Segmenting and Clustering3-.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing libraries and reading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "website_url = requests.get('https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M').text\n",
    "soup = BeautifulSoup(website_url, 'lxml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = ['PostalCode', 'Borough', 'Neighborhood'] \n",
    "neighborhoods = pd.DataFrame(columns=column_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parsing html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1A</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M2A</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Harbourfront</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PostalCode           Borough      Neighborhood\n",
       "0        M1A      Not assigned      Not assigned\n",
       "1        M2A      Not assigned      Not assigned\n",
       "2        M3A        North York         Parkwoods\n",
       "3        M4A        North York  Victoria Village\n",
       "4        M5A  Downtown Toronto      Harbourfront"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tableSoup = soup.find_all(\"table\")\n",
    "table = tableSoup[0]\n",
    "rows = table.find_all(\"tr\")\n",
    "for row in rows:\n",
    "    columns = row.find_all(\"td\")\n",
    "    headers = row.find_all(\"th\")\n",
    "    if len(columns) == 0 : continue\n",
    "    postcode = columns[0].text\n",
    "    borough = columns[1].text\n",
    "    neigh = columns[2].text.split(\"\\n\")[0]\n",
    "    neighborhoods = neighborhoods.append({'PostalCode': postcode,\n",
    "                          'Borough': borough,\n",
    "                          'Neighborhood': neigh}, ignore_index=True)\n",
    "neighborhoods.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Ignore cells with a borough that is Not assigned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Harbourfront</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Regent Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Lawrence Heights</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PostalCode           Borough      Neighborhood\n",
       "2        M3A        North York         Parkwoods\n",
       "3        M4A        North York  Victoria Village\n",
       "4        M5A  Downtown Toronto      Harbourfront\n",
       "5        M5A  Downtown Toronto       Regent Park\n",
       "6        M6A        North York  Lawrence Heights"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoods = neighborhoods[neighborhoods['Borough'].str.contains(\"[^Not assigned]\")==True]\n",
    "neighborhoods.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining neighborhoods with the same Postal Code into one row with the neighborhoods separated with a comma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge, Malvern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Highland Creek, Rouge Hill, Port Union</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Guildwood, Morningside, West Hill</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1H</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PostalCode      Borough                            Neighborhood\n",
       "0        M1B  Scarborough                          Rouge, Malvern\n",
       "1        M1C  Scarborough  Highland Creek, Rouge Hill, Port Union\n",
       "2        M1E  Scarborough       Guildwood, Morningside, West Hill\n",
       "3        M1G  Scarborough                                  Woburn\n",
       "4        M1H  Scarborough                               Cedarbrae"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoods = pd.DataFrame({'Neighborhood' : neighborhoods.groupby(by = ['PostalCode', 'Borough'])['Neighborhood'].apply(lambda x: \"%s\" % ', '.join(x))}).reset_index()\n",
    "neighborhoods.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neighborhood not assigned, then the neighborhood will be the same as the borough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge, Malvern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Highland Creek, Rouge Hill, Port Union</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Guildwood, Morningside, West Hill</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1H</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PostalCode      Borough                            Neighborhood\n",
       "0        M1B  Scarborough                          Rouge, Malvern\n",
       "1        M1C  Scarborough  Highland Creek, Rouge Hill, Port Union\n",
       "2        M1E  Scarborough       Guildwood, Morningside, West Hill\n",
       "3        M1G  Scarborough                                  Woburn\n",
       "4        M1H  Scarborough                               Cedarbrae"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoods['Neighborhood'].replace(to_replace=\"Not assigned\", value=neighborhoods['Borough'], inplace=True)\n",
    "neighborhoods.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFrame shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103, 3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoods.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading geospatial data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>Postal Code</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1B</td>\n",
       "      <td>43.806686</td>\n",
       "      <td>-79.194353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1C</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1E</td>\n",
       "      <td>43.763573</td>\n",
       "      <td>-79.188711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>43.770992</td>\n",
       "      <td>-79.216917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1H</td>\n",
       "      <td>43.773136</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postal Code   Latitude  Longitude\n",
       "0         M1B  43.806686 -79.194353\n",
       "1         M1C  43.784535 -79.160497\n",
       "2         M1E  43.763573 -79.188711\n",
       "3         M1G  43.770992 -79.216917\n",
       "4         M1H  43.773136 -79.239476"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!wget -q -o 'Geospatial_data.csv' http://cocl.us/Geospatial_data\n",
    "geoDF = pd.read_csv(\"Geospatial_data\")\n",
    "geoDF.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concatenate Latitude and Longitude to the appropriate Postal code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\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>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1B</td>\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>1</th>\n",
       "      <td>M1C</td>\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>2</th>\n",
       "      <td>M1E</td>\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>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>43.770992</td>\n",
       "      <td>-79.216917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1H</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "      <td>43.773136</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PostalCode      Borough                            Neighborhood   Latitude  \\\n",
       "0        M1B  Scarborough                          Rouge, Malvern  43.806686   \n",
       "1        M1C  Scarborough  Highland Creek, Rouge Hill, Port Union  43.784535   \n",
       "2        M1E  Scarborough       Guildwood, Morningside, West Hill  43.763573   \n",
       "3        M1G  Scarborough                                  Woburn  43.770992   \n",
       "4        M1H  Scarborough                               Cedarbrae  43.773136   \n",
       "\n",
       "   Longitude  \n",
       "0 -79.194353  \n",
       "1 -79.160497  \n",
       "2 -79.188711  \n",
       "3 -79.216917  \n",
       "4 -79.239476  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoods = pd.concat([neighborhoods, geoDF], axis=1)\n",
    "neighborhoods = neighborhoods.drop('Postal Code', axis =1)\n",
    "neighborhoods.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing map libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solving environment: done\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /home/jupyterlab/conda\n",
      "\n",
      "  added / updated specs: \n",
      "    - folium=0.5.0\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    openssl-1.0.2p             |       h470a237_1         3.1 MB  conda-forge\n",
      "    numpy-1.14.2               |   py36hdbf6ddf_0         4.0 MB\n",
      "    vincent-0.4.4              |             py_1          28 KB  conda-forge\n",
      "    conda-4.5.11               |        py36_1000         651 KB  conda-forge\n",
      "    branca-0.3.1               |             py_0          25 KB  conda-forge\n",
      "    certifi-2018.11.29         |        py36_1000         145 KB  conda-forge\n",
      "    pandas-0.23.4              |   py36hf8a1672_0        27.8 MB  conda-forge\n",
      "    altair-2.2.2               |        py36_1001         494 KB  conda-forge\n",
      "    ca-certificates-2018.11.29 |       ha4d7672_0         143 KB  conda-forge\n",
      "    folium-0.5.0               |             py_0          45 KB  conda-forge\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:        36.4 MB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "    altair:          2.2.2-py36_1001       conda-forge\n",
      "    branca:          0.3.1-py_0            conda-forge\n",
      "    folium:          0.5.0-py_0            conda-forge\n",
      "    vincent:         0.4.4-py_1            conda-forge\n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "    ca-certificates: 2018.8.24-ha4d7672_0  conda-forge --> 2018.11.29-ha4d7672_0 conda-forge\n",
      "    certifi:         2018.8.24-py36_1001   conda-forge --> 2018.11.29-py36_1000  conda-forge\n",
      "    conda:           4.5.11-py36_0         conda-forge --> 4.5.11-py36_1000      conda-forge\n",
      "    openssl:         1.0.2p-h470a237_0     conda-forge --> 1.0.2p-h470a237_1     conda-forge\n",
      "    pandas:          0.23.0-py36h637b7d7_0             --> 0.23.4-py36hf8a1672_0 conda-forge\n",
      "\n",
      "The following packages will be DOWNGRADED:\n",
      "\n",
      "    numpy:           1.14.3-py36hcd700cb_1             --> 1.14.2-py36hdbf6ddf_0            \n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "openssl-1.0.2p       | 3.1 MB    | ##################################### | 100% \n",
      "numpy-1.14.2         | 4.0 MB    | ##################################### | 100% \n",
      "vincent-0.4.4        | 28 KB     | ##################################### | 100% \n",
      "conda-4.5.11         | 651 KB    | ##################################### | 100% \n",
      "branca-0.3.1         | 25 KB     | ##################################### | 100% \n",
      "certifi-2018.11.29   | 145 KB    | ##################################### | 100% \n",
      "pandas-0.23.4        | 27.8 MB   | ##################################### | 100% \n",
      "altair-2.2.2         | 494 KB    | ##################################### | 100% \n",
      "ca-certificates-2018 | 143 KB    | ##################################### | 100% \n",
      "folium-0.5.0         | 45 KB     | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: done\n",
      "Libraries imported.\n"
     ]
    }
   ],
   "source": [
    "# import k-means from clustering stage\n",
    "from sklearn.cluster import KMeans\n",
    "import requests\n",
    "import numpy as np\n",
    "\n",
    "!conda install -c conda-forge folium=0.5.0 --yes # uncomment this line if you haven't completed the Foursquare API lab\n",
    "import folium # map rendering library\n",
    "\n",
    "print('Libraries imported.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Map for Scarborough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "<folium.folium.Map at 0x7f13d6ff3a20>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_scarborough = folium.Map(location=[43.78, -79.20], zoom_start=13)\n",
    "map_scarborough"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring Scarborough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1B</td>\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>1</th>\n",
       "      <td>M1C</td>\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>2</th>\n",
       "      <td>M1E</td>\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>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>43.770992</td>\n",
       "      <td>-79.216917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1H</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "      <td>43.773136</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PostalCode      Borough                            Neighborhood   Latitude  \\\n",
       "0        M1B  Scarborough                          Rouge, Malvern  43.806686   \n",
       "1        M1C  Scarborough  Highland Creek, Rouge Hill, Port Union  43.784535   \n",
       "2        M1E  Scarborough       Guildwood, Morningside, West Hill  43.763573   \n",
       "3        M1G  Scarborough                                  Woburn  43.770992   \n",
       "4        M1H  Scarborough                               Cedarbrae  43.773136   \n",
       "\n",
       "   Longitude  \n",
       "0 -79.194353  \n",
       "1 -79.160497  \n",
       "2 -79.188711  \n",
       "3 -79.216917  \n",
       "4 -79.239476  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Scarborough = neighborhoods.loc[neighborhoods['Borough'] == \"Scarborough\"]\n",
    "Scarborough.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding markers to the map of Scarborough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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            circle_marker_f86072a24df649ff83f0b48b446408a4.bindPopup(popup_ebaebad919b64b5383a93122830175b4);

            
        
    
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</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 0x7f13d6ff3a20>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add markers to map\n",
    "for lat, lng, postal, neighborhood in zip(Scarborough['Latitude'], Scarborough['Longitude'], Scarborough['PostalCode'], Scarborough['Neighborhood']):\n",
    "    label = '{}, {}'.format(postal,neighborhood)\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color='blue',\n",
    "        fill=True,\n",
    "        fill_color='#3186cc',\n",
    "        fill_opacity=0.7,\n",
    "        parse_html=False).add_to(map_scarborough)  \n",
    "    \n",
    "map_scarborough"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding near by venues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your credentails:\n",
      "CLIENT_ID: S0S0W4P24DNIDRQL35RZPRULG5WKL2WQE2UHZR2VS3HHHBUY\n",
      "CLIENT_SECRET:WIUPMQLQWAN13SPPU1DQZR04V5S32P1WQWQGZC3FUJYWJKV5\n"
     ]
    }
   ],
   "source": [
    "CLIENT_ID = '' # your Foursquare ID\n",
    "CLIENT_SECRET = ''  # your Foursquare Secret\n",
    "VERSION = '20180605' # Foursquare API version\n",
    "\n",
    "print('Your credentails:')\n",
    "print('CLIENT_ID: ' + CLIENT_ID)\n",
    "print('CLIENT_SECRET:' + CLIENT_SECRET)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
    "            100)\n",
    "            \n",
    "        # make the GET request\n",
    "        results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
    "        print(name, len(results))\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 = ['PostalCode', \n",
    "                  'PostalCode Latitude', \n",
    "                  'PostalCode Longitude', \n",
    "                  'Venue', \n",
    "                  'Venue Latitude', \n",
    "                  'Venue Longitude', \n",
    "                  'Venue Category']\n",
    "    \n",
    "    return(nearby_venues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "M1B\n",
      "M1B 1\n",
      "M1C\n",
      "M1C 3\n",
      "M1E\n",
      "M1E 7\n",
      "M1G\n",
      "M1G 4\n",
      "M1H\n",
      "M1H 7\n",
      "M1J\n",
      "M1J 2\n",
      "M1K\n",
      "M1K 8\n",
      "M1L\n",
      "M1L 9\n",
      "M1M\n",
      "M1M 3\n",
      "M1N\n",
      "M1N 4\n",
      "M1P\n",
      "M1P 6\n",
      "M1R\n",
      "M1R 7\n",
      "M1S\n",
      "M1S 4\n",
      "M1T\n",
      "M1T 10\n",
      "M1V\n",
      "M1V 3\n",
      "M1W\n",
      "M1W 14\n",
      "M1X\n",
      "M1X 0\n"
     ]
    }
   ],
   "source": [
    "scarborough_venues = getNearbyVenues(names=Scarborough['PostalCode'],\n",
    "                                   latitudes=Scarborough['Latitude'],\n",
    "                                   longitudes=Scarborough['Longitude']\n",
    "                                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PostalCode</th>\n",
       "      <th>PostalCode Latitude</th>\n",
       "      <th>PostalCode 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>M1B</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>M1C</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "      <td>Chris Effects Painting</td>\n",
       "      <td>43.784343</td>\n",
       "      <td>-79.163742</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1C</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>M1C</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "      <td>Affordable Toronto Movers</td>\n",
       "      <td>43.787919</td>\n",
       "      <td>-79.162977</td>\n",
       "      <td>Moving Target</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1E</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PostalCode  PostalCode Latitude  PostalCode Longitude  \\\n",
       "0        M1B            43.806686            -79.194353   \n",
       "1        M1C            43.784535            -79.160497   \n",
       "2        M1C            43.784535            -79.160497   \n",
       "3        M1C            43.784535            -79.160497   \n",
       "4        M1E            43.763573            -79.188711   \n",
       "\n",
       "                             Venue  Venue Latitude  Venue Longitude  \\\n",
       "0                          Wendy's       43.807448       -79.199056   \n",
       "1           Chris Effects Painting       43.784343       -79.163742   \n",
       "2            Royal Canadian Legion       43.782533       -79.163085   \n",
       "3        Affordable Toronto Movers       43.787919       -79.162977   \n",
       "4  Swiss Chalet Rotisserie & Grill       43.767697       -79.189914   \n",
       "\n",
       "               Venue Category  \n",
       "0        Fast Food Restaurant  \n",
       "1  Construction & Landscaping  \n",
       "2                         Bar  \n",
       "3               Moving Target  \n",
       "4                 Pizza Place  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scarborough_venues.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number of venues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(scarborough_venues['Venue'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number of venue categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(scarborough_venues['Venue Category'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analyzing venue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>M1C</td>\n",
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       "<p>5 rows × 57 columns</p>\n",
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      "text/plain": [
       "  PostalCode  American Restaurant  Athletics & Sports  Auto Garage  Bakery  \\\n",
       "0        M1B                    0                   0            0       0   \n",
       "1        M1C                    0                   0            0       0   \n",
       "2        M1C                    0                   0            0       0   \n",
       "3        M1C                    0                   0            0       0   \n",
       "4        M1E                    0                   0            0       0   \n",
       "\n",
       "   Bank  Bar  Breakfast Spot  Burger Joint  Bus Line          ...            \\\n",
       "0     0    0               0             0         0          ...             \n",
       "1     0    0               0             0         0          ...             \n",
       "2     0    1               0             0         0          ...             \n",
       "3     0    0               0             0         0          ...             \n",
       "4     0    0               0             0         0          ...             \n",
       "\n",
       "   Playground  Rental Car Location  Sandwich Place  Shopping Mall  \\\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",
       "   Skating Rink  Soccer Field  Spa  Thai Restaurant  Train Station  \\\n",
       "0             0             0    0                0              0   \n",
       "1             0             0    0                0              0   \n",
       "2             0             0    0                0              0   \n",
       "3             0             0    0                0              0   \n",
       "4             0             0    0                0              0   \n",
       "\n",
       "   Vietnamese Restaurant  \n",
       "0                      0  \n",
       "1                      0  \n",
       "2                      0  \n",
       "3                      0  \n",
       "4                      0  \n",
       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one hot encoding\n",
    "scarborough_onehot = pd.get_dummies(scarborough_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
    "\n",
    "# add neighborhood column back to dataframe\n",
    "scarborough_onehot['PostalCode'] = scarborough_venues['PostalCode'] \n",
    "\n",
    "# move neighborhood column to the first column\n",
    "fixed_columns = [scarborough_onehot.columns[-1]] + list(scarborough_onehot.columns[:-1])\n",
    "scarborough_onehot = scarborough_onehot[fixed_columns]\n",
    "\n",
    "scarborough_onehot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grouping venues with postal code by calculating the frequency using mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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      ],
      "text/plain": [
       "  PostalCode  American Restaurant  Athletics & Sports  Auto Garage    Bakery  \\\n",
       "0        M1B                  0.0            0.000000          0.0  0.000000   \n",
       "1        M1C                  0.0            0.000000          0.0  0.000000   \n",
       "2        M1E                  0.0            0.000000          0.0  0.000000   \n",
       "3        M1G                  0.0            0.000000          0.0  0.000000   \n",
       "4        M1H                  0.0            0.142857          0.0  0.142857   \n",
       "\n",
       "       Bank       Bar  Breakfast Spot  Burger Joint  Bus Line  \\\n",
       "0  0.000000  0.000000        0.000000           0.0       0.0   \n",
       "1  0.000000  0.333333        0.000000           0.0       0.0   \n",
       "2  0.000000  0.000000        0.142857           0.0       0.0   \n",
       "3  0.000000  0.000000        0.000000           0.0       0.0   \n",
       "4  0.142857  0.000000        0.000000           0.0       0.0   \n",
       "\n",
       "           ...            Playground  Rental Car Location  Sandwich Place  \\\n",
       "0          ...                   0.0             0.000000             0.0   \n",
       "1          ...                   0.0             0.000000             0.0   \n",
       "2          ...                   0.0             0.142857             0.0   \n",
       "3          ...                   0.0             0.000000             0.0   \n",
       "4          ...                   0.0             0.000000             0.0   \n",
       "\n",
       "   Shopping Mall  Skating Rink  Soccer Field       Spa  Thai Restaurant  \\\n",
       "0            0.0           0.0           0.0  0.000000         0.000000   \n",
       "1            0.0           0.0           0.0  0.000000         0.000000   \n",
       "2            0.0           0.0           0.0  0.142857         0.000000   \n",
       "3            0.0           0.0           0.0  0.000000         0.000000   \n",
       "4            0.0           0.0           0.0  0.000000         0.142857   \n",
       "\n",
       "   Train Station  Vietnamese Restaurant  \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",
       "[5 rows x 57 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scarborough_grouped = scarborough_onehot.groupby('PostalCode').mean().reset_index()\n",
    "scarborough_grouped.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 5 venues in Scarborough for each postal code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----M1B----\n",
      "                       venue  freq\n",
      "0       Fast Food Restaurant   1.0\n",
      "1        American Restaurant   0.0\n",
      "2        Japanese Restaurant   0.0\n",
      "3  Latin American Restaurant   0.0\n",
      "4                     Lounge   0.0\n",
      "\n",
      "\n",
      "----M1C----\n",
      "                        venue  freq\n",
      "0                         Bar  0.33\n",
      "1               Moving Target  0.33\n",
      "2  Construction & Landscaping  0.33\n",
      "3         American Restaurant  0.00\n",
      "4                        Park  0.00\n",
      "\n",
      "\n",
      "----M1E----\n",
      "                 venue  freq\n",
      "0                  Spa  0.14\n",
      "1       Medical Center  0.14\n",
      "2       Breakfast Spot  0.14\n",
      "3   Mexican Restaurant  0.14\n",
      "4  Rental Car Location  0.14\n",
      "\n",
      "\n",
      "----M1G----\n",
      "                           venue  freq\n",
      "0                    Coffee Shop  0.50\n",
      "1                       Pharmacy  0.25\n",
      "2              Korean Restaurant  0.25\n",
      "3                Thai Restaurant  0.00\n",
      "4  Paper / Office Supplies Store  0.00\n",
      "\n",
      "\n",
      "----M1H----\n",
      "                  venue  freq\n",
      "0  Caribbean Restaurant  0.14\n",
      "1                Bakery  0.14\n",
      "2                  Bank  0.14\n",
      "3       Thai Restaurant  0.14\n",
      "4    Athletics & Sports  0.14\n",
      "\n",
      "\n",
      "----M1J----\n",
      "                           venue  freq\n",
      "0                     Playground   0.5\n",
      "1     Construction & Landscaping   0.5\n",
      "2            American Restaurant   0.0\n",
      "3  Paper / Office Supplies Store   0.0\n",
      "4      Latin American Restaurant   0.0\n",
      "\n",
      "\n",
      "----M1K----\n",
      "              venue  freq\n",
      "0    Discount Store  0.25\n",
      "1  Department Store  0.12\n",
      "2     Train Station  0.12\n",
      "3       Coffee Shop  0.12\n",
      "4        Hobby Shop  0.12\n",
      "\n",
      "\n",
      "----M1L----\n",
      "                  venue  freq\n",
      "0              Bus Line  0.22\n",
      "1                Bakery  0.22\n",
      "2         Metro Station  0.11\n",
      "3          Soccer Field  0.11\n",
      "4  Fast Food Restaurant  0.11\n",
      "\n",
      "\n",
      "----M1M----\n",
      "                           venue  freq\n",
      "0            American Restaurant  0.33\n",
      "1                   Skating Rink  0.33\n",
      "2                          Motel  0.33\n",
      "3  Paper / Office Supplies Store  0.00\n",
      "4              Korean Restaurant  0.00\n",
      "\n",
      "\n",
      "----M1N----\n",
      "                   venue  freq\n",
      "0           Skating Rink  0.25\n",
      "1  General Entertainment  0.25\n",
      "2                   Café  0.25\n",
      "3        College Stadium  0.25\n",
      "4    American Restaurant  0.00\n",
      "\n",
      "\n",
      "----M1P----\n",
      "                       venue  freq\n",
      "0          Indian Restaurant  0.33\n",
      "1      Vietnamese Restaurant  0.17\n",
      "2         Chinese Restaurant  0.17\n",
      "3  Latin American Restaurant  0.17\n",
      "4                  Pet Store  0.17\n",
      "\n",
      "\n",
      "----M1R----\n",
      "                           venue  freq\n",
      "0      Middle Eastern Restaurant  0.29\n",
      "1  Paper / Office Supplies Store  0.14\n",
      "2                    Auto Garage  0.14\n",
      "3                 Breakfast Spot  0.14\n",
      "4                  Shopping Mall  0.14\n",
      "\n",
      "\n",
      "----M1S----\n",
      "                 venue  freq\n",
      "0               Lounge  0.25\n",
      "1       Breakfast Spot  0.25\n",
      "2         Skating Rink  0.25\n",
      "3       Clothing Store  0.25\n",
      "4  American Restaurant  0.00\n",
      "\n",
      "\n",
      "----M1T----\n",
      "                venue  freq\n",
      "0         Pizza Place   0.2\n",
      "1  Italian Restaurant   0.1\n",
      "2       Shopping Mall   0.1\n",
      "3            Pharmacy   0.1\n",
      "4  Chinese Restaurant   0.1\n",
      "\n",
      "\n",
      "----M1V----\n",
      "                       venue  freq\n",
      "0                Coffee Shop  0.33\n",
      "1                       Park  0.33\n",
      "2                 Playground  0.33\n",
      "3  Latin American Restaurant  0.00\n",
      "4                     Lounge  0.00\n",
      "\n",
      "\n",
      "----M1W----\n",
      "                  venue  freq\n",
      "0  Fast Food Restaurant  0.14\n",
      "1    Chinese Restaurant  0.14\n",
      "2           Pizza Place  0.07\n",
      "3        Sandwich Place  0.07\n",
      "4              Pharmacy  0.07\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "num_top_venues = 5\n",
    "\n",
    "for hood in scarborough_grouped['PostalCode']:\n",
    "    print(\"----\"+hood+\"----\")\n",
    "    temp = scarborough_grouped[scarborough_grouped['PostalCode'] == hood].T.reset_index()\n",
    "    temp.columns = ['venue','freq']\n",
    "    temp = temp.iloc[1:]\n",
    "    temp['freq'] = temp['freq'].astype(float)\n",
    "    temp = temp.round({'freq': 2})\n",
    "    print(temp.sort_values('freq', ascending=False).reset_index(drop=True).head(num_top_venues))\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 10 venues for each postal code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": 40,
   "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>PostalCode</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>M1B</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Train Station</td>\n",
       "      <td>Hakka Restaurant</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Department Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1C</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Moving Target</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1E</td>\n",
       "      <td>Rental Car Location</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Spa</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Medical Center</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Convenience Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Korean Restaurant</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1H</td>\n",
       "      <td>Hakka Restaurant</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Athletics &amp; Sports</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Caribbean Restaurant</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M1J</td>\n",
       "      <td>Playground</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Discount Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M1K</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Hobby Shop</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Department Store</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Train Station</td>\n",
       "      <td>Hakka Restaurant</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M1L</td>\n",
       "      <td>Bus Line</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Soccer Field</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Park</td>\n",
       "      <td>Metro Station</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Convenience Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>M1M</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Skating Rink</td>\n",
       "      <td>Motel</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Discount Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>M1N</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Skating Rink</td>\n",
       "      <td>Café</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>M1P</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Pet Store</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Latin American Restaurant</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Skating Rink</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>M1R</td>\n",
       "      <td>Middle Eastern Restaurant</td>\n",
       "      <td>Auto Garage</td>\n",
       "      <td>Paper / Office Supplies Store</td>\n",
       "      <td>Shopping Mall</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Department Store</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Convenience Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>M1S</td>\n",
       "      <td>Skating Rink</td>\n",
       "      <td>Lounge</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>M1T</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Noodle House</td>\n",
       "      <td>Rental Car Location</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Shopping Mall</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Bank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>M1V</td>\n",
       "      <td>Playground</td>\n",
       "      <td>Park</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>M1W</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Nail Salon</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Noodle House</td>\n",
       "      <td>Sandwich Place</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PostalCode      1st Most Common Venue       2nd Most Common Venue  \\\n",
       "0         M1B       Fast Food Restaurant       Vietnamese Restaurant   \n",
       "1         M1C                        Bar  Construction & Landscaping   \n",
       "2         M1E        Rental Car Location                 Pizza Place   \n",
       "3         M1G                Coffee Shop           Korean Restaurant   \n",
       "4         M1H           Hakka Restaurant             Thai Restaurant   \n",
       "5         M1J                 Playground  Construction & Landscaping   \n",
       "6         M1K             Discount Store                  Hobby Shop   \n",
       "7         M1L                   Bus Line                      Bakery   \n",
       "8         M1M        American Restaurant                Skating Rink   \n",
       "9         M1N      General Entertainment                Skating Rink   \n",
       "10        M1P          Indian Restaurant                   Pet Store   \n",
       "11        M1R  Middle Eastern Restaurant                 Auto Garage   \n",
       "12        M1S               Skating Rink                      Lounge   \n",
       "13        M1T                Pizza Place                Noodle House   \n",
       "14        M1V                 Playground                        Park   \n",
       "15        M1W         Chinese Restaurant        Fast Food Restaurant   \n",
       "\n",
       "            3rd Most Common Venue      4th Most Common Venue  \\\n",
       "0                   Train Station           Hakka Restaurant   \n",
       "1                   Moving Target      Vietnamese Restaurant   \n",
       "2                             Spa         Mexican Restaurant   \n",
       "3                        Pharmacy      Vietnamese Restaurant   \n",
       "4              Athletics & Sports                     Bakery   \n",
       "5           Vietnamese Restaurant             Clothing Store   \n",
       "6                     Coffee Shop           Department Store   \n",
       "7                    Soccer Field       Fast Food Restaurant   \n",
       "8                           Motel             Clothing Store   \n",
       "9                            Café            College Stadium   \n",
       "10             Chinese Restaurant  Latin American Restaurant   \n",
       "11  Paper / Office Supplies Store              Shopping Mall   \n",
       "12                 Breakfast Spot             Clothing Store   \n",
       "13            Rental Car Location         Italian Restaurant   \n",
       "14                    Coffee Shop      Vietnamese Restaurant   \n",
       "15                 Breakfast Spot                 Nail Salon   \n",
       "\n",
       "    5th Most Common Venue  6th Most Common Venue  7th Most Common Venue  \\\n",
       "0           Grocery Store  General Entertainment    Fried Chicken Joint   \n",
       "1             Coffee Shop          Grocery Store  General Entertainment   \n",
       "2       Electronics Store         Medical Center         Breakfast Spot   \n",
       "3          Clothing Store          Grocery Store  General Entertainment   \n",
       "4                    Bank    Fried Chicken Joint   Caribbean Restaurant   \n",
       "5           Grocery Store  General Entertainment    Fried Chicken Joint   \n",
       "6             Bus Station      Convenience Store          Train Station   \n",
       "7                    Park          Metro Station            Bus Station   \n",
       "8           Grocery Store  General Entertainment    Fried Chicken Joint   \n",
       "9   Vietnamese Restaurant         Clothing Store          Grocery Store   \n",
       "10  Vietnamese Restaurant           Skating Rink        Thai Restaurant   \n",
       "11         Sandwich Place         Breakfast Spot       Department Store   \n",
       "12  Vietnamese Restaurant            Coffee Shop          Grocery Store   \n",
       "13     Chinese Restaurant               Pharmacy    Fried Chicken Joint   \n",
       "14         Clothing Store          Grocery Store  General Entertainment   \n",
       "15          Grocery Store               Pharmacy            Pizza Place   \n",
       "\n",
       "    8th Most Common Venue       9th Most Common Venue 10th Most Common Venue  \n",
       "0       Electronics Store              Discount Store       Department Store  \n",
       "1     Fried Chicken Joint        Fast Food Restaurant      Electronics Store  \n",
       "2         College Stadium  Construction & Landscaping      Convenience Store  \n",
       "3     Fried Chicken Joint        Fast Food Restaurant      Electronics Store  \n",
       "4         College Stadium               Grocery Store  General Entertainment  \n",
       "5    Fast Food Restaurant           Electronics Store         Discount Store  \n",
       "6        Hakka Restaurant               Grocery Store  General Entertainment  \n",
       "7   Vietnamese Restaurant  Construction & Landscaping      Convenience Store  \n",
       "8    Fast Food Restaurant           Electronics Store         Discount Store  \n",
       "9     Fried Chicken Joint        Fast Food Restaurant      Electronics Store  \n",
       "10    Fried Chicken Joint        Fast Food Restaurant      Electronics Store  \n",
       "11        College Stadium  Construction & Landscaping      Convenience Store  \n",
       "12  General Entertainment         Fried Chicken Joint   Fast Food Restaurant  \n",
       "13          Shopping Mall             Thai Restaurant                   Bank  \n",
       "14    Fried Chicken Joint        Fast Food Restaurant      Electronics Store  \n",
       "15            Coffee Shop                Noodle House         Sandwich Place  "
      ]
     },
     "execution_count": 40,
     "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 = ['PostalCode']\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['PostalCode'] = scarborough_grouped['PostalCode']\n",
    "\n",
    "for ind in np.arange(scarborough_grouped.shape[0]):\n",
    "    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(scarborough_grouped.iloc[ind, :], num_top_venues)\n",
    "\n",
    "neighborhoods_venues_sorted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clustering neighborhoods with kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 2, 3, 2, 4, 2, 2, 1, 1, 2, 2, 1, 2, 3, 2], dtype=int32)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set number of clusters\n",
    "kclusters = 5\n",
    "\n",
    "scarborough_grouped_clustering = scarborough_grouped.drop('PostalCode', 1)\n",
    "\n",
    "# run k-means clustering\n",
    "kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(scarborough_grouped_clustering)\n",
    "\n",
    "# check cluster labels generated for each row in the dataframe\n",
    "kmeans.labels_[0:16]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .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>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge, Malvern</td>\n",
       "      <td>43.806686</td>\n",
       "      <td>-79.194353</td>\n",
       "      <td>0</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Train Station</td>\n",
       "      <td>Hakka Restaurant</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Department Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Highland Creek, Rouge Hill, Port Union</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "      <td>2</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Moving Target</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Guildwood, Morningside, West Hill</td>\n",
       "      <td>43.763573</td>\n",
       "      <td>-79.188711</td>\n",
       "      <td>2</td>\n",
       "      <td>Rental Car Location</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Spa</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Medical Center</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Convenience Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>43.770992</td>\n",
       "      <td>-79.216917</td>\n",
       "      <td>3</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Korean Restaurant</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1H</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "      <td>43.773136</td>\n",
       "      <td>-79.239476</td>\n",
       "      <td>2</td>\n",
       "      <td>Hakka Restaurant</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Athletics &amp; Sports</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Caribbean Restaurant</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "  PostalCode      Borough                            Neighborhood   Latitude  \\\n",
       "0        M1B  Scarborough                          Rouge, Malvern  43.806686   \n",
       "1        M1C  Scarborough  Highland Creek, Rouge Hill, Port Union  43.784535   \n",
       "2        M1E  Scarborough       Guildwood, Morningside, West Hill  43.763573   \n",
       "3        M1G  Scarborough                                  Woburn  43.770992   \n",
       "4        M1H  Scarborough                               Cedarbrae  43.773136   \n",
       "\n",
       "   Longitude  Cluster Labels 1st Most Common Venue  \\\n",
       "0 -79.194353               0  Fast Food Restaurant   \n",
       "1 -79.160497               2                   Bar   \n",
       "2 -79.188711               2   Rental Car Location   \n",
       "3 -79.216917               3           Coffee Shop   \n",
       "4 -79.239476               2      Hakka Restaurant   \n",
       "\n",
       "        2nd Most Common Venue 3rd Most Common Venue  4th Most Common Venue  \\\n",
       "0       Vietnamese Restaurant         Train Station       Hakka Restaurant   \n",
       "1  Construction & Landscaping         Moving Target  Vietnamese Restaurant   \n",
       "2                 Pizza Place                   Spa     Mexican Restaurant   \n",
       "3           Korean Restaurant              Pharmacy  Vietnamese Restaurant   \n",
       "4             Thai Restaurant    Athletics & Sports                 Bakery   \n",
       "\n",
       "  5th Most Common Venue  6th Most Common Venue  7th Most Common Venue  \\\n",
       "0         Grocery Store  General Entertainment    Fried Chicken Joint   \n",
       "1           Coffee Shop          Grocery Store  General Entertainment   \n",
       "2     Electronics Store         Medical Center         Breakfast Spot   \n",
       "3        Clothing Store          Grocery Store  General Entertainment   \n",
       "4                  Bank    Fried Chicken Joint   Caribbean Restaurant   \n",
       "\n",
       "  8th Most Common Venue       9th Most Common Venue 10th Most Common Venue  \n",
       "0     Electronics Store              Discount Store       Department Store  \n",
       "1   Fried Chicken Joint        Fast Food Restaurant      Electronics Store  \n",
       "2       College Stadium  Construction & Landscaping      Convenience Store  \n",
       "3   Fried Chicken Joint        Fast Food Restaurant      Electronics Store  \n",
       "4       College Stadium               Grocery Store  General Entertainment  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scarborough_merged = Scarborough[0:16]\n",
    "\n",
    "# add clustering labels\n",
    "scarborough_merged['Cluster Labels'] = kmeans.labels_\n",
    "\n",
    "# merge scarborough_grouped with scarborough_data to add latitude/longitude for each neighborhood\n",
    "scarborough_merged = scarborough_merged.join(neighborhoods_venues_sorted.set_index('PostalCode'), on='PostalCode')\n",
    "\n",
    "scarborough_merged.head() # check the last columns!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize the resuling clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
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            circle_marker_7896b8649eca4311b7cdcdbc239d159a.bindPopup(popup_401fbadadb194c8e8c8223434b0903b7);

            
        
    
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            circle_marker_f8c437c517524c989ddd2fcd881000a8.bindPopup(popup_bd0febeaabc34e28834848301a383f6d);

            
        
    
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}
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            var popup_516ad41395f143ca93ba953cb34e5381 = L.popup({maxWidth: '300'});

            
                var html_575b91c2f8fd4bec8ef90b75fcafb963 = $('<div id="html_575b91c2f8fd4bec8ef90b75fcafb963" style="width: 100.0%; height: 100.0%;">M1M Cluster 1</div>')[0];
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            circle_marker_2a3b969d8ab7469097501ccd5728d7be.bindPopup(popup_516ad41395f143ca93ba953cb34e5381);

            
        
    
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            var popup_76e4ecf60c8a4c25ae814d0c83a2bfd5 = L.popup({maxWidth: '300'});

            
                var html_811cdad9249a4e6bbe175d190d1faa8f = $('<div id="html_811cdad9249a4e6bbe175d190d1faa8f" style="width: 100.0%; height: 100.0%;">M1N Cluster 1</div>')[0];
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            circle_marker_2285ff594d83416488fbe1ed2b4ccb22.bindPopup(popup_76e4ecf60c8a4c25ae814d0c83a2bfd5);

            
        
    
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}
                ).addTo(map_fb94e2293f3d453e838d163ef7969f04);
            
    
            var popup_3d2aff24b7ef462aae95f47c7d1626c5 = L.popup({maxWidth: '300'});

            
                var html_5ebd201394b84254ae91dd7d188d92dc = $('<div id="html_5ebd201394b84254ae91dd7d188d92dc" style="width: 100.0%; height: 100.0%;">M1P Cluster 2</div>')[0];
                popup_3d2aff24b7ef462aae95f47c7d1626c5.setContent(html_5ebd201394b84254ae91dd7d188d92dc);
            

            circle_marker_35c49f554e734e40a3c2416edd836c07.bindPopup(popup_3d2aff24b7ef462aae95f47c7d1626c5);

            
        
    
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}
                ).addTo(map_fb94e2293f3d453e838d163ef7969f04);
            
    
            var popup_93596d54b8e2485f9f71cd2a75e1c057 = L.popup({maxWidth: '300'});

            
                var html_6b67121c763d47ed82ba48b3ee93f999 = $('<div id="html_6b67121c763d47ed82ba48b3ee93f999" style="width: 100.0%; height: 100.0%;">M1R Cluster 2</div>')[0];
                popup_93596d54b8e2485f9f71cd2a75e1c057.setContent(html_6b67121c763d47ed82ba48b3ee93f999);
            

            circle_marker_804c9a049ecc4f2995caa96d6900535b.bindPopup(popup_93596d54b8e2485f9f71cd2a75e1c057);

            
        
    
            var circle_marker_8d34ceba07d6414bad4a6f9c51b12699 = L.circleMarker(
                [43.7942003,-79.26202940000002],
                {
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}
                ).addTo(map_fb94e2293f3d453e838d163ef7969f04);
            
    
            var popup_c51d8c189edf4a6c8224472a4115d5fa = L.popup({maxWidth: '300'});

            
                var html_d9f0a82bcb3f42d29852c3cbfd9f7437 = $('<div id="html_d9f0a82bcb3f42d29852c3cbfd9f7437" style="width: 100.0%; height: 100.0%;">M1S Cluster 1</div>')[0];
                popup_c51d8c189edf4a6c8224472a4115d5fa.setContent(html_d9f0a82bcb3f42d29852c3cbfd9f7437);
            

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            circle_marker_5bf9b9f1d8c244569dc8e4ba828a4e10.bindPopup(popup_72238d3071fc40d9a9ff5ea33508c50e);

            
        
    
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                [43.8152522,-79.2845772],
                {
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  "fill": true,
  "fillColor": "#80ffb4",
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  "fillRule": "evenodd",
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                [43.799525200000005,-79.3183887],
                {
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                ).addTo(map_fb94e2293f3d453e838d163ef7969f04);
            
    
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</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 0x7f13d19d94a8>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "# create map\n",
    "map_clusters = folium.Map(location=[43.78, -79.20], zoom_start=13)\n",
    "\n",
    "# set color scheme for the clusters\n",
    "x = np.arange(kclusters)\n",
    "ys = [i+x+(i*x)**2 for i in range(kclusters)]\n",
    "colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
    "rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
    "\n",
    "# add markers to the map\n",
    "markers_colors = []\n",
    "for lat, lon, poi, cluster in zip(scarborough_merged['Latitude'], scarborough_merged['Longitude'], scarborough_merged['PostalCode'], scarborough_merged['Cluster Labels']):\n",
    "    label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lon],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color=rainbow[cluster-1],\n",
    "        fill=True,\n",
    "        fill_color=rainbow[cluster-1],\n",
    "        fill_opacity=0.7).add_to(map_clusters)\n",
    "       \n",
    "map_clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge, Malvern</td>\n",
       "      <td>43.806686</td>\n",
       "      <td>-79.194353</td>\n",
       "      <td>0</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Train Station</td>\n",
       "      <td>Hakka Restaurant</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Department Store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PostalCode      Borough    Neighborhood   Latitude  Longitude  \\\n",
       "0        M1B  Scarborough  Rouge, Malvern  43.806686 -79.194353   \n",
       "\n",
       "   Cluster Labels 1st Most Common Venue  2nd Most Common Venue  \\\n",
       "0               0  Fast Food Restaurant  Vietnamese Restaurant   \n",
       "\n",
       "  3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "0         Train Station      Hakka Restaurant         Grocery Store   \n",
       "\n",
       "   6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "0  General Entertainment   Fried Chicken Joint     Electronics Store   \n",
       "\n",
       "  9th Most Common Venue 10th Most Common Venue  \n",
       "0        Discount Store       Department Store  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scarborough_merged.loc[scarborough_merged['Cluster Labels'] == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>M1M</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cliffcrest, Cliffside, Scarborough Village West</td>\n",
       "      <td>43.716316</td>\n",
       "      <td>-79.239476</td>\n",
       "      <td>1</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Skating Rink</td>\n",
       "      <td>Motel</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Discount Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>M1N</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Birch Cliff, Cliffside West</td>\n",
       "      <td>43.692657</td>\n",
       "      <td>-79.264848</td>\n",
       "      <td>1</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Skating Rink</td>\n",
       "      <td>Café</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>M1S</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Agincourt</td>\n",
       "      <td>43.794200</td>\n",
       "      <td>-79.262029</td>\n",
       "      <td>1</td>\n",
       "      <td>Skating Rink</td>\n",
       "      <td>Lounge</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PostalCode      Borough                                     Neighborhood  \\\n",
       "8         M1M  Scarborough  Cliffcrest, Cliffside, Scarborough Village West   \n",
       "9         M1N  Scarborough                      Birch Cliff, Cliffside West   \n",
       "12        M1S  Scarborough                                        Agincourt   \n",
       "\n",
       "     Latitude  Longitude  Cluster Labels  1st Most Common Venue  \\\n",
       "8   43.716316 -79.239476               1    American Restaurant   \n",
       "9   43.692657 -79.264848               1  General Entertainment   \n",
       "12  43.794200 -79.262029               1           Skating Rink   \n",
       "\n",
       "   2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue  \\\n",
       "8           Skating Rink                 Motel        Clothing Store   \n",
       "9           Skating Rink                  Café       College Stadium   \n",
       "12                Lounge        Breakfast Spot        Clothing Store   \n",
       "\n",
       "    5th Most Common Venue  6th Most Common Venue 7th Most Common Venue  \\\n",
       "8           Grocery Store  General Entertainment   Fried Chicken Joint   \n",
       "9   Vietnamese Restaurant         Clothing Store         Grocery Store   \n",
       "12  Vietnamese Restaurant            Coffee Shop         Grocery Store   \n",
       "\n",
       "    8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n",
       "8    Fast Food Restaurant     Electronics Store         Discount Store  \n",
       "9     Fried Chicken Joint  Fast Food Restaurant      Electronics Store  \n",
       "12  General Entertainment   Fried Chicken Joint   Fast Food Restaurant  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scarborough_merged.loc[scarborough_merged['Cluster Labels'] == 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Highland Creek, Rouge Hill, Port Union</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "      <td>2</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Moving Target</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Guildwood, Morningside, West Hill</td>\n",
       "      <td>43.763573</td>\n",
       "      <td>-79.188711</td>\n",
       "      <td>2</td>\n",
       "      <td>Rental Car Location</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Spa</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Medical Center</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Convenience Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1H</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "      <td>43.773136</td>\n",
       "      <td>-79.239476</td>\n",
       "      <td>2</td>\n",
       "      <td>Hakka Restaurant</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Athletics &amp; Sports</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Caribbean Restaurant</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M1K</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>East Birchmount Park, Ionview, Kennedy Park</td>\n",
       "      <td>43.727929</td>\n",
       "      <td>-79.262029</td>\n",
       "      <td>2</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Hobby Shop</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Department Store</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Train Station</td>\n",
       "      <td>Hakka Restaurant</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M1L</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Clairlea, Golden Mile, Oakridge</td>\n",
       "      <td>43.711112</td>\n",
       "      <td>-79.284577</td>\n",
       "      <td>2</td>\n",
       "      <td>Bus Line</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Soccer Field</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Park</td>\n",
       "      <td>Metro Station</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Convenience Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>M1P</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Dorset Park, Scarborough Town Centre, Wexford ...</td>\n",
       "      <td>43.757410</td>\n",
       "      <td>-79.273304</td>\n",
       "      <td>2</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Pet Store</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Latin American Restaurant</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Skating Rink</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>M1R</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Maryvale, Wexford</td>\n",
       "      <td>43.750072</td>\n",
       "      <td>-79.295849</td>\n",
       "      <td>2</td>\n",
       "      <td>Middle Eastern Restaurant</td>\n",
       "      <td>Auto Garage</td>\n",
       "      <td>Paper / Office Supplies Store</td>\n",
       "      <td>Shopping Mall</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Department Store</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Convenience Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>M1T</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Clarks Corners, Sullivan, Tam O'Shanter</td>\n",
       "      <td>43.781638</td>\n",
       "      <td>-79.304302</td>\n",
       "      <td>2</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Noodle House</td>\n",
       "      <td>Rental Car Location</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Shopping Mall</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Bank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>M1W</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>L'Amoreaux West, Steeles West</td>\n",
       "      <td>43.799525</td>\n",
       "      <td>-79.318389</td>\n",
       "      <td>2</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Nail Salon</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Noodle House</td>\n",
       "      <td>Sandwich Place</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PostalCode      Borough                                       Neighborhood  \\\n",
       "1         M1C  Scarborough             Highland Creek, Rouge Hill, Port Union   \n",
       "2         M1E  Scarborough                  Guildwood, Morningside, West Hill   \n",
       "4         M1H  Scarborough                                          Cedarbrae   \n",
       "6         M1K  Scarborough        East Birchmount Park, Ionview, Kennedy Park   \n",
       "7         M1L  Scarborough                    Clairlea, Golden Mile, Oakridge   \n",
       "10        M1P  Scarborough  Dorset Park, Scarborough Town Centre, Wexford ...   \n",
       "11        M1R  Scarborough                                  Maryvale, Wexford   \n",
       "13        M1T  Scarborough            Clarks Corners, Sullivan, Tam O'Shanter   \n",
       "15        M1W  Scarborough                      L'Amoreaux West, Steeles West   \n",
       "\n",
       "     Latitude  Longitude  Cluster Labels      1st Most Common Venue  \\\n",
       "1   43.784535 -79.160497               2                        Bar   \n",
       "2   43.763573 -79.188711               2        Rental Car Location   \n",
       "4   43.773136 -79.239476               2           Hakka Restaurant   \n",
       "6   43.727929 -79.262029               2             Discount Store   \n",
       "7   43.711112 -79.284577               2                   Bus Line   \n",
       "10  43.757410 -79.273304               2          Indian Restaurant   \n",
       "11  43.750072 -79.295849               2  Middle Eastern Restaurant   \n",
       "13  43.781638 -79.304302               2                Pizza Place   \n",
       "15  43.799525 -79.318389               2         Chinese Restaurant   \n",
       "\n",
       "         2nd Most Common Venue          3rd Most Common Venue  \\\n",
       "1   Construction & Landscaping                  Moving Target   \n",
       "2                  Pizza Place                            Spa   \n",
       "4              Thai Restaurant             Athletics & Sports   \n",
       "6                   Hobby Shop                    Coffee Shop   \n",
       "7                       Bakery                   Soccer Field   \n",
       "10                   Pet Store             Chinese Restaurant   \n",
       "11                 Auto Garage  Paper / Office Supplies Store   \n",
       "13                Noodle House            Rental Car Location   \n",
       "15        Fast Food Restaurant                 Breakfast Spot   \n",
       "\n",
       "        4th Most Common Venue  5th Most Common Venue 6th Most Common Venue  \\\n",
       "1       Vietnamese Restaurant            Coffee Shop         Grocery Store   \n",
       "2          Mexican Restaurant      Electronics Store        Medical Center   \n",
       "4                      Bakery                   Bank   Fried Chicken Joint   \n",
       "6            Department Store            Bus Station     Convenience Store   \n",
       "7        Fast Food Restaurant                   Park         Metro Station   \n",
       "10  Latin American Restaurant  Vietnamese Restaurant          Skating Rink   \n",
       "11              Shopping Mall         Sandwich Place        Breakfast Spot   \n",
       "13         Italian Restaurant     Chinese Restaurant              Pharmacy   \n",
       "15                 Nail Salon          Grocery Store              Pharmacy   \n",
       "\n",
       "    7th Most Common Venue  8th Most Common Venue       9th Most Common Venue  \\\n",
       "1   General Entertainment    Fried Chicken Joint        Fast Food Restaurant   \n",
       "2          Breakfast Spot        College Stadium  Construction & Landscaping   \n",
       "4    Caribbean Restaurant        College Stadium               Grocery Store   \n",
       "6           Train Station       Hakka Restaurant               Grocery Store   \n",
       "7             Bus Station  Vietnamese Restaurant  Construction & Landscaping   \n",
       "10        Thai Restaurant    Fried Chicken Joint        Fast Food Restaurant   \n",
       "11       Department Store        College Stadium  Construction & Landscaping   \n",
       "13    Fried Chicken Joint          Shopping Mall             Thai Restaurant   \n",
       "15            Pizza Place            Coffee Shop                Noodle House   \n",
       "\n",
       "   10th Most Common Venue  \n",
       "1       Electronics Store  \n",
       "2       Convenience Store  \n",
       "4   General Entertainment  \n",
       "6   General Entertainment  \n",
       "7       Convenience Store  \n",
       "10      Electronics Store  \n",
       "11      Convenience Store  \n",
       "13                   Bank  \n",
       "15         Sandwich Place  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scarborough_merged.loc[scarborough_merged['Cluster Labels'] == 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .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>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>43.770992</td>\n",
       "      <td>-79.216917</td>\n",
       "      <td>3</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Korean Restaurant</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>M1V</td>\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",
       "      <td>3</td>\n",
       "      <td>Playground</td>\n",
       "      <td>Park</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PostalCode      Borough                                       Neighborhood  \\\n",
       "3         M1G  Scarborough                                             Woburn   \n",
       "14        M1V  Scarborough  Agincourt North, L'Amoreaux East, Milliken, St...   \n",
       "\n",
       "     Latitude  Longitude  Cluster Labels 1st Most Common Venue  \\\n",
       "3   43.770992 -79.216917               3           Coffee Shop   \n",
       "14  43.815252 -79.284577               3            Playground   \n",
       "\n",
       "   2nd Most Common Venue 3rd Most Common Venue  4th Most Common Venue  \\\n",
       "3      Korean Restaurant              Pharmacy  Vietnamese Restaurant   \n",
       "14                  Park           Coffee Shop  Vietnamese Restaurant   \n",
       "\n",
       "   5th Most Common Venue 6th Most Common Venue  7th Most Common Venue  \\\n",
       "3         Clothing Store         Grocery Store  General Entertainment   \n",
       "14        Clothing Store         Grocery Store  General Entertainment   \n",
       "\n",
       "   8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n",
       "3    Fried Chicken Joint  Fast Food Restaurant      Electronics Store  \n",
       "14   Fried Chicken Joint  Fast Food Restaurant      Electronics Store  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scarborough_merged.loc[scarborough_merged['Cluster Labels'] == 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<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",
       "      <th>Cluster Labels</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M1J</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Scarborough Village</td>\n",
       "      <td>43.744734</td>\n",
       "      <td>-79.239476</td>\n",
       "      <td>4</td>\n",
       "      <td>Playground</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Discount Store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PostalCode      Borough         Neighborhood   Latitude  Longitude  \\\n",
       "5        M1J  Scarborough  Scarborough Village  43.744734 -79.239476   \n",
       "\n",
       "   Cluster Labels 1st Most Common Venue       2nd Most Common Venue  \\\n",
       "5               4            Playground  Construction & Landscaping   \n",
       "\n",
       "   3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "5  Vietnamese Restaurant        Clothing Store         Grocery Store   \n",
       "\n",
       "   6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "5  General Entertainment   Fried Chicken Joint  Fast Food Restaurant   \n",
       "\n",
       "  9th Most Common Venue 10th Most Common Venue  \n",
       "5     Electronics Store         Discount Store  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scarborough_merged.loc[scarborough_merged['Cluster Labels'] == 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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