Battle of Counties_ Texas' Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *Capstone by Shivank Vatsal*\n",
    "#### Note: Maps are Not Viewable in This Notebook Due to Size Constraints (Please Refer to Report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Key Points\n",
    "* [Data Collection](#data)\n",
    "* [Accessing Foursquare API](#foursquare)\n",
    "* [One-Hot Encoding](#onehot)\n",
    "* [KMeans Clustering](#kmeans)\n",
    "* [DBSCAN Algorithm](#dbscan)\n",
    "* [Cluster Labeling](#labeling)\n",
    "* [Choropleth Map with Popups](#map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Collection <a name=\"data\"></a>\n",
    "## Import Necessary Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "\n",
    "from sklearn import metrics \n",
    "from scipy.spatial.distance import cdist \n",
    "from bs4 import BeautifulSoup\n",
    "import json\n",
    "from geopy.geocoders import Nominatim \n",
    "import requests\n",
    "from pandas.io.json import json_normalize \n",
    "from sklearn.cluster import DBSCAN \n",
    "from sklearn.preprocessing import StandardScaler \n",
    "from sklearn.preprocessing import normalize \n",
    "from sklearn.decomposition import PCA \n",
    "\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.pyplot as plt  \n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "import folium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# @hidden_cell"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import GeoJSON File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ibm_botocore.client import Config\n",
    "import ibm_boto3\n",
    "\n",
    "cos = ibm_boto3.client(service_name='s3',\n",
    "    ibm_api_key_id=credentials['IBM_API_KEY_ID'],\n",
    "    ibm_service_instance_id=credentials['IAM_SERVICE_ID'],\n",
    "    ibm_auth_endpoint=credentials['IBM_AUTH_ENDPOINT'],\n",
    "    config=Config(signature_version='oauth'),\n",
    "    endpoint_url=credentials['ENDPOINT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cos.download_file(Bucket=credentials['BUCKET'],Key='texascounties.json',Filename='texascounties.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('texascounties.json') as json_data:\n",
    "    geo_data = json.load(json_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tcounties_data = geo_data['features']\n",
    "tcounties_data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transform JSON into Dataframe: Counties and Coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>County</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Menard County</td>\n",
       "      <td>+30.8843655</td>\n",
       "      <td>-099.8539896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Calhoun County</td>\n",
       "      <td>+28.4417191</td>\n",
       "      <td>-096.5795739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hale County</td>\n",
       "      <td>+34.0684364</td>\n",
       "      <td>-101.8228879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kerr County</td>\n",
       "      <td>+30.0599530</td>\n",
       "      <td>-099.3533388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Armstrong County</td>\n",
       "      <td>+34.9641790</td>\n",
       "      <td>-101.3566363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             County     Latitude     Longitude\n",
       "0     Menard County  +30.8843655  -099.8539896\n",
       "1    Calhoun County  +28.4417191  -096.5795739\n",
       "2       Hale County  +34.0684364  -101.8228879\n",
       "3       Kerr County  +30.0599530  -099.3533388\n",
       "4  Armstrong County  +34.9641790  -101.3566363"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = ['County', 'Latitude', 'Longitude'] \n",
    "counties = pd.DataFrame(columns = column_names)\n",
    "for data in tcounties_data:\n",
    "    county_name = data['properties']['namelsad']\n",
    "    county_lat = data['properties']['intptlat']\n",
    "    county_lon = data['properties']['intptlon']\n",
    "    \n",
    "    counties = counties.append({'County': county_name,\n",
    "                                          'Latitude': county_lat,\n",
    "                                          'Longitude': county_lon}, ignore_index=True)\n",
    "counties.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>County</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Menard County</td>\n",
       "      <td>30.8843655</td>\n",
       "      <td>-99.8539896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Calhoun County</td>\n",
       "      <td>28.4417191</td>\n",
       "      <td>-96.5795739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hale County</td>\n",
       "      <td>34.0684364</td>\n",
       "      <td>-101.8228879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kerr County</td>\n",
       "      <td>30.0599530</td>\n",
       "      <td>-99.3533388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Armstrong County</td>\n",
       "      <td>34.9641790</td>\n",
       "      <td>-101.3566363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             County    Latitude     Longitude\n",
       "0     Menard County  30.8843655   -99.8539896\n",
       "1    Calhoun County  28.4417191   -96.5795739\n",
       "2       Hale County  34.0684364  -101.8228879\n",
       "3       Kerr County  30.0599530   -99.3533388\n",
       "4  Armstrong County  34.9641790  -101.3566363"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counties['Longitude'] = counties['Longitude'].apply(lambda x : x.replace('0', '', 1) if x.startswith(\"-0\") else x)\n",
    "counties['Latitude'] = counties['Latitude'].apply(lambda x : x[1:] if x.startswith(\"+\") else x)\n",
    "counties.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Web Scraping HTML to Collect Population Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pop_url = \"https://www.texas-demographics.com/counties_by_population\"\n",
    "data = []\n",
    "r = requests.get(pop_url)\n",
    "code = BeautifulSoup(r.text, 'html.parser')\n",
    "rank_table = code.find('table', class_ = 'ranklist')\n",
    "\n",
    "if rank_table is not None:\n",
    "    rows = rank_table.find_all('tr')\n",
    "    for tr in rows:\n",
    "        cols = tr.find_all('td')\n",
    "        for td in cols:\n",
    "            data.append(td.text.strip())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "index = data.index('Loving County') + 2\n",
    "data = data[:index]\n",
    "del data[::3]\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.array(data)\n",
    "data = np.reshape(data, (-1, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transform into Dataframe: County and Population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "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>County</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Harris County</td>\n",
       "      <td>4,646,630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dallas County</td>\n",
       "      <td>2,606,868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tarrant County</td>\n",
       "      <td>2,049,770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bexar County</td>\n",
       "      <td>1,952,843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Travis County</td>\n",
       "      <td>1,226,805</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           County Population\n",
       "0   Harris County  4,646,630\n",
       "1   Dallas County  2,606,868\n",
       "2  Tarrant County  2,049,770\n",
       "3    Bexar County  1,952,843\n",
       "4   Travis County  1,226,805"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = ['County', 'Population'] \n",
    "counties_pop = pd.DataFrame(data = data, columns = column_names)\n",
    "counties_pop.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge Population and Location Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>County</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Menard County</td>\n",
       "      <td>30.8843655</td>\n",
       "      <td>-99.8539896</td>\n",
       "      <td>2,119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Calhoun County</td>\n",
       "      <td>28.4417191</td>\n",
       "      <td>-96.5795739</td>\n",
       "      <td>21,668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hale County</td>\n",
       "      <td>34.0684364</td>\n",
       "      <td>-101.8228879</td>\n",
       "      <td>33,826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kerr County</td>\n",
       "      <td>30.0599530</td>\n",
       "      <td>-99.3533388</td>\n",
       "      <td>51,843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Armstrong County</td>\n",
       "      <td>34.9641790</td>\n",
       "      <td>-101.3566363</td>\n",
       "      <td>1,973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             County    Latitude     Longitude Population\n",
       "0     Menard County  30.8843655   -99.8539896      2,119\n",
       "1    Calhoun County  28.4417191   -96.5795739     21,668\n",
       "2       Hale County  34.0684364  -101.8228879     33,826\n",
       "3       Kerr County  30.0599530   -99.3533388     51,843\n",
       "4  Armstrong County  34.9641790  -101.3566363      1,973"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counties = counties.merge(counties_pop, on = 'County', how = 'left')\n",
    "counties = counties.dropna()\n",
    "counties.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# @hidden_cell"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Excel Data: Fastest Growing Cities in Texas + Sociodemographic Rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "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>County</th>\n",
       "      <th>Sociodemographic Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Frisco</td>\n",
       "      <td>Denton County</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Round Rock</td>\n",
       "      <td>Williamson County</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>McKinney</td>\n",
       "      <td>Collin County</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sugar Land</td>\n",
       "      <td>Fort Bend County</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Midland</td>\n",
       "      <td>Midland County</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         City             County Sociodemographic Rank\n",
       "0      Frisco      Denton County                     2\n",
       "1  Round Rock  Williamson County                    13\n",
       "2    McKinney      Collin County                     8\n",
       "3  Sugar Land   Fort Bend County                     4\n",
       "4     Midland     Midland County                    22"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cos = ibm_boto3.client(service_name='s3',\n",
    "    ibm_api_key_id=credentials['IBM_API_KEY_ID'],\n",
    "    ibm_service_instance_id=credentials['IAM_SERVICE_ID'],\n",
    "    ibm_auth_endpoint=credentials['IBM_AUTH_ENDPOINT'],\n",
    "    config=Config(signature_version='oauth'),\n",
    "    endpoint_url=credentials['ENDPOINT'])\n",
    "cos.download_file(Bucket=credentials['BUCKET'],Key='fastgrowingtex.xlsx',Filename='fastgrowingtex.xlsx')\n",
    "data = pd.read_excel('fastgrowingtex.xlsx', dtype = str)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge/Clean All Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>County</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Population</th>\n",
       "      <th>City</th>\n",
       "      <th>SD Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Jefferson County</td>\n",
       "      <td>29.8539997</td>\n",
       "      <td>-94.1493306</td>\n",
       "      <td>254,340</td>\n",
       "      <td>Beaumont</td>\n",
       "      <td>408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Brazos County</td>\n",
       "      <td>30.6567174</td>\n",
       "      <td>-96.3023920</td>\n",
       "      <td>222,981</td>\n",
       "      <td>College Station</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Brazos County</td>\n",
       "      <td>30.6567174</td>\n",
       "      <td>-96.3023920</td>\n",
       "      <td>222,981</td>\n",
       "      <td>Bryan</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Bell County</td>\n",
       "      <td>31.0423032</td>\n",
       "      <td>-97.4813742</td>\n",
       "      <td>348,574</td>\n",
       "      <td>Killeen</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Hidalgo County</td>\n",
       "      <td>26.3963839</td>\n",
       "      <td>-98.1809900</td>\n",
       "      <td>855,176</td>\n",
       "      <td>Edinburg</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              County    Latitude    Longitude Population             City  \\\n",
       "12  Jefferson County  29.8539997  -94.1493306    254,340         Beaumont   \n",
       "14     Brazos County  30.6567174  -96.3023920    222,981  College Station   \n",
       "15     Brazos County  30.6567174  -96.3023920    222,981            Bryan   \n",
       "16       Bell County  31.0423032  -97.4813742    348,574          Killeen   \n",
       "18    Hidalgo County  26.3963839  -98.1809900    855,176         Edinburg   \n",
       "\n",
       "   SD Rank  \n",
       "12     408  \n",
       "14      20  \n",
       "15     216  \n",
       "16      86  \n",
       "18      16  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.rename(columns = {\"Sociodemographic Rank\": \"SD Rank\"})\n",
    "counties = counties.merge(data, on = 'County', how = 'left')\n",
    "counties = counties.dropna()\n",
    "counties.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Function to Locate Coordinates of Cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Locating: \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>CityLat</th>\n",
       "      <th>CityLong</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beaumont</td>\n",
       "      <td>30.086046</td>\n",
       "      <td>-94.101846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>College Station</td>\n",
       "      <td>30.595529</td>\n",
       "      <td>-96.307104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bryan</td>\n",
       "      <td>30.674364</td>\n",
       "      <td>-96.369963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Killeen</td>\n",
       "      <td>31.117144</td>\n",
       "      <td>-97.727796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Edinburg</td>\n",
       "      <td>26.301398</td>\n",
       "      <td>-98.162450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              City    CityLat   CityLong\n",
       "0         Beaumont  30.086046 -94.101846\n",
       "1  College Station  30.595529 -96.307104\n",
       "2            Bryan  30.674364 -96.369963\n",
       "3          Killeen  31.117144 -97.727796\n",
       "4         Edinburg  26.301398 -98.162450"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def avgcoord(col):\n",
    "    lats = []\n",
    "    longs = []\n",
    "    cits = []\n",
    "    print('Locating: ')\n",
    "    for city in col:\n",
    "        cits.append(city)\n",
    "        address = '{}, TX'.format(city)\n",
    "        geolocator = Nominatim(user_agent = \"tex\")\n",
    "        location = geolocator.geocode(address)\n",
    "        longs.append(location.longitude) \n",
    "        lats.append(location.latitude)\n",
    "\n",
    "    coords = pd.DataFrame(list(zip(cits, lats, longs)), columns =['City', 'CityLat', 'CityLong']) \n",
    "    return(coords)\n",
    "\n",
    "coords = avgcoord(col = counties['City'])\n",
    "coords.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge/Clean Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "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>County</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Population</th>\n",
       "      <th>City</th>\n",
       "      <th>SD Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bell County</td>\n",
       "      <td>31.0423032</td>\n",
       "      <td>-97.4813742</td>\n",
       "      <td>348,574</td>\n",
       "      <td>Killeen</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bexar County</td>\n",
       "      <td>29.4486708</td>\n",
       "      <td>-98.5201465</td>\n",
       "      <td>1,952,843</td>\n",
       "      <td>San Antonio</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brazoria County</td>\n",
       "      <td>29.1678175</td>\n",
       "      <td>-95.4346470</td>\n",
       "      <td>360,677</td>\n",
       "      <td>Pearland</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brazos County</td>\n",
       "      <td>30.6567174</td>\n",
       "      <td>-96.3023920</td>\n",
       "      <td>222,981</td>\n",
       "      <td>College Station, Bryan</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cameron County</td>\n",
       "      <td>26.1029227</td>\n",
       "      <td>-97.4789584</td>\n",
       "      <td>421,666</td>\n",
       "      <td>Brownsville</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            County    Latitude    Longitude Population  \\\n",
       "0      Bell County  31.0423032  -97.4813742    348,574   \n",
       "1     Bexar County  29.4486708  -98.5201465  1,952,843   \n",
       "2  Brazoria County  29.1678175  -95.4346470    360,677   \n",
       "3    Brazos County  30.6567174  -96.3023920    222,981   \n",
       "4   Cameron County  26.1029227  -97.4789584    421,666   \n",
       "\n",
       "                     City  SD Rank  \n",
       "0                 Killeen        8  \n",
       "1             San Antonio       14  \n",
       "2                Pearland        1  \n",
       "3  College Station, Bryan       11  \n",
       "4             Brownsville       25  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counties[\"SD Rank\"] = pd.to_numeric(counties[\"SD Rank\"])\n",
    "counties = counties.groupby(['County', 'Latitude', 'Longitude', 'Population'], as_index = False).agg({'City': ', '.join, 'SD Rank': np.mean})\n",
    "counties['SD Rank'] = counties['SD Rank'].rank(method='max')\n",
    "counties = counties.astype({'SD Rank': 'int'})\n",
    "counties.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# @hidden_cell"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Excel Data: Median House Price by County"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "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>Unnamed: 0</th>\n",
       "      <th>County</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Median House Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Bell County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>203000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Collin County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>365000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Dallas County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Denton County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Ector County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>232500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0         County  Unnamed: 2 Median House Price\n",
       "0         NaN    Bell County         NaN             203000\n",
       "1         NaN  Collin County         NaN             365000\n",
       "2         NaN  Dallas County         NaN             280000\n",
       "3         NaN  Denton County         NaN             340000\n",
       "4         NaN   Ector County         NaN             232500"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cos = ibm_boto3.client(service_name='s3',\n",
    "    ibm_api_key_id=credentials['IBM_API_KEY_ID'],\n",
    "    ibm_service_instance_id=credentials['IAM_SERVICE_ID'],\n",
    "    ibm_auth_endpoint=credentials['IBM_AUTH_ENDPOINT'],\n",
    "    config=Config(signature_version='oauth'),\n",
    "    endpoint_url=credentials['ENDPOINT'])\n",
    "cos.download_file(Bucket=credentials['BUCKET'],Key='housepricestex.xlsx',Filename='housepricestex.xlsx')\n",
    "data = pd.read_excel('housepricestex.xlsx')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge/Clean Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "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>County</th>\n",
       "      <th>$MHP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bell County</td>\n",
       "      <td>203000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Collin County</td>\n",
       "      <td>365000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dallas County</td>\n",
       "      <td>280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Denton County</td>\n",
       "      <td>340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ector County</td>\n",
       "      <td>232500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          County    $MHP\n",
       "0    Bell County  203000\n",
       "1  Collin County  365000\n",
       "2  Dallas County  280000\n",
       "3  Denton County  340000\n",
       "4   Ector County  232500"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.rename(columns = {\"Median House Price\": \"$MHP\"})\n",
    "data = data[['County', '$MHP']]\n",
    "data = data.dropna()\n",
    "data = data.astype({'County': 'str'})\n",
    "data['County'] = data['County'].apply(lambda x: x.strip())\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "counties = counties.merge(data, on = 'County', how = 'left')\n",
    "counties = counties.dropna()\n",
    "counties = counties.rename(columns = {\"City\": \"Cities\"})\n",
    "counties['$MHP'][24] = counties['$MHP'][24][:7].replace(',', '')\n",
    "counties.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use Folium to Visualize Results So Far"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Capital Coordinates: 30.2711286, -97.7436995\n"
     ]
    },
    {
     "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(    decodeURIComponent(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7fb3a0cf4750>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "address = 'Austin, TX, United States'\n",
    "geolocator = Nominatim(user_agent = \"texas\")\n",
    "location = geolocator.geocode(address)\n",
    "longitude = location.longitude\n",
    "latitude = location.latitude\n",
    "print('Capital Coordinates: {}, {}'.format(latitude, longitude))\n",
    "\n",
    "map_tex = folium.Map(location = [latitude, longitude], zoom_start = 6)\n",
    "for lat, lng, cit, cnt in zip(counties['Latitude'], counties['Longitude'], counties['County'], counties['Cities']):\n",
    "    label = '{}: {}'.format(cnt, cit)\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "    [lat, lng], \n",
    "    radius = 5, \n",
    "    popup = label, \n",
    "    color = 'red', \n",
    "    parse_html = False).add_to(map_tex)\n",
    "    \n",
    "map_tex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# @hidden_cell"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Access Foursquare API: Get Venue Data for Cities <a name=\"foursquare\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getNearbyVenues(names, latitudes, longitudes, radius = 1200):\n",
    "    \n",
    "    venues_list=[]\n",
    "    for name, lat, lng in zip(names, latitudes, longitudes):   \n",
    "        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
    "            CLIENT_ID, \n",
    "            CLIENT_SECRET, \n",
    "            VERSION, \n",
    "            lat, \n",
    "            lng, \n",
    "            radius, \n",
    "            LIMIT)\n",
    "            \n",
    "        results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
    "        venues_list.append([(\n",
    "            name, \n",
    "            lat, \n",
    "            lng, \n",
    "            v['venue']['name'], \n",
    "            v['venue']['location']['lat'], \n",
    "            v['venue']['location']['lng'],  \n",
    "            v['venue']['categories'][0]['name']) for v in results])\n",
    "\n",
    "    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n",
    "    nearby_venues.columns = ['City', \n",
    "                  'City Latitude', \n",
    "                  'City Longitude', \n",
    "                  'Venue', \n",
    "                  'Venue Latitude', \n",
    "                  'Venue Longitude', \n",
    "                  'Venue Category']\n",
    "    \n",
    "    return(nearby_venues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1899, 7)\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>City Latitude</th>\n",
       "      <th>City Longitude</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Venue Latitude</th>\n",
       "      <th>Venue Longitude</th>\n",
       "      <th>Venue Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beaumont</td>\n",
       "      <td>30.086046</td>\n",
       "      <td>-94.101846</td>\n",
       "      <td>Chuck's Sandwich Shop</td>\n",
       "      <td>30.083338</td>\n",
       "      <td>-94.098401</td>\n",
       "      <td>Sandwich Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beaumont</td>\n",
       "      <td>30.086046</td>\n",
       "      <td>-94.101846</td>\n",
       "      <td>New York Pizza &amp; Pasta</td>\n",
       "      <td>30.079182</td>\n",
       "      <td>-94.098843</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Beaumont</td>\n",
       "      <td>30.086046</td>\n",
       "      <td>-94.101846</td>\n",
       "      <td>Suga's Deep South Cuisine &amp; Jazz Bar</td>\n",
       "      <td>30.082036</td>\n",
       "      <td>-94.099585</td>\n",
       "      <td>Café</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Beaumont</td>\n",
       "      <td>30.086046</td>\n",
       "      <td>-94.101846</td>\n",
       "      <td>Jefferson Theatre</td>\n",
       "      <td>30.082020</td>\n",
       "      <td>-94.097720</td>\n",
       "      <td>Theater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Beaumont</td>\n",
       "      <td>30.086046</td>\n",
       "      <td>-94.101846</td>\n",
       "      <td>Green Light Kitchen</td>\n",
       "      <td>30.085161</td>\n",
       "      <td>-94.097517</td>\n",
       "      <td>Café</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       City  City Latitude  City Longitude  \\\n",
       "0  Beaumont      30.086046      -94.101846   \n",
       "1  Beaumont      30.086046      -94.101846   \n",
       "2  Beaumont      30.086046      -94.101846   \n",
       "3  Beaumont      30.086046      -94.101846   \n",
       "4  Beaumont      30.086046      -94.101846   \n",
       "\n",
       "                                  Venue  Venue Latitude  Venue Longitude  \\\n",
       "0                 Chuck's Sandwich Shop       30.083338       -94.098401   \n",
       "1                New York Pizza & Pasta       30.079182       -94.098843   \n",
       "2  Suga's Deep South Cuisine & Jazz Bar       30.082036       -94.099585   \n",
       "3                     Jefferson Theatre       30.082020       -94.097720   \n",
       "4                   Green Light Kitchen       30.085161       -94.097517   \n",
       "\n",
       "       Venue Category  \n",
       "0      Sandwich Place  \n",
       "1  Italian Restaurant  \n",
       "2                Café  \n",
       "3             Theater  \n",
       "4                Café  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "texas_venues = getNearbyVenues(names = coords['City'], latitudes = coords['CityLat'], longitudes = coords['CityLong'])\n",
    "print(texas_venues.shape)\n",
    "texas_venues.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One Hot Encoding Venues in Preparation for Clustering <a name=\"onehot\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "texonehot = pd.get_dummies(texas_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
    "texonehot['City'] = texas_venues['City'] \n",
    "cit = texonehot['City']\n",
    "texonehot.drop(labels = ['City'], axis = 1, inplace = True)\n",
    "texonehot.insert(0, 'City', cit)\n",
    "texonehot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identifying Most Common Venues By City"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "texmean = texonehot.groupby('City').mean().reset_index()\n",
    "\n",
    "def most_common(row, rank):\n",
    "    row_categories = row.iloc[1:]\n",
    "    row_categories_sorted = row_categories.sort_values(ascending=False)\n",
    "    return row_categories_sorted.index.values[0:rank]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Toy / Game Store</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Thrift / Vintage Store</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Candy Store</td>\n",
       "      <td>Tree</td>\n",
       "      <td>Pizza Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Allen</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Mediterranean Restaurant</td>\n",
       "      <td>BBQ Joint</td>\n",
       "      <td>Café</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amarillo</td>\n",
       "      <td>Bar</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Hockey Arena</td>\n",
       "      <td>Convention Center</td>\n",
       "      <td>Café</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gay Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arlington</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Park</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Miscellaneous Shop</td>\n",
       "      <td>Insurance Office</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austin</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Cocktail Bar</td>\n",
       "      <td>New American Restaurant</td>\n",
       "      <td>Lounge</td>\n",
       "      <td>Juice Bar</td>\n",
       "      <td>Movie Theater</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        City  1st Most Common Venue 2nd Most Common Venue  \\\n",
       "0    Abilene          Grocery Store    Mexican Restaurant   \n",
       "1      Allen   Fast Food Restaurant    Mexican Restaurant   \n",
       "2   Amarillo                    Bar   American Restaurant   \n",
       "3  Arlington  Vietnamese Restaurant    Mexican Restaurant   \n",
       "4     Austin            Coffee Shop                 Hotel   \n",
       "\n",
       "  3rd Most Common Venue     4th Most Common Venue    5th Most Common Venue  \\\n",
       "0      Toy / Game Store               Gas Station       Chinese Restaurant   \n",
       "1              Pharmacy  Mediterranean Restaurant                BBQ Joint   \n",
       "2      Sushi Restaurant                Restaurant           Sandwich Place   \n",
       "3                  Park             Grocery Store           Discount Store   \n",
       "4                   Bar              Cocktail Bar  New American Restaurant   \n",
       "\n",
       "    6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "0  Thrift / Vintage Store  Fast Food Restaurant           Candy Store   \n",
       "1                    Café  Gym / Fitness Center        Sandwich Place   \n",
       "2            Hockey Arena     Convention Center                  Café   \n",
       "3          Sandwich Place  Fast Food Restaurant      Asian Restaurant   \n",
       "4                  Lounge             Juice Bar         Movie Theater   \n",
       "\n",
       "  9th Most Common Venue 10th Most Common Venue  \n",
       "0                  Tree            Pizza Place  \n",
       "1     Convenience Store       Asian Restaurant  \n",
       "2         Grocery Store                Gay Bar  \n",
       "3    Miscellaneous Shop       Insurance Office  \n",
       "4           Yoga Studio     Italian Restaurant  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank = 10\n",
    "suffix = ['st', 'nd', 'rd']\n",
    "columns = ['City']\n",
    "for ind in np.arange(rank):\n",
    "    try:\n",
    "        columns.append('{}{} Most Common Venue'.format(ind+1, suffix[ind]))\n",
    "    except:\n",
    "        columns.append('{}th Most Common Venue'.format(ind+1))\n",
    "\n",
    "vensort = pd.DataFrame(columns=columns)\n",
    "vensort['City'] = texmean['City']\n",
    "\n",
    "for ind in np.arange(texmean.shape[0]):\n",
    "    vensort.iloc[ind, 1:] = most_common(texmean.iloc[ind, :], rank)\n",
    "\n",
    "vensort.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Elbow Method to Distinguish Optimal K-Value for KMeans Clustering <a name=\"kmeans\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "texk = texmean.drop('City', axis = 1)\n",
    "\n",
    "distortions = []\n",
    "K = range(1,11)\n",
    "for k in K:\n",
    "    kmeanModel = KMeans(n_clusters=k)\n",
    "    kmeanModel.fit(texk)\n",
    "    distortions.append(kmeanModel.inertia_)\n",
    "    \n",
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(K, distortions, 'bx-')\n",
    "plt.xlabel('K-Values')\n",
    "plt.ylabel('Distortion')\n",
    "plt.title('Elbow Method: Optimal k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## No Distinguishable K Value/Elbow --> Use DBSCAN Algorithm for Clustering <a name=\"dbscan\"></a>"
   ]
  },
  {
   "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",
       "        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>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.024056</td>\n",
       "      <td>0.025028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.010091</td>\n",
       "      <td>0.008420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.030610</td>\n",
       "      <td>-0.074525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.050538</td>\n",
       "      <td>0.063337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.050510</td>\n",
       "      <td>-0.075062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          1         2\n",
       "0 -0.024056  0.025028\n",
       "1  0.010091  0.008420\n",
       "2  0.030610 -0.074525\n",
       "3 -0.050538  0.063337\n",
       "4  0.050510 -0.075062"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components = 2) \n",
    "texdb = pca.fit_transform(texk) \n",
    "texdb = pd.DataFrame(texdb) \n",
    "texdb.columns = ['1', '2'] \n",
    "texdb.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DBSCAN: Estimate Epsilon Value and Label Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAD4CAYAAADhNOGaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAXMElEQVR4nO3df4wc533f8ff3SPCAylQk85a2IkqlkggxhNQO5LMiJIET5k6BRBRiglKMXMNuIwOsxKhBWhg1gRZpgKCA6qZAG8BngVGJ2mgb9aJaLdHIVnK0iiCQlfJU2IqkRDajKtGVqnlHy5aNFJRJfvvHLqm95e7d7O7sr5v3C1js7cwzu8+Y8n5mn+eZ54nMRJJUXVOjroAkabQMAkmqOINAkirOIJCkijMIJKnito+6Ar2YmZnJvXv3jroakjRRnn/++bXMrLVun8gg2Lt3L8vLy6OuhiRNlIj4y3bbbRqSpIozCCSp4gwCSao4g0CSKs4gkKSKMwgkaQLMHzlE7K4RU1PE7hrzRw6V9t4GgSSNufkjhzh5/ASsrkEmrK5x8viJ0sLAIJCkMXfyiWfg/Pn1G8+fr28vgUEgSeNu7Vx327tkEEjSuJvZ1d32LhkEkjTm5g7ug+np9Runp+vbS2AQSNKYW1pYZO6Be6E2AxFQm2HugXtZWlgs5f0nctI5SaqapYVFWBjMe/uLQJIqziCQpIorJQgi4u6IeCUiTkfE0Tb7PxoRLzQez0bEB4oeK0karL6DICK2AZ8B7gFuAz4SEbe1FPvfwM9k5vuB3wSOdXGsJGmAyvhFcAdwOjNfzcy3gceBA80FMvPZzHyz8fI5YE/RYyVJg1VGENwIvN70eqWxrZNPAF/s8VhJUsnKGD4abbZl24IR+6gHwU/3cOxh4DDAzTff3H0tJUltlfGLYAW4qen1HuBMa6GIeD/wGHAgM891cyxAZh7LzNnMnK3VaiVUW5IE5QTBKeDWiLglInYA9wMnmgtExM3AF4CPZebXuzlWkjRYfTcNZeaFiHgYeBrYBhzPzJci4sHG/keBXwd2AQsRAXChcXXf9th+6yRJKi4y2zbJj7XZ2dlcXl4edTUkaaJExPOZOdu63TuLJaniDAJJqjiDQJIqziCQpIozCCSp4gwCSao4g0CSKs4gkKSKMwgkqeIMAkmqOINAkirOIJCkijMIJKniDAJJqjiDQJIqziCQpIozCCSp4gwCSao4g0CSKs4gkKSKMwgkqeIMAkmqOINAkiqulCCIiLsj4pWIOB0RR9vsf19EfCUizkfEJ1v2vRYRfxoRX42I5TLqI0kqbnu/bxAR24DPAHcBK8CpiDiRmS83FfsW8KvAL3R4m32ZudZvXSRJ3SvjF8EdwOnMfDUz3wYeBw40F8jMs5l5Cvh+CZ/Xk/kjh4jdNWJqithdY/7IoVFVRZLGShlBcCPwetPrlca2ohL4g4h4PiIOdyoUEYcjYjkilldXV7uq4PyRQ5w8fgJW1yATVtc4efyEYTBmDGtpNMoIgmizLbs4/qcy83bgHuBXIuLD7Qpl5rHMnM3M2Vqt1lUFTz7xDJw/v37j+fP17RoLhrU0OmUEwQpwU9PrPcCZogdn5pnG81ngSepNTeVaO9fddg2dYS2NThlBcAq4NSJuiYgdwP3AiSIHRsQ1EbHz8t/AzwMvllCn9WZ2dbddw2dYSyPTdxBk5gXgYeBp4M+Axcx8KSIejIgHASLivRGxAvxj4J9FxEpEXAu8B/jjiPga8D+B38/ML/Vbp1ZzB/fB9PT6jdPT9e0aD4a1NDJ9Dx8FyMyngKdatj3a9Pf/pd5k1Oot4ANl1GEjSwuLzHOo3sywdg5mdjF3cB9LC4uD/mgVNHdwX72PoLl5yLCWhiIyu+nXHQ+zs7O5vOy9Z1vN/BHDWhqkiHg+M2dbt5fyi0Aqw9LCIiyMuhZS9TjXkCRVnEEgSRVnEEhSxRkEklRxBoEkVZxBIEkVZxBIUsUZBJJUcQaBJFWcQSBJFWcQSFLFGQSSVHEGgSRVnEEgSRVnEEhSxRkEklRxBoEkVZxBIEkVZxBIUsUZBJJUcaUEQUTcHRGvRMTpiDjaZv/7IuIrEXE+Ij7ZzbGSpMHqOwgiYhvwGeAe4DbgIxFxW0uxbwG/CvxWD8dKkgaojF8EdwCnM/PVzHwbeBw40FwgM89m5ing+90eK0karDKC4Ebg9abXK41tgz5WklSCMoIg2mzLso+NiMMRsRwRy6urq4UrJ0naWBlBsALc1PR6D3Cm7GMz81hmzmbmbK1W66mikqSrlREEp4BbI+KWiNgB3A+cGMKxkqQSbO/3DTLzQkQ8DDwNbAOOZ+ZLEfFgY/+jEfFeYBm4FrgUEb8G3JaZb7U7tt86SZKKi8yizfnjY3Z2NpeXl0ddDUmaKBHxfGbOtm73zuIezR85ROyuERHEtm3159015o8cGnXVJKkrBkEP5o8c4uTxE7C6Vt9w6VL9eXWNk8dPGAaSJkplguDKFfzUVN9X7iefeAbOn2+/8/z5+n5JmhCVCIJ1V/CZ/V+5r50rtL/M8JGkQalEELS9gu/nyn1m16b7Sw8fSRqQSgRBxyv4za7sO5g7uA+mp9vvnJ5m7uC+8sNHkgakGkHQ6Qp+syv7DpYWFpl74F6ozdQ3TDX+Z6zNMPfAvSwtLJYePpI0KJUIgrZX8I0r914tLSySZ1fJTPLixfrz2dV6CEDp4SNJg1KJIFh3BR+x/sp9QAYRPpI0CN5ZPEDzRw7V+wTWzsHMLuYO7hto+EjSRjrdWdz3XEPqbGlhERZGXQtJ2lglmoYkSZ0ZBJJUcQbBBPFOZUmDYBBMCO9UljQoBsEYanfl753KkgbFIBigXppyOl35X5nyupV3Kkvqk0EwIL025XS68r8yjUUr71SW1CeDYEB6bsrpdIV/6ZJ3KksaCINgUHqddK7TFX5jWoxhTpMhqRoMgkHpcdK5jeYoujLR3aVL6ye4k6Q+GAQD0uukc6OYIE9StTnp3AA56ZykcTLQSeci4m7g3wLbgMcy85GW/dHYvx/4a+DvZ+b/aux7DfgucBG40K6Sk8pJ5yRNgr6DICK2AZ8B7gJWgFMRcSIzX24qdg9wa+PxE8BnG8+X7cvMDgPlJUmDVEYfwR3A6cx8NTPfBh4HDrSUOQB8PuueA66LiBtK+GxJUp/KCIIbgdebXq80thUtk8AfRMTzEXG404dExOGIWI6I5dXV1RKqLUmCcoIg2mxr7YHeqMxPZebt1JuPfiUiPtzuQzLzWGbOZuZsrVbrvbaSpHXKCIIV4Kam13uAM0XLZObl57PAk9SbmiRJQ1JGEJwCbo2IWyJiB3A/cKKlzAng41F3J/CdzHwjIq6JiJ0AEXEN8PPAiyXUSZJUUN+jhjLzQkQ8DDxNffjo8cx8KSIebOx/FHiK+tDR09SHj/5y4/D3AE/WR5eyHfhPmfmlfuskSSrOG8okqSI63VDmFBOSVHEGgSRVnEEgSRVnEEhSxRkEklRxBsGA9LJw/ShMSj0lDY5BMAC9Llw/bJNST0mDZRAMQM8L15egmyv8UdZT0vgwCAah14Xr+9T1Ff6I6ilpvBgEg9DjwvX96voKf0T1lDReDIIB6HXh+r51eYU/snpKGisGwQAsLSwy98C9UJuBCKjNMPfAvaUvXN/aH8C73tW+YIcr/GHVU9J4c9K5CXWlP6C5KWj79nrfwMWL72ybnvbLXRLgpHNjqfWK/vqf+1B/I34uXIBr/oZX+JK60vd6BOrNVVf0q2t8+5m1dwpcHvHDofZf5J36A777PfI7b5VfYUlblr8IRqTtFX0rR/xIGgKDYFSKjtUvacSPU0lI6sQgGJWiV+4ljPjpdSoJw0OqBkcNjUjbUT9tXLdvlje/fKqvz4rdtXoItKrNkGdXi9fPEUjSRHPU0Ji56op+qv0/xbdffK3/D+thKgnnIZKqwyAYoaWFRfLsKnnpUr3Jpp0y5v3ppWPZeYikyjAIxkWBL+te2+x7mkrCUUlSZZQSBBFxd0S8EhGnI+Jom/0REb/d2P9CRNxe9Niq2OzLup+1A3qZSsJ5iKQKycy+HsA24C+AHwJ2AF8Dbmspsx/4IhDAncCfFD223eODH/xgbkVzD92X1GaSiKQ2k3MP3XdlH7WZBK5+1GZGUh9JkwdYzjbfqWXcWXwHcDozXwWIiMeBA8DLTWUOAJ9vVOS5iLguIm4A9hY4tjKWFhZhocPOEbTZb1gfSVtGGU1DNwKvN71eaWwrUqbIsQLb7CUNTBlBEG22tQ6B6VSmyLH1N4g4HBHLEbG8utp+7PtWZpu9pEEpIwhWgJuaXu8BzhQsU+RYADLzWGbOZuZsrVbru9KTpts7ib0jWFJRZQTBKeDWiLglInYA9wMnWsqcAD7eGD10J/CdzHyj4LFqaL7vIM+uljqdhKTq6jsIMvMC8DDwNPBnwGJmvhQRD0bEg41iTwGvAqeB3wGObHRsv3WqMu8IltQt5xraYmJqqv1dyhH1O5glVZZzDVXFAEYX2ecgbW0GwRZT9uiitn0On/094geuNRCkLcIgmDCbXZ33Mp3ERjqupPbWd+2ElrYI+wgmyCjWCOjY53DZBmsaSBov9hFsASMZEbRZ34LTUksTzyCYJAXmGyqrY/fy+7Rd2ayZU1xIE88gmCSbjAiaP3KIk8e+sL5j99gXug6DdR3EGxngFBeOVJKGxyCYIJuNCDr5H74EFy+u33/xYn17Fzp2EO/cWVon9Ea8O1oaLjuLJ8z8kUP1L+q1czCzi7mD+658GUe0m8Ovrpt/51HflNaxScqOaakvnTqLy1iPQEM0lDUCZna1/yIeVn+A6yVLQ2XT0FZy7c5Ntxdpe9+oCWoobfeuvSANlUGwhcx99G7Y3vIjb/v2+naKt713uikNGErbvWsvSEPWbv3KcX9s1TWLyzDIdY+HuW7ysNdLdn1mVQEd1iy2s7hCOnYmF+wEHnUn8qCM4o5taRS8s7jiNmy+Kdr2vkXb7l3DQVVnEFTERl9qRdve27bdA6yuTfZNX45SUsUZBFWxwZdac/PHRqOC1nUit5rkm7626C8dqSiDoCo6fak1fakXGVW0tLBY/2Uw1eY/nQLNKeM4dYSjlFR57XqQx/3hqKHuzT10XzI9vX60z/R016OK5h66L9m+vX05SCL6qsOoOGpIVYCjhrTR9BRQbFRQ/MC18NZ3O3/IBtNAOHWENFpOMaHNp6coMrXERiGwWXOKnbLSWLKPQFf021a+6bh7O2WlsWQQVFS7TttC6x3v7DCf0c6dm958ZaesNKbadRwUfQDvBv4Q+Ebj+foO5e4GXgFOA0ebtv8G8H+ArzYe+4t8rp3F/emn03buofuSbdvWH7ttW+HOVTtlpdFhEJ3FEfFp4FuZ+UhEHG0EwadaymwDvg7cBawAp4CPZObLEfEbwPcy87e6+Vw7i/vTb6ftZp3OksbToDqLDwA/2/j7c8D/AD7VUuYO4HRmvtqoyOON417u87PVqz47bYeyJoKkoem3j+A9mfkGQON5d5syNwKvN71eaWy77OGIeCEijkfE9Z0+KCIOR8RyRCyvrjrUsC922kpqsmkQRMRSRLzY5nGg4Ge0m/LycnvUZ4EfBn4ceAP4153eJDOPZeZsZs7WarWCH612xrnTdhzvPJa2uk2bhjJzvtO+iPhmRNyQmW9ExA3A2TbFVoCbml7vAc403vubTe/1O8B/L1px9W5pYZF5xq+d/6rpoC9PccGhkddN2sr67Sz+V8C5ps7id2fmP2kps516Z/Ec9RFCp4C/m5kvXQ6RRrl/BPxEZt6/2efaWbw1eeexNFiD6ix+BFiMiE8AfwXc1/iwHwQey8z9mXkhIh4Gnga2Accz86XG8Z+OiB+n3lT0GvAP+qyPJpl3Hksj0VcQZOY56lf6rdvPAPubXj8FPNWm3Mf6+XxtMUWmuJBUOu8s1tgY505saSszCDQ2Ck1xIal0BoGuMsohnEsLi+TZVfLSJfLsqiEgDYFBoHWKrFImaWsxCLTOySeeeWcc/2UFlqCUNLkMAq3nEE6pcgwCrdfjPESt/QrX/9yHnCpCmhAGgdbpZQhnu36Fbz+zvGE/Q6cOaecakobPxet1lW7XG+g4NUSr2gxzB/dx8j9+6eq1j6enue4n/xbffvZP1/dRTE87hFQqSacpJgwC9S2mpupX/kVMT1/dGX3Z1BRcunT1ducakkrRKQhsGlL/ik4BMTXVOQSgfQiAHdXSgBkE6lvbfoVW09Odv+gvm+rwn6NzDUkDZRCob+2mhrhu3+xVU0VQm+n8JtPTXPcztzvXkDQCBoFK0To1xJtfPnXVVBEdfzns3MncA/fy5pdPOdeQNAJ2Fmuouh2RJKk8dhZrLDT/cpg7uI+TTzzjPQPSiBkEGgknt5PGh0GgkXByO2l8GAQaDSe3k8aGQaDR6HFyO0nlMwg0Eq5PLI0Pg2ALmMQZO12fWBoffd1HEBHvBv4zsBd4DTiUmW+2KXcc+NvA2cz8sW6Pb+V9BO+4MvrGGTslbWJQ9xEcBU5m5q3Aycbrdv49cHcfx6sDR99I6le/QXAA+Fzj788Bv9CuUGb+EfCtXo/XBhx9I6lP/QbBezLzDYDG8+5BHR8RhyNiOSKWV1edm/4KR99I6tOmQRARSxHxYpvHgWFU8LLMPJaZs5k5W6vVhvnRY83RN5L6tX2zApk532lfRHwzIm7IzDci4gbgbJef3+/xlbe0sMg8TuQmqXebBsEmTgB/D3ik8fzfhny8qIcBC6OuhaRJ1W8fwSPAXRHxDeCuxmsi4gcj4qnLhSLid4GvAD8aESsR8YmNjpckDU9fvwgy8xww12b7GWB/0+uPdHO8JGl4vLNYkirOIJCkijMIJKniJnLN4ohYBf5yBB89A6yN4HOHZaufH2z9c/T8Jtugz+9vZuZVN2JNZBCMSkQst5uwaavY6ucHW/8cPb/JNqrzs2lIkirOIJCkijMIunNs1BUYsK1+frD1z9Hzm2wjOT/7CCSp4vxFIEkVZxBIUsUZBBuIiHdHxB9GxDcaz9d3KHc8Is5GxIvDrmMvIuLuiHglIk5HxFXLg0bdbzf2vxARt4+inr0qcH7vi4ivRMT5iPjkKOrYjwLn99HGv9sLEfFsRHxgFPXsR4FzPNA4v682Fqz66VHUs1ebnV9TuQ9FxMWIODjQCmWmjw4P4NPA0cbfR4F/2aHch4HbgRdHXecC57QN+Avgh4AdwNeA21rK7Ae+CARwJ/Ano653yee3G/gQ8C+AT466zgM4v58Erm/8fc8k/ft1cY7v4p0+zvcDfz7qepd5fk3lvgw8BRwcZJ38RbCxftdkHkd3AKcz89XMfBt4nPp5NjsAfD7rngOuaywcNAk2Pb/MPJuZp4Dvj6KCfSpyfs9m5puNl88Be4Zcx34VOcfvZePbErgGmKRRL0X+PwjwD4H/whAW7DIINtbvmszj6Ebg9abXK41t3ZYZV5Nc9yK6Pb9PUP91N0kKnWNE/GJE/Dnw+8ADQ6pbGTY9v4i4EfhF4NFhVKjfFcomXkQsAe9ts+ufDrsuQxJttrVeTRUpM64mue5FFD6/iNhHPQgmqv2cgueYmU8CT0bEh4HfBDouqztmipzfvwE+lZkXI9oVL1flgyAHuybzOFoBbmp6vQc400OZcTXJdS+i0PlFxPuBx4B7sr4A1CTp6t8wM/8oIn44ImYycxImpCtyfrPA440QmAH2R8SFzPyvg6iQTUMbu7ymMmydNZVPAbdGxC0RsQO4n/p5NjsBfLwxeuhO4DuXm8gmQJHzm2Sbnl9E3Ax8AfhYZn59BHXsV5Fz/JFofEs2RrXtACYl8DY9v8y8JTP3ZuZe4AngyKBCAPxFsJlHgMXGGst/BdwH9TWZgccyc3/j9e8CPwvMRMQK8M8z89+Npsoby8wLEfEw8DT1UQnHM/OliHiwsf9R6qMU9gOngb8GfnlU9e1WkfOLiPcCy8C1wKWI+DXqozbeGlnFCyr47/frwC5gofFdeSEnaMbOguf4d6hfrHwf+H/ALzV1Ho+1guc3VE4xIUkVZ9OQJFWcQSBJFWcQSFLFGQSSVHEGgSRVnEEgSRVnEEhSxf1/ZCBItVG0s7cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "db_default = DBSCAN(eps = 0.02377, min_samples = 4).fit(texdb) \n",
    "labels = db_default.labels_ \n",
    "\n",
    "colors = {} \n",
    "colors[0] = 'r'\n",
    "colors[1] = 'g'\n",
    "colors[2] = 'c'\n",
    "colors[-1] = 'k'\n",
    "\n",
    "cvec = [colors[label] for label in labels] \n",
    "r = plt.scatter(texdb['1'], texdb['2'], color ='r'); \n",
    "g = plt.scatter(texdb['1'], texdb['2'], color ='g'); \n",
    "c = plt.scatter(texdb['1'], texdb['2'], color ='c');  \n",
    "k = plt.scatter(texdb['1'], texdb['2'], color ='k'); \n",
    "plt.figure(figsize =(9, 9)) \n",
    "plt.scatter(texdb['1'], texdb['2'], c = cvec)  \n",
    "plt.legend((r, g, c, k), ('Cluster 0', 'Cluster 1', 'Cluster 2', 'Outliers')) \n",
    "  \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging Cluster Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "final = vensort\n",
    "final.insert(1, 'Cluster', labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>CityLat</th>\n",
       "      <th>CityLong</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beaumont</td>\n",
       "      <td>30.086046</td>\n",
       "      <td>-94.101846</td>\n",
       "      <td>1</td>\n",
       "      <td>Café</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Nightclub</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Theater</td>\n",
       "      <td>Museum</td>\n",
       "      <td>Gay Bar</td>\n",
       "      <td>Other Repair Shop</td>\n",
       "      <td>Boutique</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>College Station</td>\n",
       "      <td>30.595529</td>\n",
       "      <td>-96.307104</td>\n",
       "      <td>0</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Business Service</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Storage Facility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bryan</td>\n",
       "      <td>30.674364</td>\n",
       "      <td>-96.369963</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Hot Dog Joint</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>BBQ Joint</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Café</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Killeen</td>\n",
       "      <td>31.117144</td>\n",
       "      <td>-97.727796</td>\n",
       "      <td>0</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Korean Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>BBQ Joint</td>\n",
       "      <td>Mobile Phone Shop</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Flower Shop</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Edinburg</td>\n",
       "      <td>26.301398</td>\n",
       "      <td>-98.162450</td>\n",
       "      <td>2</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Performing Arts Venue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              City    CityLat   CityLong  Cluster 1st Most Common Venue  \\\n",
       "0         Beaumont  30.086046 -94.101846        1                  Café   \n",
       "1  College Station  30.595529 -96.307104        0                 Hotel   \n",
       "2            Bryan  30.674364 -96.369963        0    Mexican Restaurant   \n",
       "3          Killeen  31.117144 -97.727796        0  Fast Food Restaurant   \n",
       "4         Edinburg  26.301398 -98.162450        2    Mexican Restaurant   \n",
       "\n",
       "  2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue  \\\n",
       "0   American Restaurant             Nightclub        Sandwich Place   \n",
       "1    Chinese Restaurant           Pizza Place                Bakery   \n",
       "2         Hot Dog Joint                   Bar   Fried Chicken Joint   \n",
       "3     Korean Restaurant    Chinese Restaurant           Pizza Place   \n",
       "4     Convenience Store    Seafood Restaurant        Discount Store   \n",
       "\n",
       "  5th Most Common Venue 6th Most Common Venue 7th Most Common Venue  \\\n",
       "0                   Bar               Theater                Museum   \n",
       "1   Japanese Restaurant          Liquor Store      Business Service   \n",
       "2          Burger Joint  Fast Food Restaurant             BBQ Joint   \n",
       "3        Cosmetics Shop             BBQ Joint     Mobile Phone Shop   \n",
       "4                Bakery          Burger Joint   Fried Chicken Joint   \n",
       "\n",
       "  8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n",
       "0               Gay Bar     Other Repair Shop               Boutique  \n",
       "1          Burger Joint   Fried Chicken Joint       Storage Facility  \n",
       "2           Pizza Place        Sandwich Place                   Café  \n",
       "3    Mexican Restaurant           Flower Shop          Grocery Store  \n",
       "4        Sandwich Place    Chinese Restaurant  Performing Arts Venue  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final = coords.merge(final, on = 'City', how = 'left')\n",
    "final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Re-Merging County and SD Rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_excel('fastgrowingtex.xlsx', dtype = str)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "final = data.merge(final, on = 'City', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>County</th>\n",
       "      <th>Sociodemographic Rank</th>\n",
       "      <th>CityLat</th>\n",
       "      <th>CityLong</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Frisco</td>\n",
       "      <td>Denton County</td>\n",
       "      <td>2</td>\n",
       "      <td>33.150674</td>\n",
       "      <td>-96.823612</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Art Gallery</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Pool</td>\n",
       "      <td>Farmers Market</td>\n",
       "      <td>Smoke Shop</td>\n",
       "      <td>Steakhouse</td>\n",
       "      <td>Sports Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Round Rock</td>\n",
       "      <td>Williamson County</td>\n",
       "      <td>13</td>\n",
       "      <td>30.508235</td>\n",
       "      <td>-97.678893</td>\n",
       "      <td>0.0</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>McKinney</td>\n",
       "      <td>Collin County</td>\n",
       "      <td>8</td>\n",
       "      <td>33.197650</td>\n",
       "      <td>-96.615447</td>\n",
       "      <td>0.0</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Café</td>\n",
       "      <td>Food</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Farmers Market</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Steakhouse</td>\n",
       "      <td>Burger Joint</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sugar Land</td>\n",
       "      <td>Fort Bend County</td>\n",
       "      <td>4</td>\n",
       "      <td>29.619679</td>\n",
       "      <td>-95.634946</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Park</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Auto Workshop</td>\n",
       "      <td>Office</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Other Great Outdoors</td>\n",
       "      <td>Big Box Store</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Midland</td>\n",
       "      <td>Midland County</td>\n",
       "      <td>22</td>\n",
       "      <td>31.997366</td>\n",
       "      <td>-102.077948</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Tex-Mex Restaurant</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Theater</td>\n",
       "      <td>Thrift / Vintage Store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         City             County Sociodemographic Rank    CityLat    CityLong  \\\n",
       "0      Frisco      Denton County                     2  33.150674  -96.823612   \n",
       "1  Round Rock  Williamson County                    13  30.508235  -97.678893   \n",
       "2    McKinney      Collin County                     8  33.197650  -96.615447   \n",
       "3  Sugar Land   Fort Bend County                     4  29.619679  -95.634946   \n",
       "4     Midland     Midland County                    22  31.997366 -102.077948   \n",
       "\n",
       "   Cluster 1st Most Common Venue 2nd Most Common Venue 3rd Most Common Venue  \\\n",
       "0      0.0    Mexican Restaurant       Thai Restaurant   American Restaurant   \n",
       "1      0.0   American Restaurant           Pizza Place           Gas Station   \n",
       "2      0.0   American Restaurant    Mexican Restaurant                  Café   \n",
       "3     -1.0                  Park          Intersection            Food Truck   \n",
       "4      0.0    Mexican Restaurant                   Bar        Ice Cream Shop   \n",
       "\n",
       "  4th Most Common Venue 5th Most Common Venue 6th Most Common Venue  \\\n",
       "0           Art Gallery          Dessert Shop                  Pool   \n",
       "1                   Bar           Coffee Shop                Bakery   \n",
       "2                  Food        Discount Store        Farmers Market   \n",
       "3         Auto Workshop                Office           Gas Station   \n",
       "4        Sandwich Place     Convenience Store                   Gym   \n",
       "\n",
       "  7th Most Common Venue 8th Most Common Venue 9th Most Common Venue  \\\n",
       "0        Farmers Market            Smoke Shop            Steakhouse   \n",
       "1         Grocery Store          Burger Joint        Ice Cream Shop   \n",
       "2        Sandwich Place                Bakery            Steakhouse   \n",
       "3  Other Great Outdoors         Big Box Store        Clothing Store   \n",
       "4    Tex-Mex Restaurant       Thai Restaurant               Theater   \n",
       "\n",
       "   10th Most Common Venue  \n",
       "0              Sports Bar  \n",
       "1      Mexican Restaurant  \n",
       "2            Burger Joint  \n",
       "3                     Gym  \n",
       "4  Thrift / Vintage Store  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "final = final.rename(columns = {\"Sociodemographic Rank\": \"SD Rank\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assigning Relative SD Rank Within Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(48, 16)\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>County</th>\n",
       "      <th>SD Rank</th>\n",
       "      <th>CityLat</th>\n",
       "      <th>CityLong</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Frisco</td>\n",
       "      <td>Denton County</td>\n",
       "      <td>1</td>\n",
       "      <td>33.150674</td>\n",
       "      <td>-96.823612</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Art Gallery</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Pool</td>\n",
       "      <td>Farmers Market</td>\n",
       "      <td>Smoke Shop</td>\n",
       "      <td>Steakhouse</td>\n",
       "      <td>Sports Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Round Rock</td>\n",
       "      <td>Williamson County</td>\n",
       "      <td>5</td>\n",
       "      <td>30.508235</td>\n",
       "      <td>-97.678893</td>\n",
       "      <td>0.0</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>McKinney</td>\n",
       "      <td>Collin County</td>\n",
       "      <td>3</td>\n",
       "      <td>33.197650</td>\n",
       "      <td>-96.615447</td>\n",
       "      <td>0.0</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Café</td>\n",
       "      <td>Food</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Farmers Market</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Steakhouse</td>\n",
       "      <td>Burger Joint</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sugar Land</td>\n",
       "      <td>Fort Bend County</td>\n",
       "      <td>2</td>\n",
       "      <td>29.619679</td>\n",
       "      <td>-95.634946</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Park</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Auto Workshop</td>\n",
       "      <td>Office</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Other Great Outdoors</td>\n",
       "      <td>Big Box Store</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Midland</td>\n",
       "      <td>Midland County</td>\n",
       "      <td>10</td>\n",
       "      <td>31.997366</td>\n",
       "      <td>-102.077948</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Tex-Mex Restaurant</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Theater</td>\n",
       "      <td>Thrift / Vintage Store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         City             County  SD Rank    CityLat    CityLong  Cluster  \\\n",
       "0      Frisco      Denton County        1  33.150674  -96.823612      0.0   \n",
       "1  Round Rock  Williamson County        5  30.508235  -97.678893      0.0   \n",
       "2    McKinney      Collin County        3  33.197650  -96.615447      0.0   \n",
       "3  Sugar Land   Fort Bend County        2  29.619679  -95.634946     -1.0   \n",
       "4     Midland     Midland County       10  31.997366 -102.077948      0.0   \n",
       "\n",
       "  1st Most Common Venue 2nd Most Common Venue 3rd Most Common Venue  \\\n",
       "0    Mexican Restaurant       Thai Restaurant   American Restaurant   \n",
       "1   American Restaurant           Pizza Place           Gas Station   \n",
       "2   American Restaurant    Mexican Restaurant                  Café   \n",
       "3                  Park          Intersection            Food Truck   \n",
       "4    Mexican Restaurant                   Bar        Ice Cream Shop   \n",
       "\n",
       "  4th Most Common Venue 5th Most Common Venue 6th Most Common Venue  \\\n",
       "0           Art Gallery          Dessert Shop                  Pool   \n",
       "1                   Bar           Coffee Shop                Bakery   \n",
       "2                  Food        Discount Store        Farmers Market   \n",
       "3         Auto Workshop                Office           Gas Station   \n",
       "4        Sandwich Place     Convenience Store                   Gym   \n",
       "\n",
       "  7th Most Common Venue 8th Most Common Venue 9th Most Common Venue  \\\n",
       "0        Farmers Market            Smoke Shop            Steakhouse   \n",
       "1         Grocery Store          Burger Joint        Ice Cream Shop   \n",
       "2        Sandwich Place                Bakery            Steakhouse   \n",
       "3  Other Great Outdoors         Big Box Store        Clothing Store   \n",
       "4    Tex-Mex Restaurant       Thai Restaurant               Theater   \n",
       "\n",
       "   10th Most Common Venue  \n",
       "0              Sports Bar  \n",
       "1      Mexican Restaurant  \n",
       "2            Burger Joint  \n",
       "3                     Gym  \n",
       "4  Thrift / Vintage Store  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final[\"SD Rank\"] = pd.to_numeric(final[\"SD Rank\"])\n",
    "final['SD Rank'] = final['SD Rank'].rank(method='max')\n",
    "final = final.astype({'SD Rank': 'int'})\n",
    "final.dropna()\n",
    "print(final.shape)\n",
    "final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "final = final.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging House Price Data and GeoJSON for Choropleth Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "prices = counties[['County', '$MHP']].copy()\n",
    "prices.reset_index(inplace=True)\n",
    "prices.drop(labels = ['index'], axis = 1, inplace = True)\n",
    "prices = prices.astype({'$MHP': 'int'})\n",
    "prices = prices.astype({'County': 'str'})\n",
    "prices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Median House Prices by County with Choropleth Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "finmap = folium.Map(location = [30.2711286, -97.7436995], zoom_start = 7)\n",
    "finmap.choropleth(\n",
    " geo_data=geo_data,\n",
    " name='choropleth',\n",
    " data=prices,\n",
    " columns=['County', '$MHP'],\n",
    " key_on='feature.properties.namelsad',\n",
    " fill_color='YlGn',\n",
    " fill_opacity=0.7,\n",
    " line_opacity=0.2,\n",
    " legend_name='Median Housing Price'\n",
    ")\n",
    "folium.LayerControl().add_to(finmap)\n",
    " \n",
    "finmap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manipulating Cluster Values for Cluster Labeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Frisco</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Round Rock</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>McKinney</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sugar Land</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Midland</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         City  Cluster\n",
       "0      Frisco      0.0\n",
       "1  Round Rock      0.0\n",
       "2    McKinney      0.0\n",
       "3  Sugar Land     -1.0\n",
       "4     Midland      0.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluslab = final[['City', 'Cluster']].copy()\n",
    "cluslab.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluslab = cluslab.merge(texmean, on = 'City', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Cluster</th>\n",
       "      <th>Accessories Store</th>\n",
       "      <th>American Restaurant</th>\n",
       "      <th>Antique Shop</th>\n",
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       "      <th>Arcade</th>\n",
       "      <th>Art Gallery</th>\n",
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       "      <th>Assisted Living</th>\n",
       "      <th>Auto Dealership</th>\n",
       "      <th>Auto Garage</th>\n",
       "      <th>Auto Workshop</th>\n",
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       "      <th>Bagel Shop</th>\n",
       "      <th>Bakery</th>\n",
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       "      <th>Baseball Field</th>\n",
       "      <th>Baseball Stadium</th>\n",
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       "      <th>Basketball Stadium</th>\n",
       "      <th>Beer Bar</th>\n",
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       "      <th>Big Box Store</th>\n",
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       "      <th>Boutique</th>\n",
       "      <th>Bowling Alley</th>\n",
       "      <th>Brazilian Restaurant</th>\n",
       "      <th>Breakfast Spot</th>\n",
       "      <th>Brewery</th>\n",
       "      <th>Burger Joint</th>\n",
       "      <th>Burrito Place</th>\n",
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       "      <th>Check Cashing Service</th>\n",
       "      <th>Chinese Restaurant</th>\n",
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       "      <th>Cocktail Bar</th>\n",
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       "      <th>Convenience Store</th>\n",
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       "      <th>Cosmetics Shop</th>\n",
       "      <th>Costume Shop</th>\n",
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       "      <th>Cycle Studio</th>\n",
       "      <th>Dance Studio</th>\n",
       "      <th>Deli / Bodega</th>\n",
       "      <th>Department Store</th>\n",
       "      <th>Dessert Shop</th>\n",
       "      <th>Diner</th>\n",
       "      <th>Discount Store</th>\n",
       "      <th>Dive Bar</th>\n",
       "      <th>Doctor's Office</th>\n",
       "      <th>Dog Run</th>\n",
       "      <th>Donut Shop</th>\n",
       "      <th>Dumpling Restaurant</th>\n",
       "      <th>Electronics Store</th>\n",
       "      <th>Empanada Restaurant</th>\n",
       "      <th>Fabric Shop</th>\n",
       "      <th>Farmers Market</th>\n",
       "      <th>Fast Food Restaurant</th>\n",
       "      <th>Field</th>\n",
       "      <th>Fishing Spot</th>\n",
       "      <th>Flower Shop</th>\n",
       "      <th>Fondue Restaurant</th>\n",
       "      <th>Food</th>\n",
       "      <th>Food &amp; Drink Shop</th>\n",
       "      <th>Food Court</th>\n",
       "      <th>Food Truck</th>\n",
       "      <th>Football Stadium</th>\n",
       "      <th>French Restaurant</th>\n",
       "      <th>Fried Chicken Joint</th>\n",
       "      <th>Furniture / Home Store</th>\n",
       "      <th>Garden</th>\n",
       "      <th>Garden Center</th>\n",
       "      <th>Gas Station</th>\n",
       "      <th>Gastropub</th>\n",
       "      <th>Gay Bar</th>\n",
       "      <th>General College &amp; University</th>\n",
       "      <th>General Entertainment</th>\n",
       "      <th>German Restaurant</th>\n",
       "      <th>Gift Shop</th>\n",
       "      <th>Gluten-free Restaurant</th>\n",
       "      <th>Greek Restaurant</th>\n",
       "      <th>Grocery Store</th>\n",
       "      <th>Gym</th>\n",
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       "      <th>Gymnastics Gym</th>\n",
       "      <th>Hardware Store</th>\n",
       "      <th>Health &amp; Beauty Service</th>\n",
       "      <th>Historic Site</th>\n",
       "      <th>History Museum</th>\n",
       "      <th>Hockey Arena</th>\n",
       "      <th>Home Service</th>\n",
       "      <th>Hot Dog Joint</th>\n",
       "      <th>Hotel</th>\n",
       "      <th>Hotel Bar</th>\n",
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       "      <th>Italian Restaurant</th>\n",
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       "      <th>Jazz Club</th>\n",
       "      <th>Jewelry Store</th>\n",
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       "      <th>Liquor Store</th>\n",
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       "      <th>Mediterranean Restaurant</th>\n",
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       "      <th>Monument / Landmark</th>\n",
       "      <th>Motel</th>\n",
       "      <th>Motorcycle Shop</th>\n",
       "      <th>Motorsports Shop</th>\n",
       "      <th>Movie Theater</th>\n",
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       "      <th>Noodle House</th>\n",
       "      <th>Notary</th>\n",
       "      <th>Office</th>\n",
       "      <th>Optical Shop</th>\n",
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       "      <th>Other Repair Shop</th>\n",
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       "      <th>Pedestrian Plaza</th>\n",
       "      <th>Performing Arts Venue</th>\n",
       "      <th>Pet Store</th>\n",
       "      <th>Pharmacy</th>\n",
       "      <th>Piano Bar</th>\n",
       "      <th>Pie Shop</th>\n",
       "      <th>Pizza Place</th>\n",
       "      <th>Platform</th>\n",
       "      <th>Playground</th>\n",
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       "      <th>Record Shop</th>\n",
       "      <th>Recording Studio</th>\n",
       "      <th>Recreation Center</th>\n",
       "      <th>Rental Car Location</th>\n",
       "      <th>Rest Area</th>\n",
       "      <th>Restaurant</th>\n",
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       "      <th>Salad Place</th>\n",
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       "      <th>Sandwich Place</th>\n",
       "      <th>Scenic Lookout</th>\n",
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       "      <th>Thai Restaurant</th>\n",
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       "      <th>Theme Park Ride / Attraction</th>\n",
       "      <th>Theme Restaurant</th>\n",
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       "      <th>Tourist Information Center</th>\n",
       "      <th>Toy / Game Store</th>\n",
       "      <th>Trail</th>\n",
       "      <th>Train Station</th>\n",
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       "      <th>Tree</th>\n",
       "      <th>Turkish Restaurant</th>\n",
       "      <th>Vegetarian / Vegan Restaurant</th>\n",
       "      <th>Video Game Store</th>\n",
       "      <th>Video Store</th>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.030303</td>\n",
       "      <td>0.030303</td>\n",
       "      <td>0.030303</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030303</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cluster  Accessories Store  American Restaurant  Antique Shop  Aquarium  \\\n",
       "0       0                0.0             0.051282      0.000000       0.0   \n",
       "1       0                0.0             0.089552      0.000000       0.0   \n",
       "2       0                0.0             0.053571      0.017857       0.0   \n",
       "3      -1                0.0             0.000000      0.000000       0.0   \n",
       "4       0                0.0             0.030303      0.000000       0.0   \n",
       "\n",
       "   Arcade  Art Gallery  Art Museum  Arts & Crafts Store  Asian Restaurant  \\\n",
       "0     0.0     0.051282         0.0             0.000000               0.0   \n",
       "1     0.0     0.000000         0.0             0.014925               0.0   \n",
       "2     0.0     0.000000         0.0             0.000000               0.0   \n",
       "3     0.0     0.000000         0.0             0.000000               0.0   \n",
       "4     0.0     0.000000         0.0             0.000000               0.0   \n",
       "\n",
       "   Assisted Living  Auto Dealership  Auto Garage  Auto Workshop  \\\n",
       "0         0.000000              0.0          0.0       0.000000   \n",
       "1         0.000000              0.0          0.0       0.000000   \n",
       "2         0.000000              0.0          0.0       0.017857   \n",
       "3         0.043478              0.0          0.0       0.043478   \n",
       "4         0.000000              0.0          0.0       0.000000   \n",
       "\n",
       "   Automotive Shop  BBQ Joint  Bagel Shop    Bakery      Bank       Bar  \\\n",
       "0         0.000000   0.000000         0.0  0.000000  0.000000  0.000000   \n",
       "1         0.000000   0.000000         0.0  0.029851  0.014925  0.029851   \n",
       "2         0.000000   0.000000         0.0  0.035714  0.000000  0.000000   \n",
       "3         0.000000   0.043478         0.0  0.000000  0.000000  0.000000   \n",
       "4         0.030303   0.030303         0.0  0.000000  0.030303  0.151515   \n",
       "\n",
       "   Baseball Field  Baseball Stadium  Basketball Court  Basketball Stadium  \\\n",
       "0        0.000000               0.0          0.000000                 0.0   \n",
       "1        0.000000               0.0          0.000000                 0.0   \n",
       "2        0.000000               0.0          0.017857                 0.0   \n",
       "3        0.043478               0.0          0.000000                 0.0   \n",
       "4        0.000000               0.0          0.000000                 0.0   \n",
       "\n",
       "   Beer Bar  Beer Garden  Beer Store  Big Box Store  Bike Shop    Bistro  \\\n",
       "0       0.0          0.0         0.0       0.000000        0.0  0.000000   \n",
       "1       0.0          0.0         0.0       0.014925        0.0  0.014925   \n",
       "2       0.0          0.0         0.0       0.000000        0.0  0.000000   \n",
       "3       0.0          0.0         0.0       0.043478        0.0  0.000000   \n",
       "4       0.0          0.0         0.0       0.000000        0.0  0.000000   \n",
       "\n",
       "   Bookstore  Boutique  Bowling Alley  Brazilian Restaurant  Breakfast Spot  \\\n",
       "0        0.0  0.000000            0.0                   0.0        0.000000   \n",
       "1        0.0  0.000000            0.0                   0.0        0.000000   \n",
       "2        0.0  0.017857            0.0                   0.0        0.017857   \n",
       "3        0.0  0.000000            0.0                   0.0        0.000000   \n",
       "4        0.0  0.000000            0.0                   0.0        0.030303   \n",
       "\n",
       "   Brewery  Burger Joint  Burrito Place  Bus Station  Bus Stop  \\\n",
       "0      0.0      0.025641            0.0          0.0       0.0   \n",
       "1      0.0      0.029851            0.0          0.0       0.0   \n",
       "2      0.0      0.035714            0.0          0.0       0.0   \n",
       "3      0.0      0.000000            0.0          0.0       0.0   \n",
       "4      0.0      0.030303            0.0          0.0       0.0   \n",
       "\n",
       "   Business Service      Café  Cajun / Creole Restaurant  Candy Store  \\\n",
       "0               0.0  0.000000                   0.000000     0.025641   \n",
       "1               0.0  0.000000                   0.014925     0.000000   \n",
       "2               0.0  0.053571                   0.017857     0.000000   \n",
       "3               0.0  0.000000                   0.000000     0.000000   \n",
       "4               0.0  0.030303                   0.000000     0.000000   \n",
       "\n",
       "   Capitol Building  Caribbean Restaurant  Check Cashing Service  \\\n",
       "0               0.0                   0.0                    0.0   \n",
       "1               0.0                   0.0                    0.0   \n",
       "2               0.0                   0.0                    0.0   \n",
       "3               0.0                   0.0                    0.0   \n",
       "4               0.0                   0.0                    0.0   \n",
       "\n",
       "   Chinese Restaurant  Chocolate Shop  Church  Clothing Store  Cocktail Bar  \\\n",
       "0            0.000000             0.0     0.0        0.000000      0.000000   \n",
       "1            0.014925             0.0     0.0        0.000000      0.014925   \n",
       "2            0.000000             0.0     0.0        0.000000      0.000000   \n",
       "3            0.000000             0.0     0.0        0.043478      0.000000   \n",
       "4            0.000000             0.0     0.0        0.000000      0.000000   \n",
       "\n",
       "   Coffee Shop  College Auditorium  Comedy Club  Concert Hall  \\\n",
       "0     0.025641                 0.0          0.0           0.0   \n",
       "1     0.029851                 0.0          0.0           0.0   \n",
       "2     0.017857                 0.0          0.0           0.0   \n",
       "3     0.043478                 0.0          0.0           0.0   \n",
       "4     0.000000                 0.0          0.0           0.0   \n",
       "\n",
       "   Construction & Landscaping  Convenience Store  Convention Center  \\\n",
       "0                    0.000000           0.000000                0.0   \n",
       "1                    0.000000           0.014925                0.0   \n",
       "2                    0.017857           0.000000                0.0   \n",
       "3                    0.000000           0.043478                0.0   \n",
       "4                    0.030303           0.060606                0.0   \n",
       "\n",
       "   Cosmetics Shop  Costume Shop  Creperie  Cupcake Shop  Cycle Studio  \\\n",
       "0             0.0           0.0       0.0      0.025641           0.0   \n",
       "1             0.0           0.0       0.0      0.000000           0.0   \n",
       "2             0.0           0.0       0.0      0.000000           0.0   \n",
       "3             0.0           0.0       0.0      0.000000           0.0   \n",
       "4             0.0           0.0       0.0      0.000000           0.0   \n",
       "\n",
       "   Dance Studio  Deli / Bodega  Department Store  Dessert Shop     Diner  \\\n",
       "0           0.0            0.0               0.0      0.051282  0.025641   \n",
       "1           0.0            0.0               0.0      0.014925  0.000000   \n",
       "2           0.0            0.0               0.0      0.035714  0.000000   \n",
       "3           0.0            0.0               0.0      0.000000  0.000000   \n",
       "4           0.0            0.0               0.0      0.000000  0.000000   \n",
       "\n",
       "   Discount Store  Dive Bar  Doctor's Office   Dog Run  Donut Shop  \\\n",
       "0        0.000000       0.0              0.0  0.000000    0.000000   \n",
       "1        0.014925       0.0              0.0  0.014925    0.014925   \n",
       "2        0.035714       0.0              0.0  0.000000    0.017857   \n",
       "3        0.000000       0.0              0.0  0.000000    0.000000   \n",
       "4        0.000000       0.0              0.0  0.000000    0.000000   \n",
       "\n",
       "   Dumpling Restaurant  Electronics Store  Empanada Restaurant  Fabric Shop  \\\n",
       "0                  0.0                0.0                  0.0          0.0   \n",
       "1                  0.0                0.0                  0.0          0.0   \n",
       "2                  0.0                0.0                  0.0          0.0   \n",
       "3                  0.0                0.0                  0.0          0.0   \n",
       "4                  0.0                0.0                  0.0          0.0   \n",
       "\n",
       "   Farmers Market  Fast Food Restaurant  Field  Fishing Spot  Flower Shop  \\\n",
       "0        0.051282              0.000000    0.0      0.025641          0.0   \n",
       "1        0.000000              0.014925    0.0      0.000000          0.0   \n",
       "2        0.035714              0.000000    0.0      0.000000          0.0   \n",
       "3        0.000000              0.043478    0.0      0.000000          0.0   \n",
       "4        0.000000              0.000000    0.0      0.000000          0.0   \n",
       "\n",
       "   Fondue Restaurant      Food  Food & Drink Shop  Food Court  Food Truck  \\\n",
       "0                0.0  0.000000                0.0         0.0    0.025641   \n",
       "1                0.0  0.000000                0.0         0.0    0.000000   \n",
       "2                0.0  0.035714                0.0         0.0    0.000000   \n",
       "3                0.0  0.000000                0.0         0.0    0.086957   \n",
       "4                0.0  0.000000                0.0         0.0    0.000000   \n",
       "\n",
       "   Football Stadium  French Restaurant  Fried Chicken Joint  \\\n",
       "0               0.0           0.000000                  0.0   \n",
       "1               0.0           0.000000                  0.0   \n",
       "2               0.0           0.017857                  0.0   \n",
       "3               0.0           0.000000                  0.0   \n",
       "4               0.0           0.000000                  0.0   \n",
       "\n",
       "   Furniture / Home Store  Garden  Garden Center  Gas Station  Gastropub  \\\n",
       "0                     0.0     0.0            0.0     0.000000        0.0   \n",
       "1                     0.0     0.0            0.0     0.029851        0.0   \n",
       "2                     0.0     0.0            0.0     0.017857        0.0   \n",
       "3                     0.0     0.0            0.0     0.043478        0.0   \n",
       "4                     0.0     0.0            0.0     0.000000        0.0   \n",
       "\n",
       "   Gay Bar  General College & University  General Entertainment  \\\n",
       "0      0.0                           0.0                    0.0   \n",
       "1      0.0                           0.0                    0.0   \n",
       "2      0.0                           0.0                    0.0   \n",
       "3      0.0                           0.0                    0.0   \n",
       "4      0.0                           0.0                    0.0   \n",
       "\n",
       "   German Restaurant  Gift Shop  Gluten-free Restaurant  Greek Restaurant  \\\n",
       "0                0.0        0.0                0.000000               0.0   \n",
       "1                0.0        0.0                0.014925               0.0   \n",
       "2                0.0        0.0                0.000000               0.0   \n",
       "3                0.0        0.0                0.000000               0.0   \n",
       "4                0.0        0.0                0.000000               0.0   \n",
       "\n",
       "   Grocery Store       Gym  Gym / Fitness Center  Gymnastics Gym  \\\n",
       "0       0.000000  0.000000                   0.0             0.0   \n",
       "1       0.029851  0.000000                   0.0             0.0   \n",
       "2       0.017857  0.000000                   0.0             0.0   \n",
       "3       0.000000  0.043478                   0.0             0.0   \n",
       "4       0.000000  0.030303                   0.0             0.0   \n",
       "\n",
       "   Hardware Store  Health & Beauty Service  Historic Site  History Museum  \\\n",
       "0             0.0                 0.000000       0.000000        0.025641   \n",
       "1             0.0                 0.000000       0.000000        0.000000   \n",
       "2             0.0                 0.000000       0.017857        0.000000   \n",
       "3             0.0                 0.043478       0.000000        0.000000   \n",
       "4             0.0                 0.000000       0.000000        0.000000   \n",
       "\n",
       "   Hockey Arena  Home Service  Hot Dog Joint     Hotel  Hotel Bar  \\\n",
       "0           0.0      0.000000            0.0  0.000000        0.0   \n",
       "1           0.0      0.000000            0.0  0.000000        0.0   \n",
       "2           0.0      0.017857            0.0  0.017857        0.0   \n",
       "3           0.0      0.043478            0.0  0.000000        0.0   \n",
       "4           0.0      0.000000            0.0  0.000000        0.0   \n",
       "\n",
       "   IT Services  Ice Cream Shop  Indian Restaurant  Indie Movie Theater  \\\n",
       "0          0.0        0.000000                0.0                  0.0   \n",
       "1          0.0        0.029851                0.0                  0.0   \n",
       "2          0.0        0.017857                0.0                  0.0   \n",
       "3          0.0        0.000000                0.0                  0.0   \n",
       "4          0.0        0.060606                0.0                  0.0   \n",
       "\n",
       "   Insurance Office  Intersection  Irish Pub  Italian Restaurant  \\\n",
       "0          0.000000      0.000000   0.000000            0.025641   \n",
       "1          0.000000      0.000000   0.000000            0.014925   \n",
       "2          0.017857      0.000000   0.035714            0.000000   \n",
       "3          0.000000      0.086957   0.000000            0.000000   \n",
       "4          0.000000      0.000000   0.000000            0.030303   \n",
       "\n",
       "   Japanese Restaurant  Jazz Club  Jewelry Store  Juice Bar  Kids Store  \\\n",
       "0             0.025641        0.0            0.0        0.0         0.0   \n",
       "1             0.000000        0.0            0.0        0.0         0.0   \n",
       "2             0.000000        0.0            0.0        0.0         0.0   \n",
       "3             0.000000        0.0            0.0        0.0         0.0   \n",
       "4             0.000000        0.0            0.0        0.0         0.0   \n",
       "\n",
       "   Korean Restaurant  Lake  Latin American Restaurant   Library  \\\n",
       "0                0.0   0.0                   0.000000  0.025641   \n",
       "1                0.0   0.0                   0.014925  0.000000   \n",
       "2                0.0   0.0                   0.000000  0.000000   \n",
       "3                0.0   0.0                   0.000000  0.000000   \n",
       "4                0.0   0.0                   0.000000  0.000000   \n",
       "\n",
       "   Light Rail Station  Lighting Store  Liquor Store  Locksmith    Lounge  \\\n",
       "0                 0.0             0.0           0.0        0.0  0.025641   \n",
       "1                 0.0             0.0           0.0        0.0  0.000000   \n",
       "2                 0.0             0.0           0.0        0.0  0.000000   \n",
       "3                 0.0             0.0           0.0        0.0  0.000000   \n",
       "4                 0.0             0.0           0.0        0.0  0.000000   \n",
       "\n",
       "   Market  Mediterranean Restaurant  Men's Store  Mexican Restaurant  \\\n",
       "0     0.0                       0.0          0.0            0.076923   \n",
       "1     0.0                       0.0          0.0            0.029851   \n",
       "2     0.0                       0.0          0.0            0.053571   \n",
       "3     0.0                       0.0          0.0            0.000000   \n",
       "4     0.0                       0.0          0.0            0.181818   \n",
       "\n",
       "   Miscellaneous Shop  Mobile Phone Shop  Monument / Landmark  Motel  \\\n",
       "0            0.000000                0.0                  0.0    0.0   \n",
       "1            0.014925                0.0                  0.0    0.0   \n",
       "2            0.000000                0.0                  0.0    0.0   \n",
       "3            0.000000                0.0                  0.0    0.0   \n",
       "4            0.000000                0.0                  0.0    0.0   \n",
       "\n",
       "   Motorcycle Shop  Motorsports Shop  Movie Theater  Museum  Music School  \\\n",
       "0              0.0               0.0            0.0     0.0      0.025641   \n",
       "1              0.0               0.0            0.0     0.0      0.000000   \n",
       "2              0.0               0.0            0.0     0.0      0.000000   \n",
       "3              0.0               0.0            0.0     0.0      0.000000   \n",
       "4              0.0               0.0            0.0     0.0      0.000000   \n",
       "\n",
       "   Music Store  Music Venue  Neighborhood  New American Restaurant  Nightclub  \\\n",
       "0          0.0          0.0           0.0                 0.000000        0.0   \n",
       "1          0.0          0.0           0.0                 0.000000        0.0   \n",
       "2          0.0          0.0           0.0                 0.017857        0.0   \n",
       "3          0.0          0.0           0.0                 0.000000        0.0   \n",
       "4          0.0          0.0           0.0                 0.000000        0.0   \n",
       "\n",
       "   Noodle House  Notary    Office  Optical Shop  Other Great Outdoors  \\\n",
       "0           0.0     0.0  0.000000      0.000000              0.000000   \n",
       "1           0.0     0.0  0.000000      0.014925              0.000000   \n",
       "2           0.0     0.0  0.000000      0.000000              0.000000   \n",
       "3           0.0     0.0  0.043478      0.000000              0.043478   \n",
       "4           0.0     0.0  0.000000      0.000000              0.000000   \n",
       "\n",
       "   Other Repair Shop  Paper / Office Supplies Store      Park  \\\n",
       "0                0.0                       0.000000  0.025641   \n",
       "1                0.0                       0.014925  0.014925   \n",
       "2                0.0                       0.000000  0.017857   \n",
       "3                0.0                       0.000000  0.130435   \n",
       "4                0.0                       0.000000  0.000000   \n",
       "\n",
       "   Pedestrian Plaza  Performing Arts Venue  Pet Store  Pharmacy  Piano Bar  \\\n",
       "0               0.0                    0.0        0.0       0.0   0.000000   \n",
       "1               0.0                    0.0        0.0       0.0   0.014925   \n",
       "2               0.0                    0.0        0.0       0.0   0.000000   \n",
       "3               0.0                    0.0        0.0       0.0   0.000000   \n",
       "4               0.0                    0.0        0.0       0.0   0.000000   \n",
       "\n",
       "   Pie Shop  Pizza Place  Platform  Playground     Plaza      Pool  Pool Hall  \\\n",
       "0  0.000000     0.025641       0.0    0.025641  0.000000  0.051282        0.0   \n",
       "1  0.000000     0.059701       0.0    0.014925  0.029851  0.000000        0.0   \n",
       "2  0.017857     0.035714       0.0    0.000000  0.000000  0.000000        0.0   \n",
       "3  0.000000     0.000000       0.0    0.000000  0.000000  0.000000        0.0   \n",
       "4  0.000000     0.030303       0.0    0.000000  0.000000  0.000000        0.0   \n",
       "\n",
       "   Post Office  Print Shop  Pub  Public Art  Record Shop  Recording Studio  \\\n",
       "0          0.0         0.0  0.0         0.0          0.0               0.0   \n",
       "1          0.0         0.0  0.0         0.0          0.0               0.0   \n",
       "2          0.0         0.0  0.0         0.0          0.0               0.0   \n",
       "3          0.0         0.0  0.0         0.0          0.0               0.0   \n",
       "4          0.0         0.0  0.0         0.0          0.0               0.0   \n",
       "\n",
       "   Recreation Center  Rental Car Location  Rest Area  Restaurant  Rock Club  \\\n",
       "0                0.0                  0.0        0.0    0.000000        0.0   \n",
       "1                0.0                  0.0        0.0    0.000000        0.0   \n",
       "2                0.0                  0.0        0.0    0.017857        0.0   \n",
       "3                0.0                  0.0        0.0    0.000000        0.0   \n",
       "4                0.0                  0.0        0.0    0.000000        0.0   \n",
       "\n",
       "   Russian Restaurant  Salad Place  Salon / Barbershop  Sandwich Place  \\\n",
       "0                 0.0     0.000000                 0.0        0.025641   \n",
       "1                 0.0     0.014925                 0.0        0.014925   \n",
       "2                 0.0     0.000000                 0.0        0.035714   \n",
       "3                 0.0     0.000000                 0.0        0.000000   \n",
       "4                 0.0     0.000000                 0.0        0.060606   \n",
       "\n",
       "   Scenic Lookout  Science Museum  Seafood Restaurant  Shanghai Restaurant  \\\n",
       "0        0.000000             0.0                 0.0                  0.0   \n",
       "1        0.014925             0.0                 0.0                  0.0   \n",
       "2        0.000000             0.0                 0.0                  0.0   \n",
       "3        0.000000             0.0                 0.0                  0.0   \n",
       "4        0.000000             0.0                 0.0                  0.0   \n",
       "\n",
       "   Shipping Store  Shoe Repair  Shoe Store  Shop & Service  Shopping Mall  \\\n",
       "0             0.0          0.0    0.000000             0.0            0.0   \n",
       "1             0.0          0.0    0.014925             0.0            0.0   \n",
       "2             0.0          0.0    0.000000             0.0            0.0   \n",
       "3             0.0          0.0    0.000000             0.0            0.0   \n",
       "4             0.0          0.0    0.000000             0.0            0.0   \n",
       "\n",
       "   Shopping Plaza  Skate Park  Smoke Shop  Snack Place  Soccer Field  \\\n",
       "0        0.000000         0.0    0.025641     0.000000      0.000000   \n",
       "1        0.014925         0.0    0.000000     0.000000      0.014925   \n",
       "2        0.000000         0.0    0.000000     0.017857      0.000000   \n",
       "3        0.000000         0.0    0.000000     0.000000      0.000000   \n",
       "4        0.000000         0.0    0.000000     0.000000      0.000000   \n",
       "\n",
       "   Soccer Stadium  South American Restaurant  Southern / Soul Food Restaurant  \\\n",
       "0        0.025641                        0.0                         0.025641   \n",
       "1        0.000000                        0.0                         0.000000   \n",
       "2        0.000000                        0.0                         0.000000   \n",
       "3        0.000000                        0.0                         0.000000   \n",
       "4        0.000000                        0.0                         0.000000   \n",
       "\n",
       "        Spa  Spanish Restaurant  Speakeasy  Sporting Goods Shop  Sports Bar  \\\n",
       "0  0.000000                 0.0        0.0             0.025641    0.025641   \n",
       "1  0.014925                 0.0        0.0             0.014925    0.029851   \n",
       "2  0.000000                 0.0        0.0             0.000000    0.000000   \n",
       "3  0.000000                 0.0        0.0             0.000000    0.000000   \n",
       "4  0.000000                 0.0        0.0             0.000000    0.000000   \n",
       "\n",
       "   Stadium  Steakhouse  Storage Facility  Supermarket  Supplement Shop  \\\n",
       "0      0.0    0.025641          0.000000          0.0              0.0   \n",
       "1      0.0    0.000000          0.014925          0.0              0.0   \n",
       "2      0.0    0.035714          0.000000          0.0              0.0   \n",
       "3      0.0    0.000000          0.000000          0.0              0.0   \n",
       "4      0.0    0.030303          0.000000          0.0              0.0   \n",
       "\n",
       "   Sushi Restaurant  Taco Place  Tanning Salon  Tea Room  Tex-Mex Restaurant  \\\n",
       "0          0.000000         0.0            0.0       0.0            0.000000   \n",
       "1          0.000000         0.0            0.0       0.0            0.000000   \n",
       "2          0.017857         0.0            0.0       0.0            0.000000   \n",
       "3          0.000000         0.0            0.0       0.0            0.000000   \n",
       "4          0.000000         0.0            0.0       0.0            0.030303   \n",
       "\n",
       "   Thai Restaurant   Theater  Theme Park Ride / Attraction  Theme Restaurant  \\\n",
       "0         0.051282  0.000000                           0.0               0.0   \n",
       "1         0.000000  0.029851                           0.0               0.0   \n",
       "2         0.000000  0.035714                           0.0               0.0   \n",
       "3         0.000000  0.000000                           0.0               0.0   \n",
       "4         0.030303  0.030303                           0.0               0.0   \n",
       "\n",
       "   Thrift / Vintage Store  Tourist Information Center  Toy / Game Store  \\\n",
       "0                0.025641                         0.0               0.0   \n",
       "1                0.000000                         0.0               0.0   \n",
       "2                0.000000                         0.0               0.0   \n",
       "3                0.000000                         0.0               0.0   \n",
       "4                0.030303                         0.0               0.0   \n",
       "\n",
       "   Trail  Train Station  Transportation Service  Travel & Transport  Tree  \\\n",
       "0    0.0            0.0                     0.0                 0.0   0.0   \n",
       "1    0.0            0.0                     0.0                 0.0   0.0   \n",
       "2    0.0            0.0                     0.0                 0.0   0.0   \n",
       "3    0.0            0.0                     0.0                 0.0   0.0   \n",
       "4    0.0            0.0                     0.0                 0.0   0.0   \n",
       "\n",
       "   Turkish Restaurant  Vegetarian / Vegan Restaurant  Video Game Store  \\\n",
       "0                 0.0                            0.0          0.000000   \n",
       "1                 0.0                            0.0          0.014925   \n",
       "2                 0.0                            0.0          0.000000   \n",
       "3                 0.0                            0.0          0.000000   \n",
       "4                 0.0                            0.0          0.000000   \n",
       "\n",
       "   Video Store  Vietnamese Restaurant  Volleyball Court  Warehouse Store  \\\n",
       "0     0.000000                    0.0               0.0         0.000000   \n",
       "1     0.014925                    0.0               0.0         0.000000   \n",
       "2     0.000000                    0.0               0.0         0.000000   \n",
       "3     0.000000                    0.0               0.0         0.043478   \n",
       "4     0.000000                    0.0               0.0         0.000000   \n",
       "\n",
       "   Water Park  Whisky Bar  Wine Bar  Wings Joint  Women's Store  Yoga Studio  \\\n",
       "0         0.0         0.0  0.000000     0.000000            0.0          0.0   \n",
       "1         0.0         0.0  0.014925     0.014925            0.0          0.0   \n",
       "2         0.0         0.0  0.035714     0.000000            0.0          0.0   \n",
       "3         0.0         0.0  0.000000     0.000000            0.0          0.0   \n",
       "4         0.0         0.0  0.000000     0.000000            0.0          0.0   \n",
       "\n",
       "   Zoo  \n",
       "0  0.0  \n",
       "1  0.0  \n",
       "2  0.0  \n",
       "3  0.0  \n",
       "4  0.0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluslab = cluslab.astype({'Cluster': 'int'})\n",
    "cluslab = cluslab.astype({'Cluster': 'str'})\n",
    "cluslab.drop(labels = ['City'], axis = 1, inplace = True)\n",
    "cluslab.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "tags = cluslab[['Cluster']]\n",
    "tags['Cluster'] = tags['Cluster'].apply(lambda x : x.replace(x, 'Outlier') if x.startswith(\"-1\") else x)\n",
    "tags['Cluster'] = tags['Cluster'].apply(lambda x : x.replace(x, 'Cluster {}'.format(x)))\n",
    "tags.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregating Venue Frequency By Cluster for Labeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>Accessories Store</th>\n",
       "      <th>American Restaurant</th>\n",
       "      <th>Antique Shop</th>\n",
       "      <th>Aquarium</th>\n",
       "      <th>Arcade</th>\n",
       "      <th>Art Gallery</th>\n",
       "      <th>Art Museum</th>\n",
       "      <th>Arts &amp; Crafts Store</th>\n",
       "      <th>Asian Restaurant</th>\n",
       "      <th>Assisted Living</th>\n",
       "      <th>Auto Dealership</th>\n",
       "      <th>Auto Garage</th>\n",
       "      <th>Auto Workshop</th>\n",
       "      <th>Automotive Shop</th>\n",
       "      <th>BBQ Joint</th>\n",
       "      <th>Bagel Shop</th>\n",
       "      <th>Bakery</th>\n",
       "      <th>Bank</th>\n",
       "      <th>Bar</th>\n",
       "      <th>Baseball Field</th>\n",
       "      <th>Baseball Stadium</th>\n",
       "      <th>Basketball Court</th>\n",
       "      <th>Basketball Stadium</th>\n",
       "      <th>Beer Bar</th>\n",
       "      <th>Beer Garden</th>\n",
       "      <th>Beer Store</th>\n",
       "      <th>Big Box Store</th>\n",
       "      <th>Bike Shop</th>\n",
       "      <th>Bistro</th>\n",
       "      <th>Bookstore</th>\n",
       "      <th>Boutique</th>\n",
       "      <th>Bowling Alley</th>\n",
       "      <th>Brazilian Restaurant</th>\n",
       "      <th>Breakfast Spot</th>\n",
       "      <th>Brewery</th>\n",
       "      <th>Burger Joint</th>\n",
       "      <th>Burrito Place</th>\n",
       "      <th>Bus Station</th>\n",
       "      <th>Bus Stop</th>\n",
       "      <th>Business Service</th>\n",
       "      <th>Café</th>\n",
       "      <th>Cajun / Creole Restaurant</th>\n",
       "      <th>Candy Store</th>\n",
       "      <th>Capitol Building</th>\n",
       "      <th>Caribbean Restaurant</th>\n",
       "      <th>Check Cashing Service</th>\n",
       "      <th>Chinese Restaurant</th>\n",
       "      <th>Chocolate Shop</th>\n",
       "      <th>Church</th>\n",
       "      <th>Clothing Store</th>\n",
       "      <th>Cocktail Bar</th>\n",
       "      <th>Coffee Shop</th>\n",
       "      <th>College Auditorium</th>\n",
       "      <th>Comedy Club</th>\n",
       "      <th>Concert Hall</th>\n",
       "      <th>Construction &amp; Landscaping</th>\n",
       "      <th>Convenience Store</th>\n",
       "      <th>Convention Center</th>\n",
       "      <th>Cosmetics Shop</th>\n",
       "      <th>Costume Shop</th>\n",
       "      <th>Creperie</th>\n",
       "      <th>Cupcake Shop</th>\n",
       "      <th>Cycle Studio</th>\n",
       "      <th>Dance Studio</th>\n",
       "      <th>Deli / Bodega</th>\n",
       "      <th>Department Store</th>\n",
       "      <th>Dessert Shop</th>\n",
       "      <th>Diner</th>\n",
       "      <th>Discount Store</th>\n",
       "      <th>Dive Bar</th>\n",
       "      <th>Doctor's Office</th>\n",
       "      <th>Dog Run</th>\n",
       "      <th>Donut Shop</th>\n",
       "      <th>Dumpling Restaurant</th>\n",
       "      <th>Electronics Store</th>\n",
       "      <th>Empanada Restaurant</th>\n",
       "      <th>Fabric Shop</th>\n",
       "      <th>Farmers Market</th>\n",
       "      <th>Fast Food Restaurant</th>\n",
       "      <th>Field</th>\n",
       "      <th>Fishing Spot</th>\n",
       "      <th>Flower Shop</th>\n",
       "      <th>Fondue Restaurant</th>\n",
       "      <th>Food</th>\n",
       "      <th>Food &amp; Drink Shop</th>\n",
       "      <th>Food Court</th>\n",
       "      <th>Food Truck</th>\n",
       "      <th>Football Stadium</th>\n",
       "      <th>French Restaurant</th>\n",
       "      <th>Fried Chicken Joint</th>\n",
       "      <th>Furniture / Home Store</th>\n",
       "      <th>Garden</th>\n",
       "      <th>Garden Center</th>\n",
       "      <th>Gas Station</th>\n",
       "      <th>Gastropub</th>\n",
       "      <th>Gay Bar</th>\n",
       "      <th>General College &amp; University</th>\n",
       "      <th>General Entertainment</th>\n",
       "      <th>German Restaurant</th>\n",
       "      <th>Gift Shop</th>\n",
       "      <th>Gluten-free Restaurant</th>\n",
       "      <th>Greek Restaurant</th>\n",
       "      <th>Grocery Store</th>\n",
       "      <th>Gym</th>\n",
       "      <th>Gym / Fitness Center</th>\n",
       "      <th>Gymnastics Gym</th>\n",
       "      <th>Hardware Store</th>\n",
       "      <th>Health &amp; Beauty Service</th>\n",
       "      <th>Historic Site</th>\n",
       "      <th>History Museum</th>\n",
       "      <th>Hockey Arena</th>\n",
       "      <th>Home Service</th>\n",
       "      <th>Hot Dog Joint</th>\n",
       "      <th>Hotel</th>\n",
       "      <th>Hotel Bar</th>\n",
       "      <th>IT Services</th>\n",
       "      <th>Ice Cream Shop</th>\n",
       "      <th>Indian Restaurant</th>\n",
       "      <th>Indie Movie Theater</th>\n",
       "      <th>Insurance Office</th>\n",
       "      <th>Intersection</th>\n",
       "      <th>Irish Pub</th>\n",
       "      <th>Italian Restaurant</th>\n",
       "      <th>Japanese Restaurant</th>\n",
       "      <th>Jazz Club</th>\n",
       "      <th>Jewelry Store</th>\n",
       "      <th>Juice Bar</th>\n",
       "      <th>Kids Store</th>\n",
       "      <th>Korean Restaurant</th>\n",
       "      <th>Lake</th>\n",
       "      <th>Latin American Restaurant</th>\n",
       "      <th>Library</th>\n",
       "      <th>Light Rail Station</th>\n",
       "      <th>Lighting Store</th>\n",
       "      <th>Liquor Store</th>\n",
       "      <th>Locksmith</th>\n",
       "      <th>Lounge</th>\n",
       "      <th>Market</th>\n",
       "      <th>Mediterranean Restaurant</th>\n",
       "      <th>Men's Store</th>\n",
       "      <th>Mexican Restaurant</th>\n",
       "      <th>Miscellaneous Shop</th>\n",
       "      <th>Mobile Phone Shop</th>\n",
       "      <th>Monument / Landmark</th>\n",
       "      <th>Motel</th>\n",
       "      <th>Motorcycle Shop</th>\n",
       "      <th>Motorsports Shop</th>\n",
       "      <th>Movie Theater</th>\n",
       "      <th>Museum</th>\n",
       "      <th>Music School</th>\n",
       "      <th>Music Store</th>\n",
       "      <th>Music Venue</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>New American Restaurant</th>\n",
       "      <th>Nightclub</th>\n",
       "      <th>Noodle House</th>\n",
       "      <th>Notary</th>\n",
       "      <th>Office</th>\n",
       "      <th>Optical Shop</th>\n",
       "      <th>Other Great Outdoors</th>\n",
       "      <th>Other Repair Shop</th>\n",
       "      <th>Paper / Office Supplies Store</th>\n",
       "      <th>Park</th>\n",
       "      <th>Pedestrian Plaza</th>\n",
       "      <th>Performing Arts Venue</th>\n",
       "      <th>Pet Store</th>\n",
       "      <th>Pharmacy</th>\n",
       "      <th>Piano Bar</th>\n",
       "      <th>Pie Shop</th>\n",
       "      <th>Pizza Place</th>\n",
       "      <th>Platform</th>\n",
       "      <th>Playground</th>\n",
       "      <th>Plaza</th>\n",
       "      <th>Pool</th>\n",
       "      <th>Pool Hall</th>\n",
       "      <th>Post Office</th>\n",
       "      <th>Print Shop</th>\n",
       "      <th>Pub</th>\n",
       "      <th>Public Art</th>\n",
       "      <th>Record Shop</th>\n",
       "      <th>Recording Studio</th>\n",
       "      <th>Recreation Center</th>\n",
       "      <th>Rental Car Location</th>\n",
       "      <th>Rest Area</th>\n",
       "      <th>Restaurant</th>\n",
       "      <th>Rock Club</th>\n",
       "      <th>Russian Restaurant</th>\n",
       "      <th>Salad Place</th>\n",
       "      <th>Salon / Barbershop</th>\n",
       "      <th>Sandwich Place</th>\n",
       "      <th>Scenic Lookout</th>\n",
       "      <th>Science Museum</th>\n",
       "      <th>Seafood Restaurant</th>\n",
       "      <th>Shanghai Restaurant</th>\n",
       "      <th>Shipping Store</th>\n",
       "      <th>Shoe Repair</th>\n",
       "      <th>Shoe Store</th>\n",
       "      <th>Shop &amp; Service</th>\n",
       "      <th>Shopping Mall</th>\n",
       "      <th>Shopping Plaza</th>\n",
       "      <th>Skate Park</th>\n",
       "      <th>Smoke Shop</th>\n",
       "      <th>Snack Place</th>\n",
       "      <th>Soccer Field</th>\n",
       "      <th>Soccer Stadium</th>\n",
       "      <th>South American Restaurant</th>\n",
       "      <th>Southern / Soul Food Restaurant</th>\n",
       "      <th>Spa</th>\n",
       "      <th>Spanish Restaurant</th>\n",
       "      <th>Speakeasy</th>\n",
       "      <th>Sporting Goods Shop</th>\n",
       "      <th>Sports Bar</th>\n",
       "      <th>Stadium</th>\n",
       "      <th>Steakhouse</th>\n",
       "      <th>Storage Facility</th>\n",
       "      <th>Supermarket</th>\n",
       "      <th>Supplement Shop</th>\n",
       "      <th>Sushi Restaurant</th>\n",
       "      <th>Taco Place</th>\n",
       "      <th>Tanning Salon</th>\n",
       "      <th>Tea Room</th>\n",
       "      <th>Tex-Mex Restaurant</th>\n",
       "      <th>Thai Restaurant</th>\n",
       "      <th>Theater</th>\n",
       "      <th>Theme Park Ride / Attraction</th>\n",
       "      <th>Theme Restaurant</th>\n",
       "      <th>Thrift / Vintage Store</th>\n",
       "      <th>Tourist Information Center</th>\n",
       "      <th>Toy / Game Store</th>\n",
       "      <th>Trail</th>\n",
       "      <th>Train Station</th>\n",
       "      <th>Transportation Service</th>\n",
       "      <th>Travel &amp; Transport</th>\n",
       "      <th>Tree</th>\n",
       "      <th>Turkish Restaurant</th>\n",
       "      <th>Vegetarian / Vegan Restaurant</th>\n",
       "      <th>Video Game Store</th>\n",
       "      <th>Video Store</th>\n",
       "      <th>Vietnamese Restaurant</th>\n",
       "      <th>Volleyball Court</th>\n",
       "      <th>Warehouse Store</th>\n",
       "      <th>Water Park</th>\n",
       "      <th>Whisky Bar</th>\n",
       "      <th>Wine Bar</th>\n",
       "      <th>Wings Joint</th>\n",
       "      <th>Women's Store</th>\n",
       "      <th>Yoga Studio</th>\n",
       "      <th>Zoo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cluster 0</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.755814</td>\n",
       "      <td>0.129989</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.095245</td>\n",
       "      <td>0.165962</td>\n",
       "      <td>0.046129</td>\n",
       "      <td>0.026974</td>\n",
       "      <td>0.058232</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.092503</td>\n",
       "      <td>0.149079</td>\n",
       "      <td>0.601460</td>\n",
       "      <td>0.02439</td>\n",
       "      <td>0.316827</td>\n",
       "      <td>0.268110</td>\n",
       "      <td>0.884269</td>\n",
       "      <td>0.131030</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017857</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.033787</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.112568</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.014925</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.044884</td>\n",
       "      <td>0.012987</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.226017</td>\n",
       "      <td>0.153058</td>\n",
       "      <td>0.672722</td>\n",
       "      <td>0.077749</td>\n",
       "      <td>0.075565</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.098547</td>\n",
       "      <td>0.406399</td>\n",
       "      <td>0.071244</td>\n",
       "      <td>0.075641</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.435414</td>\n",
       "      <td>0.022222</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.134655</td>\n",
       "      <td>0.039316</td>\n",
       "      <td>0.617141</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.177819</td>\n",
       "      <td>0.508583</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.181770</td>\n",
       "      <td>0.012048</td>\n",
       "      <td>0.012048</td>\n",
       "      <td>0.064103</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.02439</td>\n",
       "      <td>0.046531</td>\n",
       "      <td>0.128949</td>\n",
       "      <td>0.171377</td>\n",
       "      <td>0.887042</td>\n",
       "      <td>0.022222</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.014925</td>\n",
       "      <td>0.256611</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.055742</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.012987</td>\n",
       "      <td>0.154173</td>\n",
       "      <td>1.337932</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.025641</td>\n",
       "      <td>0.077807</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.172134</td>\n",
       "      <td>0.049249</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.071960</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.065327</td>\n",
       "      <td>0.513137</td>\n",
       "      <td>0.246225</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.463721</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.024390</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.012048</td>\n",
       "      <td>0.038889</td>\n",
       "      <td>0.065433</td>\n",
       "      <td>0.014925</td>\n",
       "      <td>0.102526</td>\n",
       "      <td>0.538582</td>\n",
       "      <td>0.099971</td>\n",
       "      <td>0.131322</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017857</td>\n",
       "      <td>0.052668</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.334224</td>\n",
       "      <td>0.091057</td>\n",
       "      <td>0.311110</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.325983</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.012048</td>\n",
       "      <td>0.054894</td>\n",
       "      <td>0.096868</td>\n",
       "      <td>0.074176</td>\n",
       "      <td>0.199304</td>\n",
       "      <td>0.119326</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.095674</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.129032</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026974</td>\n",
       "      <td>0.064103</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.012987</td>\n",
       "      <td>0.100503</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.127486</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.115884</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.988521</td>\n",
       "      <td>0.014925</td>\n",
       "      <td>0.176486</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.012987</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.034270</td>\n",
       "      <td>0.025641</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.035209</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.100280</td>\n",
       "      <td>0.060241</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.068979</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024390</td>\n",
       "      <td>0.096166</td>\n",
       "      <td>0.413974</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.012048</td>\n",
       "      <td>0.088069</td>\n",
       "      <td>0.400073</td>\n",
       "      <td>0.014925</td>\n",
       "      <td>0.017857</td>\n",
       "      <td>1.185461</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.077603</td>\n",
       "      <td>0.111693</td>\n",
       "      <td>0.088319</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.100583</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.012048</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.102351</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.209428</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.014925</td>\n",
       "      <td>0.114941</td>\n",
       "      <td>0.859732</td>\n",
       "      <td>0.014925</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.093604</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.074185</td>\n",
       "      <td>0.03427</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.027912</td>\n",
       "      <td>0.012987</td>\n",
       "      <td>0.101593</td>\n",
       "      <td>0.017857</td>\n",
       "      <td>0.014925</td>\n",
       "      <td>0.025641</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.025641</td>\n",
       "      <td>0.064949</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.174386</td>\n",
       "      <td>0.12842</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.267591</td>\n",
       "      <td>0.110536</td>\n",
       "      <td>0.179286</td>\n",
       "      <td>0.029654</td>\n",
       "      <td>0.073599</td>\n",
       "      <td>0.187848</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.072944</td>\n",
       "      <td>0.04329</td>\n",
       "      <td>0.118962</td>\n",
       "      <td>0.373422</td>\n",
       "      <td>0.012048</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.188333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076316</td>\n",
       "      <td>0.012048</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.091976</td>\n",
       "      <td>0.102751</td>\n",
       "      <td>0.097748</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012987</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.084910</td>\n",
       "      <td>0.071606</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.067041</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster 1</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.277387</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.037027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.037027</td>\n",
       "      <td>0.374414</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.051667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.047027</td>\n",
       "      <td>0.121667</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.170360</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.187027</td>\n",
       "      <td>0.277027</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.037027</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.047027</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.078694</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.057027</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.577027</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.138694</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.181667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.051667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.123333</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.077027</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.087027</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.051667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.081667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.114054</td>\n",
       "      <td>0.0200</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.247387</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.084054</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.193333</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.077027</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster 2</th>\n",
       "      <td>0.0625</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.190132</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.230349</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.074371</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.093478</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.074371</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.046739</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.140217</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.084239</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.158610</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.282981</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.161871</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.532437</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.115132</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.158610</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.186871</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.096739</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.02500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.099371</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster Outlier</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.159892</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.143478</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.210726</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.373950</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.067869</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.024390</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.152632</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024390</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.296110</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.177778</td>\n",
       "      <td>0.441819</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.279190</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024390</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.024390</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.092259</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.302632</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.175108</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.186335</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.146867</td>\n",
       "      <td>0.338933</td>\n",
       "      <td>0.485501</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.310726</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.124390</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024390</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.836089</td>\n",
       "      <td>0.024390</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.067869</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.471282</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.122476</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.234745</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.069845</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.146341</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Accessories Store  American Restaurant  Antique Shop  \\\n",
       "Cluster                                                                 \n",
       "Cluster 0                   0.0000             0.755814      0.129989   \n",
       "Cluster 1                   0.0000             0.277387      0.000000   \n",
       "Cluster 2                   0.0625             0.052632      0.000000   \n",
       "Cluster Outlier             0.0000             0.142857      0.000000   \n",
       "\n",
       "                 Aquarium    Arcade  Art Gallery  Art Museum  \\\n",
       "Cluster                                                        \n",
       "Cluster 0            0.00  0.095245     0.165962    0.046129   \n",
       "Cluster 1            0.01  0.000000     0.041667    0.010000   \n",
       "Cluster 2            0.00  0.000000     0.000000    0.021739   \n",
       "Cluster Outlier      0.00  0.000000     0.000000    0.000000   \n",
       "\n",
       "                 Arts & Crafts Store  Asian Restaurant  Assisted Living  \\\n",
       "Cluster                                                                   \n",
       "Cluster 0                   0.026974          0.058232         0.000000   \n",
       "Cluster 1                   0.000000          0.037027         0.000000   \n",
       "Cluster 2                   0.000000          0.000000         0.000000   \n",
       "Cluster Outlier             0.000000          0.159892         0.043478   \n",
       "\n",
       "                 Auto Dealership  Auto Garage  Auto Workshop  Automotive Shop  \\\n",
       "Cluster                                                                         \n",
       "Cluster 0               0.047619     0.047619       0.092503         0.149079   \n",
       "Cluster 1               0.000000     0.000000       0.000000         0.000000   \n",
       "Cluster 2               0.000000     0.000000       0.000000         0.025000   \n",
       "Cluster Outlier         0.000000     0.000000       0.143478         0.000000   \n",
       "\n",
       "                 BBQ Joint  Bagel Shop    Bakery      Bank       Bar  \\\n",
       "Cluster                                                                \n",
       "Cluster 0         0.601460     0.02439  0.316827  0.268110  0.884269   \n",
       "Cluster 1         0.060000     0.00000  0.040000  0.037027  0.374414   \n",
       "Cluster 2         0.025000     0.00000  0.190132  0.021739  0.000000   \n",
       "Cluster Outlier   0.210726     0.00000  0.373950  0.000000  0.000000   \n",
       "\n",
       "                 Baseball Field  Baseball Stadium  Basketball Court  \\\n",
       "Cluster                                                               \n",
       "Cluster 0              0.131030          0.000000          0.017857   \n",
       "Cluster 1              0.000000          0.027027          0.000000   \n",
       "Cluster 2              0.000000          0.000000          0.000000   \n",
       "Cluster Outlier        0.067869          0.000000          0.000000   \n",
       "\n",
       "                 Basketball Stadium  Beer Bar  Beer Garden  Beer Store  \\\n",
       "Cluster                                                                  \n",
       "Cluster 0                      0.00      0.00     0.033787        0.00   \n",
       "Cluster 1                      0.01      0.03     0.010000        0.01   \n",
       "Cluster 2                      0.00      0.00     0.000000        0.00   \n",
       "Cluster Outlier                0.00      0.00     0.000000        0.00   \n",
       "\n",
       "                 Big Box Store  Bike Shop    Bistro  Bookstore  Boutique  \\\n",
       "Cluster                                                                    \n",
       "Cluster 0             0.112568       0.00  0.014925   0.054054  0.044884   \n",
       "Cluster 1             0.000000       0.01  0.030000   0.010000  0.051667   \n",
       "Cluster 2             0.000000       0.00  0.000000   0.062500  0.021739   \n",
       "Cluster Outlier       0.043478       0.00  0.066667   0.024390  0.000000   \n",
       "\n",
       "                 Bowling Alley  Brazilian Restaurant  Breakfast Spot  \\\n",
       "Cluster                                                                \n",
       "Cluster 0             0.012987                  0.00        0.226017   \n",
       "Cluster 1             0.000000                  0.02        0.020000   \n",
       "Cluster 2             0.000000                  0.00        0.062500   \n",
       "Cluster Outlier       0.000000                  0.00        0.000000   \n",
       "\n",
       "                  Brewery  Burger Joint  Burrito Place  Bus Station  Bus Stop  \\\n",
       "Cluster                                                                         \n",
       "Cluster 0        0.153058      0.672722       0.077749     0.075565  0.027027   \n",
       "Cluster 1        0.047027      0.121667       0.027027     0.000000  0.000000   \n",
       "Cluster 2        0.000000      0.230349       0.000000     0.000000  0.000000   \n",
       "Cluster Outlier  0.000000      0.152632       0.066667     0.000000  0.000000   \n",
       "\n",
       "                 Business Service      Café  Cajun / Creole Restaurant  \\\n",
       "Cluster                                                                  \n",
       "Cluster 0                0.098547  0.406399                   0.071244   \n",
       "Cluster 1                0.000000  0.170360                   0.030000   \n",
       "Cluster 2                0.074371  0.000000                   0.000000   \n",
       "Cluster Outlier          0.100000  0.000000                   0.100000   \n",
       "\n",
       "                 Candy Store  Capitol Building  Caribbean Restaurant  \\\n",
       "Cluster                                                                \n",
       "Cluster 0           0.075641              0.00                  0.00   \n",
       "Cluster 1           0.000000              0.02                  0.01   \n",
       "Cluster 2           0.000000              0.00                  0.00   \n",
       "Cluster Outlier     0.000000              0.00                  0.00   \n",
       "\n",
       "                 Check Cashing Service  Chinese Restaurant  Chocolate Shop  \\\n",
       "Cluster                                                                      \n",
       "Cluster 0                     0.027027            0.435414        0.022222   \n",
       "Cluster 1                     0.000000            0.030000        0.000000   \n",
       "Cluster 2                     0.000000            0.093478        0.000000   \n",
       "Cluster Outlier               0.000000            0.024390        0.000000   \n",
       "\n",
       "                 Church  Clothing Store  Cocktail Bar  Coffee Shop  \\\n",
       "Cluster                                                              \n",
       "Cluster 0          0.00        0.134655      0.039316     0.617141   \n",
       "Cluster 1          0.01        0.010000      0.187027     0.277027   \n",
       "Cluster 2          0.00        0.052632      0.000000     0.074371   \n",
       "Cluster Outlier    0.00        0.043478      0.000000     0.296110   \n",
       "\n",
       "                 College Auditorium  Comedy Club  Concert Hall  \\\n",
       "Cluster                                                          \n",
       "Cluster 0                     0.000         0.00          0.00   \n",
       "Cluster 1                     0.000         0.03          0.06   \n",
       "Cluster 2                     0.025         0.00          0.00   \n",
       "Cluster Outlier               0.000         0.00          0.00   \n",
       "\n",
       "                 Construction & Landscaping  Convenience Store  \\\n",
       "Cluster                                                          \n",
       "Cluster 0                          0.177819           0.508583   \n",
       "Cluster 1                          0.027027           0.027027   \n",
       "Cluster 2                          0.000000           0.385612   \n",
       "Cluster Outlier                    0.177778           0.441819   \n",
       "\n",
       "                 Convention Center  Cosmetics Shop  Costume Shop  Creperie  \\\n",
       "Cluster                                                                      \n",
       "Cluster 0                 0.000000        0.181770      0.012048  0.012048   \n",
       "Cluster 1                 0.027027        0.010000      0.000000  0.000000   \n",
       "Cluster 2                 0.000000        0.021739      0.000000  0.000000   \n",
       "Cluster Outlier           0.000000        0.000000      0.000000  0.000000   \n",
       "\n",
       "                 Cupcake Shop  Cycle Studio  Dance Studio  Deli / Bodega  \\\n",
       "Cluster                                                                    \n",
       "Cluster 0            0.064103          0.00      0.037037        0.02439   \n",
       "Cluster 1            0.010000          0.01      0.020000        0.02000   \n",
       "Cluster 2            0.000000          0.00      0.025000        0.00000   \n",
       "Cluster Outlier      0.000000          0.00      0.000000        0.00000   \n",
       "\n",
       "                 Department Store  Dessert Shop     Diner  Discount Store  \\\n",
       "Cluster                                                                     \n",
       "Cluster 0                0.046531      0.128949  0.171377        0.887042   \n",
       "Cluster 1                0.010000      0.020000  0.037027        0.027027   \n",
       "Cluster 2                0.046739      0.000000  0.000000        0.140217   \n",
       "Cluster Outlier          0.000000      0.000000  0.000000        0.279190   \n",
       "\n",
       "                 Dive Bar  Doctor's Office   Dog Run  Donut Shop  \\\n",
       "Cluster                                                            \n",
       "Cluster 0        0.022222         0.045455  0.014925    0.256611   \n",
       "Cluster 1        0.047027         0.010000  0.000000    0.010000   \n",
       "Cluster 2        0.000000         0.000000  0.000000    0.084239   \n",
       "Cluster Outlier  0.000000         0.024390  0.000000    0.111111   \n",
       "\n",
       "                 Dumpling Restaurant  Electronics Store  Empanada Restaurant  \\\n",
       "Cluster                                                                        \n",
       "Cluster 0                       0.00           0.055742                 0.00   \n",
       "Cluster 1                       0.01           0.000000                 0.01   \n",
       "Cluster 2                       0.00           0.000000                 0.00   \n",
       "Cluster Outlier                 0.00           0.024390                 0.00   \n",
       "\n",
       "                 Fabric Shop  Farmers Market  Fast Food Restaurant  Field  \\\n",
       "Cluster                                                                     \n",
       "Cluster 0           0.012987        0.154173              1.337932   0.00   \n",
       "Cluster 1           0.000000        0.040000              0.010000   0.01   \n",
       "Cluster 2           0.000000        0.000000              0.158610   0.00   \n",
       "Cluster Outlier     0.000000        0.000000              0.092259   0.00   \n",
       "\n",
       "                 Fishing Spot  Flower Shop  Fondue Restaurant      Food  \\\n",
       "Cluster                                                                   \n",
       "Cluster 0            0.025641     0.077807               0.00  0.172134   \n",
       "Cluster 1            0.000000     0.000000               0.01  0.000000   \n",
       "Cluster 2            0.000000     0.000000               0.00  0.000000   \n",
       "Cluster Outlier      0.000000     0.000000               0.00  0.136364   \n",
       "\n",
       "                 Food & Drink Shop  Food Court  Food Truck  Football Stadium  \\\n",
       "Cluster                                                                        \n",
       "Cluster 0                 0.049249        0.00    0.071960          0.037037   \n",
       "Cluster 1                 0.000000        0.02    0.070000          0.000000   \n",
       "Cluster 2                 0.000000        0.00    0.021739          0.000000   \n",
       "Cluster Outlier           0.000000        0.00    0.086957          0.302632   \n",
       "\n",
       "                 French Restaurant  Fried Chicken Joint  \\\n",
       "Cluster                                                   \n",
       "Cluster 0                 0.065327             0.513137   \n",
       "Cluster 1                 0.050000             0.010000   \n",
       "Cluster 2                 0.000000             0.282981   \n",
       "Cluster Outlier           0.000000             0.175108   \n",
       "\n",
       "                 Furniture / Home Store    Garden  Garden Center  Gas Station  \\\n",
       "Cluster                                                                         \n",
       "Cluster 0                      0.246225  0.026316           0.00     0.463721   \n",
       "Cluster 1                      0.000000  0.010000           0.01     0.041667   \n",
       "Cluster 2                      0.000000  0.000000           0.00     0.043478   \n",
       "Cluster Outlier                0.000000  0.000000           0.00     0.186335   \n",
       "\n",
       "                 Gastropub   Gay Bar  General College & University  \\\n",
       "Cluster                                                              \n",
       "Cluster 0         0.027027  0.024390                          0.00   \n",
       "Cluster 1         0.020000  0.078694                          0.01   \n",
       "Cluster 2         0.000000  0.000000                          0.00   \n",
       "Cluster Outlier   0.000000  0.000000                          0.00   \n",
       "\n",
       "                 General Entertainment  German Restaurant  Gift Shop  \\\n",
       "Cluster                                                                \n",
       "Cluster 0                     0.012048           0.038889   0.065433   \n",
       "Cluster 1                     0.000000           0.010000   0.010000   \n",
       "Cluster 2                     0.000000           0.000000   0.000000   \n",
       "Cluster Outlier               0.000000           0.000000   0.000000   \n",
       "\n",
       "                 Gluten-free Restaurant  Greek Restaurant  Grocery Store  \\\n",
       "Cluster                                                                    \n",
       "Cluster 0                      0.014925          0.102526       0.538582   \n",
       "Cluster 1                      0.000000          0.010000       0.057027   \n",
       "Cluster 2                      0.000000          0.000000       0.161871   \n",
       "Cluster Outlier                0.000000          0.000000       0.146867   \n",
       "\n",
       "                      Gym  Gym / Fitness Center  Gymnastics Gym  \\\n",
       "Cluster                                                           \n",
       "Cluster 0        0.099971              0.131322        0.047619   \n",
       "Cluster 1        0.080000              0.030000        0.000000   \n",
       "Cluster 2        0.000000              0.000000        0.000000   \n",
       "Cluster Outlier  0.338933              0.485501        0.000000   \n",
       "\n",
       "                 Hardware Store  Health & Beauty Service  Historic Site  \\\n",
       "Cluster                                                                   \n",
       "Cluster 0              0.026316                 0.000000       0.017857   \n",
       "Cluster 1              0.000000                 0.000000       0.040000   \n",
       "Cluster 2              0.000000                 0.000000       0.000000   \n",
       "Cluster Outlier        0.000000                 0.043478       0.000000   \n",
       "\n",
       "                 History Museum  Hockey Arena  Home Service  Hot Dog Joint  \\\n",
       "Cluster                                                                      \n",
       "Cluster 0              0.052668      0.000000      0.334224       0.091057   \n",
       "Cluster 1              0.050000      0.054054      0.000000       0.000000   \n",
       "Cluster 2              0.025000      0.000000      0.000000       0.000000   \n",
       "Cluster Outlier        0.000000      0.000000      0.310726       0.000000   \n",
       "\n",
       "                    Hotel  Hotel Bar  IT Services  Ice Cream Shop  \\\n",
       "Cluster                                                             \n",
       "Cluster 0        0.311110       0.00     0.016667        0.325983   \n",
       "Cluster 1        0.577027       0.02     0.010000        0.020000   \n",
       "Cluster 2        0.000000       0.00     0.000000        0.000000   \n",
       "Cluster Outlier  0.136364       0.00     0.000000        0.052632   \n",
       "\n",
       "                 Indian Restaurant  Indie Movie Theater  Insurance Office  \\\n",
       "Cluster                                                                     \n",
       "Cluster 0                 0.027027             0.012048          0.054894   \n",
       "Cluster 1                 0.010000             0.010000          0.000000   \n",
       "Cluster 2                 0.000000             0.000000          0.000000   \n",
       "Cluster Outlier           0.100000             0.000000          0.124390   \n",
       "\n",
       "                 Intersection  Irish Pub  Italian Restaurant  \\\n",
       "Cluster                                                        \n",
       "Cluster 0            0.096868   0.074176            0.199304   \n",
       "Cluster 1            0.041667   0.000000            0.138694   \n",
       "Cluster 2            0.000000   0.000000            0.000000   \n",
       "Cluster Outlier      0.086957   0.000000            0.000000   \n",
       "\n",
       "                 Japanese Restaurant  Jazz Club  Jewelry Store  Juice Bar  \\\n",
       "Cluster                                                                     \n",
       "Cluster 0                   0.119326       0.00       0.095674       0.00   \n",
       "Cluster 1                   0.010000       0.02       0.000000       0.02   \n",
       "Cluster 2                   0.000000       0.00       0.000000       0.00   \n",
       "Cluster Outlier             0.000000       0.00       0.000000       0.00   \n",
       "\n",
       "                 Kids Store  Korean Restaurant      Lake  \\\n",
       "Cluster                                                    \n",
       "Cluster 0          0.034483           0.129032  0.000000   \n",
       "Cluster 1          0.000000           0.010000  0.000000   \n",
       "Cluster 2          0.000000           0.000000  0.021739   \n",
       "Cluster Outlier    0.000000           0.000000  0.000000   \n",
       "\n",
       "                 Latin American Restaurant   Library  Light Rail Station  \\\n",
       "Cluster                                                                    \n",
       "Cluster 0                         0.026974  0.064103            0.026316   \n",
       "Cluster 1                         0.010000  0.000000            0.000000   \n",
       "Cluster 2                         0.000000  0.000000            0.000000   \n",
       "Cluster Outlier                   0.000000  0.000000            0.000000   \n",
       "\n",
       "                 Lighting Store  Liquor Store  Locksmith    Lounge    Market  \\\n",
       "Cluster                                                                        \n",
       "Cluster 0              0.012987      0.100503   0.021739  0.127486  0.000000   \n",
       "Cluster 1              0.000000      0.000000   0.000000  0.090000  0.000000   \n",
       "Cluster 2              0.000000      0.000000   0.000000  0.000000  0.000000   \n",
       "Cluster Outlier        0.000000      0.000000   0.024390  0.000000  0.045455   \n",
       "\n",
       "                 Mediterranean Restaurant  Men's Store  Mexican Restaurant  \\\n",
       "Cluster                                                                      \n",
       "Cluster 0                        0.115884         0.00            1.988521   \n",
       "Cluster 1                        0.020000         0.02            0.181667   \n",
       "Cluster 2                        0.000000         0.00            0.532437   \n",
       "Cluster Outlier                  0.000000         0.00            0.836089   \n",
       "\n",
       "                 Miscellaneous Shop  Mobile Phone Shop  Monument / Landmark  \\\n",
       "Cluster                                                                       \n",
       "Cluster 0                  0.014925           0.176486                 0.00   \n",
       "Cluster 1                  0.000000           0.020000                 0.01   \n",
       "Cluster 2                  0.000000           0.000000                 0.00   \n",
       "Cluster Outlier            0.024390           0.000000                 0.00   \n",
       "\n",
       "                    Motel  Motorcycle Shop  Motorsports Shop  Movie Theater  \\\n",
       "Cluster                                                                       \n",
       "Cluster 0        0.032258         0.012987          0.000000           0.00   \n",
       "Cluster 1        0.000000         0.000000          0.000000           0.04   \n",
       "Cluster 2        0.000000         0.000000          0.000000           0.00   \n",
       "Cluster Outlier  0.000000         0.000000          0.142857           0.00   \n",
       "\n",
       "                   Museum  Music School  Music Store  Music Venue  \\\n",
       "Cluster                                                             \n",
       "Cluster 0        0.034270      0.025641         0.00     0.035209   \n",
       "Cluster 1        0.051667      0.000000         0.01     0.050000   \n",
       "Cluster 2        0.021739      0.000000         0.00     0.000000   \n",
       "Cluster Outlier  0.066667      0.000000         0.00     0.000000   \n",
       "\n",
       "                 Neighborhood  New American Restaurant  Nightclub  \\\n",
       "Cluster                                                             \n",
       "Cluster 0                0.00                 0.100280   0.060241   \n",
       "Cluster 1                0.01                 0.100000   0.123333   \n",
       "Cluster 2                0.00                 0.021739   0.000000   \n",
       "Cluster Outlier          0.00                 0.000000   0.000000   \n",
       "\n",
       "                 Noodle House    Notary    Office  Optical Shop  \\\n",
       "Cluster                                                           \n",
       "Cluster 0                0.00  0.047619  0.000000      0.068979   \n",
       "Cluster 1                0.01  0.000000  0.000000      0.010000   \n",
       "Cluster 2                0.00  0.000000  0.000000      0.021739   \n",
       "Cluster Outlier          0.00  0.000000  0.067869      0.000000   \n",
       "\n",
       "                 Other Great Outdoors  Other Repair Shop  \\\n",
       "Cluster                                                    \n",
       "Cluster 0                    0.000000           0.024390   \n",
       "Cluster 1                    0.000000           0.041667   \n",
       "Cluster 2                    0.000000           0.000000   \n",
       "Cluster Outlier              0.043478           0.000000   \n",
       "\n",
       "                 Paper / Office Supplies Store      Park  Pedestrian Plaza  \\\n",
       "Cluster                                                                      \n",
       "Cluster 0                             0.096166  0.413974              0.00   \n",
       "Cluster 1                             0.000000  0.120000              0.01   \n",
       "Cluster 2                             0.025000  0.115132              0.00   \n",
       "Cluster Outlier                       0.000000  0.471282              0.00   \n",
       "\n",
       "                 Performing Arts Venue  Pet Store  Pharmacy  Piano Bar  \\\n",
       "Cluster                                                                  \n",
       "Cluster 0                     0.012048   0.088069  0.400073   0.014925   \n",
       "Cluster 1                     0.077027   0.010000  0.010000   0.030000   \n",
       "Cluster 2                     0.025000   0.000000  0.025000   0.000000   \n",
       "Cluster Outlier               0.000000   0.000000  0.000000   0.000000   \n",
       "\n",
       "                 Pie Shop  Pizza Place  Platform  Playground     Plaza  \\\n",
       "Cluster                                                                  \n",
       "Cluster 0        0.017857     1.185461  0.000000    0.077603  0.111693   \n",
       "Cluster 1        0.000000     0.087027  0.027027    0.000000  0.090000   \n",
       "Cluster 2        0.000000     0.158610  0.000000    0.000000  0.000000   \n",
       "Cluster Outlier  0.000000     0.122476  0.000000    0.000000  0.000000   \n",
       "\n",
       "                     Pool  Pool Hall  Post Office  Print Shop       Pub  \\\n",
       "Cluster                                                                   \n",
       "Cluster 0        0.088319   0.000000     0.000000    0.026316  0.100583   \n",
       "Cluster 1        0.010000   0.000000     0.051667    0.000000  0.020000   \n",
       "Cluster 2        0.000000   0.000000     0.000000    0.000000  0.000000   \n",
       "Cluster Outlier  0.000000   0.142857     0.000000    0.000000  0.000000   \n",
       "\n",
       "                 Public Art  Record Shop  Recording Studio  Recreation Center  \\\n",
       "Cluster                                                                         \n",
       "Cluster 0          0.026316     0.012048          0.027027           0.027027   \n",
       "Cluster 1          0.020000     0.000000          0.000000           0.000000   \n",
       "Cluster 2          0.000000     0.000000          0.000000           0.000000   \n",
       "Cluster Outlier    0.000000     0.000000          0.000000           0.000000   \n",
       "\n",
       "                 Rental Car Location  Rest Area  Restaurant  Rock Club  \\\n",
       "Cluster                                                                  \n",
       "Cluster 0                   0.102351   0.026316    0.209428     0.0000   \n",
       "Cluster 1                   0.081667   0.000000    0.114054     0.0200   \n",
       "Cluster 2                   0.025000   0.000000    0.000000     0.0625   \n",
       "Cluster Outlier             0.000000   0.000000    0.000000     0.0000   \n",
       "\n",
       "                 Russian Restaurant  Salad Place  Salon / Barbershop  \\\n",
       "Cluster                                                                \n",
       "Cluster 0                      0.00     0.014925            0.114941   \n",
       "Cluster 1                      0.01     0.010000            0.000000   \n",
       "Cluster 2                      0.00     0.000000            0.000000   \n",
       "Cluster Outlier                0.00     0.000000            0.000000   \n",
       "\n",
       "                 Sandwich Place  Scenic Lookout  Science Museum  \\\n",
       "Cluster                                                           \n",
       "Cluster 0              0.859732        0.014925        0.043478   \n",
       "Cluster 1              0.247387        0.010000        0.000000   \n",
       "Cluster 2              0.186871        0.000000        0.000000   \n",
       "Cluster Outlier        0.234745        0.000000        0.000000   \n",
       "\n",
       "                 Seafood Restaurant  Shanghai Restaurant  Shipping Store  \\\n",
       "Cluster                                                                    \n",
       "Cluster 0                  0.093604             0.016667        0.016667   \n",
       "Cluster 1                  0.120000             0.000000        0.000000   \n",
       "Cluster 2                  0.096739             0.000000        0.000000   \n",
       "Cluster Outlier            0.000000             0.000000        0.000000   \n",
       "\n",
       "                 Shoe Repair  Shoe Store  Shop & Service  Shopping Mall  \\\n",
       "Cluster                                                                   \n",
       "Cluster 0           0.016667    0.074185         0.03427           0.00   \n",
       "Cluster 1           0.000000    0.010000         0.00000           0.01   \n",
       "Cluster 2           0.000000    0.000000         0.00000           0.00   \n",
       "Cluster Outlier     0.000000    0.000000         0.00000           0.00   \n",
       "\n",
       "                 Shopping Plaza  Skate Park  Smoke Shop  Snack Place  \\\n",
       "Cluster                                                                \n",
       "Cluster 0              0.027912    0.012987    0.101593     0.017857   \n",
       "Cluster 1              0.000000    0.000000    0.000000     0.000000   \n",
       "Cluster 2              0.000000    0.000000    0.000000     0.000000   \n",
       "Cluster Outlier        0.000000    0.000000    0.000000     0.045455   \n",
       "\n",
       "                 Soccer Field  Soccer Stadium  South American Restaurant  \\\n",
       "Cluster                                                                    \n",
       "Cluster 0            0.014925        0.025641                   0.027027   \n",
       "Cluster 1            0.000000        0.000000                   0.000000   \n",
       "Cluster 2            0.000000        0.000000                   0.000000   \n",
       "Cluster Outlier      0.000000        0.000000                   0.000000   \n",
       "\n",
       "                 Southern / Soul Food Restaurant       Spa  \\\n",
       "Cluster                                                      \n",
       "Cluster 0                               0.025641  0.064949   \n",
       "Cluster 1                               0.040000  0.010000   \n",
       "Cluster 2                               0.000000  0.000000   \n",
       "Cluster Outlier                         0.000000  0.000000   \n",
       "\n",
       "                 Spanish Restaurant  Speakeasy  Sporting Goods Shop  \\\n",
       "Cluster                                                               \n",
       "Cluster 0                  0.032258       0.00             0.174386   \n",
       "Cluster 1                  0.010000       0.02             0.000000   \n",
       "Cluster 2                  0.000000       0.00             0.000000   \n",
       "Cluster Outlier            0.000000       0.00             0.052632   \n",
       "\n",
       "                 Sports Bar   Stadium  Steakhouse  Storage Facility  \\\n",
       "Cluster                                                               \n",
       "Cluster 0           0.12842  0.038462    0.267591          0.110536   \n",
       "Cluster 1           0.02000  0.000000    0.150000          0.000000   \n",
       "Cluster 2           0.02500  0.000000    0.000000          0.000000   \n",
       "Cluster Outlier     0.00000  0.000000    0.000000          0.000000   \n",
       "\n",
       "                 Supermarket  Supplement Shop  Sushi Restaurant  Taco Place  \\\n",
       "Cluster                                                                       \n",
       "Cluster 0           0.179286         0.029654          0.073599    0.187848   \n",
       "Cluster 1           0.010000         0.000000          0.084054    0.050000   \n",
       "Cluster 2           0.000000         0.000000          0.000000    0.099371   \n",
       "Cluster Outlier     0.000000         0.000000          0.045455    0.069845   \n",
       "\n",
       "                 Tanning Salon  Tea Room  Tex-Mex Restaurant  Thai Restaurant  \\\n",
       "Cluster                                                                         \n",
       "Cluster 0             0.037037  0.072944             0.04329         0.118962   \n",
       "Cluster 1             0.000000  0.000000             0.01000         0.020000   \n",
       "Cluster 2             0.000000  0.000000             0.00000         0.000000   \n",
       "Cluster Outlier       0.000000  0.000000             0.00000         0.000000   \n",
       "\n",
       "                  Theater  Theme Park Ride / Attraction  Theme Restaurant  \\\n",
       "Cluster                                                                     \n",
       "Cluster 0        0.373422                      0.012048              0.00   \n",
       "Cluster 1        0.193333                      0.027027              0.01   \n",
       "Cluster 2        0.021739                      0.000000              0.00   \n",
       "Cluster Outlier  0.250000                      0.000000              0.00   \n",
       "\n",
       "                 Thrift / Vintage Store  Tourist Information Center  \\\n",
       "Cluster                                                               \n",
       "Cluster 0                      0.188333                    0.000000   \n",
       "Cluster 1                      0.000000                    0.027027   \n",
       "Cluster 2                      0.000000                    0.000000   \n",
       "Cluster Outlier                0.000000                    0.000000   \n",
       "\n",
       "                 Toy / Game Store     Trail  Train Station  \\\n",
       "Cluster                                                      \n",
       "Cluster 0                0.076316  0.012048       0.026316   \n",
       "Cluster 1                0.000000  0.030000       0.010000   \n",
       "Cluster 2                0.000000  0.000000       0.000000   \n",
       "Cluster Outlier          0.000000  0.000000       0.000000   \n",
       "\n",
       "                 Transportation Service  Travel & Transport  Tree  \\\n",
       "Cluster                                                             \n",
       "Cluster 0                           0.0            0.026316  0.05   \n",
       "Cluster 1                           0.0            0.000000  0.00   \n",
       "Cluster 2                           0.0            0.000000  0.00   \n",
       "Cluster Outlier                     0.1            0.000000  0.00   \n",
       "\n",
       "                 Turkish Restaurant  Vegetarian / Vegan Restaurant  \\\n",
       "Cluster                                                              \n",
       "Cluster 0                      0.00                       0.038462   \n",
       "Cluster 1                      0.01                       0.000000   \n",
       "Cluster 2                      0.00                       0.000000   \n",
       "Cluster Outlier                0.00                       0.100000   \n",
       "\n",
       "                 Video Game Store  Video Store  Vietnamese Restaurant  \\\n",
       "Cluster                                                                 \n",
       "Cluster 0                0.091976     0.102751               0.097748   \n",
       "Cluster 1                0.000000     0.020000               0.010000   \n",
       "Cluster 2                0.000000     0.000000               0.000000   \n",
       "Cluster Outlier          0.000000     0.000000               0.146341   \n",
       "\n",
       "                 Volleyball Court  Warehouse Store  Water Park  Whisky Bar  \\\n",
       "Cluster                                                                      \n",
       "Cluster 0                0.033333         0.000000    0.012987        0.00   \n",
       "Cluster 1                0.000000         0.000000    0.000000        0.04   \n",
       "Cluster 2                0.000000         0.000000    0.000000        0.00   \n",
       "Cluster Outlier          0.000000         0.043478    0.000000        0.00   \n",
       "\n",
       "                 Wine Bar  Wings Joint  Women's Store  Yoga Studio       Zoo  \n",
       "Cluster                                                                       \n",
       "Cluster 0        0.084910     0.071606       0.026316     0.067041  0.000000  \n",
       "Cluster 1        0.077027     0.010000       0.000000     0.020000  0.000000  \n",
       "Cluster 2        0.000000     0.000000       0.000000     0.000000  0.021739  \n",
       "Cluster Outlier  0.000000     0.000000       0.000000     0.000000  0.000000  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluslab.drop(labels = ['Cluster'], axis = 1, inplace = True)\n",
    "cluslab.insert(0, 'Cluster', tags)\n",
    "cluslab = cluslab.groupby(['Cluster']).agg('sum')\n",
    "cluslab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluslab = cluslab.T\n",
    "cluslab = cluslab.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Cluster</th>\n",
       "      <th>index</th>\n",
       "      <th>Cluster 0</th>\n",
       "      <th>Cluster 1</th>\n",
       "      <th>Cluster 2</th>\n",
       "      <th>Cluster Outlier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Accessories Store</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>0.755814</td>\n",
       "      <td>0.277387</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Antique Shop</td>\n",
       "      <td>0.129989</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aquarium</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arcade</td>\n",
       "      <td>0.095245</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Cluster                index  Cluster 0  Cluster 1  Cluster 2  Cluster Outlier\n",
       "0          Accessories Store   0.000000   0.000000   0.062500         0.000000\n",
       "1        American Restaurant   0.755814   0.277387   0.052632         0.142857\n",
       "2               Antique Shop   0.129989   0.000000   0.000000         0.000000\n",
       "3                   Aquarium   0.000000   0.010000   0.000000         0.000000\n",
       "4                     Arcade   0.095245   0.000000   0.000000         0.000000"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluslab.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Venue Frequencies by Cluster for Labeling <a name=\"labeling\"></a>\n",
    "### Cluster 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yxaLdghBSGiiSCSFodhycWFwf92aQCUUpBUsAS4QRcISQ0kCRTAjBn37zebz9978+7s0gE4qrFCwRWMKx1ISQ8kCRTAjBhbU2ltY7FDAkF1wFXyQLXHfcW0MIIemgSCaEwPGNog4NoyQHXKUgAljCsdSEkPJAkUwIge2LY5simeSA63p2CxHmJBNCygNFMiEkqCCzykfywLNbAJZFTzIhpDxQJBNCaLcguRI27nGYCCGkPFAkE0KCWC42VZE8UAoQASoicKiRCSElgSKZEALHVy7MsCV54CqFiiUQNu4RQkoERTIhJBDHtFuQPDDtFvQkE0LKAkUyIYSNeyRXXAUIc5IJISVjoEgWkTtF5IyIHOpx/btF5An/3zdE5HXGdcdE5EkReUxEDma54YSQ7GAEHMkTPZaadgtCSJlIU0m+C8Atfa5/DsCblFKvBfAxAHfErn+zUuoGpdSBjW0iISRvXF1JpkgmOeC4ZrrFuLeGEELSUR10A6XU10Rkf5/rv2H8+i0A+za/WYSQUWL7a+D0JJM8YE4yIaSMZO1J/o8AvmT8rgB8RUQeFpHbMv5bhJCMcHyfKNMtSB54Y6nFj4DjZ4wQUg4GVpLTIiJvhieS/41x8U1KqZMi8hIAfyci31VKfa3H/W8DcBsAXH311VltFiEkBY5fSabdguSBUl4VmWOpCSFlIpNKsoi8FsAfA3iHUuqcvlwpddL//wyAewDc2OsxlFJ3KKUOKKUO7N27N4vNIoSkRA94YJWP5IGrFCoisIR2C0JIedi0SBaRqwH8FYCfV0p9z7h8VkS26Z8BvAVAYkIGIWS86EqyzXFoJAc8TzLHUhNCysVAu4WIfA7AzQD2iMg8gI8CqAGAUupTAH4NwG4AfygiAGD7SRaXArjHv6wK4M+VUl/O4TkQQjaJFscUMCQPPE8ymJNMCCkVadIt3jXg+l8E8IsJlx8F8LruexBCiobLiXskR5Q/cY85yYSQMsGJe4SQYIgIBQzJAzMnmR8xQkhZoEgmhASpFg6XwkkOeGOpgYrFCDhCSHmgSCaEBJVk2i1IHtBuQQgpIxTJhJBAHFMkkzxw/ZxkjqVOxnZc/Ok3j6HDpRxCCgVFMiEkFMms8pEcYE5yfx55YRH/971P4eCxC+PeFEKIAUUyISQQyZy4R/LA8yQzJ7kXuoLMSjIhxYIimRASVJBptyB54HmS/bHU1IFdsCeAkGJCkUwICYaJ0G5B8iCMgGPjXhJ64iVFMiHFgiKZEBIIF9otSB64frpFxaLdIgntsrD5/SOkUFAkE0KCgzMP0iQPdE4y0y2SYSWZkGJCkUwICRv3WOUjOcCc5P7oSjLtToQUC4pkQghzkkmuuMqbtsex1MnYQSWZXY2EFAmKZEIIRTLJFVcp327BSnISDsfCE1JIKJIJIbRbkFxxFfx0CzbuJRGKZKpkQooERTIhhJUskivMSe4Pv3+EFBOKZEKIMUyER2mSPTonuWJxtSIJm5VkQgoJRTIhWxylFD3JJFc4lro/+jVhBCMhxYIimZAtjimMHR6jSQ5E7Bb8jHURTLzki0NIoaBIJmSLY2azcuIeyQM9cc8STzCTKK6iSCakiFAkE7LFiVaSeZAm2WPmJFMHdhN4kvn9I6RQUCQTssWJiGQqGJIDzEnuT9ATQL8TIYWCIpmQLQ5FMskb5eckexFw/IzFcVhJJqSQUCQTssWhSCZ54/qNexWLdoskbKbLEFJIKJIJ2eKYB2YuhZM80DnJtFsko/ORGQFHSLGgSCZki2OzkkxyRjEnuS960h6tKIQUC4pkQrY4TLcgeeMyJ7kvrCQTUkwokgnZ4kTsFjxIkxxgTnJ/dCWZKzmEFAuKZEK2OGb1WB+sCckSVwEWc5J7oivJFMmEFIuBIllE7hSRMyJyqMf1IiL/U0SOiMgTIvJ647pbROSwf91HstxwQkg2RNMtqJJJ9uix1GzcS4bDRAgpJmkqyXcBuKXP9W8D8Er/320A/ggARKQC4JP+9dcCeJeIXLuZjSWEZA89ySRvXD8n2bIEStFyEScYS81hIoQUioEiWSn1NQDn+9zkHQD+RHl8C8BOEbkcwI0Ajiiljiql2gA+79+WEFIgopXkMW4ImVjcoJIs/u9j3qCCYfvimI17hBSLLDzJVwI4bvw+71/W63JCSIGw2bhHcsZxlR8B5/1Oy0UUfaLK14WQYpGFSJaEy1Sfy5MfROQ2ETkoIgcXFhYy2CxCSBpotyB5Y46lBigG4+jvHSvJhBSLLETyPICrjN/3ATjZ5/JElFJ3KKUOKKUO7N27N4PNIoSkgRFwJG/idgtq5ChaHPP7R0ixyEIk3wfgF/yUizcCWFJKnQLwEIBXisg1IlIHcKt/W0JIgTBFMitZJA9cpfwIuPB3EqLFsc10GUIKRXXQDUTkcwBuBrBHROYBfBRADQCUUp8CcD+AtwM4AmANwHv962wR+QCABwBUANyplHoqh+dACNkEtFuQvAnSLdi4l0gQAUeNTEihGCiSlVLvGnC9AvD+HtfdD09EE0IKiimMudxL8iDISfZLyRyaEcUJRDJVMiFFghP3CNnimAdmiheSB2El2fudOclRHJeNe4QUEYpkQrY4tjHAgF5RkgeOy5zkfjACjpBiQpFMyBZHH5hrFWElmWSOrhozJ7k3QSWZE/cIKRQUyYRscfQSb71icbmXZI7+SDEnuTesJBNSTCiSCdni6AN0vWrxIE0yR3+mmJPcGx39xpNUQooFRTIhWxxTJNNuQbImEMlGTjI/Z1G0y4LpMoQUC4pkQrY4tllJZgIVyRhl2C10BBxXLKI4rCQTUkgokgnZ4ujqVa1icZgIyRzaLQajG/ZYYSekWFAkE7LFMRv3eJAmWWM27jHdIhn9evD7R0ixoEgmZIujD9D0JJM80J8pYU5yT8Kx1HxhCCkSFMmEbHH0Uu8URTLJARXYLQTCSnIi2vJEuxMhxYIiuaScXWnhkw8e4XjXLcDiWhs/9BtfxWPHF3N5fLOSTPFCsia0W5ieZH7OTGwOEyGkkFAkl5SvPv0ifvuBw5i/sD7uTSE58+LFFhaWWzi6sJLL49tm4x4rySRjohFwnkh2mKISgcNECCkmFMklpWV7R5k2jzYTT8d/j/OqMjlm4x4P0iRjXMNuUbGilxGPYCw1T1IJKRQUySUlb+FEioM+EerkFGKsD9C1qsVhBiRzzJxkjqVOxmHjHiGFhCK5pOhKcoeV5ImnY+d7QmRGwLGSRbKGOcmDmZR0i/d85ju459H5cW8GIZlBkVxS2hTJW4aOk+9SrOsqVCxBxRJWkknmMCd5MO6EiORvHDmHJ+aXxr0ZhGQGRXJJCZbgabeYeEJrTT4nRLYWySL0JJPMcZmTPJBJqCQrpdB2XFoAyURBkVxSWEneOugTotwqyUqhIgLLEqYOkMxxmZM8EGcCJu6FzYfciZDJgSK5pFAkbx06Tr7vte0oVC0veYDihWRNYLewjEpyicVgHgSNeyX+/ulVTa5ukkmCIrmkhCKZO6RJJ/8IOBeWtltQvJCMiUbA0W4RRykVSbco66CVds62MELGAUVyScnbp0qKQ8f2KzR5RcApr5Ls2S3KeYAmxYVjqfujv3K1SrlPIOwgqrKkT4CQBCiSS0rL4TCRrYIWx3kOE7EsQZUimeRANN2COclxtIe37k9aKaunN0jh4TGJTBAUySWFdoutQ5iTnN8wkaCSTPFCMoY5yf3RmniqVgFQ3uY9DrgikwhFcklp5yycSHEIGmJyOniaEXBsqCJZo0WgMCc5kXgluewimXYLMklQJJcUpltsHbSlxsmpQmMOE2ElmWSNWUkW5iR3oUVxvVp2kUy7BZk8KJJLSjvwJJdzh0rSE1Zo8h0mYolAKcZzkWwx0y2CSjI/YwGTI5JptyCTB0VySaHdYuuQfwScN0xEx3OxmkyyxMxJDiPg+BnTBCJ5YuwWPCaRyYEiuaTQbrF1CJYx84qAM+wW+ndCsiJaSabdIo4drySX9AQitFuUc/sJSSKVSBaRW0TksIgcEZGPJFz/X0TkMf/fIRFxROQS/7pjIvKkf93BrJ/AViWcwsYd0qSTd5JJXCSzykeyhDnJ/dEnpVO+SC6ryMx7Migh42CgSBaRCoBPAngbgGsBvEtErjVvo5T6baXUDUqpGwD8MoB/UkqdN27yZv/6Axluey7Yjot7HztReM9ci5XkLUPeg2P0MJGKsJJMsicpJ7msU+XyYOI8ySXdfkKSSFNJvhHAEaXUUaVUG8DnAbyjz+3fBeBzWWzcOPj2c+fxS59/DI/PL457U/rS5ln7liHvg4+uJFu6ksyPFMkQXXAwc5Kpo0K0vWJqYuwW3IGQySGNSL4SwHHj93n/si5EZAbALQD+0rhYAfiKiDwsIrf1+iMicpuIHBSRgwsLCyk2Kx/W2g4AYN3/v6hwmMjWIchJznGYiJeT7P9e0oM0KSb688Sc5GQmrZLMYxKZJNKIZEm4rNe34KcB/EvManGTUur18Owa7xeRH0u6o1LqDqXUAaXUgb1796bYrHzQZ8Gtgp8Ns3Fv65B3uoXNxj2SIyqwW4Q5yfyMhejv9aSkW5R1rDYhSaQRyfMArjJ+3wfgZI/b3oqY1UIpddL//wyAe+DZNwpLkD9sF/uLTrvF1mFUdouKVe6DNCkmQbqFcSLGQnKIG9gtyj6WmukWZPJII5IfAvBKEblGROrwhPB98RuJyA4AbwJwr3HZrIhs0z8DeAuAQ1lseF7oL3iRRbLjqmBHyh3S5DOaCDgLfiGLdguSKa5RSabdopuuCLjSimQWbsjkUR10A6WULSIfAPAAgAqAO5VST4nI7f71n/Jv+k4AX1FKrRp3vxTAPf4SWxXAnyulvpzlE8iaMnzRzW1rF3g7STaMZpiI0VRV0oM0KSbMSe6P45/8apFc1nQIm+kWZAIZKJIBQCl1P4D7Y5d9Kvb7XQDuil12FMDrNrWFI6ZTArtFy9g2VpInn7z952ElmX5Rkj3MSe6P/lpPlbyS3KbdgkwgnLgXQy9tF7lCawr4Ile8STaMwpNctTiWmuSDdgkxJzkZO1ZJLqtI5lhqMolQJMcoQyW5TbvFliLvhhjbdb2cZNotSA64QQQcc5KT0Jqy7CJZ2y2UKu9zICQORXIMXa0rsvhs026xpcjbJ+8qoGIJqn4lmZ5CkiVRT7J3GUVUSFBJrpR7mEjbOBZxhZNMChTJMbQALXQlmXaLLUU7Z7tFUEmmJ5nkQJBuYSH4jNFuEaK/b6EnuZz7dPNYxBNtMilQJMcohd2CInlLEaZb5FRJduFP3NNL4TzAkexgukV/uifujXNrNo65f+JoajIpUCTHCOwWRRbJjjcyu16xCj8C9J5H5/GvP/73rE5ugo6tc5JzrCQLJ+6RfGBOcn+6RXJxjz396ETsFnx/yWRAkRyjDOOe275omp2qFHo7AeDowipOLjXRsp1xb0pp0Z7F/HKSgUoltFtQwJAsiUbAsZIcxw7sFpXI72WjHbFbFPu4REhaKJJj6C93oRv3/G2bqVcLv0NtlcDjXXSCE7fcJu65XgSc6EpyLn+GbFGSGvfoSQ7Rr0/QuFfwfXovonaLcj4HQuJQJMfQS9utAos6LZrmpqqFF5+tjldBLvp2Fhm9dJlXtJLtKliG3YJVIJIlSTnJXK0I0YKy7BFwHaZbkAmEIjlGmRr3ymC30CcbRT7pKDodxw0mleXxfruxYSLUyCRLknKSC77bGik68q3sIrnNdAsygVAkx+j4X+4ii0/duDc7VSK7RYFfzyLjugq2qzBTy8+vaLvKS7fw9wZlzWklxSSwW1gCy4peRpIi4Mr52ph2iyIfPwkZBorkGJ0SeGj1ts3UK8H2FhXdsNfqFHs7i4r2IU/XqwDyiVZyleLEPZIb0XQL5iTHsePpFiV9bUy7BT3JZFKgSI5RisY9bbeoV3Nr5soKLY6L/HoWGX3gmalXIr9nSVhJZgQcyR7mJPfHnZBKcofpFmQCoUiOoUdrFrqS7OgIuGrh8yibdnEb91xX4b/e/QQOnVga96b0pGOsGgDZH0BdV0H5Y6kDv2hJK1mkmOiPrDAnOZGgklzxLVUF36f3ohOxW5TzORAShyI5RmC3KPCXPGzcq8JxVaGXx7IyMxoAACAASURBVINKcgFF8sVmB184eBxff/bsuDelJ/rAMx1UkrN9HbUgrlqCaoWVZJI92lpRYU5yIm7MblHWE4iOo4LnUFahT0gciuQYgd1iTKLOdRX+5omTfYVKaLfwhVOBl7bCxr3iDRNpl8F/rkVyTo17+nNmRXKSeYAj2aFFoF6psISeZBM7ZrcoejN2LzqOG9rCCnxMImQYKJJjhHaL8Yi6R49fwAf+/FF8+7lzPW/TdhxULMFUzXv7iry01Sqw3aLIAl4T9yRn3binBXHV4sQ9kg9h454E//NELESPoS59BJzthik8BT4mETIMFMkxtAgZV6PZctMGAKy1egu3tu2iXrFQ8zO7ipxwUeScZL1NRT7J0PaKRi2fxj3bqPKxkkzyIMhJ9o82liW0WxjoQ03ZRbLtKjRyOpknZFxQJMfQokRP3hs1TT2hrs9Opm27qFcNkVzgpa0ie5LLYLfoBCPItd0i2211jUoy0y1IHpjpFt7/tFuYOK43LKha8u9f1G5RzudASByK5Bi6UjeuSnIzhahsOy5qFQs1v9GqyJXQwG5RwMpCkOFcaJGs7RbVyO9ZoSvJFdotSE6YOcne/8LPmIHtqqCpsWKV14rSsV3M1PLLcydkHFAkxxj3WGpdSW718US3bYUpo5Jc5B2SFv1FHCZSpkrydE7LmFqsVCwrqGSVtXGIFJPuSjLtFiaOP8wH8BJAyvr960TsFuV8DoTEoUiOMW6RvN4Z3OjWdmJ2i4KKZKVUoSvJ7TH7z9MQ5CTnlG5hm417nLhHckAFlWTv8yXC1QoTxzFEslXeKnvHCRv3imwBJGQYKJJj2IbdYhy+uaDy2k8k247fuCf+78XcqdquCipGRazWhn7p4qZbtOOV5Kwj4BwjAq7knkhSTMIIOPj/C0qqA3PBrCRXLSltFbZju0YKTzmfAyFxKJJjtMc8NWi9M9gnG2/cK+oIUPM5FFEkt8e8apCGuCc58wg4Y5hIkG7B4xvJkO4IOJ6ImTiuCqxOVpkryYbdoqirm4QMC0VyjI7jdRoD41mGbw1ht6gW3G6hnwtQTEtDu0QRcEHXeMbbqjNaLUtg+XsD2i1IlgQRcP5+tcyWgjyw3VgluaBFj34opSJ2i7L6qgmJQ5Ecw3YUZv2q3TgqjKkj4Epgtyh6JbnIg040XY17GR9A9ccsEgE34QLm20fP4TUffQCLa+1xb8qWwFUKIghGUgsb9yK4hki2LEEB6wkDcVwFpfIbekTIuKBINlBKwXaVUbUb/Rc9sFv0SYPQdot6iewWRYxZ0+K4VeAdut7G6ZwmWenPjiVbx5N89Owqlls2ziy3xr0pWwJXqcBqATAnOY7tKlT9ZZyqJcHqTpnQleNGTitehIwLimQD/cWenRpnJXnwqOSW7eUkF95uYTTEFbFa2ypFBJz3mZzO6cTNNSvJW2Ti3nrbifxP8sVVYdMewJzkOI6rAquTVdIIOL3yWa9YqJTUMkJIEqlEsojcIiKHReSIiHwk4fqbRWRJRB7z//1a2vsWCS1AZqc8QTKO6mczhSe547h+TnKxh4k0jWp4IT3JQeNeccVSYLfILQLOe/xKZXSV5I7j4vsLK7n+jX40/fe72Snu+z5JeHYLs5JMu4WJY1aSK1LKngAdVVmrWKVO6CAkzkCRLCIVAJ8E8DYA1wJ4l4hcm3DTryulbvD//fqQ9y0EYZPU+CrJqdItSpKTHGncK6AQDSLghnj9/uXIWTx4+Exem9RF11jqrNMt9MQ9f+LXKDJs73/yFN76u1/D+dXxeIKbupJMkTwSlEKwSgEwJzmO46qg0u5VYcv32uht9ibBWoUt3BAyLGkqyTcCOKKUOqqUagP4PIB3pHz8zdx35Ogv9tyUHgE8jnSLFGOpg8a9govkonuSNxAB9wf/8Cx+76vP5rVJXeQ9llqL5Kox8SvvSvLCcgu2q3BuZTyeYC2OWUkeDa4hAgG/klxCIZgXZiW5UlIrSjuoJAuqFdotyOSQRiRfCeC48fu8f1mcHxGRx0XkSyJy3ZD3hYjcJiIHReTgwsJCis3KnnjVbhwWgWaKxIUwJ7nYdgstjBs1q5C+341EwK207EiFPG/i6RZZC1j9eJbZXZ/zQVp/Li42O7n+nV6EIrl4n8lJxPMkhyrZi4Ab4wYVDDMCrlJSq4LeT3l2C1aSyeSQRiRLwmXxb8AjAF6qlHodgD8A8MUh7utdqNQdSqkDSqkDe/fuTbFZ2aN3TuOMgNPNRMMMEyluJdl7LtsatUKK5I1EwK22nJE+l47jwhJgquq/11lHwKloJblq5V/l05/xi+t2rn+nF1oc024xGnQEnIZ2iyiO65Z+LHXUbiGMgCMTQxqRPA/gKuP3fQBOmjdQSl1USq34P98PoCYie9Lct0i0g8a9MaZbpKkkxzzJRa08aOvItka1mI17G0i3WG3ZI12mbztu4PMD8oiAi1aSKyPortev37gryUy3GA2uUsHnC+BY6jiOQmyYSPlenG67RfmeAyFJpBHJDwF4pYhcIyJ1ALcCuM+8gYhcJn77sojc6D/uuTT3LRLxdIux2C06/bN7XVeh46jIMJHiVpK1SC5mJTkQyY6bOrd1tWWP1F/dsVUQqySSQ+OeE60kW6OoJAcieUyVZF8cNwvYTDqJJOUkl7FamheO60a+f2WMYDTtFjXLKuwxiZBhqQ66gVLKFpEPAHgAQAXAnUqpp0Tkdv/6TwH4twDeJyI2gHUAtypPdSTeN6fnsmnsWJPUWCrJ2m7Ro1qpl9vNSnIRq7RAWDHc3qjixPp4qob9iEwEdFxMVSt9b++6CqttJxJnlTcdx0WtGg4a6GTtSfbFirncm7cnWZ8IXhzTZyKIgGMleSQwJ7k/Xk5yWEkuo0g27RbVSjl91YQkMVAkA4GF4v7YZZ8yfv4EgE+kvW9R0WJzTleSx2m36CF89TbVc1yCzwotQrc3ajhqr455a7ox39+2PVgkr40hFaHjuMGKQdWy8ouA05UsyX8sblq7xd8+cQp/88RJ/NF/eEOmf389qCQX8+Ry0lCxnGSOpY7iuCrYl5d1mEjHtFtYFtMtyMTAiXsGdpBu4VeSR1yhtR036AruJdADkVwNl+CLurSlG+PmpgrqSXaiInkQqy3PHmC7amSNKdqTDHiDBnKPgLOQu90iEMkDGvceOnYef/f0i5n//XXduMdK8khw3WhOsiX5f8bKhJluUdZhInpfWvNTl5huQSYFimSD+FjqUYtPs7LVy/cajP/0l+BrFauQAhTwnkO9YhU2Aq5lRICl2alrkQyM7gSq44RVplol+wpNEAEnYbU6d7uFna6S3Ow4sF2V+fdQi3SmW4wGz5Mc/l7WBIe8cCMRcFYpK8l6NbNmWajmsJ8iZFxQJBt0Ne6NWNjpg3fFksGVZF841StWce0WHW989lStElSVi0Rr6Epy+BxGlbHbsU27RfZ+xbCS7C/3WvmPpQ4j4PqL5PWcxGyTw0RGiqtAu0UfbFcZw3zK2dQYNO5VxeudKOgxiZBhoUg2iI+lHvWUOC0etjeqqewWgF6CL+ZZe8t2MFWzUK+MppJ8amkdH/ubp1OLvIgn2RksmFaMSvKoRL/tupFKcl52C18jj2Tinj7BWB6QbhGI2YxtEZy4N1qUUsHnC2C6RRxvLLVRSS6hwGyb6RaV7HsnCBkXFMkGod1iPJVkLby2T9d6irCW3W23KOpZe8tvhqtXLbgq+/iyOP94eAH/+5+fw/Pn0jUJtmwnqNKmOSEy7RatEVWS24bdoppDSL/dVUkeRbpFWruFG/k/K/TJKO0Wo8HpioBjTrKJ4ypUK0ZPQAlfnKjdopzNh4QkQZFsoH1UOoN45J5kXwzsmK71FJWBJ1lXF63iVpKbHQdTVSsQ9Hn7eIcVP23bxdwQg2NW26FIHlXGbsf3dQMjioCT/BuH0jbu5eEddl0VnBBxLPVoiI+lZiU5iuMqVCz9HS+nJzlqtyhmDwohG4Ei2SCcGjQ6i4CJFgM7pmve9iSIyk68klwtbnB7yx+frUVe3q/nsMvobdvFtkYt+HkQpid5VJVkLyfZExh5LGM6/uNFcpJHNkxkQCXZzn58tLliwHSL0dA9lrqcWcC9OHOxia88dXrD93eUgl9InoxhIqwkkwmCItlA2xZqFQu16uhTI8LhG55wSxJiSekWRfWwtWwXjVolrCSnFMnfPnpuQ2IwqCS30923ZVaSU/y9iN1iRCdQHSMCrmJlH9KvH87MSc67yqcruG3b7XtC08rBO2wKbnqSR4NSqisCbpIKyX/+nRdw+2cf3nCxwnbMSnJZRXIs3aKghRtChoUi2UDbLWoVGUslWYuH7X0qyfF0i6olxY2Ai9kt0gjLY2dX8XN3fAtffebM0H9v2DSEtu1irpE+7m8cjXtRT7KVvd3CjVaS8/YTKqWw3nFwyWwdQP9qch52C/1YIhTJo8J1o3aLSYuAW1rvwFWDP6fzF9bw5PxS1+WOmW5RWpEc2i1qTLcgEwRFsoEWoNWKJ+xGLT677BYJojKeblEvuN1iqlbB1BCe5HOrLQDAhbX20H9Pv35r7f5eV03bcbFtGE+yIZJHFgHnuBH/efYT97z/q5GJe/kd4PSJ0ku2TQHo70sOGvcytEU0je8YG/dGQ9xuMWljqfV+YdDn9Hf/7ln80ucf7brcUeFY6lGky+SBabfwTrSLeUwiZFgokg10Ba2uRfKYcpK3T/eOoCub3WJqSE/yiu/7NQVpWoJxwynEj+24cFwVVJJTpVu0R19JjoylruRgt/APZmEEVb4CRluIXrK9AQBY7ldJtp3I/1mgPyO7ZuoUySMi3rg3aTnJeoVp0Odpca2NpYRs8EgluaR+Xl05rlri2y3K9xwISYIi2aATVJLHY7doxSrJSUKsVSa7he34w0TS2y1W/OzclU2I5DQNWfo1GyrdouUEtoSRNe7Zbq4T98IIuNFUsrSQCCrJfbKSh/WYp0GfQO2cqaHZcaEmqKJZVJJykifpddcn9oNE8nLLxlrCvsl23Gi6TAlfG30yLyKFTlwiZFgokg30F7tqSeHtFlOG3aKoTRLexL0K6pX0udMrLa/SknQwGcRa4GEd/Hf0tgTpFikb93bNeF7aUUXAtR2FqnFClHWVyXW9pXC93GtZnoc0L5pxkdxj6p5SYVRblhVfbeG4xH8fRz0waCuSlJNcwmJpT/Sq16B91mrLxnrH6YpYdFU0Xaao+/N+mA3G3ljqCXqDx8iXD50q5LTarQRFskHHVahXLIiMuXGvTyyZ6f0CUOgRoMHEvSHSLZY3UUluBpXHwfcNRXL6SvJKy8aeOV9cjdSTrO0W2Q+Osd1o8kAl52Ei3ZXkZJFsitc80i12+iKZMXD5Ex9LbUn+o89HiV79GuRJ1vu0+Am27boT0LgXWkbysIVtRY6cWcHtn30EX316+CZ2kh0UyQYd2w0mH42jIa7Z8SbAzdS9ymuiJzlx4l4xKw+tjotG1YiASzH6eXUznuQh0hD0azvsMBGdyjCqCqTtmHaLHBr3lAqqWIA/FjfHg7QWvJf6nuRejXumMM5DJO+a8U5ER7UisJVRSsGK5SSX0VLQi7Se5F4VZ9cNV3KqI5h4mQcdxw2PSZaFDhv3Ns25Fa+JfVCePMkXimQD21UR/+c4holERGUakVz4dIthG/e8HYI5uCMtOtViIyI5zWu42nJCu8WImr46jkKtmt80LseJiWRBrhP3zEpurSI9DwBmekiW1V5d7ds1y0ryqHBjOcmVCRtLvZpyv6NXyeKfucmoJJt2C+/9LePzKBK6X2MjBSOSHRTJBm0jSaBetUbuV2x2XDTqlb5jnNuOC5Gw0aqomZSuq9B23KFzknVVZiM7Bi2s0jR6acE+O5XeL73asrGtUUW9MprPhlLK/0ya1pq8K8k5R8D579F0vYLtjVpPT3KkkpxhtVc/lj7ZYcJF/sRzki1rcsZSK6UCu0W/E66O4wb7DLOSrJTq8iSXUVx2HBWswur9VVGLN2VBJ6FspD+HZAdFsoG5tD2Oxr1Wx0GjZmGqqu0W3V+Otu0GvmmguHYL/dpNVY2c5CE8yWmzjk1Cu8Xg++rXdsqv3LdSNu7NTlUxVbNG0kwRRhLmGQEXFcl5Z9jq96hRs7B9uha833FMYZxluoUWMpfM+naLEXnLtzJJY6knRSS3bDf4nvY74TJP+s19mxbElUgEY/nSPzqxk3kAbN7bJBTJxYAi2aBjTDebGoP4TGO3aNmh9wvIp5krC3TF0KwkDzP6eSONe4HdIk0EnGFbmUphrXFdhdW244nkamUk4qqrSTOnCLjqCCvJukI8XatgW6Oaym6RZ+Nelo9tOy7+4O+f7Zv9vBVRsZxka4LsFqb47bffMU8GzdtpIVmphBFwQPmsCubQI53GU8aUjiJxMRDJtFuME4pkg7bjRpaMxjFMZLpe6evh1RYGTb1SzExKXQkc3pOs7RbDiRfXVaHdIoXwMYeypBkco+Pl5qa8yvgoKskd2ztQBj75HKw1brySnHPjkH6PGrX0dousI+DqVStojs1SJD9zahn//e++hwcPL6S+z7MvLuPFi83MtqGIOAk5yZNSSTb3U30ryW2zkhzerquSXClnFTZqt5DgMrJxdCV5I/05JDsokg1s42x4PBP3vDSIfsM3tN1C403cK55IDivJ/Z9PHF1xWR3y7Nl87DQ5yfFK96D3WleMZupVNGqj8SRrIR807lWszCtM8Qi4as6V5MBuUa1g+3S15zARfbuqJZkK2WbHwXStgulaJfJ3smDZbzpNmqrWi9s/+zB++4HDmW1DEXETcpLLmOCQhH7Pgf6fpRXjc75m3E6/DuYwEaCcleTQbuFXkplwsSn0Klsa+yDJD4pkA/NseBwieb3jRCqvWog9ffIibvyNr+KFc2vezqgMdgs79J5urJJsD+XLM5ekBuWVAgmV5AEnGlokz/l2i1HkJOsVAtOTnPWqgeuqoHoF5D9xTwveRt3qW0luBbaIWqZCdr3t+f4bWiRn6Pdb8ys+S2vtyOX9IpzOrbZxfrXd8/pJIJ6TLJLvwJpRYlb5+u13ViK2DMOTbIxzBkKxXLaTiHi6BQBmJW+Si6wkFwKKZINOrHFv1OJTV7niovLZM8s4s9zCPY+e6Kok1yveWOqiNXpogT9VraBasWBJ+gQJwDuwDuP7NYXUWooz78CTXPFE/CDxqXdUo2zci3uSazlEwMUryZYluUbANTsORLzXfft0baAneedMPVMhu+5/x7RIbmZ4IqxXP8xK8jOnLuKG//YVfO/F5a7bK6Ww2rIjVcZJJJ6T7EXAFWt/tVGiDXnpRHLEbhGrJGux7JRMYHYcFRyXQrvFhJwJjYklepILAUWyQUQkV6yRi89mx0GjVoFlSSRmTC9J/80TJz2RXI3aLYDiLc+F6RHp00KUUlhp2UF28TCWCy2ktjeqqdIQgu2rVVL5z/VBbnaqgsaIK8lmhcZxVaafya4IuJyXwvWJoIhge6OKZsdNfO11xXnXTC1Ta4v+jk1rT3KGAlx/RhbXQpH83NlVuAo4fn6t6/Yt20XHUVie8BzUeE7yJI2l1u+dyBB2iyRPsm9RKGsl2Tb6eUK7RbmeQ9HQg5ZYSR4vFMkGXrpFaLcA0iUyZEWz46JR6/ZE6x3ss2dW8PSpi13pFnrbi4Tp+QWQasy3Fg2XbvdGFg+TlawPUJfM1lN5WCOV5BSZ2BG7xYgqye2gcS+eP5qhSHYSGvdy/Miv+yIVALZPezFsSWkQTSOFIvNKcr2Chv+5zNLKEdgtjEryBd96kZTWoi+b9DQM142NpZ6gnGS9X9g103+/s9IjBUMLydBuUcyixyDajopMBgVYSd4sej/CLPfxQpFsYMcqyUA6i0BWNG0naCjyKq/el2O52YElXlf4qaVmrHHP3yEVzOQX2C2C51MZKET1gUSPLB7mDHrdmKS23nEGVlv1tqRNt9BV7Zl6FVNVazwRcEH+aHZ/23ZVcGAGgErOAqbZcYPP+LaGt2KQ1LynbRA7p7P1JDf9mMVqxUKtkm1ToP78miJZV5WTRPJm4g7LhJs4lnp825Ml+j3cOzfVv5IcNP5WIpVkbW2yApHsXV42kRyJgNOV5IIVbspGmG4x2fuHokORbNB2VPAF19XaUVZovaYiT0BMVa2gGrvSsrFjuoYfefnuyLYBRnVxxE2Gg4jbLaaGSJAIRPIQdgvdMb57tg7Hn/bXf/vCSvdUqsY9HQFXRaNWGdEwkahI1hXfLD+TrormJFetfNNSdHMqAGxveJXkpOa9wG6RcmUgLc2OG1gtGrVKpgJ8NUEkX/Cb8pIOdDrJZaU5XJNq2ejOSS7fsIxe6Pdw91y9ryd5tWVjpl7B7FQ1klYwKZXkiN2ikv3J/FajbbvBvonDRMYLRbKB7bioV2N2ixGJT6UUWrZrVF5D4bbctDHXqOKnXnsFAERykvNYgs+CZtxukUKI6gOOFsnDVNi0t1SPG24O8CXHG/fSCvjZICd5BBFw8ZzkHEL6bVcFVSwgf79oy6/kAqHdIql5r9nxKlMzdW8FIqtmQt24B3gDTbIU4KvtbrvFeW23SKiW68+37aqRfJ7GRXdO8uREwK22bEzXfPE7oHFvdqraVUl2elSSy+bn7UTsFsU8JpUJvU/cMV3DWnuyT6KLTiqRLCK3iMhhETkiIh9JuP7dIvKE/+8bIvI647pjIvKkiDwmIgez3PisMRv3aiO2W+iDZGC3MITbctPGtqkabrnuMlQtiVWSi+n/CiPgzOfTX5CEdgvPk7w2jN1Ce5Ln6pHfe9F2XNQq4jVJpqhyB417dT1xb3TpFvrErZrDoAG3a+JevlUss5IbVpIT7BZ+xTlMocjm9V5vh5XsRi3byYmrCY17+uek5jyzutxrPPck4I2ljp2IlUwE9mK17RUwZur99wkrLQfbpqqYriWL5LJXkttJY6knXCR/8dET+IfvvpjLY+vVtct3NOCqdDMGSD5UB91ARCoAPgngJwDMA3hIRO5TSj1t3Ow5AG9SSl0QkbcBuAPADxvXv1kpdTbD7c6FToLdQvuC80ZXIXTj3pQxsGK52cFco4pds3X857e+Cvt3zwb3C8/ai/UlMu0MgPd8hrZbDFFJ1gee3bPpRHKr42LKr2jWUkXAeculliUjqyQnRcAB2Ypk23W7IuDyrPJ5jXu+3WJae5KT7RYNY+hHs+PCXyTYFM1YJTnLpkD9eV3vOEEKjW7cS/osmyslKy0be7dNZbYtRWKSx1IvN700nukB1p0Vfx9etSTWuOd9x8s+TMT2iw6A0Uw+4XaLP/iHZ7F32xR+/Acuzfyx9WrUZTsa+O7pZay27KBgQEZLmkryjQCOKKWOKqXaAD4P4B3mDZRS31BKXfB//RaAfdlu5mjomHaLoJI8mp1VM7HyGnqSt/tNTre/6eW45frLgvsVdWkrSLcwnk/6xj0/3WIIT3IYGeYpqUHZkm3HCU6E0jbuzdS998DzJI8nAg7obbc4fHoZb/jY3+H0Uvoxx47bHQGXd06yFqmDPMl5TMYz/36jZmXrSTY+c/og169xz6weT3JWcrxxb7LGUtteLOSAE67VloPZehUz9Wpk36R1ZDCW2iqnSO4kpFtMeiV5ab2DMxdbuT02AFy+YxoAfcnjJI1IvhLAceP3ef+yXvxHAF8yflcAviIiD4vIbcNv4ujoOG5QSZ4acQScXvadDhr3KhG7hc4OjlMtuN1iagghGvckDxUB1w4j4AAMtEOYQ1nSTdxzMDcVNlU6rsr9NW87UU/yoLi/756+iHOrbRxdWEn9N7pE8ggqyfrEaaZeQcWSRKuBjkPU1ogsKr5KqSACDtB2iywryeFjLa17FeQwAq7770TtFpMbA9eVk2xNUrqF41WS6/0rycstz5YxHfMkB5XkIGO4nCI5arfIvneiaCilsLjWwemLzVz8wjrx5/Id3rGQInl8pBHJknBZ4qdCRN4MTyT/V+Pim5RSrwfwNgDvF5Ef63Hf20TkoIgcXFhYSLFZ2WMnNB+MypMct1t42b3eZSstG9v8qluceoHtFpaEO/00QlRX2y6ZraNesRKFRS/WOg5qFQmawQYNFPGaJMO4vzQ5ybP+iYq+X97V5I7RXAgAtQERcLpq2W8McpwkkawUcqsmt4wIOD1QZHG9eyxz047bLTZ/kGg7LlwVrtZMD/CRDsuqseKztN6B46qgIrSS8J6Y1eVJHigSz0mWCaokL7dCu0XH6X3ivNLqYM5v3DPFdLcnubzDRMI8dx1LWq7nMAwrLRu2q7DWdnL57pp2C2C4VVWSLWlE8jyAq4zf9wE4Gb+RiLwWwB8DeIdS6py+XCl10v//DIB74Nk3ulBK3aGUOqCUOrB37970zyBD2o6L2pjSLZrGBDj991u2N/FPe5KTCBIPCrZDatme51cfHNMmSFjiVdNnpipDjePU8Xlpl+fNSnKaeLoVQyRrkdXKuXkvsFtUo16/XsuYesea1AjXCycWARd4InM6SJueZMCzx1xY6+FJrhqT8TJ4rfVqjX7/GtWMI+DaNq7Y6S2PLq51sLTeCby3W9luER9LPUmeZH3yPDPgc6orzr3SLbrtFsUqevTDcRVc1b3iNcmVZLM598Uh7G1pMRv3gOGa2Em2pBHJDwF4pYhcIyJ1ALcCuM+8gYhcDeCvAPy8Uup7xuWzIrJN/wzgLQAOZbXxWdNx3KA5KsxJHpXdwvsShHYLr/Kqp9Bt6yGSA7tFAbpfl9Y7+NKTpwCE6QSatHaL2akqRASz9epQEXDrbQcz9UoggFKJ5Fg8Xb9ls9V2aHnRFpLcK8k9PMm9PpN6x72U4PHthe3EIuByXu41PcEAsHOmhsW1hEpyx42kW2QhZuPfMa+SnGW6hYMrfZG8tN4JrBb1ipU4GGelZQefpUkeKOIm5CSXrVLai1W/kjzoc7ri79uma9GouHAsdVQkl8nP23Po8h6T/gAAIABJREFUUYmew7CYIvn0xXxE8lTVSt1jQ/JjoEhWStkAPgDgAQDPAPgLpdRTInK7iNzu3+zXAOwG8IexqLdLAfyziDwO4DsA/lYp9eXMn0UGxM+GdZVxVNEr+gDeMCrJbdsNqk3beniSA1tIAc7av/joCbzvzx7BkTMraHXcIA8XQKrRzystO3ies1OVocdST9fCyuP6gJ2KV+kO32ul+lfjV1tOUC3SqRh5x8B1eZIDu0XydmrbwjB2i/gwEX2QzmM5XHuCzS7tXTN1XFgdnG6RhSc5bmnKcpiIUipSSV5a7wTif9+u6UTP8WrLDpZTJ1kkJ+UkT4rdYsWwWwDJ+ewt20HbcbGtoSvJYe6tPlmIi+QynUSEIlnbLSY/3cK0iA3TKJ2WpfUOdkzXgmMOPcnjY2AEHAAope4HcH/ssk8ZP/8igF9MuN9RAK+LX15E9BddV+t0ysWoG/eCCDhfVOqDZy+7RX3AEvwoObfidfo+/Px5tOxoJTnNVDtdbQGA2anqUDsGLb5mUooqs5JcM1YN9A4+jq4YAeF7NLpKsm7q6b+6oZfohqokx4aJ5BlB5VXrERXJs3U8c+pi121btotGrWLkJG/+tV6PVZIbNSuzEx1vFHroIVxc6+C8L/73XTKD58+vQcXygldaNi6ZrePUYnOic5Ljz1vbLeKXl42Ov9I36zfuAcBap/t91KsIs/5tdO5to1YJTni7RHLB7HP96HQ1GE9+Jdm0iL2YRyW52cH26VpwPKQneXxw4p6P3lkFiQcVb4c2Mk9yl92i4leSvS/jtqnkxr0ipVvoHcfDz1+IVGqBaFpHL3QwP+CNf96I3SKoJA9YRm85LurVMJ4O6P9eRxr3/PvlLZLt2DLmoGiloHFvCJHsxIaJaMGcRxEo7gkGgF0ztWAqncl628F0zQpOSJoZVFKC1Zp69hP39Gd1+3QN2xrViN3iql3TcFzVZe3QqTVzjSpWWpOcboEuTzKA0vuSwymcVWMFq3eKyVwjrAzq2zmObtyLjp4vgkg+urCCf/3xv8fJxfW+t+uV516EY1JeLPnf7YoludgtuirJ9CSPDYpkH+3pDSvJo/2ir/ewW+iGnkGNe0WwWyyux0Vy1G6RxpOsq7Uz9crQE/em65VAmKfxJJvxdPqyJFxXYbXtGCLZF24jsltoEasbYnodQJc2UEmOp1tUc1zuDS1F4W5n50wdzY7b9VrG0y2ysEXEK8k6kSCLBqM1o1q4Y7oWsVtcdckMAGA5JoT16sS2RnVDleS/fvxkop+7aHRFwEl4eZnRJ0bbDLtF0udUv7dzRoPfmn87/T3TdpQiRcA9eWIJJ5eaOPzict/bxVdh85gMWjR0QWj/7hmcXso+KzkUyd4xh3aL8UGR7BM/Gx55uoWushnVzZbtBHmJvRr3ihTcrg/Y319YxemlZqSSrJ9PP7you9BuMWwlebrmpWl4k9QGeZKjw0S8y5Lfa31AC3KSa6OpJHccL4FDL0nrA2jPxj2dbjGE4HLc7gxboHfM3GaIr5YA4fCXCzGxF3iSM023iJ6IZmnlWDGqijtnan4luYOqJeFwnNhJn/azzk1Vh063OLW0jg9+7lH8f994ftPbnjeuG7NbBL73cW1RNpjveb+oQr1U7uUpe/s3vX8KI+C8fZBVoIl7Z1f8rO/V/idi2m5R77JbjL9wkxeLa16k375dM/nYLda9OMmKP+GVjXvjgyLZpxOzW2jxOWq7RaMeepJdFQ4l6GW3KNJY6gtr7UDkPnP6Yle6hav67zhXmjZm66HdYhgflldJDqvQqSrJRgQc0Lsar5dL9Vl9kG6RdwScHWaPAv3j/pRSG64kV42/oQVzHnaL+GoJAFwy632uzeY9pTxrQqNqBSeN2aRbRAf2NPoskQ9LsKQ+VQ0qyRdW29g5Uw++u3EhvNL07EVzU9Whs1afP7cGAHh8fnHT25438bHUMiGV5NBuYTYMd39xzNVA3TOhK4NxT7L+LhZDJHsV0vMDRXIvu8X4n0NeLK61sWO6hsu2N1LbLR5+/gJ++a+eTDV8RFeSAe8kLOlY+MkHj+DTXzs63IaToaFI9ulltxhd454DEUSmwAHh2XzvSrLuJB7/DmlxrYObXr4nGEgRt1sA/V/P1VboSZ6pV4ezW/geVsBPLRgwTKQdGyaiL+u1XQC6Gvd0BfJ3HjiMP/569jurjuMGTYVAf/95s+MG2z+sJ9kUMLpvMR+7RVSkAp7dAohWknWFfqpWgWUJ6tVsxkdrMWzaLbzt2vxja9EzU69g53Qdi2ttXFhr45LZsPnGXBlRSmGlHdothq0kv3DeE8lPzC/mMvErS7rHUocJKsr/V0b0sKNtjbCSnFTxWw72H5WutAI33rgnxbEqnF0eViR7225ZAkvyWY0qCovrHeyareHSHQ2cXWkFr8HXn13AkTPJ9pS/fvwkPvedFwa+nq6rgsY9AF3Z2pr7HjuJv3mia2QFyRiKZB/9hY5HwI2yktwwh2/44uicL5J7e5KLk5O8uNbB5TsbuO6K7QCi3tNBr6freqJBR8DNTVXQdtzUr7+OgAPSTVJrxcZS99s201MIGI17/t+47/GT+PKh06m2cxjaxgRIIKzQJFlrdPV421R1uEpyLAIuEDADDtJKKXz50KmhVjC0SJ2KDRMBYiI51uA3XasEl22G9ZgnOjjZyUAkrxgnUtuna1hat3FhreNVkhvdInmt7aVhBHaLISvJx32RfHaljVM5RFBliasQzeKW8PL3ffYRfOQvnxzTlm0OfWJjNu4l2i2Cz0atq8HPDuwW0XSLIlTZdSU5boWKE0+3ALz+iSJYADfDcrOD9//ZI5i/sNZ13YW1NnZO13HZ9gaUAhaWW7AdF7f/6cP4/b8/kvh4z51dBYCB39eVtg2lEFSSe/XnnFttBUU0kh8UyT5tW3/RvZ2UiHhT4nqIgAurbdz98Hxmf183nmm0EDu/2kKjZvWMJiuK3aLtx9Xtmqnj9VfvAtCjktzH96sUIpVkAKmzktfbod1iujZ4Wl8kAm7Aa6gPFnu2ed7S+FjqsystnFnOvnnjxOJ6ZAUhbIjp3k6d23nVJTNo2d2NcL1wYsNE0nbXP3XyIm7/7CN44Kn0Jwd6qmQ83QKIRiqFtwvFbBaWiKR0CyAbK0dgyQnsFm0srrWxa8asJIfP0Yx29NIthhfJegHgiYJbLhylID0qyU+eWMLTCRGAZSCwW9T7N+6FYrrS1YjVVUku0CCOhQ3aLQCgZknp7RZPnljC3z55Cn/6rW7f/9JaBztnarhsh3dMOH2xiadPXcRq2+mZBnLsnCeSB+UqL/n7wrCS3G23cFyF86ttnF1plXYlpixQJPskfdH7JTJ85hvH8J//z+NBRWezaA+m+bcB4NxqG3M9/MhAcTqJdfVy10wNb3ipFsndz6dXs5tZlQHCqm0aX7LtuGg7bmQZfaAn2QnTNwYJeC2S92qRbAwTWW3ZWGs7OLPczHRndWppHf/87ALefv3lwWVh415CJdnfsb50t5ekkHagiO0mDxMZZLfQB4JjfnUkDc2Y3QEI7RaLxoE4ELPV9O9nqr+fkG4BZORJ9h9jru417nUchZOLTeyaqQef5RWjGmRWnuemahuyW7z+6l2oWoLH55c2vf154o2lTl6tOLPcxEIOJ5ijIEi3aBgT95I8yYaYDu0W3mU9c5ILIHzOLuvGvf77krjdAvArySW3W+jP5b2PnuwqGlxYa2PnTA2Xbvdy0V9cauI7z50HkCyCO46L+QvePvPUAA+zPpZub2hPcrfd4sJaO8jbXmXyRa5QJPvE7Rbez9JTOB085n0hdAPNZtHd/BrTk7y9h9UCCJfgR2UL6YVOttg5U08UyYOa41Zivt8gRD2FL1kLKH0Amq5X+uYk244Lx1Xd6RY9tk3vLHfPeoLOHCaiBXSz4w7dfNWPuw/Ow1XAvztwVXCZjoBLan7UyRZX+3FjF9fTbYs3DW34SrKuMg3z+U+qJNerFuamqpGs5MC7XA9TKLKwRKx3HFQtCb7jUxmmW4SV5EqwTLrSsrFr1hDJhhBeMSw82xpVtJ301X8AeOH8Ol75kjn8wOXbCl9J7s5J9v4/t9pGx1E4u9IaaO8pIma6RcX3zicNE9EpJpYlRo67HwHnH3eCxj2rf8zjqFBK4dyqX0lOabeoRo6dVukryXq/f/piE98+ei643HW9JuldM57dQt9Gi+QXLza73r/j59eCy04v9c+d1gUOvR+ZrnUP1tLHHSD0jpN8oEj20XYLs9O/XrUSl+A7jotHX/AOTHoJZbOstOyIeJgKPMmtnn5kwPP6VS0Z+1m7Xi7fOVPDFTun8fbXXIYbr9kdXD81oFprVmUAT2wA6SrJ6wnL6P2GT2ihHojkAX7pheUWthvVInNkubmzOnMxm52V6yp84eBx3PSK3bjarwwD/VcNdPVBZ/Km8SWvtW20bTeoWADpJ+7p5/rCECspuspmVpIB7zOzaNgtur3D2VSS19tu5DuWaSW5ZaNe9WxRO6fD13PXTA2NmoWKJcl2C18km5cNYq1t4+xKC1ddMoPX7tuJJ+aXCi0yu3KSfUGoK262q4KTvDJhvueAd5KetN/xBhFVgtsAod1Cf8/066PzksctkpfWO+g4nk1moN3C32/WYwWmskfALSy3UK94J/H3PHoiuHy5acP1PcOXzNZRr1g4vdTEQ8fOo16xYLsqmD6rMXXCIE+ybrwO0y267YPnDC+yPpkh+UCR7KNFZj2F3eKZUxeDg/YwIqEXHcfFw89fwGuu3BH524C3g+qVbKGpVsbv/9LNHboR6w/f/Qb85GtDq8AgS0NYWav5/6f3JDdj4mt6QASc3gYt3AcJ+IWVVmC1ADy/ujc23MHCcrizOrOcTQPVN75/DvMX1iNVZMBo3EsSyWuxSnIKu8XJRW979+2aDi6zUlaStQd7GLtR0jARwPvMXFjrb7fIJCfZjq7WZJnBvNoOB+HsMETyzpk6RMSLNEywW8z6jXtAd0RcL46f9ypRV10yg9deuQPLTTuzk/Ws8dIrEMlJ1j+bYqGMlouVVthoDPS2BS2bI+2rPSLgKsWqJOuT/5deMoPFtXbf7QlWYaum3ULGbgHcLAvL3n7/bddfhi8dOh3sJ3T/xy7/u/2S7VP4l++fxYW1Dm5+1V4AwMmYEH7urLefvGbP7EBPsl4F3D4d9ufEV1TN4ox5DCLZQ5HsE04Nig3ASDgbfujYBQBelej5DA5ODz9/ActNG2/+gZcEl+kMX9tVwQ62F7XK4Gl2ebNkVJKT0GO+e3qS/SpbvOKSRiTrJc4ZY3k+KTLnw59/FA9+90ywDXG7Rb9KsimS9d9oddzAdqBvlwVfOHgcO6ZreOt1l0Uu7xfSv7TeQcUSXLHTE7xpYuC0r1jfBzBykgd4IvVzPXWxOXBIjCbJbgEAu2br0ca9jk7BSHfSk5Zm28F0Pfx+m+kW620nsFBthNWWE3x2t0cqyd5J49xUdKreijEkKPQspxXJ3gH3ar+SDABPFNSXrD9GVsLEvVNGg1NZRfJsl0hOzknW77FlRYcd6e9ZkG5RkAg4Lbxeeek2uKr//qQdG60NeCf0424m3yy6OPLOH7wSKy0bX33mRQDRVVMAuHR7A4dOeM2n77jhSgDdlopjZ1exvVHFqy/fNrhxL1ZJnql3D8c6y0ryyKBI9omnWwBeg1bS8tnBY+exb9c0Xn/1rkw8yQ9+9wxqFcG/eeWe8G8b1bZtjd6Ne4DOBR6veT9eSY4zsJKsM0e7KskpPMmxhrCZhAi4pfUOvvjYSdz3+MlgG8LBMf390mdX2ti7rRG5TFeSTT9YFnaLlZaNBw6dxjt/8MouMdmvcW9x3fOu6x3rhkVyWk+yXzVXCkFDyiCabS8L3PSqA97J5mKCJ1mLWK+SnE0EXKOaYLfoOPh///Zp/F//65sbnp610goH4ZgninpYildJtiO315drO1Xa0dQvGCL5X106h0bNGmqoyHdPX8QP/cZXcaJHF/6wPHj4DD5+/zOJ12kRmJSTbFbbFlaKHWOXxGpMJPfaD5v570A091aLYf2a6IryuO0zulL5ry6dA9Dfl5xkt6hWpBAJHZvhzEVPJP/wy3bj0u1TuO8xL5PY7L8BEPiS926bwhtfdgmAbkvFsXOruGbPLC7fMY1TS/2bvJfWO7DE6M+pV7DWcSKfibMrrWBffY4xcLlCkeyTZLe4bEej68OulMJDxy7gh/Zfgqt3z+CF82ubTjX4h++ewY3XXBKpGOvKK4CBleQrdk7j5IBmgLy5sNZBrSJBNTdOOEwkWfSu+PaAuUascW8YT3Is3cJ8X074Qu7w6eXIsApz23pVPhaWW9gzFxX/UzULrY7nSd41U8NU1crEbvHk/BLajhss25mI9PafL63b2DlTD5bo0oymPrm4DkuAS40qedqc1oXlFq7ZMwsAeCHliWLTdiNZ4JpdM/WI77EVqzhPZRQBF49Z1I9/+PQyvvDQcSjlvf4bYa0dCqa43QLwVkhWEkTy7FQ1nMiXspL8wvk1zE1VsWumhmrFwnVX7BiqkvyNI+ewsNzCkxk1/N19cB6f/vrRxO+PTmmINIeK9iSvBydCZa0kR+wW9QrWezTu6ROo4Hbak+wUtZKsRfI2AP1HUyfaLawJSLfwK8kVS3DTK/bg0ePe92UxoZIMADdec4nnUa5aXbrhubOr2L9nFpfvaGC94/RtrNaDRPR+cmaqCqXClTjA61XaM1fHjulaxHpBsoci2SfJbrFv1zSOx4LEnz+3hrMrLRzYvwv7d89ire1EltyH5fj5NTx7ZgVvftVLIpfXq2Ylub9I3rdrOnU1rxdPzC/iwe+e2fD9l9bbgf8yiUHNcaFo8ISLPqikEQ76gGOmWziuilRcdSD8kYWVoAkizTCRtbaNlZbdbbeoVtC0ncCK8ZLtU5lkJT95wtsRm/50k14VmsW1NrZP1zBVraBRs1I17p1YbOKy7Y3IZz5NTqtSCgsrrSDFJK0vf73tdPmRAe9gs9y0AxtJM+GkJxNPcixBZqpqQQT4Pw/Po1oRiHjZqBthpeVE4gv16xjYLRq1SPrJSstGreJ528PGvXTNa8fPr2Hfrungu3bdFdtx+PRy6pP1w6eX/cfJ5sT6+wsrcBUi+bC/88Bh/Ic//nZgtzB3C/rnU0tN7N89i0bNKqVINi02gF4WT8hJ7lNJ1icR8Qi4+Enqk/NLmcWNpkFXKl+2x6skn+sjkvV+M2K3KECfzGboOC7Or7bxEn+/f+3l27Gw3MLZlVZQSdbfbZ2VfOP+SyAiuDxWXGvZXnby/t2zuGyHJ6hPXez93Zu/sB78XcCrJAPRVdVzK23smZvCnrk6K8k5Q5HsE04NCvfmV+2awXLTjgiOh3zfoq4kA+kraUk8eNgTpj/+A1GRPDWkSD5xYX1TS3T//Svfw6/cs/HJVxdWO8FgiCQG5SQvt2zUK1aQQdyoWbAEqUZT60qyGRkGRFML9NJy23bxvRdXAISvsZlWEUdnhe6di4pks5K8Z24KL9nWyMRu8eSJi7hy5zR2x/6exvP6db/PF9c7QarCjulaaruFabUAwmXffjmti2te5/urL9+O6VoltUiOi1TNJX60nk44COwWVdNukUUl2Y0ka4gIGlXvhOq9N12Dl++dw1MnNyaSV1t2cDATEeyYrkEkrCpvi9stfJ+qiGzIbqEbNAHgpbtnsdKyB6YQaA6/6IvkhEliw+K4Ckf9rGxTdH/r6Dl857nzQfEhKSf59MUmLtvRwN5tU6UUyXFPcqOXJ9lo3AOA6XoVa0EEnDeyW5/wJJ2kXlht4+fu+Cb+3f/6ZtD7kTdnV1rYPVvHbn8FrV8lef7COuoVK7L/L3tOshaeew2RDHhN+9qTrKNZX753DpYAN73CS3O6bHsj4kk+fn4NroJvt/BFcg9fslIKjx1fxOv8XgMAwZAs83h2dqWF3XNT2D03takiXVqUUmPvexoXFMk+ScNEdNe/eQZ/8NgFbG9U8Yq9c3ipf6A6tgmR/A/fPYP9u2fwsr1zkcvNSnK/YSKAJ+bbjrupSuZzZ1dxaqk5cFJdL/SYzl4MSpCI+/ZEBLMpx/UmeZKB6PSrE0al/ZBfLYyL5KRtW4gNEgmfT8WPgGtj77Yp7J2bysRucejEEq6/cnvP6yuVZLvF4nonEGTbG7VUleSTS90iOahk9dkf6s/ZpduncPUlM6l9+ebocJNgoIhfoWkmnPTE7TMbYbVld/396XoF2xtV3P5jL8f1V2zfcCV5LSaYdkzXsGO6Fryes1OVSHqF6WfVAqqfSD7nZwkrpXD8QlQk7/dP1p9PcbLiugrf80XyRlafDh47j4/eeyh4L05cWA++N6boPnZuFW3HDU5OI55kf9e2uNbBpdsa2DuiA/39T57CH3/9aGaPt9KyIwWMpJM5pZS3bzM+GzNG4543zCfc1+vXyTxJ/cy/POetWC638KtffDLV9+AbR87i/7nvqdTP5fj5Nfzgr38lsBvp/ZqulvbzJD97ZgUv2zsbWZGqjnDi3mPHF/GOT/wzllMOUEqD3pfr4sirDZH8/7d33uFxVFcbf+/2vtKueu8usmzLFTdsCMaA6dXUACGUAElIIf0LCUloIYROIAFCi+lgDDYGbNx7tyTL6r233dVq+3x/TNmZ3dmVbGwM+P6ex4+lraO5M/eee857zhka8cOsUwl/75njU7D+l2egKIWVpmQk6CVGMF/ZIi/JiDQrO9/GSt5r7nejf9iHcq5rLSDyJIvW5l6XD0kmDZJN2qhyc8ebPc0DOPuxDZh036e48aUdWL6j+ZTq8keNZA4++UBsJPM1Z8W923c3D2B6biIUCoKsRAMUBGg+xgoXI74gttb1YVGE1AI4erlF5HEeDb5ASHhvY++xfcbQiD9mZQtArEmWt77aBz1Csw4eo0Y1JqM90pMs1yK2dWAEWYl6KEi4jS9/TAoFYRvHyBwb7+GKrm6hgMcfDHuSj4PcwuHxo6F3WKhYIIcqhidZfP6tevWoJeBCIQYdgx4ZI5n9P54nWTgnJi2ybYYxh4E9/pCgAxfDe6D6hyM9yWH5TIiJfe2MBV8ghKY+Vhco5pLyTNx3YSmsBjUmZVrR5fAe02Yn0lto1aslSawmrVriSRaXBdOqFFArScwNYf+wDwsf+RI/fXMfepxeePwhSf1svsuiuNLOX1ZW4oev7Ir6rNaBEbi5BMpjCd+/vasV/93aJBi/dT0u4Tn+85wev5B939DDHpOcJxlgN1pflyf5xU0NeGDV4VGrC4yFYIiBY8Qv9RDLJO55AyH4g0xMuUUoxEBkIwt5B3yTEYfHj5e2NOKc0jTcs7gEKw90SGr2xuL17c14eUsjOsaYq7Kpli1h9iUX2eTnNb1GCb1aGdeTXNPtRDGnXeZRKxVRVXg21/bi0TXVYzqeo+Hzyi7sbx3Cvpbj11Qnct5PNLKNQ6o6nBhw+yT3NiFEsBUANpepy+ERIrt8V9J8uxEpZi0UJLYnme+/UJ4TXgMMWr6VOTs/MAwjjI/dpJFUujieMAyDBz6pwmXPbsGwN4CrZmSjvmcYv37vID460HFCvvObCDWSOfhECbHcImx8shONxx9EfY8LZZwRo1EpkJGgH5MHR44vDnfBGwhh8cTUqOfEcot4zUTY4+SN+WPTGDZz4SDg2JujRE4ckcTzJIdCDPY0D0gmBoD1vh1LdYtYcovCZBPy7EZUdrDlesQbEU2MMnqCJzlSbqFSot/tg9sX5OQWWjg9ga8kC+A93JNi6JEB9voMRrh5+Q5QgidZP7onuXfYC18whMwEadUOccvgWPBGZIpFh9yjSF71+IPQy2iS+euGr5Ay4g9Co1QIyV78eH6VChe13S74gwwmZki99H84fyIunZYFIKwDr+DKOY0VhmEw7AtKklYLko0oFEWHTFolXL6AcF5dnrAXkq+jHKtO8vt72+DyBrBifzseXXMEACSLclaiAYRIux9uqOnBxpqeqColhzvZv21aTiJaB0aO2iNU0cFeo/tb2P95IznBoBZkN+Lj4KUYYsNYnLeQYvl65BYMw6Cm24VgiMHync1f+fOOdLEJwLyHEWA3c5Gbeqeos6L4dSOi6hZiTzLAbtp5+/LVrU1wegK468wi3L6wEDPzEvF/H1aMugne08yWKd3TNDbDcS/3ej45rdfJGmEAK4eKpUl2+wJo4bo/ipGrk/z45zV4cm3tcZeM8PP5sUaB5OCvxxRLeH6ckG5GVYcDg+74DqF0q47tJMmVZmvoG0aiQQ2rQQ21UoFkszZm1729zQMwaJRCwiQQ3YBm2BeENxBCkkmDJJMWQyP+EyKF2N00gH9tqMdl07Lw6T2n4/6LJ+HLXyyCVa/GppqeMX/OgdZByWb62wY1kjl8MnILq14Ns1YleEhqu9kElXGiCzjXbjhmucV7e9qQZtHhtAJ71HNiAy5eW2rgq3uSG3qHZX8eKwzDYMDtR4JxdE9ybbcLDb3Dkkz4+l4XBt1+zMi1Sd4jllvEM9r4yUNI9BLkFuEFq3XAjcxEPUpSzYKxpRWVA1PHaBzT4/SCkLBulkerUggSjiSTBilcibivstjzRnKspD1APnHP6Q2AYcL6V1aTHN8DzzcSSbdKPcmxmhlsq+8Tqk4IC4iZlVuM+MeWvBpLk5xojJZbiEsgiusZHyv8QlqaEVvKwhvQh45ysfUG2DbnYrnFQ5dNxjPXThN+N+nYDHVeizrsk8ozTDp5aRHDMHhrZwsmZ1kxt9CON3e1AGAlVjw6tRLpFp1gnPoCIdT3DMPjD0V5i3mpxZnjUzDiDx6VF8ofDOFIJ7vY8SXn6npcsBk1mJRhRQt3P4g32vWCJzn8OeKfUy06JJt0GHCfmIWep8flFWqJ/29H81eu4buriTUqxXOWXhNdqpA/33n2cARDkrgXYgRJDg/vSXb7Avj3xnqcMS4ZkzKvcF5bAAAgAElEQVStUCoI7j6zGC5vAIfiVDPpGBoRPJW8sTwavAdzX8sg56n0IcnM3peJRnVMT3JdNzu+fKm48N8gjXh1OzzY2cTm8+w7zm3UK9vZe/toN7fx4Oc4cVWjCekW1Ha70O30ChIxOYRW1dwYNPYOI1c0/mlWfWxPMqdHFl8ThojEPb7sqN2oFTTjfD6Cxx88bvfRZ5VdUCsJ/njBRKEMrUJBcFqBDVvq+kZ5NwvDMLjppZ1Y+sRGfLhv9AjINxFqJHMEhMS98CkhhCDLZhA8tHxW+Li0sJGcYzMek9yix+nF+iM9uGRaZtQkCUhL0Y2mSdaplUg2a485W50PB5m1qmMykke4GzO+JlkJq16N17c344y/f4nF/1gvGGK7uOYsM/ISJe8xalQ41DaEcx/fiPF/WB0zqcrjD0KrUgjnUdAkc534hr0BDLj9yErUS8Yu0pMst3D2ONkEFrHeDuCaiXCTUbJZi2QL63X5KrrkA61DyEzQRxnkYtQKBfwRBizvmQlrklWjepLlaiQDYb2o2At0qG0Iy57fhrd2sgZat9MLg0YJo1YlhP0jjbHt9X1R4U9PQF6TzMst+IQYb2RnvOPQPrqifQh6tVJirERi1qlRkGQ8ao8UL6MQewvVSoVsXgH/WnGDCf55OU3yvpZBVHc5cfWsHDx21VTh2hB3SQTY5D1eblHf6xLG7zA3Z/Ec7nQi26YXNvpHs7Gu63EJzgR+bOu6h1GYbES2TY/Wfl6yxR5Hjs2A+l7WqJYrAQeE5RbAiW2KUMsl694wJxddDi8+r+z6Sp+3u7EfyWYtsm3hcdCrlfAFQxKZAe+hnZItCp+LZGRyRrKSsF7Yzyq7MOD24/aFhcJz/EaO3/TJwXuPrXr1mIzkoRE/arpdyErUo3/Yh0NtDviCISF6ZjNq0R/D+1vTzV5fvB6XJ7It9eqKTqHSyb7m+EbyfzY14OHVh0c9boA1Dju52ubH05Pc7fTCylUL4pmQbkEgxKC60yFpPR8JP6fyjoiG3mGhXCYApFt0ggG9u6kfz3xZC4BdxyrbHdERVY1UbsHfJ0lmreDt58vALXt+G37/wbEn4PMwDIM1lV04rcAe1adhbmESWgdGxiTXah/yoG/YB51aiZ8s34cHVx3+1umZqZHM4Q+GQAiiJixxGbjqLic0KoWQKAOwSTMDbv+YEqXErNjfjmCIwaXlmbLPE0KERXY0TTJ/nK2Dx+ZJru9lw0GlmZZjMpL5upHxqlsoFQTrf7kIb902Bz9aVIjGPje217O70Z2NA7AbNZKJBADyk41wegKw6lXwBUPYcKRX9rMj699GapJ5/WRmQhwjOYYnmdd+RSKWw/ByCyC6oYgvEML+MWrlDrUNxfUiA7wnWXqc/LXHezesejWcHn9c73u76JyIkStBtfpQJwA2/AawCwj/9/IJZJHJez9/ez/ueG235JyyJeCijWS9WgmNSiF4qzz+kKRUHD+enjF29gNY/eO2+rC3o7LdgfHpZtkNqZjSTCsq2o/OI8V7eGLVCAfCpQ15Q9gZkfRl1qlkS8C9ubMFBo0SF0zJQKpFh39dPx33njMu6jzm2sMJlNUiw5j3ZPJUdzoxLtUsyDVaZCRatd1O2WQg3lM3vygJh9qGEAwxqOtxoTDZhKxEA/qGfRj2BtDY50aqRYsJ6WbBkyzXlhpgvW68kXwiJRf8ebj19AJkJujx2vamUd+zpbYXZfd9KttgZlfTAGbkJkr+lvB1Gr7m97UMojDZKKmdLe4gGZAzkpUEoRCDvc2D0KuVQqlFgJ1rUi1awXsqx57mAWhVClw2LQsVbY5RO2Ly89ONc/MAAJ9VdgrfBQA2Q2xP8pEuF9RKIujiedjqFuE55OMDHShOMaE4xYR9LbENd4Zh8Nz6Ojy3vm5MzW6quM3CvCI7mvvdRy3laOobxkYZ6YBcl1VeWhNi4q91fJm3zqERVLQPoWPIIzF806yskcwwDH73/iE8vLoa64/04FDbEAIhRpK0B4g8yT4+kseOhd2oETzdvVykZF/LIL6o6v7KzWjqetiI79kyUtA5hWzke+sYvMl8VO7562fgqhnZeG59nSDpAdhqYfe+s/+kN8+JBzWSOXzBkMSLzJOdaBC0e9WdThQlmyRexdwYZeDWVHTGLfL97u5WTM6yRiU8iOFbU4+mSQZYXeKxapIbel3ITzIiP8l0TEbyQEQHolgkGDSYlW/D3WcWw6BRCuL/3U39mBax4ADAXy+ehEN/WoLlt85Bnt0Qc3J1+4IwiIwGXaSRzJ2XLE5uwaONMJLlWpDLTZaR7002awW5RWTy3oubG3DxM5tHLRM4NOJHY58bZVmjGMkyiXuDI+z5F2uSQwzg4jPoZf6utsERGDVKofkID+/lE8stPq1gF839XIi3x+kRzglbr1daK7lzyIPWATbk+wGXZFTb7URTnzvKAwqwRlOiQS1cR56IznhyGvN4tA+O4Iev7MLP39oPhmErQlR2OOJKLXjKMi1oGxwZczk1IJx1Hq/pD28Qiz3J4gYT5oi21QAEHfLSsnThs2fm2fCjRUVRn59rN6Jv2AeXN4AjXU6oFGy91mqRkewNBNHQO4xxaWbZyj0Amyh28dNb8PO390d9R2WHAzq1AheXZ8LtC2JnYz/6hn0oTDYJm6XWgREhvJxnNwobODm5hYIAdpP2azGSa7pdsOhUSLPocM3sHGyu7RtVJ7mmsgtOTwCba6Wb8y4He32LjVcA0Ana0XCC1d7mwWijR62EP8jAHwwhFGKERiI8vCd5X8sgyjKtUVGsiemW+J7k5gGUZVoxK98GXzAktEyOxd7mQRACXDE9GwaNEp9Vscl7vJGcaNTENJJru53ITzJGrZ1qBREicz1OL3Y09uPcsnRMzU4QJB1yVLQ70OP0IsQA/9s+unac3yxcOSObe//ReZN//e5B3PTSzqj7oMflldQqBtgSbvzm3RpnrbMZNNAoFehwePDO7lZolApcOCVDeD7dqoPTG8CqQ5043OmERqnA3z6uEiQ8U7OlnmQ+cW8kwpOcLPIk97l8QkJ637APR7qlm+OjZQ0XaTlLxkguTjEhyaTBljp5p5WYinYHFISVEP7+/AnQqhR4f09YdvH3T6vx1q7Wr3y8JxJqJHMEgoxE4sCTlaiH2xdE/7CP9cKkSY3aHBvr/WzqDxuXOxr6ceuru/HU2lrZ76rqcKCywxHTi8zDezpNmtGN5OxEPdoHR0ZtJyxHY68b+Ukm5CcZ0D/si9qNN/e5cc0L2/DWrhbZz4/sQDQaeo0SZ01IxapDHegc8qCxz40ZEQsOIPWml+ckYk+z/OQ64g8KCxT/+QCEluJ8SDkr0YA8u0H4zDEl7jm9UUl7ACSePJtRA7tRA6WCRMktVh3sAMMAm0eZUCrGoEcGuDBmKJYnOWwkA2zt5C+qujD5T2uistz5GsmRGxNFhCe5vseFmm4XMhP0aOgdxqDbx3mS2U2BVsXqYcWbgF2c9jDRoMZz6+sQDDH400eV0GuUuPX0Atm/K9GgEeQWkdrlyE3PaNy3ogJuXxBtgyM42DaE1oEROD0BTEyPf24BYFIG+5rRdMn+YAj/WFONtsERwfA1xjGSxc1xgiEGI/6gZPMrp0n++EA73L4gls3KHvW4xRUuqjvZklylGVYcEXmV63uGEQgxGJdmgVGrgt2oiZJbvLmjBS5vAF9W96C2W2pEVrQPYXyaRfCK8YtdYYox7Jnud6Oxz418u1FSSUSuugXfzezrMpKLU80ghOAyLlFzNMnFjoZ+yf88YXmYNIeC36h7OJlX68AI+oZ9UeFzvSgRKxBiJOcGYKM5Hn8Ile0OTM2JrnQzMYPVxspp9L2BICraHJiWm4hpuex7944iudjbMoCSFDOsBjXKMq2Cd5bXJNsMGji9AVmPNH9eIxHnTvBSi6Vl6Ziak4ABtz9mbfX1R1iv7rScBCzf2Tyqvrayw4E0iw4LitkOpYeOwkg+0uXE1vo+BEIMnviiRvKcnHNEqSCCTCmeJ1mhIEi1atHS78aH+9qxeGKqxIHEe5r/9kkVUi1aPHLFZFR3OfHMulpk2/RR38tHKMKaZHbDYjNqhHr6vS6voCsH2K6aY8EXCEmaAPF8VtmFskxrVM4KwK7LcwqTsKWub1TpREXbEIpSTNBrlDDr1Di7NA0fHWiHLxBCbbcT27l7a3t9f9zPOZlQI5nDHwxBpYwOxfKTf0W7A50OT7SRzC1OfAIDwzCCnmrDkXAYZ9gbwEVPb8a8B9fi6he2QaUguHBqfCNZq1LApFVJ9HyxyEo0wB9kZEOD8XD7Auh0eJCfZEA+112pIUJj/cLGemyp68O97xzA0ic2RmlNw3KL+J5kMedPTseg248n1rKTU6QeOZLynAT0OL1ol0l48PikWlf+Z96j0zrIFrtPNmmhUipQxFUdEG+KtDJyC76zXDxPss2ogZqrxJBk0kjkFu2DI4L3dbREB147OLrcQoFAkIHbF8ArWxvh8gaE8y+ukwywxvOH+1hD6/MqaTfFdpnyb0C0J/nTCtaQuGdxCQDWmxy5gOTYDagRGVS7mwagVytx34WlqO8dxq/ePYCNNb2456ySmE1SEg0aUeKetOmHsOkZg5H8WWUX1lR24faFhVApCD452CnIJyIrW8hRyp3/0cbrg71teGJtLf67pTGqW6Qc4oYhLhkNs1x1izd3tqAoxYRpOfHvDUBsJLtR3eVASaoZ49LYyBBv3PAyjPHcHCbOtwDYiMPLWxoxKdMCjUqBl7c0CM8xDIPKdgcmZliQbzfCrFPhk4NsJKgw2YRszjNd1eFAr8uL3CSDJAQv1iHzune+nS8fMj6RRnJtt0uowJBm1WFcqhkba8Ib14beYVz27BZhM+nw+FHFVQLZ0RhhJDf1Q6dWREUmwgnD7Pnm7+koz6CoOUQwFL3uqBQEFe1se/opMuUgJ6ZbEQgxUZsYAIKeeFpOAlLMOmQl6uPqksPebvZ7xF5vsScZCM/zPCO+IJr73VGVLQBpM5FVBztQkGxESapJOBexyrWtO9yNSZkW/OSsEvS6fFjNRbFiwV+TNqMGmQl6HDyK5L1XtjZCo1Lg0vJMvLe3TYiiMgwT0znCSy5GcwilW/X4vLIb/cM+XD49K+o5gN1E3TwvHxdOycCM3EQ4PAFMzY6+15UKAr1aKdEkJ3CVMowatsNq37AP+1oGUZRiQq7dMKbEOoZhcM+b+7D4H+slNaa7nR7saxmUrbrFM7fQjm6nV6heE4tD7UOC4wEALi3PxKDbj3XV3Xh9ezPUSnbd3N4wNqP+ZECNZA5/kJGVW/BhybVcy+ZxEbtmk1aF6bmJeH5DHSrah7Cuuhu7mgYwKdOC+t5hwcO2prIT+1sGMTnLiu+NT8V9F5bGTdACWE/nWPTI4uM8WskFXxeZ9yQDrPyCx+Hx4909rbh0WiaevLocQyN+3Pn6HolXYUBo0zk2TzIALByXDLNOheU7mqFRKeKWPQPCC42cV8QdUX4r3Ewk7NHJSNAJmw1+oxMpt4g0kh2eAHyBkLyRzBlx4uznFLNOIrdYw03wU7ITsLWuV9h1ewNBiW60sXcYz62vx6x8m7AgxULFhTHvW1GB//uwAk9+USN4ksXVLQC2CyJf93RtldRrJtdtDwhrksNGcifKMq1YUpoKQtiEPKdH2qZ7QXEyDrYNCRrC3U0DmJJtxfmTM5CfZMQ7u1tRnGLC9XNyY/5diUZ12JMciFXdIr5Xye0L4L4VFShJNeHnZ5dgTqEdqw51oLJ9CAoSfe/KYdWrsbQsHS9srI/yIPIEQwyeXV8nnB/ewxPPk2wWJe7JGsk6laRt9ZEuJ/Y0D2LZzOyYrd7F8NnzVR0OtPSPYFyqGSWpZgRCjLD4H+50Qq0kgvY/O1EvCTOvruhE2+AIfvK9Elw8NQPv7m4TNi5tgyNweAKYmG6BQkEwJStB6JKZlWiAzaiBQaPERk6akG83SnIMpG2p2V/E0QirXn3CGor0ubzoH/ZJPJ4LipOwo7FfkPD8b0czdjcN4N3drQDYa5hhgIUlyajvGZZI53Y3DWBKVkLUehGZC8FriiOvO4NIlhFkpBsIgPVE8jKZWJ5kALK6ZH5+5DdW03IS45aBa+gdxtCIX2Qks/8rFURwevDrVKQEqa7HBYYBilOi7ys110ykpsuJbfV9WFqWDkJYT6xerZR4PXmG3H7saR7AGeNSsKAoCbl2A17bGls77vEHUdfjErrhlWZYhIjcaDg8fry3pw0XTsnAr88bD7WS4EnOm+zyBjDiD8rO+2EjOf48nW7VwRcMIcWsxYLipKjnAFZidfXsHBBC8LulE0AIMCvfJvdxkooofS6f0FOAEAK7UYtepxd7mwdQnp2AuYV2bK/vk5XZiXlvTxs+PtiBYV9QsG8A4IuqbjAM4hrJc7iKXPGM8W6nB10Or+B4ANj7Lsmkwf92NOPd3a04Z1I6FhQnY0dD/zc2oY8ayRz+YAhqGY8tb3x+zhkZJWnRE8Iz106DVa/GzS/vxAOfHEau3YDHrpwKAPjyCHvxrdjXjgyrDk9fMw2PXjkF150W22Dg0aqUcXWOcsc5lmz19/e24jfvsZ2b+AU0L8mAbBvbHKVB1FDk3d2tcPuCuHFuHi6YkoGHLpuMtsERLN/RIryGX0itR2Eka1VKnD0xDSEGmJJllWQRyzE+zQKtSiE7uY5EhOd541esSc4UaWEvnJKBi6ZmSIwPtVIR1ayiN0a3PfF3iJP6UszShiKfVnShOMWEa2fnoNflE9phP7jqMJb8cwPuX1kJlzeAO9/YA6WC4LGrpsY9BwAbxjzYNoS3drUiyaTBS1saUdXhgFalEM4BrzNee7gbDk8AeXYDNtf1CZ4Ijz+IvmFfVI1kQGokdw6xHoUlpakw69QoSjbhMy5ELdbrnVeWDoD1GLl9AVS0OzA9NxFKBcGPFhWCEOCPF5TKbkJ5Egxh3WNkgt9Yq1v88/MatA2O4G+XlEGtVOC8snQ09bnxwb52FCabJMmd8XjgsjLk2Ay48409stVK1lR0or5nGPOLktDU5xa08sY4sijey+zyBsLVMHRSTbIvEBI2n2/ubIFaSXDJKJIsHhMnn+DHpyTNLGwG+Q1ZRfsQCpNNwjhkJRrQxkm0GIbBCxsbkGc34HvjU3DTvHyM+INYzlU04b3xvPd0Sja78OUnGaFUELahQqJBMNJy7UakmnXCfSInt0i1hK+hZLNWuN8+PtAhJPXGwhcI4YYXd+C37x+UbQ7y0OrDOP/JjQgEQ0KUQ+zxPL0kGb5ACNsb+hAKMfhofzsANqEaYCUWaiXBbZw8aBfnTeavb7nIV6R2fh/nFInUFIvlFsFQSLYEHMOwc0uGNfoezbUZYNAoZXXJe5oHkJmgF+r7luckoNPhkQ2ps6/nm1ewf08554ywcfIx/mcgujU178mOLP8G8BGvEP68shImrQo3zcsXHi/LtMp6kjfU9CDEAIvGpUChILh2dg52NPbHTFKs7WaruPCbhrJMK+p7h8fUee89bl37/pw8pJh1uGFOHj7Y14babpeoRnL0vD+/OImtkjTKhpuXVFwyLTNq/FMtOpi1KtwwN1eI+pXnJOKLny3Espny0iqDNmwk90QkkyeZtdjTPIABtx9TcxIwpzAJTm8gbgJyS78bf1xRgVl5NqSYtUJUCGDX/Dy7QYg4yZFrNyDDqsPmmtgyQv77J4kiLiqlAhdOycSX1T1weAK4dnYOTiuwodfl+8bWUqZGMoc/GIJaFX06zDo1EgxqtA6MwKxVyU5aqRYdXrxpJoa9QdR0u/CzxSUoSmGTWdZX92Bg2IeNNb24YErGmKQTPEfjSc4UEnHie5LdvgDuX1mF/+1oxqbaXsFrnGc3QqtSIjNRLxjOoRCDV7c2oTwnQegCt6A4CbPybXhqXa2wGAy6/TBolKMaupGcP4U1rqbnyu+exWhU7OQa6UlmGAYOj18SnieESFrEtg2OICshHPo9Y3wKHl9WHvX5kSXgwrUy43mSRUayRYsezqgaGPZhR2M/lpSmYW4hv+vuxZDbjzd3tiAzQY//bGrAgofWoqLdgb9fMSWq0oQcKoUCbl8QU7KsePeOuQCAlQc6JOE/3pO8Yn871EqC35w3Ab5ASNCpxSr/BoiMZIbBGi7LfUlpGgDWI84bHOKNQ36SEaUZFnx8sAP7WgYRDDFC/djLp2dhy6/PxPwIb0okiQY1Bkf8CARD8AZCskbyf7c24qHVh7GzMdrDW9XhwH82NWDZzGxBK3r2xFS2I2a/e0xSCx6LTo3nrpsOlyeAu9/YK9HhMwyDZ76sQ57dgEeumAxCgA/2sYZVPE8ybxC7vAHZBhN8maWmPje8gSDe29OKxRNTY8pT5Mi1G4SSb+NSzShIMkGlIKjudKK224lNtb0S71C2TS9ItHY09GN/yyB+MD8fCgXBhHQL5hba8fLmRvS6vKjkEnDGp3FGMjcfFKYYoz4PYDfdCgURSu6J+2XwntM0UaOGZBPbUKSxdxg/Xr4XN7y4I66W9v29rdhwpAfLdzRj4SPr8I/PjgieqF6XFy9uasChNgdWHepEDeeVLRYZc7PybdCoFNhY04tdTQPoGPJgVp4NR7pcONzpwM6GfkzKtGJGng06tULQTkZe32LE9dm9gWBMTbFBJMuQKwHHrxFTsxNkowgKBcH4NHOUkRwKMdjdNIBpovwO3qO8XiT9a+gdxnX/3o6lT2zEQ6sPw6xVCRK0FIsOmQl6ybwmeJIjWlPzCaK5MmUVVUqCYV+QlVktLpFETafmJKCyPbrqxrrqbiQY1ELU8MoZ2TBqlELUBmA3K0se24CNNT2C8cx7d/lopNioHvYG8M/Pj+BHr+/GVf/aioue3oybX96JZ76sw9TsBCFR+rbTC6BTK/H4FzWijqLRa31hsgmbfnWm7NwpJt9uBCHA5dOyop7TqBRY+4tF+NnicZLHC0Qb2EiMGpWwue6LNJKNGqFXQ3l2osTL6w0E8fDqw0JEEWCvEz4x99Erp+DcSWn4sroHw94ADrYOYVfTAK6fkxc3gkUIwZJJafi8qitmKTjeqx859146jd34FyYbMTvfhtn57PFu+4bqkqmRzBGIIbcAwoX7S9LMMS+c8WkWvHjjTNyxqBAXTGa9lIvGJWNLXR8+2NeGQIjBBaIM17GQn2SUDWXJoVUpkWrRCp7kddXdsh6WN7Y3o3/YB7NWhSe+qEF97zDSLDphgc9PMgl1TjfV9qK+dxjfn5MnvJ8Qgl8uGYcepxevbG0EwNa3PRo9Ms/8oiT8YH4+roqxe46kPCcBhyIm188qu1DfM4zZEQ1ZTDoVqjud8PiD6HF6JZ5kOeQS92K1pAbCnmTxc8kmLfqGfQgEQ/i8qgvBEIMlpWnISjQgx8bqxN7Y0Qy3L4gXbpiBJ68uhz/I4PaFhXFDW2IMGiUMGiX+uawcuXajULZJXGKKT9zrdXkxO9+OM8alwKRV4QsupMbX75Sb6Hkv36Dbj6fX1WJSpgVFnAdOrK3kQ+U8SyenY2/zoOCR4xdnQohs8kck03MTEQwxeHlLI1fdQlpib9nMbPa8bajHNS9sE5KLAHbS/+37B2HVq/Grc8YLj9tNWmECnpg+diMZYCU5f76oFNsb+vGGqFzYhppeHGwbwh2LCpFu1aM8O0G4TuJpkrUqpdB6Wk5usaA4CVa9Gtf+ezueWluLAbcfV83MOapj5o0VnVqBbBuboFqQbMSRLieeWVcHnUopePSA8LzW2DeM+z6qRJpFh8tE+smfnlWCAbcPFz65CZ9XdSE/ySgYgvy1IK6Py+dvpJi1gu6W1yVLPcns/6liI5nruvfEFzVQK9lkvh++skt2AQ4EQ3jmyzqUZVqx/pdn4KyJqXjiixp8zHnDXt7cCF8whHSrDs9+WYcjXS6YtSqJUa5TKzE734aNNT1Ysb8NOrUCj145BUoFwdu7WnGgdQiz8lhDujw7ETsb2XDwi5saoVUpJIYoj7g+e0U7qw0ul9GY8q+rbHeg0+GV9SSz5zi2BG1ihgVV7Q5JiPr1Hc3ocnglZbsmZlgwMd2CP3xwCG/tasGB1kFc/uwWVLQPId2qw5QsK+5ZXCJx3tx6egGunBG+Dvi5PVJuUdPtQl6SUZIAzaPmdkVFKaaoqOnU7AT4giG8u7sNm2t7hX8bjvTg9OJk4XwkGDS4bk4uVh5oR32PC4FgCL97/xCqu5y46aWdeHlLIwwaJXK56443klce6EBttxPrqrtx9mMb8PgXNTjc6QQDtoZ8t9MDrVqBu88MV4mxm7T4/tw8rDzQLlQzkZv3x8ql07Lw6U9Pj1m9ik9aHSt6kdyi1+WTyPx4g1mvVqIk1YRksxYlqSZ8XtWF6/+9A898WYefLN8nRGqW72zBjoZ+/N/5E5FtM+C8snR4AyGsPdwtnNMrZkQb95HcdnohFIQIdZ4jOdTmQJ7dEFVnuTTDgmtm5+Dec8aDELZ8YKpFK2xEv2mMyU1JCDkHwOMAlAD+zTDMgxHPE+758wC4AdzIMMyesbz3m4IvGIoqxcOTbdPjYNtQVNJeJLPybRJN0aJxyXhlaxP++XkNl21+dAv1E1eXj/4iEXwZuP9sasD9KyuRbdPjndvnCouRxx/EvzbUY06BHUtKU3HfR5Wo7nRKdnr5dgP2NA1g0O3DI59WI8mkwbllaZLvmZlnw8KSZDy7vg7nTkrHoNs35soWYtRKBf5w/sQxv748JxEvbGxAVYcTU7MT4PYF8KePKjEu1YwbIvSuN83Lw8Orq/Hommru3IxiJMtoksMehdjVLcQ7+mSLDgwD/P6DQ9jdxIY9J2Wy53ZuoR0fH+zAwdYhzCuys4tXhgXnTEqLK0OI5FfnjMedZxQJes8fLSrE8h3Nkk2KSaOCgrD1PM8cnwKNSoEFxUlYe7gLDDNJKJMk57nmJ+7nN9TD6fHj+etnCBtDiZEcEYpcWmxAyzsAABH2SURBVJaOh1dX482dLShJNR2V9AYAzhiXgu+NT8Gja46AASPxJCsUBA9eNhkAu1Cf/dh6/Oyt/fjwznnQqBR4cXMD9jYP4tErpkRpus8rS8PW+j6UZoxe2SKSy6dn4cN97Xh4dTXOLk2DLxDCL9/ej8wEPS7mZBBnl6ZhT/MgVAoiWx1HjEmrgmPEL+hexYtwQbIJ79w+Bze8uANPrq1FZoIe84vie98j4Q3SktRwPeiSVDM21fbC6Qngprl5Eo8eb9Q+tLoaVR0OPHPtNMG4Bdj57N075uLWV3ahot0hKWOVwtVsFpdB441ucVUL/jqVSJs4oyrNKjWSWwdG0Nzvxi0LWCPt0me24KaXd+KVm2dJNnQfH+xAU58bz103Hdk2Ax6/aiqa+9y4b0UFpmQl4JWtjTinNA1njE/Bve8cQOuAG0WppigHx4LiJPztk8PoGPTgrAmpyLYZMLfQjle3NsEXDAlz+cx8G55aW4P/bmnE51Vd+P3SCZJNKY9Yk8zXkI2sbAGEG8v8cUUFAETVolUI91vshM2J6Va8tq0ZrQMjyLYZ0OXw4OFVhzGvyI7zJ6eHz7VSgeW3nYY7X9+De985AI1KgRSzFq/cPAsFydEyCQD4Prfx5uHndrGR3NLvxp6mAcwukI8C8k6EP5w/MWp+m56bCAUBfvt+dNOLyJJjt8wvwMubG/Hsl3WYmGFBdZcTj1w+Ge/sbsX2hn72sxThainjUs14dVsTXt3GbmwLk4145/Y5Y4pW3rqgAK9ubcJzG+oBIKoE3NGgUSkk5Ua/KkaNCgNuH3yBEIZG/JIIE991r0wk7ZlbmISXt7CJifeeMw6PfXYEf1lZid8unYAHVlXhtAKbYAjPyLMh2azFa9uasLd5EMtmZQsykHikWXVYNisbb2xvxp1nFCHVosOTa2th1qrwg/n5qOgYEiLQYggh+NslZZLfZ+fbsa2erZYxlhyMr5NRjWRCiBLA0wAWA2gFsJMQsoJhmErRy84FUMz9mw3gWQCzx/jebwSBYEh2RwywxicwtsQfMacV2KFRKjA04sdN8+KHL44H2Yl6rDrUia31fZhXZMfe5kHc8J8dePO205Bg0ODNnS2st2ZZOcpzEvDUujr0urxCVQuAXdRcXCWOjkEPnrymXFZG8ZvzxuPK57bigqc2QadWjNnj/VXgF5y9zQOYmp2Ax79gNahv3z4naiK+/fRCbK3rwwsb2Qz90aQMGpVC6KDH0+vyQq0ksgtiWJMcNjrKsxOQYtZiFdd8445FhcKYzym0Y/nOFjg9ATxwaXiCOBoDGZAaIADrbXnxxpmSz1EoCMw6NYZG/PjehBQArLG86lAnfvH2Aby3txVTshPiVrcYGvHjlvn5kk5h49LM0KrYJgG2iMhBrt2IskwrDrYNjWlBioQQgj9fPAmL/7EeIz5pMxExNqMGD1w6GT98ZRf+vqYag24f3trVikXjkoUwnpgrZmRDo1IIBfCP9pj+cvEkLPnnBvzq3QNcpYgQXv3BbOGeWFKahgdXHYZRqxr1/jbpVHh7dyt8gRB+cXZJVJi6ONWMd++Yi3ve3IdLyuU7ccZDbCTzjE8zY+WBDmiUCvwwovxeRoIOhLDNJBaWJOPcSdLNMMB651bcPR8PrjocVbKSl+Hw8Ea3uNkS/zeK/5RpOYl45PLJmCfaBCSbtQiEGBg0Stx2egHsJi3+df0M3PLfnbjgyU148ppyzC1MQijE4Km1tShJNQnGpUqpwEOXTcaFT23CZc9ugcMTwO0LCzEh3YJ/rDmCTodHtgIDWzbsMJzegLABuHBKBjbW9IKQcMvp2fk2PMEAf1pZiVl5Nok3Xgy/sXtpcwMqOxyYlpMg8ZbzlKSa8JeLJ8GsU2FCugUFEfc0X+0iXs103rFR0e5Ats2AP31UAW8whL9eXBZ1HVp0arx440zcv7IShzucePKactnjioVaqYBFpxI0yRXtQ7jxpZ0IhBhJN0AxV8zIRl6SEQtLkqOeS7XosOae09E/LNUOa1QKTI5I4E42a3H1rBy8tq0Jqw91YkFxEi6fnoULpmTg4dXVUXKWFXfPQ02XC9WdTngDIVw6LVO2gZEciUYNbp6XhyfW1sac908W5TkJeHJtLa7/z3YAUucM/7N4Q3ZxeSZ2NvbjTxeWYkaeDR5fEE+srUVNtwtefwh/vSR8nSgVBOeUpgkbixtEkePRuGNRIZbvaMGDqw6j2+EVKsFsrO1FS/8Irpk1eu4VAMwusGHF/na2fGRS7K6oJ4OxeJJnAahlGKYeAAghywFcBEBs6F4E4BWGjf1sI4QkEELSAeSN4b3fCPzB6KLuPHx5o9E8yZEYNCrMLrBhY02vxAtzoshKNMAbCGFekR0v3jgTuxoHuL7pm5Bi0aK2y4WZeYk4rcAGQtiklL9+UiVUtQDCRli/y4eXb56JuYXy3qzxaRZ8dPd83P7aHlR1OKJqhp4I0q16pFl0eHpdHVbsb8fB1iFcPj0LM2W+W8Elwp37+MYxyS20KgW6nR5c8sxm4bGWfjfsRq2sjlxObjEp04odvztL9vN5I60wWX7h+CrInXurXo0kk0YwUhaNSwEhECqV/PXiMlkjTMkt0Nk2PX52donkObWSrULSOuCWPSdLJ6dzRvLoJcvkyEzQ42eLS/CXj6viLmyLJ6bi0mmZeH5DPQgB7jyjED89q0TWSNWplUctWxCTl2TEj79XjEc+rYZBo8Rrt8yWzAOsJMokhELjYdSo4AuM4GeLS3DXmcWyr8lI0OPN2+Yc07HyYy3ezPMG8+UzsqIMI61KiVSzDgNuH/58UWlMIz/JpMXfr5gy6vfzLZrFG7m8pGi5hVJBcMUMqcSKj9bcNC9P8JLNKbTjw7vm47ZXd+G6f2/H5KwE+AJsIt7jy6ZKrsGJGRbctrAAT6+rw7wiu7C5u2VBPv7ycZXsJn58mhlJJi28gSAWjmPvySWT0vC7Dw6hIMkoREPKcxKgUhColQo8csXkmJsXXopS0e7AspnZ+L8L5KNkhJC4idtKhSKqS18k41LNUBDg/pWVePbLWuxvHcIvzi6J2kTzqJUK/PmiSTE/bzTsJi0+OtCBA21DONLphEWvxhu3z4kpJ0iz6uLKCyPbWMfjtoUFeH17EzyBIO67kL1OdWql7PnVqpSYlGkdtVpSLH4wvwAvbWkcc+nVr4ufLS5BokGDB1ex5WXtIucM/3O5yKExNTsBH/94gfD7j84owkcHOlDR7sA9Z5WgMCKKcF5ZOl7d1oTTS5IFed1YSLfqccWMLLy+vRl6tRKPL5uKQbcf969kTTw+kjoap3Fyye31fd9KIzkTQIvo91aw3uLRXpM5xvcCAAghtwK4FQByco59UTtWSjMtQvHzSM6amIqqTmdUvcuxcMfCQkzNTogZ2jqenFuWhr5hH363dAK0KiXmFSXhX9dPx8tbGhFiGEzPS8Q9ImPi2tNyUN87LPEIzc6344Y5ubhqZvaoIepcuxHv3TEXT62rES7yE81dZxYJHeDOn5yO3543IeZrk0xa/Ov66Xh/TxsyRtHFnleWjrZBj0TjNyHdElW+h2dabiKunpU95s1BilmHu88swqx829cy+d6yIF8iE0k2a/HLJeNgN2pw5YzYZcWMGiVunJuHi6ZmSELvPLcvLERbjAoqV0zPQl23C4snjE1fLceNc/PQNjiCs0b5jD9eUAq1QoGLpmZg7lHKEo6WHy4oQI/TiyWlabI1i3+xZJzQ1TEe156Wi0AwFNMT+VXhtX7nicLtcwrtuGJ6Fn4cwyi/fWEBLHq1bPLV0VKUbML35+RiaVn4+6flJOKa2TmyG1kx84uTcNWMbNx6utQzWZRiwod3zcfDqw8LCcVXzsiSfAfP3WcWs81XRJuia2bnoK7HhXNkvOSEENx7zjiEQowQGbDo1PjNueMlm1+DRoWfnlWMwmRT3PNk1Chx1xlFmJBuwdLJ0cc3Vq6bnRMzqsmj1yjxo0VF2M91WbvutJyoc3c8uWFOrlAm7IzxKfjteRNGTV47XqRb9bj/oklQKxVRxt3xxmpQ42+XlJ3Qmt3HAiEEN8/Px6x8G17f3iRZb+cXJWHZzGyhoYocOrUSTywrx7t7WnH7ouiGTrPybbj+tNwx5weJ+cn3iuEPhnDz/Hwhsbc0w4IV+9tHve95CpKMmF+UNGav/9cJGa02HSHkCgBLGIa5hfv9egCzGIa5W/SajwE8wDDMJu73LwDcC6BgtPfKMWPGDGbXrl3H/ldRKBQKhUKhUCijQAjZzTDMDLnnxuJJbgUg3l5kAWgf42s0Y3gvhUKhUCgUCoXyjWIsWUM7ARQTQvIJIRoAywCsiHjNCgA3EJbTAAwxDNMxxvdSKBQKhUKhUCjfKEb1JDMMEyCE3AXgU7Bl3F5kGKaCEHI79/xzAD4BW/6tFmwJuJvivfeE/CUUCoVCoVAoFMpxYlRN8smAapIpFAqFQqFQKCeaeJpk2nGPQqFQKBQKhUKJgBrJFAqFQqFQKBRKBNRIplAoFAqFQqFQIqBGMoVCoVAoFAqFEgE1kikUCoVCoVAolAiokUyhUCgUCoVCoURAjWQKhUKhUCgUCiUCaiRTKBQKhUKhUCgRUCOZQqFQKBQKhUKJ4BvZcY8Q0gOg6SR8dRKA3pPwvZSvHzrWpw50rE8t6HifOtCxPnU4kWOdyzBMstwT30gj+WRBCNkVqzUh5bsFHetTBzrWpxZ0vE8d6FifOpyssaZyCwqFQqFQKBQKJQJqJFMoFAqFQqFQKBFQI1nK8yf7AChfG3SsTx3oWJ9a0PE+daBjfepwUsaaapIpFAqFQqFQKJQIqCeZQqFQKBQKhUKJgBrJAAgh5xBCqgkhtYSQX5/s46EcfwghjYSQg4SQfYSQXdxjNkLIZ4SQGu7/xJN9nJSjhxDyIiGkmxBySPRYzLElhPyGu9erCSFLTs5RU46FGGN9HyGkjbu39xFCzhM9R8f6WwohJJsQso4QUkUIqSCE/IR7nN7b3zHijPVJv7dPebkFIUQJ4AiAxQBaAewEcDXDMJUn9cAoxxVCSCOAGQzD9IoeexhAP8MwD3Kbo0SGYX51so6RcmwQQk4H4ALwCsMwk7jHZMeWEDIRwP8AzAKQAeBzACUMwwRP0uFTjoIYY30fABfDMH+PeC0d628xhJB0AOkMw+whhJgB7AZwMYAbQe/t7xRxxvpKnOR7m3qS2ZNcyzBMPcMwPgDLAVx0ko+J8vVwEYD/cj//F+xNSfmWwTDMBgD9EQ/HGtuLACxnGMbLMEwDgFqwcwDlW0CMsY4FHetvMQzDdDAMs4f72QmgCkAm6L39nSPOWMfiaxtraiSzA9Ei+r0V8QeH8u2EAbCGELKbEHIr91gqwzAdAHuTAkg5aUdHOd7EGlt6v383uYsQcoCTY/DhdzrW3xEIIXkAygFsB723v9NEjDVwku9taiQDROaxU1uD8t1kHsMw0wCcC+BOLmxLOfWg9/t3j2cBFAKYCqADwKPc43SsvwMQQkwA3gXwU4ZhHPFeKvMYHe9vETJjfdLvbWokszuQbNHvWQDaT9KxUE4QDMO0c/93A3gfbGimi9NC8Zqo7pN3hJTjTKyxpff7dwyGYboYhgkyDBMC8ALCYVc61t9yCCFqsEbT6wzDvMc9TO/t7yByY/1NuLepkcwm6hUTQvIJIRoAywCsOMnHRDmOEEKMXDIACCFGAGcDOAR2nL/Pvez7AD48OUdIOQHEGtsVAJYRQrSEkHwAxQB2nITjoxwneIOJ4xKw9zZAx/pbDSGEAPgPgCqGYf4heore298xYo31N+HeVp2ID/02wTBMgBByF4BPASgBvMgwTMVJPizK8SUVwPvsfQgVgDcYhllNCNkJ4C1CyA8ANAO44iQeI+UYIYT8D8AiAEmEkFYAfwTwIGTGlmGYCkLIWwAqAQQA3Emz3789xBjrRYSQqWDDrY0AbgPoWH8HmAfgegAHCSH7uMd+C3pvfxeJNdZXn+x7+5QvAUehUCgUCoVCoURC5RYUCoVCoVAoFEoE1EimUCgUCoVCoVAioEYyhUKhUCgUCoUSATWSKRQKhUKhUCiUCKiRTKFQKBQKhUKhRECNZAqFQqFQKBQKJQJqJFMoFAqFQqFQKBFQI5lCoVAoFAqFQong/wHy8IIZodB7kwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluslab['Cluster 0'].plot(legend=True, figsize=(12,6))  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluslab['Cluster 1'].plot(legend=True, figsize=(12,6))  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluslab['Cluster 2'].plot(legend=True, figsize=(12,6))  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering Common Venue Categories for Labeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "clus0 = cluslab[['Cluster 0', 'index']]\n",
    "clus1 = cluslab[['Cluster 1', 'index']]\n",
    "clus2 = cluslab[['Cluster 2', 'index']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster 0: Fast Food and Mexican Cuisine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>Cluster</th>\n",
       "      <th>Cluster 0</th>\n",
       "      <th>index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>1.337932</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1.988521</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>1.185461</td>\n",
       "      <td>Pizza Place</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Cluster  Cluster 0                 index\n",
       "78        1.337932  Fast Food Restaurant\n",
       "140       1.988521    Mexican Restaurant\n",
       "169       1.185461           Pizza Place"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clus0 = clus0[clus0['Cluster 0'] > 1]\n",
    "clus0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster 1: Hotel and Social Venues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Cluster</th>\n",
       "      <th>Cluster 1</th>\n",
       "      <th>index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.374414</td>\n",
       "      <td>Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>0.577027</td>\n",
       "      <td>Hotel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Cluster  Cluster 1  index\n",
       "18        0.374414    Bar\n",
       "113       0.577027  Hotel"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clus1 = clus1[clus1['Cluster 1'] > 0.3]\n",
    "clus1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster 2: Convenience Stores and Mexican Cuisine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Cluster</th>\n",
       "      <th>Cluster 2</th>\n",
       "      <th>index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0.385612</td>\n",
       "      <td>Convenience Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>0.532437</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Cluster  Cluster 2               index\n",
       "56        0.385612   Convenience Store\n",
       "140       0.532437  Mexican Restaurant"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clus2 = clus2[clus2['Cluster 2'] > 0.3]\n",
    "clus2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Referencing Final Dataframe for Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>County</th>\n",
       "      <th>SD Rank</th>\n",
       "      <th>CityLat</th>\n",
       "      <th>CityLong</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Frisco</td>\n",
       "      <td>Denton County</td>\n",
       "      <td>1</td>\n",
       "      <td>33.150674</td>\n",
       "      <td>-96.823612</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Art Gallery</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Pool</td>\n",
       "      <td>Farmers Market</td>\n",
       "      <td>Smoke Shop</td>\n",
       "      <td>Steakhouse</td>\n",
       "      <td>Sports Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Round Rock</td>\n",
       "      <td>Williamson County</td>\n",
       "      <td>5</td>\n",
       "      <td>30.508235</td>\n",
       "      <td>-97.678893</td>\n",
       "      <td>0</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>McKinney</td>\n",
       "      <td>Collin County</td>\n",
       "      <td>3</td>\n",
       "      <td>33.197650</td>\n",
       "      <td>-96.615447</td>\n",
       "      <td>0</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Café</td>\n",
       "      <td>Food</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Farmers Market</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Steakhouse</td>\n",
       "      <td>Burger Joint</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sugar Land</td>\n",
       "      <td>Fort Bend County</td>\n",
       "      <td>2</td>\n",
       "      <td>29.619679</td>\n",
       "      <td>-95.634946</td>\n",
       "      <td>-1</td>\n",
       "      <td>Park</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Auto Workshop</td>\n",
       "      <td>Office</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Other Great Outdoors</td>\n",
       "      <td>Big Box Store</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Midland</td>\n",
       "      <td>Midland County</td>\n",
       "      <td>10</td>\n",
       "      <td>31.997366</td>\n",
       "      <td>-102.077948</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Tex-Mex Restaurant</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Theater</td>\n",
       "      <td>Thrift / Vintage Store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         City             County  SD Rank    CityLat    CityLong  Cluster  \\\n",
       "0      Frisco      Denton County        1  33.150674  -96.823612        0   \n",
       "1  Round Rock  Williamson County        5  30.508235  -97.678893        0   \n",
       "2    McKinney      Collin County        3  33.197650  -96.615447        0   \n",
       "3  Sugar Land   Fort Bend County        2  29.619679  -95.634946       -1   \n",
       "4     Midland     Midland County       10  31.997366 -102.077948        0   \n",
       "\n",
       "  1st Most Common Venue 2nd Most Common Venue 3rd Most Common Venue  \\\n",
       "0    Mexican Restaurant       Thai Restaurant   American Restaurant   \n",
       "1   American Restaurant           Pizza Place           Gas Station   \n",
       "2   American Restaurant    Mexican Restaurant                  Café   \n",
       "3                  Park          Intersection            Food Truck   \n",
       "4    Mexican Restaurant                   Bar        Ice Cream Shop   \n",
       "\n",
       "  4th Most Common Venue 5th Most Common Venue 6th Most Common Venue  \\\n",
       "0           Art Gallery          Dessert Shop                  Pool   \n",
       "1                   Bar           Coffee Shop                Bakery   \n",
       "2                  Food        Discount Store        Farmers Market   \n",
       "3         Auto Workshop                Office           Gas Station   \n",
       "4        Sandwich Place     Convenience Store                   Gym   \n",
       "\n",
       "  7th Most Common Venue 8th Most Common Venue 9th Most Common Venue  \\\n",
       "0        Farmers Market            Smoke Shop            Steakhouse   \n",
       "1         Grocery Store          Burger Joint        Ice Cream Shop   \n",
       "2        Sandwich Place                Bakery            Steakhouse   \n",
       "3  Other Great Outdoors         Big Box Store        Clothing Store   \n",
       "4    Tex-Mex Restaurant       Thai Restaurant               Theater   \n",
       "\n",
       "   10th Most Common Venue  \n",
       "0              Sports Bar  \n",
       "1      Mexican Restaurant  \n",
       "2            Burger Joint  \n",
       "3                     Gym  \n",
       "4  Thrift / Vintage Store  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final = final.astype({'Cluster': 'int'})\n",
    "# final['Cluster'] = final['Cluster'].apply(lambda x : x.replace(x, 'Outlier') if x.startswith(\"-1\") else x)\n",
    "final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summarized Visual: Choropleth and Color-Coded Cities <a name=\"map\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "\n",
    "def get_string(num):\n",
    "    if num == -1:\n",
    "        clus = 'Outlier'\n",
    "    else:\n",
    "        clus = str(num)\n",
    "    return clus\n",
    "\n",
    "def get_desc(num):\n",
    "    desc = ''\n",
    "    if num == 0:\n",
    "        desc = 'Fast Food and Mexican Cuis.'\n",
    "        return desc\n",
    "    elif num == 1:\n",
    "        desc = 'Hotel and Social Venues'\n",
    "        return desc\n",
    "    elif num == 2:\n",
    "        desc = 'Conv. Store and Mexican Cuis.'\n",
    "        return desc\n",
    "    else:\n",
    "        return desc\n",
    "\n",
    "clusters = 4\n",
    "x = np.arange(clusters)\n",
    "ys = [i + x + (i*x)**2 for i in range(clusters)]\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, cit, cty, cluster, rank in zip(final['CityLat'], final['CityLong'], final['City'], final['County'], final['Cluster'], final['SD Rank']):\n",
    "    label = folium.Popup(str(cit) + ', ' + str(cty) + '| ' + 'Cluster ' + get_string(cluster) + ': ' + str(get_desc(cluster)) + '| ' + 'SD Rank: ' + str(rank), parse_html=True, max_width=300, min_width=300)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lon],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color=rainbow[cluster-1] if cluster > -1 else 'black',\n",
    "        fill=True,\n",
    "        fill_color=rainbow[cluster-1],\n",
    "        fill_opacity=0.7).add_to(finmap)\n",
    "       \n",
    "finmap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## End of Notebook"
   ]
  }
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