(popup_5fec0c705df94438bcf7ca9e9788d802)
        ;

        
    
    
            var circle_marker_bb994397c9a342da8afa2751a7b481eb = L.circleMarker(
                [26.7153424, -80.0533746],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_29dd5ab6631c4bd3831f95e7f09eb1ee);
        
    
        var popup_0dec49dbf55144e2be4e472cae14b988 = L.popup({"maxWidth": "100%"});

        
            var html_3bcb6d31dece49868e9f5c8132a2e17c = $(`<div id="html_3bcb6d31dece49868e9f5c8132a2e17c" style="width: 100.0%; height: 100.0%;">West Palm Beach Cluster1</div>`)[0];
            popup_0dec49dbf55144e2be4e472cae14b988.setContent(html_3bcb6d31dece49868e9f5c8132a2e17c);
        

        circle_marker_bb994397c9a342da8afa2751a7b481eb.bindPopup(popup_0dec49dbf55144e2be4e472cae14b988)
        ;

        
    
    
            var circle_marker_489d1e6a0b684cffa2e3166ae39ac736 = L.circleMarker(
                [26.100365399999998, -80.3997748],
                {"bubblingMouseEvents": true, "color": "#80ffb4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#80ffb4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_29dd5ab6631c4bd3831f95e7f09eb1ee);
        
    
        var popup_2693ca03aca246fc97918bd092a42c53 = L.popup({"maxWidth": "100%"});

        
            var html_a9cb1b8488d8432f906a6b1f445dbb3b = $(`<div id="html_a9cb1b8488d8432f906a6b1f445dbb3b" style="width: 100.0%; height: 100.0%;">Weston Cluster2</div>`)[0];
            popup_2693ca03aca246fc97918bd092a42c53.setContent(html_a9cb1b8488d8432f906a6b1f445dbb3b);
        

        circle_marker_489d1e6a0b684cffa2e3166ae39ac736.bindPopup(popup_2693ca03aca246fc97918bd092a42c53)
        ;

        
    
</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x1a1ed42d250>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create map\n",
    "import matplotlib.colors as colors\n",
    "map_clusters = folium.Map(location=[latitude, longitude], zoom_start=8)\n",
    "\n",
    "# set color scheme for the clusters\n",
    "x = np.arange(kclusters)\n",
    "ys = [i + x + (i*x)**2 for i in range(kclusters)]\n",
    "colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
    "rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
    "\n",
    "# add markers to the map\n",
    "markers_colors = []\n",
    "for lat, lon, poi, cluster in zip(pb_cty_merged['Latitude'], pb_cty_merged['Longitude'], pb_cty_merged['City'], pb_cty_merged['Cluster Labels']):\n",
    "    label = folium.Popup(str(poi) + ' Cluster' + str(cluster), parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lon],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color=rainbow[cluster-1],\n",
    "        fill=True,\n",
    "        fill_color=rainbow[cluster-1],\n",
    "        fill_opacity=0.7).add_to(map_clusters)\n",
    "       \n",
    "map_clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heat Map of Cities based on number of businesses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x1a1ed488f10>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cityloc['Businesses'] = cityloc['Businesses'].astype(float) # Converted dtype to float for heat map\n",
    "from folium.plugins import HeatMap\n",
    "from sklearn import preprocessing\n",
    "\n",
    "m = folium.Map([latitude, longitude], zoom_start=8)\n",
    "data = cityloc[['Latitude', 'Longitude', 'Businesses']].values\n",
    "heat_data = [[row['Latitude'], row['Longitude'], index] for index, row in cityloc.iterrows()]\n",
    "HeatMap(data, \n",
    "        max_val=max(cityloc['Businesses'].values)).add_to(m)\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examine Clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 1 - North Cluster (Palm Beach Area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>City</th>\n",
       "      <th>Businesses</th>\n",
       "      <th>LifeScience</th>\n",
       "      <th>InfoTech</th>\n",
       "      <th>Financial</th>\n",
       "      <th>Clean Tech</th>\n",
       "      <th>MedD_PB</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Boynton Beach</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14.730</td>\n",
       "      <td>26.531787</td>\n",
       "      <td>-80.090547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Delray Beach</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>19.510</td>\n",
       "      <td>26.461462</td>\n",
       "      <td>-80.072820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Greenacres</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.490</td>\n",
       "      <td>26.627628</td>\n",
       "      <td>-80.135390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Juno Beach</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>9.970</td>\n",
       "      <td>26.879782</td>\n",
       "      <td>-80.053374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Jupiter</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>15.150</td>\n",
       "      <td>26.934225</td>\n",
       "      <td>-80.094209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Lake Worth</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8.515</td>\n",
       "      <td>26.616756</td>\n",
       "      <td>-80.068448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Loxahatchee</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12.490</td>\n",
       "      <td>26.771624</td>\n",
       "      <td>-80.238888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>North Palm Beach</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>9.970</td>\n",
       "      <td>26.817116</td>\n",
       "      <td>-80.059080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Palm Beach</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.085</td>\n",
       "      <td>26.705621</td>\n",
       "      <td>-80.036430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Palm Beach Gardens</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>9.235</td>\n",
       "      <td>26.839610</td>\n",
       "      <td>-80.101914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Palm Springs</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.660</td>\n",
       "      <td>26.635901</td>\n",
       "      <td>-80.096154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Riviera Beach</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.810</td>\n",
       "      <td>26.775341</td>\n",
       "      <td>-80.058097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Royal Palm Beach</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.380</td>\n",
       "      <td>26.708398</td>\n",
       "      <td>-80.230602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Wellington</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.140</td>\n",
       "      <td>26.661763</td>\n",
       "      <td>-80.268357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  City  Businesses  LifeScience  InfoTech  Financial  \\\n",
       "2   Boynton Beach       7           6            0         1           \n",
       "9   Delray Beach        15          7            5         2           \n",
       "11  Greenacres          1           1            0         0           \n",
       "15  Juno Beach          10          0            3         1           \n",
       "16  Jupiter             13          7            1         5           \n",
       "17  Lake Worth          6           4            1         1           \n",
       "20  Loxahatchee         3           2            0         1           \n",
       "23  North Palm Beach    8           0            1         2           \n",
       "25  Palm Beach          4           1            1         2           \n",
       "26  Palm Beach Gardens  24          15           4         4           \n",
       "27  Palm Springs        2           2            0         0           \n",
       "31  Riviera Beach       3           3            0         0           \n",
       "32  Royal Palm Beach    1           1            0         0           \n",
       "35  Wellington          2           2            0         0           \n",
       "\n",
       "    Clean Tech  MedD_PB   Latitude  Longitude  \n",
       "2   0           14.730   26.531787 -80.090547  \n",
       "9   1           19.510   26.461462 -80.072820  \n",
       "11  0           8.490    26.627628 -80.135390  \n",
       "15  6           9.970    26.879782 -80.053374  \n",
       "16  0           15.150   26.934225 -80.094209  \n",
       "17  0           8.515    26.616756 -80.068448  \n",
       "20  0           12.490   26.771624 -80.238888  \n",
       "23  5           9.970    26.817116 -80.059080  \n",
       "25  0           1.085    26.705621 -80.036430  \n",
       "26  1           9.235    26.839610 -80.101914  \n",
       "27  0           6.660    26.635901 -80.096154  \n",
       "31  0           5.810    26.775341 -80.058097  \n",
       "32  0           9.380    26.708398 -80.230602  \n",
       "35  0           11.140   26.661763 -80.268357  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb_cty_merged.loc[pb_cty_merged['Cluster Labels'] == 0, pb_cty_merged.columns[[0] + list(range(2, pb_cty_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 2 - High Density Cluster Cities with Highest Economic Activity "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>City</th>\n",
       "      <th>Businesses</th>\n",
       "      <th>LifeScience</th>\n",
       "      <th>InfoTech</th>\n",
       "      <th>Financial</th>\n",
       "      <th>Clean Tech</th>\n",
       "      <th>MedD_PB</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boca Raton</td>\n",
       "      <td>89</td>\n",
       "      <td>39</td>\n",
       "      <td>34</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>22.610</td>\n",
       "      <td>26.368306</td>\n",
       "      <td>-80.128932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>98</td>\n",
       "      <td>35</td>\n",
       "      <td>44</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>37.235</td>\n",
       "      <td>26.122439</td>\n",
       "      <td>-80.137317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>57</td>\n",
       "      <td>31</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>2.920</td>\n",
       "      <td>26.715342</td>\n",
       "      <td>-80.053375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               City  Businesses  LifeScience  InfoTech  Financial  Clean Tech  \\\n",
       "1   Boca Raton       89          39           34        16         0            \n",
       "10  Fort Lauderdale  98          35           44        18         1            \n",
       "36  West Palm Beach  57          31           11        11         4            \n",
       "\n",
       "    MedD_PB   Latitude  Longitude  \n",
       "1   22.610   26.368306 -80.128932  \n",
       "10  37.235   26.122439 -80.137317  \n",
       "36  2.920    26.715342 -80.053375  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb_cty_merged.loc[pb_cty_merged['Cluster Labels'] == 1, pb_cty_merged.columns[[0] + list(range(2, pb_cty_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 3 - South Cluster - (Ft Lauderdale Area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>City</th>\n",
       "      <th>Businesses</th>\n",
       "      <th>LifeScience</th>\n",
       "      <th>InfoTech</th>\n",
       "      <th>Financial</th>\n",
       "      <th>Clean Tech</th>\n",
       "      <th>MedD_PB</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Belle Glade</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38.130</td>\n",
       "      <td>26.684510</td>\n",
       "      <td>-80.667558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Coconut Creek</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.200</td>\n",
       "      <td>26.251748</td>\n",
       "      <td>-80.178935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Coral Springs</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>33.270</td>\n",
       "      <td>26.271192</td>\n",
       "      <td>-80.270604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dania</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46.650</td>\n",
       "      <td>26.052311</td>\n",
       "      <td>-80.143934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dania Beach</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45.870</td>\n",
       "      <td>26.052311</td>\n",
       "      <td>-80.143934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Davie</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>45.880</td>\n",
       "      <td>26.076478</td>\n",
       "      <td>-80.252116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Deerfield Beach</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28.690</td>\n",
       "      <td>26.318412</td>\n",
       "      <td>-80.099766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Hallandale</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50.910</td>\n",
       "      <td>25.981202</td>\n",
       "      <td>-80.148379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Hallandale Beach</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>50.750</td>\n",
       "      <td>25.981202</td>\n",
       "      <td>-80.148379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Hollywood</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>48.740</td>\n",
       "      <td>26.011201</td>\n",
       "      <td>-80.149490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Lauderdale Lakes</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.985</td>\n",
       "      <td>26.166474</td>\n",
       "      <td>-80.208381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Lighthouse Point</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.410</td>\n",
       "      <td>26.275636</td>\n",
       "      <td>-80.087265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Margate</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>33.145</td>\n",
       "      <td>26.244526</td>\n",
       "      <td>-80.206436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Miramar</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.275</td>\n",
       "      <td>25.986076</td>\n",
       "      <td>-80.303560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Oakland Park</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36.980</td>\n",
       "      <td>26.172307</td>\n",
       "      <td>-80.131989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Pembroke Pines</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>50.950</td>\n",
       "      <td>26.007765</td>\n",
       "      <td>-80.296256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Plantation</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>42.860</td>\n",
       "      <td>26.127586</td>\n",
       "      <td>-80.233104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Pompano Beach</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.480</td>\n",
       "      <td>26.237860</td>\n",
       "      <td>-80.124767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Sunrise</td>\n",
       "      <td>27</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>43.510</td>\n",
       "      <td>26.166971</td>\n",
       "      <td>-80.256595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Tamarac</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38.260</td>\n",
       "      <td>26.212861</td>\n",
       "      <td>-80.249771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Weston</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47.830</td>\n",
       "      <td>26.100365</td>\n",
       "      <td>-80.399775</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                City  Businesses  LifeScience  InfoTech  Financial  \\\n",
       "0   Belle Glade       1           1            0         0           \n",
       "3   Coconut Creek     1           1            0         0           \n",
       "4   Coral Springs     14          7            4         3           \n",
       "5   Dania             1           0            1         0           \n",
       "6   Dania Beach       2           1            1         0           \n",
       "7   Davie             5           3            1         1           \n",
       "8   Deerfield Beach   9           3            5         1           \n",
       "12  Hallandale        1           0            1         0           \n",
       "13  Hallandale Beach  4           0            2         2           \n",
       "14  Hollywood         17          8            5         4           \n",
       "18  Lauderdale Lakes  4           4            0         0           \n",
       "19  Lighthouse Point  2           2            0         0           \n",
       "21  Margate           4           2            0         2           \n",
       "22  Miramar           12          7            4         1           \n",
       "24  Oakland Park      3           2            1         0           \n",
       "28  Pembroke Pines    3           2            0         1           \n",
       "29  Plantation        13          8            4         1           \n",
       "30  Pompano Beach     17          11           6         0           \n",
       "33  Sunrise           27          16           5         6           \n",
       "34  Tamarac           3           1            2         0           \n",
       "37  Weston            9           6            3         0           \n",
       "\n",
       "    Clean Tech  MedD_PB   Latitude  Longitude  \n",
       "0   0           38.130   26.684510 -80.667558  \n",
       "3   0           29.200   26.251748 -80.178935  \n",
       "4   0           33.270   26.271192 -80.270604  \n",
       "5   0           46.650   26.052311 -80.143934  \n",
       "6   0           45.870   26.052311 -80.143934  \n",
       "7   0           45.880   26.076478 -80.252116  \n",
       "8   0           28.690   26.318412 -80.099766  \n",
       "12  0           50.910   25.981202 -80.148379  \n",
       "13  0           50.750   25.981202 -80.148379  \n",
       "14  0           48.740   26.011201 -80.149490  \n",
       "18  0           37.985   26.166474 -80.208381  \n",
       "19  0           30.410   26.275636 -80.087265  \n",
       "21  0           33.145   26.244526 -80.206436  \n",
       "22  0           53.275   25.986076 -80.303560  \n",
       "24  0           36.980   26.172307 -80.131989  \n",
       "28  0           50.950   26.007765 -80.296256  \n",
       "29  0           42.860   26.127586 -80.233104  \n",
       "30  0           33.480   26.237860 -80.124767  \n",
       "33  0           43.510   26.166971 -80.256595  \n",
       "34  0           38.260   26.212861 -80.249771  \n",
       "37  0           47.830   26.100365 -80.399775  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb_cty_merged.loc[pb_cty_merged['Cluster Labels'] == 2, pb_cty_merged.columns[[0] + list(range(2, pb_cty_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of Busineses by Key Industry for each Cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Cluster Labels</th>\n",
       "      <th>Total</th>\n",
       "      <th>LifeScience</th>\n",
       "      <th>InfoTech</th>\n",
       "      <th>Financial</th>\n",
       "      <th>CleanTech</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>North Cluster</td>\n",
       "      <td>99</td>\n",
       "      <td>51</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>High Density Cluster</td>\n",
       "      <td>244</td>\n",
       "      <td>105</td>\n",
       "      <td>89</td>\n",
       "      <td>45</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>South Cluster</td>\n",
       "      <td>152</td>\n",
       "      <td>85</td>\n",
       "      <td>45</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Cluster Labels  Total  LifeScience  InfoTech  Financial  CleanTech\n",
       "0  North Cluster         99     51           16        19         13       \n",
       "1  High Density Cluster  244    105          89        45         5        \n",
       "2  South Cluster         152    85           45        22         0        "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sum = pb_cty_merged.groupby('Cluster Labels').agg(\n",
    "    Total=pd.NamedAgg(column=\"Businesses\", aggfunc=\"sum\"),\n",
    "    LifeScience=pd.NamedAgg(column=\"LifeScience\", aggfunc=\"sum\"),\n",
    "    InfoTech=pd.NamedAgg(column=\"InfoTech\", aggfunc=\"sum\"),\n",
    "    Financial=pd.NamedAgg(column=\"Financial\", aggfunc=\"sum\"),\n",
    "    CleanTech=pd.NamedAgg(column=\"Clean Tech\", aggfunc=\"sum\")).reset_index(drop=False)\n",
    "df_sum['Cluster Labels'] = df_sum['Cluster Labels'].replace([0,1,2 ],['North Cluster', 'High Density Cluster','South Cluster'])\n",
    "df_sum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Thank You!  - Conclusion and Recommendations are within report "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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