{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "73898b7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "import requests\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px              ### To create interactive plots in python\n",
    "import chart_studio\n",
    "username ='janice.gbedemah'              ### An account needs to be created online to retrieve API keys\n",
    "api_key = '7fuMphA2ExbPtpp9j3Bv'\n",
    "chart_studio.tools.set_credentials_file(username=username,api_key=api_key)\n",
    "import chart_studio.plotly as py\n",
    "import chart_studio.tools as tls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "49d01bc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "alberta_data = {'address':[], 'bedroom': [], 'bathroom':[], 'price':[]}\n",
    "ontario_data = {'address':[], 'bedroom': [], 'bathroom':[], 'price':[]}\n",
    "BC_data = {'address':[], 'bedroom': [], 'bathroom':[], 'price':[]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3e87652a",
   "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>address</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>bathroom</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>618 5 Avenue ...</td>\n",
       "      <td>1 Bed</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$1,399 CA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9721 92 Ave.,...</td>\n",
       "      <td>1 Bed</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$999 CAD ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10717 107 Str...</td>\n",
       "      <td>Studio</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$699 CAD ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6707 Elbow Dr...</td>\n",
       "      <td>Studio</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$1,050 CA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3011 James Mo...</td>\n",
       "      <td>2 Beds</td>\n",
       "      <td>2 Baths</td>\n",
       "      <td>$1,429 CA...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             address   bedroom  bathroom  \\\n",
       "0                                   618 5 Avenue ...    1 Bed     1 Bath   \n",
       "1                                   9721 92 Ave.,...    1 Bed     1 Bath   \n",
       "2                                   10717 107 Str...   Studio     1 Bath   \n",
       "3                                   6707 Elbow Dr...   Studio     1 Bath   \n",
       "4                                   3011 James Mo...   2 Beds    2 Baths   \n",
       "\n",
       "                                               price  \n",
       "0                                       $1,399 CA...  \n",
       "1                                       $999 CAD ...  \n",
       "2                                       $699 CAD ...  \n",
       "3                                       $1,050 CA...  \n",
       "4                                       $1,429 CA...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## SCRAPING LISTINGS IN ALBERTA\n",
    "for i in range(1,54):         ## TO loop through all the url pages from page 1 to 53\n",
    "    url='https://apartmentlove.com/apartments-for-rent/alberta/canada/apartment/page'+str(i)\n",
    "    page = requests.get(url) ## requesting to have access/connection to that page from the url\n",
    "    soup = BeautifulSoup(page.content, 'html.parser') ## used to parse the html imformation from that page\n",
    "    lists = soup.find_all('div', class_=\"property-details\")## The parent class for all the information for each listing\n",
    "    for list in lists:\n",
    "        address = list.find('div', class_='property-details-heading').h3.a.text.replace(\"\\n\",\" \")\n",
    "        bedroom = list.find('ul', class_='d-flex').li.text.replace(\"\\n\",\" \")\n",
    "        bathroom = list.find('i', class_='bath-icon').next_sibling ## to extract the first string/text next to a tag/element \n",
    "        price = list.find('span', class_='price-text').text.replace(\"\\n\",\" \")\n",
    "        if address:\n",
    "            alberta_data['address'].append(address)\n",
    "        if bedroom:\n",
    "            alberta_data['bedroom'].append(bedroom)\n",
    "        if bathroom:\n",
    "            alberta_data['bathroom'].append(bathroom)\n",
    "        if price:\n",
    "            alberta_data['price'].append(price) \n",
    "alberta = pd.DataFrame(alberta_data)\n",
    "alberta.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0f540caa",
   "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>address</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>bathroom</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>185 Lyon Stre...</td>\n",
       "      <td>1 Bed</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$1,900 CA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50 Prince Art...</td>\n",
       "      <td>1 Bed</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$1,800 CA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2 Quebec Stre...</td>\n",
       "      <td>1 Bed</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$1,580 CA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Klaskamp, Han...</td>\n",
       "      <td>1 Bed</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$980 CAD ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60 Conway Dr....</td>\n",
       "      <td>1 Bed</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$1,359 CA...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             address  bedroom bathroom  \\\n",
       "0                                   185 Lyon Stre...   1 Bed    1 Bath   \n",
       "1                                   50 Prince Art...   1 Bed    1 Bath   \n",
       "2                                   2 Quebec Stre...   1 Bed    1 Bath   \n",
       "3                                   Klaskamp, Han...   1 Bed    1 Bath   \n",
       "4                                   60 Conway Dr....   1 Bed    1 Bath   \n",
       "\n",
       "                                               price  \n",
       "0                                       $1,900 CA...  \n",
       "1                                       $1,800 CA...  \n",
       "2                                       $1,580 CA...  \n",
       "3                                       $980 CAD ...  \n",
       "4                                       $1,359 CA...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## SCRAPING LISTINGS IN ONTARIO\n",
    "for j in range(1,9):\n",
    "    url2 = 'https://apartmentlove.com/apartments-for-rent/ontario/canada/apartment/page'+str(j)\n",
    "    page2 = requests.get(url2)\n",
    "    soup2 = BeautifulSoup(page2.content, \"html.parser\")\n",
    "    lists2 = soup2.find_all('div', class_=\"property-details\")\n",
    "    for list in lists2:\n",
    "        address2 = list.find('div', class_='property-details-heading').h3.a.text.replace(\"\\n\",\" \")\n",
    "        bedroom2 = list.find('ul', class_='d-flex').li.text.replace(\"\\n\",\" \")\n",
    "        bathroom2= list.find('i', class_='bath-icon').next_sibling ## to extract the first string/text next to a tag/element \n",
    "        price2 = list.find('span', class_='price-text').text.replace(\"\\n\",\" \")\n",
    "        if address2:\n",
    "            ontario_data['address'].append(address2)\n",
    "        if bedroom:\n",
    "            ontario_data['bedroom'].append(bedroom2)\n",
    "        if bathroom:\n",
    "            ontario_data['bathroom'].append(bathroom2)\n",
    "        if price:\n",
    "            ontario_data['price'].append(price2) \n",
    "ontario = pd.DataFrame(ontario_data)\n",
    "ontario.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "96428266",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(156, 4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ontario.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fa0cca89",
   "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>address</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>bathroom</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>610 Granville...</td>\n",
       "      <td>1 Bed</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$1,650 CA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>828 Howe Stre...</td>\n",
       "      <td>1 Bed</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$1,650 CA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>833 Seymour S...</td>\n",
       "      <td>1 Bed</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$1,400 CA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alexander Str...</td>\n",
       "      <td>1 Bed</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$1,600 CA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>928 Homer Str...</td>\n",
       "      <td>1 Bed</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$1,400 CA...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             address  bedroom bathroom  \\\n",
       "0                                   610 Granville...   1 Bed    1 Bath   \n",
       "1                                   828 Howe Stre...   1 Bed    1 Bath   \n",
       "2                                   833 Seymour S...   1 Bed    1 Bath   \n",
       "3                                   Alexander Str...   1 Bed    1 Bath   \n",
       "4                                   928 Homer Str...   1 Bed    1 Bath   \n",
       "\n",
       "                                               price  \n",
       "0                                       $1,650 CA...  \n",
       "1                                       $1,650 CA...  \n",
       "2                                       $1,400 CA...  \n",
       "3                                       $1,600 CA...  \n",
       "4                                       $1,400 CA...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## SCRAPING LISTINGS IN BRITISH COLUMBIA\n",
    "for k in range(1,7):\n",
    "    url3 = 'https://apartmentlove.com/apartments-for-rent/british-columbia/canada/apartment/page'+str(k)\n",
    "    page3 = requests.get(url3)\n",
    "    soup3 = BeautifulSoup(page3.content, \"html.parser\")\n",
    "    lists3 = soup3.find_all('div', class_=\"property-details\")\n",
    "    for list in lists3:\n",
    "        address3 = list.find('div', class_='property-details-heading').h3.a.text.replace(\"\\n\",\" \")\n",
    "        bedroom3 = list.find('ul', class_='d-flex').li.text.replace(\"\\n\",\" \")\n",
    "        bathroom3= list.find('i', class_='bath-icon').next_sibling ## to extract the first string/text next to a tag/element \n",
    "        price3 = list.find('span', class_='price-text').text.replace(\"\\n\",\" \")\n",
    "        if address3:\n",
    "            BC_data['address'].append(address3)\n",
    "        if bedroom:\n",
    "            BC_data['bedroom'].append(bedroom3)\n",
    "        if bathroom:\n",
    "            BC_data['bathroom'].append(bathroom3)\n",
    "        if price:\n",
    "            BC_data['price'].append(price3) \n",
    "BC = pd.DataFrame(BC_data)\n",
    "BC.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eeb3724d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(144, 4)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BC.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a32f488e",
   "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>address</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>bathroom</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>618 5 Avenue ...</td>\n",
       "      <td>1 Bed</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$1,399 CA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9721 92 Ave.,...</td>\n",
       "      <td>1 Bed</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$999 CAD ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10717 107 Str...</td>\n",
       "      <td>Studio</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$699 CAD ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6707 Elbow Dr...</td>\n",
       "      <td>Studio</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$1,050 CA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3011 James Mo...</td>\n",
       "      <td>2 Beds</td>\n",
       "      <td>2 Baths</td>\n",
       "      <td>$1,429 CA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1970 Fitzgera...</td>\n",
       "      <td>2 Beds</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$1,295 CA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1970 Fitzgera...</td>\n",
       "      <td>3 Beds</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$1,495 CA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>144 Brunswick...</td>\n",
       "      <td>1 Bed</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$1,250 CA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>908 Sixth Ave...</td>\n",
       "      <td>2 Beds</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$1,995 CA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>144 Brunswick...</td>\n",
       "      <td>2 Beds</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$1,395 CA...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1572 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               address   bedroom  bathroom  \\\n",
       "0                                     618 5 Avenue ...    1 Bed     1 Bath   \n",
       "1                                     9721 92 Ave.,...    1 Bed     1 Bath   \n",
       "2                                     10717 107 Str...   Studio     1 Bath   \n",
       "3                                     6707 Elbow Dr...   Studio     1 Bath   \n",
       "4                                     3011 James Mo...   2 Beds    2 Baths   \n",
       "..                                                 ...       ...       ...   \n",
       "139                                   1970 Fitzgera...   2 Beds     1 Bath   \n",
       "140                                   1970 Fitzgera...   3 Beds     1 Bath   \n",
       "141                                   144 Brunswick...    1 Bed     1 Bath   \n",
       "142                                   908 Sixth Ave...   2 Beds     1 Bath   \n",
       "143                                   144 Brunswick...   2 Beds     1 Bath   \n",
       "\n",
       "                                                 price  \n",
       "0                                         $1,399 CA...  \n",
       "1                                         $999 CAD ...  \n",
       "2                                         $699 CAD ...  \n",
       "3                                         $1,050 CA...  \n",
       "4                                         $1,429 CA...  \n",
       "..                                                 ...  \n",
       "139                                       $1,295 CA...  \n",
       "140                                       $1,495 CA...  \n",
       "141                                       $1,250 CA...  \n",
       "142                                       $1,995 CA...  \n",
       "143                                       $1,395 CA...  \n",
       "\n",
       "[1572 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_listings = pd.concat([alberta,ontario,BC], axis = 0)\n",
    "all_listings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6670c0d3",
   "metadata": {},
   "source": [
    "   ##  DATA INSPECTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a658bcc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1572, 4)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " all_listings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "539c3348",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "address     0\n",
       "bedroom     0\n",
       "bathroom    0\n",
       "price       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_listings.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "08620257",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>address</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>bathroom</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1572</td>\n",
       "      <td>1572</td>\n",
       "      <td>1572</td>\n",
       "      <td>1572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>565</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>150-8930 Jasp...</td>\n",
       "      <td>2 Beds</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$999 CAD ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>13</td>\n",
       "      <td>587</td>\n",
       "      <td>1472</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "                                                  address   bedroom bathroom  \\\n",
       "count                                                1572      1572     1572   \n",
       "unique                                                565         8        3   \n",
       "top                                      150-8930 Jasp...   2 Beds    1 Bath   \n",
       "freq                                                   13       587     1472   \n",
       "\n",
       "                                                    price  \n",
       "count                                                1572  \n",
       "unique                                                432  \n",
       "top                                          $999 CAD ...  \n",
       "freq                                                   37  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_listings.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "964c11f4",
   "metadata": {},
   "source": [
    "## DATA CLEANING/WRANGLING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5abdc611",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>address</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>bathroom</th>\n",
       "      <th>price</th>\n",
       "      <th>city</th>\n",
       "      <th>province</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>618 5 Avenue ...</td>\n",
       "      <td>1 Bed</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$1,399 CA...</td>\n",
       "      <td>Calgary</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9721 92 Ave.</td>\n",
       "      <td>1 Bed</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$999 CAD ...</td>\n",
       "      <td>Grande Prairie</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10717 107 Str...</td>\n",
       "      <td>Studio</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$699 CAD ...</td>\n",
       "      <td>Edmonton</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6707 Elbow Dr...</td>\n",
       "      <td>Studio</td>\n",
       "      <td>1 Bath</td>\n",
       "      <td>$1,050 CA...</td>\n",
       "      <td>Calgary</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3011 James Mo...</td>\n",
       "      <td>2 Beds</td>\n",
       "      <td>2 Baths</td>\n",
       "      <td>$1,429 CA...</td>\n",
       "      <td>Edmonton</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             address   bedroom  bathroom  \\\n",
       "0                                   618 5 Avenue ...    1 Bed     1 Bath   \n",
       "1                                       9721 92 Ave.    1 Bed     1 Bath   \n",
       "2                                   10717 107 Str...   Studio     1 Bath   \n",
       "3                                   6707 Elbow Dr...   Studio     1 Bath   \n",
       "4                                   3011 James Mo...   2 Beds    2 Baths   \n",
       "\n",
       "                                               price             city  \\\n",
       "0                                       $1,399 CA...          Calgary   \n",
       "1                                       $999 CAD ...   Grande Prairie   \n",
       "2                                       $699 CAD ...         Edmonton   \n",
       "3                                       $1,050 CA...          Calgary   \n",
       "4                                       $1,429 CA...         Edmonton   \n",
       "\n",
       "                          province  \n",
       "0   AB                              \n",
       "1   AB                              \n",
       "2   AB                              \n",
       "3   AB                              \n",
       "4   AB                              "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_listings[['address','city','province']] = all_listings['address'].str.rsplit(',', n=2,expand=True)\n",
    "all_listings.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "60e1b99b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>address</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>bathroom</th>\n",
       "      <th>price</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>618 5 Avenue ...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>$1,399 CA...</td>\n",
       "      <td>Calgary</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9721 92 Ave.</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>$999 CAD ...</td>\n",
       "      <td>Grande Prairie</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10717 107 Str...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>$699 CAD ...</td>\n",
       "      <td>Edmonton</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6707 Elbow Dr...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>$1,050 CA...</td>\n",
       "      <td>Calgary</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3011 James Mo...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>$1,429 CA...</td>\n",
       "      <td>Edmonton</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             address bedroom bathroom  \\\n",
       "0                                   618 5 Avenue ...     1         1    \n",
       "1                                       9721 92 Ave.     1         1    \n",
       "2                                   10717 107 Str...      0        1    \n",
       "3                                   6707 Elbow Dr...      0        1    \n",
       "4                                   3011 James Mo...     2         2    \n",
       "\n",
       "                                               price             city  \\\n",
       "0                                       $1,399 CA...          Calgary   \n",
       "1                                       $999 CAD ...   Grande Prairie   \n",
       "2                                       $699 CAD ...         Edmonton   \n",
       "3                                       $1,050 CA...          Calgary   \n",
       "4                                       $1,429 CA...         Edmonton   \n",
       "\n",
       "                          province  \n",
       "0   AB                              \n",
       "1   AB                              \n",
       "2   AB                              \n",
       "3   AB                              \n",
       "4   AB                              "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## DATA MANIPULATION\n",
    "all_listings['bedroom'] = all_listings['bedroom'].str.replace('Beds','')\n",
    "all_listings['bedroom'] = all_listings['bedroom'].str.replace('Bed','')\n",
    "all_listings['bathroom'] = all_listings['bathroom'].str.replace('Baths','')\n",
    "all_listings['bathroom'] = all_listings['bathroom'].str.replace('Bath','')\n",
    "all_listings['bedroom'] = all_listings['bedroom'].str.replace('Studio',\"0\")\n",
    "all_listings.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0d2b5baa",
   "metadata": {},
   "outputs": [],
   "source": [
    "## CHANGE THE DATA TYPES\n",
    "all_listings[['bedroom','bathroom']] = all_listings[['bedroom','bathroom']].apply(pd.to_numeric)\n",
    "all_listings['price'] = all_listings['price'].str.replace('CAD / month','')\n",
    "all_listings['price'] = all_listings['price'].str.replace('\\$','',regex = True)\n",
    "all_listings['price'] = pd.to_numeric(all_listings['price'].replace({',':''}, regex = True)).astype(float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "75873759",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3], dtype=int64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_listings.bathroom.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "51e5733b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1. , 0. , 2. , 3. , 5. , 4. , 2.5, 1.5])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_listings.bedroom.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "25f6ea39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "address      object\n",
       "bedroom     float64\n",
       "bathroom      int64\n",
       "price       float64\n",
       "city         object\n",
       "province     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_listings.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f22cca67",
   "metadata": {},
   "outputs": [],
   "source": [
    "## SAVING DATA TO A CSV FILE\n",
    "all_listings.to_csv('apartment_listings.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "8119545a",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Analysis is performed for only 0-3 bedroom apartments\n",
    "apartments = all_listings[(all_listings['bedroom']== 0) | (all_listings['bedroom']==1)|(all_listings['bedroom']==2)|(all_listings['bedroom']==3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c542e25b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th>address</th>\n",
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       "      <th>bathroom</th>\n",
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       "      <th>city</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>618 5 Avenue ...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1399.0</td>\n",
       "      <td>Calgary</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>9721 92 Ave.</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>999.0</td>\n",
       "      <td>Grande Prairie</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>10717 107 Str...</td>\n",
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       "      <td>1</td>\n",
       "      <td>699.0</td>\n",
       "      <td>Edmonton</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>6707 Elbow Dr...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>Calgary</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>3011 James Mo...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1429.0</td>\n",
       "      <td>Edmonton</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1567</th>\n",
       "      <td>139</td>\n",
       "      <td>1970 Fitzgera...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1295.0</td>\n",
       "      <td>Courtenay</td>\n",
       "      <td>BC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1568</th>\n",
       "      <td>140</td>\n",
       "      <td>1970 Fitzgera...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1495.0</td>\n",
       "      <td>Courtenay</td>\n",
       "      <td>BC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1569</th>\n",
       "      <td>141</td>\n",
       "      <td>144 Brunswick...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>Penticton</td>\n",
       "      <td>BC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1570</th>\n",
       "      <td>142</td>\n",
       "      <td>908 Sixth Avenue</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>New Westminster</td>\n",
       "      <td>BC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1571</th>\n",
       "      <td>143</td>\n",
       "      <td>144 Brunswick St</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>Penticton</td>\n",
       "      <td>BC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1563 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                                            address  bedroom  \\\n",
       "0              0                                   618 5 Avenue ...      1.0   \n",
       "1              1                                       9721 92 Ave.      1.0   \n",
       "2              2                                   10717 107 Str...      0.0   \n",
       "3              3                                   6707 Elbow Dr...      0.0   \n",
       "4              4                                   3011 James Mo...      2.0   \n",
       "...          ...                                                ...      ...   \n",
       "1567         139                                   1970 Fitzgera...      2.0   \n",
       "1568         140                                   1970 Fitzgera...      3.0   \n",
       "1569         141                                   144 Brunswick...      1.0   \n",
       "1570         142                                   908 Sixth Avenue      2.0   \n",
       "1571         143                                   144 Brunswick St      2.0   \n",
       "\n",
       "      bathroom   price              city                         province  \n",
       "0            1  1399.0           Calgary   AB                              \n",
       "1            1   999.0    Grande Prairie   AB                              \n",
       "2            1   699.0          Edmonton   AB                              \n",
       "3            1  1050.0           Calgary   AB                              \n",
       "4            2  1429.0          Edmonton   AB                              \n",
       "...        ...     ...               ...                              ...  \n",
       "1567         1  1295.0         Courtenay   BC                              \n",
       "1568         1  1495.0         Courtenay   BC                              \n",
       "1569         1  1250.0         Penticton   BC                              \n",
       "1570         1  1995.0   New Westminster   BC                              \n",
       "1571         1  1395.0         Penticton   BC                              \n",
       "\n",
       "[1563 rows x 7 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apartments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "23416349",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Price Distribution for the dataset\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.hist(apartments['price'],bins =range(500,5000,200),color='purple') \n",
    "plt.xlabel('Rent Prices/month')\n",
    "plt.ylabel('Count of listings')\n",
    "plt.title('Price Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b67d74ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Histogram of prices for each province\n",
    "apartments['price'].hist(by=apartments['province'],color='green',bins =range(500,4000,200),figsize=(10,8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "7d68488b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Boxplot for each province \n",
    "apartments.boxplot(column='price', by='province',color= \"green\", figsize=(8,8), showmeans=True)\n",
    "plt.show()\n",
    "## The ouliers in the plot below can be attributed to the natural variations in the provinces. That is, the cities in each province have their own unique characteristics which may cause the disparity in prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f3ae0cdb",
   "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>bedroom</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>province</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AB</th>\n",
       "      <td>882.786517</td>\n",
       "      <td>1067.613924</td>\n",
       "      <td>1285.652720</td>\n",
       "      <td>1533.129496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BC</th>\n",
       "      <td>1046.944444</td>\n",
       "      <td>1401.557377</td>\n",
       "      <td>1593.260870</td>\n",
       "      <td>1876.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ON</th>\n",
       "      <td>1245.500000</td>\n",
       "      <td>1600.820000</td>\n",
       "      <td>2019.190476</td>\n",
       "      <td>2254.785714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "bedroom                                  0.0          1.0          2.0  \\\n",
       "province                                                                 \n",
       " AB                               882.786517  1067.613924  1285.652720   \n",
       " BC                              1046.944444  1401.557377  1593.260870   \n",
       " ON                              1245.500000  1600.820000  2019.190476   \n",
       "\n",
       "bedroom                                  3.0  \n",
       "province                                      \n",
       " AB                              1533.129496  \n",
       " BC                              1876.666667  \n",
       " ON                              2254.785714  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## A pivot table for the average rent prices for 0-3 bedroom apartments in each province\n",
    "pivot1 = pd.pivot_table(apartments,\n",
    "                       index=[\"province\"],\n",
    "                       columns ='bedroom',\n",
    "                       values='price',\n",
    "                       aggfunc = 'mean')\n",
    "pivot1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "27c1b4fd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plotly.com"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "bingroup": "x",
         "histfunc": "sum",
         "hovertemplate": "bedroom=0.0<br>province=%{x}<br>sum of price=%{y}<extra></extra>",
         "legendgroup": "0.0",
         "marker": {
          "color": "#FFA15A",
          "pattern": {
           "shape": ""
          }
         },
         "name": "0.0",
         "offsetgroup": "0.0",
         "orientation": "v",
         "showlegend": true,
         "texttemplate": "%{value}",
         "type": "histogram",
         "x": [
          " AB                            ",
          " BC                            ",
          " ON                            "
         ],
         "xaxis": "x",
         "y": [
          882.7865168539325,
          1046.9444444444443,
          1245.5
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "bingroup": "x",
         "histfunc": "sum",
         "hovertemplate": "bedroom=1.0<br>province=%{x}<br>sum of price=%{y}<extra></extra>",
         "legendgroup": "1.0",
         "marker": {
          "color": "#19d3f3",
          "pattern": {
           "shape": ""
          }
         },
         "name": "1.0",
         "offsetgroup": "1.0",
         "orientation": "v",
         "showlegend": true,
         "texttemplate": "%{value}",
         "type": "histogram",
         "x": [
          " AB                            ",
          " BC                            ",
          " ON                            "
         ],
         "xaxis": "x",
         "y": [
          1067.613924050633,
          1401.5573770491803,
          1600.82
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "bingroup": "x",
         "histfunc": "sum",
         "hovertemplate": "bedroom=2.0<br>province=%{x}<br>sum of price=%{y}<extra></extra>",
         "legendgroup": "2.0",
         "marker": {
          "color": "#FF6692",
          "pattern": {
           "shape": ""
          }
         },
         "name": "2.0",
         "offsetgroup": "2.0",
         "orientation": "v",
         "showlegend": true,
         "texttemplate": "%{value}",
         "type": "histogram",
         "x": [
          " AB                            ",
          " BC                            ",
          " ON                            "
         ],
         "xaxis": "x",
         "y": [
          1285.652719665272,
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         ],
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RENT PRICE\"}},\"legend\":{\"title\":{\"text\":\"BEDROOM SIZE\"},\"tracegroupgap\":0},\"margin\":{\"t\":60},\"barmode\":\"group\",\"font\":{\"size\":13},\"title\":{\"text\":\"AVERAGE RENT PRICES FOR 0-3 BEDROOM APARTMENTS\"},\"paper_bgcolor\":\"rgba(0,0,0,0)\",\"plot_bgcolor\":\"rgba(0,0,0,0)\"},                        {\"responsive\": true}                    ).then(function(){\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 72x72 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table1 = apartments.groupby(['province','bedroom'], as_index=False)['price'].mean()\n",
    "plt.figure(figsize=(1,1))\n",
    "fig=px.histogram(data_frame=pivot2, x='province',y ='price', color='bedroom', barmode=\"group\",text_auto=True,color_discrete_map={\n",
    "                \"0.0\": \"gold\",\n",
    "                \"1.0\": \"bronze\",\n",
    "                \"2.0\": \"blue\",\n",
    "                \"3.0\": \"goldenrod\"})\n",
    "\n",
    "fig.update_layout(\n",
    "    title='AVERAGE RENT PRICES FOR 0-3 BEDROOM APARTMENTS',\n",
    "    xaxis_title='PROVINCE',\n",
    "    yaxis_title='AVERAGE RENT PRICE',\n",
    "    legend_title='BEDROOM SIZE',\n",
    "    paper_bgcolor=\"rgba(0,0,0,0)\",  ## Sets the background to white\n",
    "    plot_bgcolor=\"rgba(0,0,0,0)\",   ## Sets the background of the plot to white/clear\n",
    "    font_size=13,\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7bc6304c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://plotly.com/~janice.gbedemah/1/'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "py.plot(fig, filename=\"housing affordability study\", auto_open=True) ## This saves the plot to your account and can be retrieved online"
   ]
  }
 ],
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