Week3-Part2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b0249a5d",
   "metadata": {},
   "source": [
    "# IBM Apliend data science capstone\n",
    "\n",
    "## Segmenting and Clustering Neighborhoods in Toronto\n",
    "### Part 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d39bcc1",
   "metadata": {},
   "source": [
    "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, we need to get the latitude and the longitude coordinates of each neighborhood. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30b45f79",
   "metadata": {},
   "source": [
    "### Library "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9c42585f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.set_option(\"display.max_columns\", None)\n",
    "pd.set_option(\"display.max_rows\", None)\n",
    "\n",
    "# Map rendering library\n",
    "import folium "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec6cb840",
   "metadata": {},
   "source": [
    "### Load Postal Code from the csv file of the Part 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0c4b1d83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Lawrence Manor, Lawrence Heights</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M7A</td>\n",
       "      <td>Queen's Park</td>\n",
       "      <td>Ontario Provincial Government</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PostalCode           Borough                      Neighborhood\n",
       "0        M3A        North York                         Parkwoods\n",
       "1        M4A        North York                  Victoria Village\n",
       "2        M5A  Downtown Toronto         Regent Park, Harbourfront\n",
       "3        M6A        North York  Lawrence Manor, Lawrence Heights\n",
       "4        M7A      Queen's Park     Ontario Provincial Government"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the coordinates from the csv file on Coursera\n",
    "df = pd.read_csv(\"Toronto_PostalCode.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f0ebe58",
   "metadata": {},
   "source": [
    "### Load the coordinates from the csv file on Coursera"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "01e330d2",
   "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",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the coordinates from the csv file on Coursera\n",
    "coordinates = pd.read_csv(\"Geospatial_Coordinates.csv\")\n",
    "coordinates.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0232a9ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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": [
       "  PostalCode   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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename the column \"Postal Code\" to \"PostalCode\" like the image in the assignment\n",
    "coordinates.rename(columns={\"Postal Code\": \"PostalCode\"}, inplace=True)\n",
    "coordinates.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95e1b9fb",
   "metadata": {},
   "source": [
    "### Merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9bd2e6b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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>Queen's Park</td>\n",
       "      <td>Ontario Provincial Government</td>\n",
       "      <td>43.662301</td>\n",
       "      <td>-79.389494</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PostalCode           Borough                      Neighborhood   Latitude  \\\n",
       "0        M3A        North York                         Parkwoods  43.753259   \n",
       "1        M4A        North York                  Victoria Village  43.725882   \n",
       "2        M5A  Downtown Toronto         Regent Park, Harbourfront  43.654260   \n",
       "3        M6A        North York  Lawrence Manor, Lawrence Heights  43.718518   \n",
       "4        M7A      Queen's Park     Ontario Provincial Government  43.662301   \n",
       "\n",
       "   Longitude  \n",
       "0 -79.329656  \n",
       "1 -79.315572  \n",
       "2 -79.360636  \n",
       "3 -79.464763  \n",
       "4 -79.389494  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge two table on the column \"PostalCode\"\n",
    "df_merge = df.merge(coordinates, on=\"PostalCode\", how=\"left\")\n",
    "df_merge.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab7efd5e",
   "metadata": {},
   "source": [
    "### Save the Final DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "55575b08",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merge.to_csv(\"Toronto_PostalCode_LL.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a16a4fc",
   "metadata": {},
   "source": [
    "### DataFrame showed in the assigment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0435c3bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M5G</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Central Bay Street</td>\n",
       "      <td>43.657952</td>\n",
       "      <td>-79.387383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M2H</td>\n",
       "      <td>North York</td>\n",
       "      <td>Hillcrest Village</td>\n",
       "      <td>43.803762</td>\n",
       "      <td>-79.363452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M4B</td>\n",
       "      <td>East York</td>\n",
       "      <td>Parkview Hill, Woodbine Gardens</td>\n",
       "      <td>43.706397</td>\n",
       "      <td>-79.309937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M4G</td>\n",
       "      <td>East York</td>\n",
       "      <td>Leaside</td>\n",
       "      <td>43.709060</td>\n",
       "      <td>-79.363452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M4M</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>Studio District</td>\n",
       "      <td>43.659526</td>\n",
       "      <td>-79.340923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M1R</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Wexford, Maryvale</td>\n",
       "      <td>43.750071</td>\n",
       "      <td>-79.295849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M9V</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>South Steeles, Silverstone, Humbergate, Jamest...</td>\n",
       "      <td>43.739416</td>\n",
       "      <td>-79.588437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>M9L</td>\n",
       "      <td>North York</td>\n",
       "      <td>Humber Summit</td>\n",
       "      <td>43.756303</td>\n",
       "      <td>-79.565963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>M5V</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>CN Tower, King and Spadina, Railway Lands, Har...</td>\n",
       "      <td>43.628947</td>\n",
       "      <td>-79.394420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Malvern, Rouge</td>\n",
       "      <td>43.806686</td>\n",
       "      <td>-79.194353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PostalCode           Borough  \\\n",
       "0         M5G  Downtown Toronto   \n",
       "1         M2H        North York   \n",
       "2         M4B         East York   \n",
       "3         M1J       Scarborough   \n",
       "4         M4G         East York   \n",
       "5         M4M      East Toronto   \n",
       "6         M1R       Scarborough   \n",
       "7         M9V         Etobicoke   \n",
       "8         M9L        North York   \n",
       "9         M5V  Downtown Toronto   \n",
       "10        M1B       Scarborough   \n",
       "11        M5A  Downtown Toronto   \n",
       "\n",
       "                                         Neighborhood   Latitude  Longitude  \n",
       "0                                  Central Bay Street  43.657952 -79.387383  \n",
       "1                                   Hillcrest Village  43.803762 -79.363452  \n",
       "2                     Parkview Hill, Woodbine Gardens  43.706397 -79.309937  \n",
       "3                                 Scarborough Village  43.744734 -79.239476  \n",
       "4                                             Leaside  43.709060 -79.363452  \n",
       "5                                     Studio District  43.659526 -79.340923  \n",
       "6                                   Wexford, Maryvale  43.750071 -79.295849  \n",
       "7   South Steeles, Silverstone, Humbergate, Jamest...  43.739416 -79.588437  \n",
       "8                                       Humber Summit  43.756303 -79.565963  \n",
       "9   CN Tower, King and Spadina, Railway Lands, Har...  43.628947 -79.394420  \n",
       "10                                     Malvern, Rouge  43.806686 -79.194353  \n",
       "11                          Regent Park, Harbourfront  43.654260 -79.360636  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df_merge.set_index('PostalCode')\n",
    "\n",
    "df1.loc[[\"M5G\", \"M2H\", \"M4B\", \"M1J\", \"M4G\", \"M4M\", \"M1R\", \"M9V\", \"M9L\", \"M5V\", \"M1B\", \"M5A\"]].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d7e2fa8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b3a6b6e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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