dc_neighborhoods.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DC Neighborhoods - Capstone"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import folium\n",
    "from geopy.geocoders import Nominatim\n",
    "import requests\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Upload Coordinate/Neighborhood Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Zip</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Shaw</td>\n",
       "      <td>20001</td>\n",
       "      <td>38.907711</td>\n",
       "      <td>-77.017320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>H Street Corridor</td>\n",
       "      <td>20002</td>\n",
       "      <td>38.901811</td>\n",
       "      <td>-76.990970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Capitol Hill</td>\n",
       "      <td>20003</td>\n",
       "      <td>38.881762</td>\n",
       "      <td>-76.994470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Downtown, Chinatown</td>\n",
       "      <td>20004</td>\n",
       "      <td>38.895268</td>\n",
       "      <td>-77.027600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Mt Vernon Triangle</td>\n",
       "      <td>20005</td>\n",
       "      <td>38.904461</td>\n",
       "      <td>-77.030880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>Dupont Circle</td>\n",
       "      <td>20579</td>\n",
       "      <td>38.904311</td>\n",
       "      <td>-77.044649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>Foggy Bottom</td>\n",
       "      <td>20586</td>\n",
       "      <td>38.902211</td>\n",
       "      <td>-77.047399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>L'Enfant Plaza</td>\n",
       "      <td>20594</td>\n",
       "      <td>38.884940</td>\n",
       "      <td>-77.018372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>Gateway</td>\n",
       "      <td>20022</td>\n",
       "      <td>38.918560</td>\n",
       "      <td>-76.961414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>Carver</td>\n",
       "      <td>20027</td>\n",
       "      <td>38.899903</td>\n",
       "      <td>-76.979776</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Neighborhood    Zip   Latitude  Longitude\n",
       "0                  Shaw  20001  38.907711 -77.017320\n",
       "1     H Street Corridor  20002  38.901811 -76.990970\n",
       "2          Capitol Hill  20003  38.881762 -76.994470\n",
       "3   Downtown, Chinatown  20004  38.895268 -77.027600\n",
       "4    Mt Vernon Triangle  20005  38.904461 -77.030880\n",
       "..                  ...    ...        ...        ...\n",
       "56        Dupont Circle  20579  38.904311 -77.044649\n",
       "57         Foggy Bottom  20586  38.902211 -77.047399\n",
       "58       L'Enfant Plaza  20594  38.884940 -77.018372\n",
       "59              Gateway  20022  38.918560 -76.961414\n",
       "60               Carver  20027  38.899903 -76.979776\n",
       "\n",
       "[61 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('dc_neighborhoods.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Coordinates DC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The geograpical coordinate of Washington, DC are 38.8949855, -77.0365708.\n"
     ]
    }
   ],
   "source": [
    "address = 'Washington, DC'\n",
    "\n",
    "geolocator = Nominatim(user_agent=\"DC\")\n",
    "location = geolocator.geocode(address)\n",
    "latitude_dc = location.latitude\n",
    "longitude_dc = location.longitude\n",
    "print('The geograpical coordinate of Washington, DC are {}, {}.'.format(latitude_dc, longitude_dc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Map of DC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_fc38059c23ba40398fdf0e022a6d65aa);
        
    
        var popup_9519b4a883c948c5b54d5b603c4642a5 = L.popup({"maxWidth": "100%"});

        
            var html_434fec38260c4364a8eaad611d15d68d = $(`<div id="html_434fec38260c4364a8eaad611d15d68d" style="width: 100.0%; height: 100.0%;">Sursum Corda</div>`)[0];
            popup_9519b4a883c948c5b54d5b603c4642a5.setContent(html_434fec38260c4364a8eaad611d15d68d);
        

        circle_marker_bd1076c9e126474ebb55eb054f2bad93.bindPopup(popup_9519b4a883c948c5b54d5b603c4642a5)
        ;

        
    
    
            var circle_marker_84053c406fc94cf58c6f6a80e037f311 = L.circleMarker(
                [38.887405, -77.004663],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_fc38059c23ba40398fdf0e022a6d65aa);
        
    
        var popup_58d062cac3b44526afe34ec25b708b5a = L.popup({"maxWidth": "100%"});

        
            var html_0efe7118b9474f78a892a5730aa4d826 = $(`<div id="html_0efe7118b9474f78a892a5730aa4d826" style="width: 100.0%; height: 100.0%;">Capitol Hill</div>`)[0];
            popup_58d062cac3b44526afe34ec25b708b5a.setContent(html_0efe7118b9474f78a892a5730aa4d826);
        

        circle_marker_84053c406fc94cf58c6f6a80e037f311.bindPopup(popup_58d062cac3b44526afe34ec25b708b5a)
        ;

        
    
    
            var circle_marker_0ea3acaba1e3447b8a30469bb0b88df6 = L.circleMarker(
                [38.940761, -77.028348],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_fc38059c23ba40398fdf0e022a6d65aa);
        
    
        var popup_bddcbd83a0b24e90b9e9838d025498b5 = L.popup({"maxWidth": "100%"});

        
            var html_b684b8d112f04d86947d5488a9915088 = $(`<div id="html_b684b8d112f04d86947d5488a9915088" style="width: 100.0%; height: 100.0%;">Petworth</div>`)[0];
            popup_bddcbd83a0b24e90b9e9838d025498b5.setContent(html_b684b8d112f04d86947d5488a9915088);
        

        circle_marker_0ea3acaba1e3447b8a30469bb0b88df6.bindPopup(popup_bddcbd83a0b24e90b9e9838d025498b5)
        ;

        
    
    
            var circle_marker_71bfbba340db4e278da0ed07d5aaa228 = L.circleMarker(
                [38.890976, -77.02109200000001],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_fc38059c23ba40398fdf0e022a6d65aa);
        
    
        var popup_7c73c5a3507c4bebbf9331e5901addf8 = L.popup({"maxWidth": "100%"});

        
            var html_062f0ceed95c4b3eac21020b4329d682 = $(`<div id="html_062f0ceed95c4b3eac21020b4329d682" style="width: 100.0%; height: 100.0%;">National Mall</div>`)[0];
            popup_7c73c5a3507c4bebbf9331e5901addf8.setContent(html_062f0ceed95c4b3eac21020b4329d682);
        

        circle_marker_71bfbba340db4e278da0ed07d5aaa228.bindPopup(popup_7c73c5a3507c4bebbf9331e5901addf8)
        ;

        
    
    
            var circle_marker_d89c064107d44c90b9b72bf357a0b5be = L.circleMarker(
                [38.887333, -77.02311800000001],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_fc38059c23ba40398fdf0e022a6d65aa);
        
    
        var popup_85d5662b908b44db9c813f8a46fec728 = L.popup({"maxWidth": "100%"});

        
            var html_36a208d37fdf4bf5ba40a75d43530d5a = $(`<div id="html_36a208d37fdf4bf5ba40a75d43530d5a" style="width: 100.0%; height: 100.0%;">National Mall</div>`)[0];
            popup_85d5662b908b44db9c813f8a46fec728.setContent(html_36a208d37fdf4bf5ba40a75d43530d5a);
        

        circle_marker_d89c064107d44c90b9b72bf357a0b5be.bindPopup(popup_85d5662b908b44db9c813f8a46fec728)
        ;

        
    
    
            var circle_marker_6dfa0c7d1fb5401abb088b9a30871680 = L.circleMarker(
                [38.897073999999996, -77.05535],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_fc38059c23ba40398fdf0e022a6d65aa);
        
    
        var popup_88fc0fbd4012448b87cf16f99b570ce5 = L.popup({"maxWidth": "100%"});

        
            var html_458581f371a4433e9292169df3fa4f05 = $(`<div id="html_458581f371a4433e9292169df3fa4f05" style="width: 100.0%; height: 100.0%;">Foggy Bottom</div>`)[0];
            popup_88fc0fbd4012448b87cf16f99b570ce5.setContent(html_458581f371a4433e9292169df3fa4f05);
        

        circle_marker_6dfa0c7d1fb5401abb088b9a30871680.bindPopup(popup_88fc0fbd4012448b87cf16f99b570ce5)
        ;

        
    
    
            var circle_marker_cbba87620c5745879f044fe6a628d588 = L.circleMarker(
                [38.893662, -77.02359200000001],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_fc38059c23ba40398fdf0e022a6d65aa);
        
    
        var popup_1bc52d550875455a85b439da6a7361f7 = L.popup({"maxWidth": "100%"});

        
            var html_a2336e5ad5b54366ac29f7b81ce6b7c0 = $(`<div id="html_a2336e5ad5b54366ac29f7b81ce6b7c0" style="width: 100.0%; height: 100.0%;">Chinatown</div>`)[0];
            popup_1bc52d550875455a85b439da6a7361f7.setContent(html_a2336e5ad5b54366ac29f7b81ce6b7c0);
        

        circle_marker_cbba87620c5745879f044fe6a628d588.bindPopup(popup_1bc52d550875455a85b439da6a7361f7)
        ;

        
    
    
            var circle_marker_050a16486e0e4c19be4d9d217dd4028f = L.circleMarker(
                [38.904311, -77.04464899999999],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_fc38059c23ba40398fdf0e022a6d65aa);
        
    
        var popup_cce9ab57e25c409da71541f3d50d559f = L.popup({"maxWidth": "100%"});

        
            var html_16ecbab9c0224534879c69b50de82fa2 = $(`<div id="html_16ecbab9c0224534879c69b50de82fa2" style="width: 100.0%; height: 100.0%;">Dupont Circle</div>`)[0];
            popup_cce9ab57e25c409da71541f3d50d559f.setContent(html_16ecbab9c0224534879c69b50de82fa2);
        

        circle_marker_050a16486e0e4c19be4d9d217dd4028f.bindPopup(popup_cce9ab57e25c409da71541f3d50d559f)
        ;

        
    
    
            var circle_marker_4a12a66728b045e8b27580368c3eb527 = L.circleMarker(
                [38.902211, -77.047399],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_fc38059c23ba40398fdf0e022a6d65aa);
        
    
        var popup_c8f7801323fe4c15980dfac051190718 = L.popup({"maxWidth": "100%"});

        
            var html_7da617a573b74653b530eabf4b1b9a65 = $(`<div id="html_7da617a573b74653b530eabf4b1b9a65" style="width: 100.0%; height: 100.0%;">Foggy Bottom</div>`)[0];
            popup_c8f7801323fe4c15980dfac051190718.setContent(html_7da617a573b74653b530eabf4b1b9a65);
        

        circle_marker_4a12a66728b045e8b27580368c3eb527.bindPopup(popup_c8f7801323fe4c15980dfac051190718)
        ;

        
    
    
            var circle_marker_6f2212f9ff21473da08063b5e13f4835 = L.circleMarker(
                [38.88494, -77.018372],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_fc38059c23ba40398fdf0e022a6d65aa);
        
    
        var popup_9d2ee0ad1b384eab91b1a48ba7308425 = L.popup({"maxWidth": "100%"});

        
            var html_07784b5909604a2089d17cb76579c141 = $(`<div id="html_07784b5909604a2089d17cb76579c141" style="width: 100.0%; height: 100.0%;">L&#39;Enfant Plaza</div>`)[0];
            popup_9d2ee0ad1b384eab91b1a48ba7308425.setContent(html_07784b5909604a2089d17cb76579c141);
        

        circle_marker_6f2212f9ff21473da08063b5e13f4835.bindPopup(popup_9d2ee0ad1b384eab91b1a48ba7308425)
        ;

        
    
    
            var circle_marker_5d1b3b3d423340ba99272acf9b71e1db = L.circleMarker(
                [38.91856, -76.961414],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_fc38059c23ba40398fdf0e022a6d65aa);
        
    
        var popup_e8fb45d9986f41f3b67de2d8776f9599 = L.popup({"maxWidth": "100%"});

        
            var html_5d55f037756f4b1fba6697b02940da11 = $(`<div id="html_5d55f037756f4b1fba6697b02940da11" style="width: 100.0%; height: 100.0%;">Gateway</div>`)[0];
            popup_e8fb45d9986f41f3b67de2d8776f9599.setContent(html_5d55f037756f4b1fba6697b02940da11);
        

        circle_marker_5d1b3b3d423340ba99272acf9b71e1db.bindPopup(popup_e8fb45d9986f41f3b67de2d8776f9599)
        ;

        
    
    
            var circle_marker_d11b3d8a50e04c11bdd89c054a94bb86 = L.circleMarker(
                [38.899903, -76.979776],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_fc38059c23ba40398fdf0e022a6d65aa);
        
    
        var popup_e90544306c144383879dc6acdcd6eb85 = L.popup({"maxWidth": "100%"});

        
            var html_13f48642b814487d9dcf075f8bff813f = $(`<div id="html_13f48642b814487d9dcf075f8bff813f" style="width: 100.0%; height: 100.0%;">Carver</div>`)[0];
            popup_e90544306c144383879dc6acdcd6eb85.setContent(html_13f48642b814487d9dcf075f8bff813f);
        

        circle_marker_d11b3d8a50e04c11bdd89c054a94bb86.bindPopup(popup_e90544306c144383879dc6acdcd6eb85)
        ;

        
    
</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7fee2f21a8d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_dc = folium.Map(location=[latitude_dc, longitude_dc], zoom_start=10)\n",
    "\n",
    "# add markers to map\n",
    "for lat, lng, Neighbourhood in zip(df['Latitude'], df['Longitude'], df['Neighborhood']):\n",
    "    label = '{}'.format(Neighbourhood)\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color='blue',\n",
    "        fill=True,\n",
    "        fill_color='#3186cc',\n",
    "        fill_opacity=0.7,\n",
    "        parse_html=False).add_to(map_dc)  \n",
    "    \n",
    "map_dc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Foursquare Credentials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLIENT_ID = 'SRLY14ZNRWBDPI4MVMO0NDUY5ANDISOI3X3441XJQYDKGFNC'\n",
    "CLIENT_SECRET = 'JSH2S0P5VZSXZCDQK3S0T5NB3501DI3IZWSXHLV33ZNPX41G'\n",
    "VERSION = '20180604'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Neighborhood Coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Latitude and longitude values of Shaw are 38.907711, -77.01732.\n"
     ]
    }
   ],
   "source": [
    "neighborhood_latitude = df.loc[0, 'Latitude'] # neighborhood latitude value\n",
    "neighborhood_longitude = df.loc[0, 'Longitude'] # neighborhood longitude value\n",
    "\n",
    "neighborhood_name = df.loc[0, 'Neighborhood'] # neighborhood name\n",
    "\n",
    "print('Latitude and longitude values of {} are {}, {}.'.format(neighborhood_name, \n",
    "                                                               neighborhood_latitude, \n",
    "                                                               neighborhood_longitude))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create and Send GET Request"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://api.foursquare.com/v2/venues/explore?&client_id=SRLY14ZNRWBDPI4MVMO0NDUY5ANDISOI3X3441XJQYDKGFNC&client_secret=JSH2S0P5VZSXZCDQK3S0T5NB3501DI3IZWSXHLV33ZNPX41G&v=20180604&ll=38.907711,-77.01732&radius=500&limit=100'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LIMIT = 100\n",
    "radius = 500\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",
    "    neighborhood_latitude, \n",
    "    neighborhood_longitude, \n",
    "    radius, \n",
    "    LIMIT)\n",
    "url"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore DC Neighborhoods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getNearbyVenues(names, latitudes, longitudes, radius=500):\n",
    "    \n",
    "    venues_list=[]\n",
    "    for name, lat, lng in zip(names, latitudes, longitudes):\n",
    "        print(name)\n",
    "            \n",
    "        # create the API request URL\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",
    "        # make the GET request\n",
    "        results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
    "        \n",
    "        # return only relevant information for each nearby venue\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 = ['Neighborhood', \n",
    "                  'Neighborhood Latitude', \n",
    "                  'Neighborhood Longitude', \n",
    "                  'Venue', \n",
    "                  'Venue Latitude', \n",
    "                  'Venue Longitude', \n",
    "                  'Venue Category']\n",
    "    \n",
    "    return(nearby_venues)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Venue Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shaw\n",
      "H Street Corridor\n",
      "Capitol Hill\n",
      "Downtown, Chinatown\n",
      "Mt Vernon Triangle\n",
      "Georgetown\n",
      "Woodley Park, Forest Hills\n",
      "Adams Morgan\n",
      "Park View, Mount Pleasant\n",
      "Brightwood\n",
      "Shepherd Park\n",
      "East End\n",
      "Chevy Chase\n",
      "Cathedral Heights\n",
      "Brookland\n",
      "Langdon\n",
      "Benning Heights\n",
      "Southeast Washington\n",
      "Southwest Waterfront\n",
      "Congress Heights\n",
      "Dupont Circle\n",
      "Foggy Bottom\n",
      "Foggy Bottom\n",
      "Mt Vernon Square\n",
      "Pleasant Plains\n",
      "Dupont Circle\n",
      "University Heights\n",
      "L'Enfant Plaza\n",
      "Capitol Hill\n",
      "Southwest Washington\n",
      "National Mall\n",
      "National Mall\n",
      "McMillan\n",
      "Southwest Waterfront\n",
      "Joint Base Anacostia-Bolling\n",
      "Joint Base Anacostia-Bolling\n",
      "Congress Heights\n",
      "Blue Plains\n",
      "Navy Yard\n",
      "East End\n",
      "L'Enfant Plaza\n",
      "Georgetown\n",
      "Kalorama Heights\n",
      "Columbia Heights\n",
      "L'Enfant Plaza\n",
      "Southwest Washington\n",
      "Foggy Bottom\n",
      "Foggy Bottom\n",
      "East End\n",
      "Sursum Corda\n",
      "Capitol Hill\n",
      "Petworth\n",
      "National Mall\n",
      "National Mall\n",
      "Foggy Bottom\n",
      "Chinatown\n",
      "Dupont Circle\n",
      "Foggy Bottom\n",
      "L'Enfant Plaza\n",
      "Gateway\n",
      "Carver\n"
     ]
    }
   ],
   "source": [
    "dc_venues = getNearbyVenues(names=df['Neighborhood'],\n",
    "                                   latitudes=df['Latitude'],\n",
    "                                   longitudes=df['Longitude']\n",
    "                                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Neighborhood Latitude</th>\n",
       "      <th>Neighborhood 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",
       "      <td>0</td>\n",
       "      <td>Shaw</td>\n",
       "      <td>38.907711</td>\n",
       "      <td>-77.01732</td>\n",
       "      <td>Saint's Paradise Cafeteria</td>\n",
       "      <td>38.905810</td>\n",
       "      <td>-77.020216</td>\n",
       "      <td>Southern / Soul Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Shaw</td>\n",
       "      <td>38.907711</td>\n",
       "      <td>-77.01732</td>\n",
       "      <td>Beau Thai</td>\n",
       "      <td>38.909882</td>\n",
       "      <td>-77.022019</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Shaw</td>\n",
       "      <td>38.907711</td>\n",
       "      <td>-77.01732</td>\n",
       "      <td>Compass Coffee</td>\n",
       "      <td>38.910569</td>\n",
       "      <td>-77.021703</td>\n",
       "      <td>Coffee Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Shaw</td>\n",
       "      <td>38.907711</td>\n",
       "      <td>-77.01732</td>\n",
       "      <td>Giant Food</td>\n",
       "      <td>38.908984</td>\n",
       "      <td>-77.022755</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Shaw</td>\n",
       "      <td>38.907711</td>\n",
       "      <td>-77.01732</td>\n",
       "      <td>Ivy and Coney</td>\n",
       "      <td>38.910646</td>\n",
       "      <td>-77.021692</td>\n",
       "      <td>Dive Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Shaw</td>\n",
       "      <td>38.907711</td>\n",
       "      <td>-77.01732</td>\n",
       "      <td>Unleashed by Petco</td>\n",
       "      <td>38.910073</td>\n",
       "      <td>-77.022046</td>\n",
       "      <td>Pet Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Shaw</td>\n",
       "      <td>38.907711</td>\n",
       "      <td>-77.01732</td>\n",
       "      <td>Bundy Dog Park</td>\n",
       "      <td>38.909506</td>\n",
       "      <td>-77.017807</td>\n",
       "      <td>Dog Run</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Shaw</td>\n",
       "      <td>38.907711</td>\n",
       "      <td>-77.01732</td>\n",
       "      <td>Primal Fitness</td>\n",
       "      <td>38.905880</td>\n",
       "      <td>-77.014334</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Shaw</td>\n",
       "      <td>38.907711</td>\n",
       "      <td>-77.01732</td>\n",
       "      <td>Popeyes Louisiana Kitchen</td>\n",
       "      <td>38.907921</td>\n",
       "      <td>-77.021408</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Shaw</td>\n",
       "      <td>38.907711</td>\n",
       "      <td>-77.01732</td>\n",
       "      <td>Bread for the City</td>\n",
       "      <td>38.910201</td>\n",
       "      <td>-77.021844</td>\n",
       "      <td>Building</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Neighborhood  Neighborhood Latitude  Neighborhood Longitude  \\\n",
       "0         Shaw              38.907711               -77.01732   \n",
       "1         Shaw              38.907711               -77.01732   \n",
       "2         Shaw              38.907711               -77.01732   \n",
       "3         Shaw              38.907711               -77.01732   \n",
       "4         Shaw              38.907711               -77.01732   \n",
       "5         Shaw              38.907711               -77.01732   \n",
       "6         Shaw              38.907711               -77.01732   \n",
       "7         Shaw              38.907711               -77.01732   \n",
       "8         Shaw              38.907711               -77.01732   \n",
       "9         Shaw              38.907711               -77.01732   \n",
       "\n",
       "                        Venue  Venue Latitude  Venue Longitude  \\\n",
       "0  Saint's Paradise Cafeteria       38.905810       -77.020216   \n",
       "1                   Beau Thai       38.909882       -77.022019   \n",
       "2              Compass Coffee       38.910569       -77.021703   \n",
       "3                  Giant Food       38.908984       -77.022755   \n",
       "4               Ivy and Coney       38.910646       -77.021692   \n",
       "5          Unleashed by Petco       38.910073       -77.022046   \n",
       "6              Bundy Dog Park       38.909506       -77.017807   \n",
       "7              Primal Fitness       38.905880       -77.014334   \n",
       "8   Popeyes Louisiana Kitchen       38.907921       -77.021408   \n",
       "9          Bread for the City       38.910201       -77.021844   \n",
       "\n",
       "                    Venue Category  \n",
       "0  Southern / Soul Food Restaurant  \n",
       "1                  Thai Restaurant  \n",
       "2                      Coffee Shop  \n",
       "3                    Grocery Store  \n",
       "4                         Dive Bar  \n",
       "5                        Pet Store  \n",
       "6                          Dog Run  \n",
       "7                              Gym  \n",
       "8              Fried Chicken Joint  \n",
       "9                         Building  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_venues.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2643, 7)\n",
      "There are 282 uniques categories.\n"
     ]
    }
   ],
   "source": [
    "print(dc_venues.shape)\n",
    "dc_venues.groupby('Neighborhood').count()\n",
    "print('There are {} uniques categories.'.format(len(dc_venues['Venue Category'].unique())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze Each Neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2643, 283)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_onehot = pd.get_dummies(dc_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
    "dc_onehot.insert(loc=0, column='Neighborhood', value=dc_venues['Neighborhood'] )\n",
    "dc_onehot.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Acupuncturist</th>\n",
       "      <th>African Restaurant</th>\n",
       "      <th>American Restaurant</th>\n",
       "      <th>Arepa Restaurant</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>Athletics &amp; Sports</th>\n",
       "      <th>...</th>\n",
       "      <th>Waterfront</th>\n",
       "      <th>Weight Loss Center</th>\n",
       "      <th>Whisky Bar</th>\n",
       "      <th>Wine Bar</th>\n",
       "      <th>Wine Shop</th>\n",
       "      <th>Winery</th>\n",
       "      <th>Wings Joint</th>\n",
       "      <th>Women's Store</th>\n",
       "      <th>Xinjiang Restaurant</th>\n",
       "      <th>Yoga Studio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Adams Morgan</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>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017544</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Benning Heights</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076923</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>...</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Blue Plains</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>...</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Brightwood</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.058824</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>...</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Brookland</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>...</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Capitol Hill</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.016129</td>\n",
       "      <td>0.008065</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008065</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008065</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008065</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Carver</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.031250</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>...</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>0.03125</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Cathedral Heights</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>0.076923</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Chevy Chase</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>0.200000</td>\n",
       "      <td>...</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Chinatown</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Columbia Heights</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</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>0.000000</td>\n",
       "      <td>0.037037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Congress Heights</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>...</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Downtown, Chinatown</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</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>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>Dupont Circle</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>East End</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007246</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Foggy Bottom</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.037288</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003390</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003390</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006780</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>Gateway</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>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>Georgetown</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.039604</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029703</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009901</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009901</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>H Street Corridor</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013889</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>Joint Base Anacostia-Bolling</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>...</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>Kalorama Heights</td>\n",
       "      <td>0.010638</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010638</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.021277</td>\n",
       "      <td>0.010638</td>\n",
       "      <td>0.010638</td>\n",
       "      <td>0.021277</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010638</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>0.000000</td>\n",
       "      <td>0.021277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>L'Enfant Plaza</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.022936</td>\n",
       "      <td>0.027523</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>Langdon</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>McMillan</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>...</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>Mt Vernon Square</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>Mt Vernon Triangle</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>National Mall</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003891</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.027237</td>\n",
       "      <td>0.050584</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>Navy Yard</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>...</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>0.041667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>Park View, Mount Pleasant</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.057143</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>Petworth</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>...</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>Pleasant Plains</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014085</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>...</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>Shaw</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>...</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>Shepherd Park</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>...</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>0.090909</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>Southeast Washington</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>...</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>Southwest Washington</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.032258</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>...</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>0.000000</td>\n",
       "      <td>0.032258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>Southwest Waterfront</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>...</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>Sursum Corda</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.081633</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>University Heights</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>Woodley Park, Forest Hills</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>0.027778</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39 rows × 283 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Neighborhood  Acupuncturist  African Restaurant  \\\n",
       "0                   Adams Morgan       0.000000            0.000000   \n",
       "1                Benning Heights       0.000000            0.000000   \n",
       "2                    Blue Plains       0.000000            0.000000   \n",
       "3                     Brightwood       0.000000            0.000000   \n",
       "4                      Brookland       0.000000            0.000000   \n",
       "5                   Capitol Hill       0.000000            0.000000   \n",
       "6                         Carver       0.000000            0.000000   \n",
       "7              Cathedral Heights       0.000000            0.000000   \n",
       "8                    Chevy Chase       0.000000            0.000000   \n",
       "9                      Chinatown       0.000000            0.000000   \n",
       "10              Columbia Heights       0.000000            0.000000   \n",
       "11              Congress Heights       0.000000            0.000000   \n",
       "12           Downtown, Chinatown       0.000000            0.000000   \n",
       "13                 Dupont Circle       0.000000            0.000000   \n",
       "14                      East End       0.000000            0.000000   \n",
       "15                  Foggy Bottom       0.000000            0.000000   \n",
       "16                       Gateway       0.000000            0.000000   \n",
       "17                    Georgetown       0.000000            0.000000   \n",
       "18             H Street Corridor       0.000000            0.000000   \n",
       "19  Joint Base Anacostia-Bolling       0.000000            0.000000   \n",
       "20              Kalorama Heights       0.010638            0.000000   \n",
       "21                L'Enfant Plaza       0.000000            0.000000   \n",
       "22                       Langdon       0.000000            0.000000   \n",
       "23                      McMillan       0.000000            0.000000   \n",
       "24              Mt Vernon Square       0.000000            0.000000   \n",
       "25            Mt Vernon Triangle       0.000000            0.000000   \n",
       "26                 National Mall       0.000000            0.000000   \n",
       "27                     Navy Yard       0.000000            0.000000   \n",
       "28     Park View, Mount Pleasant       0.000000            0.000000   \n",
       "29                      Petworth       0.000000            0.000000   \n",
       "30               Pleasant Plains       0.000000            0.014085   \n",
       "31                          Shaw       0.000000            0.000000   \n",
       "32                 Shepherd Park       0.000000            0.000000   \n",
       "33          Southeast Washington       0.000000            0.000000   \n",
       "34          Southwest Washington       0.000000            0.000000   \n",
       "35          Southwest Waterfront       0.000000            0.000000   \n",
       "36                  Sursum Corda       0.000000            0.000000   \n",
       "37            University Heights       0.000000            0.000000   \n",
       "38    Woodley Park, Forest Hills       0.000000            0.000000   \n",
       "\n",
       "    American Restaurant  Arepa Restaurant  Art Gallery  Art Museum  \\\n",
       "0              0.000000              0.00     0.000000    0.000000   \n",
       "1              0.076923              0.00     0.000000    0.000000   \n",
       "2              0.000000              0.00     0.000000    0.000000   \n",
       "3              0.058824              0.00     0.000000    0.000000   \n",
       "4              0.000000              0.00     0.000000    0.000000   \n",
       "5              0.032258              0.00     0.016129    0.008065   \n",
       "6              0.031250              0.00     0.000000    0.000000   \n",
       "7              0.000000              0.00     0.000000    0.076923   \n",
       "8              0.000000              0.00     0.000000    0.000000   \n",
       "9              0.030000              0.00     0.000000    0.050000   \n",
       "10             0.000000              0.00     0.037037    0.037037   \n",
       "11             0.000000              0.00     0.000000    0.000000   \n",
       "12             0.040000              0.00     0.000000    0.030000   \n",
       "13             0.030000              0.00     0.000000    0.000000   \n",
       "14             0.007246              0.00     0.000000    0.014493   \n",
       "15             0.037288              0.00     0.000000    0.000000   \n",
       "16             0.000000              0.00     0.000000    0.000000   \n",
       "17             0.039604              0.00     0.000000    0.000000   \n",
       "18             0.013889              0.00     0.055556    0.000000   \n",
       "19             0.000000              0.00     0.000000    0.000000   \n",
       "20             0.010638              0.00     0.021277    0.010638   \n",
       "21             0.000000              0.00     0.022936    0.027523   \n",
       "22             0.038462              0.00     0.038462    0.000000   \n",
       "23             0.000000              0.00     0.000000    0.000000   \n",
       "24             0.070000              0.00     0.010000    0.020000   \n",
       "25             0.020000              0.01     0.010000    0.000000   \n",
       "26             0.003891              0.00     0.027237    0.050584   \n",
       "27             0.000000              0.00     0.000000    0.000000   \n",
       "28             0.028571              0.00     0.000000    0.000000   \n",
       "29             0.000000              0.00     0.000000    0.000000   \n",
       "30             0.000000              0.00     0.000000    0.000000   \n",
       "31             0.000000              0.00     0.000000    0.000000   \n",
       "32             0.000000              0.00     0.000000    0.000000   \n",
       "33             0.000000              0.00     0.000000    0.000000   \n",
       "34             0.032258              0.00     0.000000    0.000000   \n",
       "35             0.000000              0.00     0.000000    0.000000   \n",
       "36             0.081633              0.00     0.000000    0.000000   \n",
       "37             0.026316              0.00     0.052632    0.000000   \n",
       "38             0.000000              0.00     0.000000    0.000000   \n",
       "\n",
       "    Arts & Crafts Store  Asian Restaurant  Athletics & Sports  ...  \\\n",
       "0              0.000000          0.000000            0.000000  ...   \n",
       "1              0.000000          0.000000            0.000000  ...   \n",
       "2              0.000000          0.000000            0.000000  ...   \n",
       "3              0.000000          0.000000            0.000000  ...   \n",
       "4              0.000000          0.000000            0.000000  ...   \n",
       "5              0.000000          0.008065            0.000000  ...   \n",
       "6              0.000000          0.000000            0.000000  ...   \n",
       "7              0.000000          0.000000            0.000000  ...   \n",
       "8              0.000000          0.000000            0.200000  ...   \n",
       "9              0.000000          0.000000            0.000000  ...   \n",
       "10             0.000000          0.074074            0.000000  ...   \n",
       "11             0.000000          0.000000            0.000000  ...   \n",
       "12             0.000000          0.010000            0.000000  ...   \n",
       "13             0.010000          0.000000            0.000000  ...   \n",
       "14             0.000000          0.000000            0.000000  ...   \n",
       "15             0.003390          0.000000            0.000000  ...   \n",
       "16             0.000000          0.000000            0.000000  ...   \n",
       "17             0.000000          0.029703            0.000000  ...   \n",
       "18             0.000000          0.027778            0.000000  ...   \n",
       "19             0.000000          0.000000            0.000000  ...   \n",
       "20             0.010638          0.021277            0.000000  ...   \n",
       "21             0.000000          0.000000            0.000000  ...   \n",
       "22             0.000000          0.000000            0.000000  ...   \n",
       "23             0.000000          0.000000            0.000000  ...   \n",
       "24             0.000000          0.020000            0.000000  ...   \n",
       "25             0.020000          0.000000            0.000000  ...   \n",
       "26             0.000000          0.000000            0.000000  ...   \n",
       "27             0.000000          0.000000            0.000000  ...   \n",
       "28             0.000000          0.057143            0.028571  ...   \n",
       "29             0.000000          0.000000            0.000000  ...   \n",
       "30             0.000000          0.000000            0.000000  ...   \n",
       "31             0.000000          0.000000            0.000000  ...   \n",
       "32             0.000000          0.000000            0.000000  ...   \n",
       "33             0.000000          0.000000            0.000000  ...   \n",
       "34             0.000000          0.000000            0.000000  ...   \n",
       "35             0.000000          0.000000            0.000000  ...   \n",
       "36             0.000000          0.020408            0.000000  ...   \n",
       "37             0.000000          0.000000            0.000000  ...   \n",
       "38             0.027778          0.027778            0.000000  ...   \n",
       "\n",
       "    Waterfront  Weight Loss Center  Whisky Bar  Wine Bar  Wine Shop    Winery  \\\n",
       "0     0.000000            0.000000    0.017544  0.000000   0.000000  0.000000   \n",
       "1     0.000000            0.000000    0.000000  0.000000   0.000000  0.000000   \n",
       "2     0.000000            0.000000    0.000000  0.000000   0.000000  0.000000   \n",
       "3     0.000000            0.000000    0.000000  0.000000   0.000000  0.000000   \n",
       "4     0.000000            0.000000    0.000000  0.000000   0.000000  0.000000   \n",
       "5     0.000000            0.000000    0.008065  0.000000   0.008065  0.000000   \n",
       "6     0.000000            0.000000    0.000000  0.000000   0.000000  0.000000   \n",
       "7     0.000000            0.000000    0.000000  0.000000   0.000000  0.000000   \n",
       "8     0.000000            0.000000    0.000000  0.000000   0.000000  0.000000   \n",
       "9     0.000000            0.000000    0.000000  0.000000   0.000000  0.000000   \n",
       "10    0.000000            0.000000    0.000000  0.000000   0.000000  0.000000   \n",
       "11    0.000000            0.000000    0.000000  0.000000   0.000000  0.000000   \n",
       "12    0.000000            0.000000    0.010000  0.000000   0.000000  0.000000   \n",
       "13    0.000000            0.003333    0.000000  0.003333   0.000000  0.000000   \n",
       "14    0.000000            0.000000    0.000000  0.000000   0.000000  0.000000   \n",
       "15    0.000000            0.000000    0.003390  0.000000   0.006780  0.000000   \n",
       "16    0.000000            0.000000    0.000000  0.000000   0.166667  0.000000   \n",
       "17    0.009901            0.000000    0.000000  0.009901   0.000000  0.000000   \n",
       "18    0.000000            0.000000    0.000000  0.027778   0.000000  0.000000   \n",
       "19    0.000000            0.000000    0.000000  0.000000   0.000000  0.000000   \n",
       "20    0.000000            0.000000    0.010638  0.000000   0.000000  0.000000   \n",
       "21    0.000000            0.000000    0.000000  0.000000   0.000000  0.000000   \n",
       "22    0.000000            0.000000    0.000000  0.000000   0.000000  0.000000   \n",
       "23    0.000000            0.000000    0.000000  0.000000   0.000000  0.000000   \n",
       "24    0.000000            0.000000    0.000000  0.000000   0.010000  0.000000   \n",
       "25    0.000000            0.000000    0.000000  0.000000   0.000000  0.000000   \n",
       "26    0.000000            0.000000    0.000000  0.000000   0.000000  0.000000   \n",
       "27    0.000000            0.000000    0.000000  0.000000   0.000000  0.041667   \n",
       "28    0.000000            0.000000    0.000000  0.028571   0.000000  0.000000   \n",
       "29    0.000000            0.000000    0.000000  0.000000   0.000000  0.000000   \n",
       "30    0.000000            0.000000    0.000000  0.000000   0.000000  0.000000   \n",
       "31    0.000000            0.000000    0.000000  0.000000   0.000000  0.000000   \n",
       "32    0.000000            0.000000    0.000000  0.000000   0.000000  0.000000   \n",
       "33    0.000000            0.000000    0.000000  0.000000   0.000000  0.000000   \n",
       "34    0.000000            0.000000    0.000000  0.000000   0.000000  0.000000   \n",
       "35    0.000000            0.000000    0.000000  0.000000   0.000000  0.000000   \n",
       "36    0.000000            0.000000    0.000000  0.000000   0.020408  0.000000   \n",
       "37    0.000000            0.000000    0.000000  0.000000   0.026316  0.000000   \n",
       "38    0.000000            0.000000    0.000000  0.000000   0.027778  0.000000   \n",
       "\n",
       "    Wings Joint  Women's Store  Xinjiang Restaurant  Yoga Studio  \n",
       "0      0.000000        0.00000             0.000000     0.000000  \n",
       "1      0.000000        0.00000             0.000000     0.000000  \n",
       "2      0.000000        0.00000             0.000000     0.000000  \n",
       "3      0.000000        0.00000             0.000000     0.000000  \n",
       "4      0.000000        0.00000             0.000000     0.000000  \n",
       "5      0.000000        0.00000             0.000000     0.008065  \n",
       "6      0.000000        0.03125             0.000000     0.000000  \n",
       "7      0.000000        0.00000             0.000000     0.000000  \n",
       "8      0.000000        0.00000             0.000000     0.000000  \n",
       "9      0.000000        0.00000             0.000000     0.000000  \n",
       "10     0.000000        0.00000             0.000000     0.037037  \n",
       "11     0.000000        0.00000             0.000000     0.000000  \n",
       "12     0.000000        0.00000             0.000000     0.000000  \n",
       "13     0.000000        0.00000             0.000000     0.026667  \n",
       "14     0.000000        0.00000             0.000000     0.000000  \n",
       "15     0.000000        0.00000             0.000000     0.010169  \n",
       "16     0.000000        0.00000             0.000000     0.000000  \n",
       "17     0.000000        0.00000             0.000000     0.009901  \n",
       "18     0.000000        0.00000             0.000000     0.000000  \n",
       "19     0.000000        0.00000             0.000000     0.000000  \n",
       "20     0.000000        0.00000             0.000000     0.021277  \n",
       "21     0.000000        0.00000             0.000000     0.000000  \n",
       "22     0.000000        0.00000             0.000000     0.000000  \n",
       "23     0.000000        0.00000             0.000000     0.000000  \n",
       "24     0.000000        0.00000             0.000000     0.010000  \n",
       "25     0.000000        0.00000             0.000000     0.000000  \n",
       "26     0.000000        0.00000             0.000000     0.000000  \n",
       "27     0.000000        0.00000             0.000000     0.000000  \n",
       "28     0.000000        0.00000             0.000000     0.028571  \n",
       "29     0.000000        0.00000             0.000000     0.000000  \n",
       "30     0.000000        0.00000             0.000000     0.000000  \n",
       "31     0.000000        0.00000             0.000000     0.000000  \n",
       "32     0.090909        0.00000             0.000000     0.000000  \n",
       "33     0.000000        0.00000             0.000000     0.000000  \n",
       "34     0.000000        0.00000             0.000000     0.032258  \n",
       "35     0.000000        0.00000             0.000000     0.000000  \n",
       "36     0.000000        0.00000             0.000000     0.000000  \n",
       "37     0.000000        0.00000             0.000000     0.000000  \n",
       "38     0.000000        0.00000             0.027778     0.000000  \n",
       "\n",
       "[39 rows x 283 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_grouped = dc_onehot.groupby('Neighborhood').mean().reset_index()\n",
    "dc_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Adams Morgan----\n",
      "                       venue  freq\n",
      "0  Latin American Restaurant  0.05\n",
      "1                     Bakery  0.04\n",
      "2                        Bar  0.04\n",
      "3                      Diner  0.04\n",
      "4                Pizza Place  0.04\n",
      "\n",
      "\n",
      "----Benning Heights----\n",
      "                 venue  freq\n",
      "0    Convenience Store  0.08\n",
      "1       Breakfast Spot  0.08\n",
      "2  American Restaurant  0.08\n",
      "3                 Park  0.08\n",
      "4              Parking  0.08\n",
      "\n",
      "\n",
      "----Blue Plains----\n",
      "          venue  freq\n",
      "0    Food Court  0.14\n",
      "1   Post Office  0.14\n",
      "2           Gym  0.14\n",
      "3  Intersection  0.14\n",
      "4    Food Truck  0.14\n",
      "\n",
      "\n",
      "----Brightwood----\n",
      "                    venue  freq\n",
      "0             Pizza Place  0.12\n",
      "1      Chinese Restaurant  0.06\n",
      "2                    Café  0.06\n",
      "3  Thrift / Vintage Store  0.06\n",
      "4       Mobile Phone Shop  0.06\n",
      "\n",
      "\n",
      "----Brookland----\n",
      "                      venue  freq\n",
      "0                 Gastropub  0.05\n",
      "1      Ethiopian Restaurant  0.05\n",
      "2                  Pharmacy  0.05\n",
      "3            Boarding House  0.05\n",
      "4  Bike Rental / Bike Share  0.05\n",
      "\n",
      "\n",
      "----Capitol Hill----\n",
      "                 venue  freq\n",
      "0        Historic Site  0.04\n",
      "1  Monument / Landmark  0.03\n",
      "2                  Bar  0.03\n",
      "3                 Café  0.03\n",
      "4           Food Truck  0.03\n",
      "\n",
      "\n",
      "----Carver----\n",
      "                  venue  freq\n",
      "0  Fast Food Restaurant  0.09\n",
      "1                  Park  0.06\n",
      "2    Light Rail Station  0.06\n",
      "3           Pizza Place  0.06\n",
      "4          Liquor Store  0.06\n",
      "\n",
      "\n",
      "----Cathedral Heights----\n",
      "               venue  freq\n",
      "0        Coffee Shop  0.15\n",
      "1     Sandwich Place  0.08\n",
      "2       Burger Joint  0.08\n",
      "3  College Cafeteria  0.08\n",
      "4     Shipping Store  0.08\n",
      "\n",
      "\n",
      "----Chevy Chase----\n",
      "                  venue  freq\n",
      "0  Fast Food Restaurant   0.2\n",
      "1     Recreation Center   0.2\n",
      "2    Athletics & Sports   0.2\n",
      "3        Farmers Market   0.2\n",
      "4         Grocery Store   0.2\n",
      "\n",
      "\n",
      "----Chinatown----\n",
      "                 venue  freq\n",
      "0       Science Museum  0.12\n",
      "1           Art Museum  0.05\n",
      "2       History Museum  0.05\n",
      "3              Theater  0.04\n",
      "4  American Restaurant  0.03\n",
      "\n",
      "\n",
      "----Columbia Heights----\n",
      "                  venue  freq\n",
      "0  Gym / Fitness Center  0.07\n",
      "1                  Park  0.07\n",
      "2      Asian Restaurant  0.07\n",
      "3           Yoga Studio  0.04\n",
      "4           Coffee Shop  0.04\n",
      "\n",
      "\n",
      "----Congress Heights----\n",
      "                     venue  freq\n",
      "0  Health & Beauty Service  0.25\n",
      "1           Baseball Field  0.25\n",
      "2                     Park  0.25\n",
      "3                      Gym  0.25\n",
      "4       Persian Restaurant  0.00\n",
      "\n",
      "\n",
      "----Downtown, Chinatown----\n",
      "                 venue  freq\n",
      "0                Hotel  0.06\n",
      "1       Science Museum  0.04\n",
      "2  American Restaurant  0.04\n",
      "3          Coffee Shop  0.04\n",
      "4              Exhibit  0.04\n",
      "\n",
      "\n",
      "----Dupont Circle----\n",
      "                 venue  freq\n",
      "0          Coffee Shop  0.07\n",
      "1       Sandwich Place  0.07\n",
      "2                Hotel  0.05\n",
      "3          Yoga Studio  0.03\n",
      "4  American Restaurant  0.03\n",
      "\n",
      "\n",
      "----East End----\n",
      "                 venue  freq\n",
      "0           Food Truck  0.09\n",
      "1                Hotel  0.08\n",
      "2       Sandwich Place  0.07\n",
      "3  Monument / Landmark  0.07\n",
      "4                 Café  0.05\n",
      "\n",
      "\n",
      "----Foggy Bottom----\n",
      "                 venue  freq\n",
      "0                Hotel  0.07\n",
      "1       Sandwich Place  0.07\n",
      "2          Coffee Shop  0.06\n",
      "3          Pizza Place  0.05\n",
      "4  American Restaurant  0.04\n",
      "\n",
      "\n",
      "----Gateway----\n",
      "                    venue  freq\n",
      "0             Gas Station  0.17\n",
      "1            Home Service  0.17\n",
      "2  Thrift / Vintage Store  0.17\n",
      "3               Wine Shop  0.17\n",
      "4          Shipping Store  0.17\n",
      "\n",
      "\n",
      "----Georgetown----\n",
      "                 venue  freq\n",
      "0                Hotel  0.06\n",
      "1   Italian Restaurant  0.06\n",
      "2  American Restaurant  0.04\n",
      "3        Grocery Store  0.04\n",
      "4          Coffee Shop  0.04\n",
      "\n",
      "\n",
      "----H Street Corridor----\n",
      "                 venue  freq\n",
      "0          Pizza Place  0.06\n",
      "1          Art Gallery  0.06\n",
      "2                  Bar  0.06\n",
      "3    Convenience Store  0.04\n",
      "4  Fried Chicken Joint  0.03\n",
      "\n",
      "\n",
      "----Joint Base Anacostia-Bolling----\n",
      "            venue  freq\n",
      "0      Playground  0.33\n",
      "1            Park  0.17\n",
      "2  Baseball Field  0.17\n",
      "3     Post Office  0.17\n",
      "4      Donut Shop  0.17\n",
      "\n",
      "\n",
      "----Kalorama Heights----\n",
      "                venue  freq\n",
      "0  Italian Restaurant  0.03\n",
      "1      Massage Studio  0.03\n",
      "2                 Spa  0.03\n",
      "3                 Bar  0.02\n",
      "4               Hotel  0.02\n",
      "\n",
      "\n",
      "----L'Enfant Plaza----\n",
      "                venue  freq\n",
      "0      Sandwich Place  0.09\n",
      "1          Food Truck  0.09\n",
      "2         Coffee Shop  0.06\n",
      "3               Hotel  0.05\n",
      "4  Mexican Restaurant  0.04\n",
      "\n",
      "\n",
      "----Langdon----\n",
      "           venue  freq\n",
      "0           Park  0.08\n",
      "1       Bus Stop  0.04\n",
      "2        Dog Run  0.04\n",
      "3    Coffee Shop  0.04\n",
      "4  Grocery Store  0.04\n",
      "\n",
      "\n",
      "----McMillan----\n",
      "                      venue  freq\n",
      "0                 Cafeteria  0.12\n",
      "1               Coffee Shop  0.12\n",
      "2  College Basketball Court  0.06\n",
      "3               Pizza Place  0.06\n",
      "4                Food Truck  0.06\n",
      "\n",
      "\n",
      "----Mt Vernon Square----\n",
      "                     venue  freq\n",
      "0      American Restaurant  0.07\n",
      "1                    Hotel  0.05\n",
      "2         Ramen Restaurant  0.04\n",
      "3       Italian Restaurant  0.04\n",
      "4  New American Restaurant  0.03\n",
      "\n",
      "\n",
      "----Mt Vernon Triangle----\n",
      "                venue  freq\n",
      "0               Hotel  0.14\n",
      "1      Sandwich Place  0.06\n",
      "2         Coffee Shop  0.06\n",
      "3  Salon / Barbershop  0.04\n",
      "4           Hotel Bar  0.04\n",
      "\n",
      "\n",
      "----National Mall----\n",
      "            venue  freq\n",
      "0  Science Museum  0.12\n",
      "1         Exhibit  0.08\n",
      "2  History Museum  0.08\n",
      "3      Art Museum  0.05\n",
      "4      Food Truck  0.05\n",
      "\n",
      "\n",
      "----Navy Yard----\n",
      "                                      venue  freq\n",
      "0                            Sandwich Place  0.08\n",
      "1                               Salad Place  0.04\n",
      "2  Residential Building (Apartment / Condo)  0.04\n",
      "3                                Taco Place  0.04\n",
      "4           Southern / Soul Food Restaurant  0.04\n",
      "\n",
      "\n",
      "----Park View, Mount Pleasant----\n",
      "              venue  freq\n",
      "0               Bar  0.14\n",
      "1            Bakery  0.09\n",
      "2              Café  0.06\n",
      "3       Pizza Place  0.06\n",
      "4  Asian Restaurant  0.06\n",
      "\n",
      "\n",
      "----Petworth----\n",
      "                    venue  freq\n",
      "0           Grocery Store  0.10\n",
      "1             Pizza Place  0.10\n",
      "2             Coffee Shop  0.10\n",
      "3     Japanese Restaurant  0.05\n",
      "4  Thrift / Vintage Store  0.05\n",
      "\n",
      "\n",
      "----Pleasant Plains----\n",
      "            venue  freq\n",
      "0             Bar  0.10\n",
      "1     Pizza Place  0.06\n",
      "2         Gay Bar  0.04\n",
      "3             Gym  0.04\n",
      "4  Sandwich Place  0.04\n",
      "\n",
      "\n",
      "----Shaw----\n",
      "                             venue  freq\n",
      "0                    Grocery Store  0.13\n",
      "1                     Intersection  0.13\n",
      "2  Southern / Soul Food Restaurant  0.07\n",
      "3                        Pet Store  0.07\n",
      "4                         Dive Bar  0.07\n",
      "\n",
      "\n",
      "----Shepherd Park----\n",
      "                  venue  freq\n",
      "0  Fast Food Restaurant  0.18\n",
      "1     Convenience Store  0.09\n",
      "2          Liquor Store  0.09\n",
      "3           Bus Station  0.09\n",
      "4                   Gym  0.09\n",
      "\n",
      "\n",
      "----Southeast Washington----\n",
      "                 venue  freq\n",
      "0  Fried Chicken Joint   1.0\n",
      "1        Acupuncturist   0.0\n",
      "2     Pedestrian Plaza   0.0\n",
      "3             Pie Shop   0.0\n",
      "4             Pharmacy   0.0\n",
      "\n",
      "\n",
      "----Southwest Washington----\n",
      "           venue  freq\n",
      "0           Park  0.13\n",
      "1  Boat or Ferry  0.13\n",
      "2        Theater  0.10\n",
      "3     Food Truck  0.06\n",
      "4  Grocery Store  0.06\n",
      "\n",
      "\n",
      "----Southwest Waterfront----\n",
      "                venue  freq\n",
      "0             Theater  0.06\n",
      "1               Hotel  0.05\n",
      "2     Harbor / Marina  0.05\n",
      "3         Fish Market  0.05\n",
      "4  Mexican Restaurant  0.05\n",
      "\n",
      "\n",
      "----Sursum Corda----\n",
      "                 venue  freq\n",
      "0  American Restaurant  0.08\n",
      "1  Japanese Restaurant  0.04\n",
      "2          Salad Place  0.04\n",
      "3           Taco Place  0.04\n",
      "4    Indian Restaurant  0.04\n",
      "\n",
      "\n",
      "----University Heights----\n",
      "            venue  freq\n",
      "0     Pizza Place  0.08\n",
      "1  Sandwich Place  0.05\n",
      "2       Bookstore  0.05\n",
      "3     Art Gallery  0.05\n",
      "4     Coffee Shop  0.05\n",
      "\n",
      "\n",
      "----Woodley Park, Forest Hills----\n",
      "                      venue  freq\n",
      "0           Thai Restaurant  0.06\n",
      "1        Italian Restaurant  0.06\n",
      "2  Mediterranean Restaurant  0.06\n",
      "3        Mexican Restaurant  0.06\n",
      "4          Department Store  0.03\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "num_top_venues = 5\n",
    "\n",
    "for hood in dc_grouped['Neighborhood']:\n",
    "    print(\"----\"+hood+\"----\")\n",
    "    temp = dc_grouped[dc_grouped['Neighborhood'] == hood].T.reset_index()\n",
    "    temp.columns = ['venue','freq']\n",
    "    temp = temp.iloc[1:]\n",
    "    temp['freq'] = temp['freq'].astype(float)\n",
    "    temp = temp.round({'freq': 2})\n",
    "    print(temp.sort_values('freq', ascending=False).reset_index(drop=True).head(num_top_venues))\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Most Common Venue Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_most_common_venues(row, num_top_venues):\n",
    "    row_categories = row.iloc[1:]\n",
    "    row_categories_sorted = row_categories.sort_values(ascending=False)\n",
    "    \n",
    "    return row_categories_sorted.index.values[0:num_top_venues]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</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",
       "      <td>0</td>\n",
       "      <td>Adams Morgan</td>\n",
       "      <td>Latin American Restaurant</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Park</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Ethiopian Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Taco Place</td>\n",
       "      <td>Cocktail Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Benning Heights</td>\n",
       "      <td>Shop &amp; Service</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Parking</td>\n",
       "      <td>Park</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "      <td>Dry Cleaner</td>\n",
       "      <td>Donut Shop</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Convenience Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Blue Plains</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Food Court</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Post Office</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Exhibit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Brightwood</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Big Box Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Bus Stop</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Mobile Phone Shop</td>\n",
       "      <td>Historic Site</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Brookland</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Rental Car Location</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Ethiopian Restaurant</td>\n",
       "      <td>Spa</td>\n",
       "      <td>Business Service</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Neighborhood      1st Most Common Venue  2nd Most Common Venue  \\\n",
       "0     Adams Morgan  Latin American Restaurant  Vietnamese Restaurant   \n",
       "1  Benning Heights             Shop & Service            Gas Station   \n",
       "2      Blue Plains               Intersection             Food Truck   \n",
       "3       Brightwood                Pizza Place         Sandwich Place   \n",
       "4        Brookland                Gas Station                    Bar   \n",
       "\n",
       "  3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "0                  Park           Pizza Place  Ethiopian Restaurant   \n",
       "1               Parking                  Park    Seafood Restaurant   \n",
       "2            Food Court        Sandwich Place                 Hotel   \n",
       "3  Fast Food Restaurant         Big Box Store           Coffee Shop   \n",
       "4   Rental Car Location     Convenience Store              Pharmacy   \n",
       "\n",
       "  6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "0                   Bar                Bakery                 Diner   \n",
       "1           Dry Cleaner            Donut Shop        Sandwich Place   \n",
       "2                   Gym           Post Office           Yoga Studio   \n",
       "3              Bus Stop    Mexican Restaurant    Chinese Restaurant   \n",
       "4  Ethiopian Restaurant                   Spa      Business Service   \n",
       "\n",
       "  9th Most Common Venue 10th Most Common Venue  \n",
       "0            Taco Place           Cocktail Bar  \n",
       "1        Breakfast Spot      Convenience Store  \n",
       "2  Fast Food Restaurant                Exhibit  \n",
       "3     Mobile Phone Shop          Historic Site  \n",
       "4             Gastropub          Grocery Store  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_top_venues = 10\n",
    "\n",
    "indicators = ['st', 'nd', 'rd']\n",
    "\n",
    "# create columns according to number of top venues\n",
    "columns = ['Neighborhood']\n",
    "for ind in np.arange(num_top_venues):\n",
    "    try:\n",
    "        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))\n",
    "    except:\n",
    "        columns.append('{}th Most Common Venue'.format(ind+1))\n",
    "\n",
    "# create a new dataframe\n",
    "neighborhoods_venues_sorted = pd.DataFrame(columns=columns)\n",
    "neighborhoods_venues_sorted['Neighborhood'] = dc_grouped['Neighborhood']\n",
    "\n",
    "for ind in np.arange(dc_grouped.shape[0]):\n",
    "    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(dc_grouped.iloc[ind, :], num_top_venues)\n",
    "\n",
    "neighborhoods_venues_sorted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster Neighborhoods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "dc_grouped_clustering = dc_grouped.drop('Neighborhood', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "distortions = []\n",
    "K = range(1,40)\n",
    "for k in K:\n",
    "    kmeanModel = KMeans(n_clusters=k)\n",
    "    kmeanModel.fit(dc_grouped_clustering)\n",
    "    distortions.append(kmeanModel.inertia_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configure Elbow Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": [
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(K, distortions, 'bx-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Distortion')\n",
    "plt.title('The Elbow Method showing the optimal k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-means Cluster Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  4, 10,  0,  1,  4,  9,  4,  3,  4], dtype=int32)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set number of clusters\n",
    "kclusters = 11\n",
    "\n",
    "# run k-means clustering\n",
    "kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(dc_grouped_clustering)\n",
    "\n",
    "# check cluster labels generated for each row in the dataframe\n",
    "kmeans.labels_[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Neighborhood</th>\n",
       "      <th>Zip</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Cluster Labels</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",
       "      <td>0</td>\n",
       "      <td>Shaw</td>\n",
       "      <td>20001</td>\n",
       "      <td>38.907711</td>\n",
       "      <td>-77.01732</td>\n",
       "      <td>8</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>Dive Bar</td>\n",
       "      <td>Dog Run</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Pet Store</td>\n",
       "      <td>Southern / Soul Food Restaurant</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>H Street Corridor</td>\n",
       "      <td>20002</td>\n",
       "      <td>38.901811</td>\n",
       "      <td>-76.99097</td>\n",
       "      <td>1</td>\n",
       "      <td>Art Gallery</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Bike Rental / Bike Share</td>\n",
       "      <td>Cocktail Bar</td>\n",
       "      <td>Wine Bar</td>\n",
       "      <td>Taco Place</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Capitol Hill</td>\n",
       "      <td>20003</td>\n",
       "      <td>38.881762</td>\n",
       "      <td>-76.99447</td>\n",
       "      <td>4</td>\n",
       "      <td>Historic Site</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Café</td>\n",
       "      <td>Monument / Landmark</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Taco Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Downtown, Chinatown</td>\n",
       "      <td>20004</td>\n",
       "      <td>38.895268</td>\n",
       "      <td>-77.02760</td>\n",
       "      <td>4</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Science Museum</td>\n",
       "      <td>Exhibit</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>History Museum</td>\n",
       "      <td>Art Museum</td>\n",
       "      <td>Theater</td>\n",
       "      <td>Museum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Mt Vernon Triangle</td>\n",
       "      <td>20005</td>\n",
       "      <td>38.904461</td>\n",
       "      <td>-77.03088</td>\n",
       "      <td>4</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Salon / Barbershop</td>\n",
       "      <td>Hotel Bar</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>Latin American Restaurant</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Neighborhood    Zip   Latitude  Longitude  Cluster Labels  \\\n",
       "0                 Shaw  20001  38.907711  -77.01732               8   \n",
       "1    H Street Corridor  20002  38.901811  -76.99097               1   \n",
       "2         Capitol Hill  20003  38.881762  -76.99447               4   \n",
       "3  Downtown, Chinatown  20004  38.895268  -77.02760               4   \n",
       "4   Mt Vernon Triangle  20005  38.904461  -77.03088               4   \n",
       "\n",
       "  1st Most Common Venue 2nd Most Common Venue 3rd Most Common Venue  \\\n",
       "0          Intersection         Grocery Store  Gym / Fitness Center   \n",
       "1           Art Gallery           Pizza Place                   Bar   \n",
       "2         Historic Site            Food Truck   American Restaurant   \n",
       "3                 Hotel   American Restaurant           Coffee Shop   \n",
       "4                 Hotel           Coffee Shop        Sandwich Place   \n",
       "\n",
       "  4th Most Common Venue 5th Most Common Venue     6th Most Common Venue  \\\n",
       "0              Dive Bar               Dog Run               Coffee Shop   \n",
       "1     Convenience Store          Liquor Store  Bike Rental / Bike Share   \n",
       "2           Pizza Place                  Café       Monument / Landmark   \n",
       "3        Science Museum               Exhibit            Sandwich Place   \n",
       "4    Salon / Barbershop             Hotel Bar        Italian Restaurant   \n",
       "\n",
       "  7th Most Common Venue            8th Most Common Venue  \\\n",
       "0             Pet Store  Southern / Soul Food Restaurant   \n",
       "1          Cocktail Bar                         Wine Bar   \n",
       "2      Sushi Restaurant                              Bar   \n",
       "3        History Museum                       Art Museum   \n",
       "4  Gym / Fitness Center        Latin American Restaurant   \n",
       "\n",
       "  9th Most Common Venue 10th Most Common Venue  \n",
       "0   Fried Chicken Joint        Thai Restaurant  \n",
       "1            Taco Place       Asian Restaurant  \n",
       "2                 Hotel             Taco Place  \n",
       "3               Theater                 Museum  \n",
       "4      Sushi Restaurant          Deli / Bodega  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add clustering labels\n",
    "\n",
    "neighborhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_)\n",
    "\n",
    "dc_merged = df\n",
    "\n",
    "# merge dc_grouped with df to add latitude/longitude for each neighborhood\n",
    "dc_merged = dc_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')\n",
    "\n",
    "dc_merged.head() # check the last columns!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=<!DOCTYPE html>
<head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    
        <script>
            L_NO_TOUCH = false;
            L_DISABLE_3D = false;
        </script>
    
    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.6.0/dist/leaflet.js"></script>
    <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>
    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.6.0/dist/leaflet.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css"/>
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>
    <link rel="stylesheet" href="https://rawcdn.githack.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css"/>
    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
    
            <meta name="viewport" content="width=device-width,
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                    position: relative;
                    width: 100.0%;
                    height: 100.0%;
                    left: 0.0%;
                    top: 0.0%;
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            </style>
        
</head>
<body>    
    
            <div class="folium-map" id="map_c3212abcef4e409caf6515e188ccdbae" ></div>
        
</body>
<script>    
    
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                {
                    center: [38.8949855, -77.0365708],
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                    zoomControl: true,
                    preferCanvas: false,
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            popup_a97cda62684f4b1d8a2799a8a3482e7a.setContent(html_44ca118963924c479475b5da582a2a08);
        

        circle_marker_af312a80ff424cf186773b671c4d21af.bindPopup(popup_a97cda62684f4b1d8a2799a8a3482e7a)
        ;

        
    
    
            var circle_marker_762db94cfe294ad0acf26c5b63f1e23d = L.circleMarker(
                [38.96508, -77.02858],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_4d61cf9a3f5f446b8f4c1df27ffcaca0 = L.popup({"maxWidth": "100%"});

        
            var html_13a70817ca02433c8dcac2be8537c585 = $(`<div id="html_13a70817ca02433c8dcac2be8537c585" style="width: 100.0%; height: 100.0%;">Brightwood Cluster 0</div>`)[0];
            popup_4d61cf9a3f5f446b8f4c1df27ffcaca0.setContent(html_13a70817ca02433c8dcac2be8537c585);
        

        circle_marker_762db94cfe294ad0acf26c5b63f1e23d.bindPopup(popup_4d61cf9a3f5f446b8f4c1df27ffcaca0)
        ;

        
    
    
            var circle_marker_4bf9ed6e8b764437befaef92cfdbf715 = L.circleMarker(
                [38.97808, -77.02858],
                {"bubblingMouseEvents": true, "color": "#ff964f", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff964f", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_1834206b6bc44252a66fb7b090c6d994 = L.popup({"maxWidth": "100%"});

        
            var html_fbdce2893b054eb6a716264ca9483d89 = $(`<div id="html_fbdce2893b054eb6a716264ca9483d89" style="width: 100.0%; height: 100.0%;">Shepherd Park Cluster 9</div>`)[0];
            popup_1834206b6bc44252a66fb7b090c6d994.setContent(html_fbdce2893b054eb6a716264ca9483d89);
        

        circle_marker_4bf9ed6e8b764437befaef92cfdbf715.bindPopup(popup_1834206b6bc44252a66fb7b090c6d994)
        ;

        
    
    
            var circle_marker_723ce193c97d4ab59be53843f4f54645 = L.circleMarker(
                [38.893311, -77.014647],
                {"bubblingMouseEvents": true, "color": "#18cde4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#18cde4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_b261d39db28d4e4e814d4905e52ea13e = L.popup({"maxWidth": "100%"});

        
            var html_455a942af5d649e491bd50096a426ed3 = $(`<div id="html_455a942af5d649e491bd50096a426ed3" style="width: 100.0%; height: 100.0%;">East End Cluster 4</div>`)[0];
            popup_b261d39db28d4e4e814d4905e52ea13e.setContent(html_455a942af5d649e491bd50096a426ed3);
        

        circle_marker_723ce193c97d4ab59be53843f4f54645.bindPopup(popup_b261d39db28d4e4e814d4905e52ea13e)
        ;

        
    
    
            var circle_marker_a627538b7a274fc68f0c48b2575806f9 = L.circleMarker(
                [38.96516, -77.06737],
                {"bubblingMouseEvents": true, "color": "#1996f3", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#1996f3", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_6b43c6a78a60449d90802ca7b00efe6d = L.popup({"maxWidth": "100%"});

        
            var html_ffd3c77933b04c43a87c1da5d51384ec = $(`<div id="html_ffd3c77933b04c43a87c1da5d51384ec" style="width: 100.0%; height: 100.0%;">Chevy Chase Cluster 3</div>`)[0];
            popup_6b43c6a78a60449d90802ca7b00efe6d.setContent(html_ffd3c77933b04c43a87c1da5d51384ec);
        

        circle_marker_a627538b7a274fc68f0c48b2575806f9.bindPopup(popup_6b43c6a78a60449d90802ca7b00efe6d)
        ;

        
    
    
            var circle_marker_c0fdcf7cd55b4e2e928effc8504c8f8c = L.circleMarker(
                [38.94081, -77.08723],
                {"bubblingMouseEvents": true, "color": "#18cde4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#18cde4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_e7afd0cdf79143fbb08e332eae434e22 = L.popup({"maxWidth": "100%"});

        
            var html_e7f395a6d0304fcfa69da40f4f8a195c = $(`<div id="html_e7f395a6d0304fcfa69da40f4f8a195c" style="width: 100.0%; height: 100.0%;">Cathedral Heights Cluster 4</div>`)[0];
            popup_e7afd0cdf79143fbb08e332eae434e22.setContent(html_e7f395a6d0304fcfa69da40f4f8a195c);
        

        circle_marker_c0fdcf7cd55b4e2e928effc8504c8f8c.bindPopup(popup_e7afd0cdf79143fbb08e332eae434e22)
        ;

        
    
    
            var circle_marker_147edd23fb0642d2afd5c83892ea0970 = L.circleMarker(
                [38.937478000000006, -76.99125],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_dd11cb1831bd4383b7e239d74a9728fe = L.popup({"maxWidth": "100%"});

        
            var html_143c8b86cc264cfc89b2fd13d3060c38 = $(`<div id="html_143c8b86cc264cfc89b2fd13d3060c38" style="width: 100.0%; height: 100.0%;">Brookland Cluster 1</div>`)[0];
            popup_dd11cb1831bd4383b7e239d74a9728fe.setContent(html_143c8b86cc264cfc89b2fd13d3060c38);
        

        circle_marker_147edd23fb0642d2afd5c83892ea0970.bindPopup(popup_dd11cb1831bd4383b7e239d74a9728fe)
        ;

        
    
    
            var circle_marker_110e1e31780342798a5647b36dc0bac2 = L.circleMarker(
                [38.928761, -76.97523000000001],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_2ffd2df66cbc460da9222aa083b6ee2c = L.popup({"maxWidth": "100%"});

        
            var html_d20722454a4e45bbaee8133657c4831d = $(`<div id="html_d20722454a4e45bbaee8133657c4831d" style="width: 100.0%; height: 100.0%;">Langdon Cluster 1</div>`)[0];
            popup_2ffd2df66cbc460da9222aa083b6ee2c.setContent(html_d20722454a4e45bbaee8133657c4831d);
        

        circle_marker_110e1e31780342798a5647b36dc0bac2.bindPopup(popup_2ffd2df66cbc460da9222aa083b6ee2c)
        ;

        
    
    
            var circle_marker_76c6af80ff6642d8a69748e5dd1654e6 = L.circleMarker(
                [38.891445000000004, -76.93811],
                {"bubblingMouseEvents": true, "color": "#18cde4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#18cde4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_c18c1329a0944c48a1b33ce3fbe09627 = L.popup({"maxWidth": "100%"});

        
            var html_504dfe8dfd734edfbe8339fec48a365d = $(`<div id="html_504dfe8dfd734edfbe8339fec48a365d" style="width: 100.0%; height: 100.0%;">Benning Heights Cluster 4</div>`)[0];
            popup_c18c1329a0944c48a1b33ce3fbe09627.setContent(html_504dfe8dfd734edfbe8339fec48a365d);
        

        circle_marker_76c6af80ff6642d8a69748e5dd1654e6.bindPopup(popup_c18c1329a0944c48a1b33ce3fbe09627)
        ;

        
    
    
            var circle_marker_61338c6dfd6943f5ba2806a683d77744 = L.circleMarker(
                [38.863079, -76.97138000000001],
                {"bubblingMouseEvents": true, "color": "#4e4dfc", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#4e4dfc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_fe538cc3fd75480c8d3f69af29ccd988 = L.popup({"maxWidth": "100%"});

        
            var html_88534f11758441a592bcf912664c7a70 = $(`<div id="html_88534f11758441a592bcf912664c7a70" style="width: 100.0%; height: 100.0%;">Southeast Washington Cluster 2</div>`)[0];
            popup_fe538cc3fd75480c8d3f69af29ccd988.setContent(html_88534f11758441a592bcf912664c7a70);
        

        circle_marker_61338c6dfd6943f5ba2806a683d77744.bindPopup(popup_fe538cc3fd75480c8d3f69af29ccd988)
        ;

        
    
    
            var circle_marker_0a3853a0d9c34f89985ca6eadb2f1fa3 = L.circleMarker(
                [38.880112, -77.02362],
                {"bubblingMouseEvents": true, "color": "#18cde4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#18cde4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_8dc1d9d5e3474c2bb3eb059ec739e443 = L.popup({"maxWidth": "100%"});

        
            var html_5926f95501e04eefbb3947729d0b4383 = $(`<div id="html_5926f95501e04eefbb3947729d0b4383" style="width: 100.0%; height: 100.0%;">Southwest Waterfront Cluster 4</div>`)[0];
            popup_8dc1d9d5e3474c2bb3eb059ec739e443.setContent(html_5926f95501e04eefbb3947729d0b4383);
        

        circle_marker_0a3853a0d9c34f89985ca6eadb2f1fa3.bindPopup(popup_8dc1d9d5e3474c2bb3eb059ec739e443)
        ;

        
    
    
            var circle_marker_8ecd0a15f56f4aefac750f129ca2a331 = L.circleMarker(
                [38.836145, -77.00058],
                {"bubblingMouseEvents": true, "color": "#4df3ce", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#4df3ce", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_39040e7fb52e4852abb405870776d508 = L.popup({"maxWidth": "100%"});

        
            var html_484185c4149c4868bf74ab165cdc0163 = $(`<div id="html_484185c4149c4868bf74ab165cdc0163" style="width: 100.0%; height: 100.0%;">Congress Heights Cluster 5</div>`)[0];
            popup_39040e7fb52e4852abb405870776d508.setContent(html_484185c4149c4868bf74ab165cdc0163);
        

        circle_marker_8ecd0a15f56f4aefac750f129ca2a331.bindPopup(popup_39040e7fb52e4852abb405870776d508)
        ;

        
    
    
            var circle_marker_3d6a7923c7444d68a9bb63e66b5471f7 = L.circleMarker(
                [38.906778, -77.04148],
                {"bubblingMouseEvents": true, "color": "#18cde4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#18cde4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_b4cc2b70b9124c1e81b824d47e90205e = L.popup({"maxWidth": "100%"});

        
            var html_72b3cbbbbbef459bb9da13e8d6322948 = $(`<div id="html_72b3cbbbbbef459bb9da13e8d6322948" style="width: 100.0%; height: 100.0%;">Dupont Circle Cluster 4</div>`)[0];
            popup_b4cc2b70b9124c1e81b824d47e90205e.setContent(html_72b3cbbbbbef459bb9da13e8d6322948);
        

        circle_marker_3d6a7923c7444d68a9bb63e66b5471f7.bindPopup(popup_b4cc2b70b9124c1e81b824d47e90205e)
        ;

        
    
    
            var circle_marker_f86ea5fa7f5c4e6082cfce2523bbcbd7 = L.circleMarker(
                [38.900394, -77.05126],
                {"bubblingMouseEvents": true, "color": "#18cde4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#18cde4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_16ae5909a04a4216ad441927f2c73c98 = L.popup({"maxWidth": "100%"});

        
            var html_be263c2aac9547efb9516709cd7fa3e7 = $(`<div id="html_be263c2aac9547efb9516709cd7fa3e7" style="width: 100.0%; height: 100.0%;">Foggy Bottom Cluster 4</div>`)[0];
            popup_16ae5909a04a4216ad441927f2c73c98.setContent(html_be263c2aac9547efb9516709cd7fa3e7);
        

        circle_marker_f86ea5fa7f5c4e6082cfce2523bbcbd7.bindPopup(popup_16ae5909a04a4216ad441927f2c73c98)
        ;

        
    
    
            var circle_marker_58486cdcb66b4775a4a15333dee3bf76 = L.circleMarker(
                [38.90014, -77.047899],
                {"bubblingMouseEvents": true, "color": "#18cde4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#18cde4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_21410a807cb84602b4a784b49ea1f0a0 = L.popup({"maxWidth": "100%"});

        
            var html_3b5255370a5548d1b02aed6fb7497b15 = $(`<div id="html_3b5255370a5548d1b02aed6fb7497b15" style="width: 100.0%; height: 100.0%;">Foggy Bottom Cluster 4</div>`)[0];
            popup_21410a807cb84602b4a784b49ea1f0a0.setContent(html_3b5255370a5548d1b02aed6fb7497b15);
        

        circle_marker_58486cdcb66b4775a4a15333dee3bf76.bindPopup(popup_21410a807cb84602b4a784b49ea1f0a0)
        ;

        
    
    
            var circle_marker_068ed6c884e142a7a539abaab312346b = L.circleMarker(
                [38.901633000000004, -77.02095899999999],
                {"bubblingMouseEvents": true, "color": "#18cde4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#18cde4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_793abc9d0a1a48da9ebde934a99c4b53 = L.popup({"maxWidth": "100%"});

        
            var html_b247a406ac954f11816bd67fc9081c7f = $(`<div id="html_b247a406ac954f11816bd67fc9081c7f" style="width: 100.0%; height: 100.0%;">Mt Vernon Square Cluster 4</div>`)[0];
            popup_793abc9d0a1a48da9ebde934a99c4b53.setContent(html_b247a406ac954f11816bd67fc9081c7f);
        

        circle_marker_068ed6c884e142a7a539abaab312346b.bindPopup(popup_793abc9d0a1a48da9ebde934a99c4b53)
        ;

        
    
    
            var circle_marker_ffe73d44e3a04600beb9ad240736a2f0 = L.circleMarker(
                [38.918011, -77.020398],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_9260b6e3b5a6419bb3141179b79e8d87 = L.popup({"maxWidth": "100%"});

        
            var html_771c875c2ba44a179d8e0869ea3de2c7 = $(`<div id="html_771c875c2ba44a179d8e0869ea3de2c7" style="width: 100.0%; height: 100.0%;">Pleasant Plains Cluster 1</div>`)[0];
            popup_9260b6e3b5a6419bb3141179b79e8d87.setContent(html_771c875c2ba44a179d8e0869ea3de2c7);
        

        circle_marker_ffe73d44e3a04600beb9ad240736a2f0.bindPopup(popup_9260b6e3b5a6419bb3141179b79e8d87)
        ;

        
    
    
            var circle_marker_9dea2584cb054be5a51d9df185df66b5 = L.circleMarker(
                [38.905317, -77.046645],
                {"bubblingMouseEvents": true, "color": "#18cde4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#18cde4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_d84d46c62ba749fc9a6e7719f1df9894 = L.popup({"maxWidth": "100%"});

        
            var html_fb48dff2f2f647f49740302a241cc4b8 = $(`<div id="html_fb48dff2f2f647f49740302a241cc4b8" style="width: 100.0%; height: 100.0%;">Dupont Circle Cluster 4</div>`)[0];
            popup_d84d46c62ba749fc9a6e7719f1df9894.setContent(html_fb48dff2f2f647f49740302a241cc4b8);
        

        circle_marker_9dea2584cb054be5a51d9df185df66b5.bindPopup(popup_d84d46c62ba749fc9a6e7719f1df9894)
        ;

        
    
    
            var circle_marker_d744d172ff644266b05b92e5391a047a = L.circleMarker(
                [38.933161, -76.996348],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_825b4ae16e4c4ae0af343eceed560722 = L.popup({"maxWidth": "100%"});

        
            var html_8f3159ec7a324f5f9599fc07856ce295 = $(`<div id="html_8f3159ec7a324f5f9599fc07856ce295" style="width: 100.0%; height: 100.0%;">University Heights Cluster 1</div>`)[0];
            popup_825b4ae16e4c4ae0af343eceed560722.setContent(html_8f3159ec7a324f5f9599fc07856ce295);
        

        circle_marker_d744d172ff644266b05b92e5391a047a.bindPopup(popup_825b4ae16e4c4ae0af343eceed560722)
        ;

        
    
    
            var circle_marker_ca1385cf4fa2492cb393038b31ae4d2a = L.circleMarker(
                [38.883412, -77.028198],
                {"bubblingMouseEvents": true, "color": "#18cde4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#18cde4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_1307a85192de4811bcf6462117451c83 = L.popup({"maxWidth": "100%"});

        
            var html_a106921147aa4e15adaa1ad9945f952e = $(`<div id="html_a106921147aa4e15adaa1ad9945f952e" style="width: 100.0%; height: 100.0%;">L&#39;Enfant Plaza Cluster 4</div>`)[0];
            popup_1307a85192de4811bcf6462117451c83.setContent(html_a106921147aa4e15adaa1ad9945f952e);
        

        circle_marker_ca1385cf4fa2492cb393038b31ae4d2a.bindPopup(popup_1307a85192de4811bcf6462117451c83)
        ;

        
    
    
            var circle_marker_273d9664acac40d0abd68bc6a6aa713f = L.circleMarker(
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        circle_marker_273d9664acac40d0abd68bc6a6aa713f.bindPopup(popup_473dc52e01f6495ca23c81a3c8c23a90)
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            var circle_marker_4ae455465aed42e38bd9cf229966259b = L.circleMarker(
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                {"bubblingMouseEvents": true, "color": "#18cde4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#18cde4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
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            popup_ef41f3ca42b1409b985628f43bbf9487.setContent(html_e5af6e5366ec470191753e8d4ae844b9);
        

        circle_marker_4ae455465aed42e38bd9cf229966259b.bindPopup(popup_ef41f3ca42b1409b985628f43bbf9487)
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                {"bubblingMouseEvents": true, "color": "#18cde4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#18cde4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
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            popup_e3b06eac796d4181acf72349d42ab375.setContent(html_e85febfdfd7b40309ff72ddf37097636);
        

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            popup_5141c46785434ebe82807a1a5c7fd3d0.setContent(html_36500414e727462f87fb781fc2abf96c);
        

        circle_marker_c63c5e0013464e1d927b95e13268c7f5.bindPopup(popup_5141c46785434ebe82807a1a5c7fd3d0)
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        circle_marker_93c6493a39ad44cd8d31208328f1844f.bindPopup(popup_3650e5cec4b44bc396a5e06a836ad8fa)
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            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_8dcf69846e394fb9afd2b1973cc25ef6 = L.popup({"maxWidth": "100%"});

        
            var html_95518480d5b047bd8eb275caf589e555 = $(`<div id="html_95518480d5b047bd8eb275caf589e555" style="width: 100.0%; height: 100.0%;">Foggy Bottom Cluster 4</div>`)[0];
            popup_8dcf69846e394fb9afd2b1973cc25ef6.setContent(html_95518480d5b047bd8eb275caf589e555);
        

        circle_marker_bb58719c83fa434c888b6ca650e4f14f.bindPopup(popup_8dcf69846e394fb9afd2b1973cc25ef6)
        ;

        
    
    
            var circle_marker_d377834ab66746f49a470f17ad783f41 = L.circleMarker(
                [38.894075, -77.01254],
                {"bubblingMouseEvents": true, "color": "#18cde4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#18cde4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
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        var popup_dc1cca777ddd42b3a47819453dec6a20 = L.popup({"maxWidth": "100%"});

        
            var html_7c170750760d4aa7a4f1f6d9af3d4edb = $(`<div id="html_7c170750760d4aa7a4f1f6d9af3d4edb" style="width: 100.0%; height: 100.0%;">East End Cluster 4</div>`)[0];
            popup_dc1cca777ddd42b3a47819453dec6a20.setContent(html_7c170750760d4aa7a4f1f6d9af3d4edb);
        

        circle_marker_d377834ab66746f49a470f17ad783f41.bindPopup(popup_dc1cca777ddd42b3a47819453dec6a20)
        ;

        
    
    
            var circle_marker_16bffa90a2d64720ab1eab61942fe10f = L.circleMarker(
                [38.901210999999996, -77.016948],
                {"bubblingMouseEvents": true, "color": "#18cde4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#18cde4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
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        var popup_461e7c97d8de4f7a8828c046c720228f = L.popup({"maxWidth": "100%"});

        
            var html_ac66904bb0774c8a970cb45e8cc7d5c5 = $(`<div id="html_ac66904bb0774c8a970cb45e8cc7d5c5" style="width: 100.0%; height: 100.0%;">Sursum Corda Cluster 4</div>`)[0];
            popup_461e7c97d8de4f7a8828c046c720228f.setContent(html_ac66904bb0774c8a970cb45e8cc7d5c5);
        

        circle_marker_16bffa90a2d64720ab1eab61942fe10f.bindPopup(popup_461e7c97d8de4f7a8828c046c720228f)
        ;

        
    
    
            var circle_marker_473adfbf2fa44e47b26dfb91f8bd60ec = L.circleMarker(
                [38.887405, -77.004663],
                {"bubblingMouseEvents": true, "color": "#18cde4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#18cde4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
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        var popup_ff2fedf98a7041bcb7d58b66cfb16b4b = L.popup({"maxWidth": "100%"});

        
            var html_8708d462408d455e8282d6817eb51dc8 = $(`<div id="html_8708d462408d455e8282d6817eb51dc8" style="width: 100.0%; height: 100.0%;">Capitol Hill Cluster 4</div>`)[0];
            popup_ff2fedf98a7041bcb7d58b66cfb16b4b.setContent(html_8708d462408d455e8282d6817eb51dc8);
        

        circle_marker_473adfbf2fa44e47b26dfb91f8bd60ec.bindPopup(popup_ff2fedf98a7041bcb7d58b66cfb16b4b)
        ;

        
    
    
            var circle_marker_4f4fc54c16b948c8ae09b26c1a338262 = L.circleMarker(
                [38.940761, -77.028348],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_18fe6aba645149a4bf3cc3ff00b38a06 = L.popup({"maxWidth": "100%"});

        
            var html_d588421ae66443acbbc739765376ace3 = $(`<div id="html_d588421ae66443acbbc739765376ace3" style="width: 100.0%; height: 100.0%;">Petworth Cluster 1</div>`)[0];
            popup_18fe6aba645149a4bf3cc3ff00b38a06.setContent(html_d588421ae66443acbbc739765376ace3);
        

        circle_marker_4f4fc54c16b948c8ae09b26c1a338262.bindPopup(popup_18fe6aba645149a4bf3cc3ff00b38a06)
        ;

        
    
    
            var circle_marker_6cff71477e6d4e7ab75c608e4e6fe6df = L.circleMarker(
                [38.890976, -77.02109200000001],
                {"bubblingMouseEvents": true, "color": "#18cde4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#18cde4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_824c0b79ee0d4eff9c12ed329f626693 = L.popup({"maxWidth": "100%"});

        
            var html_c4e96238d25c4cedb1233ab9ac78b44c = $(`<div id="html_c4e96238d25c4cedb1233ab9ac78b44c" style="width: 100.0%; height: 100.0%;">National Mall Cluster 4</div>`)[0];
            popup_824c0b79ee0d4eff9c12ed329f626693.setContent(html_c4e96238d25c4cedb1233ab9ac78b44c);
        

        circle_marker_6cff71477e6d4e7ab75c608e4e6fe6df.bindPopup(popup_824c0b79ee0d4eff9c12ed329f626693)
        ;

        
    
    
            var circle_marker_dff68778cf1d4a7f88d071e7b1943492 = L.circleMarker(
                [38.887333, -77.02311800000001],
                {"bubblingMouseEvents": true, "color": "#18cde4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#18cde4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_a47940ca5af5474a91dabbc663579088 = L.popup({"maxWidth": "100%"});

        
            var html_790383a4ffae41dea529b905f0903145 = $(`<div id="html_790383a4ffae41dea529b905f0903145" style="width: 100.0%; height: 100.0%;">National Mall Cluster 4</div>`)[0];
            popup_a47940ca5af5474a91dabbc663579088.setContent(html_790383a4ffae41dea529b905f0903145);
        

        circle_marker_dff68778cf1d4a7f88d071e7b1943492.bindPopup(popup_a47940ca5af5474a91dabbc663579088)
        ;

        
    
    
            var circle_marker_55fd996cab5e427291fe0b92568331bd = L.circleMarker(
                [38.897073999999996, -77.05535],
                {"bubblingMouseEvents": true, "color": "#18cde4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#18cde4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_555fa5b41da84ba3937f618c0834d737 = L.popup({"maxWidth": "100%"});

        
            var html_a323125d2b104c579a442ed6fbf005f3 = $(`<div id="html_a323125d2b104c579a442ed6fbf005f3" style="width: 100.0%; height: 100.0%;">Foggy Bottom Cluster 4</div>`)[0];
            popup_555fa5b41da84ba3937f618c0834d737.setContent(html_a323125d2b104c579a442ed6fbf005f3);
        

        circle_marker_55fd996cab5e427291fe0b92568331bd.bindPopup(popup_555fa5b41da84ba3937f618c0834d737)
        ;

        
    
    
            var circle_marker_fb90b7f0828946daae2a714aa8f46c55 = L.circleMarker(
                [38.893662, -77.02359200000001],
                {"bubblingMouseEvents": true, "color": "#18cde4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#18cde4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_2d2168abe2964827b986a1903bc78f9c = L.popup({"maxWidth": "100%"});

        
            var html_6bea1c1252604b4f864c26f0b80ed5ac = $(`<div id="html_6bea1c1252604b4f864c26f0b80ed5ac" style="width: 100.0%; height: 100.0%;">Chinatown Cluster 4</div>`)[0];
            popup_2d2168abe2964827b986a1903bc78f9c.setContent(html_6bea1c1252604b4f864c26f0b80ed5ac);
        

        circle_marker_fb90b7f0828946daae2a714aa8f46c55.bindPopup(popup_2d2168abe2964827b986a1903bc78f9c)
        ;

        
    
    
            var circle_marker_3c7659b9021c4cb1b5ad4d1f43a13bce = L.circleMarker(
                [38.904311, -77.04464899999999],
                {"bubblingMouseEvents": true, "color": "#18cde4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#18cde4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_888f46e7da99418296ffd65a0e0865bf = L.popup({"maxWidth": "100%"});

        
            var html_49f31ac4302946a4af44d36f5ed53f25 = $(`<div id="html_49f31ac4302946a4af44d36f5ed53f25" style="width: 100.0%; height: 100.0%;">Dupont Circle Cluster 4</div>`)[0];
            popup_888f46e7da99418296ffd65a0e0865bf.setContent(html_49f31ac4302946a4af44d36f5ed53f25);
        

        circle_marker_3c7659b9021c4cb1b5ad4d1f43a13bce.bindPopup(popup_888f46e7da99418296ffd65a0e0865bf)
        ;

        
    
    
            var circle_marker_5606dc135749417f92938df93abaa045 = L.circleMarker(
                [38.902211, -77.047399],
                {"bubblingMouseEvents": true, "color": "#18cde4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#18cde4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_ad28c62a4887460aaf00acb0fd0c5595 = L.popup({"maxWidth": "100%"});

        
            var html_c4f9659b7fe84f28bc49464717f05613 = $(`<div id="html_c4f9659b7fe84f28bc49464717f05613" style="width: 100.0%; height: 100.0%;">Foggy Bottom Cluster 4</div>`)[0];
            popup_ad28c62a4887460aaf00acb0fd0c5595.setContent(html_c4f9659b7fe84f28bc49464717f05613);
        

        circle_marker_5606dc135749417f92938df93abaa045.bindPopup(popup_ad28c62a4887460aaf00acb0fd0c5595)
        ;

        
    
    
            var circle_marker_535e7cb1234e4da4ac4c241ff710dd89 = L.circleMarker(
                [38.88494, -77.018372],
                {"bubblingMouseEvents": true, "color": "#18cde4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#18cde4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_04fa4517a6684dd582068877e658ef2f = L.popup({"maxWidth": "100%"});

        
            var html_73bbee104dd14f44bfe93913dda9d7e6 = $(`<div id="html_73bbee104dd14f44bfe93913dda9d7e6" style="width: 100.0%; height: 100.0%;">L&#39;Enfant Plaza Cluster 4</div>`)[0];
            popup_04fa4517a6684dd582068877e658ef2f.setContent(html_73bbee104dd14f44bfe93913dda9d7e6);
        

        circle_marker_535e7cb1234e4da4ac4c241ff710dd89.bindPopup(popup_04fa4517a6684dd582068877e658ef2f)
        ;

        
    
    
            var circle_marker_9994d810b3ec469a9048d65aff3f3acb = L.circleMarker(
                [38.91856, -76.961414],
                {"bubblingMouseEvents": true, "color": "#b2f396", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#b2f396", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_ed9281ff8f2b4da7830050fb7926f2b6 = L.popup({"maxWidth": "100%"});

        
            var html_f1dc5c4f392b4d4cb05eda9dd34b70a2 = $(`<div id="html_f1dc5c4f392b4d4cb05eda9dd34b70a2" style="width: 100.0%; height: 100.0%;">Gateway Cluster 7</div>`)[0];
            popup_ed9281ff8f2b4da7830050fb7926f2b6.setContent(html_f1dc5c4f392b4d4cb05eda9dd34b70a2);
        

        circle_marker_9994d810b3ec469a9048d65aff3f3acb.bindPopup(popup_ed9281ff8f2b4da7830050fb7926f2b6)
        ;

        
    
    
            var circle_marker_01406c13ee094fa5b53abe3488df58c8 = L.circleMarker(
                [38.899903, -76.979776],
                {"bubblingMouseEvents": true, "color": "#ff964f", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff964f", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_c3212abcef4e409caf6515e188ccdbae);
        
    
        var popup_b073c24ed1734ff2ae5c0e8abcf15183 = L.popup({"maxWidth": "100%"});

        
            var html_4a13e57c137f47e6b6e3a223b94fa94d = $(`<div id="html_4a13e57c137f47e6b6e3a223b94fa94d" style="width: 100.0%; height: 100.0%;">Carver Cluster 9</div>`)[0];
            popup_b073c24ed1734ff2ae5c0e8abcf15183.setContent(html_4a13e57c137f47e6b6e3a223b94fa94d);
        

        circle_marker_01406c13ee094fa5b53abe3488df58c8.bindPopup(popup_b073c24ed1734ff2ae5c0e8abcf15183)
        ;

        
    
</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7fee2f74b190>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_clusters = folium.Map(location=[latitude_dc, longitude_dc], zoom_start=11)\n",
    "\n",
    "# set color scheme for the clusters\n",
    "x = np.arange(kclusters)\n",
    "ys = [i + x + (i*x)**2 for i in range(kclusters)]\n",
    "colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
    "rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
    "\n",
    "# add markers to the map\n",
    "markers_colors = []\n",
    "for lat, lon, poi, cluster in zip(dc_merged['Latitude'], dc_merged['Longitude'], dc_merged['Neighborhood'], dc_merged['Cluster Labels']):\n",
    "    label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lon],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color=rainbow[cluster-1],\n",
    "        fill=True,\n",
    "        fill_color=rainbow[cluster-1],\n",
    "        fill_opacity=0.7).add_to(map_clusters)\n",
    "       \n",
    "map_clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examine Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Neighborhood</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",
       "      <td>9</td>\n",
       "      <td>Brightwood</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Big Box Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Bus Stop</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Mobile Phone Shop</td>\n",
       "      <td>Historic Site</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Neighborhood 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "9   Brightwood           Pizza Place        Sandwich Place   \n",
       "\n",
       "  3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "9  Fast Food Restaurant         Big Box Store           Coffee Shop   \n",
       "\n",
       "  6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "9              Bus Stop    Mexican Restaurant    Chinese Restaurant   \n",
       "\n",
       "  9th Most Common Venue 10th Most Common Venue  \n",
       "9     Mobile Phone Shop          Historic Site  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_merged.loc[dc_merged['Cluster Labels'] == 0, dc_merged.columns[[0] + list(range(5, dc_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</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",
       "      <td>1</td>\n",
       "      <td>H Street Corridor</td>\n",
       "      <td>Art Gallery</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Bike Rental / Bike Share</td>\n",
       "      <td>Cocktail Bar</td>\n",
       "      <td>Wine Bar</td>\n",
       "      <td>Taco Place</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Woodley Park, Forest Hills</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Mediterranean Restaurant</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Steakhouse</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Gift Shop</td>\n",
       "      <td>Supplement Shop</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Adams Morgan</td>\n",
       "      <td>Latin American Restaurant</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Park</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Ethiopian Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Taco Place</td>\n",
       "      <td>Cocktail Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Park View, Mount Pleasant</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Café</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Filipino Restaurant</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Shipping Store</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "      <td>Beer Garden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>Brookland</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Rental Car Location</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Ethiopian Restaurant</td>\n",
       "      <td>Spa</td>\n",
       "      <td>Business Service</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Langdon</td>\n",
       "      <td>Park</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "      <td>Martial Arts School</td>\n",
       "      <td>Men's Store</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Bus Stop</td>\n",
       "      <td>Paper / Office Supplies Store</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Clothing Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>Pleasant Plains</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Gay Bar</td>\n",
       "      <td>New American Restaurant</td>\n",
       "      <td>Hookah Bar</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Nightclub</td>\n",
       "      <td>Cocktail Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>University Heights</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Spa</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Art Gallery</td>\n",
       "      <td>Bookstore</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Ethiopian Restaurant</td>\n",
       "      <td>Martial Arts School</td>\n",
       "      <td>College Cafeteria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>McMillan</td>\n",
       "      <td>Cafeteria</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Ethiopian Restaurant</td>\n",
       "      <td>Salon / Barbershop</td>\n",
       "      <td>College Basketball Court</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Vegetarian / Vegan Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>Kalorama Heights</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Massage Studio</td>\n",
       "      <td>Spa</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Bookstore</td>\n",
       "      <td>Cocktail Bar</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Hotel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>Columbia Heights</td>\n",
       "      <td>Park</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Concert Hall</td>\n",
       "      <td>Spa</td>\n",
       "      <td>New American Restaurant</td>\n",
       "      <td>Bus Stop</td>\n",
       "      <td>Soccer Field</td>\n",
       "      <td>Supermarket</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>Petworth</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Donut Shop</td>\n",
       "      <td>New American Restaurant</td>\n",
       "      <td>Southern / Soul Food Restaurant</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Farmers Market</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Neighborhood      1st Most Common Venue  \\\n",
       "1            H Street Corridor                Art Gallery   \n",
       "6   Woodley Park, Forest Hills         Italian Restaurant   \n",
       "7                 Adams Morgan  Latin American Restaurant   \n",
       "8    Park View, Mount Pleasant                        Bar   \n",
       "14                   Brookland                Gas Station   \n",
       "15                     Langdon                       Park   \n",
       "24             Pleasant Plains                        Bar   \n",
       "26          University Heights                Pizza Place   \n",
       "32                    McMillan                  Cafeteria   \n",
       "42            Kalorama Heights         Italian Restaurant   \n",
       "43            Columbia Heights                       Park   \n",
       "51                    Petworth                Coffee Shop   \n",
       "\n",
       "    2nd Most Common Venue     3rd Most Common Venue 4th Most Common Venue  \\\n",
       "1             Pizza Place                       Bar     Convenience Store   \n",
       "6         Thai Restaurant  Mediterranean Restaurant    Mexican Restaurant   \n",
       "7   Vietnamese Restaurant                      Park           Pizza Place   \n",
       "8                  Bakery                      Café           Pizza Place   \n",
       "14                    Bar       Rental Car Location     Convenience Store   \n",
       "15         Ice Cream Shop       Martial Arts School           Men's Store   \n",
       "24            Pizza Place            Sandwich Place                   Gym   \n",
       "26            Coffee Shop                       Spa        Sandwich Place   \n",
       "32            Coffee Shop      Ethiopian Restaurant    Salon / Barbershop   \n",
       "42         Massage Studio                       Spa      Asian Restaurant   \n",
       "43       Asian Restaurant      Gym / Fitness Center           Yoga Studio   \n",
       "51          Grocery Store               Pizza Place    Italian Restaurant   \n",
       "\n",
       "       5th Most Common Venue     6th Most Common Venue  \\\n",
       "1               Liquor Store  Bike Rental / Bike Share   \n",
       "6                 Steakhouse             Grocery Store   \n",
       "7       Ethiopian Restaurant                       Bar   \n",
       "8           Asian Restaurant       Filipino Restaurant   \n",
       "14                  Pharmacy      Ethiopian Restaurant   \n",
       "15               Gas Station       Fried Chicken Joint   \n",
       "24                   Gay Bar   New American Restaurant   \n",
       "26               Art Gallery                 Bookstore   \n",
       "32  College Basketball Court        Chinese Restaurant   \n",
       "42                       Bar                 Bookstore   \n",
       "43              Concert Hall                       Spa   \n",
       "51                Donut Shop   New American Restaurant   \n",
       "\n",
       "              7th Most Common Venue          8th Most Common Venue  \\\n",
       "1                      Cocktail Bar                       Wine Bar   \n",
       "6                      Liquor Store                      Gift Shop   \n",
       "7                            Bakery                          Diner   \n",
       "8                Mexican Restaurant                 Shipping Store   \n",
       "14                              Spa               Business Service   \n",
       "15                         Bus Stop  Paper / Office Supplies Store   \n",
       "24                       Hookah Bar                Thai Restaurant   \n",
       "26                   Breakfast Spot           Ethiopian Restaurant   \n",
       "32                     Liquor Store                    Pizza Place   \n",
       "42                     Cocktail Bar                  Grocery Store   \n",
       "43          New American Restaurant                       Bus Stop   \n",
       "51  Southern / Soul Food Restaurant                    Gas Station   \n",
       "\n",
       "   9th Most Common Venue         10th Most Common Venue  \n",
       "1             Taco Place               Asian Restaurant  \n",
       "6        Supplement Shop                  Deli / Bodega  \n",
       "7             Taco Place                   Cocktail Bar  \n",
       "8     Seafood Restaurant                    Beer Garden  \n",
       "14             Gastropub                  Grocery Store  \n",
       "15    Chinese Restaurant                 Clothing Store  \n",
       "24             Nightclub                   Cocktail Bar  \n",
       "26   Martial Arts School              College Cafeteria  \n",
       "32                   Bar  Vegetarian / Vegan Restaurant  \n",
       "42       Thai Restaurant                          Hotel  \n",
       "43          Soccer Field                    Supermarket  \n",
       "51        Farmers Market             Mexican Restaurant  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_merged.loc[dc_merged['Cluster Labels'] == 1, dc_merged.columns[[0] + list(range(5, dc_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</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",
       "      <td>17</td>\n",
       "      <td>Southeast Washington</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "      <td>Falafel Restaurant</td>\n",
       "      <td>Farmers Market</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Filipino Restaurant</td>\n",
       "      <td>Fish Market</td>\n",
       "      <td>Flea Market</td>\n",
       "      <td>Food Court</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Neighborhood 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "17  Southeast Washington   Fried Chicken Joint           Yoga Studio   \n",
       "\n",
       "   3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "17     Food & Drink Shop    Falafel Restaurant        Farmers Market   \n",
       "\n",
       "   6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "17  Fast Food Restaurant   Filipino Restaurant           Fish Market   \n",
       "\n",
       "   9th Most Common Venue 10th Most Common Venue  \n",
       "17           Flea Market             Food Court  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_merged.loc[dc_merged['Cluster Labels'] == 2, dc_merged.columns[[0] + list(range(5, dc_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</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",
       "      <td>12</td>\n",
       "      <td>Chevy Chase</td>\n",
       "      <td>Athletics &amp; Sports</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Recreation Center</td>\n",
       "      <td>Farmers Market</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Flea Market</td>\n",
       "      <td>Eye Doctor</td>\n",
       "      <td>Falafel Restaurant</td>\n",
       "      <td>Filipino Restaurant</td>\n",
       "      <td>Fish Market</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Neighborhood 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "12  Chevy Chase    Athletics & Sports         Grocery Store   \n",
       "\n",
       "   3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "12     Recreation Center        Farmers Market  Fast Food Restaurant   \n",
       "\n",
       "   6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "12           Flea Market            Eye Doctor    Falafel Restaurant   \n",
       "\n",
       "   9th Most Common Venue 10th Most Common Venue  \n",
       "12   Filipino Restaurant            Fish Market  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_merged.loc[dc_merged['Cluster Labels'] == 3, dc_merged.columns[[0] + list(range(5, dc_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</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",
       "      <td>2</td>\n",
       "      <td>Capitol Hill</td>\n",
       "      <td>Historic Site</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Café</td>\n",
       "      <td>Monument / Landmark</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Taco Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Downtown, Chinatown</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Science Museum</td>\n",
       "      <td>Exhibit</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>History Museum</td>\n",
       "      <td>Art Museum</td>\n",
       "      <td>Theater</td>\n",
       "      <td>Museum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Mt Vernon Triangle</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Salon / Barbershop</td>\n",
       "      <td>Hotel Bar</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>Latin American Restaurant</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Georgetown</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>French Restaurant</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Hotel Bar</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>East End</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Monument / Landmark</td>\n",
       "      <td>Café</td>\n",
       "      <td>Park</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Scenic Lookout</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>Cathedral Heights</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Student Center</td>\n",
       "      <td>Bagel Shop</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Park</td>\n",
       "      <td>Tennis Court</td>\n",
       "      <td>Shipping Store</td>\n",
       "      <td>College Cafeteria</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Bank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>Benning Heights</td>\n",
       "      <td>Shop &amp; Service</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Parking</td>\n",
       "      <td>Park</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "      <td>Dry Cleaner</td>\n",
       "      <td>Donut Shop</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Convenience Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>Southwest Waterfront</td>\n",
       "      <td>Theater</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Harbor / Marina</td>\n",
       "      <td>Park</td>\n",
       "      <td>Music Venue</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Fish Market</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>Dupont Circle</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Salad Place</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Hotel Bar</td>\n",
       "      <td>Mediterranean Restaurant</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>Foggy Bottom</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Vegetarian / Vegan Restaurant</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Café</td>\n",
       "      <td>Performing Arts Venue</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>Foggy Bottom</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Vegetarian / Vegan Restaurant</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Café</td>\n",
       "      <td>Performing Arts Venue</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>Mt Vernon Square</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Ramen Restaurant</td>\n",
       "      <td>Plaza</td>\n",
       "      <td>New American Restaurant</td>\n",
       "      <td>Mediterranean Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Salad Place</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>Dupont Circle</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Salad Place</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Hotel Bar</td>\n",
       "      <td>Mediterranean Restaurant</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>L'Enfant Plaza</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Exhibit</td>\n",
       "      <td>Mediterranean Restaurant</td>\n",
       "      <td>Donut Shop</td>\n",
       "      <td>Fish Market</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>Capitol Hill</td>\n",
       "      <td>Historic Site</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Café</td>\n",
       "      <td>Monument / Landmark</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Taco Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>Southwest Washington</td>\n",
       "      <td>Boat or Ferry</td>\n",
       "      <td>Park</td>\n",
       "      <td>Theater</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "      <td>Bike Rental / Bike Share</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>National Mall</td>\n",
       "      <td>Science Museum</td>\n",
       "      <td>History Museum</td>\n",
       "      <td>Exhibit</td>\n",
       "      <td>Art Museum</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Art Gallery</td>\n",
       "      <td>Museum</td>\n",
       "      <td>Café</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>National Mall</td>\n",
       "      <td>Science Museum</td>\n",
       "      <td>History Museum</td>\n",
       "      <td>Exhibit</td>\n",
       "      <td>Art Museum</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Art Gallery</td>\n",
       "      <td>Museum</td>\n",
       "      <td>Café</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>Southwest Waterfront</td>\n",
       "      <td>Theater</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Harbor / Marina</td>\n",
       "      <td>Park</td>\n",
       "      <td>Music Venue</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Fish Market</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>Navy Yard</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Residential Building (Apartment / Condo)</td>\n",
       "      <td>Brewery</td>\n",
       "      <td>Southern / Soul Food Restaurant</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "      <td>Salad Place</td>\n",
       "      <td>River</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>East End</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Monument / Landmark</td>\n",
       "      <td>Café</td>\n",
       "      <td>Park</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Scenic Lookout</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>L'Enfant Plaza</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Exhibit</td>\n",
       "      <td>Mediterranean Restaurant</td>\n",
       "      <td>Donut Shop</td>\n",
       "      <td>Fish Market</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>Georgetown</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>French Restaurant</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Hotel Bar</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>L'Enfant Plaza</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Exhibit</td>\n",
       "      <td>Mediterranean Restaurant</td>\n",
       "      <td>Donut Shop</td>\n",
       "      <td>Fish Market</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>Southwest Washington</td>\n",
       "      <td>Boat or Ferry</td>\n",
       "      <td>Park</td>\n",
       "      <td>Theater</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Gastropub</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Seafood Restaurant</td>\n",
       "      <td>Bike Rental / Bike Share</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>Foggy Bottom</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Vegetarian / Vegan Restaurant</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Café</td>\n",
       "      <td>Performing Arts Venue</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>Foggy Bottom</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Vegetarian / Vegan Restaurant</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Café</td>\n",
       "      <td>Performing Arts Venue</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>East End</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Monument / Landmark</td>\n",
       "      <td>Café</td>\n",
       "      <td>Park</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Scenic Lookout</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>Sursum Corda</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Ramen Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Salad Place</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Taco Place</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Cycle Studio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>Capitol Hill</td>\n",
       "      <td>Historic Site</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Café</td>\n",
       "      <td>Monument / Landmark</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Taco Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>National Mall</td>\n",
       "      <td>Science Museum</td>\n",
       "      <td>History Museum</td>\n",
       "      <td>Exhibit</td>\n",
       "      <td>Art Museum</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Art Gallery</td>\n",
       "      <td>Museum</td>\n",
       "      <td>Café</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>National Mall</td>\n",
       "      <td>Science Museum</td>\n",
       "      <td>History Museum</td>\n",
       "      <td>Exhibit</td>\n",
       "      <td>Art Museum</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Art Gallery</td>\n",
       "      <td>Museum</td>\n",
       "      <td>Café</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>Foggy Bottom</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Vegetarian / Vegan Restaurant</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Café</td>\n",
       "      <td>Performing Arts Venue</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>Chinatown</td>\n",
       "      <td>Science Museum</td>\n",
       "      <td>History Museum</td>\n",
       "      <td>Art Museum</td>\n",
       "      <td>Theater</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Plaza</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Sculpture Garden</td>\n",
       "      <td>Salad Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>Dupont Circle</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Salad Place</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Hotel Bar</td>\n",
       "      <td>Mediterranean Restaurant</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>Foggy Bottom</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Vegetarian / Vegan Restaurant</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Café</td>\n",
       "      <td>Performing Arts Venue</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>L'Enfant Plaza</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Exhibit</td>\n",
       "      <td>Mediterranean Restaurant</td>\n",
       "      <td>Donut Shop</td>\n",
       "      <td>Fish Market</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Neighborhood 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "2           Capitol Hill         Historic Site            Food Truck   \n",
       "3    Downtown, Chinatown                 Hotel   American Restaurant   \n",
       "4     Mt Vernon Triangle                 Hotel           Coffee Shop   \n",
       "5             Georgetown    Italian Restaurant                 Hotel   \n",
       "11              East End            Food Truck                 Hotel   \n",
       "13     Cathedral Heights           Coffee Shop        Student Center   \n",
       "16       Benning Heights        Shop & Service           Gas Station   \n",
       "18  Southwest Waterfront               Theater                 Hotel   \n",
       "20         Dupont Circle           Coffee Shop        Sandwich Place   \n",
       "21          Foggy Bottom                 Hotel        Sandwich Place   \n",
       "22          Foggy Bottom                 Hotel        Sandwich Place   \n",
       "23      Mt Vernon Square   American Restaurant                 Hotel   \n",
       "25         Dupont Circle           Coffee Shop        Sandwich Place   \n",
       "27        L'Enfant Plaza            Food Truck        Sandwich Place   \n",
       "28          Capitol Hill         Historic Site            Food Truck   \n",
       "29  Southwest Washington         Boat or Ferry                  Park   \n",
       "30         National Mall        Science Museum        History Museum   \n",
       "31         National Mall        Science Museum        History Museum   \n",
       "33  Southwest Waterfront               Theater                 Hotel   \n",
       "38             Navy Yard        Sandwich Place    Italian Restaurant   \n",
       "39              East End            Food Truck                 Hotel   \n",
       "40        L'Enfant Plaza            Food Truck        Sandwich Place   \n",
       "41            Georgetown    Italian Restaurant                 Hotel   \n",
       "44        L'Enfant Plaza            Food Truck        Sandwich Place   \n",
       "45  Southwest Washington         Boat or Ferry                  Park   \n",
       "46          Foggy Bottom                 Hotel        Sandwich Place   \n",
       "47          Foggy Bottom                 Hotel        Sandwich Place   \n",
       "48              East End            Food Truck                 Hotel   \n",
       "49          Sursum Corda   American Restaurant    Italian Restaurant   \n",
       "50          Capitol Hill         Historic Site            Food Truck   \n",
       "52         National Mall        Science Museum        History Museum   \n",
       "53         National Mall        Science Museum        History Museum   \n",
       "54          Foggy Bottom                 Hotel        Sandwich Place   \n",
       "55             Chinatown        Science Museum        History Museum   \n",
       "56         Dupont Circle           Coffee Shop        Sandwich Place   \n",
       "57          Foggy Bottom                 Hotel        Sandwich Place   \n",
       "58        L'Enfant Plaza            Food Truck        Sandwich Place   \n",
       "\n",
       "                       3rd Most Common Venue 4th Most Common Venue  \\\n",
       "2                        American Restaurant           Pizza Place   \n",
       "3                                Coffee Shop        Science Museum   \n",
       "4                             Sandwich Place    Salon / Barbershop   \n",
       "5                                Coffee Shop         Grocery Store   \n",
       "11                            Sandwich Place   Monument / Landmark   \n",
       "13                                Bagel Shop          Burger Joint   \n",
       "16                                   Parking                  Park   \n",
       "18                           Harbor / Marina                  Park   \n",
       "20                                     Hotel           Salad Place   \n",
       "21                               Coffee Shop           Pizza Place   \n",
       "22                               Coffee Shop           Pizza Place   \n",
       "23                        Italian Restaurant      Ramen Restaurant   \n",
       "25                                     Hotel           Salad Place   \n",
       "27                               Coffee Shop                 Hotel   \n",
       "28                       American Restaurant           Pizza Place   \n",
       "29                                   Theater            Food Truck   \n",
       "30                                   Exhibit            Art Museum   \n",
       "31                                   Exhibit            Art Museum   \n",
       "33                           Harbor / Marina                  Park   \n",
       "38  Residential Building (Apartment / Condo)               Brewery   \n",
       "39                            Sandwich Place   Monument / Landmark   \n",
       "40                               Coffee Shop                 Hotel   \n",
       "41                               Coffee Shop         Grocery Store   \n",
       "44                               Coffee Shop                 Hotel   \n",
       "45                                   Theater            Food Truck   \n",
       "46                               Coffee Shop           Pizza Place   \n",
       "47                               Coffee Shop           Pizza Place   \n",
       "48                            Sandwich Place   Monument / Landmark   \n",
       "49                          Ramen Restaurant                   Bar   \n",
       "50                       American Restaurant           Pizza Place   \n",
       "52                                   Exhibit            Art Museum   \n",
       "53                                   Exhibit            Art Museum   \n",
       "54                               Coffee Shop           Pizza Place   \n",
       "55                                Art Museum               Theater   \n",
       "56                                     Hotel           Salad Place   \n",
       "57                               Coffee Shop           Pizza Place   \n",
       "58                               Coffee Shop                 Hotel   \n",
       "\n",
       "              5th Most Common Venue          6th Most Common Venue  \\\n",
       "2                              Café            Monument / Landmark   \n",
       "3                           Exhibit                 Sandwich Place   \n",
       "4                         Hotel Bar             Italian Restaurant   \n",
       "5               American Restaurant              French Restaurant   \n",
       "11                             Café                           Park   \n",
       "13                             Park                   Tennis Court   \n",
       "16               Seafood Restaurant                    Dry Cleaner   \n",
       "18                      Music Venue             Mexican Restaurant   \n",
       "20              American Restaurant                    Pizza Place   \n",
       "21              American Restaurant  Vegetarian / Vegan Restaurant   \n",
       "22              American Restaurant  Vegetarian / Vegan Restaurant   \n",
       "23                            Plaza        New American Restaurant   \n",
       "25              American Restaurant                    Pizza Place   \n",
       "27                     Burger Joint             Mexican Restaurant   \n",
       "28                             Café            Monument / Landmark   \n",
       "29                    Grocery Store                    Yoga Studio   \n",
       "30                       Food Truck                 Sandwich Place   \n",
       "31                       Food Truck                 Sandwich Place   \n",
       "33                      Music Venue             Mexican Restaurant   \n",
       "38  Southern / Soul Food Restaurant                 Clothing Store   \n",
       "39                             Café                           Park   \n",
       "40                     Burger Joint             Mexican Restaurant   \n",
       "41              American Restaurant              French Restaurant   \n",
       "44                     Burger Joint             Mexican Restaurant   \n",
       "45                    Grocery Store                    Yoga Studio   \n",
       "46              American Restaurant  Vegetarian / Vegan Restaurant   \n",
       "47              American Restaurant  Vegetarian / Vegan Restaurant   \n",
       "48                             Café                           Park   \n",
       "49              Japanese Restaurant                    Salad Place   \n",
       "50                             Café            Monument / Landmark   \n",
       "52                       Food Truck                 Sandwich Place   \n",
       "53                       Food Truck                 Sandwich Place   \n",
       "54              American Restaurant  Vegetarian / Vegan Restaurant   \n",
       "55              American Restaurant                    Coffee Shop   \n",
       "56              American Restaurant                    Pizza Place   \n",
       "57              American Restaurant  Vegetarian / Vegan Restaurant   \n",
       "58                     Burger Joint             Mexican Restaurant   \n",
       "\n",
       "       7th Most Common Venue      8th Most Common Venue  \\\n",
       "2           Sushi Restaurant                        Bar   \n",
       "3             History Museum                 Art Museum   \n",
       "4       Gym / Fitness Center  Latin American Restaurant   \n",
       "5           Asian Restaurant                Pizza Place   \n",
       "11             Deli / Bodega             Scenic Lookout   \n",
       "13            Shipping Store          College Cafeteria   \n",
       "16                Donut Shop             Sandwich Place   \n",
       "18               Fish Market         Seafood Restaurant   \n",
       "20               Yoga Studio                  Hotel Bar   \n",
       "21                    Bakery                       Café   \n",
       "22                    Bakery                       Café   \n",
       "23  Mediterranean Restaurant                Coffee Shop   \n",
       "25               Yoga Studio                  Hotel Bar   \n",
       "27                   Exhibit   Mediterranean Restaurant   \n",
       "28          Sushi Restaurant                        Bar   \n",
       "29                 Gastropub                Coffee Shop   \n",
       "30               Coffee Shop                Art Gallery   \n",
       "31               Coffee Shop                Art Gallery   \n",
       "33               Fish Market         Seafood Restaurant   \n",
       "38               Coffee Shop         Seafood Restaurant   \n",
       "39             Deli / Bodega             Scenic Lookout   \n",
       "40                   Exhibit   Mediterranean Restaurant   \n",
       "41          Asian Restaurant                Pizza Place   \n",
       "44                   Exhibit   Mediterranean Restaurant   \n",
       "45                 Gastropub                Coffee Shop   \n",
       "46                    Bakery                       Café   \n",
       "47                    Bakery                       Café   \n",
       "48             Deli / Bodega             Scenic Lookout   \n",
       "49                     Hotel                 Taco Place   \n",
       "50          Sushi Restaurant                        Bar   \n",
       "52               Coffee Shop                Art Gallery   \n",
       "53               Coffee Shop                Art Gallery   \n",
       "54                    Bakery                       Café   \n",
       "55                     Plaza                Pizza Place   \n",
       "56               Yoga Studio                  Hotel Bar   \n",
       "57                    Bakery                       Café   \n",
       "58                   Exhibit   Mediterranean Restaurant   \n",
       "\n",
       "       9th Most Common Venue    10th Most Common Venue  \n",
       "2                      Hotel                Taco Place  \n",
       "3                    Theater                    Museum  \n",
       "4           Sushi Restaurant             Deli / Bodega  \n",
       "5                  Hotel Bar            Cosmetics Shop  \n",
       "11               Coffee Shop      Gym / Fitness Center  \n",
       "13            Sandwich Place                      Bank  \n",
       "16            Breakfast Spot         Convenience Store  \n",
       "18               Coffee Shop            Ice Cream Shop  \n",
       "20  Mediterranean Restaurant           Thai Restaurant  \n",
       "21     Performing Arts Venue         Indian Restaurant  \n",
       "22     Performing Arts Venue         Indian Restaurant  \n",
       "23               Salad Place       Japanese Restaurant  \n",
       "25  Mediterranean Restaurant           Thai Restaurant  \n",
       "27                Donut Shop               Fish Market  \n",
       "28                     Hotel                Taco Place  \n",
       "29        Seafood Restaurant  Bike Rental / Bike Share  \n",
       "30                    Museum                      Café  \n",
       "31                    Museum                      Café  \n",
       "33               Coffee Shop            Ice Cream Shop  \n",
       "38               Salad Place                     River  \n",
       "39               Coffee Shop      Gym / Fitness Center  \n",
       "40                Donut Shop               Fish Market  \n",
       "41                 Hotel Bar            Cosmetics Shop  \n",
       "44                Donut Shop               Fish Market  \n",
       "45        Seafood Restaurant  Bike Rental / Bike Share  \n",
       "46     Performing Arts Venue         Indian Restaurant  \n",
       "47     Performing Arts Venue         Indian Restaurant  \n",
       "48               Coffee Shop      Gym / Fitness Center  \n",
       "49         Indian Restaurant              Cycle Studio  \n",
       "50                     Hotel                Taco Place  \n",
       "52                    Museum                      Café  \n",
       "53                    Museum                      Café  \n",
       "54     Performing Arts Venue         Indian Restaurant  \n",
       "55          Sculpture Garden               Salad Place  \n",
       "56  Mediterranean Restaurant           Thai Restaurant  \n",
       "57     Performing Arts Venue         Indian Restaurant  \n",
       "58                Donut Shop               Fish Market  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_merged.loc[dc_merged['Cluster Labels'] == 4, dc_merged.columns[[0] + list(range(5, dc_merged.shape[1]))]]"
   ]
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  {
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   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Neighborhood</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",
       "      <td>19</td>\n",
       "      <td>Congress Heights</td>\n",
       "      <td>Health &amp; Beauty Service</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Baseball Field</td>\n",
       "      <td>Park</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Fish Market</td>\n",
       "      <td>Falafel Restaurant</td>\n",
       "      <td>Farmers Market</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Filipino Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>Congress Heights</td>\n",
       "      <td>Health &amp; Beauty Service</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Baseball Field</td>\n",
       "      <td>Park</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Fish Market</td>\n",
       "      <td>Falafel Restaurant</td>\n",
       "      <td>Farmers Market</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Filipino Restaurant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Neighborhood    1st Most Common Venue 2nd Most Common Venue  \\\n",
       "19  Congress Heights  Health & Beauty Service                   Gym   \n",
       "36  Congress Heights  Health & Beauty Service                   Gym   \n",
       "\n",
       "   3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "19        Baseball Field                  Park           Yoga Studio   \n",
       "36        Baseball Field                  Park           Yoga Studio   \n",
       "\n",
       "   6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "19           Fish Market    Falafel Restaurant        Farmers Market   \n",
       "36           Fish Market    Falafel Restaurant        Farmers Market   \n",
       "\n",
       "   9th Most Common Venue 10th Most Common Venue  \n",
       "19  Fast Food Restaurant    Filipino Restaurant  \n",
       "36  Fast Food Restaurant    Filipino Restaurant  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "dc_merged.loc[dc_merged['Cluster Labels'] == 5, dc_merged.columns[[0] + list(range(5, dc_merged.shape[1]))]]"
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   "cell_type": "code",
   "execution_count": 33,
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   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Neighborhood</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",
       "      <td>34</td>\n",
       "      <td>Joint Base Anacostia-Bolling</td>\n",
       "      <td>Playground</td>\n",
       "      <td>Park</td>\n",
       "      <td>Donut Shop</td>\n",
       "      <td>Baseball Field</td>\n",
       "      <td>Post Office</td>\n",
       "      <td>Falafel Restaurant</td>\n",
       "      <td>Farmers Market</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Filipino Restaurant</td>\n",
       "      <td>Fish Market</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>Joint Base Anacostia-Bolling</td>\n",
       "      <td>Playground</td>\n",
       "      <td>Park</td>\n",
       "      <td>Donut Shop</td>\n",
       "      <td>Baseball Field</td>\n",
       "      <td>Post Office</td>\n",
       "      <td>Falafel Restaurant</td>\n",
       "      <td>Farmers Market</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Filipino Restaurant</td>\n",
       "      <td>Fish Market</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Neighborhood 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "34  Joint Base Anacostia-Bolling            Playground                  Park   \n",
       "35  Joint Base Anacostia-Bolling            Playground                  Park   \n",
       "\n",
       "   3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "34            Donut Shop        Baseball Field           Post Office   \n",
       "35            Donut Shop        Baseball Field           Post Office   \n",
       "\n",
       "   6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "34    Falafel Restaurant        Farmers Market  Fast Food Restaurant   \n",
       "35    Falafel Restaurant        Farmers Market  Fast Food Restaurant   \n",
       "\n",
       "   9th Most Common Venue 10th Most Common Venue  \n",
       "34   Filipino Restaurant            Fish Market  \n",
       "35   Filipino Restaurant            Fish Market  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_merged.loc[dc_merged['Cluster Labels'] == 6, dc_merged.columns[[0] + list(range(5, dc_merged.shape[1]))]]"
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  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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",
       "      <td>59</td>\n",
       "      <td>Gateway</td>\n",
       "      <td>Thrift / Vintage Store</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Wine Shop</td>\n",
       "      <td>Shipping Store</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Home Service</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Eye Doctor</td>\n",
       "      <td>Falafel Restaurant</td>\n",
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       "   Neighborhood   1st Most Common Venue 2nd Most Common Venue  \\\n",
       "59      Gateway  Thrift / Vintage Store           Gas Station   \n",
       "\n",
       "   3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "59             Wine Shop        Shipping Store          Liquor Store   \n",
       "\n",
       "   6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "59          Home Service           Yoga Studio  Fast Food Restaurant   \n",
       "\n",
       "   9th Most Common Venue 10th Most Common Venue  \n",
       "59            Eye Doctor     Falafel Restaurant  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
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    "dc_merged.loc[dc_merged['Cluster Labels'] == 7, dc_merged.columns[[0] + list(range(5, dc_merged.shape[1]))]]"
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   "execution_count": 35,
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       "      <th>2nd Most Common Venue</th>\n",
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       "      <td>Shaw</td>\n",
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       "      <td>Dive Bar</td>\n",
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       "      <td>Pet Store</td>\n",
       "      <td>Southern / Soul Food Restaurant</td>\n",
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       "  Neighborhood 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "0         Shaw          Intersection         Grocery Store   \n",
       "\n",
       "  3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "0  Gym / Fitness Center              Dive Bar               Dog Run   \n",
       "\n",
       "  6th Most Common Venue 7th Most Common Venue  \\\n",
       "0           Coffee Shop             Pet Store   \n",
       "\n",
       "             8th Most Common Venue 9th Most Common Venue  \\\n",
       "0  Southern / Soul Food Restaurant   Fried Chicken Joint   \n",
       "\n",
       "  10th Most Common Venue  \n",
       "0        Thai Restaurant  "
      ]
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     "execution_count": 35,
     "metadata": {},
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    }
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    "dc_merged.loc[dc_merged['Cluster Labels'] == 8, dc_merged.columns[[0] + list(range(5, dc_merged.shape[1]))]]"
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       "      <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",
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       "      <th>10th Most Common Venue</th>\n",
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       "      <td>10</td>\n",
       "      <td>Shepherd Park</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Donut Shop</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Caribbean Restaurant</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Gym</td>\n",
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       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>Carver</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Light Rail Station</td>\n",
       "      <td>Park</td>\n",
       "      <td>Pool</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Women's Store</td>\n",
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       "     Neighborhood 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "10  Shepherd Park  Fast Food Restaurant            Donut Shop   \n",
       "60         Carver  Fast Food Restaurant          Liquor Store   \n",
       "\n",
       "   3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "10     Convenience Store           Pizza Place          Liquor Store   \n",
       "60           Pizza Place        Sandwich Place    Light Rail Station   \n",
       "\n",
       "   6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "10  Caribbean Restaurant                  Bank                   Gym   \n",
       "60                  Park                  Pool              Pharmacy   \n",
       "\n",
       "   9th Most Common Venue 10th Most Common Venue  \n",
       "10           Wings Joint            Bus Station  \n",
       "60         Women's Store          Grocery Store  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_merged.loc[dc_merged['Cluster Labels'] == 9, dc_merged.columns[[0] + list(range(5, dc_merged.shape[1]))]]"
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   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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       "      <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",
       "      <td>37</td>\n",
       "      <td>Blue Plains</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Food Court</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Post Office</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Exhibit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Neighborhood 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "37  Blue Plains          Intersection            Food Truck   \n",
       "\n",
       "   3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "37            Food Court        Sandwich Place                 Hotel   \n",
       "\n",
       "   6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "37                   Gym           Post Office           Yoga Studio   \n",
       "\n",
       "   9th Most Common Venue 10th Most Common Venue  \n",
       "37  Fast Food Restaurant                Exhibit  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_merged.loc[dc_merged['Cluster Labels'] == 10, dc_merged.columns[[0] + list(range(5, dc_merged.shape[1]))]]"
   ]
  }
 ],
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