DP0701EN-3-3-3-Neighborhoods-Toronto-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://cognitiveclass.ai\"><img src = \"https://ibm.box.com/shared/static/9gegpsmnsoo25ikkbl4qzlvlyjbgxs5x.png\" width = 400> </a>\n",
    "\n",
    "<h1 align=center><font size = 5>Segmenting and Clustering Neighborhoods in Toronto</font></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "\n",
    "<font size = 3>\n",
    "\n",
    "1. <a href=\"#item1\">Download and Explore Dataset</a>\n",
    "\n",
    "2. <a href=\"#item2\">Explore Neighborhoods in Toronto</a>\n",
    "\n",
    "3. <a href=\"#item3\">Analyze Each Neighborhood</a>\n",
    "\n",
    "4. <a href=\"#item4\">Cluster Neighborhoods</a>\n",
    "\n",
    "5. <a href=\"#item5\">Examine Clusters</a>    \n",
    "</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install and access required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solving environment: done\n",
      "\n",
      "\n",
      "==> WARNING: A newer version of conda exists. <==\n",
      "  current version: 4.5.11\n",
      "  latest version: 4.7.12\n",
      "\n",
      "Please update conda by running\n",
      "\n",
      "    $ conda update -n base -c defaults conda\n",
      "\n",
      "\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n",
      "Solving environment: done\n",
      "\n",
      "\n",
      "==> WARNING: A newer version of conda exists. <==\n",
      "  current version: 4.5.11\n",
      "  latest version: 4.7.12\n",
      "\n",
      "Please update conda by running\n",
      "\n",
      "    $ conda update -n base -c defaults conda\n",
      "\n",
      "\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /home/jupyterlab/conda/envs/python\n",
      "\n",
      "  added / updated specs: \n",
      "    - geopy\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    geopy-1.20.0               |             py_0          57 KB  conda-forge\n",
      "    geographiclib-1.49         |             py_0          32 KB  conda-forge\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:          90 KB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "    geographiclib: 1.49-py_0   conda-forge\n",
      "    geopy:         1.20.0-py_0 conda-forge\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "geopy-1.20.0         | 57 KB     | ##################################### | 100% \n",
      "geographiclib-1.49   | 32 KB     | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: done\n",
      "Libraries imported.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # library to handle data in a vectorized manner\n",
    "\n",
    "import pandas as pd # library for data analsysis\n",
    "\n",
    "import json # library to handle JSON files\n",
    "\n",
    "import requests # library to handle requests\n",
    "\n",
    "from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe\n",
    "\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors # Matplotlib and associated plotting modules\n",
    "\n",
    "from sklearn.cluster import KMeans # import k-means from clustering stage\n",
    "\n",
    "!conda install -c conda-forge beautifulsoup4 --yes\n",
    "from bs4 import BeautifulSoup # website scraping libraries and packages in Python from BeautifulSoup \n",
    "\n",
    "!conda install -c conda-forge geopy --yes\n",
    "from geopy.geocoders import Nominatim  # convert an address into latitude and longitude values\n",
    "\n",
    "print(\"Libraries imported.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import folium # map rendering library\n",
    "\n",
    "from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Download Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the Wikipage we need Canada postal code dataset.\n",
    "We will essentially need a dataset that contains the 5 boroughs and the neighborhoods that exist in each borough as well as the the latitude and longitude coordinates of each neighborhood.\n",
    "\n",
    "Luckily, this dataset exists for free on the web. Feel free to try to find this dataset on your own, but here is the link to the dataset: https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get the html from the wikipedia page with the postal codes of Canada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GET request\n",
    "data = requests.get('https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M').text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "soup = BeautifulSoup(data, 'html.parser')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3 List in Array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "postalCodeList = []\n",
    "boroughList = []\n",
    "neighborhoodList = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Locate the   table and postal code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "soup.find('table').find_all('tr')\n",
    "\n",
    "# find all the rows of the table\n",
    "soup.find('table').find_all('tr')\n",
    "\n",
    "# for each row of the table, find all the table data\n",
    "for row in soup.find('table').find_all('tr'):\n",
    "    cells = row.find_all('td')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "for row in soup.find('table').find_all('tr'):\n",
    "    cells = row.find_all('td')\n",
    "    if(len(cells) > 0):\n",
    "        postalCodeList.append(cells[0].text)\n",
    "        boroughList.append(cells[1].text)\n",
    "        neighborhoodList.append(cells[2].text.rstrip('\\n'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load the pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
       "      <td>0</td>\n",
       "      <td>M1A</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>M2A</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toronto_df = pd.DataFrame({\"PostalCode\": postalCodeList,\n",
    "                           \"Borough\": boroughList,\n",
    "                           \"Neighborhood\": neighborhoodList})\n",
    "\n",
    "toronto_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remove no-assigned columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
       "      <td>0</td>\n",
       "      <td>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Harbourfront</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Regent Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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",
       "0        M3A        North York         Parkwoods\n",
       "1        M4A        North York  Victoria Village\n",
       "2        M5A  Downtown Toronto      Harbourfront\n",
       "3        M5A  Downtown Toronto       Regent Park\n",
       "4        M6A        North York  Lawrence Heights"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toronto_df_drop = toronto_df[toronto_df.Borough != \"Not assigned\"].reset_index(drop=True)\n",
    "toronto_df_drop.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Group neighbors in same borough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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",
       "      <td>0</td>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge, Malvern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Highland Creek, Rouge Hill, Port Union</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Guildwood, Morningside, West Hill</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toronto_df_grouped = toronto_df_drop.groupby([\"PostalCode\", \"Borough\"], as_index=False).agg(lambda x: \", \".join(x))\n",
    "toronto_df_grouped.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Neighborhood WHICH IS \"Not assigned\", make the value the same as Borough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
       "      <td>0</td>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge, Malvern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Highland Creek, Rouge Hill, Port Union</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Guildwood, Morningside, West Hill</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for index, row in toronto_df_grouped.iterrows():\n",
    "    if row[\"Neighborhood\"] == \"Not assigned\":\n",
    "        row[\"Neighborhood\"] = row[\"Borough\"]\n",
    "        \n",
    "toronto_df_grouped.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1 Question requirement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PostalCode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>M5G</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Central Bay Street</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>M2H</td>\n",
       "      <td>North York</td>\n",
       "      <td>Hillcrest Village</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>M4B</td>\n",
       "      <td>East York</td>\n",
       "      <td>Woodbine Gardens, Parkview Hill</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>M1J</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Scarborough Village</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>M4G</td>\n",
       "      <td>East York</td>\n",
       "      <td>Leaside</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>M4M</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>Studio District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>M1R</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Maryvale, Wexford</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>M9V</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Albion Gardens, Beaumond Heights, Humbergate, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>M9L</td>\n",
       "      <td>North York</td>\n",
       "      <td>Humber Summit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>M5V</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>CN Tower, Bathurst Quay, Island airport, Harbo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge, Malvern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Harbourfront, Regent Park</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PostalCode           Borough  \\\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  \n",
       "0                                  Central Bay Street  \n",
       "1                                   Hillcrest Village  \n",
       "2                     Woodbine Gardens, Parkview Hill  \n",
       "3                                 Scarborough Village  \n",
       "4                                             Leaside  \n",
       "5                                     Studio District  \n",
       "6                                   Maryvale, Wexford  \n",
       "7   Albion Gardens, Beaumond Heights, Humbergate, ...  \n",
       "8                                       Humber Summit  \n",
       "9   CN Tower, Bathurst Quay, Island airport, Harbo...  \n",
       "10                                     Rouge, Malvern  \n",
       "11                          Harbourfront, Regent Park  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = [\"PostalCode\", \"Borough\", \"Neighborhood\"]\n",
    "test_df = pd.DataFrame(columns=column_names)\n",
    "\n",
    "test_list = [\"M5G\", \"M2H\", \"M4B\", \"M1J\", \"M4G\", \"M4M\", \"M1R\", \"M9V\", \"M9L\", \"M5V\", \"M1B\", \"M5A\"]\n",
    "\n",
    "for postcode in test_list:\n",
    "    test_df = test_df.append(toronto_df_grouped[toronto_df_grouped[\"PostalCode\"]==postcode], ignore_index=True)\n",
    "    \n",
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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