Rent_Price.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Analyzing renting price in kamloops region"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The price data will be scraped from kijiji website"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 921,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "import urllib.request\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get Renting data from kijiji website"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pc=[]\n",
    "renting_info=[]\n",
    "startLink='https://www.kijiji.ca/v-house-rental/kamloops'\n",
    "for i in range(10):\n",
    "    url='https://www.kijiji.ca/b-house-rental/kamloops/page-'+str(i)+'/c43l1700227'\n",
    "    with urllib.request.urlopen(url) as response:\n",
    "        html_doc = response.read()\n",
    "        sp = BeautifulSoup(html_doc, 'html.parser')\n",
    "        prices=sp.select('.price')\n",
    "        links=sp.select('.title a')        \n",
    "        for i in range(len(sp.select('.price'))):\n",
    "            rent_price = prices[i].get_text().strip().strip('$').replace(',','')\n",
    "            mlink=startLink+links[i].get('href')\n",
    "            with urllib.request.urlopen(mlink) as response:\n",
    "                html_doc = response.read()\n",
    "                soup = BeautifulSoup(html_doc, 'html.parser')\n",
    "                features = soup.select('.attributeValue-1550499923')\n",
    "                try:\n",
    "                    bedrooms = features[0].get_text()\n",
    "                except:\n",
    "                    bedrooms = 'Not Available'\n",
    "                try:\n",
    "                    bathrooms = features[1].get_text()\n",
    "                except:\n",
    "                    bathrooms = 'Not Available'\n",
    "#                 try:\n",
    "#                     furnished = features[2].get_text()\n",
    "#                 except:\n",
    "#                     furnished = 'Not Availabel'\n",
    "#                 try:\n",
    "#                     petfriendly = features[3].get_text()\n",
    "#                 except:\n",
    "#                     petfriendly = 'Not Availabel'\n",
    "                try:\n",
    "                    location = soup.select('.address-2932131783')[0].get_text()\n",
    "                except:\n",
    "                    location='Not Availabel'\n",
    "                print(rent_price,bedrooms,bathrooms,location)\n",
    "                renting_info.append({'price':rent_price, 'bedrooms':bedrooms,'bathrooms':bathrooms,'location':location })\n",
    "                time.sleep(1)\n",
    "    time.sleep(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "renting_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "rent_info=pd.DataFrame(renting_info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "rent_info.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "rent_info.to_csv('rent_info_kamloops.'+str(int(time.time()))+'.csv', sep=',',header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Processing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 922,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>location</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 bathroom</td>\n",
       "      <td>2 bedrooms</td>\n",
       "      <td>Linthorpe Road, Kamloops V2B 7S3 BC</td>\n",
       "      <td>1525.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>3 bedrooms</td>\n",
       "      <td>Merritt, BC V1K0B2, Canada</td>\n",
       "      <td>Please Contact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>3 bedrooms</td>\n",
       "      <td>, Kamloops V2B 3T9 BC</td>\n",
       "      <td>1800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>4 bedrooms</td>\n",
       "      <td>Ave NE, Salmon Arm, BC V1E 3G4, Canada</td>\n",
       "      <td>2800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1 bathroom</td>\n",
       "      <td>2 bedrooms</td>\n",
       "      <td>Kamloops, BC V2B 3K5, Canada</td>\n",
       "      <td>950.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     bathrooms    bedrooms                                location  \\\n",
       "0   1 bathroom  2 bedrooms     Linthorpe Road, Kamloops V2B 7S3 BC   \n",
       "1  2 bathrooms  3 bedrooms              Merritt, BC V1K0B2, Canada   \n",
       "2  2 bathrooms  3 bedrooms                   , Kamloops V2B 3T9 BC   \n",
       "3  2 bathrooms  4 bedrooms  Ave NE, Salmon Arm, BC V1E 3G4, Canada   \n",
       "4   1 bathroom  2 bedrooms            Kamloops, BC V2B 3K5, Canada   \n",
       "\n",
       "            price  \n",
       "0         1525.00  \n",
       "1  Please Contact  \n",
       "2         1800.00  \n",
       "3         2800.00  \n",
       "4          950.00  "
      ]
     },
     "execution_count": 922,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('rent_info_kamloops.1528247317.csv', names = [\"bathrooms\", \"bedrooms\", \"location\", \"price\"]).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 923,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "read_in_rent_info=pd.read_csv('rent_info_kamloops.1528247317.csv', names = [\"bathrooms\", \"bedrooms\", \"location\", \"price\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 924,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>location</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 bathroom</td>\n",
       "      <td>2 bedrooms</td>\n",
       "      <td>Linthorpe Road, Kamloops V2B 7S3 BC</td>\n",
       "      <td>1525.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>3 bedrooms</td>\n",
       "      <td>Merritt, BC V1K0B2, Canada</td>\n",
       "      <td>Please Contact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>3 bedrooms</td>\n",
       "      <td>, Kamloops V2B 3T9 BC</td>\n",
       "      <td>1800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>4 bedrooms</td>\n",
       "      <td>Ave NE, Salmon Arm, BC V1E 3G4, Canada</td>\n",
       "      <td>2800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1 bathroom</td>\n",
       "      <td>2 bedrooms</td>\n",
       "      <td>Kamloops, BC V2B 3K5, Canada</td>\n",
       "      <td>950.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     bathrooms    bedrooms                                location  \\\n",
       "0   1 bathroom  2 bedrooms     Linthorpe Road, Kamloops V2B 7S3 BC   \n",
       "1  2 bathrooms  3 bedrooms              Merritt, BC V1K0B2, Canada   \n",
       "2  2 bathrooms  3 bedrooms                   , Kamloops V2B 3T9 BC   \n",
       "3  2 bathrooms  4 bedrooms  Ave NE, Salmon Arm, BC V1E 3G4, Canada   \n",
       "4   1 bathroom  2 bedrooms            Kamloops, BC V2B 3K5, Canada   \n",
       "\n",
       "            price  \n",
       "0         1525.00  \n",
       "1  Please Contact  \n",
       "2         1800.00  \n",
       "3         2800.00  \n",
       "4          950.00  "
      ]
     },
     "execution_count": 924,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_in_rent_info.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 925,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>location</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>3 bedrooms</td>\n",
       "      <td>, Kamloops V2E 1E9 BC, Canada</td>\n",
       "      <td>Please Contact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>1.5 bathrooms</td>\n",
       "      <td>3 bedrooms</td>\n",
       "      <td>460 Dalgleish Dr, Kamloops, BC V2C 5W7, Canada</td>\n",
       "      <td>1550.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>3 bedrooms</td>\n",
       "      <td>, Kamloops V2B 3E1 BC, Canada</td>\n",
       "      <td>1400.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>3 bathrooms</td>\n",
       "      <td>4 bedrooms</td>\n",
       "      <td>, kamloops v2c1k7 BC, Canada</td>\n",
       "      <td>2650.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>No</td>\n",
       "      <td>1 bathroom</td>\n",
       "      <td>, Kamloops V1S 1A3 BC, Canada</td>\n",
       "      <td>2500.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         bathrooms    bedrooms  \\\n",
       "205    2 bathrooms  3 bedrooms   \n",
       "206  1.5 bathrooms  3 bedrooms   \n",
       "207    2 bathrooms  3 bedrooms   \n",
       "208    3 bathrooms  4 bedrooms   \n",
       "209             No  1 bathroom   \n",
       "\n",
       "                                           location           price  \n",
       "205                   , Kamloops V2E 1E9 BC, Canada  Please Contact  \n",
       "206  460 Dalgleish Dr, Kamloops, BC V2C 5W7, Canada         1550.00  \n",
       "207                   , Kamloops V2B 3E1 BC, Canada         1400.00  \n",
       "208                    , kamloops v2c1k7 BC, Canada         2650.00  \n",
       "209                   , Kamloops V1S 1A3 BC, Canada         2500.00  "
      ]
     },
     "execution_count": 925,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_in_rent_info.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove bedrooms info not provided records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 926,
   "metadata": {},
   "outputs": [],
   "source": [
    "level_one_cleaned = read_in_rent_info[read_in_rent_info.bedrooms!='No']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 927,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>location</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 bathroom</td>\n",
       "      <td>2 bedrooms</td>\n",
       "      <td>Linthorpe Road, Kamloops V2B 7S3 BC</td>\n",
       "      <td>1525.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>3 bedrooms</td>\n",
       "      <td>Merritt, BC V1K0B2, Canada</td>\n",
       "      <td>Please Contact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>3 bedrooms</td>\n",
       "      <td>, Kamloops V2B 3T9 BC</td>\n",
       "      <td>1800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>4 bedrooms</td>\n",
       "      <td>Ave NE, Salmon Arm, BC V1E 3G4, Canada</td>\n",
       "      <td>2800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1 bathroom</td>\n",
       "      <td>2 bedrooms</td>\n",
       "      <td>Kamloops, BC V2B 3K5, Canada</td>\n",
       "      <td>950.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     bathrooms    bedrooms                                location  \\\n",
       "0   1 bathroom  2 bedrooms     Linthorpe Road, Kamloops V2B 7S3 BC   \n",
       "1  2 bathrooms  3 bedrooms              Merritt, BC V1K0B2, Canada   \n",
       "2  2 bathrooms  3 bedrooms                   , Kamloops V2B 3T9 BC   \n",
       "3  2 bathrooms  4 bedrooms  Ave NE, Salmon Arm, BC V1E 3G4, Canada   \n",
       "4   1 bathroom  2 bedrooms            Kamloops, BC V2B 3K5, Canada   \n",
       "\n",
       "            price  \n",
       "0         1525.00  \n",
       "1  Please Contact  \n",
       "2         1800.00  \n",
       "3         2800.00  \n",
       "4          950.00  "
      ]
     },
     "execution_count": 927,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "level_one_cleaned.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 928,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "210"
      ]
     },
     "execution_count": 928,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(read_in_rent_info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove price info not provided records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 929,
   "metadata": {},
   "outputs": [],
   "source": [
    "level_two_cleaned = level_one_cleaned[read_in_rent_info.price != 'Please Contact']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 930,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>location</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 bathroom</td>\n",
       "      <td>2 bedrooms</td>\n",
       "      <td>Linthorpe Road, Kamloops V2B 7S3 BC</td>\n",
       "      <td>1525.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>3 bedrooms</td>\n",
       "      <td>, Kamloops V2B 3T9 BC</td>\n",
       "      <td>1800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>4 bedrooms</td>\n",
       "      <td>Ave NE, Salmon Arm, BC V1E 3G4, Canada</td>\n",
       "      <td>2800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1 bathroom</td>\n",
       "      <td>2 bedrooms</td>\n",
       "      <td>Kamloops, BC V2B 3K5, Canada</td>\n",
       "      <td>950.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1 bathroom</td>\n",
       "      <td>3 bedrooms</td>\n",
       "      <td>Airfield Rd, Barrière, BC V0E 1E0, Canada</td>\n",
       "      <td>1800.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     bathrooms    bedrooms                                   location    price\n",
       "0   1 bathroom  2 bedrooms        Linthorpe Road, Kamloops V2B 7S3 BC  1525.00\n",
       "2  2 bathrooms  3 bedrooms                      , Kamloops V2B 3T9 BC  1800.00\n",
       "3  2 bathrooms  4 bedrooms     Ave NE, Salmon Arm, BC V1E 3G4, Canada  2800.00\n",
       "4   1 bathroom  2 bedrooms               Kamloops, BC V2B 3K5, Canada   950.00\n",
       "5   1 bathroom  3 bedrooms  Airfield Rd, Barrière, BC V0E 1E0, Canada  1800.00"
      ]
     },
     "execution_count": 930,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "level_two_cleaned.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 931,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "154"
      ]
     },
     "execution_count": 931,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(level_two_cleaned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 932,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2 bedrooms'"
      ]
     },
     "execution_count": 932,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "level_two_cleaned.iloc[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 933,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1 bathroom'"
      ]
     },
     "execution_count": 933,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "level_two_cleaned.iloc[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 934,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(level_two_cleaned)):\n",
    "    level_two_cleaned.iloc[i][0]=level_two_cleaned.iloc[i][0].strip('bathrooms ')\n",
    "    level_two_cleaned.iloc[i][1]=level_two_cleaned.iloc[i][1].strip('bedrooms bath bedrooms and den')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 935,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>location</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Linthorpe Road, Kamloops V2B 7S3 BC</td>\n",
       "      <td>1525.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>, Kamloops V2B 3T9 BC</td>\n",
       "      <td>1800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Ave NE, Salmon Arm, BC V1E 3G4, Canada</td>\n",
       "      <td>2800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Kamloops, BC V2B 3K5, Canada</td>\n",
       "      <td>950.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Airfield Rd, Barrière, BC V0E 1E0, Canada</td>\n",
       "      <td>1800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>992 Columbia St, Kamloops, BC V2C 2V9, Canada</td>\n",
       "      <td>1200.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2082 Westsyde Rd, Kamloops, BC V2B 7C2, Canada</td>\n",
       "      <td>1050.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2215 Doubletree Crescent, Kamloops, BC V2B 6E6...</td>\n",
       "      <td>1100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1924 Gloaming Dr, Kamloops, BC V1S 1R2, Canada</td>\n",
       "      <td>2300.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1657 Selwyn Rd, Kamloops, BC V2E 1P1, Canada</td>\n",
       "      <td>1400.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bathrooms bedrooms                                           location  \\\n",
       "0          1        2                Linthorpe Road, Kamloops V2B 7S3 BC   \n",
       "2          2        3                              , Kamloops V2B 3T9 BC   \n",
       "3          2        4             Ave NE, Salmon Arm, BC V1E 3G4, Canada   \n",
       "4          1        2                       Kamloops, BC V2B 3K5, Canada   \n",
       "5          1        3          Airfield Rd, Barrière, BC V0E 1E0, Canada   \n",
       "6          1        1      992 Columbia St, Kamloops, BC V2C 2V9, Canada   \n",
       "8          1        2     2082 Westsyde Rd, Kamloops, BC V2B 7C2, Canada   \n",
       "9          1        1  2215 Doubletree Crescent, Kamloops, BC V2B 6E6...   \n",
       "10         4        6     1924 Gloaming Dr, Kamloops, BC V1S 1R2, Canada   \n",
       "12         1        2       1657 Selwyn Rd, Kamloops, BC V2E 1P1, Canada   \n",
       "\n",
       "      price  \n",
       "0   1525.00  \n",
       "2   1800.00  \n",
       "3   2800.00  \n",
       "4    950.00  \n",
       "5   1800.00  \n",
       "6   1200.00  \n",
       "8   1050.00  \n",
       "9   1100.00  \n",
       "10  2300.00  \n",
       "12  1400.00  "
      ]
     },
     "execution_count": 935,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "level_two_cleaned.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 936,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(level_two_cleaned)):\n",
    "    if(level_two_cleaned.iloc[i][1]=='Bachelor or studi'):\n",
    "        level_two_cleaned.iloc[i][1]=1\n",
    "    else:\n",
    "        level_two_cleaned.iloc[i][1]=int(level_two_cleaned.iloc[i][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 937,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(level_two_cleaned)):\n",
    "    try:\n",
    "        level_two_cleaned.iloc[i][0]=math.floor(int(level_two_cleaned.iloc[i][0]))\n",
    "    except:\n",
    "        level_two_cleaned.iloc[i][0]=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 938,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>location</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Linthorpe Road, Kamloops V2B 7S3 BC</td>\n",
       "      <td>1525.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>, Kamloops V2B 3T9 BC</td>\n",
       "      <td>1800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Ave NE, Salmon Arm, BC V1E 3G4, Canada</td>\n",
       "      <td>2800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Kamloops, BC V2B 3K5, Canada</td>\n",
       "      <td>950.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Airfield Rd, Barrière, BC V0E 1E0, Canada</td>\n",
       "      <td>1800.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  bathrooms bedrooms                                   location    price\n",
       "0         1        2        Linthorpe Road, Kamloops V2B 7S3 BC  1525.00\n",
       "2         2        3                      , Kamloops V2B 3T9 BC  1800.00\n",
       "3         2        4     Ave NE, Salmon Arm, BC V1E 3G4, Canada  2800.00\n",
       "4         1        2               Kamloops, BC V2B 3K5, Canada   950.00\n",
       "5         1        3  Airfield Rd, Barrière, BC V0E 1E0, Canada  1800.00"
      ]
     },
     "execution_count": 938,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "level_two_cleaned.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 939,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "154"
      ]
     },
     "execution_count": 939,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(level_two_cleaned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 940,
   "metadata": {},
   "outputs": [],
   "source": [
    "bathroom_numbers=level_two_cleaned.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 941,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bedroom_numbers=level_two_cleaned.iloc[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 942,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119a9c6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bathroom_numbers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 943,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119aa8978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bedroom_numbers, bins='auto')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing bedroom number price relationship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 944,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>location</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Linthorpe Road, Kamloops V2B 7S3 BC</td>\n",
       "      <td>1525.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>, Kamloops V2B 3T9 BC</td>\n",
       "      <td>1800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Ave NE, Salmon Arm, BC V1E 3G4, Canada</td>\n",
       "      <td>2800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Kamloops, BC V2B 3K5, Canada</td>\n",
       "      <td>950.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Airfield Rd, Barrière, BC V0E 1E0, Canada</td>\n",
       "      <td>1800.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  bathrooms bedrooms                                   location    price\n",
       "0         1        2        Linthorpe Road, Kamloops V2B 7S3 BC  1525.00\n",
       "2         2        3                      , Kamloops V2B 3T9 BC  1800.00\n",
       "3         2        4     Ave NE, Salmon Arm, BC V1E 3G4, Canada  2800.00\n",
       "4         1        2               Kamloops, BC V2B 3K5, Canada   950.00\n",
       "5         1        3  Airfield Rd, Barrière, BC V0E 1E0, Canada  1800.00"
      ]
     },
     "execution_count": 944,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "level_two_cleaned.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert price to number type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 945,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1525.00'"
      ]
     },
     "execution_count": 945,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "level_two_cleaned.iloc[0,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 946,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xiaosheng/anaconda/lib/python3.5/site-packages/pandas/core/indexing.py:141: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n",
      "/Users/xiaosheng/anaconda/lib/python3.5/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "for i in range(len(level_two_cleaned)):\n",
    "    level_two_cleaned.iloc[i,3]=float(level_two_cleaned.iloc[i,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 947,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>location</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Linthorpe Road, Kamloops V2B 7S3 BC</td>\n",
       "      <td>1525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>, Kamloops V2B 3T9 BC</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Ave NE, Salmon Arm, BC V1E 3G4, Canada</td>\n",
       "      <td>2800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Kamloops, BC V2B 3K5, Canada</td>\n",
       "      <td>950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Airfield Rd, Barrière, BC V0E 1E0, Canada</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  bathrooms bedrooms                                   location price\n",
       "0         1        2        Linthorpe Road, Kamloops V2B 7S3 BC  1525\n",
       "2         2        3                      , Kamloops V2B 3T9 BC  1800\n",
       "3         2        4     Ave NE, Salmon Arm, BC V1E 3G4, Canada  2800\n",
       "4         1        2               Kamloops, BC V2B 3K5, Canada   950\n",
       "5         1        3  Airfield Rd, Barrière, BC V0E 1E0, Canada  1800"
      ]
     },
     "execution_count": 947,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "level_two_cleaned.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 948,
   "metadata": {},
   "outputs": [],
   "source": [
    "level_two_cleaned.to_csv('cleaned_data.csv',sep=',',header=None )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 949,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cleaned_data=pd.read_csv('cleaned_data.csv',  names = [\"bathrooms\", \"bedrooms\", \"location\", \"price\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 950,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>location</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Linthorpe Road, Kamloops V2B 7S3 BC</td>\n",
       "      <td>1525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>, Kamloops V2B 3T9 BC</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Ave NE, Salmon Arm, BC V1E 3G4, Canada</td>\n",
       "      <td>2800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Kamloops, BC V2B 3K5, Canada</td>\n",
       "      <td>950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Airfield Rd, Barrière, BC V0E 1E0, Canada</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bathrooms  bedrooms                                   location   price\n",
       "0          1         2        Linthorpe Road, Kamloops V2B 7S3 BC  1525.0\n",
       "2          2         3                      , Kamloops V2B 3T9 BC  1800.0\n",
       "3          2         4     Ave NE, Salmon Arm, BC V1E 3G4, Canada  2800.0\n",
       "4          1         2               Kamloops, BC V2B 3K5, Canada   950.0\n",
       "5          1         3  Airfield Rd, Barrière, BC V0E 1E0, Canada  1800.0"
      ]
     },
     "execution_count": 950,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 951,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "154"
      ]
     },
     "execution_count": 951,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cleaned_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 952,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 952,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_data.iloc[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 953,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1525.0"
      ]
     },
     "execution_count": 953,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_data.iloc[0,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1008,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bedroom_price_pair={}\n",
    "for i in range(len(cleaned_data)):\n",
    "    if(cleaned_data.iloc[i,1] in bedroom_price_pair):\n",
    "        bedroom_price_pair[cleaned_data.iloc[i,1]].append(cleaned_data.iloc[i,3])\n",
    "    else:\n",
    "        bedroom_price_pair[cleaned_data.iloc[i,1]] =[cleaned_data.iloc[i,3]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing one bedroom housing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1009,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 1009,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(bedroom_price_pair[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1010,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "992.17142857142858"
      ]
     },
     "execution_count": 1010,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(bedroom_price_pair[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1011,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2500.0"
      ]
     },
     "execution_count": 1011,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(bedroom_price_pair[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1012,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 1012,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(bedroom_price_pair[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1013,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 1013,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bedroom_price_pair[1].index(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1014,
   "metadata": {},
   "outputs": [],
   "source": [
    "del bedroom_price_pair[1][11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1015,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300.0"
      ]
     },
     "execution_count": 1015,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(bedroom_price_pair[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average price for one room"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1016,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1021.3235294117648"
      ]
     },
     "execution_count": 1016,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(bedroom_price_pair[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Price distribution for one room"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1017,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXqP5y14AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1196554e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bedroom_price_pair[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing two bedroom housing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1018,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49"
      ]
     },
     "execution_count": 1018,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(bedroom_price_pair[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1019,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1650.0"
      ]
     },
     "execution_count": 1019,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(bedroom_price_pair[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1020,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "585.0"
      ]
     },
     "execution_count": 1020,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(bedroom_price_pair[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1021,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1323.7755102040817"
      ]
     },
     "execution_count": 1021,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(bedroom_price_pair[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average price for one room at renting two bedroom house"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1022,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "661.88775510204084"
      ]
     },
     "execution_count": 1022,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(bedroom_price_pair[2])/2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Price distribution for one room"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1023,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMOiS1IRBl6QmDLokNWHQJakJgy5JTfwfZAlWL3vrbfcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119b883c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "one_bedroom_price=[]\n",
    "for val in bedroom_price_pair[2]:\n",
    "    one_bedroom_price.append(val/2)\n",
    "plt.hist(one_bedroom_price)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing three bedroom housing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1024,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46"
      ]
     },
     "execution_count": 1024,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(bedroom_price_pair[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1025,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1779.7826086956522"
      ]
     },
     "execution_count": 1025,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(bedroom_price_pair[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1026,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2500.0"
      ]
     },
     "execution_count": 1026,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(bedroom_price_pair[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1027,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1250.0"
      ]
     },
     "execution_count": 1027,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(bedroom_price_pair[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average price for one room at renting three bedroom house"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1028,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "593.26086956521738"
      ]
     },
     "execution_count": 1028,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(bedroom_price_pair[3])/3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Price distribution for one room"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1029,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxYY0FujYfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119613cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "one_bedroom_price=[]\n",
    "for val in bedroom_price_pair[3]:\n",
    "    one_bedroom_price.append(val/3)\n",
    "plt.hist(one_bedroom_price)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing four bedroom housing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1030,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 1030,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(bedroom_price_pair[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1031,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2228.8095238095239"
      ]
     },
     "execution_count": 1031,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(bedroom_price_pair[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1032,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3250.0"
      ]
     },
     "execution_count": 1032,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(bedroom_price_pair[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1033,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1600.0"
      ]
     },
     "execution_count": 1033,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(bedroom_price_pair[4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average price for one room at renting four bedroom house"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1034,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "557.20238095238096"
      ]
     },
     "execution_count": 1034,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(bedroom_price_pair[4])/4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Price distribution for one room"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1035,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a11d0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "one_bedroom_price=[]\n",
    "for val in bedroom_price_pair[4]:\n",
    "    one_bedroom_price.append(val/4)\n",
    "plt.hist(one_bedroom_price)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing five bedroom housing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1036,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 1036,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(bedroom_price_pair[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1037,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1850.0"
      ]
     },
     "execution_count": 1037,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(bedroom_price_pair[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1038,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1850.0"
      ]
     },
     "execution_count": 1038,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(bedroom_price_pair[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1039,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1850.0"
      ]
     },
     "execution_count": 1039,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(bedroom_price_pair[5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average price for one room at renting five bedroom house"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1040,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "370.0"
      ]
     },
     "execution_count": 1040,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(bedroom_price_pair[5])/5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Price distribution for one room"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1041,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jR++irwPe1419Fnh+N74VOAF8gdGTJU8eO9a7gJlz7X8hvKbY87MZPUlzjNFTCs9Y7V4n\n9dxtuwm4ZoF9Xs/o6YkTdE8BLdDz44F/Y/SL7GPA41a71xXoeZbRfeo7utf+1e512j3Pm38PD6Gn\nZfyEqiQ16KK9LSNJLTPcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lq0P8DQVk12ZxrJEgA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b110eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "one_bedroom_price=[]\n",
    "for val in bedroom_price_pair[5]:\n",
    "    one_bedroom_price.append(val/5)\n",
    "plt.hist(one_bedroom_price)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can expect that on average in Kamloops region it costs an individule less if rent a house with more rooms.\n",
    "\n",
    "Note:<br>\n",
    "    ```` Above analysis did not consider the number of bathrooms and location relationship to house renting price\n",
    "\n",
    "This is a personal project on scraping data from web and analyzing data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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