Toronto_Neighborhoods.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BATTLE BETWEEN NEIGHBORHOODS OF TORONTO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Objectives - Part 1</b>:\n",
    "\n",
    "1)The dataframe will consist of three columns: PostalCode, Borough, and Neighborhood\n",
    "\n",
    "\n",
    "2)Only process the cells that have an assigned borough. Ignore cells with a borough that is Not assigned.\n",
    "\n",
    "\n",
    "3)More than one neighborhood can exist in one postal code area. For example, in the table on the Wikipedia page, you will notice that M5A is listed twice and has two neighborhoods: Harbourfront and Regent Park. These two rows will be combined into one row with the neighborhoods separated with a comma as shown in row 11 in the above table.\n",
    "\n",
    "\n",
    "4)If a cell has a borough but a Not assigned neighborhood, then the neighborhood will be the same as the borough.\n",
    "Clean your Notebook and add Markdown cells to explain your work and any assumptions you are making.\n",
    "\n",
    "\n",
    "5)In the last cell of your notebook, use the .shape method to print the number of rows of your dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "</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>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>Regent Park, Harbourfront</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>M5Z</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>M6Z</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>M7Z</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>M8Z</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Mimico NW, The Queensway West, South of Bloor,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>M9Z</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Postal Code           Borough  \\\n",
       "0           M1A      Not assigned   \n",
       "1           M2A      Not assigned   \n",
       "2           M3A        North York   \n",
       "3           M4A        North York   \n",
       "4           M5A  Downtown Toronto   \n",
       "..          ...               ...   \n",
       "175         M5Z      Not assigned   \n",
       "176         M6Z      Not assigned   \n",
       "177         M7Z      Not assigned   \n",
       "178         M8Z         Etobicoke   \n",
       "179         M9Z      Not assigned   \n",
       "\n",
       "                                          Neighborhood  \n",
       "0                                         Not assigned  \n",
       "1                                         Not assigned  \n",
       "2                                            Parkwoods  \n",
       "3                                     Victoria Village  \n",
       "4                            Regent Park, Harbourfront  \n",
       "..                                                 ...  \n",
       "175                                       Not assigned  \n",
       "176                                       Not assigned  \n",
       "177                                       Not assigned  \n",
       "178  Mimico NW, The Queensway West, South of Bloor,...  \n",
       "179                                       Not assigned  \n",
       "\n",
       "[180 rows x 3 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_html('https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M')[0]\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "</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>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>Regent Park, Harbourfront</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Lawrence Manor, Lawrence Heights</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M7A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Queen's Park, Ontario Provincial Government</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>M8X</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>The Kingsway, Montgomery Road, Old Mill North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>M4Y</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Church and Wellesley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>M7Y</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>Business reply mail Processing Centre, South C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>M8Y</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Old Mill South, King's Mill Park, Sunnylea, Hu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>M8Z</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Mimico NW, The Queensway West, South of Bloor,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Postal Code           Borough  \\\n",
       "2           M3A        North York   \n",
       "3           M4A        North York   \n",
       "4           M5A  Downtown Toronto   \n",
       "5           M6A        North York   \n",
       "6           M7A  Downtown Toronto   \n",
       "..          ...               ...   \n",
       "160         M8X         Etobicoke   \n",
       "165         M4Y  Downtown Toronto   \n",
       "168         M7Y      East Toronto   \n",
       "169         M8Y         Etobicoke   \n",
       "178         M8Z         Etobicoke   \n",
       "\n",
       "                                          Neighborhood  \n",
       "2                                            Parkwoods  \n",
       "3                                     Victoria Village  \n",
       "4                            Regent Park, Harbourfront  \n",
       "5                     Lawrence Manor, Lawrence Heights  \n",
       "6          Queen's Park, Ontario Provincial Government  \n",
       "..                                                 ...  \n",
       "160      The Kingsway, Montgomery Road, Old Mill North  \n",
       "165                               Church and Wellesley  \n",
       "168  Business reply mail Processing Centre, South C...  \n",
       "169  Old Mill South, King's Mill Park, Sunnylea, Hu...  \n",
       "178  Mimico NW, The Queensway West, South of Bloor,...  \n",
       "\n",
       "[103 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df.Borough != 'Not assigned']\n",
    "df\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Postal Code</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>Malvern, Rouge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge Hill, Port Union, Highland Creek</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>M9N</td>\n",
       "      <td>York</td>\n",
       "      <td>Weston</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>M9P</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Westmount</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>M9R</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Kingsview Village, St. Phillips, Martin Grove ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>M9V</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>South Steeles, Silverstone, Humbergate, Jamest...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>M9W</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Northwest, West Humber - Clairville</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Postal Code      Borough  \\\n",
       "0           M1B  Scarborough   \n",
       "1           M1C  Scarborough   \n",
       "2           M1E  Scarborough   \n",
       "3           M1G  Scarborough   \n",
       "4           M1H  Scarborough   \n",
       "..          ...          ...   \n",
       "98          M9N         York   \n",
       "99          M9P    Etobicoke   \n",
       "100         M9R    Etobicoke   \n",
       "101         M9V    Etobicoke   \n",
       "102         M9W    Etobicoke   \n",
       "\n",
       "                                          Neighborhood  \n",
       "0                                       Malvern, Rouge  \n",
       "1               Rouge Hill, Port Union, Highland Creek  \n",
       "2                    Guildwood, Morningside, West Hill  \n",
       "3                                               Woburn  \n",
       "4                                            Cedarbrae  \n",
       "..                                                 ...  \n",
       "98                                              Weston  \n",
       "99                                           Westmount  \n",
       "100  Kingsview Village, St. Phillips, Martin Grove ...  \n",
       "101  South Steeles, Silverstone, Humbergate, Jamest...  \n",
       "102                Northwest, West Humber - Clairville  \n",
       "\n",
       "[103 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Postal Code','Borough'])['Neighborhood'].apply(lambda x: ','.join(x.astype(str))).reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "df.loc[df['Neighborhood'] =='Not assigned' , 'Neighborhood'] = df['Borough']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Objectives - Part 2</b>:\n",
    "\n",
    "1)Now that you have built a dataframe of the postal code of each neighborhood along with the borough name and neighborhood name, in order to utilize the Foursquare location data.\n",
    "\n",
    "2)Explore and cluster the neighborhoods in Toronto.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Importing longitudes and latitudes for postal codes </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>M9N</td>\n",
       "      <td>43.706876</td>\n",
       "      <td>-79.518188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>M9P</td>\n",
       "      <td>43.696319</td>\n",
       "      <td>-79.532242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>M9R</td>\n",
       "      <td>43.688905</td>\n",
       "      <td>-79.554724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>M9V</td>\n",
       "      <td>43.739416</td>\n",
       "      <td>-79.588437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>M9W</td>\n",
       "      <td>43.706748</td>\n",
       "      <td>-79.594054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 3 columns</p>\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\n",
       "..          ...        ...        ...\n",
       "98          M9N  43.706876 -79.518188\n",
       "99          M9P  43.696319 -79.532242\n",
       "100         M9R  43.688905 -79.554724\n",
       "101         M9V  43.739416 -79.588437\n",
       "102         M9W  43.706748 -79.594054\n",
       "\n",
       "[103 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv(\"http://cocl.us/Geospatial_data\")\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Postal Code</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>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "      <td>43.753259</td>\n",
       "      <td>-79.329656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "      <td>43.725882</td>\n",
       "      <td>-79.315572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Regent Park, Harbourfront</td>\n",
       "      <td>43.654260</td>\n",
       "      <td>-79.360636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Lawrence Manor, Lawrence Heights</td>\n",
       "      <td>43.718518</td>\n",
       "      <td>-79.464763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M7A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Queen's Park, Ontario Provincial Government</td>\n",
       "      <td>43.662301</td>\n",
       "      <td>-79.389494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>M8X</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>The Kingsway, Montgomery Road, Old Mill North</td>\n",
       "      <td>43.653654</td>\n",
       "      <td>-79.506944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>M4Y</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Church and Wellesley</td>\n",
       "      <td>43.665860</td>\n",
       "      <td>-79.383160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>M7Y</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>Business reply mail Processing Centre, South C...</td>\n",
       "      <td>43.662744</td>\n",
       "      <td>-79.321558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>M8Y</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Old Mill South, King's Mill Park, Sunnylea, Hu...</td>\n",
       "      <td>43.636258</td>\n",
       "      <td>-79.498509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>M8Z</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Mimico NW, The Queensway West, South of Bloor,...</td>\n",
       "      <td>43.628841</td>\n",
       "      <td>-79.520999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 5 columns</p>\n",
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      ],
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       "    Postal Code           Borough  \\\n",
       "0           M3A        North York   \n",
       "1           M4A        North York   \n",
       "2           M5A  Downtown Toronto   \n",
       "3           M6A        North York   \n",
       "4           M7A  Downtown Toronto   \n",
       "..          ...               ...   \n",
       "98          M8X         Etobicoke   \n",
       "99          M4Y  Downtown Toronto   \n",
       "100         M7Y      East Toronto   \n",
       "101         M8Y         Etobicoke   \n",
       "102         M8Z         Etobicoke   \n",
       "\n",
       "                                          Neighborhood   Latitude  Longitude  \n",
       "0                                            Parkwoods  43.753259 -79.329656  \n",
       "1                                     Victoria Village  43.725882 -79.315572  \n",
       "2                            Regent Park, Harbourfront  43.654260 -79.360636  \n",
       "3                     Lawrence Manor, Lawrence Heights  43.718518 -79.464763  \n",
       "4          Queen's Park, Ontario Provincial Government  43.662301 -79.389494  \n",
       "..                                                 ...        ...        ...  \n",
       "98       The Kingsway, Montgomery Road, Old Mill North  43.653654 -79.506944  \n",
       "99                                Church and Wellesley  43.665860 -79.383160  \n",
       "100  Business reply mail Processing Centre, South C...  43.662744 -79.321558  \n",
       "101  Old Mill South, King's Mill Park, Sunnylea, Hu...  43.636258 -79.498509  \n",
       "102  Mimico NW, The Queensway West, South of Bloor,...  43.628841 -79.520999  \n",
       "\n",
       "[103 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df, df1, on = \"Postal Code\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Objectives - Part 3</b>:\n",
    "\n",
    "To generate maps to visualize your neighborhoods and how they cluster together.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Importing necessary libraries for map visualization and analysis </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Libraries imported.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n",
    "\n",
    "import requests # library to handle requests\n",
    "from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe\n",
    "\n",
    "# Matplotlib and associated plotting modules\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "\n",
    "# import k-means from clustering stage\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    " \n",
    "import folium # map rendering library\n",
    "\n",
    "print('Libraries imported.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The geograpical coordinate of Toronto, ON, Canada are 43.6534817, -79.3839347.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "address = 'Toronto, ON, Canada'\n",
    "\n",
    "geolocator = Nominatim(user_agent=\"to_explorer\")\n",
    "location = geolocator.geocode(address)\n",
    "latitude = location.latitude\n",
    "longitude = location.longitude\n",
    "print('The geograpical coordinate of Toronto, ON, Canada are {}, {}.'.format(latitude, longitude))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
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</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x262adc244c8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create map of Toronto using latitude and longitude values\n",
    "map_Toronto = folium.Map(location=[latitude,longitude], zoom_start=10)\n",
    "\n",
    "# add markers to map\n",
    "for lat, lng, borough, neighborhood in zip(df['Latitude'], df['Longitude'], df['Borough'], df['Neighborhood']):\n",
    "    label = '{}, {}'.format(neighborhood,borough)\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_Toronto)  \n",
    "    \n",
    "map_Toronto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![map1.JPG](https://raw.githubusercontent.com/sarthak815/Coursera_Capstone/master/map1.JPG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Defining foursquare credentials:</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "CLIENT_ID = 'DB4RZDGTHFHJM3OWBQ3R33SVWA5U41ZDL52HN4YI3GGSNZP1' # your Foursquare ID\n",
    "CLIENT_SECRET = '2B4CQVWFQ4SOVVJ5CFIS25XEPWL5W2JRU1H5YFE3ZHDKOCYO' # your Foursquare Secret\n",
    "VERSION = '20180605' # Foursquare API version\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# defining radius and limit of venues to get\n",
    "radius=500\n",
    "LIMIT=100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "def getNearbyVenues(names, latitudes, longitudes, radius=500):\n",
    "    \n",
    "    venues_list=[]\n",
    "    for name, lat, lng in zip(names, latitudes, longitudes):\n",
    "        print(name)\n",
    "            \n",
    "        # create the API request URL\n",
    "        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
    "            CLIENT_ID, \n",
    "            CLIENT_SECRET, \n",
    "            VERSION, \n",
    "            lat, \n",
    "            lng, \n",
    "            radius, \n",
    "            LIMIT)\n",
    "            \n",
    "        # make the GET request\n",
    "        results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
    "        \n",
    "        # return only relevant information for each nearby venue\n",
    "        venues_list.append([(\n",
    "            name, \n",
    "            lat, \n",
    "            lng, \n",
    "            v['venue']['name'], \n",
    "            v['venue']['location']['lat'], \n",
    "            v['venue']['location']['lng'],  \n",
    "            v['venue']['categories'][0]['name']) for v in results])\n",
    "\n",
    "    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n",
    "    nearby_venues.columns = ['Neighbourhood', \n",
    "                  'Neighbourhood Latitude', \n",
    "                  'Neighbourhood Longitude', \n",
    "                  'Venue', \n",
    "                  'Venue Latitude', \n",
    "                  'Venue Longitude', \n",
    "                  'Venue Category']\n",
    "    \n",
    "    return(nearby_venues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parkwoods\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'CLIENT_ID' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-0af917010a47>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m toronto_venues = getNearbyVenues(names=df['Neighborhood'],\n\u001b[0;32m      2\u001b[0m                                    \u001b[0mlatitudes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Latitude'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m                                    \u001b[0mlongitudes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Longitude'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m                                   )\n",
      "\u001b[1;32m<ipython-input-12-d194f1c67c83>\u001b[0m in \u001b[0;36mgetNearbyVenues\u001b[1;34m(names, latitudes, longitudes, radius)\u001b[0m\n\u001b[0;32m      7\u001b[0m         \u001b[1;31m# create the API request URL\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m         url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n\u001b[1;32m----> 9\u001b[1;33m             \u001b[0mCLIENT_ID\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m             \u001b[0mCLIENT_SECRET\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m             \u001b[0mVERSION\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'CLIENT_ID' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "toronto_venues = getNearbyVenues(names=df['Neighborhood'],\n",
    "                                   latitudes=df['Latitude'],\n",
    "                                   longitudes=df['Longitude']\n",
    "                                  )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Checking size of resulting dataframe:</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "print(toronto_venues.shape)\n",
    "toronto_venues.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>The number of venues per neighborhood:</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "toronto_venues.groupby('Neighbourhood').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Let's find out how many unique categories can be curated from all the returned venues</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "print('There are {} uniques categories.'.format(len(toronto_venues['Venue Category'].unique())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Analyzing each neighborhood</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# one hot encoding\n",
    "toronto_onehot = pd.get_dummies(toronto_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
    "\n",
    "# add neighborhood column back to dataframe\n",
    "toronto_onehot['Neighborhood'] = toronto_venues['Neighbourhood'] \n",
    "\n",
    "# move neighborhood column to the first column\n",
    "fixed_columns = [toronto_onehot.columns[-1]] + list(toronto_onehot.columns[:-1])\n",
    "toronto_onehot = toronto_onehot[fixed_columns]\n",
    "\n",
    "toronto_onehot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "toronto_onehot.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Next, let's group rows by neighborhood and by taking the mean of the frequency of occurrence of each category</b>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "toronto_grouped = toronto_onehot.groupby('Neighborhood').mean().reset_index()\n",
    "toronto_grouped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<b>Let's print each neighborhood along with the top 5 most common venues</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "num_top_venues = 5\n",
    "\n",
    "for hood in toronto_grouped['Neighborhood']:\n",
    "    print(\"----\"+hood+\"----\")\n",
    "    temp = toronto_grouped[toronto_grouped['Neighborhood'] == hood].T.reset_index()\n",
    "    temp.columns = ['venue','freq']\n",
    "    temp = temp.iloc[1:]\n",
    "    temp['freq'] = temp['freq'].astype(float)\n",
    "    temp = temp.round({'freq': 2})\n",
    "    print(temp.sort_values('freq', ascending=False).reset_index(drop=True).head(num_top_venues))\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Let's put that into a pandas dataframe</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "num_top_venues = 10\n",
    "\n",
    "indicators = ['st', 'nd', 'rd']\n",
    "\n",
    "# create columns according to number of top venues\n",
    "columns = ['Neighborhood']\n",
    "for ind in np.arange(num_top_venues):\n",
    "    try:\n",
    "        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))\n",
    "    except:\n",
    "        columns.append('{}th Most Common Venue'.format(ind+1))\n",
    "\n",
    "# create a new dataframe\n",
    "neighborhoods_venues_sorted = pd.DataFrame(columns=columns)\n",
    "neighborhoods_venues_sorted['Neighborhood'] = toronto_grouped['Neighborhood']\n",
    "\n",
    "for ind in np.arange(toronto_grouped.shape[0]):\n",
    "    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(toronto_grouped.iloc[ind, :], num_top_venues)\n",
    "\n",
    "neighborhoods_venues_sorted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Cluster Neighborhoods</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# set number of clusters\n",
    "kclusters = 5\n",
    "\n",
    "toronto_grouped_clustering = toronto_grouped.drop('Neighborhood', 1)\n",
    "\n",
    "# run k-means clustering\n",
    "kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(toronto_grouped_clustering)\n",
    "\n",
    "# check cluster labels generated for each row in the dataframe\n",
    "kmeans.labels_[0:10] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# add clustering labels\n",
    "neighborhoods_venues_sorted.insert(0, 'Cluster_Labels', kmeans.labels_)\n",
    "\n",
    "\n",
    "toronto_merged = df\n",
    "\n",
    "# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood\n",
    "toronto_merged = toronto_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')\n",
    "\n",
    "toronto_merged.head() # check the last columns!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "toronto_merged=toronto_merged.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "toronto_merged['Cluster_Labels'] = toronto_merged.Cluster_Labels.astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing all the clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# create map\n",
    "map_clusters = folium.Map(location=[latitude, longitude], zoom_start=11)\n",
    "\n",
    "# set color scheme for the clusters\n",
    "x = np.arange(kclusters)\n",
    "ys = [i + x + (i*x)**2 for i in range(kclusters)]\n",
    "colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
    "rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
    "\n",
    "# add markers to the map\n",
    "markers_colors = []\n",
    "for lat, lon, poi, cluster in zip(toronto_merged['Latitude'], toronto_merged['Longitude'], toronto_merged['Neighborhood'], toronto_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": [
    "![map2.JPG](https://raw.githubusercontent.com/sarthak815/Coursera_Capstone/master/map2.JPG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<b>Examine Clusters</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "toronto_merged.loc[toronto_merged['Cluster_Labels'] == 0, toronto_merged.columns[[1] + list(range(5, toronto_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "toronto_merged.loc[toronto_merged['Cluster_Labels'] == 1, toronto_merged.columns[[1] + list(range(5, toronto_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "toronto_merged.loc[toronto_merged['Cluster_Labels'] == 2, toronto_merged.columns[[1] + list(range(5, toronto_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "toronto_merged.loc[toronto_merged['Cluster_Labels'] == 3, toronto_merged.columns[[1] + list(range(5, toronto_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "toronto_merged.loc[toronto_merged['Cluster_Labels'] == 4, toronto_merged.columns[[1] + list(range(5, toronto_merged.shape[1]))]]\n",
    "\n"
   ]
  }
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