week-3-project-analysis.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Toronto Neighborhoods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "import requests\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from geopy.geocoders import Nominatim\n",
    "import folium\n",
    "import json\n",
    "from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- will use 'https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M'\n",
    "- check for status code and headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M\n",
      "200\n",
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     ]
    }
   ],
   "source": [
    "url = 'https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M'\n",
    "result = requests.get(url)\n",
    "print(url)\n",
    "print(result.status_code)\n",
    "print(result.headers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- will get data + clean it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "soup = BeautifulSoup(result.content, 'html.parser')\n",
    "table = soup.find('table')\n",
    "trs = table.find_all('tr')\n",
    "rows = []\n",
    "for tr in trs:\n",
    "    i = tr.find_all('td')\n",
    "    if i:\n",
    "        rows.append(i)\n",
    "        \n",
    "lst = []\n",
    "for row in rows:\n",
    "    postalcode = row[0].text.rstrip()\n",
    "    borough = row[1].text.rstrip()\n",
    "    neighborhood = row[2].text.rstrip()\n",
    "    if borough != 'Not assigned':\n",
    "        if neighborhood == 'Not assigned':\n",
    "            neighborhood = borough\n",
    "        lst.append([postalcode, borough, neighborhood])\n",
    "\n",
    "# lst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- convert into a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(212, 3)\n"
     ]
    }
   ],
   "source": [
    "cols = ['PostalCode', 'Borough', 'Neighborhood']\n",
    "df = pd.DataFrame(lst, columns=cols)\n",
    "print(df.shape)\n",
    "# df[df.duplicated(['PostalCode'], keep=False)] - this would have shown the duplicate PostalCodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- custom groupby / agg to merge Neighborhoods\n",
    "    - groupby PostalCode, keep the first Borough and join() Neighborhoods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.groupby('PostalCode').agg(\n",
    "    {\n",
    "        'Borough':'first', \n",
    "        'Neighborhood': ', '.join,}\n",
    "    ).reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- will check if 'M5A' example is done correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Harbourfront, Regent Park</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PostalCode           Borough               Neighborhood\n",
       "53        M5A  Downtown Toronto  Harbourfront, Regent Park"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['PostalCode'] == 'M5A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- will check the df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103, 3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- will read geo data from a CSV\n",
    "- need to rename a column so the merge (below) will work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfgeo = pd.read_csv(\"Geospatial_Coordinates.csv\")\n",
    "dfgeo.rename(columns={'Postal Code': 'PostalCode'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- need to merge 2 dataframes based on a PostalCode column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.merge(df, dfgeo, on=\"PostalCode\", how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- let's check if the example returns the expected value(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>M5G</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Central Bay Street</td>\n",
       "      <td>43.657952</td>\n",
       "      <td>-79.387383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   PostalCode           Borough        Neighborhood   Latitude  Longitude\n",
       "57        M5G  Downtown Toronto  Central Bay Street  43.657952 -79.387383"
      ]
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     "metadata": {},
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   ],
   "source": [
    "df2.loc[df2['PostalCode'] == 'M5G']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "      <th>PostalCode</th>\n",
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       "      <th>Neighborhood</th>\n",
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       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>M9V</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Albion Gardens, Beaumond Heights, Humbergate, ...</td>\n",
       "      <td>43.739416</td>\n",
       "      <td>-79.588437</td>\n",
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       "    PostalCode    Borough                                       Neighborhood  \\\n",
       "101        M9V  Etobicoke  Albion Gardens, Beaumond Heights, Humbergate, ...   \n",
       "\n",
       "      Latitude  Longitude  \n",
       "101  43.739416 -79.588437  "
      ]
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     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "df2.loc[df2['PostalCode'] == 'M9V']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Toronto map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The geograpical coordinates of Toronto are 43.653963, -79.387207.\n"
     ]
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            var circle_marker_949c9292ea3248ed93423d28183ee263 = L.circleMarker(
                [43.6362579, -79.49850909999999],
                {
  "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_b6276eb402fb49e99713c98069ddc2d0);
            
    
            var popup_bbfe995622f84cea8f057e8c0c251f48 = L.popup({maxWidth: '300'
            
            });

            
                var html_aeabdbbcc31649388dae690de9cea651 = $(`<div id="html_aeabdbbcc31649388dae690de9cea651" style="width: 100.0%; height: 100.0%;">M8Y</div>`)[0];
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            circle_marker_949c9292ea3248ed93423d28183ee263.bindPopup(popup_bbfe995622f84cea8f057e8c0c251f48)
            ;

            
        
    
            var circle_marker_57ede363c94f4dc5a62a60ad1e57d255 = L.circleMarker(
                [43.6288408, -79.52099940000001],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
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  "fillRule": "evenodd",
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}
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            var popup_48e782eb9f134775bbb9982a967350f7 = L.popup({maxWidth: '300'
            
            });

            
                var html_ef5b462678d94f589f8c2e92f4d081e5 = $(`<div id="html_ef5b462678d94f589f8c2e92f4d081e5" style="width: 100.0%; height: 100.0%;">M8Z</div>`)[0];
                popup_48e782eb9f134775bbb9982a967350f7.setContent(html_ef5b462678d94f589f8c2e92f4d081e5);
            

            circle_marker_57ede363c94f4dc5a62a60ad1e57d255.bindPopup(popup_48e782eb9f134775bbb9982a967350f7)
            ;

            
        
    
            var circle_marker_40e95c477a52493da880a4c944990962 = L.circleMarker(
                [43.6678556, -79.53224240000002],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
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  "dashOffset": null,
  "fill": true,
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}
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            var popup_83d66caea80e422e9cf18df5eafc83ea = L.popup({maxWidth: '300'
            
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                var html_60ccd6bb6ae043ef9f11957d6cbdd184 = $(`<div id="html_60ccd6bb6ae043ef9f11957d6cbdd184" style="width: 100.0%; height: 100.0%;">M9A</div>`)[0];
                popup_83d66caea80e422e9cf18df5eafc83ea.setContent(html_60ccd6bb6ae043ef9f11957d6cbdd184);
            

            circle_marker_40e95c477a52493da880a4c944990962.bindPopup(popup_83d66caea80e422e9cf18df5eafc83ea)
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            var circle_marker_ad8fa875c66f4fa2beed1a3b0cfa22c5 = L.circleMarker(
                [43.6509432, -79.55472440000001],
                {
  "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_b6276eb402fb49e99713c98069ddc2d0);
            
    
            var popup_2562a71adcfb4a28a5ca37261821b442 = L.popup({maxWidth: '300'
            
            });

            
                var html_5d1c38edcd0641f2a35c4512512aaf7d = $(`<div id="html_5d1c38edcd0641f2a35c4512512aaf7d" style="width: 100.0%; height: 100.0%;">M9B</div>`)[0];
                popup_2562a71adcfb4a28a5ca37261821b442.setContent(html_5d1c38edcd0641f2a35c4512512aaf7d);
            

            circle_marker_ad8fa875c66f4fa2beed1a3b0cfa22c5.bindPopup(popup_2562a71adcfb4a28a5ca37261821b442)
            ;

            
        
    
            var circle_marker_39896cd96c0a43b890b87633087725ae = L.circleMarker(
                [43.6435152, -79.57720079999999],
                {
  "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_b6276eb402fb49e99713c98069ddc2d0);
            
    
            var popup_2047d26041a44cc3a1cea4c30a694179 = L.popup({maxWidth: '300'
            
            });

            
                var html_bbf4fb6b641c44828ee8c97f8594b559 = $(`<div id="html_bbf4fb6b641c44828ee8c97f8594b559" style="width: 100.0%; height: 100.0%;">M9C</div>`)[0];
                popup_2047d26041a44cc3a1cea4c30a694179.setContent(html_bbf4fb6b641c44828ee8c97f8594b559);
            

            circle_marker_39896cd96c0a43b890b87633087725ae.bindPopup(popup_2047d26041a44cc3a1cea4c30a694179)
            ;

            
        
    
            var circle_marker_4d63d362b8ff4f82be986ed79072ccce = L.circleMarker(
                [43.7563033, -79.56596329999999],
                {
  "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_b6276eb402fb49e99713c98069ddc2d0);
            
    
            var popup_61c82eff925b4d2fabb8af5a2ff904e1 = L.popup({maxWidth: '300'
            
            });

            
                var html_fce2f13ba52247ceb61d4ae4249989e9 = $(`<div id="html_fce2f13ba52247ceb61d4ae4249989e9" style="width: 100.0%; height: 100.0%;">M9L</div>`)[0];
                popup_61c82eff925b4d2fabb8af5a2ff904e1.setContent(html_fce2f13ba52247ceb61d4ae4249989e9);
            

            circle_marker_4d63d362b8ff4f82be986ed79072ccce.bindPopup(popup_61c82eff925b4d2fabb8af5a2ff904e1)
            ;

            
        
    
            var circle_marker_986fd9d787ea45a8bc4e50904155f0f4 = L.circleMarker(
                [43.7247659, -79.53224240000002],
                {
  "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_b6276eb402fb49e99713c98069ddc2d0);
            
    
            var popup_a96e3527f62b406c8c42fc909f7cc90a = L.popup({maxWidth: '300'
            
            });

            
                var html_a0e74f2ac73a425090a1d13584d9c8ab = $(`<div id="html_a0e74f2ac73a425090a1d13584d9c8ab" style="width: 100.0%; height: 100.0%;">M9M</div>`)[0];
                popup_a96e3527f62b406c8c42fc909f7cc90a.setContent(html_a0e74f2ac73a425090a1d13584d9c8ab);
            

            circle_marker_986fd9d787ea45a8bc4e50904155f0f4.bindPopup(popup_a96e3527f62b406c8c42fc909f7cc90a)
            ;

            
        
    
            var circle_marker_66f441352bb14c9db21458b1ee244288 = L.circleMarker(
                [43.706876, -79.51818840000001],
                {
  "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_b6276eb402fb49e99713c98069ddc2d0);
            
    
            var popup_96b0ef6c189246dfb1c0a0a1edff8750 = L.popup({maxWidth: '300'
            
            });

            
                var html_24f9f9febaa445298c80b7123ae074b2 = $(`<div id="html_24f9f9febaa445298c80b7123ae074b2" style="width: 100.0%; height: 100.0%;">M9N</div>`)[0];
                popup_96b0ef6c189246dfb1c0a0a1edff8750.setContent(html_24f9f9febaa445298c80b7123ae074b2);
            

            circle_marker_66f441352bb14c9db21458b1ee244288.bindPopup(popup_96b0ef6c189246dfb1c0a0a1edff8750)
            ;

            
        
    
            var circle_marker_d158a8134ea141caaee6fba3e69266d2 = L.circleMarker(
                [43.696319, -79.53224240000002],
                {
  "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_b6276eb402fb49e99713c98069ddc2d0);
            
    
            var popup_7e342893a99442db9a8606769ac5358e = L.popup({maxWidth: '300'
            
            });

            
                var html_cfa0df5b080f414e8c975a23d3439fa8 = $(`<div id="html_cfa0df5b080f414e8c975a23d3439fa8" style="width: 100.0%; height: 100.0%;">M9P</div>`)[0];
                popup_7e342893a99442db9a8606769ac5358e.setContent(html_cfa0df5b080f414e8c975a23d3439fa8);
            

            circle_marker_d158a8134ea141caaee6fba3e69266d2.bindPopup(popup_7e342893a99442db9a8606769ac5358e)
            ;

            
        
    
            var circle_marker_fa8b596388b54fb28fd303b7961d0e21 = L.circleMarker(
                [43.6889054, -79.55472440000001],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
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  "radius": 5,
  "stroke": true,
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}
                )
                .addTo(map_b6276eb402fb49e99713c98069ddc2d0);
            
    
            var popup_d45db9aebfe84996a2063828bb392d00 = L.popup({maxWidth: '300'
            
            });

            
                var html_9fba9e6f59b54b5fabae159aaf6737f5 = $(`<div id="html_9fba9e6f59b54b5fabae159aaf6737f5" style="width: 100.0%; height: 100.0%;">M9R</div>`)[0];
                popup_d45db9aebfe84996a2063828bb392d00.setContent(html_9fba9e6f59b54b5fabae159aaf6737f5);
            

            circle_marker_fa8b596388b54fb28fd303b7961d0e21.bindPopup(popup_d45db9aebfe84996a2063828bb392d00)
            ;

            
        
    
            var circle_marker_c6c92f135c91492db5182c351cb08d04 = L.circleMarker(
                [43.739416399999996, -79.5884369],
                {
  "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_b6276eb402fb49e99713c98069ddc2d0);
            
    
            var popup_9a05fd5a5d7a4f628e5d347b8a7987a5 = L.popup({maxWidth: '300'
            
            });

            
                var html_e5e00b23e6434b37a38051b1247ae5c9 = $(`<div id="html_e5e00b23e6434b37a38051b1247ae5c9" style="width: 100.0%; height: 100.0%;">M9V</div>`)[0];
                popup_9a05fd5a5d7a4f628e5d347b8a7987a5.setContent(html_e5e00b23e6434b37a38051b1247ae5c9);
            

            circle_marker_c6c92f135c91492db5182c351cb08d04.bindPopup(popup_9a05fd5a5d7a4f628e5d347b8a7987a5)
            ;

            
        
    
            var circle_marker_5971aa94fd8b4c54b7599db042363226 = L.circleMarker(
                [43.706748299999994, -79.5940544],
                {
  "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_b6276eb402fb49e99713c98069ddc2d0);
            
    
            var popup_50d3424048884000a75ad2c80a1a4022 = L.popup({maxWidth: '300'
            
            });

            
                var html_f55bfac8f87b45d3bc06d7efad92fc03 = $(`<div id="html_f55bfac8f87b45d3bc06d7efad92fc03" style="width: 100.0%; height: 100.0%;">M9W</div>`)[0];
                popup_50d3424048884000a75ad2c80a1a4022.setContent(html_f55bfac8f87b45d3bc06d7efad92fc03);
            

            circle_marker_5971aa94fd8b4c54b7599db042363226.bindPopup(popup_50d3424048884000a75ad2c80a1a4022)
            ;

            
        
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x115769f28>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geolocator = Nominatim(user_agent=\"coursera\")\n",
    "address = 'Toronto'\n",
    "try:\n",
    "    location = geolocator.geocode(address)\n",
    "    latitude = location.latitude\n",
    "    longitude = location.longitude\n",
    "    print('The geograpical coordinates of {} are {}, {}.'.format(address, latitude, longitude))\n",
    "except AttributeError:\n",
    "    print('Cannot find: {}, will drop index: {}'.format(address, index))\n",
    "\n",
    "my_map = folium.Map(location=[latitude, longitude], zoom_start=11)\n",
    "\n",
    "# add markers to map\n",
    "for lat, lng, label in zip(df2['Latitude'], df2['Longitude'], df2['PostalCode']):\n",
    "    label = folium.Popup(label)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color='blue',\n",
    "        fill_color='#3186cc',\n",
    "        fill_opacity=0.7).add_to(my_map)  \n",
    "    \n",
    "my_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Will analyze with 4Square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLIENT_ID = '...' # Foursquare ID\n",
    "CLIENT_SECRET = '...' # Foursquare Secret\n",
    "VERSION = '20180605' # API version"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- will focus on Etobicoke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.set_index('PostalCode', inplace = True) \n",
    "neighborhood_latitude = df2.loc['M9V']['Latitude']\n",
    "neighborhood_longitude = df2.loc['M9V']['Longitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://api.foursquare.com/v2/venues/explore?&client_id=NE044IXTOCA202APNIBVZCC1NBTIV11D4JBNGW1P2ZRV3ONP&client_secret=F5LRZGFOSQPHT45EUX4DNN0MGYUDSROA4TQP1NMLR5RY4QLU&v=20180605&ll=43.739416399999996,-79.5884369&radius=500&limit=100'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LIMIT = 100 # limit of number of venues returned by Foursquare API\n",
    "radius = 500 # define radius\n",
    "# create 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",
    "    neighborhood_latitude, \n",
    "    neighborhood_longitude, \n",
    "    radius, \n",
    "    LIMIT)\n",
    "url # display URL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = requests.get(url).json()\n",
    "# results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function that extracts the category of the venue\n",
    "def get_category_type(row):\n",
    "    try:\n",
    "        categories_list = row['categories']\n",
    "    except:\n",
    "        categories_list = row['venue.categories']\n",
    "        \n",
    "    if len(categories_list) == 0:\n",
    "        return None\n",
    "    else:\n",
    "        return categories_list[0]['name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "venues = results['response']['groups'][0]['items']"
   ]
  },
  {
   "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>reasons.count</th>\n",
       "      <th>reasons.items</th>\n",
       "      <th>referralId</th>\n",
       "      <th>venue.categories</th>\n",
       "      <th>venue.id</th>\n",
       "      <th>venue.location.address</th>\n",
       "      <th>venue.location.cc</th>\n",
       "      <th>venue.location.city</th>\n",
       "      <th>venue.location.country</th>\n",
       "      <th>venue.location.crossStreet</th>\n",
       "      <th>venue.location.distance</th>\n",
       "      <th>venue.location.formattedAddress</th>\n",
       "      <th>venue.location.labeledLatLngs</th>\n",
       "      <th>venue.location.lat</th>\n",
       "      <th>venue.location.lng</th>\n",
       "      <th>venue.location.postalCode</th>\n",
       "      <th>venue.location.state</th>\n",
       "      <th>venue.name</th>\n",
       "      <th>venue.photos.count</th>\n",
       "      <th>venue.photos.groups</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
       "      <td>e-0-4b04a05bf964a520c45522e3-0</td>\n",
       "      <td>[{'id': '4bf58dd8d48988d118951735', 'name': 'G...</td>\n",
       "      <td>4b04a05bf964a520c45522e3</td>\n",
       "      <td>1530 Albion Rd</td>\n",
       "      <td>CA</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Canada</td>\n",
       "      <td>at Finch Ave. W.</td>\n",
       "      <td>318</td>\n",
       "      <td>[1530 Albion Rd (at Finch Ave. W.), Etobicoke ...</td>\n",
       "      <td>[{'label': 'display', 'lat': 43.74196806166214...</td>\n",
       "      <td>43.741968</td>\n",
       "      <td>-79.586639</td>\n",
       "      <td>M9V 1B4</td>\n",
       "      <td>ON</td>\n",
       "      <td>Sheriff's No Frills</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
       "      <td>e-0-4c633939e1621b8d48842553-1</td>\n",
       "      <td>[{'id': '4bf58dd8d48988d1c5941735', 'name': 'S...</td>\n",
       "      <td>4c633939e1621b8d48842553</td>\n",
       "      <td>6210 Finch Ave. W Suite 103</td>\n",
       "      <td>CA</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Canada</td>\n",
       "      <td>at Albion Rd.</td>\n",
       "      <td>344</td>\n",
       "      <td>[6210 Finch Ave. W Suite 103 (at Albion Rd.), ...</td>\n",
       "      <td>[{'label': 'display', 'lat': 43.74242142838322...</td>\n",
       "      <td>43.742421</td>\n",
       "      <td>-79.589471</td>\n",
       "      <td>M9V 3Y5</td>\n",
       "      <td>ON</td>\n",
       "      <td>Subway</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
       "      <td>e-0-4be70e26cf200f47e334153c-2</td>\n",
       "      <td>[{'id': '4d4ae6fc7a7b7dea34424761', 'name': 'F...</td>\n",
       "      <td>4be70e26cf200f47e334153c</td>\n",
       "      <td>1530 Albion Rd.</td>\n",
       "      <td>CA</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Canada</td>\n",
       "      <td>at Kipling Ave. (Albion Centre)</td>\n",
       "      <td>370</td>\n",
       "      <td>[1530 Albion Rd. (at Kipling Ave. (Albion Cent...</td>\n",
       "      <td>[{'label': 'display', 'lat': 43.74120165509377...</td>\n",
       "      <td>43.741202</td>\n",
       "      <td>-79.584545</td>\n",
       "      <td>M9V 1B4</td>\n",
       "      <td>ON</td>\n",
       "      <td>Popeyes Louisiana Kitchen</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
       "      <td>e-0-4cd4738cdfb4a1cd4337535c-3</td>\n",
       "      <td>[{'id': '5370f356bcbc57f1066c94c2', 'name': 'B...</td>\n",
       "      <td>4cd4738cdfb4a1cd4337535c</td>\n",
       "      <td>1530 Albion Rd</td>\n",
       "      <td>CA</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Near Finch Ave. W.</td>\n",
       "      <td>413</td>\n",
       "      <td>[1530 Albion Rd (Near Finch Ave. W.), Etobicok...</td>\n",
       "      <td>[{'label': 'display', 'lat': 43.7416936, 'lng'...</td>\n",
       "      <td>43.741694</td>\n",
       "      <td>-79.584373</td>\n",
       "      <td>M9V 1B4</td>\n",
       "      <td>ON</td>\n",
       "      <td>The Beer Store</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
       "      <td>e-0-4be58dc4cf200f479154133c-4</td>\n",
       "      <td>[{'id': '4bf58dd8d48988d10f951735', 'name': 'P...</td>\n",
       "      <td>4be58dc4cf200f479154133c</td>\n",
       "      <td>1530 Albion Rd</td>\n",
       "      <td>CA</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Albion Mall</td>\n",
       "      <td>438</td>\n",
       "      <td>[1530 Albion Rd (Albion Mall), Etobicoke ON M9...</td>\n",
       "      <td>[{'label': 'display', 'lat': 43.74083239297218...</td>\n",
       "      <td>43.740832</td>\n",
       "      <td>-79.583347</td>\n",
       "      <td>M9V 1B4</td>\n",
       "      <td>ON</td>\n",
       "      <td>Shoppers Drug Mart</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
       "      <td>e-0-4d8ba6910c4e41bdaaf7667f-5</td>\n",
       "      <td>[{'id': '4bf58dd8d48988d1ca941735', 'name': 'P...</td>\n",
       "      <td>4d8ba6910c4e41bdaaf7667f</td>\n",
       "      <td>1530 Albion Road, Unit T25</td>\n",
       "      <td>CA</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Canada</td>\n",
       "      <td>NaN</td>\n",
       "      <td>397</td>\n",
       "      <td>[1530 Albion Road, Unit T25, Etobicoke ON M9V ...</td>\n",
       "      <td>[{'label': 'display', 'lat': 43.74156896801906...</td>\n",
       "      <td>43.741569</td>\n",
       "      <td>-79.584489</td>\n",
       "      <td>M9V 1B4</td>\n",
       "      <td>ON</td>\n",
       "      <td>Pizza Pizza</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
       "      <td>e-0-5112b872e4b0c0a78d7bcd27-6</td>\n",
       "      <td>[{'id': '4bf58dd8d48988d118951735', 'name': 'G...</td>\n",
       "      <td>5112b872e4b0c0a78d7bcd27</td>\n",
       "      <td>1620 Albion road</td>\n",
       "      <td>CA</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Albion Road and Finch Ave</td>\n",
       "      <td>319</td>\n",
       "      <td>[1620 Albion road (Albion Road and Finch Ave),...</td>\n",
       "      <td>[{'label': 'display', 'lat': 43.74184023292111...</td>\n",
       "      <td>43.741840</td>\n",
       "      <td>-79.590561</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ON</td>\n",
       "      <td>Sunny Foodmart</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
       "      <td>e-0-4cd9d00734bb8cfa6576babf-7</td>\n",
       "      <td>[{'id': '4bf58dd8d48988d1e0931735', 'name': 'C...</td>\n",
       "      <td>4cd9d00734bb8cfa6576babf</td>\n",
       "      <td>6220 Finch Ave West</td>\n",
       "      <td>CA</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Albion</td>\n",
       "      <td>306</td>\n",
       "      <td>[6220 Finch Ave West (Albion), Etobicoke ON M9...</td>\n",
       "      <td>[{'label': 'display', 'lat': 43.7420146612893,...</td>\n",
       "      <td>43.742015</td>\n",
       "      <td>-79.589690</td>\n",
       "      <td>M9V 0A1</td>\n",
       "      <td>ON</td>\n",
       "      <td>Tim Hortons</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
       "      <td>e-0-4c1951d6834e2d7f2d3a2a80-8</td>\n",
       "      <td>[{'id': '4bf58dd8d48988d16e941735', 'name': 'F...</td>\n",
       "      <td>4c1951d6834e2d7f2d3a2a80</td>\n",
       "      <td>1530 Albion Road</td>\n",
       "      <td>CA</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>Canada</td>\n",
       "      <td>NaN</td>\n",
       "      <td>404</td>\n",
       "      <td>[1530 Albion Road, Toronto ON M9V 1B4, Canada]</td>\n",
       "      <td>[{'label': 'display', 'lat': 43.74163461048035...</td>\n",
       "      <td>43.741635</td>\n",
       "      <td>-79.584446</td>\n",
       "      <td>M9V 1B4</td>\n",
       "      <td>ON</td>\n",
       "      <td>McDonald's</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>[{'summary': 'This spot is popular', 'type': '...</td>\n",
       "      <td>e-0-4da73b855da37feee89a1143-9</td>\n",
       "      <td>[{'id': '4bf58dd8d48988d1ca941735', 'name': 'P...</td>\n",
       "      <td>4da73b855da37feee89a1143</td>\n",
       "      <td>1701 Martingrove Road</td>\n",
       "      <td>CA</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>Canada</td>\n",
       "      <td>NaN</td>\n",
       "      <td>315</td>\n",
       "      <td>[1701 Martingrove Road, Toronto ON M9V 4N4, Ca...</td>\n",
       "      <td>[{'label': 'display', 'lat': 43.73676056243852...</td>\n",
       "      <td>43.736761</td>\n",
       "      <td>-79.589817</td>\n",
       "      <td>M9V 4N4</td>\n",
       "      <td>ON</td>\n",
       "      <td>Pizza Nova</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   reasons.count                                      reasons.items  \\\n",
       "0              0  [{'summary': 'This spot is popular', 'type': '...   \n",
       "1              0  [{'summary': 'This spot is popular', 'type': '...   \n",
       "2              0  [{'summary': 'This spot is popular', 'type': '...   \n",
       "3              0  [{'summary': 'This spot is popular', 'type': '...   \n",
       "4              0  [{'summary': 'This spot is popular', 'type': '...   \n",
       "5              0  [{'summary': 'This spot is popular', 'type': '...   \n",
       "6              0  [{'summary': 'This spot is popular', 'type': '...   \n",
       "7              0  [{'summary': 'This spot is popular', 'type': '...   \n",
       "8              0  [{'summary': 'This spot is popular', 'type': '...   \n",
       "9              0  [{'summary': 'This spot is popular', 'type': '...   \n",
       "\n",
       "                       referralId  \\\n",
       "0  e-0-4b04a05bf964a520c45522e3-0   \n",
       "1  e-0-4c633939e1621b8d48842553-1   \n",
       "2  e-0-4be70e26cf200f47e334153c-2   \n",
       "3  e-0-4cd4738cdfb4a1cd4337535c-3   \n",
       "4  e-0-4be58dc4cf200f479154133c-4   \n",
       "5  e-0-4d8ba6910c4e41bdaaf7667f-5   \n",
       "6  e-0-5112b872e4b0c0a78d7bcd27-6   \n",
       "7  e-0-4cd9d00734bb8cfa6576babf-7   \n",
       "8  e-0-4c1951d6834e2d7f2d3a2a80-8   \n",
       "9  e-0-4da73b855da37feee89a1143-9   \n",
       "\n",
       "                                    venue.categories  \\\n",
       "0  [{'id': '4bf58dd8d48988d118951735', 'name': 'G...   \n",
       "1  [{'id': '4bf58dd8d48988d1c5941735', 'name': 'S...   \n",
       "2  [{'id': '4d4ae6fc7a7b7dea34424761', 'name': 'F...   \n",
       "3  [{'id': '5370f356bcbc57f1066c94c2', 'name': 'B...   \n",
       "4  [{'id': '4bf58dd8d48988d10f951735', 'name': 'P...   \n",
       "5  [{'id': '4bf58dd8d48988d1ca941735', 'name': 'P...   \n",
       "6  [{'id': '4bf58dd8d48988d118951735', 'name': 'G...   \n",
       "7  [{'id': '4bf58dd8d48988d1e0931735', 'name': 'C...   \n",
       "8  [{'id': '4bf58dd8d48988d16e941735', 'name': 'F...   \n",
       "9  [{'id': '4bf58dd8d48988d1ca941735', 'name': 'P...   \n",
       "\n",
       "                   venue.id       venue.location.address venue.location.cc  \\\n",
       "0  4b04a05bf964a520c45522e3               1530 Albion Rd                CA   \n",
       "1  4c633939e1621b8d48842553  6210 Finch Ave. W Suite 103                CA   \n",
       "2  4be70e26cf200f47e334153c              1530 Albion Rd.                CA   \n",
       "3  4cd4738cdfb4a1cd4337535c               1530 Albion Rd                CA   \n",
       "4  4be58dc4cf200f479154133c               1530 Albion Rd                CA   \n",
       "5  4d8ba6910c4e41bdaaf7667f   1530 Albion Road, Unit T25                CA   \n",
       "6  5112b872e4b0c0a78d7bcd27             1620 Albion road                CA   \n",
       "7  4cd9d00734bb8cfa6576babf          6220 Finch Ave West                CA   \n",
       "8  4c1951d6834e2d7f2d3a2a80             1530 Albion Road                CA   \n",
       "9  4da73b855da37feee89a1143        1701 Martingrove Road                CA   \n",
       "\n",
       "  venue.location.city venue.location.country       venue.location.crossStreet  \\\n",
       "0           Etobicoke                 Canada                 at Finch Ave. W.   \n",
       "1           Etobicoke                 Canada                    at Albion Rd.   \n",
       "2           Etobicoke                 Canada  at Kipling Ave. (Albion Centre)   \n",
       "3           Etobicoke                 Canada               Near Finch Ave. W.   \n",
       "4           Etobicoke                 Canada                      Albion Mall   \n",
       "5           Etobicoke                 Canada                              NaN   \n",
       "6             Toronto                 Canada        Albion Road and Finch Ave   \n",
       "7           Etobicoke                 Canada                           Albion   \n",
       "8             Toronto                 Canada                              NaN   \n",
       "9             Toronto                 Canada                              NaN   \n",
       "\n",
       "   venue.location.distance                    venue.location.formattedAddress  \\\n",
       "0                      318  [1530 Albion Rd (at Finch Ave. W.), Etobicoke ...   \n",
       "1                      344  [6210 Finch Ave. W Suite 103 (at Albion Rd.), ...   \n",
       "2                      370  [1530 Albion Rd. (at Kipling Ave. (Albion Cent...   \n",
       "3                      413  [1530 Albion Rd (Near Finch Ave. W.), Etobicok...   \n",
       "4                      438  [1530 Albion Rd (Albion Mall), Etobicoke ON M9...   \n",
       "5                      397  [1530 Albion Road, Unit T25, Etobicoke ON M9V ...   \n",
       "6                      319  [1620 Albion road (Albion Road and Finch Ave),...   \n",
       "7                      306  [6220 Finch Ave West (Albion), Etobicoke ON M9...   \n",
       "8                      404     [1530 Albion Road, Toronto ON M9V 1B4, Canada]   \n",
       "9                      315  [1701 Martingrove Road, Toronto ON M9V 4N4, Ca...   \n",
       "\n",
       "                       venue.location.labeledLatLngs  venue.location.lat  \\\n",
       "0  [{'label': 'display', 'lat': 43.74196806166214...           43.741968   \n",
       "1  [{'label': 'display', 'lat': 43.74242142838322...           43.742421   \n",
       "2  [{'label': 'display', 'lat': 43.74120165509377...           43.741202   \n",
       "3  [{'label': 'display', 'lat': 43.7416936, 'lng'...           43.741694   \n",
       "4  [{'label': 'display', 'lat': 43.74083239297218...           43.740832   \n",
       "5  [{'label': 'display', 'lat': 43.74156896801906...           43.741569   \n",
       "6  [{'label': 'display', 'lat': 43.74184023292111...           43.741840   \n",
       "7  [{'label': 'display', 'lat': 43.7420146612893,...           43.742015   \n",
       "8  [{'label': 'display', 'lat': 43.74163461048035...           43.741635   \n",
       "9  [{'label': 'display', 'lat': 43.73676056243852...           43.736761   \n",
       "\n",
       "   venue.location.lng venue.location.postalCode venue.location.state  \\\n",
       "0          -79.586639                   M9V 1B4                   ON   \n",
       "1          -79.589471                   M9V 3Y5                   ON   \n",
       "2          -79.584545                   M9V 1B4                   ON   \n",
       "3          -79.584373                   M9V 1B4                   ON   \n",
       "4          -79.583347                   M9V 1B4                   ON   \n",
       "5          -79.584489                   M9V 1B4                   ON   \n",
       "6          -79.590561                       NaN                   ON   \n",
       "7          -79.589690                   M9V 0A1                   ON   \n",
       "8          -79.584446                   M9V 1B4                   ON   \n",
       "9          -79.589817                   M9V 4N4                   ON   \n",
       "\n",
       "                  venue.name  venue.photos.count venue.photos.groups  \n",
       "0        Sheriff's No Frills                   0                  []  \n",
       "1                     Subway                   0                  []  \n",
       "2  Popeyes Louisiana Kitchen                   0                  []  \n",
       "3             The Beer Store                   0                  []  \n",
       "4         Shoppers Drug Mart                   0                  []  \n",
       "5                Pizza Pizza                   0                  []  \n",
       "6             Sunny Foodmart                   0                  []  \n",
       "7                Tim Hortons                   0                  []  \n",
       "8                 McDonald's                   0                  []  \n",
       "9                 Pizza Nova                   0                  []  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nearby_venues = json_normalize(venues) # flatten JSON\n",
    "nearby_venues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>name</th>\n",
       "      <th>categories</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sheriff's No Frills</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>43.741968</td>\n",
       "      <td>-79.586639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Subway</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>43.742421</td>\n",
       "      <td>-79.589471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Popeyes Louisiana Kitchen</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>43.741202</td>\n",
       "      <td>-79.584545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Beer Store</td>\n",
       "      <td>Beer Store</td>\n",
       "      <td>43.741694</td>\n",
       "      <td>-79.584373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shoppers Drug Mart</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>43.740832</td>\n",
       "      <td>-79.583347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name           categories        lat        lng\n",
       "0        Sheriff's No Frills        Grocery Store  43.741968 -79.586639\n",
       "1                     Subway       Sandwich Place  43.742421 -79.589471\n",
       "2  Popeyes Louisiana Kitchen  Fried Chicken Joint  43.741202 -79.584545\n",
       "3             The Beer Store           Beer Store  43.741694 -79.584373\n",
       "4         Shoppers Drug Mart             Pharmacy  43.740832 -79.583347"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter columns\n",
    "filtered_columns = ['venue.name', 'venue.categories', 'venue.location.lat', 'venue.location.lng']\n",
    "nearby_venues = nearby_venues.loc[:, filtered_columns]\n",
    "\n",
    "# filter the category for each row\n",
    "nearby_venues['venue.categories'] = nearby_venues.apply(get_category_type, axis=1)\n",
    "\n",
    "# clean columns\n",
    "nearby_venues.columns = [col.split(\".\")[-1] for col in nearby_venues.columns]\n",
    "\n",
    "nearby_venues.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 venues were returned by Foursquare.\n"
     ]
    }
   ],
   "source": [
    "print('{} venues were returned by Foursquare.'.format(nearby_venues.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PostalCode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>M1B</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge, Malvern</td>\n",
       "      <td>43.806686</td>\n",
       "      <td>-79.194353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M1C</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Highland Creek, Rouge Hill, Port Union</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M1E</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Guildwood, Morningside, West Hill</td>\n",
       "      <td>43.763573</td>\n",
       "      <td>-79.188711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M1G</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>43.770992</td>\n",
       "      <td>-79.216917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M1H</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "      <td>43.773136</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M1J</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Scarborough Village</td>\n",
       "      <td>43.744734</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M1K</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>East Birchmount Park, Ionview, Kennedy Park</td>\n",
       "      <td>43.727929</td>\n",
       "      <td>-79.262029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M1L</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Clairlea, Golden Mile, Oakridge</td>\n",
       "      <td>43.711112</td>\n",
       "      <td>-79.284577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M1M</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cliffcrest, Cliffside, Scarborough Village West</td>\n",
       "      <td>43.716316</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M1N</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Birch Cliff, Cliffside West</td>\n",
       "      <td>43.692657</td>\n",
       "      <td>-79.264848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M1P</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Dorset Park, Scarborough Town Centre, Wexford ...</td>\n",
       "      <td>43.757410</td>\n",
       "      <td>-79.273304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M1R</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Maryvale, Wexford</td>\n",
       "      <td>43.750072</td>\n",
       "      <td>-79.295849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M1S</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Agincourt</td>\n",
       "      <td>43.794200</td>\n",
       "      <td>-79.262029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M1T</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Clarks Corners, Sullivan, Tam O'Shanter</td>\n",
       "      <td>43.781638</td>\n",
       "      <td>-79.304302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M1V</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Agincourt North, L'Amoreaux East, Milliken, St...</td>\n",
       "      <td>43.815252</td>\n",
       "      <td>-79.284577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M1W</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>L'Amoreaux West, Steeles West</td>\n",
       "      <td>43.799525</td>\n",
       "      <td>-79.318389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M1X</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Upper Rouge</td>\n",
       "      <td>43.836125</td>\n",
       "      <td>-79.205636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M2H</th>\n",
       "      <td>North York</td>\n",
       "      <td>Hillcrest Village</td>\n",
       "      <td>43.803762</td>\n",
       "      <td>-79.363452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M2J</th>\n",
       "      <td>North York</td>\n",
       "      <td>Fairview, Henry Farm, Oriole</td>\n",
       "      <td>43.778517</td>\n",
       "      <td>-79.346556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M2K</th>\n",
       "      <td>North York</td>\n",
       "      <td>Bayview Village</td>\n",
       "      <td>43.786947</td>\n",
       "      <td>-79.385975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M2L</th>\n",
       "      <td>North York</td>\n",
       "      <td>Silver Hills, York Mills</td>\n",
       "      <td>43.757490</td>\n",
       "      <td>-79.374714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M2M</th>\n",
       "      <td>North York</td>\n",
       "      <td>Newtonbrook, Willowdale</td>\n",
       "      <td>43.789053</td>\n",
       "      <td>-79.408493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M2N</th>\n",
       "      <td>North York</td>\n",
       "      <td>Willowdale South</td>\n",
       "      <td>43.770120</td>\n",
       "      <td>-79.408493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M2P</th>\n",
       "      <td>North York</td>\n",
       "      <td>York Mills West</td>\n",
       "      <td>43.752758</td>\n",
       "      <td>-79.400049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M2R</th>\n",
       "      <td>North York</td>\n",
       "      <td>Willowdale West</td>\n",
       "      <td>43.782736</td>\n",
       "      <td>-79.442259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M3A</th>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "      <td>43.753259</td>\n",
       "      <td>-79.329656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M3B</th>\n",
       "      <td>North York</td>\n",
       "      <td>Don Mills North</td>\n",
       "      <td>43.745906</td>\n",
       "      <td>-79.352188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M3C</th>\n",
       "      <td>North York</td>\n",
       "      <td>Flemingdon Park, Don Mills South</td>\n",
       "      <td>43.725900</td>\n",
       "      <td>-79.340923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M3H</th>\n",
       "      <td>North York</td>\n",
       "      <td>Bathurst Manor, Downsview North, Wilson Heights</td>\n",
       "      <td>43.754328</td>\n",
       "      <td>-79.442259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M3J</th>\n",
       "      <td>North York</td>\n",
       "      <td>Northwood Park, York University</td>\n",
       "      <td>43.767980</td>\n",
       "      <td>-79.487262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M6C</th>\n",
       "      <td>York</td>\n",
       "      <td>Humewood-Cedarvale</td>\n",
       "      <td>43.693781</td>\n",
       "      <td>-79.428191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M6E</th>\n",
       "      <td>York</td>\n",
       "      <td>Caledonia-Fairbanks</td>\n",
       "      <td>43.689026</td>\n",
       "      <td>-79.453512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M6G</th>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Christie</td>\n",
       "      <td>43.669542</td>\n",
       "      <td>-79.422564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M6H</th>\n",
       "      <td>West Toronto</td>\n",
       "      <td>Dovercourt Village, Dufferin</td>\n",
       "      <td>43.669005</td>\n",
       "      <td>-79.442259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M6J</th>\n",
       "      <td>West Toronto</td>\n",
       "      <td>Little Portugal, Trinity</td>\n",
       "      <td>43.647927</td>\n",
       "      <td>-79.419750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M6K</th>\n",
       "      <td>West Toronto</td>\n",
       "      <td>Brockton, Exhibition Place, Parkdale Village</td>\n",
       "      <td>43.636847</td>\n",
       "      <td>-79.428191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M6L</th>\n",
       "      <td>North York</td>\n",
       "      <td>Maple Leaf Park, North Park, Upwood Park</td>\n",
       "      <td>43.713756</td>\n",
       "      <td>-79.490074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M6M</th>\n",
       "      <td>York</td>\n",
       "      <td>Del Ray, Keelesdale, Mount Dennis, Silverthorn</td>\n",
       "      <td>43.691116</td>\n",
       "      <td>-79.476013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M6N</th>\n",
       "      <td>York</td>\n",
       "      <td>The Junction North, Runnymede</td>\n",
       "      <td>43.673185</td>\n",
       "      <td>-79.487262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M6P</th>\n",
       "      <td>West Toronto</td>\n",
       "      <td>High Park, The Junction South</td>\n",
       "      <td>43.661608</td>\n",
       "      <td>-79.464763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M6R</th>\n",
       "      <td>West Toronto</td>\n",
       "      <td>Parkdale, Roncesvalles</td>\n",
       "      <td>43.648960</td>\n",
       "      <td>-79.456325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M6S</th>\n",
       "      <td>West Toronto</td>\n",
       "      <td>Runnymede, Swansea</td>\n",
       "      <td>43.651571</td>\n",
       "      <td>-79.484450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M7A</th>\n",
       "      <td>Queen's Park</td>\n",
       "      <td>Queen's Park</td>\n",
       "      <td>43.662301</td>\n",
       "      <td>-79.389494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M7R</th>\n",
       "      <td>Mississauga</td>\n",
       "      <td>Canada Post Gateway Processing Centre</td>\n",
       "      <td>43.636966</td>\n",
       "      <td>-79.615819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M7Y</th>\n",
       "      <td>East Toronto</td>\n",
       "      <td>Business reply mail Processing Centre969 Eastern</td>\n",
       "      <td>43.662744</td>\n",
       "      <td>-79.321558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M8V</th>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Humber Bay Shores, Mimico South, New Toronto</td>\n",
       "      <td>43.605647</td>\n",
       "      <td>-79.501321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M8W</th>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Alderwood, Long Branch</td>\n",
       "      <td>43.602414</td>\n",
       "      <td>-79.543484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M8X</th>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>The Kingsway, Montgomery Road, Old Mill North</td>\n",
       "      <td>43.653654</td>\n",
       "      <td>-79.506944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M8Y</th>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Humber Bay, King's Mill Park, Kingsway Park So...</td>\n",
       "      <td>43.636258</td>\n",
       "      <td>-79.498509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M8Z</th>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Kingsway Park South West, Mimico NW, The Queen...</td>\n",
       "      <td>43.628841</td>\n",
       "      <td>-79.520999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M9A</th>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Islington Avenue</td>\n",
       "      <td>43.667856</td>\n",
       "      <td>-79.532242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M9B</th>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Cloverdale, Islington, Martin Grove, Princess ...</td>\n",
       "      <td>43.650943</td>\n",
       "      <td>-79.554724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M9C</th>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Bloordale Gardens, Eringate, Markland Wood, Ol...</td>\n",
       "      <td>43.643515</td>\n",
       "      <td>-79.577201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M9L</th>\n",
       "      <td>North York</td>\n",
       "      <td>Humber Summit</td>\n",
       "      <td>43.756303</td>\n",
       "      <td>-79.565963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M9M</th>\n",
       "      <td>North York</td>\n",
       "      <td>Emery, Humberlea</td>\n",
       "      <td>43.724766</td>\n",
       "      <td>-79.532242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M9N</th>\n",
       "      <td>York</td>\n",
       "      <td>Weston</td>\n",
       "      <td>43.706876</td>\n",
       "      <td>-79.518188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M9P</th>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Westmount</td>\n",
       "      <td>43.696319</td>\n",
       "      <td>-79.532242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M9R</th>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Kingsview Village, Martin Grove Gardens, Richv...</td>\n",
       "      <td>43.688905</td>\n",
       "      <td>-79.554724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M9V</th>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Albion Gardens, Beaumond Heights, Humbergate, ...</td>\n",
       "      <td>43.739416</td>\n",
       "      <td>-79.588437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M9W</th>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Northwest</td>\n",
       "      <td>43.706748</td>\n",
       "      <td>-79.594054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Borough  \\\n",
       "PostalCode                     \n",
       "M1B              Scarborough   \n",
       "M1C              Scarborough   \n",
       "M1E              Scarborough   \n",
       "M1G              Scarborough   \n",
       "M1H              Scarborough   \n",
       "M1J              Scarborough   \n",
       "M1K              Scarborough   \n",
       "M1L              Scarborough   \n",
       "M1M              Scarborough   \n",
       "M1N              Scarborough   \n",
       "M1P              Scarborough   \n",
       "M1R              Scarborough   \n",
       "M1S              Scarborough   \n",
       "M1T              Scarborough   \n",
       "M1V              Scarborough   \n",
       "M1W              Scarborough   \n",
       "M1X              Scarborough   \n",
       "M2H               North York   \n",
       "M2J               North York   \n",
       "M2K               North York   \n",
       "M2L               North York   \n",
       "M2M               North York   \n",
       "M2N               North York   \n",
       "M2P               North York   \n",
       "M2R               North York   \n",
       "M3A               North York   \n",
       "M3B               North York   \n",
       "M3C               North York   \n",
       "M3H               North York   \n",
       "M3J               North York   \n",
       "...                      ...   \n",
       "M6C                     York   \n",
       "M6E                     York   \n",
       "M6G         Downtown Toronto   \n",
       "M6H             West Toronto   \n",
       "M6J             West Toronto   \n",
       "M6K             West Toronto   \n",
       "M6L               North York   \n",
       "M6M                     York   \n",
       "M6N                     York   \n",
       "M6P             West Toronto   \n",
       "M6R             West Toronto   \n",
       "M6S             West Toronto   \n",
       "M7A             Queen's Park   \n",
       "M7R              Mississauga   \n",
       "M7Y             East Toronto   \n",
       "M8V                Etobicoke   \n",
       "M8W                Etobicoke   \n",
       "M8X                Etobicoke   \n",
       "M8Y                Etobicoke   \n",
       "M8Z                Etobicoke   \n",
       "M9A                Etobicoke   \n",
       "M9B                Etobicoke   \n",
       "M9C                Etobicoke   \n",
       "M9L               North York   \n",
       "M9M               North York   \n",
       "M9N                     York   \n",
       "M9P                Etobicoke   \n",
       "M9R                Etobicoke   \n",
       "M9V                Etobicoke   \n",
       "M9W                Etobicoke   \n",
       "\n",
       "                                                 Neighborhood   Latitude  \\\n",
       "PostalCode                                                                 \n",
       "M1B                                            Rouge, Malvern  43.806686   \n",
       "M1C                    Highland Creek, Rouge Hill, Port Union  43.784535   \n",
       "M1E                         Guildwood, Morningside, West Hill  43.763573   \n",
       "M1G                                                    Woburn  43.770992   \n",
       "M1H                                                 Cedarbrae  43.773136   \n",
       "M1J                                       Scarborough Village  43.744734   \n",
       "M1K               East Birchmount Park, Ionview, Kennedy Park  43.727929   \n",
       "M1L                           Clairlea, Golden Mile, Oakridge  43.711112   \n",
       "M1M           Cliffcrest, Cliffside, Scarborough Village West  43.716316   \n",
       "M1N                               Birch Cliff, Cliffside West  43.692657   \n",
       "M1P         Dorset Park, Scarborough Town Centre, Wexford ...  43.757410   \n",
       "M1R                                         Maryvale, Wexford  43.750072   \n",
       "M1S                                                 Agincourt  43.794200   \n",
       "M1T                   Clarks Corners, Sullivan, Tam O'Shanter  43.781638   \n",
       "M1V         Agincourt North, L'Amoreaux East, Milliken, St...  43.815252   \n",
       "M1W                             L'Amoreaux West, Steeles West  43.799525   \n",
       "M1X                                               Upper Rouge  43.836125   \n",
       "M2H                                         Hillcrest Village  43.803762   \n",
       "M2J                              Fairview, Henry Farm, Oriole  43.778517   \n",
       "M2K                                           Bayview Village  43.786947   \n",
       "M2L                                  Silver Hills, York Mills  43.757490   \n",
       "M2M                                   Newtonbrook, Willowdale  43.789053   \n",
       "M2N                                          Willowdale South  43.770120   \n",
       "M2P                                           York Mills West  43.752758   \n",
       "M2R                                           Willowdale West  43.782736   \n",
       "M3A                                                 Parkwoods  43.753259   \n",
       "M3B                                           Don Mills North  43.745906   \n",
       "M3C                          Flemingdon Park, Don Mills South  43.725900   \n",
       "M3H           Bathurst Manor, Downsview North, Wilson Heights  43.754328   \n",
       "M3J                           Northwood Park, York University  43.767980   \n",
       "...                                                       ...        ...   \n",
       "M6C                                        Humewood-Cedarvale  43.693781   \n",
       "M6E                                       Caledonia-Fairbanks  43.689026   \n",
       "M6G                                                  Christie  43.669542   \n",
       "M6H                              Dovercourt Village, Dufferin  43.669005   \n",
       "M6J                                  Little Portugal, Trinity  43.647927   \n",
       "M6K              Brockton, Exhibition Place, Parkdale Village  43.636847   \n",
       "M6L                  Maple Leaf Park, North Park, Upwood Park  43.713756   \n",
       "M6M            Del Ray, Keelesdale, Mount Dennis, Silverthorn  43.691116   \n",
       "M6N                             The Junction North, Runnymede  43.673185   \n",
       "M6P                             High Park, The Junction South  43.661608   \n",
       "M6R                                    Parkdale, Roncesvalles  43.648960   \n",
       "M6S                                        Runnymede, Swansea  43.651571   \n",
       "M7A                                              Queen's Park  43.662301   \n",
       "M7R                     Canada Post Gateway Processing Centre  43.636966   \n",
       "M7Y          Business reply mail Processing Centre969 Eastern  43.662744   \n",
       "M8V              Humber Bay Shores, Mimico South, New Toronto  43.605647   \n",
       "M8W                                    Alderwood, Long Branch  43.602414   \n",
       "M8X             The Kingsway, Montgomery Road, Old Mill North  43.653654   \n",
       "M8Y         Humber Bay, King's Mill Park, Kingsway Park So...  43.636258   \n",
       "M8Z         Kingsway Park South West, Mimico NW, The Queen...  43.628841   \n",
       "M9A                                          Islington Avenue  43.667856   \n",
       "M9B         Cloverdale, Islington, Martin Grove, Princess ...  43.650943   \n",
       "M9C         Bloordale Gardens, Eringate, Markland Wood, Ol...  43.643515   \n",
       "M9L                                             Humber Summit  43.756303   \n",
       "M9M                                          Emery, Humberlea  43.724766   \n",
       "M9N                                                    Weston  43.706876   \n",
       "M9P                                                 Westmount  43.696319   \n",
       "M9R         Kingsview Village, Martin Grove Gardens, Richv...  43.688905   \n",
       "M9V         Albion Gardens, Beaumond Heights, Humbergate, ...  43.739416   \n",
       "M9W                                                 Northwest  43.706748   \n",
       "\n",
       "            Longitude  \n",
       "PostalCode             \n",
       "M1B        -79.194353  \n",
       "M1C        -79.160497  \n",
       "M1E        -79.188711  \n",
       "M1G        -79.216917  \n",
       "M1H        -79.239476  \n",
       "M1J        -79.239476  \n",
       "M1K        -79.262029  \n",
       "M1L        -79.284577  \n",
       "M1M        -79.239476  \n",
       "M1N        -79.264848  \n",
       "M1P        -79.273304  \n",
       "M1R        -79.295849  \n",
       "M1S        -79.262029  \n",
       "M1T        -79.304302  \n",
       "M1V        -79.284577  \n",
       "M1W        -79.318389  \n",
       "M1X        -79.205636  \n",
       "M2H        -79.363452  \n",
       "M2J        -79.346556  \n",
       "M2K        -79.385975  \n",
       "M2L        -79.374714  \n",
       "M2M        -79.408493  \n",
       "M2N        -79.408493  \n",
       "M2P        -79.400049  \n",
       "M2R        -79.442259  \n",
       "M3A        -79.329656  \n",
       "M3B        -79.352188  \n",
       "M3C        -79.340923  \n",
       "M3H        -79.442259  \n",
       "M3J        -79.487262  \n",
       "...               ...  \n",
       "M6C        -79.428191  \n",
       "M6E        -79.453512  \n",
       "M6G        -79.422564  \n",
       "M6H        -79.442259  \n",
       "M6J        -79.419750  \n",
       "M6K        -79.428191  \n",
       "M6L        -79.490074  \n",
       "M6M        -79.476013  \n",
       "M6N        -79.487262  \n",
       "M6P        -79.464763  \n",
       "M6R        -79.456325  \n",
       "M6S        -79.484450  \n",
       "M7A        -79.389494  \n",
       "M7R        -79.615819  \n",
       "M7Y        -79.321558  \n",
       "M8V        -79.501321  \n",
       "M8W        -79.543484  \n",
       "M8X        -79.506944  \n",
       "M8Y        -79.498509  \n",
       "M8Z        -79.520999  \n",
       "M9A        -79.532242  \n",
       "M9B        -79.554724  \n",
       "M9C        -79.577201  \n",
       "M9L        -79.565963  \n",
       "M9M        -79.532242  \n",
       "M9N        -79.518188  \n",
       "M9P        -79.532242  \n",
       "M9R        -79.554724  \n",
       "M9V        -79.588437  \n",
       "M9W        -79.594054  \n",
       "\n",
       "[103 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "Scarborough\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "North York\n",
      "East York\n",
      "East York\n",
      "East Toronto\n",
      "East York\n",
      "East York\n",
      "East York\n",
      "East Toronto\n",
      "East Toronto\n",
      "East Toronto\n",
      "Central Toronto\n",
      "Central Toronto\n",
      "Central Toronto\n",
      "Central Toronto\n",
      "Central Toronto\n",
      "Central Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "North York\n",
      "Central Toronto\n",
      "Central Toronto\n",
      "Central Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "Downtown Toronto\n",
      "North York\n",
      "North York\n",
      "York\n",
      "York\n",
      "Downtown Toronto\n",
      "West Toronto\n",
      "West Toronto\n",
      "West Toronto\n",
      "North York\n",
      "York\n",
      "York\n",
      "West Toronto\n",
      "West Toronto\n",
      "West Toronto\n",
      "Queen's Park\n",
      "Mississauga\n",
      "East Toronto\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "North York\n",
      "North York\n",
      "York\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n",
      "Etobicoke\n"
     ]
    }
   ],
   "source": [
    "venues = getNearbyVenues(names=df2['Borough'],latitudes=df2['Latitude'],longitudes=df2['Longitude'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2247, 7)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>0</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.806686</td>\n",
       "      <td>-79.194353</td>\n",
       "      <td>Wendy's</td>\n",
       "      <td>43.807448</td>\n",
       "      <td>-79.199056</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "      <td>RIGHT WAY TO GOLF</td>\n",
       "      <td>43.785177</td>\n",
       "      <td>-79.161108</td>\n",
       "      <td>Golf Course</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "      <td>Royal Canadian Legion</td>\n",
       "      <td>43.782533</td>\n",
       "      <td>-79.163085</td>\n",
       "      <td>Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.763573</td>\n",
       "      <td>-79.188711</td>\n",
       "      <td>Swiss Chalet Rotisserie &amp; Grill</td>\n",
       "      <td>43.767697</td>\n",
       "      <td>-79.189914</td>\n",
       "      <td>Pizza Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>43.763573</td>\n",
       "      <td>-79.188711</td>\n",
       "      <td>G &amp; G Electronics</td>\n",
       "      <td>43.765309</td>\n",
       "      <td>-79.191537</td>\n",
       "      <td>Electronics Store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Neighborhood  Neighborhood Latitude  Neighborhood Longitude  \\\n",
       "0  Scarborough              43.806686              -79.194353   \n",
       "1  Scarborough              43.784535              -79.160497   \n",
       "2  Scarborough              43.784535              -79.160497   \n",
       "3  Scarborough              43.763573              -79.188711   \n",
       "4  Scarborough              43.763573              -79.188711   \n",
       "\n",
       "                             Venue  Venue Latitude  Venue Longitude  \\\n",
       "0                          Wendy's       43.807448       -79.199056   \n",
       "1                RIGHT WAY TO GOLF       43.785177       -79.161108   \n",
       "2            Royal Canadian Legion       43.782533       -79.163085   \n",
       "3  Swiss Chalet Rotisserie & Grill       43.767697       -79.189914   \n",
       "4                G & G Electronics       43.765309       -79.191537   \n",
       "\n",
       "         Venue Category  \n",
       "0  Fast Food Restaurant  \n",
       "1           Golf Course  \n",
       "2                   Bar  \n",
       "3           Pizza Place  \n",
       "4     Electronics Store  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(venues.shape)\n",
    "venues.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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 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",
       "    <tr>\n",
       "      <th>Neighborhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Central Toronto</th>\n",
       "      <td>117</td>\n",
       "      <td>117</td>\n",
       "      <td>117</td>\n",
       "      <td>117</td>\n",
       "      <td>117</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Downtown Toronto</th>\n",
       "      <td>1276</td>\n",
       "      <td>1276</td>\n",
       "      <td>1276</td>\n",
       "      <td>1276</td>\n",
       "      <td>1276</td>\n",
       "      <td>1276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East Toronto</th>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East York</th>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Etobicoke</th>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mississauga</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North York</th>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Queen's Park</th>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scarborough</th>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Toronto</th>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>York</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Neighborhood Latitude  Neighborhood Longitude  Venue  \\\n",
       "Neighborhood                                                             \n",
       "Central Toronto                     117                     117    117   \n",
       "Downtown Toronto                   1276                    1276   1276   \n",
       "East Toronto                        128                     128    128   \n",
       "East York                            71                      71     71   \n",
       "Etobicoke                            69                      69     69   \n",
       "Mississauga                          10                      10     10   \n",
       "North York                          239                     239    239   \n",
       "Queen's Park                         44                      44     44   \n",
       "Scarborough                          91                      91     91   \n",
       "West Toronto                        187                     187    187   \n",
       "York                                 15                      15     15   \n",
       "\n",
       "                  Venue Latitude  Venue Longitude  Venue Category  \n",
       "Neighborhood                                                       \n",
       "Central Toronto              117              117             117  \n",
       "Downtown Toronto            1276             1276            1276  \n",
       "East Toronto                 128              128             128  \n",
       "East York                     71               71              71  \n",
       "Etobicoke                     69               69              69  \n",
       "Mississauga                   10               10              10  \n",
       "North York                   239              239             239  \n",
       "Queen's Park                  44               44              44  \n",
       "Scarborough                   91               91              91  \n",
       "West Toronto                 187              187             187  \n",
       "York                          15               15              15  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "venues.groupby('Neighborhood').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 274 unique categories.\n"
     ]
    }
   ],
   "source": [
    "print('There are {} unique categories.'.format(len(venues['Venue Category'].unique())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accessories Store</th>\n",
       "      <th>Adult Boutique</th>\n",
       "      <th>Afghan Restaurant</th>\n",
       "      <th>Airport</th>\n",
       "      <th>Airport Food Court</th>\n",
       "      <th>Airport Gate</th>\n",
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       "      <th>Warehouse Store</th>\n",
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       "      <th>Yoga Studio</th>\n",
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       "<p>5 rows × 274 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accessories Store  Adult Boutique  Afghan Restaurant  Airport  \\\n",
       "0                  0               0                  0        0   \n",
       "1                  0               0                  0        0   \n",
       "2                  0               0                  0        0   \n",
       "3                  0               0                  0        0   \n",
       "4                  0               0                  0        0   \n",
       "\n",
       "   Airport Food Court  Airport Gate  Airport Lounge  Airport Service  \\\n",
       "0                   0             0               0                0   \n",
       "1                   0             0               0                0   \n",
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       "4                 0                    0     ...                        0   \n",
       "\n",
       "   Vegetarian / Vegan Restaurant  Video Game Store  Video Store  \\\n",
       "0                              0                 0            0   \n",
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       "2                              0                 0            0   \n",
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       "4                              0                 0            0   \n",
       "\n",
       "   Vietnamese Restaurant  Warehouse Store  Wine Bar  Wings Joint  \\\n",
       "0                      0                0         0            0   \n",
       "1                      0                0         0            0   \n",
       "2                      0                0         0            0   \n",
       "3                      0                0         0            0   \n",
       "4                      0                0         0            0   \n",
       "\n",
       "   Women's Store  Yoga Studio  \n",
       "0              0            0  \n",
       "1              0            0  \n",
       "2              0            0  \n",
       "3              0            0  \n",
       "4              0            0  \n",
       "\n",
       "[5 rows x 274 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one hot encoding\n",
    "onehot = pd.get_dummies(venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
    "\n",
    "# add neighborhood column back to dataframe\n",
    "onehot['Neighborhood'] = venues['Neighborhood'] \n",
    "\n",
    "onehot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2247, 274)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "onehot.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>Accessories Store</th>\n",
       "      <th>Adult Boutique</th>\n",
       "      <th>Afghan Restaurant</th>\n",
       "      <th>Airport</th>\n",
       "      <th>Airport Food Court</th>\n",
       "      <th>Airport Gate</th>\n",
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       "      <th>...</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central Toronto</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.001567</td>\n",
       "      <td>0.003918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>East Toronto</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0.023438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>East York</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.014085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mississauga</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>North York</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004184</td>\n",
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       "      <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>0.004184</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.008368</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.012552</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Queen's Park</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.022727</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>0.000000</td>\n",
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       "      <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>0.010989</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>West Toronto</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010695</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010695</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005348</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>York</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.066667</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>0.066667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 274 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Neighborhood  Accessories Store  Adult Boutique  Afghan Restaurant  \\\n",
       "0    Central Toronto           0.000000        0.000000           0.000000   \n",
       "1   Downtown Toronto           0.000000        0.000784           0.000784   \n",
       "2       East Toronto           0.000000        0.000000           0.000000   \n",
       "3          East York           0.000000        0.000000           0.000000   \n",
       "4          Etobicoke           0.000000        0.000000           0.000000   \n",
       "5        Mississauga           0.000000        0.000000           0.000000   \n",
       "6         North York           0.004184        0.000000           0.000000   \n",
       "7       Queen's Park           0.000000        0.000000           0.000000   \n",
       "8        Scarborough           0.000000        0.000000           0.000000   \n",
       "9       West Toronto           0.000000        0.000000           0.000000   \n",
       "10              York           0.000000        0.000000           0.000000   \n",
       "\n",
       "     Airport  Airport Food Court  Airport Gate  Airport Lounge  \\\n",
       "0   0.000000            0.000000      0.000000        0.000000   \n",
       "1   0.000784            0.000784      0.000784        0.001567   \n",
       "2   0.000000            0.000000      0.000000        0.000000   \n",
       "3   0.000000            0.000000      0.000000        0.000000   \n",
       "4   0.000000            0.000000      0.000000        0.000000   \n",
       "5   0.000000            0.000000      0.000000        0.000000   \n",
       "6   0.004184            0.000000      0.000000        0.000000   \n",
       "7   0.000000            0.000000      0.000000        0.000000   \n",
       "8   0.000000            0.000000      0.000000        0.000000   \n",
       "9   0.000000            0.000000      0.000000        0.000000   \n",
       "10  0.000000            0.000000      0.000000        0.000000   \n",
       "\n",
       "    Airport Service  Airport Terminal     ...       Turkish Restaurant  \\\n",
       "0          0.000000          0.000000     ...                 0.000000   \n",
       "1          0.001567          0.001567     ...                 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          0.000000     ...                 0.000000   \n",
       "6          0.000000          0.000000     ...                 0.000000   \n",
       "7          0.000000          0.000000     ...                 0.000000   \n",
       "8          0.000000          0.000000     ...                 0.000000   \n",
       "9          0.000000          0.000000     ...                 0.000000   \n",
       "10         0.000000          0.000000     ...                 0.066667   \n",
       "\n",
       "    Vegetarian / Vegan Restaurant  Video Game Store  Video Store  \\\n",
       "0                        0.008547          0.000000     0.000000   \n",
       "1                        0.007837          0.001567     0.000000   \n",
       "2                        0.000000          0.000000     0.000000   \n",
       "3                        0.000000          0.000000     0.014085   \n",
       "4                        0.000000          0.000000     0.000000   \n",
       "5                        0.000000          0.000000     0.000000   \n",
       "6                        0.000000          0.004184     0.004184   \n",
       "7                        0.000000          0.000000     0.000000   \n",
       "8                        0.000000          0.000000     0.000000   \n",
       "9                        0.010695          0.000000     0.000000   \n",
       "10                       0.000000          0.000000     0.000000   \n",
       "\n",
       "    Vietnamese Restaurant  Warehouse Store  Wine Bar  Wings Joint  \\\n",
       "0                0.008547         0.000000  0.000000     0.000000   \n",
       "1                0.005486         0.000000  0.006270     0.000784   \n",
       "2                0.000000         0.000000  0.000000     0.000000   \n",
       "3                0.000000         0.014085  0.000000     0.000000   \n",
       "4                0.000000         0.000000  0.000000     0.014493   \n",
       "5                0.000000         0.000000  0.000000     0.000000   \n",
       "6                0.008368         0.000000  0.000000     0.004184   \n",
       "7                0.000000         0.000000  0.000000     0.022727   \n",
       "8                0.010989         0.000000  0.000000     0.000000   \n",
       "9                0.010695         0.000000  0.005348     0.000000   \n",
       "10               0.000000         0.000000  0.000000     0.000000   \n",
       "\n",
       "    Women's Store  Yoga Studio  \n",
       "0        0.000000     0.008547  \n",
       "1        0.001567     0.003918  \n",
       "2        0.000000     0.023438  \n",
       "3        0.000000     0.014085  \n",
       "4        0.000000     0.000000  \n",
       "5        0.000000     0.000000  \n",
       "6        0.012552     0.000000  \n",
       "7        0.000000     0.022727  \n",
       "8        0.000000     0.000000  \n",
       "9        0.000000     0.005348  \n",
       "10       0.066667     0.000000  \n",
       "\n",
       "[11 rows x 274 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = onehot.groupby('Neighborhood').mean().reset_index()\n",
    "grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11, 274)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Central Toronto----\n",
      "            venue  freq\n",
      "0     Coffee Shop  0.08\n",
      "1  Sandwich Place  0.07\n",
      "2            Park  0.05\n",
      "3            Café  0.04\n",
      "4     Pizza Place  0.04\n",
      "\n",
      "\n",
      "----Downtown Toronto----\n",
      "         venue  freq\n",
      "0  Coffee Shop  0.09\n",
      "1         Café  0.06\n",
      "2   Restaurant  0.04\n",
      "3       Bakery  0.03\n",
      "4        Hotel  0.03\n",
      "\n",
      "\n",
      "----East Toronto----\n",
      "                venue  freq\n",
      "0    Greek Restaurant  0.08\n",
      "1         Coffee Shop  0.06\n",
      "2                Café  0.04\n",
      "3      Ice Cream Shop  0.04\n",
      "4  Italian Restaurant  0.04\n",
      "\n",
      "\n",
      "----East York----\n",
      "                 venue  freq\n",
      "0          Coffee Shop  0.06\n",
      "1                 Park  0.06\n",
      "2                 Bank  0.04\n",
      "3  Sporting Goods Shop  0.04\n",
      "4          Pizza Place  0.04\n",
      "\n",
      "\n",
      "----Etobicoke----\n",
      "                  venue  freq\n",
      "0           Pizza Place  0.13\n",
      "1        Sandwich Place  0.07\n",
      "2              Pharmacy  0.06\n",
      "3  Fast Food Restaurant  0.04\n",
      "4           Coffee Shop  0.04\n",
      "\n",
      "\n",
      "----Mississauga----\n",
      "                      venue  freq\n",
      "0               Coffee Shop   0.2\n",
      "1                     Hotel   0.2\n",
      "2  Mediterranean Restaurant   0.1\n",
      "3      Gym / Fitness Center   0.1\n",
      "4       American Restaurant   0.1\n",
      "\n",
      "\n",
      "----North York----\n",
      "                  venue  freq\n",
      "0           Coffee Shop  0.07\n",
      "1        Clothing Store  0.05\n",
      "2  Fast Food Restaurant  0.05\n",
      "3           Pizza Place  0.03\n",
      "4                  Park  0.03\n",
      "\n",
      "\n",
      "----Queen's Park----\n",
      "              venue  freq\n",
      "0       Coffee Shop  0.23\n",
      "1  Sushi Restaurant  0.05\n",
      "2             Diner  0.05\n",
      "3               Gym  0.05\n",
      "4    Sandwich Place  0.02\n",
      "\n",
      "\n",
      "----Scarborough----\n",
      "                  venue  freq\n",
      "0    Chinese Restaurant  0.05\n",
      "1  Fast Food Restaurant  0.05\n",
      "2                Bakery  0.04\n",
      "3           Coffee Shop  0.04\n",
      "4        Breakfast Spot  0.04\n",
      "\n",
      "\n",
      "----West Toronto----\n",
      "                venue  freq\n",
      "0                 Bar  0.07\n",
      "1         Coffee Shop  0.06\n",
      "2                Café  0.06\n",
      "3         Pizza Place  0.03\n",
      "4  Italian Restaurant  0.03\n",
      "\n",
      "\n",
      "----York----\n",
      "          venue  freq\n",
      "0          Park  0.13\n",
      "1   Pizza Place  0.07\n",
      "2         Trail  0.07\n",
      "3  Skating Rink  0.07\n",
      "4      Pharmacy  0.07\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "num_top_venues = 5\n",
    "\n",
    "for hood in grouped['Neighborhood']:\n",
    "    print(\"----\"+hood+\"----\")\n",
    "    temp = grouped[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": "code",
   "execution_count": 34,
   "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": 35,
   "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",
       "      <th>0</th>\n",
       "      <td>Central Toronto</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Park</td>\n",
       "      <td>Café</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Pub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>American Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>East Toronto</td>\n",
       "      <td>Greek Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "      <td>Park</td>\n",
       "      <td>Brewery</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Light Rail Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>East York</td>\n",
       "      <td>Park</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Sporting Goods Shop</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Beer Store</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Café</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Beer Store</td>\n",
       "      <td>Bakery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mississauga</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Mediterranean Restaurant</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Burrito Place</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Eastern European Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>North York</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Shopping Mall</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Queen's Park</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Café</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Spa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Playground</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Sandwich Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>West Toronto</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Café</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Music Venue</td>\n",
       "      <td>Diner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>York</td>\n",
       "      <td>Park</td>\n",
       "      <td>Trail</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Field</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Skating Rink</td>\n",
       "      <td>Tennis Court</td>\n",
       "      <td>Hockey Arena</td>\n",
       "      <td>Market</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Neighborhood 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "0    Central Toronto           Coffee Shop        Sandwich Place   \n",
       "1   Downtown Toronto           Coffee Shop                  Café   \n",
       "2       East Toronto      Greek Restaurant           Coffee Shop   \n",
       "3          East York                  Park           Coffee Shop   \n",
       "4          Etobicoke           Pizza Place        Sandwich Place   \n",
       "5        Mississauga           Coffee Shop                 Hotel   \n",
       "6         North York           Coffee Shop  Fast Food Restaurant   \n",
       "7       Queen's Park           Coffee Shop      Sushi Restaurant   \n",
       "8        Scarborough  Fast Food Restaurant    Chinese Restaurant   \n",
       "9       West Toronto                   Bar                  Café   \n",
       "10              York                  Park                 Trail   \n",
       "\n",
       "       3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "0                       Park                  Café           Pizza Place   \n",
       "1                 Restaurant                 Hotel                Bakery   \n",
       "2                       Café    Italian Restaurant        Ice Cream Shop   \n",
       "3               Burger Joint   Sporting Goods Shop                  Bank   \n",
       "4                   Pharmacy                  Café         Grocery Store   \n",
       "5   Mediterranean Restaurant   Fried Chicken Joint        Sandwich Place   \n",
       "6             Clothing Store           Pizza Place            Restaurant   \n",
       "7                        Gym                 Diner                  Café   \n",
       "8             Breakfast Spot                Bakery           Coffee Shop   \n",
       "9                Coffee Shop    Italian Restaurant           Pizza Place   \n",
       "10      Fast Food Restaurant                 Field        Sandwich Place   \n",
       "\n",
       "   6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "0         Clothing Store      Sushi Restaurant          Dessert Shop   \n",
       "1     Italian Restaurant                   Bar   Japanese Restaurant   \n",
       "2                   Park               Brewery           Yoga Studio   \n",
       "3          Grocery Store           Pizza Place        Sandwich Place   \n",
       "4                    Gym  Fast Food Restaurant           Coffee Shop   \n",
       "5          Burrito Place   American Restaurant  Gym / Fitness Center   \n",
       "6          Shopping Mall   Japanese Restaurant        Sandwich Place   \n",
       "7     Mexican Restaurant                   Bar    Italian Restaurant   \n",
       "8            Pizza Place            Playground       Thai Restaurant   \n",
       "9                 Bakery            Restaurant        Breakfast Spot   \n",
       "10            Restaurant          Skating Rink          Tennis Court   \n",
       "\n",
       "   9th Most Common Venue       10th Most Common Venue  \n",
       "0             Restaurant                          Pub  \n",
       "1            Pizza Place          American Restaurant  \n",
       "2         Sandwich Place           Light Rail Station  \n",
       "3             Beer Store         Fast Food Restaurant  \n",
       "4             Beer Store                       Bakery  \n",
       "5      Electronics Store  Eastern European Restaurant  \n",
       "6          Grocery Store                         Park  \n",
       "7    Japanese Restaurant                          Spa  \n",
       "8           Intersection               Sandwich Place  \n",
       "9            Music Venue                        Diner  \n",
       "10          Hockey Arena                       Market  "
      ]
     },
     "execution_count": 35,
     "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'] = grouped['Neighborhood']\n",
    "\n",
    "for ind in np.arange(grouped.shape[0]):\n",
    "    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(grouped.iloc[ind, :], num_top_venues)\n",
    "\n",
    "neighborhoods_venues_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 4, 4, 1, 0, 3, 4, 0], dtype=int32)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set number of clusters\n",
    "kclusters = 5\n",
    "\n",
    "grouped_clustering = grouped.drop('Neighborhood', 1)\n",
    "\n",
    "# run k-means clustering\n",
    "kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(grouped_clustering)\n",
    "\n",
    "# check cluster labels generated for each row in the dataframe\n",
    "kmeans.labels_[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Accessories Store</th>\n",
       "      <th>Adult Boutique</th>\n",
       "      <th>Afghan Restaurant</th>\n",
       "      <th>Airport</th>\n",
       "      <th>Airport Food Court</th>\n",
       "      <th>Airport Gate</th>\n",
       "      <th>Airport Lounge</th>\n",
       "      <th>Airport Service</th>\n",
       "      <th>Airport Terminal</th>\n",
       "      <th>...</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central Toronto</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Park</td>\n",
       "      <td>Café</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Pub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000784</td>\n",
       "      <td>0.000784</td>\n",
       "      <td>0.000784</td>\n",
       "      <td>0.000784</td>\n",
       "      <td>0.000784</td>\n",
       "      <td>0.001567</td>\n",
       "      <td>0.001567</td>\n",
       "      <td>0.001567</td>\n",
       "      <td>...</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>American Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>East Toronto</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>Greek Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "      <td>Park</td>\n",
       "      <td>Brewery</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Light Rail Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>East York</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>Park</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Burger Joint</td>\n",
       "      <td>Sporting Goods Shop</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Beer Store</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Café</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Beer Store</td>\n",
       "      <td>Bakery</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 285 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Neighborhood  Accessories Store  Adult Boutique  Afghan Restaurant  \\\n",
       "0   Central Toronto                0.0        0.000000           0.000000   \n",
       "1  Downtown Toronto                0.0        0.000784           0.000784   \n",
       "2      East Toronto                0.0        0.000000           0.000000   \n",
       "3         East York                0.0        0.000000           0.000000   \n",
       "4         Etobicoke                0.0        0.000000           0.000000   \n",
       "\n",
       "    Airport  Airport Food Court  Airport Gate  Airport Lounge  \\\n",
       "0  0.000000            0.000000      0.000000        0.000000   \n",
       "1  0.000784            0.000784      0.000784        0.001567   \n",
       "2  0.000000            0.000000      0.000000        0.000000   \n",
       "3  0.000000            0.000000      0.000000        0.000000   \n",
       "4  0.000000            0.000000      0.000000        0.000000   \n",
       "\n",
       "   Airport Service  Airport Terminal           ...            \\\n",
       "0         0.000000          0.000000           ...             \n",
       "1         0.001567          0.001567           ...             \n",
       "2         0.000000          0.000000           ...             \n",
       "3         0.000000          0.000000           ...             \n",
       "4         0.000000          0.000000           ...             \n",
       "\n",
       "   1st Most Common Venue  2nd Most Common Venue  3rd Most Common Venue  \\\n",
       "0            Coffee Shop         Sandwich Place                   Park   \n",
       "1            Coffee Shop                   Café             Restaurant   \n",
       "2       Greek Restaurant            Coffee Shop                   Café   \n",
       "3                   Park            Coffee Shop           Burger Joint   \n",
       "4            Pizza Place         Sandwich Place               Pharmacy   \n",
       "\n",
       "   4th Most Common Venue  5th Most Common Venue  6th Most Common Venue  \\\n",
       "0                   Café            Pizza Place         Clothing Store   \n",
       "1                  Hotel                 Bakery     Italian Restaurant   \n",
       "2     Italian Restaurant         Ice Cream Shop                   Park   \n",
       "3    Sporting Goods Shop                   Bank          Grocery Store   \n",
       "4                   Café          Grocery Store                    Gym   \n",
       "\n",
       "   7th Most Common Venue  8th Most Common Venue  9th Most Common Venue  \\\n",
       "0       Sushi Restaurant           Dessert Shop             Restaurant   \n",
       "1                    Bar    Japanese Restaurant            Pizza Place   \n",
       "2                Brewery            Yoga Studio         Sandwich Place   \n",
       "3            Pizza Place         Sandwich Place             Beer Store   \n",
       "4   Fast Food Restaurant            Coffee Shop             Beer Store   \n",
       "\n",
       "   10th Most Common Venue  \n",
       "0                     Pub  \n",
       "1     American Restaurant  \n",
       "2      Light Rail Station  \n",
       "3    Fast Food Restaurant  \n",
       "4                  Bakery  \n",
       "\n",
       "[5 rows x 285 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = grouped\n",
    "\n",
    "# add clustering labels\n",
    "merged['Cluster Labels'] = kmeans.labels_\n",
    "\n",
    "# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood\n",
    "merged = merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')\n",
    "\n",
    "merged.head() # check the last columns!"
   ]
  }
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