Second_SCNT_Capstone_Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Segmenting and Clustering Neighborhoods in Toronto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we will scrape the following Wikipedia page ([List of postal codes of Canada: M](https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M)) in order to obtain the data that is in the table of postal codes and transform it into a pandas dataframe. For this purpose, we will use the **BeautifulSoup** package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to import the **requests** library, which allows to send organic, grass-fed HTTP/1.1 requests, without the need for manual labor. We take the link of the website through which we are going to scrape the data and assign it to variable named wiki_url."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "wiki_url = requests.get('https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M')\n",
    "soup = BeautifulSoup(wiki_url.content,'lxml')\n",
    "#print(soup.prettify())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can uncomment the last line in order to take a look at the whole HTML script.\n",
    "\n",
    "Now, we need to find the class ‘wikitable sortable’ in the HTML script and assign it to the variable `my_table`. Let's take a look to the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_table = soup.find('table',{'class':'wikitable sortable'})\n",
    "#my_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can deduce every `<tr>`...`<\\tr>` section corresponds to a row. We can use the `find_all()` method in order to find all the `<tr>` tags in the document and get a set. Now let's see what is the length of this set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "290"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(my_table.find_all(\"tr\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The lenght of the set is 290, which means our table contains 290 rows. Now let's import the numpy library to create an empty array of 290 rows and 3 columns, as the table shown in the wiki page."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "matrix = np.empty((290, 3), dtype=object)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will fill our array with the values of the table using a for loop. Note that we use the `stripped_strings` generator.\n",
    "When there’s more than one thing inside a tag (as it is our case), you can still look at just the strings using the `.strings` generator. Since these strings tend to have a lot of extra whitespace, you can remove it by using the `.stripped_strings` generator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, val in enumerate(my_table.find_all(\"tr\")):\n",
    "    for j,string in enumerate(val.stripped_strings):\n",
    "        matrix[i][j]=string\n",
    "#matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But we wish to keep only the rows that have an assigned borough. We will eliminate the rows where the borough is Not assigned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "(3, 213)\n"
     ]
    }
   ],
   "source": [
    "matrix2=matrix.transpose()\n",
    "indices = [i for (i,v) in enumerate(matrix2[1]) if v=='Not assigned']\n",
    "matrix2 = np.delete(matrix2, indices, 1)\n",
    "matrix2.shape\n",
    "for (i,v) in enumerate(matrix2[2]):\n",
    "    if v=='Not assigned':\n",
    "        matrix2[2][i]=matrix2[1][i]\n",
    "# to check if we eliminate the desired rows succesfully the next statement should give 'False' as a result\n",
    "print('Not assigned' in matrix2[1])\n",
    "print(matrix2.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We eliminated the unassigned boroughs and we are left with 213 rows. Let's convert it to a **pandas** dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Postcode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighbourhood</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>Harbourfront</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Regent Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</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": [
       "  Postcode           Borough     Neighbourhood\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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "matrix3=matrix2.transpose()\n",
    "df_torpc=pd.DataFrame(data=matrix3[1:,0:],\n",
    "                       columns=matrix3[0,0:])\n",
    "df_torpc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Postcode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighbourhood</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>Harbourfront, Regent Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Lawrence Heights, Lawrence Manor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M7A</td>\n",
       "      <td>Queen's Park</td>\n",
       "      <td>Queen's Park</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postcode           Borough                     Neighbourhood\n",
       "0      M3A        North York                         Parkwoods\n",
       "1      M4A        North York                  Victoria Village\n",
       "2      M5A  Downtown Toronto         Harbourfront, Regent Park\n",
       "3      M6A        North York  Lawrence Heights, Lawrence Manor\n",
       "4      M7A      Queen's Park                      Queen's Park"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = df_torpc.groupby(by=['Postcode','Borough'],sort=False).agg( ', '.join)\n",
    "result.reset_index(inplace=True)\n",
    "result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103, 3)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have have built a dataframe of the postal codes 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",
    "\n",
    "I tried to use the Geocoder Python package (https://geocoder.readthedocs.io/index.html) but it didn't work, so I will use the csv file provided here: http://cocl.us/Geospatial_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df_latlng = pd.read_csv('http://cocl.us/Geospatial_data')\n",
    "df_latlng.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rename \"Postcode\" to \"Postal Code\" and \"Neighbourhood\" to \"Neighborhood\""
   ]
  },
  {
   "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>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>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>Harbourfront, Regent Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Lawrence Heights, Lawrence Manor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M7A</td>\n",
       "      <td>Queen's Park</td>\n",
       "      <td>Queen's Park</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postal Code           Borough                      Neighborhood\n",
       "0         M3A        North York                         Parkwoods\n",
       "1         M4A        North York                  Victoria Village\n",
       "2         M5A  Downtown Toronto         Harbourfront, Regent Park\n",
       "3         M6A        North York  Lawrence Heights, Lawrence Manor\n",
       "4         M7A      Queen's Park                      Queen's Park"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.rename(columns={'Postcode': 'Postal Code','Neighbourhood': 'Neighborhood'}, inplace=True)\n",
    "result.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge both dataframes using the key 'Postal Code'"
   ]
  },
  {
   "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>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>Harbourfront, Regent Park</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 Heights, Lawrence Manor</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>Queen's Park</td>\n",
       "      <td>43.662301</td>\n",
       "      <td>-79.389494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M9A</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Islington Avenue</td>\n",
       "      <td>43.667856</td>\n",
       "      <td>-79.532242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</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>7</th>\n",
       "      <td>M3B</td>\n",
       "      <td>North York</td>\n",
       "      <td>Don Mills North</td>\n",
       "      <td>43.745906</td>\n",
       "      <td>-79.352188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>M4B</td>\n",
       "      <td>East York</td>\n",
       "      <td>Woodbine Gardens, Parkview Hill</td>\n",
       "      <td>43.706397</td>\n",
       "      <td>-79.309937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>M5B</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Ryerson, Garden District</td>\n",
       "      <td>43.657162</td>\n",
       "      <td>-79.378937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postal Code           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         Harbourfront, Regent Park  43.654260   \n",
       "3         M6A        North York  Lawrence Heights, Lawrence Manor  43.718518   \n",
       "4         M7A      Queen's Park                      Queen's Park  43.662301   \n",
       "5         M9A         Etobicoke                  Islington Avenue  43.667856   \n",
       "6         M1B       Scarborough                    Rouge, Malvern  43.806686   \n",
       "7         M3B        North York                   Don Mills North  43.745906   \n",
       "8         M4B         East York   Woodbine Gardens, Parkview Hill  43.706397   \n",
       "9         M5B  Downtown Toronto          Ryerson, Garden District  43.657162   \n",
       "\n",
       "   Longitude  \n",
       "0 -79.329656  \n",
       "1 -79.315572  \n",
       "2 -79.360636  \n",
       "3 -79.464763  \n",
       "4 -79.389494  \n",
       "5 -79.532242  \n",
       "6 -79.194353  \n",
       "7 -79.352188  \n",
       "8 -79.309937  \n",
       "9 -79.378937  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_neighb=pd.merge(result, df_latlng, on='Postal Code', how='inner')\n",
    "df_neighb.head(10)"
   ]
  }
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