D - Codes_for_Project_Analysis_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import urllib.request \n",
    "import urllib.parse\n",
    "import urllib.error\n",
    "import ssl\n",
    "import re\n",
    "from urllib.request import Request, urlopen\n",
    "from bs4 import BeautifulSoup as BS\n",
    "import json\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from geopy.geocoders import Nominatim\n",
    "geolocator = Nominatim(user_agent=\"test\")\n",
    "import statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For ignoring SSL certificate errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ctx = ssl.create_default_context()\n",
    "ctx.check_hostname = False\n",
    "ctx.verify_mode = ssl.CERT_NONE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function takes url and returns the beautiful soup object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def house_doc(house_url):\n",
    "    req = Request(house_url, headers={'User-Agent': 'Mozilla/5.0'})\n",
    "    webpage = urlopen(req).read()\n",
    "    house_page_doc = BS(webpage,'html.parser')\n",
    "    return house_page_doc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function takes mainurl and gives list of urls of all the pages "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def page_url(mainurl):\n",
    "    mainurldoc = house_doc(mainurl)\n",
    "    base_url = \"https://www.redfin.com\"\n",
    "    url_list =[mainurl]\n",
    "    for hrefs in mainurldoc.findAll(\"a\",{\"class\":\"clickable goToPage\"}, href = True):\n",
    "        next_page = base_url+hrefs['href']\n",
    "        url_list.append(next_page)\n",
    "    return url_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mainurls =[\"https://www.redfin.com/city/6671/CA/Fremont/filter/property-type=house+condo,status=active+comingsoon+contingent+pending\",\n",
    "       \"https://www.redfin.com/city/4561/CA/Cupertino/filter/property-type=house+condo,status=active+comingsoon+contingent+pending\",\n",
    "       \"https://www.redfin.com/city/14325/CA/Palo-Alto/filter/property-type=house+condo,status=active+comingsoon+contingent+pending\",\n",
    "       \"https://www.redfin.com/city/17675/CA/Santa-Clara/filter/property-type=house+condo,status=active+comingsoon+contingent+pending\",\n",
    "       \"https://www.redfin.com/city/19457/CA/Sunnyvale/filter/property-type=house+condo,status=active+comingsoon+contingent+pending\",\n",
    "       \"https://www.redfin.com/city/5159/CA/Dublin/filter/property-type=house+condo,status=active+comingsoon+contingent+pending\",\n",
    "       \"https://www.redfin.com/city/8439/CA/Hayward/filter/property-type=house+condo,status=active+comingsoon+contingent+pending\",\n",
    "       \"https://www.redfin.com/city/17420/CA/San-Jose/filter/property-type=house,status=active+comingsoon+contingent+pending\",\n",
    "       \"https://www.redfin.com/city/17519/CA/San-Ramon/filter/property-type=house+condo,status=active+comingsoon+contingent+pending\",\n",
    "       \"https://www.redfin.com/city/14986/CA/Pleasanton/filter/property-type=house+condo,status=active+comingsoon+contingent+pending\"]\n",
    "       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [],
   "source": [
    "pageurls = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://www.redfin.com/city/6671/CA/Fremont/filter/property-type=house+condo,status=active+comingsoon+contingent+pending\n",
      "https://www.redfin.com/city/4561/CA/Cupertino/filter/property-type=house+condo,status=active+comingsoon+contingent+pending\n",
      "https://www.redfin.com/city/14325/CA/Palo-Alto/filter/property-type=house+condo,status=active+comingsoon+contingent+pending\n",
      "https://www.redfin.com/city/17675/CA/Santa-Clara/filter/property-type=house+condo,status=active+comingsoon+contingent+pending\n",
      "https://www.redfin.com/city/19457/CA/Sunnyvale/filter/property-type=house+condo,status=active+comingsoon+contingent+pending\n",
      "https://www.redfin.com/city/5159/CA/Dublin/filter/property-type=house+condo,status=active+comingsoon+contingent+pending\n",
      "https://www.redfin.com/city/8439/CA/Hayward/filter/property-type=house+condo,status=active+comingsoon+contingent+pending\n",
      "https://www.redfin.com/city/17420/CA/San-Jose/filter/property-type=house,status=active+comingsoon+contingent+pending\n",
      "https://www.redfin.com/city/17519/CA/San-Ramon/filter/property-type=house+condo,status=active+comingsoon+contingent+pending\n",
      "https://www.redfin.com/city/14986/CA/Pleasanton/filter/property-type=house+condo,status=active+comingsoon+contingent+pending\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['https://www.redfin.com/city/6671/CA/Fremont/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
       " 'https://www.redfin.com/city/6671/CA/Fremont/page-2',\n",
       " 'https://www.redfin.com/city/6671/CA/Fremont/page-3',\n",
       " 'https://www.redfin.com/city/6671/CA/Fremont/page-4',\n",
       " 'https://www.redfin.com/city/6671/CA/Fremont/page-5',\n",
       " 'https://www.redfin.com/city/6671/CA/Fremont/page-6',\n",
       " 'https://www.redfin.com/city/6671/CA/Fremont/page-7',\n",
       " 'https://www.redfin.com/city/6671/CA/Fremont/page-8',\n",
       " 'https://www.redfin.com/city/6671/CA/Fremont/page-9',\n",
       " 'https://www.redfin.com/city/4561/CA/Cupertino/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
       " 'https://www.redfin.com/city/4561/CA/Cupertino/page-2',\n",
       " 'https://www.redfin.com/city/14325/CA/Palo-Alto/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
       " 'https://www.redfin.com/city/14325/CA/Palo-Alto/page-2',\n",
       " 'https://www.redfin.com/city/14325/CA/Palo-Alto/page-3',\n",
       " 'https://www.redfin.com/city/17675/CA/Santa-Clara/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
       " 'https://www.redfin.com/city/17675/CA/Santa-Clara/page-2',\n",
       " 'https://www.redfin.com/city/17675/CA/Santa-Clara/page-3',\n",
       " 'https://www.redfin.com/city/17675/CA/Santa-Clara/page-4',\n",
       " 'https://www.redfin.com/city/17675/CA/Santa-Clara/page-5',\n",
       " 'https://www.redfin.com/city/19457/CA/Sunnyvale/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
       " 'https://www.redfin.com/city/19457/CA/Sunnyvale/page-2',\n",
       " 'https://www.redfin.com/city/19457/CA/Sunnyvale/page-3',\n",
       " 'https://www.redfin.com/city/19457/CA/Sunnyvale/page-4',\n",
       " 'https://www.redfin.com/city/19457/CA/Sunnyvale/page-5',\n",
       " 'https://www.redfin.com/city/5159/CA/Dublin/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
       " 'https://www.redfin.com/city/8439/CA/Hayward/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
       " 'https://www.redfin.com/city/8439/CA/Hayward/page-2',\n",
       " 'https://www.redfin.com/city/8439/CA/Hayward/page-3',\n",
       " 'https://www.redfin.com/city/17420/CA/San-Jose/filter/property-type=house,status=active+comingsoon+contingent+pending',\n",
       " 'https://www.redfin.com/city/17420/CA/San-Jose/page-2',\n",
       " 'https://www.redfin.com/city/17420/CA/San-Jose/page-3',\n",
       " 'https://www.redfin.com/city/17420/CA/San-Jose/page-4',\n",
       " 'https://www.redfin.com/city/17420/CA/San-Jose/page-5',\n",
       " 'https://www.redfin.com/city/17420/CA/San-Jose/page-6',\n",
       " 'https://www.redfin.com/city/17420/CA/San-Jose/page-7',\n",
       " 'https://www.redfin.com/city/17420/CA/San-Jose/page-8',\n",
       " 'https://www.redfin.com/city/17420/CA/San-Jose/page-9',\n",
       " 'https://www.redfin.com/city/17519/CA/San-Ramon/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
       " 'https://www.redfin.com/city/17519/CA/San-Ramon/page-2',\n",
       " 'https://www.redfin.com/city/17519/CA/San-Ramon/page-3',\n",
       " 'https://www.redfin.com/city/14986/CA/Pleasanton/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
       " 'https://www.redfin.com/city/14986/CA/Pleasanton/page-2',\n",
       " 'https://www.redfin.com/city/6671/CA/Fremont/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
       " 'https://www.redfin.com/city/6671/CA/Fremont/page-2',\n",
       " 'https://www.redfin.com/city/6671/CA/Fremont/page-3',\n",
       " 'https://www.redfin.com/city/6671/CA/Fremont/page-4',\n",
       " 'https://www.redfin.com/city/4561/CA/Cupertino/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
       " 'https://www.redfin.com/city/4561/CA/Cupertino/page-2',\n",
       " 'https://www.redfin.com/city/6671/CA/Fremont/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
       " 'https://www.redfin.com/city/6671/CA/Fremont/page-2',\n",
       " 'https://www.redfin.com/city/6671/CA/Fremont/page-3',\n",
       " 'https://www.redfin.com/city/6671/CA/Fremont/page-4',\n",
       " 'https://www.redfin.com/city/4561/CA/Cupertino/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
       " 'https://www.redfin.com/city/4561/CA/Cupertino/page-2',\n",
       " 'https://www.redfin.com/city/14325/CA/Palo-Alto/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
       " 'https://www.redfin.com/city/14325/CA/Palo-Alto/page-2',\n",
       " 'https://www.redfin.com/city/17675/CA/Santa-Clara/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
       " 'https://www.redfin.com/city/17675/CA/Santa-Clara/page-2',\n",
       " 'https://www.redfin.com/city/19457/CA/Sunnyvale/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
       " 'https://www.redfin.com/city/19457/CA/Sunnyvale/page-2',\n",
       " 'https://www.redfin.com/city/19457/CA/Sunnyvale/page-3',\n",
       " 'https://www.redfin.com/city/19457/CA/Sunnyvale/page-4',\n",
       " 'https://www.redfin.com/city/19457/CA/Sunnyvale/page-5',\n",
       " 'https://www.redfin.com/city/5159/CA/Dublin/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
       " 'https://www.redfin.com/city/8439/CA/Hayward/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
       " 'https://www.redfin.com/city/8439/CA/Hayward/page-2',\n",
       " 'https://www.redfin.com/city/8439/CA/Hayward/page-3',\n",
       " 'https://www.redfin.com/city/8439/CA/Hayward/page-4',\n",
       " 'https://www.redfin.com/city/8439/CA/Hayward/page-5',\n",
       " 'https://www.redfin.com/city/8439/CA/Hayward/page-6',\n",
       " 'https://www.redfin.com/city/17420/CA/San-Jose/filter/property-type=house,status=active+comingsoon+contingent+pending',\n",
       " 'https://www.redfin.com/city/17420/CA/San-Jose/page-2',\n",
       " 'https://www.redfin.com/city/17420/CA/San-Jose/page-3',\n",
       " 'https://www.redfin.com/city/17420/CA/San-Jose/page-4',\n",
       " 'https://www.redfin.com/city/17420/CA/San-Jose/page-5',\n",
       " 'https://www.redfin.com/city/17420/CA/San-Jose/page-6',\n",
       " 'https://www.redfin.com/city/17420/CA/San-Jose/page-7',\n",
       " 'https://www.redfin.com/city/17420/CA/San-Jose/page-8',\n",
       " 'https://www.redfin.com/city/17420/CA/San-Jose/page-9',\n",
       " 'https://www.redfin.com/city/17519/CA/San-Ramon/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
       " 'https://www.redfin.com/city/14986/CA/Pleasanton/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
       " 'https://www.redfin.com/city/14986/CA/Pleasanton/page-2']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mainurls along with all the page urls\n",
    "for i in mainurls:\n",
    "    pageurls = pageurls + page_url(i)\n",
    "    time.sleep(5)\n",
    "    print(i)\n",
    "pageurls    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pageurls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Copying the result into pageurls object to avoid repeated runs\n",
    "pageurls = ['https://www.redfin.com/city/6671/CA/Fremont/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
    " 'https://www.redfin.com/city/6671/CA/Fremont/page-2',\n",
    " 'https://www.redfin.com/city/6671/CA/Fremont/page-3',\n",
    " 'https://www.redfin.com/city/6671/CA/Fremont/page-4',\n",
    " 'https://www.redfin.com/city/6671/CA/Fremont/page-5',\n",
    " 'https://www.redfin.com/city/6671/CA/Fremont/page-6',\n",
    " 'https://www.redfin.com/city/6671/CA/Fremont/page-7',\n",
    " 'https://www.redfin.com/city/6671/CA/Fremont/page-8',\n",
    " 'https://www.redfin.com/city/6671/CA/Fremont/page-9',\n",
    " 'https://www.redfin.com/city/4561/CA/Cupertino/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
    " 'https://www.redfin.com/city/4561/CA/Cupertino/page-2',\n",
    " 'https://www.redfin.com/city/14325/CA/Palo-Alto/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
    " 'https://www.redfin.com/city/14325/CA/Palo-Alto/page-2',\n",
    " 'https://www.redfin.com/city/14325/CA/Palo-Alto/page-3',\n",
    " 'https://www.redfin.com/city/17675/CA/Santa-Clara/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
    " 'https://www.redfin.com/city/17675/CA/Santa-Clara/page-2',\n",
    " 'https://www.redfin.com/city/17675/CA/Santa-Clara/page-3',\n",
    " 'https://www.redfin.com/city/17675/CA/Santa-Clara/page-4',\n",
    " 'https://www.redfin.com/city/17675/CA/Santa-Clara/page-5',\n",
    " 'https://www.redfin.com/city/19457/CA/Sunnyvale/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
    " 'https://www.redfin.com/city/19457/CA/Sunnyvale/page-2',\n",
    " 'https://www.redfin.com/city/19457/CA/Sunnyvale/page-3',\n",
    " 'https://www.redfin.com/city/19457/CA/Sunnyvale/page-4',\n",
    " 'https://www.redfin.com/city/19457/CA/Sunnyvale/page-5',\n",
    " 'https://www.redfin.com/city/5159/CA/Dublin/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
    " 'https://www.redfin.com/city/8439/CA/Hayward/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
    " 'https://www.redfin.com/city/8439/CA/Hayward/page-2',\n",
    " 'https://www.redfin.com/city/8439/CA/Hayward/page-3',\n",
    " 'https://www.redfin.com/city/17420/CA/San-Jose/filter/property-type=house,status=active+comingsoon+contingent+pending',\n",
    " 'https://www.redfin.com/city/17420/CA/San-Jose/page-2',\n",
    " 'https://www.redfin.com/city/17420/CA/San-Jose/page-3',\n",
    " 'https://www.redfin.com/city/17420/CA/San-Jose/page-4',\n",
    " 'https://www.redfin.com/city/17420/CA/San-Jose/page-5',\n",
    " 'https://www.redfin.com/city/17420/CA/San-Jose/page-6',\n",
    " 'https://www.redfin.com/city/17420/CA/San-Jose/page-7',\n",
    " 'https://www.redfin.com/city/17420/CA/San-Jose/page-8',\n",
    " 'https://www.redfin.com/city/17420/CA/San-Jose/page-9',\n",
    " 'https://www.redfin.com/city/17519/CA/San-Ramon/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
    " 'https://www.redfin.com/city/17519/CA/San-Ramon/page-2',\n",
    " 'https://www.redfin.com/city/17519/CA/San-Ramon/page-3',\n",
    " 'https://www.redfin.com/city/14986/CA/Pleasanton/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
    " 'https://www.redfin.com/city/14986/CA/Pleasanton/page-2',\n",
    " 'https://www.redfin.com/city/6671/CA/Fremont/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
    " 'https://www.redfin.com/city/6671/CA/Fremont/page-2',\n",
    " 'https://www.redfin.com/city/6671/CA/Fremont/page-3',\n",
    " 'https://www.redfin.com/city/6671/CA/Fremont/page-4',\n",
    " 'https://www.redfin.com/city/4561/CA/Cupertino/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
    " 'https://www.redfin.com/city/4561/CA/Cupertino/page-2',\n",
    " 'https://www.redfin.com/city/6671/CA/Fremont/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
    " 'https://www.redfin.com/city/6671/CA/Fremont/page-2',\n",
    " 'https://www.redfin.com/city/6671/CA/Fremont/page-3',\n",
    " 'https://www.redfin.com/city/6671/CA/Fremont/page-4',\n",
    " 'https://www.redfin.com/city/4561/CA/Cupertino/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
    " 'https://www.redfin.com/city/4561/CA/Cupertino/page-2',\n",
    " 'https://www.redfin.com/city/14325/CA/Palo-Alto/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
    " 'https://www.redfin.com/city/14325/CA/Palo-Alto/page-2',\n",
    " 'https://www.redfin.com/city/17675/CA/Santa-Clara/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
    " 'https://www.redfin.com/city/17675/CA/Santa-Clara/page-2',\n",
    " 'https://www.redfin.com/city/19457/CA/Sunnyvale/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
    " 'https://www.redfin.com/city/19457/CA/Sunnyvale/page-2',\n",
    " 'https://www.redfin.com/city/19457/CA/Sunnyvale/page-3',\n",
    " 'https://www.redfin.com/city/19457/CA/Sunnyvale/page-4',\n",
    " 'https://www.redfin.com/city/19457/CA/Sunnyvale/page-5',\n",
    " 'https://www.redfin.com/city/5159/CA/Dublin/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
    " 'https://www.redfin.com/city/8439/CA/Hayward/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
    " 'https://www.redfin.com/city/8439/CA/Hayward/page-2',\n",
    " 'https://www.redfin.com/city/8439/CA/Hayward/page-3',\n",
    " 'https://www.redfin.com/city/8439/CA/Hayward/page-4',\n",
    " 'https://www.redfin.com/city/8439/CA/Hayward/page-5',\n",
    " 'https://www.redfin.com/city/8439/CA/Hayward/page-6',\n",
    " 'https://www.redfin.com/city/17420/CA/San-Jose/filter/property-type=house,status=active+comingsoon+contingent+pending',\n",
    " 'https://www.redfin.com/city/17420/CA/San-Jose/page-2',\n",
    " 'https://www.redfin.com/city/17420/CA/San-Jose/page-3',\n",
    " 'https://www.redfin.com/city/17420/CA/San-Jose/page-4',\n",
    " 'https://www.redfin.com/city/17420/CA/San-Jose/page-5',\n",
    " 'https://www.redfin.com/city/17420/CA/San-Jose/page-6',\n",
    " 'https://www.redfin.com/city/17420/CA/San-Jose/page-7',\n",
    " 'https://www.redfin.com/city/17420/CA/San-Jose/page-8',\n",
    " 'https://www.redfin.com/city/17420/CA/San-Jose/page-9',\n",
    " 'https://www.redfin.com/city/17519/CA/San-Ramon/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
    " 'https://www.redfin.com/city/14986/CA/Pleasanton/filter/property-type=house+condo,status=active+comingsoon+contingent+pending',\n",
    " 'https://www.redfin.com/city/14986/CA/Pleasanton/page-2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mainurls along with all the page urls\n",
    "len(pageurls) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function takes list of url list of pageurls (main url and page urls) and returns a list of urls of each house property"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def price_address_url(url_list):\n",
    "    house_urls =[]\n",
    "    for urls in url_list:\n",
    "        doc = house_doc(urls)\n",
    "        url_list = doc.find_all('div', {'class':'bottomV2'})\n",
    "        for divs in url_list:\n",
    "            house_urls.append('https://www.redfin.com/'+divs.a[\"href\"])\n",
    "    return house_urls\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['https://www.redfin.com//CA/Fremont/40519-Blacow-Rd-94538/home/1953063',\n",
       " 'https://www.redfin.com//CA/Fremont/39951-Fremont-Blvd-94538/unit-325/home/1511710',\n",
       " 'https://www.redfin.com//CA/Fremont/40219-Laiolo-Rd-94538/home/1109299',\n",
       " 'https://www.redfin.com//CA/Fremont/5428-Twilight-Cmn-94555/home/1433676',\n",
       " 'https://www.redfin.com//CA/Newark/39975-Cedar-Blvd-94560/unit-143/home/1158407',\n",
       " 'https://www.redfin.com//CA/Fremont/43402-Lindenwood-St-94538/home/1054002',\n",
       " 'https://www.redfin.com//CA/Fremont/77-Shaniko-Cmn-94539/unit-20/home/1014737',\n",
       " 'https://www.redfin.com//CA/Fremont/2358-Velocity-CMN-94539/unit-306/home/166904282',\n",
       " 'https://www.redfin.com//CA/Fremont/4634-Sloat-Rd-94538/home/2023466',\n",
       " 'https://www.redfin.com//CA/Fremont/5416-Cleveland-Pl-94538/home/1771968',\n",
       " 'https://www.redfin.com//CA/Fremont/1616-Sioux-Dr-94539/home/654837',\n",
       " 'https://www.redfin.com//CA/Fremont/3651-Knollwood-Ter-94536/unit-112/home/1921696',\n",
       " 'https://www.redfin.com//CA/Fremont/60-Aventura-Cmn-94536/home/179273151',\n",
       " 'https://www.redfin.com//CA/Union-City/2772-Meadowlark-Dr-94587/home/1025455',\n",
       " 'https://www.redfin.com//CA/Fremont/4696-Amiens-Ave-94555/home/2010870',\n",
       " 'https://www.redfin.com//CA/Newark/35013-Dorchester-Ct-94560/home/951826',\n",
       " 'https://www.redfin.com//CA/Fremont/4706-Jaques-Ct-94555/home/1144489',\n",
       " 'https://www.redfin.com//CA/Fremont/4747-Cortez-Ave-94536/home/1990316',\n",
       " 'https://www.redfin.com//CA/Fremont/45158-Tom-Blalock-St-94539/unit-106-B4/home/179261781',\n",
       " 'https://www.redfin.com//CA/Fremont/442-D-St-94536/home/1474743',\n",
       " 'https://www.redfin.com//CA/Newark/6372-Buena-Vista-Dr-94560/unit-B/home/1927973',\n",
       " 'https://www.redfin.com//CA/Fremont/4449-Cherrywood-Ave-94538/home/1113660',\n",
       " 'https://www.redfin.com//CA/Fremont/38620-Canyon-Heights-Dr-94536/home/1470853',\n",
       " 'https://www.redfin.com//CA/Fremont/47465-Mantis-St-94539/home/1089971',\n",
       " 'https://www.redfin.com//CA/Fremont/3658-Capitol-Ave-94538/unit-402A/home/168824273',\n",
       " 'https://www.redfin.com//CA/Newark/36288-Saxon-Ct-94560/home/860305',\n",
       " 'https://www.redfin.com//CA/Fremont/3648-Birchwood-Ter-94536/unit-113/home/1214153',\n",
       " 'https://www.redfin.com//CA/Fremont/3580-Birchwood-Ter-94536/unit-206/home/1558678',\n",
       " 'https://www.redfin.com//CA/Fremont/3897-Jersey-Rd-94538/home/1513188',\n",
       " 'https://www.redfin.com//CA/Fremont/2633-Forrest-Ct-94536/home/1636220',\n",
       " 'https://www.redfin.com//CA/Fremont/34643-Musk-Ter-94555/home/1143770',\n",
       " 'https://www.redfin.com//CA/Fremont/2585-Cabot-Ct-94536/home/1061704',\n",
       " 'https://www.redfin.com//CA/Fremont/3754-Ferry-Ln-94555/home/1817726',\n",
       " 'https://www.redfin.com//CA/Fremont/63-Blaisdell-Way-94536/home/1926695',\n",
       " 'https://www.redfin.com//CA/Fremont/45188-Tom-Blalock-St-94539/unit-211/home/168828161',\n",
       " 'https://www.redfin.com//CA/Fremont/430-Panorama-Ct-94539/home/1216566',\n",
       " 'https://www.redfin.com//CA/Fremont/43437-Lindenwood-St-94538/home/1432472',\n",
       " 'https://www.redfin.com//CA/Newark/38130-Luma-Ter-94560/home/95473971',\n",
       " 'https://www.redfin.com//CA/Fremont/1935-Castillejo-Way-94539/home/95474035',\n",
       " 'https://www.redfin.com//CA/Fremont/49076-Larkspur-Ter-94539/home/2112535',\n",
       " 'https://www.redfin.com//CA/Fremont/2585-Cabot-Ct-94536/home/1061704',\n",
       " 'https://www.redfin.com//CA/Fremont/37908-Bright-Cmn-94536/home/1061903',\n",
       " 'https://www.redfin.com//CA/Fremont/3754-Ferry-Ln-94555/home/1817726',\n",
       " 'https://www.redfin.com//CA/Fremont/63-Blaisdell-Way-94536/home/1926695',\n",
       " 'https://www.redfin.com//CA/Fremont/45188-Tom-Blalock-St-94539/unit-211/home/168828161',\n",
       " 'https://www.redfin.com//CA/Fremont/430-Panorama-Ct-94539/home/1216566',\n",
       " 'https://www.redfin.com//CA/Fremont/43437-Lindenwood-St-94538/home/1432472',\n",
       " 'https://www.redfin.com//CA/Fremont/44819-S-Grimmer-Blvd-94538/unit-1005/home/179246354',\n",
       " 'https://www.redfin.com//CA/Fremont/39142-Declaration-St-94538/home/168824215',\n",
       " 'https://www.redfin.com//CA/Newark/38130-Luma-Ter-94560/home/95473971',\n",
       " 'https://www.redfin.com//CA/Fremont/1935-Castillejo-Way-94539/home/95474035',\n",
       " 'https://www.redfin.com//CA/Fremont/38627-Cherry-Ln-94536/unit-8/home/1814542',\n",
       " 'https://www.redfin.com//CA/Fremont/49076-Larkspur-Ter-94539/home/2112535',\n",
       " 'https://www.redfin.com//CA/Fremont/34192-Finnigan-Ter-94555/home/2010137',\n",
       " 'https://www.redfin.com//CA/Fremont/5634-Impatiens-Cmn-94538/home/742690',\n",
       " 'https://www.redfin.com//CA/Fremont/521-Emerson-St-94539/home/1666368',\n",
       " 'https://www.redfin.com//CA/Newark/5527-Azalea-Way-94560/home/143115530',\n",
       " 'https://www.redfin.com//CA/Fremont/35228-Erving-Ct-94536/home/1115167',\n",
       " 'https://www.redfin.com//CA/Fremont/38612-Country-Ter-94536/home/28442262',\n",
       " 'https://www.redfin.com//CA/Fremont/4789-Calaveras-Ave-94538/home/2021008',\n",
       " 'https://www.redfin.com//CA/Fremont/1150-Bennett-Ct-94536/home/897828',\n",
       " 'https://www.redfin.com//CA/Fremont/2045-Castillejo-Way-94539/home/1712303',\n",
       " 'https://www.redfin.com//CA/Fremont/38053-Davy-Ct-94536/home/938989',\n",
       " 'https://www.redfin.com//CA/Fremont/3748-Fulmar-Ter-94555/home/1149405',\n",
       " 'https://www.redfin.com//CA/Fremont/40326-Blacow-Rd-94538/home/1109722',\n",
       " 'https://www.redfin.com//CA/Fremont/4059-Lorenzo-Ter-94536/home/1582476',\n",
       " 'https://www.redfin.com//CA/Fremont/3280-Neal-Ter-94538/home/1105960',\n",
       " 'https://www.redfin.com//CA/Fremont/48252-Cottonwood-St-94539/home/1088807',\n",
       " 'https://www.redfin.com//CA/Fremont/45158-Tom-Blalock-St-94539/unit-304-B4/home/179177386',\n",
       " 'https://www.redfin.com//CA/Fremont/38372-Redwood-Ter-94536/unit-23/home/1887324',\n",
       " 'https://www.redfin.com//CA/Fremont/3237-Lake-Arrowhead-Ave-94555/home/1722455',\n",
       " 'https://www.redfin.com//CA/Fremont/44434-Camellia-Dr-94539/home/1900642',\n",
       " 'https://www.redfin.com//CA/Fremont/34378-Torrington-Ct-94555/home/1960119',\n",
       " 'https://www.redfin.com//CA/Newark/5632-Pandorea-Ter-94560/home/143117456',\n",
       " 'https://www.redfin.com//CA/Fremont/45138-Challenge-Cmn-94538/unit-1001/home/175930843',\n",
       " 'https://www.redfin.com//CA/Fremont/711-Old-Canyon-Rd-94536/unit-49/home/1397389',\n",
       " 'https://www.redfin.com//CA/Fremont/5278-Keystone-Dr-94536/home/1986301',\n",
       " 'https://www.redfin.com//CA/Fremont/38507-Overacker-Ave-94536/home/2009598',\n",
       " 'https://www.redfin.com//CA/Fremont/344-Goleta-Ter-94536/home/799873',\n",
       " 'https://www.redfin.com//CA/Fremont/47262-Mission-Falls-Ct-94539/home/178764593',\n",
       " 'https://www.redfin.com//CA/Fremont/38321-Timpanogas-Cir-94536/home/22713384',\n",
       " 'https://www.redfin.com//CA/Fremont/38630-Country-Cmn-94536/unit-311/home/1335305',\n",
       " 'https://www.redfin.com//CA/Fremont/230-Barton-Dr-94536/home/1990604',\n",
       " 'https://www.redfin.com//CA/Fremont/5235-Earle-St-94536/home/1301546',\n",
       " 'https://www.redfin.com//CA/Fremont/36509-Cabrillo-Dr-94536/home/1769862',\n",
       " 'https://www.redfin.com//CA/Fremont/32728-Shaver-Lake-St-94555/home/1132749',\n",
       " 'https://www.redfin.com//CA/Fremont/42102-Cam-Santa-Barbara-94539/home/1085321',\n",
       " 'https://www.redfin.com//CA/Fremont/34910-Sea-Cliff-Ter-94555/home/1540351',\n",
       " 'https://www.redfin.com//CA/Fremont/47282-Mission-Falls-Ct-94539/home/179138819',\n",
       " 'https://www.redfin.com//CA/Fremont/47292-Mission-Falls-Ct-94539/home/179138815',\n",
       " 'https://www.redfin.com//CA/Fremont/47312-Mission-Falls-Ct-94539/home/179138816',\n",
       " 'https://www.redfin.com//CA/Fremont/47302-Mission-Falls-Ct-94539/home/179138817',\n",
       " 'https://www.redfin.com//CA/Fremont/4435-Kennett-Ter-94536/home/112886472',\n",
       " 'https://www.redfin.com//CA/Fremont/5585-Beau-Ct-94538/home/1603007',\n",
       " 'https://www.redfin.com//CA/Fremont/3768-Capitol-Ave-94538/unit-518B/home/168824362',\n",
       " 'https://www.redfin.com//CA/Fremont/1460-Gilbert-Ave-94536/home/2034857',\n",
       " 'https://www.redfin.com//CA/Fremont/41260-Norman-Ct-94539/home/1792143',\n",
       " 'https://www.redfin.com//CA/Fremont/900-Hunter-Ln-94539/home/1324861',\n",
       " 'https://www.redfin.com//CA/Fremont/39224-Guardino-Dr-94538/unit-218/home/1233551',\n",
       " 'https://www.redfin.com//CA/Fremont/3445-Little-Ct-94538/home/1586595',\n",
       " 'https://www.redfin.com//CA/Fremont/5338-Sayre-Ave-94536/home/1058193',\n",
       " 'https://www.redfin.com//CA/Fremont/3800-Adams-Ave-94538/home/704456',\n",
       " 'https://www.redfin.com//CA/Fremont/37373-Sequoia-Rd-94536/home/12117639',\n",
       " 'https://www.redfin.com//CA/Fremont/38280-Corrigan-Ct-94536/home/527183',\n",
       " 'https://www.redfin.com//CA/Fremont/4226-Nerissa-Cir-94555/home/1109542',\n",
       " 'https://www.redfin.com//CA/Fremont/4141-Deep-Creek-Rd-94555/unit-145/home/1153572',\n",
       " 'https://www.redfin.com//CA/Fremont/45083-Cougar-Cir-94539/home/1559513',\n",
       " 'https://www.redfin.com//CA/Fremont/48867-Sauvignon-Ct-94539/home/1959642',\n",
       " 'https://www.redfin.com//CA/Fremont/289-Winnipeg-Ter-94538/unit-289/home/63744779',\n",
       " 'https://www.redfin.com//CA/Fremont/752-Topawa-Dr-94539/home/1263780',\n",
       " 'https://www.redfin.com//CA/Fremont/357-Paso-Roble-Cmn-94539/home/40627336',\n",
       " 'https://www.redfin.com//CA/Fremont/311-Sandstone-Dr-94536/home/1492571',\n",
       " 'https://www.redfin.com//CA/Fremont/34671-Agree-Ter-94555/unit-73/home/1355165',\n",
       " 'https://www.redfin.com//CA/Fremont/46885-Fernald-Cmn-94539/unit-46/home/1650559',\n",
       " 'https://www.redfin.com//CA/Fremont/45347-Lumen-Ter-94539/home/178993931',\n",
       " 'https://www.redfin.com//CA/Fremont/45158-Tom-Blalock-St-94539/home/177943120',\n",
       " 'https://www.redfin.com//CA/Fremont/45318-Silhouette-Cmn-94539/unit-101-F1/home/178974349',\n",
       " 'https://www.redfin.com//CA/Fremont/1908-Reflection-Cmn-94539/unit-101-F3/home/178974115',\n",
       " 'https://www.redfin.com//CA/Fremont/45128-Warm-Springs-Blvd-94539/unit-406/home/168827911',\n",
       " 'https://www.redfin.com//CA/Fremont/45158-Tom-Blalock-St-94539/unit-307-B4/home/178960638',\n",
       " 'https://www.redfin.com//CA/Fremont/45249-Electric-Ter-94539/unit-101/home/168828105',\n",
       " 'https://www.redfin.com//CA/Fremont/2632-Middlefield-Ave-94539/home/1108109',\n",
       " 'https://www.redfin.com//CA/Fremont/37965-Stenhammer-Dr-94536/home/1260309',\n",
       " 'https://www.redfin.com//CA/Fremont/44873-Camellia-Dr-94539/home/883190',\n",
       " 'https://www.redfin.com//CA/Fremont/38739-Aurora-Ter-94536/unit-16/home/1858362',\n",
       " 'https://www.redfin.com//CA/Fremont/35879-Adobe-Dr-94536/home/1793925',\n",
       " 'https://www.redfin.com//CA/Fremont/47252-Mission-Falls-Ct-94539/home/178764595',\n",
       " 'https://www.redfin.com//CA/Fremont/4434-Kennett-Ter-94536/home/112886447',\n",
       " 'https://www.redfin.com//CA/Fremont/711-Old-Canyon-Rd-94536/unit-58/home/1921911',\n",
       " 'https://www.redfin.com//CA/Fremont/4767-Mendocino-Ter-94555/home/1603468',\n",
       " 'https://www.redfin.com//CA/Fremont/4117-Argyle-Ct-94536/unit-A/home/740249',\n",
       " 'https://www.redfin.com//CA/Fremont/661-Monticello-Ter-94539/home/1080643',\n",
       " 'https://www.redfin.com//CA/Fremont/3207-Washington-Blvd-94539/home/1377816',\n",
       " 'https://www.redfin.com//CA/Fremont/38539-Mission-Blvd-94536/home/1074436',\n",
       " 'https://www.redfin.com//CA/Fremont/45158-Tom-Blalock-St-94539/unit-209-B4/home/178601459',\n",
       " 'https://www.redfin.com//CA/Fremont/45158-Tom-Blalock-St-94539/unit-206-B4/home/178553372',\n",
       " 'https://www.redfin.com//CA/Fremont/28-Eagle-Grn-94538/home/57180338',\n",
       " 'https://www.redfin.com//CA/Fremont/240-Manitoba-Grn-94538/unit-240/home/178529656',\n",
       " 'https://www.redfin.com//CA/Fremont/711-Old-Canyon-Rd-94536/unit-56/home/126930350',\n",
       " 'https://www.redfin.com//CA/Fremont/202-Mission-Falls-Ln-94539/home/178482826',\n",
       " 'https://www.redfin.com//CA/Fremont/44936-Winding-Ln-94539/home/957815',\n",
       " 'https://www.redfin.com//CA/Union-City/489-Seema-Cir-94587/unit-489/home/39988743',\n",
       " 'https://www.redfin.com//CA/Fremont/3509-Pepperwood-Ter-94536/unit-206/home/1182011',\n",
       " 'https://www.redfin.com//CA/Fremont/3998-Fossano-Cmn-94538/home/143117736',\n",
       " 'https://www.redfin.com//CA/Newark/36614-Charles-St-94560/home/1612792',\n",
       " 'https://www.redfin.com//CA/Fremont/253-Corte-San-Pablo-94539/home/1977683',\n",
       " 'https://www.redfin.com//CA/Fremont/42410-Paseo-Padre-Pkwy-94539/home/1353712',\n",
       " 'https://www.redfin.com//CA/Fremont/772-Sunrise-Dr-94539/home/1721628',\n",
       " 'https://www.redfin.com//CA/Fremont/35578-Rockland-Ct-94536/home/1959310',\n",
       " 'https://www.redfin.com//CA/Fremont/39109-Guardino-Dr-94538/unit-335/home/2006755',\n",
       " 'https://www.redfin.com//CA/Fremont/4940-Everglades-Park-Dr-94538/home/2035134',\n",
       " 'https://www.redfin.com//CA/Fremont/5963-Via-Lugano-94555/home/17185478',\n",
       " 'https://www.redfin.com//CA/Fremont/3303-Owen-Cmn-94536/home/2039128',\n",
       " 'https://www.redfin.com//CA/Newark/5720-Oceanspray-Rd-94560/home/112885676',\n",
       " 'https://www.redfin.com//CA/Fremont/33308-Jamie-Cir-94555/home/1670311',\n",
       " 'https://www.redfin.com//CA/Fremont/42589-Fern-Cir-94538/home/1796637',\n",
       " 'https://www.redfin.com//CA/Fremont/3312-Trafalgar-Rd-94555/home/1817691',\n",
       " 'https://www.redfin.com//CA/Fremont/486-Niles-Cmn-94536/home/22656722',\n",
       " 'https://www.redfin.com//CA/Fremont/37860-Blacow-Rd-94536/home/1052934',\n",
       " 'https://www.redfin.com//CA/Fremont/5124-Eggers-Dr-94536/home/1432580',\n",
       " 'https://www.redfin.com//CA/Fremont/26-Essanay-Ave-94536/unit-8/home/1665659',\n",
       " 'https://www.redfin.com//CA/Newark/37924-Luma-Ter-94560/home/112885845',\n",
       " 'https://www.redfin.com//CA/Fremont/47232-Mission-Falls-Ct-94539/home/178764591',\n",
       " 'https://www.redfin.com//CA/Fremont/3467-Pennsylvania-Cmn-94536/unit-16A/home/545192',\n",
       " 'https://www.redfin.com//CA/Fremont/3975-Lake-Tahoe-Ter-94555/home/589272',\n",
       " 'https://www.redfin.com//CA/Fremont/4960-Friar-Ave-94555/home/646077',\n",
       " 'https://www.redfin.com//CA/Union-City/4920-Bridgepointe-Pl-94587/unit-41/home/768668',\n",
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       " 'https://www.redfin.com//CA/Hayward/1151-Tamalpais-Pl-94542/home/112886715',\n",
       " 'https://www.redfin.com//CA/Hayward/24412-Karina-St-94542/home/22726923',\n",
       " 'https://www.redfin.com//CA/Hayward/27950-Lancelot-Ct-94544/home/1848843',\n",
       " 'https://www.redfin.com//CA/Hayward/Unknown-94541/home/178306938',\n",
       " 'https://www.redfin.com//CA/Hayward/24731-2nd-St-94541/home/177844913',\n",
       " 'https://www.redfin.com//CA/Hayward/21628-Vallejo-St-94541/home/974034',\n",
       " 'https://www.redfin.com//CA/Hayward/26736-Hayward-Blvd-94542/home/22691436',\n",
       " 'https://www.redfin.com//CA/Hayward/24072-Myrtle-St-94541/home/177621464',\n",
       " 'https://www.redfin.com//CA/Hayward/27828-Mandarin-Ave-94544/home/1664853',\n",
       " 'https://www.redfin.com//CA/Hayward/23965-2nd-St-94541/home/995216',\n",
       " 'https://www.redfin.com//CA/HAYWARD/OVERHILL-DR-94544/home/22677230',\n",
       " 'https://www.redfin.com//CA/Hayward/26520-Fairview-Ave-94542/home/1989755',\n",
       " 'https://www.redfin.com//CA/Hayward/Mission-Crossing/Residence-4/home/171424242',\n",
       " 'https://www.redfin.com//CA/Hayward/Mission-Crossing/Residence-3/home/171424243',\n",
       " 'https://www.redfin.com//CA/San-Jose/2262-Mt-Davidson-Dr-95124/home/1582493',\n",
       " 'https://www.redfin.com//CA/San-Jose/3294-Mirage-Way-95135/home/962164',\n",
       " 'https://www.redfin.com//CA/San-Jose/15148-Bel-Escou-Dr-95124/home/833562',\n",
       " 'https://www.redfin.com//CA/San-Jose/3826-Carrera-Ct-95148/home/1800001',\n",
       " 'https://www.redfin.com//CA/San-Jose/2323-Marshglen-Ct-95133/home/1527253',\n",
       " 'https://www.redfin.com//CA/San-Jose/3167-Whiteleaf-Ct-95148/home/1263116',\n",
       " 'https://www.redfin.com//CA/San-Jose/1618-Shasta-Ave-95128/home/691093',\n",
       " 'https://www.redfin.com//CA/San-Jose/427-Page-St-95126/home/1232274',\n",
       " 'https://www.redfin.com//CA/San-Jose/160-Whirlaway-Dr-95111/home/981207',\n",
       " 'https://www.redfin.com//CA/San-Jose/3280-Flintdale-Dr-95148/home/951197',\n",
       " 'https://www.redfin.com//CA/San-Jose/317-Avenida-Pinos-95123/home/1664985',\n",
       " 'https://www.redfin.com//CA/San-Jose/7055-Glenview-Dr-95120/home/933501',\n",
       " 'https://www.redfin.com//CA/San-Jose/3727-Chambord-Ct-95127/home/946858',\n",
       " 'https://www.redfin.com//CA/San-Jose/2181-Carobwood-Ln-95132/home/1689715',\n",
       " 'https://www.redfin.com//CA/San-Jose/1560-Coleman-Rd-95120/home/1066324',\n",
       " 'https://www.redfin.com//CA/San-Jose/2549-Castleton-Dr-95148/home/1092226',\n",
       " 'https://www.redfin.com//CA/San-Jose/3130-Brandywine-Dr-95121/home/1153983',\n",
       " 'https://www.redfin.com//CA/San-Jose/125-Cadwell-Ct-95138/home/1331906',\n",
       " 'https://www.redfin.com//CA/San-Jose/3285-Woodmont-Dr-95118/home/857001',\n",
       " 'https://www.redfin.com//CA/San-Jose/8815-Wine-Valley-Cir-95135/home/750886',\n",
       " 'https://www.redfin.com//CA/San-Jose/1885-Kirkland-Ave-95125/home/1234776',\n",
       " 'https://www.redfin.com//CA/San-Jose/699-Azule-Ave-95123/home/655684',\n",
       " 'https://www.redfin.com//CA/San-Jose/1925-Trenton-Dr-95124/home/1286055',\n",
       " 'https://www.redfin.com//CA/San-Jose/1248-Malone-Rd-95125/home/1648307',\n",
       " 'https://www.redfin.com//CA/San-Jose/83-Cashew-Blossom-Dr-95123/home/1019116',\n",
       " 'https://www.redfin.com//CA/San-Jose/14936-Donner-Dr-95124/home/1088942',\n",
       " 'https://www.redfin.com//CA/San-Jose/1053-Wallace-Dr-95120/home/1694532',\n",
       " 'https://www.redfin.com//CA/Saratoga/18941-Fernbrook-Ct-95070/home/1466595',\n",
       " 'https://www.redfin.com//CA/San-Jose/14650-E-Hills-Dr-95127/home/948063',\n",
       " 'https://www.redfin.com//CA/San-Jose/1056-Foxhurst-Way-95120/home/930795',\n",
       " 'https://www.redfin.com//CA/San-Jose/183-Venado-Way-95123/home/1184300',\n",
       " 'https://www.redfin.com//CA/Campbell/679-Elden-Dr-95008/home/167451576',\n",
       " 'https://www.redfin.com//CA/San-Jose/774-Pershing-Ave-95126/home/1788679',\n",
       " 'https://www.redfin.com//CA/San-Jose/5414-Century-Meadow-Ct-95111/home/1520797',\n",
       " 'https://www.redfin.com//CA/San-Jose/3335-Quesada-Dr-95148/home/953277',\n",
       " 'https://www.redfin.com//CA/San-Jose/131-Brice-Ct-95111/home/1255590',\n",
       " 'https://www.redfin.com//CA/San-Jose/1451-Sunny-Ct-95116/home/17292401',\n",
       " 'https://www.redfin.com//CA/San-Jose/3415-Gradell-Pl-95148/home/1650998',\n",
       " 'https://www.redfin.com//CA/San-Jose/2246-Timberpeak-Ct-95116/home/1035802',\n",
       " 'https://www.redfin.com//CA/San-Jose/1308-Whitegate-Ave-95125/home/1676268',\n",
       " 'https://www.redfin.com//CA/San-Jose/14936-Donner-Dr-95124/home/1088942',\n",
       " 'https://www.redfin.com//CA/San-Jose/1053-Wallace-Dr-95120/home/1694532',\n",
       " 'https://www.redfin.com//CA/Saratoga/18941-Fernbrook-Ct-95070/home/1466595',\n",
       " 'https://www.redfin.com//CA/San-Jose/2370-Winepol-Loop-95125/home/167457256',\n",
       " 'https://www.redfin.com//CA/San-Jose/6973-Chantel-Ct-95129/home/1740407',\n",
       " 'https://www.redfin.com//CA/San-Jose/14650-E-Hills-Dr-95127/home/948063',\n",
       " 'https://www.redfin.com//CA/San-Jose/1056-Foxhurst-Way-95120/home/930795',\n",
       " 'https://www.redfin.com//CA/San-Jose/91-Castlebridge-Dr-95116/home/885864',\n",
       " 'https://www.redfin.com//CA/San-Jose/183-Venado-Way-95123/home/1184300',\n",
       " 'https://www.redfin.com//CA/San-Jose/2729-Ginoso-Ct-95111/unit-6/home/167458115',\n",
       " 'https://www.redfin.com//CA/San-Jose/174-N-18th-St-95112/home/179306661',\n",
       " 'https://www.redfin.com//CA/Campbell/679-Elden-Dr-95008/home/167451576',\n",
       " ...]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "property_url_list = price_address_url(pageurls)\n",
    "property_url_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### writing the urls of all the properties of all the cities in 'property_url_list' file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"D:\\BAN 612\\Project\\PRoject jupyter files\\property_url_list.txt\", \"w\") as output:\n",
    "    output.write(str(property_url_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Saving the property urls in 'property_url_list' object to use these urls repeatedly without scraping them again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "property_url_list = ['https://www.redfin.com//CA/Fremont/40519-Blacow-Rd-94538/home/1953063', 'https://www.redfin.com//CA/Fremont/39951-Fremont-Blvd-94538/unit-325/home/1511710', 'https://www.redfin.com//CA/Fremont/40219-Laiolo-Rd-94538/home/1109299', 'https://www.redfin.com//CA/Fremont/5428-Twilight-Cmn-94555/home/1433676', 'https://www.redfin.com//CA/Newark/39975-Cedar-Blvd-94560/unit-143/home/1158407', 'https://www.redfin.com//CA/Fremont/43402-Lindenwood-St-94538/home/1054002', 'https://www.redfin.com//CA/Fremont/77-Shaniko-Cmn-94539/unit-20/home/1014737', 'https://www.redfin.com//CA/Fremont/2358-Velocity-CMN-94539/unit-306/home/166904282', 'https://www.redfin.com//CA/Fremont/4634-Sloat-Rd-94538/home/2023466', 'https://www.redfin.com//CA/Fremont/5416-Cleveland-Pl-94538/home/1771968', 'https://www.redfin.com//CA/Fremont/1616-Sioux-Dr-94539/home/654837', 'https://www.redfin.com//CA/Fremont/3651-Knollwood-Ter-94536/unit-112/home/1921696', 'https://www.redfin.com//CA/Fremont/60-Aventura-Cmn-94536/home/179273151', 'https://www.redfin.com//CA/Union-City/2772-Meadowlark-Dr-94587/home/1025455', 'https://www.redfin.com//CA/Fremont/4696-Amiens-Ave-94555/home/2010870', 'https://www.redfin.com//CA/Newark/35013-Dorchester-Ct-94560/home/951826', 'https://www.redfin.com//CA/Fremont/4706-Jaques-Ct-94555/home/1144489', 'https://www.redfin.com//CA/Fremont/4747-Cortez-Ave-94536/home/1990316', 'https://www.redfin.com//CA/Fremont/45158-Tom-Blalock-St-94539/unit-106-B4/home/179261781', 'https://www.redfin.com//CA/Fremont/442-D-St-94536/home/1474743', 'https://www.redfin.com//CA/Newark/6372-Buena-Vista-Dr-94560/unit-B/home/1927973', 'https://www.redfin.com//CA/Fremont/4449-Cherrywood-Ave-94538/home/1113660', 'https://www.redfin.com//CA/Fremont/38620-Canyon-Heights-Dr-94536/home/1470853', 'https://www.redfin.com//CA/Fremont/47465-Mantis-St-94539/home/1089971', 'https://www.redfin.com//CA/Fremont/3658-Capitol-Ave-94538/unit-402A/home/168824273', 'https://www.redfin.com//CA/Newark/36288-Saxon-Ct-94560/home/860305', 'https://www.redfin.com//CA/Fremont/3648-Birchwood-Ter-94536/unit-113/home/1214153', 'https://www.redfin.com//CA/Fremont/3580-Birchwood-Ter-94536/unit-206/home/1558678', 'https://www.redfin.com//CA/Fremont/3897-Jersey-Rd-94538/home/1513188', 'https://www.redfin.com//CA/Fremont/2633-Forrest-Ct-94536/home/1636220', 'https://www.redfin.com//CA/Fremont/34643-Musk-Ter-94555/home/1143770', 'https://www.redfin.com//CA/Fremont/2585-Cabot-Ct-94536/home/1061704', 'https://www.redfin.com//CA/Fremont/3754-Ferry-Ln-94555/home/1817726', 'https://www.redfin.com//CA/Fremont/63-Blaisdell-Way-94536/home/1926695', 'https://www.redfin.com//CA/Fremont/45188-Tom-Blalock-St-94539/unit-211/home/168828161', 'https://www.redfin.com//CA/Fremont/430-Panorama-Ct-94539/home/1216566', 'https://www.redfin.com//CA/Fremont/43437-Lindenwood-St-94538/home/1432472', 'https://www.redfin.com//CA/Newark/38130-Luma-Ter-94560/home/95473971', 'https://www.redfin.com//CA/Fremont/1935-Castillejo-Way-94539/home/95474035', 'https://www.redfin.com//CA/Fremont/49076-Larkspur-Ter-94539/home/2112535', 'https://www.redfin.com//CA/Fremont/2585-Cabot-Ct-94536/home/1061704', 'https://www.redfin.com//CA/Fremont/37908-Bright-Cmn-94536/home/1061903', 'https://www.redfin.com//CA/Fremont/3754-Ferry-Ln-94555/home/1817726', 'https://www.redfin.com//CA/Fremont/63-Blaisdell-Way-94536/home/1926695', 'https://www.redfin.com//CA/Fremont/45188-Tom-Blalock-St-94539/unit-211/home/168828161', 'https://www.redfin.com//CA/Fremont/430-Panorama-Ct-94539/home/1216566', 'https://www.redfin.com//CA/Fremont/43437-Lindenwood-St-94538/home/1432472', 'https://www.redfin.com//CA/Fremont/44819-S-Grimmer-Blvd-94538/unit-1005/home/179246354', 'https://www.redfin.com//CA/Fremont/39142-Declaration-St-94538/home/168824215', 'https://www.redfin.com//CA/Newark/38130-Luma-Ter-94560/home/95473971', 'https://www.redfin.com//CA/Fremont/1935-Castillejo-Way-94539/home/95474035', 'https://www.redfin.com//CA/Fremont/38627-Cherry-Ln-94536/unit-8/home/1814542', 'https://www.redfin.com//CA/Fremont/49076-Larkspur-Ter-94539/home/2112535', 'https://www.redfin.com//CA/Fremont/34192-Finnigan-Ter-94555/home/2010137', 'https://www.redfin.com//CA/Fremont/5634-Impatiens-Cmn-94538/home/742690', 'https://www.redfin.com//CA/Fremont/521-Emerson-St-94539/home/1666368', 'https://www.redfin.com//CA/Newark/5527-Azalea-Way-94560/home/143115530', 'https://www.redfin.com//CA/Fremont/35228-Erving-Ct-94536/home/1115167', 'https://www.redfin.com//CA/Fremont/38612-Country-Ter-94536/home/28442262', 'https://www.redfin.com//CA/Fremont/4789-Calaveras-Ave-94538/home/2021008', 'https://www.redfin.com//CA/Fremont/1150-Bennett-Ct-94536/home/897828', 'https://www.redfin.com//CA/Fremont/2045-Castillejo-Way-94539/home/1712303', 'https://www.redfin.com//CA/Fremont/38053-Davy-Ct-94536/home/938989', 'https://www.redfin.com//CA/Fremont/3748-Fulmar-Ter-94555/home/1149405', 'https://www.redfin.com//CA/Fremont/40326-Blacow-Rd-94538/home/1109722', 'https://www.redfin.com//CA/Fremont/4059-Lorenzo-Ter-94536/home/1582476', 'https://www.redfin.com//CA/Fremont/3280-Neal-Ter-94538/home/1105960', 'https://www.redfin.com//CA/Fremont/48252-Cottonwood-St-94539/home/1088807', 'https://www.redfin.com//CA/Fremont/45158-Tom-Blalock-St-94539/unit-304-B4/home/179177386', 'https://www.redfin.com//CA/Fremont/38372-Redwood-Ter-94536/unit-23/home/1887324', 'https://www.redfin.com//CA/Fremont/3237-Lake-Arrowhead-Ave-94555/home/1722455', 'https://www.redfin.com//CA/Fremont/44434-Camellia-Dr-94539/home/1900642', 'https://www.redfin.com//CA/Fremont/34378-Torrington-Ct-94555/home/1960119', 'https://www.redfin.com//CA/Newark/5632-Pandorea-Ter-94560/home/143117456', 'https://www.redfin.com//CA/Fremont/45138-Challenge-Cmn-94538/unit-1001/home/175930843', 'https://www.redfin.com//CA/Fremont/711-Old-Canyon-Rd-94536/unit-49/home/1397389', 'https://www.redfin.com//CA/Fremont/5278-Keystone-Dr-94536/home/1986301', 'https://www.redfin.com//CA/Fremont/38507-Overacker-Ave-94536/home/2009598', 'https://www.redfin.com//CA/Fremont/344-Goleta-Ter-94536/home/799873', 'https://www.redfin.com//CA/Fremont/47262-Mission-Falls-Ct-94539/home/178764593', 'https://www.redfin.com//CA/Fremont/38321-Timpanogas-Cir-94536/home/22713384', 'https://www.redfin.com//CA/Fremont/38630-Country-Cmn-94536/unit-311/home/1335305', 'https://www.redfin.com//CA/Fremont/230-Barton-Dr-94536/home/1990604', 'https://www.redfin.com//CA/Fremont/5235-Earle-St-94536/home/1301546', 'https://www.redfin.com//CA/Fremont/36509-Cabrillo-Dr-94536/home/1769862', 'https://www.redfin.com//CA/Fremont/32728-Shaver-Lake-St-94555/home/1132749', 'https://www.redfin.com//CA/Fremont/42102-Cam-Santa-Barbara-94539/home/1085321', 'https://www.redfin.com//CA/Fremont/34910-Sea-Cliff-Ter-94555/home/1540351', 'https://www.redfin.com//CA/Fremont/47282-Mission-Falls-Ct-94539/home/179138819', 'https://www.redfin.com//CA/Fremont/47292-Mission-Falls-Ct-94539/home/179138815', 'https://www.redfin.com//CA/Fremont/47312-Mission-Falls-Ct-94539/home/179138816', 'https://www.redfin.com//CA/Fremont/47302-Mission-Falls-Ct-94539/home/179138817', 'https://www.redfin.com//CA/Fremont/4435-Kennett-Ter-94536/home/112886472', 'https://www.redfin.com//CA/Fremont/5585-Beau-Ct-94538/home/1603007', 'https://www.redfin.com//CA/Fremont/3768-Capitol-Ave-94538/unit-518B/home/168824362', 'https://www.redfin.com//CA/Fremont/1460-Gilbert-Ave-94536/home/2034857', 'https://www.redfin.com//CA/Fremont/41260-Norman-Ct-94539/home/1792143', 'https://www.redfin.com//CA/Fremont/900-Hunter-Ln-94539/home/1324861', 'https://www.redfin.com//CA/Fremont/39224-Guardino-Dr-94538/unit-218/home/1233551', 'https://www.redfin.com//CA/Fremont/3445-Little-Ct-94538/home/1586595', 'https://www.redfin.com//CA/Fremont/5338-Sayre-Ave-94536/home/1058193', 'https://www.redfin.com//CA/Fremont/3800-Adams-Ave-94538/home/704456', 'https://www.redfin.com//CA/Fremont/37373-Sequoia-Rd-94536/home/12117639', 'https://www.redfin.com//CA/Fremont/38280-Corrigan-Ct-94536/home/527183', 'https://www.redfin.com//CA/Fremont/4226-Nerissa-Cir-94555/home/1109542', 'https://www.redfin.com//CA/Fremont/4141-Deep-Creek-Rd-94555/unit-145/home/1153572', 'https://www.redfin.com//CA/Fremont/45083-Cougar-Cir-94539/home/1559513', 'https://www.redfin.com//CA/Fremont/48867-Sauvignon-Ct-94539/home/1959642', 'https://www.redfin.com//CA/Fremont/289-Winnipeg-Ter-94538/unit-289/home/63744779', 'https://www.redfin.com//CA/Fremont/752-Topawa-Dr-94539/home/1263780', 'https://www.redfin.com//CA/Fremont/357-Paso-Roble-Cmn-94539/home/40627336', 'https://www.redfin.com//CA/Fremont/311-Sandstone-Dr-94536/home/1492571', 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'https://www.redfin.com//CA/San-Jose/867-Pinewood-Dr-95129/home/1531885', 'https://www.redfin.com//CA/San-Jose/481-N-18th-St-95112/home/565999', 'https://www.redfin.com//CA/San-Jose/14305-Branham-Ln-95124/home/651185', 'https://www.redfin.com//CA/Cupertino/943-Brookgrove-Ln-95014/home/1065812', 'https://www.redfin.com//CA/San-Jose/1456-Kinsport-Ln-95120/home/1712897', 'https://www.redfin.com//CA/San-Jose/3596-Calico-Ave-95124/home/828061', 'https://www.redfin.com//CA/San-Jose/818-Fountain-Palm-Ct-95133/home/1241799', 'https://www.redfin.com//CA/San-Jose/1505-Michigan-Ave-95002/home/1733895', 'https://www.redfin.com//CA/San-Jose/1399-Beattie-Ct-95120/home/1503760', 'https://www.redfin.com//CA/San-Jose/1722-Canna-Ln-95124/home/1187166', 'https://www.redfin.com//CA/San-Jose/2785-Flint-Ave-95148/home/1599178', 'https://www.redfin.com//CA/San-Jose/1038-Summermist-Ct-95122/home/1339249', 'https://www.redfin.com//CA/San-Jose/1445-Jeffery-Ave-95118/home/1419807', 'https://www.redfin.com//CA/San-Jose/4943-Scarlett-Way-95111/home/1768779', 'https://www.redfin.com//CA/San-Jose/1037-Cumberland-Pl-95125/home/849943', 'https://www.redfin.com//CA/San-Jose/6918-Rainbow-Dr-95129/home/1714708', 'https://www.redfin.com//CA/San-Jose/1494-Medallion-Dr-95120/home/1535123', 'https://www.redfin.com//CA/San-Jose/435-Colony-Cove-Dr-95123/home/1085807', 'https://www.redfin.com//CA/San-Jose/166-Sunwood-Meadows-Pl-95119/home/1810377', 'https://www.redfin.com//CA/San-Jose/784-Colleen-Dr-95123/home/1492378', 'https://www.redfin.com//CA/San-Jose/3371-Kuykendall-Pl-95148/home/1678288', 'https://www.redfin.com//CA/San-Jose/461-Northlake-Dr-95117/unit-52/home/1499451', 'https://www.redfin.com//CA/San-Jose/285-E-St-James-St-95112/unit-1/home/841273', 'https://www.redfin.com//CA/San-Jose/130-E-San-Fernando-St-95112/unit-404/home/1379040', 'https://www.redfin.com//CA/San-Jose/1182-Leigh-Ave-95126/unit-3/home/729117', 'https://www.redfin.com//CA/San-Jose/756-Villa-Teresa-Way-95123/unit-756/home/179310910', 'https://www.redfin.com//CA/San-Jose/655-S-34th-St-95116/unit-67/home/179256399', 'https://www.redfin.com//CA/San-Jose/3453-Ravendale-Ct-95111/home/1387031', 'https://www.redfin.com//CA/San-Jose/2803-Cicero-Way-95148/home/797094', 'https://www.redfin.com//CA/San-Jose/555-Marble-Arch-Ave-95136/home/1047918', 'https://www.redfin.com//CA/San-Jose/57-Deer-Run-Cir-95136/home/985890', 'https://www.redfin.com//CA/San-Jose/5244-Makati-Cir-95123/home/1115345', 'https://www.redfin.com//CA/San-Jose/6128-Glen-Harbor-Dr-95123/home/1668993', 'https://www.redfin.com//CA/Campbell/230-S-Midway-St-95008/home/1682478', 'https://www.redfin.com//CA/Campbell/2713-Montavo-Pl-95008/home/12180568', 'https://www.redfin.com//CA/San-Jose/1629-Andalusia-Way-95125/home/570810', 'https://www.redfin.com//CA/San-Jose/988-Orchid-Way-95117/home/1177112', 'https://www.redfin.com//CA/San-Jose/2368-Fairgrove-Ct-95125/home/1327769', 'https://www.redfin.com//CA/San-Jose/1054-Princess-Anne-Dr-95128/home/1724641', 'https://www.redfin.com//CA/San-Jose/102-Balboa-Ave-95116/home/1769529', 'https://www.redfin.com//CA/San-Jose/5782-Orchard-Park-Dr-95123/home/1772365', 'https://www.redfin.com//CA/San-Jose/23-Monte-Verano-Ct-95116/home/12178186', 'https://www.redfin.com//CA/San-Jose/2507-Riparian-Ct-95133/home/12180018', 'https://www.redfin.com//CA/San-Jose/6813-Moselle-Dr-95119/home/682873', 'https://www.redfin.com//CA/San-Jose/343-Mayellen-Ave-95126/home/722059', 'https://www.redfin.com//CA/Campbell/2300-Middletown-Dr-95008/home/1701484', 'https://www.redfin.com//CA/San-Jose/1106-Yarwood-Ct-95128/home/12180308', 'https://www.redfin.com//CA/San-Jose/1259-Westbury-Dr-95131/home/28894958', 'https://www.redfin.com//CA/San-Jose/555-N-10th-St-95112/home/695569', 'https://www.redfin.com//CA/San-Jose/4735-Lago-Vista-Cir-95129/home/711325', 'https://www.redfin.com//CA/San-Jose/107-Spyglass-Hill-Rd-95127/home/948655', 'https://www.redfin.com//CA/San-Jose/2834-Buena-Knoll-Ct-95121/home/972177', 'https://www.redfin.com//CA/San-Jose/244-Blossom-Hill-Rd-95123/home/997795', 'https://www.redfin.com//CA/San-Jose/377-Springpark-Cir-95136/home/1385314', 'https://www.redfin.com//CA/San-Jose/3216-Oriole-Dr-95117/home/1566190', 'https://www.redfin.com//CA/San-Jose/2065-Nola-Ranch-Way-95133/home/95957745', 'https://www.redfin.com//CA/San-Jose/919-Roosevelt-St-95112/home/748467', 'https://www.redfin.com//CA/San-Jose/305-Coty-Way-95136/home/864840', 'https://www.redfin.com//CA/Campbell/1594-Summerfield-Dr-95008/home/872505', 'https://www.redfin.com//CA/San-Jose/3312-Merrimac-Dr-95117/home/1034908', 'https://www.redfin.com//CA/San-Jose/3113-Silbury-Ct-95148/home/1364153', 'https://www.redfin.com//CA/San-Jose/611-Fenton-St-95127/home/1557132', 'https://www.redfin.com//CA/San-Jose/1726-Midfield-Ave-95122/home/1582010', 'https://www.redfin.com//CA/San-Jose/1230-Glenbrae-Ln-95118/home/1626274', 'https://www.redfin.com//CA/San-Jose/1752-Harte-Dr-95124/home/1660788', 'https://www.redfin.com//CA/San-Jose/749-Promenade-Ln-95138/home/1768813', 'https://www.redfin.com//CA/San-Ramon/109-Rush-Creek-Ct-94582/home/1172040', 'https://www.redfin.com//CA/San-Ramon/4716-Norris-Canyon-Rd-94583/unit-202/home/56527873', 'https://www.redfin.com//CA/San-Ramon/260-Mara-Pl-94583/home/1940844', 'https://www.redfin.com//CA/San-Ramon/48-Longwood-Ct-94582/home/961405', 'https://www.redfin.com//CA/San-Ramon/9592-Ernwood-St-94583/home/1696061', 'https://www.redfin.com//CA/San-Ramon/3081-Wrangler-Rd-94582/home/1458639', 'https://www.redfin.com//CA/San-Ramon/51103-Via-Vicenza-94583/home/179260091', 'https://www.redfin.com//CA/San-Ramon/9819-Broadmoor-Dr-94583/home/831009', 'https://www.redfin.com//CA/San-Ramon/290-Ludlow-Pl-94583/home/955159', 'https://www.redfin.com//CA/San-Ramon/201-Via-Encanto-94583/home/168816401', 'https://www.redfin.com//CA/San-Ramon/844-Ladera-Corte-94583/home/1304235', 'https://www.redfin.com//CA/San-Ramon/205-Hat-Creek-Ct-94582/home/880764', 'https://www.redfin.com//CA/San-Ramon/4840-Cornflower-St-94582/home/40090937', 'https://www.redfin.com//CA/San-Ramon/5030-Rowan-Dr-94582/home/95952558', 'https://www.redfin.com//CA/San-Ramon/1018-Lakeridge-Pl-94582/home/1398278', 'https://www.redfin.com//CA/San-Ramon/1489-Allanmere-Dr-94582/home/1712964', 'https://www.redfin.com//CA/San-Ramon/542-Casella-Ct-94582/home/1817137', 'https://www.redfin.com//CA/San-Ramon/537-Santander-Dr-94583/home/940685', 'https://www.redfin.com//CA/San-Ramon/3832-Aragon-Ln-94583/home/940643', 'https://www.redfin.com//CA/San-Ramon/238-Copper-Ridge-Rd-94582/home/2109286', 'https://www.redfin.com//CA/San-Ramon/3268-El-Suyo-Dr-94583/home/1923651', 'https://www.redfin.com//CA/San-Ramon/653-Greylyn-Dr-94583/home/1792129', 'https://www.redfin.com//CA/San-Ramon/8985-Alcosta-Blvd-94583/unit-182/home/951923', 'https://www.redfin.com//CA/San-Ramon/216-Copper-Ridge-Rd-94582/home/2109264', 'https://www.redfin.com//CA/San-Ramon/9725-Alcosta-Blvd-94583/home/1878245', 'https://www.redfin.com//CA/San-Ramon/608-Sunflower-Ct-94582/home/1977658', 'https://www.redfin.com//CA/San-Ramon/2131-Canyon-Crest-Ave-94582/home/1516784', 'https://www.redfin.com//CA/San-Ramon/721-Ardleigh-Ct-94583/home/2102676', 'https://www.redfin.com//CA/Danville/1520-Lawrence-Rd-94506/home/1969347', 'https://www.redfin.com//CA/Pleasanton/3128-Joanne-Cir-94588/home/1199195', 'https://www.redfin.com//CA/Pleasanton/4029-Moselle-Ct-94566/home/1669068', 'https://www.redfin.com//CA/Pleasanton/6082-Allbrook-Cir-94588/home/746059', 'https://www.redfin.com//CA/Pleasanton/2219-Segundo-Ct-94588/unit-3/home/179312231', 'https://www.redfin.com//CA/Dublin/7412-Kenwood-Rd-94568/home/143118730', 'https://www.redfin.com//CA/Pleasanton/1224-Harvest-Rd-94566/home/1923639', 'https://www.redfin.com//CA/Pleasanton/1398-Benedict-Ct-94566/home/1755694', 'https://www.redfin.com//CA/Dublin/4403-Healdsburg-Way-94568/home/112887016', 'https://www.redfin.com//CA/Pleasanton/789-Bonita-Ave-94566/home/1407879', 'https://www.redfin.com//CA/Pleasanton/410-Vineyard-Pl-94566/home/1396132', 'https://www.redfin.com//CA/Pleasanton/2511-Yolanda-Ct-94566/home/22690495', 'https://www.redfin.com//CA/Pleasanton/4270-Krause-Ct-94588/home/1275310', 'https://www.redfin.com//CA/Pleasanton/7-Deer-Oaks-Dr-94588/home/1601038', 'https://www.redfin.com//CA/Pleasanton/1622-Tanglewood-Ct-94566/home/1456477', 'https://www.redfin.com//CA/Pleasanton/5492-Greenfield-Way-94566/home/675537', 'https://www.redfin.com//CA/Pleasanton/7795-Olive-Dr-94588/home/1171753', 'https://www.redfin.com//CA/Pleasanton/3209-Monmouth-Ct-94588/home/1185876', 'https://www.redfin.com//CA/Pleasanton/277-Mavis-Dr-94566/home/1407799', 'https://www.redfin.com//CA/Pleasanton/4065-Churchill-Dr-94588/home/1806924', 'https://www.redfin.com//CA/Pleasanton/715-Sycamore-Rd-94566/home/729778', 'https://www.redfin.com//CA/Pleasanton/5812-Corte-Mente-94566/home/1911692', 'https://www.redfin.com//CA/Pleasanton/7251-Beaumont-Ct-94566/home/784004', 'https://www.redfin.com//CA/Pleasanton/6832-Corte-de-Flores-94566/home/1687091', 'https://www.redfin.com//CA/Pleasanton/6930-Corte-Mercado-94566/home/1998563', 'https://www.redfin.com//CA/Pleasanton/544-Sycamore-Rd-94566/home/747006', 'https://www.redfin.com//CA/Pleasanton/1084-Riesling-Dr-94566/home/2044313', 'https://www.redfin.com//CA/Pleasanton/1650-Calle-Santa-Anna-94566/home/1195081', 'https://www.redfin.com//CA/Pleasanton/4427-Downing-Ct-94588/home/1268461', 'https://www.redfin.com//CA/Pleasanton/6053-Sterling-Greens-Cir-94566/home/1287225', 'https://www.redfin.com//CA/Pleasanton/4092-Graham-St-94566/home/1766759', 'https://www.redfin.com//CA/Pleasanton/6831-Herrin-Way-94588/home/1954394', 'https://www.redfin.com//CA/Pleasanton/3772-Stanley-Blvd-94566/home/161408661', 'https://www.redfin.com//CA/Pleasanton/5327-Blackbird-Dr-94566/home/1880261', 'https://www.redfin.com//CA/Pleasanton/4708-Del-Valle-Pkwy-94566/home/1562115', 'https://www.redfin.com//CA/Pleasanton/878-E-Angela-St-94566/home/1191550', 'https://www.redfin.com//CA/Pleasanton/2509-Leona-Pl-94566/home/2114639', 'https://www.redfin.com//CA/Pleasanton/1344-Orloff-Dr-94566/home/1473919', 'https://www.redfin.com//CA/Pleasanton/5538-Paseo-Navarro-94566/home/1196648', 'https://www.redfin.com//CA/Pleasanton/211-Tomas-Way-94566/home/1202693', 'https://www.redfin.com//CA/Pleasanton/6014-Sequoia-Ct-94588/home/1896878', 'https://www.redfin.com//CA/Pleasanton/2509-Leona-Pl-94566/home/2114639', 'https://www.redfin.com//CA/Pleasanton/1344-Orloff-Dr-94566/home/1473919', 'https://www.redfin.com//CA/Pleasanton/5538-Paseo-Navarro-94566/home/1196648', 'https://www.redfin.com//CA/Pleasanton/211-Tomas-Way-94566/home/1202693', 'https://www.redfin.com//CA/Pleasanton/6014-Sequoia-Ct-94588/home/1896878', 'https://www.redfin.com//CA/Dublin/7061-Calistoga-Ln-94568/home/167330969', 'https://www.redfin.com//CA/Pleasanton/4649-Denker-Dr-94588/home/1583340', 'https://www.redfin.com//CA/Pleasanton/4115-School-St-94566/home/1782112', 'https://www.redfin.com//CA/Pleasanton/4150-Alba-Ct-94588/home/1184501', 'https://www.redfin.com//CA/Pleasanton/5474-Greenfield-Way-94566/home/2012805', 'https://www.redfin.com//CA/Pleasanton/103-Castlewood-Dr-94566/home/177303076', 'https://www.redfin.com//CA/Pleasanton/4937-Monaco-Dr-94566/home/2039967', 'https://www.redfin.com//CA/Pleasanton/1926-Clover-Ct-94588/home/1469272', 'https://www.redfin.com//CA/Pleasanton/3984-Foothill-Rd-94588/home/22691781', 'https://www.redfin.com//CA/Danville/5621-HIGHLAND-Rd-94506/home/35616237', 'https://www.redfin.com//CA/Danville/3100-Finley-Rd-94506/home/28508283', 'https://www.redfin.com//CA/Pleasanton/4407-Yuma-Ct-94588/home/1191432', 'https://www.redfin.com//CA/DANVILLE/6909-JOHNSTON-Rd-94506/home/17518226', 'https://www.redfin.com//CA/Pleasanton/Diamond-Canyon/Dresden/home/177955150', 'https://www.redfin.com//CA/Pleasanton/Diamond-Canyon/Regent/home/177955151', 'https://www.redfin.com//CA/Pleasanton/Diamond-Canyon/Fortuna/home/177955152', 'https://www.redfin.com//CA/Pleasanton/Twin-Oaks-Arbor/Sable-2/home/178961071', 'https://www.redfin.com//CA/Pleasanton/Twin-Oaks-Arbor/Sable-1/home/178961072', 'https://www.redfin.com//CA/Pleasanton/Twin-Oaks-Arbor/Victoria-2/home/178961073', 'https://www.redfin.com//CA/Pleasanton/Twin-Oaks-Arbor/Victoria-1/home/178961074']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2864"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(property_url_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function for getting information out of each property- Listprice, address, beds, baths, living sqft, Status, Time on Redfin, Price/sqft, Year built, Walk score, transit score, groceries, food&drink, services, shopping, emergency, drought risk, neighbourhood sold homes, Schools and other attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def house_details(doc):\n",
    "    \n",
    "    SuperCenters = ['Costco','Target','Walmart','Safeway','Lucky','FoodMaxx','Trader Joe','Walgreens']\n",
    "    Major_Indian_Grocery = ['New India','Apna Bazaar','India Cash And Carry','Trinetra','Namaste','Saudagar','Bharat','Nilgiris']\n",
    "    Major_Entertainment =['AMC','Cinemark','Theater']\n",
    "    Boba = ['Bubble Tea','Boba']\n",
    "    Healthcare = ['Hospital','Clinic','Kaiser Permanante','United Health','Health']\n",
    "    \n",
    "    table_dict = {}\n",
    "    for i in doc.findAll('div', {'class':'keyDetail font-weight-roman font-size-base'}): \n",
    "        x = i.findChildren('span')[0].get_text().split(\"\\n\")\n",
    "        y = i.findChildren('span')[1].get_text().split()\n",
    "        z = zip(x,y)\n",
    "        table_dict.update(dict(list(z)))\n",
    "    \n",
    "    try:\n",
    "        price = doc.find('div',attrs={'class':'statsValue'}).text.split('$')[1]\n",
    "    except:\n",
    "        price=np.nan\n",
    "    try:\n",
    "        Address = doc.find('h1', attrs = {'data-rf-test-id':'abp-homeinfo-homeaddress'}).text\n",
    "    except:\n",
    "        Address = np.nan\n",
    "        \n",
    "    try:\n",
    "        bed_str = doc.find('div',attrs={'data-rf-test-id':'abp-beds'}).text.split('B')[0]\n",
    "        beds = int(bed_str)\n",
    "    except:\n",
    "        beds=np.nan\n",
    "    \n",
    "    try:\n",
    "        str = doc.find('div',attrs ={'data-rf-test-id':'abp-baths'}).text.split('B')[0]\n",
    "        bath = float(str)\n",
    "    except:\n",
    "        bath = np.nan\n",
    "\n",
    "    try:\n",
    "        sqft_str =doc.find('div',attrs ={'data-rf-test-id':'abp-sqFt'}).text.split('S')[0].replace(',','')\n",
    "        living_sqft = float(sqft_str)\n",
    "    except:\n",
    "        living_sqft=np.nan\n",
    "        \n",
    "    try:\n",
    "        d_score = int(doc.find('tspan', attrs = {'class':'riskValue redOrange'}).text) \n",
    "    except:\n",
    "        d_score = np.nan\n",
    "    \n",
    "    try:\n",
    "        nbd_homes = int(doc.find('div',attrs ={'class':'MarketInsightsRegionCard--cardMetrics row'}).text.split()[-3].strip(\"Sale-to-List\").strip(\"#\"))\n",
    "    except:\n",
    "        nbd_homes = np.nan\n",
    "    \n",
    "    try:\n",
    "        wa_score = int(doc.findAll('div', {'class':'score inline-block not-last'})[0].get_text().split(\" \")[0])\n",
    "    except:\n",
    "        wa_score = np.nan\n",
    "    try:\n",
    "        trans_score = int(doc.findAll('span', {'class':'value fair'})[0].get_text())\n",
    "    except:\n",
    "        trans_score = np.nan\n",
    "        \n",
    "    for div in doc.findAll('div', {'class':'PointOfInterestWidget'}):\n",
    "        groceries = int(div.findAll('div', {'class':'Tag__text'})[1].get_text())\n",
    "        services = int(div.findAll('div', {'class':'Tag__text'})[-1].get_text())\n",
    "        emergency = int(div.findAll('div', {'class':'Tag__text'})[-2].get_text())\n",
    "        shopping = int(div.findAll('div', {'class':'Tag__text'})[4].get_text())\n",
    "        food_drink = int(div.findAll('div', {'class':'Tag__text'})[2].get_text())\n",
    "    \n",
    "    #places nearby items\n",
    "    try:\n",
    "        Nearby_Items = doc.find('div',attrs={'class':'PointOfInterestWidget'}).text\n",
    "    except:\n",
    "        Nearby_Items = np.nan\n",
    "        \n",
    "    #competitive score\n",
    "    try:\n",
    "        Competitive_Score = doc.find('div',attrs={'class':'score most'}).text\n",
    "    except:\n",
    "        Competitive_Score = np.nan\n",
    "        \n",
    "    # activity views\n",
    "    # views, favorite, favrotie all time, x-outs, all-time x-outs, tours, all- time tours\n",
    "    try:\n",
    "        activity_lis = [activity.get_text() for activity in doc.findAll('span',attrs={'class':'count','data-rf-test-name':'activity-count-label'})]\n",
    "    except:\n",
    "        activity_lis = np.nan\n",
    "        \n",
    "    try:    \n",
    "        view_count = activity_lis[0]\n",
    "    except:\n",
    "        view_count=np.nan\n",
    "\n",
    "\n",
    "    try:    \n",
    "        fav_count_30 = activity_lis[1]\n",
    "    except:\n",
    "        fav_count_30=np.nan\n",
    "\n",
    "    try:\n",
    "        fav_all_time = activity_lis[2]\n",
    "    except:\n",
    "        fav_all_time=np.nan\n",
    "\n",
    "     \n",
    "    school_dict = {}\n",
    "    name_lis = [school.get_text() for school in doc.findAll('div', {'class':'school-name font-size-base font-weight-bold'})]\n",
    "    #dis_lis= [float(dis.get_text().strip(\"mi\")) for dis in doc.findAll('div', {'class':'subsection-number'}) if \"mi\" in dis.get_text()]\n",
    "    rating_lis = [school.get_text() for school in doc.findAll('span', {'class':'rating-num font-size-base font-weight-bold'})]\n",
    "    for x,y in zip(name_lis,rating_lis):\n",
    "        school_dict[x] = y\n",
    "\n",
    "\n",
    "    has_supercenter = 0;\n",
    "    has_major_indian_grocery = 0;\n",
    "    has_major_entertainment = 0;\n",
    "    has_indian_restaurant = 0;\n",
    "    has_chinese_restaurant = 0;\n",
    "    has_mexican_restaurant = 0;\n",
    "    has_boba = 0;\n",
    "    has_starbucks = 0;\n",
    "    has_healthcare_support = 0;\n",
    "    has_mall = 0; \n",
    "\n",
    "    \n",
    "    try:\n",
    "        if any(centre in Nearby_Items for centre in SuperCenters):\n",
    "            has_supercenter = 1;\n",
    "    except:\n",
    "        has_supercenter = 0;       \n",
    "    \n",
    "    try:\n",
    "        if any(grocery in Nearby_Items for grocery in Major_Indian_Grocery):\n",
    "            has_major_indian_grocery = 1;\n",
    "    except:\n",
    "        has_major_indian_grocery = 0;\n",
    "\n",
    "    try:\n",
    "        if any(entertainment in Nearby_Items for entertainment in Major_Entertainment):\n",
    "            has_major_entertainment = 1;\n",
    "    except:\n",
    "        has_major_entertainment = 0;\n",
    "        \n",
    "    try:\n",
    "        if 'Indian Restaurant' in Nearby_Items:\n",
    "            has_indian_restaurant = 1;\n",
    "    except:\n",
    "        has_indian_restaurant = 0;\n",
    "\n",
    "    try:\n",
    "        if 'Chinese Restaurant' in Nearby_Items:\n",
    "            has_chinese_restaurant = 1;\n",
    "    except:\n",
    "        has_chinese_restaurant = 0;       \n",
    " \n",
    "    try:\n",
    "        if 'Mexican Restaurant' in Nearby_Items:\n",
    "            has_mexican_restaurant = 1;\n",
    "    except:\n",
    "        has_mexican_restaurant = 0;       \n",
    "        \n",
    "    try:\n",
    "        if any(boba_tea in Nearby_Items for boba_tea in Boba):\n",
    "            has_boba = 1;\n",
    "    except:\n",
    "        has_boba = 0;\n",
    "\n",
    "    try:\n",
    "        if 'Starbucks' in Nearby_Items:\n",
    "            has_starbucks = 1;\n",
    "    except:\n",
    "        has_starbucks = 0;\n",
    "        \n",
    "    try:\n",
    "        if any(health in Nearby_Items for health in Healthcare):\n",
    "            has_healthcare_support = 1;\n",
    "    except:\n",
    "        has_healthcare_support = 0;\n",
    "        \n",
    "\n",
    "    try:\n",
    "        if 'Mall' in Nearby_Items:\n",
    "            has_mall = 1;\n",
    "    except:\n",
    "        has_mall = 0;\n",
    "\n",
    "        \n",
    "        \n",
    "    l = {\n",
    "       'List_price' : price,\n",
    "         'Address' : Address,\n",
    "         'Beds' : beds,\n",
    "         'Baths' : bath,\n",
    "         'Living_sqft' : living_sqft,\n",
    "        'Drought_Score': d_score, \n",
    "        'Walk_score':  wa_score, \n",
    "        'Neighbourhood_Homes':nbd_homes,\n",
    "        'Transit_score':trans_score,\n",
    "        'Groceries_stores': groceries, \n",
    "        'Services':services,\n",
    "        'Emergency': emergency, \n",
    "        'Shopping':shopping,\n",
    "        'Food_and_Drink': food_drink,\n",
    "        'Schools': [school_dict],\n",
    "        'Competitive_Score':Competitive_Score,\n",
    "        'page_view_count':view_count,\n",
    "        'page_fav_count_30':fav_count_30,\n",
    "        'page_fav_all_time_count':fav_all_time,\n",
    "        'has_supercenter':has_supercenter,\n",
    "        'has_major_indian_grocery':has_major_indian_grocery,\n",
    "        'has_major_entertainment':has_major_entertainment,\n",
    "        'has_indian_restaurant':has_indian_restaurant,\n",
    "        'has_chinese_restaurant':has_chinese_restaurant,\n",
    "        'has_mexican_restaurant':has_mexican_restaurant,\n",
    "        'has_boba':has_boba,\n",
    "        'has_starbucks':has_starbucks,\n",
    "        'has_healthcare_support':has_healthcare_support,\n",
    "        'has_mall':has_mall\n",
    "    }\n",
    "    table_dict.update(l) \n",
    "    return table_dict    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fetching 1st quarter records(2864 / 4 = 716) and writing to csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "701\n",
      "702\n",
      "703\n",
      "704\n",
      "705\n",
      "706\n",
      "707\n",
      "708\n",
      "709\n",
      "710\n",
      "711\n",
      "712\n",
      "713\n",
      "714\n",
      "715\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>List_price</th>\n",
       "      <th>Address</th>\n",
       "      <th>Beds</th>\n",
       "      <th>Baths</th>\n",
       "      <th>Living_sqft</th>\n",
       "      <th>Status</th>\n",
       "      <th>Time on Redfin</th>\n",
       "      <th>Property Type</th>\n",
       "      <th>HOA Dues</th>\n",
       "      <th>Year Built</th>\n",
       "      <th>...</th>\n",
       "      <th>Drought_Score</th>\n",
       "      <th>Walk_score</th>\n",
       "      <th>Neighbourhood_Homes</th>\n",
       "      <th>Transit_score</th>\n",
       "      <th>Groceries_stores</th>\n",
       "      <th>Services</th>\n",
       "      <th>Emergency</th>\n",
       "      <th>Shopping</th>\n",
       "      <th>Food_and_Drink</th>\n",
       "      <th>Schools</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1,599,000</td>\n",
       "      <td>40519 Blacow Rd,Fremont, CA 94538</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1763.0</td>\n",
       "      <td>Coming</td>\n",
       "      <td>16</td>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1960</td>\n",
       "      <td>...</td>\n",
       "      <td>66</td>\n",
       "      <td>54</td>\n",
       "      <td>29</td>\n",
       "      <td>54</td>\n",
       "      <td>4</td>\n",
       "      <td>71</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>53</td>\n",
       "      <td>[{'Steven Millard Elementary School': 0.5, 'G....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>799,000</td>\n",
       "      <td>39951 Fremont Blvd #325,Fremont, CA 94538</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1465.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Condo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1987</td>\n",
       "      <td>...</td>\n",
       "      <td>66</td>\n",
       "      <td>57</td>\n",
       "      <td>11</td>\n",
       "      <td>57</td>\n",
       "      <td>10</td>\n",
       "      <td>63</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>[{'Brier Elementary School': 0.6, 'G. M. Walte...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1,480,000</td>\n",
       "      <td>40219 Laiolo Rd,FREMONT, CA 94538</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1169.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>7</td>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1959</td>\n",
       "      <td>...</td>\n",
       "      <td>66</td>\n",
       "      <td>52</td>\n",
       "      <td>29</td>\n",
       "      <td>52</td>\n",
       "      <td>10</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>57</td>\n",
       "      <td>[{'John Blacow Elementary School': 0.4, 'G. M....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1,590,000</td>\n",
       "      <td>5428 Twilight Cmn,Fremont, CA 94555</td>\n",
       "      <td>3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1658.0</td>\n",
       "      <td>New</td>\n",
       "      <td>10</td>\n",
       "      <td>Single</td>\n",
       "      <td>$50/month</td>\n",
       "      <td>1989</td>\n",
       "      <td>...</td>\n",
       "      <td>66</td>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>81</td>\n",
       "      <td>[{'Forest Park Elementary School': 0.3, 'Thorn...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>605,000</td>\n",
       "      <td>39975 Cedar Blvd #143,Newark, CA 94560</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1071.0</td>\n",
       "      <td>New</td>\n",
       "      <td>14</td>\n",
       "      <td>Condo</td>\n",
       "      <td>$467/month</td>\n",
       "      <td>1985</td>\n",
       "      <td>...</td>\n",
       "      <td>66</td>\n",
       "      <td>76</td>\n",
       "      <td>9</td>\n",
       "      <td>56</td>\n",
       "      <td>6</td>\n",
       "      <td>52</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>69</td>\n",
       "      <td>[{'Birch Grove Primary': 1.5, 'Newark Junior H...</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>711</th>\n",
       "      <td>1,598,000</td>\n",
       "      <td>595 Balsam Ave,SUNNYVALE, CA 94085</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1229.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1952</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>75</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>58</td>\n",
       "      <td>[{'Bishop Elementary School': 0.5, 'Columbia M...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>2,199,000</td>\n",
       "      <td>899 W Washington Ave,Sunnyvale, CA 94086</td>\n",
       "      <td>4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2257.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1946</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>6</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>75</td>\n",
       "      <td>[{'Vargas Elementary School': 0.4, 'Sunnyvale ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>799,000</td>\n",
       "      <td>976 Belmont Ter #6,Sunnyvale, CA 94086</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>854.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>22</td>\n",
       "      <td>Condo</td>\n",
       "      <td>$560/month</td>\n",
       "      <td>1986</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>75</td>\n",
       "      <td>[{'Vargas Elementary School': 0.4, 'Sunnyvale ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>1,399,888</td>\n",
       "      <td>802 Lori Ave,SUNNYVALE, CA 94086</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1950</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54</td>\n",
       "      <td>4</td>\n",
       "      <td>54</td>\n",
       "      <td>5</td>\n",
       "      <td>65</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>71</td>\n",
       "      <td>[{'Vargas Elementary School': 0.7, 'Sunnyvale ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>1,599,000</td>\n",
       "      <td>2920 Feliz Rd,SANTA CLARA, CA 95051</td>\n",
       "      <td>2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1466.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Condo</td>\n",
       "      <td>$336</td>\n",
       "      <td>2020</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45</td>\n",
       "      <td>8</td>\n",
       "      <td>65</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>54</td>\n",
       "      <td>[{'Bracher Elementary School': 1.1, 'Juan Cabr...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>716 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    List_price                                    Address Beds  Baths  \\\n",
       "0    1,599,000          40519 Blacow Rd,Fremont, CA 94538    4    3.0   \n",
       "1      799,000  39951 Fremont Blvd #325,Fremont, CA 94538    2    2.0   \n",
       "2    1,480,000          40219 Laiolo Rd,FREMONT, CA 94538    3    2.0   \n",
       "3    1,590,000        5428 Twilight Cmn,Fremont, CA 94555    3    2.5   \n",
       "4      605,000     39975 Cedar Blvd #143,Newark, CA 94560    2    2.0   \n",
       "..         ...                                        ...  ...    ...   \n",
       "711  1,598,000         595 Balsam Ave,SUNNYVALE, CA 94085    3    1.0   \n",
       "712  2,199,000   899 W Washington Ave,Sunnyvale, CA 94086    4    3.5   \n",
       "713    799,000     976 Belmont Ter #6,Sunnyvale, CA 94086    2    1.0   \n",
       "714  1,399,888           802 Lori Ave,SUNNYVALE, CA 94086    3    1.0   \n",
       "715  1,599,000        2920 Feliz Rd,SANTA CLARA, CA 95051    2    2.5   \n",
       "\n",
       "     Living_sqft  Status Time on Redfin Property Type    HOA Dues Year Built  \\\n",
       "0         1763.0  Coming             16        Single         NaN       1960   \n",
       "1         1465.0     NaN            NaN         Condo         NaN       1987   \n",
       "2         1169.0  Active              7        Single         NaN       1959   \n",
       "3         1658.0     New             10        Single   $50/month       1989   \n",
       "4         1071.0     New             14         Condo  $467/month       1985   \n",
       "..           ...     ...            ...           ...         ...        ...   \n",
       "711       1229.0     NaN            NaN        Single         NaN       1952   \n",
       "712       2257.0     NaN            NaN        Single         NaN       1946   \n",
       "713        854.0  Active             22         Condo  $560/month       1986   \n",
       "714       1003.0     NaN            NaN        Single         NaN       1950   \n",
       "715       1466.0     NaN            NaN         Condo        $336       2020   \n",
       "\n",
       "     ... Drought_Score Walk_score Neighbourhood_Homes Transit_score  \\\n",
       "0    ...            66         54                  29            54   \n",
       "1    ...            66         57                  11            57   \n",
       "2    ...            66         52                  29            52   \n",
       "3    ...            66         30                  28           NaN   \n",
       "4    ...            66         76                   9            56   \n",
       "..   ...           ...        ...                 ...           ...   \n",
       "711  ...           NaN         75                   4           NaN   \n",
       "712  ...           NaN         69                   1            69   \n",
       "713  ...           NaN         49                   5           NaN   \n",
       "714  ...           NaN         54                   4            54   \n",
       "715  ...           NaN         45                   8            65   \n",
       "\n",
       "    Groceries_stores Services Emergency Shopping Food_and_Drink  \\\n",
       "0                  4       71         7       14             53   \n",
       "1                 10       63         4       13             48   \n",
       "2                 10       84         1       16             57   \n",
       "3                 11       86         1       17             81   \n",
       "4                  6       52         7       16             69   \n",
       "..               ...      ...       ...      ...            ...   \n",
       "711                7       75         4       13             58   \n",
       "712                6       63         2        8             75   \n",
       "713                6       54         3        9             75   \n",
       "714                5       65         2        7             71   \n",
       "715                3       24         1       12             54   \n",
       "\n",
       "                                               Schools  \n",
       "0    [{'Steven Millard Elementary School': 0.5, 'G....  \n",
       "1    [{'Brier Elementary School': 0.6, 'G. M. Walte...  \n",
       "2    [{'John Blacow Elementary School': 0.4, 'G. M....  \n",
       "3    [{'Forest Park Elementary School': 0.3, 'Thorn...  \n",
       "4    [{'Birch Grove Primary': 1.5, 'Newark Junior H...  \n",
       "..                                                 ...  \n",
       "711  [{'Bishop Elementary School': 0.5, 'Columbia M...  \n",
       "712  [{'Vargas Elementary School': 0.4, 'Sunnyvale ...  \n",
       "713  [{'Vargas Elementary School': 0.4, 'Sunnyvale ...  \n",
       "714  [{'Vargas Elementary School': 0.7, 'Sunnyvale ...  \n",
       "715  [{'Bracher Elementary School': 1.1, 'Juan Cabr...  \n",
       "\n",
       "[716 rows x 22 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "details_df =pd.DataFrame(columns = ['List_price', 'Address', 'Beds', 'Baths',\n",
    "                    'Living_sqft','Status', 'Time on Redfin', 'Property Type', 'HOA Dues',\n",
    "                   'Year Built', 'Est. Mo. Payment', 'Price/Sq.Ft.', 'Drought_Score', \n",
    "                'Walk_score', \n",
    "                'Neighbourhood_Homes',\n",
    "                'Transit_score',\n",
    "                'Groceries_stores', \n",
    "                'Services',\n",
    "                'Emergency', \n",
    "                'Shopping',\n",
    "                'Food_and_Drink',\n",
    "                'Schools',\n",
    "                'Competitive_Score',\n",
    "            'page_view_count',\n",
    "            'page_fav_count_30',\n",
    "            'page_fav_all_time_count'e,\n",
    "            'has_supercenter',\n",
    "            'has_major_indian_grocery',\n",
    "            'has_major_entertainment',\n",
    "            'has_indian_restaurant',\n",
    "            'has_chinese_restaurant',\n",
    "            'has_mexican_restaurant',\n",
    "            'has_boba',\n",
    "            'has_starbucks',\n",
    "            'has_healthcare_support',\n",
    "            'has_mall'])\n",
    "for i in range((int(len(property_url_list)/4))):\n",
    "    house_dict = house_details(house_doc(property_url_list[i]))\n",
    "    details_df.loc[len(details_df)] = house_dict\n",
    "    details_df.to_csv('Records.csv', index = False)\n",
    "    print(i)\n",
    "#     time.sleep(3)\n",
    "details_df "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fetching 2nd quarter records(2864 / 4 = 716) and writing to csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "716\n",
      "717\n",
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       "      <td>63</td>\n",
       "      <td>1.0</td>\n",
       "      <td>63</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>31</td>\n",
       "      <td>[{'Donlon Elementary School': 0.6, 'Thomas S. ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>1,248,000</td>\n",
       "      <td>7061 Calistoga Ln,Dublin, CA 94568</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>15</td>\n",
       "      <td>Condo</td>\n",
       "      <td>$393/month</td>\n",
       "      <td>2018</td>\n",
       "      <td>...</td>\n",
       "      <td>66.0</td>\n",
       "      <td>2</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>75</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>95</td>\n",
       "      <td>[{'The Quarry Lane School': 0.5, 'John Green E...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>1,649,000</td>\n",
       "      <td>4649 Denker Dr,Pleasanton, CA 94588</td>\n",
       "      <td>4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1918.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1968</td>\n",
       "      <td>...</td>\n",
       "      <td>66.0</td>\n",
       "      <td>55</td>\n",
       "      <td>5.0</td>\n",
       "      <td>55</td>\n",
       "      <td>4</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>33</td>\n",
       "      <td>[{'Donlon Elementary School': 0.4, 'Thomas S. ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>1,499,950</td>\n",
       "      <td>4115 School,Pleasanton, CA 94566</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2149.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1956</td>\n",
       "      <td>...</td>\n",
       "      <td>66.0</td>\n",
       "      <td>70</td>\n",
       "      <td>5.0</td>\n",
       "      <td>67</td>\n",
       "      <td>3</td>\n",
       "      <td>73</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>35</td>\n",
       "      <td>[{'Alisal Elementary School': 0.2, 'Harvest Pa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>1,050,000</td>\n",
       "      <td>4150 Alba Ct,Pleasanton, CA 94588</td>\n",
       "      <td>2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1316.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Townhouse</td>\n",
       "      <td>$239/month</td>\n",
       "      <td>1997</td>\n",
       "      <td>...</td>\n",
       "      <td>66.0</td>\n",
       "      <td>45</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>53</td>\n",
       "      <td>[{'Donlon Elementary School': 1.1, 'Thomas S. ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>716 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     List_price                                  Address Beds  Baths  \\\n",
       "0       130,000  1220 Tasman Dr #325,SUNNYVALE, CA 94089    1    1.0   \n",
       "1       349,000   1111 Morse Ave #27,SUNNYVALE, CA 94089    3    2.0   \n",
       "2     8,495,000       767 S Wolfe Rd,SUNNYVALE, CA 94086   41   22.5   \n",
       "3    15,000,000     1270 Coronado Dr,SUNNYVALE, CA 94086   36   26.0   \n",
       "4     1,998,888     102 Brisbane Ter,SUNNYVALE, CA 94086    3    2.5   \n",
       "..          ...                                      ...  ...    ...   \n",
       "711   1,598,000     6014 Sequoia Ct,PLEASANTON, CA 94588    3    2.0   \n",
       "712   1,248,000       7061 Calistoga Ln,Dublin, CA 94568    3    3.5   \n",
       "713   1,649,000      4649 Denker Dr,Pleasanton, CA 94588    4    2.5   \n",
       "714   1,499,950         4115 School,Pleasanton, CA 94566    4    2.0   \n",
       "715   1,050,000        4150 Alba Ct,Pleasanton, CA 94588    2    2.5   \n",
       "\n",
       "     Living_sqft  Status Time on Redfin Property Type    HOA Dues Year Built  \\\n",
       "0          624.0     NaN            NaN   Single-Wide         NaN       1963   \n",
       "1         1560.0  Active             28   Double-Wide         NaN       2010   \n",
       "2        17354.0     NaN            NaN            5+         NaN       1964   \n",
       "3        18463.0  Active             29            5+         NaN       1963   \n",
       "4         1777.0     NaN            NaN        Single        $330       1988   \n",
       "..           ...     ...            ...           ...         ...        ...   \n",
       "711       1752.0     NaN            NaN        Single         NaN       1968   \n",
       "712       1994.0  Active             15         Condo  $393/month       2018   \n",
       "713       1918.0     NaN            NaN        Single         NaN       1968   \n",
       "714       2149.0     NaN            NaN        Single         NaN       1956   \n",
       "715       1316.0     NaN            NaN     Townhouse  $239/month       1997   \n",
       "\n",
       "     ... Drought_Score Walk_score  Neighbourhood_Homes Transit_score  \\\n",
       "0    ...           NaN         54                  NaN            54   \n",
       "1    ...           NaN         49                  NaN           NaN   \n",
       "2    ...           NaN         74                  NaN           NaN   \n",
       "3    ...           NaN         45                  NaN           NaN   \n",
       "4    ...           NaN         78                 18.0           NaN   \n",
       "..   ...           ...        ...                  ...           ...   \n",
       "711  ...          66.0         63                  1.0            63   \n",
       "712  ...          66.0          2                 14.0           NaN   \n",
       "713  ...          66.0         55                  5.0            55   \n",
       "714  ...          66.0         70                  5.0            67   \n",
       "715  ...          66.0         45                  8.0           NaN   \n",
       "\n",
       "     Groceries_stores Services Emergency Shopping Food_and_Drink  \\\n",
       "0                   3       30         1       15             56   \n",
       "1                   8       52         2        7             59   \n",
       "2                   6       65         1       23             65   \n",
       "3                   8       59         6       17             53   \n",
       "4                   7       70         2       11             82   \n",
       "..                ...      ...       ...      ...            ...   \n",
       "711                 3       32         1        6             31   \n",
       "712                10       75         9       21             95   \n",
       "713                 4       40         2        7             33   \n",
       "714                 3       73         2       11             35   \n",
       "715                 3       44         2       12             53   \n",
       "\n",
       "                                               Schools  \n",
       "0    [{'George Mayne Elementary School': 2.1, 'Mari...  \n",
       "1    [{'Lakewood Elementary School': 0.9, 'Columbia...  \n",
       "2    [{'Braly Elementary School': 0.2, 'Marian A. P...  \n",
       "3    [{'Vargas Elementary School': 0.4, 'Sunnyvale ...  \n",
       "4    [{'Bishop Elementary School': 0.5, 'Columbia M...  \n",
       "..                                                 ...  \n",
       "711  [{'Donlon Elementary School': 0.6, 'Thomas S. ...  \n",
       "712  [{'The Quarry Lane School': 0.5, 'John Green E...  \n",
       "713  [{'Donlon Elementary School': 0.4, 'Thomas S. ...  \n",
       "714  [{'Alisal Elementary School': 0.2, 'Harvest Pa...  \n",
       "715  [{'Donlon Elementary School': 1.1, 'Thomas S. ...  \n",
       "\n",
       "[716 rows x 22 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "details_df =pd.DataFrame(columns = ['List_price', 'Address', 'Beds', 'Baths',\n",
    "                    'Living_sqft','Status', 'Time on Redfin', 'Property Type', 'HOA Dues',\n",
    "                   'Year Built', 'Est. Mo. Payment', 'Price/Sq.Ft.', 'Drought_Score', \n",
    "                'Walk_score', \n",
    "                'Neighbourhood_Homes',\n",
    "                'Transit_score',\n",
    "                'Groceries_stores', \n",
    "                'Services',\n",
    "                'Emergency', \n",
    "                'Shopping',\n",
    "                'Food_and_Drink',\n",
    "                'Schools',\n",
    "                'Competitive_Score',\n",
    "            'page_view_count',\n",
    "            'page_fav_count_30',\n",
    "            'page_fav_all_time_count'e,\n",
    "            'has_supercenter',\n",
    "            'has_major_indian_grocery',\n",
    "            'has_major_entertainment',\n",
    "            'has_indian_restaurant',\n",
    "            'has_chinese_restaurant',\n",
    "            'has_mexican_restaurant',\n",
    "            'has_boba',\n",
    "            'has_starbucks',\n",
    "            'has_healthcare_support',\n",
    "            'has_mall'])\n",
    "for i in range((int(len(property_url_list)/4)),(int(len(property_url_list)/2))):\n",
    "    house_dict = house_details(house_doc(property_url_list[i]))\n",
    "    details_df.loc[len(details_df)] = house_dict\n",
    "    details_df.to_csv('Records1.csv', index = False)\n",
    "    print(i)\n",
    "#     time.sleep(3)\n",
    "details_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Fetching 3rd quarter records(2864 / 4 = 716) and writing to csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>List_price</th>\n",
       "      <th>Address</th>\n",
       "      <th>Beds</th>\n",
       "      <th>Baths</th>\n",
       "      <th>Living_sqft</th>\n",
       "      <th>Status</th>\n",
       "      <th>Time on Redfin</th>\n",
       "      <th>Property Type</th>\n",
       "      <th>HOA Dues</th>\n",
       "      <th>Year Built</th>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1,750,000</td>\n",
       "      <td>5474 Greenfield Way,Pleasanton, CA 94566</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1882.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1966</td>\n",
       "      <td>...</td>\n",
       "      <td>66</td>\n",
       "      <td>53</td>\n",
       "      <td>7</td>\n",
       "      <td>53</td>\n",
       "      <td>3</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>36</td>\n",
       "      <td>[{'Walnut Grove Elementary School': 0.2, 'Harv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1,600,000</td>\n",
       "      <td>103 Castlewood Dr,Pleasanton, CA 94566</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Active</td>\n",
       "      <td>36</td>\n",
       "      <td>Vacant</td>\n",
       "      <td>$6/month</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>79</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>59</td>\n",
       "      <td>[{'Phoebe Apperson Hearst Elementary School': ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2,798,000</td>\n",
       "      <td>4937 Monaco Dr,Pleasanton, CA 94566</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3233.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>48</td>\n",
       "      <td>Single</td>\n",
       "      <td>$68/month</td>\n",
       "      <td>1986</td>\n",
       "      <td>...</td>\n",
       "      <td>66</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "      <td>[{'Phoebe Apperson Hearst Elementary School': ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4,998,000</td>\n",
       "      <td>1926 Clover Ct,Pleasanton, CA 94588</td>\n",
       "      <td>5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>7014.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Single</td>\n",
       "      <td>$320/month</td>\n",
       "      <td>1997</td>\n",
       "      <td>...</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>76</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>56</td>\n",
       "      <td>[{'Phoebe Apperson Hearst Elementary School': ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3,800,000</td>\n",
       "      <td>3984 Foothill Rd,Pleasanton, CA 94588</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Active</td>\n",
       "      <td>149</td>\n",
       "      <td>Vacant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>66</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>47</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>37</td>\n",
       "      <td>[{'Lydiksen Elementary School': 1.1, 'Thomas S...</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>2,298,000</td>\n",
       "      <td>536 Hawthorn Ave,SUNNYVALE, CA 94086</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1967</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69</td>\n",
       "      <td>32</td>\n",
       "      <td>69</td>\n",
       "      <td>9</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>73</td>\n",
       "      <td>[{'Ellis Elementary School': 0.3, 'Sunnyvale M...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>3,088,000</td>\n",
       "      <td>18771 E Homestead Rd #3,SUNNYVALE, CA 94087</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>82</td>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59</td>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>7</td>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>104</td>\n",
       "      <td>[{'Laurelwood Elementary School': 0.4, 'Marian...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>1,698,000</td>\n",
       "      <td>251 Ficus Ter,SUNNYVALE, CA 94086</td>\n",
       "      <td>3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1783.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>91</td>\n",
       "      <td>Townhouse</td>\n",
       "      <td>$298</td>\n",
       "      <td>2006</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61</td>\n",
       "      <td>27</td>\n",
       "      <td>61</td>\n",
       "      <td>6</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>92</td>\n",
       "      <td>[{'Ellis Elementary School': 0.3, 'Sunnyvale M...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>625,888</td>\n",
       "      <td>880 E Fremont Ave #304,SUNNYVALE, CA 94087</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>733.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Condo</td>\n",
       "      <td>$419</td>\n",
       "      <td>1987</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>75</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>85</td>\n",
       "      <td>[{'Louis E. Stocklmeir Elementary School': 0.9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>3,500,000</td>\n",
       "      <td>842 Sunnyvale Saratoga Rd,SUNNYVALE, CA 94087</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Active</td>\n",
       "      <td>146</td>\n",
       "      <td>Residential</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66</td>\n",
       "      <td>8</td>\n",
       "      <td>75</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>62</td>\n",
       "      <td>[{'Ellis Elementary School': 0.6, 'Sunnyvale M...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>716 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    List_price                                        Address Beds  Baths  \\\n",
       "0    1,750,000       5474 Greenfield Way,Pleasanton, CA 94566    4    2.0   \n",
       "1    1,600,000         103 Castlewood Dr,Pleasanton, CA 94566  NaN    NaN   \n",
       "2    2,798,000            4937 Monaco Dr,Pleasanton, CA 94566    5    3.0   \n",
       "3    4,998,000            1926 Clover Ct,Pleasanton, CA 94588    5    5.5   \n",
       "4    3,800,000          3984 Foothill Rd,Pleasanton, CA 94588  NaN    NaN   \n",
       "..         ...                                            ...  ...    ...   \n",
       "711  2,298,000           536 Hawthorn Ave,SUNNYVALE, CA 94086    3    2.0   \n",
       "712  3,088,000    18771 E Homestead Rd #3,SUNNYVALE, CA 94087    3    3.0   \n",
       "713  1,698,000              251 Ficus Ter,SUNNYVALE, CA 94086    3    2.5   \n",
       "714    625,888     880 E Fremont Ave #304,SUNNYVALE, CA 94087    1    1.0   \n",
       "715  3,500,000  842 Sunnyvale Saratoga Rd,SUNNYVALE, CA 94087  NaN    NaN   \n",
       "\n",
       "     Living_sqft  Status Time on Redfin Property Type    HOA Dues Year Built  \\\n",
       "0         1882.0     NaN            NaN        Single         NaN       1966   \n",
       "1            NaN  Active             36        Vacant    $6/month        NaN   \n",
       "2         3233.0  Active             48        Single   $68/month       1986   \n",
       "3         7014.0     NaN            NaN        Single  $320/month       1997   \n",
       "4            NaN  Active            149        Vacant         NaN        NaN   \n",
       "..           ...     ...            ...           ...         ...        ...   \n",
       "711       1360.0     NaN            NaN        Single         NaN       1967   \n",
       "712       2400.0  Active             82        Single         NaN       2022   \n",
       "713       1783.0  Active             91     Townhouse        $298       2006   \n",
       "714        733.0     NaN            NaN         Condo        $419       1987   \n",
       "715          NaN  Active            146   Residential         NaN        NaN   \n",
       "\n",
       "     ... Drought_Score Walk_score Neighbourhood_Homes Transit_score  \\\n",
       "0    ...            66         53                   7            53   \n",
       "1    ...            66          0                 NaN           NaN   \n",
       "2    ...            66         76                   1           NaN   \n",
       "3    ...            66          0                   2           NaN   \n",
       "4    ...            66          3                 NaN           NaN   \n",
       "..   ...           ...        ...                 ...           ...   \n",
       "711  ...           NaN         69                  32            69   \n",
       "712  ...           NaN         59                   6            59   \n",
       "713  ...           NaN         61                  27            61   \n",
       "714  ...           NaN         81                   4           NaN   \n",
       "715  ...           NaN         66                 NaN            66   \n",
       "\n",
       "    Groceries_stores Services Emergency Shopping Food_and_Drink  \\\n",
       "0                  3       58         1       11             36   \n",
       "1                  5       79         2        8             59   \n",
       "2                  6       99         1        6             60   \n",
       "3                  6       76         2        8             56   \n",
       "4                  5       47         2       12             37   \n",
       "..               ...      ...       ...      ...            ...   \n",
       "711                9       80         1       21             73   \n",
       "712                7       80         3       19            104   \n",
       "713                6       78         1       23             92   \n",
       "714                9       75         7       16             85   \n",
       "715                8       75         2       19             62   \n",
       "\n",
       "                                               Schools  \n",
       "0    [{'Walnut Grove Elementary School': 0.2, 'Harv...  \n",
       "1    [{'Phoebe Apperson Hearst Elementary School': ...  \n",
       "2    [{'Phoebe Apperson Hearst Elementary School': ...  \n",
       "3    [{'Phoebe Apperson Hearst Elementary School': ...  \n",
       "4    [{'Lydiksen Elementary School': 1.1, 'Thomas S...  \n",
       "..                                                 ...  \n",
       "711  [{'Ellis Elementary School': 0.3, 'Sunnyvale M...  \n",
       "712  [{'Laurelwood Elementary School': 0.4, 'Marian...  \n",
       "713  [{'Ellis Elementary School': 0.3, 'Sunnyvale M...  \n",
       "714  [{'Louis E. Stocklmeir Elementary School': 0.9...  \n",
       "715  [{'Ellis Elementary School': 0.6, 'Sunnyvale M...  \n",
       "\n",
       "[716 rows x 22 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "details_df =pd.DataFrame(columns = ['List_price', 'Address', 'Beds', 'Baths',\n",
    "                    'Living_sqft','Status', 'Time on Redfin', 'Property Type', 'HOA Dues',\n",
    "                   'Year Built', 'Est. Mo. Payment', 'Price/Sq.Ft.', 'Drought_Score', \n",
    "                'Walk_score', \n",
    "                'Neighbourhood_Homes',\n",
    "                'Transit_score',\n",
    "                'Groceries_stores', \n",
    "                'Services',\n",
    "                'Emergency', \n",
    "                'Shopping',\n",
    "                'Food_and_Drink',\n",
    "                'Schools',\n",
    "                'Competitive_Score',\n",
    "            'page_view_count',\n",
    "            'page_fav_count_30',\n",
    "            'page_fav_all_time_count'e,\n",
    "            'has_supercenter',\n",
    "            'has_major_indian_grocery',\n",
    "            'has_major_entertainment',\n",
    "            'has_indian_restaurant',\n",
    "            'has_chinese_restaurant',\n",
    "            'has_mexican_restaurant',\n",
    "            'has_boba',\n",
    "            'has_starbucks',\n",
    "            'has_healthcare_support',\n",
    "            'has_mall'])\n",
    "for i in range((int(len(property_url_list)/2)),(int(3*len(property_url_list)/4))):\n",
    "    house_dict = house_details(house_doc(property_url_list[i]))\n",
    "    details_df.loc[len(details_df)] = house_dict\n",
    "    details_df.to_csv('Records2.csv', index = False)\n",
    "    print(i)\n",
    "#     time.sleep(3)\n",
    "details_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1432"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(len(property_url_list)/2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fetching 4th quarter records(2864 / 4 = 716) and writing to csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>List_price</th>\n",
       "      <th>Address</th>\n",
       "      <th>Beds</th>\n",
       "      <th>Baths</th>\n",
       "      <th>Living_sqft</th>\n",
       "      <th>Status</th>\n",
       "      <th>Time on Redfin</th>\n",
       "      <th>Property Type</th>\n",
       "      <th>HOA Dues</th>\n",
       "      <th>Year Built</th>\n",
       "      <th>...</th>\n",
       "      <th>Drought_Score</th>\n",
       "      <th>Walk_score</th>\n",
       "      <th>Neighbourhood_Homes</th>\n",
       "      <th>Transit_score</th>\n",
       "      <th>Groceries_stores</th>\n",
       "      <th>Services</th>\n",
       "      <th>Emergency</th>\n",
       "      <th>Shopping</th>\n",
       "      <th>Food_and_Drink</th>\n",
       "      <th>Schools</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>299,950</td>\n",
       "      <td>1050 Borregas Ave, Space #124 #124,SUNNYVALE, ...</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>190</td>\n",
       "      <td>Double-Wide</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1976</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>48</td>\n",
       "      <td>[{'Lakewood Elementary School': 1.0, 'Columbia...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>220,000</td>\n",
       "      <td>1220 Tasman Dr #159,SUNNYVALE, CA 94089</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1344.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>244</td>\n",
       "      <td>Double-Wide</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1968</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58.0</td>\n",
       "      <td>5</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>62</td>\n",
       "      <td>[{'George Mayne Elementary School': 2.2, 'Mari...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5,750,000</td>\n",
       "      <td>295 Commercial St,Sunnyvale, CA 94085</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Active</td>\n",
       "      <td>258</td>\n",
       "      <td>Vacant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>66</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>62</td>\n",
       "      <td>[{'San Miguel Elementary School': 0.7, 'Columb...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3,488,000</td>\n",
       "      <td>18771 #4 Homestead Rd,SUNNYVALE, CA 94087</td>\n",
       "      <td>4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3075.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>8</td>\n",
       "      <td>87</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>79</td>\n",
       "      <td>[{}]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14,500,000</td>\n",
       "      <td>214 W Ahwanee Ave,SUNNYVALE, CA 94085</td>\n",
       "      <td>20</td>\n",
       "      <td>24.0</td>\n",
       "      <td>31696.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>549</td>\n",
       "      <td>5+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.0</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>45</td>\n",
       "      <td>[{'Bishop Elementary School': 0.7, 'Columbia M...</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>711</th>\n",
       "      <td>3,699,995+</td>\n",
       "      <td>Fortuna,Pleasanton, CA 94566</td>\n",
       "      <td>5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4035.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>66.0</td>\n",
       "      <td>20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>56</td>\n",
       "      <td>[{'Valley View Elementary School': 1.0, 'Pleas...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>1,868,995+</td>\n",
       "      <td>Sable 2,Pleasanton, CA 94588</td>\n",
       "      <td>4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2860.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>66.0</td>\n",
       "      <td>56</td>\n",
       "      <td>21.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>[{'Lydiksen Elementary School': 0.8, 'Thomas S...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>1,852,995+</td>\n",
       "      <td>Sable 1,Pleasanton, CA 94588</td>\n",
       "      <td>4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2846.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>66.0</td>\n",
       "      <td>56</td>\n",
       "      <td>21.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>[{'Lydiksen Elementary School': 0.8, 'Thomas S...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>1,926,995+</td>\n",
       "      <td>Victoria 2,Pleasanton, CA 94588</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2833.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>66.0</td>\n",
       "      <td>56</td>\n",
       "      <td>21.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>[{'Lydiksen Elementary School': 0.8, 'Thomas S...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>1,904,995+</td>\n",
       "      <td>Victoria 1,Pleasanton, CA 94588</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2813.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>66.0</td>\n",
       "      <td>56</td>\n",
       "      <td>21.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>[{'Lydiksen Elementary School': 0.8, 'Thomas S...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>716 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     List_price                                            Address Beds  \\\n",
       "0       299,950  1050 Borregas Ave, Space #124 #124,SUNNYVALE, ...    4   \n",
       "1       220,000            1220 Tasman Dr #159,SUNNYVALE, CA 94089    2   \n",
       "2     5,750,000              295 Commercial St,Sunnyvale, CA 94085  NaN   \n",
       "3     3,488,000          18771 #4 Homestead Rd,SUNNYVALE, CA 94087    4   \n",
       "4    14,500,000              214 W Ahwanee Ave,SUNNYVALE, CA 94085   20   \n",
       "..          ...                                                ...  ...   \n",
       "711  3,699,995+                       Fortuna,Pleasanton, CA 94566    5   \n",
       "712  1,868,995+                       Sable 2,Pleasanton, CA 94588    4   \n",
       "713  1,852,995+                       Sable 1,Pleasanton, CA 94588    4   \n",
       "714  1,926,995+                    Victoria 2,Pleasanton, CA 94588    4   \n",
       "715  1,904,995+                    Victoria 1,Pleasanton, CA 94588    4   \n",
       "\n",
       "     Baths  Living_sqft  Status Time on Redfin Property Type HOA Dues  \\\n",
       "0      3.0       1920.0  Active            190   Double-Wide      NaN   \n",
       "1      2.0       1344.0  Active            244   Double-Wide      NaN   \n",
       "2      NaN          NaN  Active            258        Vacant      NaN   \n",
       "3      3.5       3075.0     NaN            NaN        Single      NaN   \n",
       "4     24.0      31696.0  Active            549            5+      NaN   \n",
       "..     ...          ...     ...            ...           ...      ...   \n",
       "711    5.5       4035.0  Active            NaN        Single      NaN   \n",
       "712    3.5       2860.0  Active            NaN        Single      NaN   \n",
       "713    3.5       2846.0     NaN            NaN        Single      NaN   \n",
       "714    4.0       2833.0  Active            NaN        Single      NaN   \n",
       "715    4.0       2813.0  Active            NaN        Single      NaN   \n",
       "\n",
       "    Year Built  ... Drought_Score Walk_score  Neighbourhood_Homes  \\\n",
       "0         1976  ...           NaN         46                  NaN   \n",
       "1         1968  ...           NaN         58                  NaN   \n",
       "2          NaN  ...           NaN         40                  NaN   \n",
       "3         2020  ...           NaN         68                  9.0   \n",
       "4         2020  ...           NaN         57                  NaN   \n",
       "..         ...  ...           ...        ...                  ...   \n",
       "711        NaN  ...          66.0         20                  1.0   \n",
       "712        NaN  ...          66.0         56                 21.0   \n",
       "713        NaN  ...          66.0         56                 21.0   \n",
       "714        NaN  ...          66.0         56                 21.0   \n",
       "715        NaN  ...          66.0         56                 21.0   \n",
       "\n",
       "    Transit_score  Groceries_stores  Services Emergency Shopping  \\\n",
       "0             NaN                 7        44         2        5   \n",
       "1            58.0                 5        37         1       14   \n",
       "2             NaN                 4        66         4       15   \n",
       "3            68.0                 8        87         1       14   \n",
       "4            57.0                 6        43         1        6   \n",
       "..            ...               ...       ...       ...      ...   \n",
       "711           NaN                 6        96         1        7   \n",
       "712          56.0                 2        36         1       11   \n",
       "713          56.0                 2        36         1       11   \n",
       "714          56.0                 2        36         1       11   \n",
       "715          56.0                 2        36         1       11   \n",
       "\n",
       "    Food_and_Drink                                            Schools  \n",
       "0               48  [{'Lakewood Elementary School': 1.0, 'Columbia...  \n",
       "1               62  [{'George Mayne Elementary School': 2.2, 'Mari...  \n",
       "2               62  [{'San Miguel Elementary School': 0.7, 'Columb...  \n",
       "3               79                                               [{}]  \n",
       "4               45  [{'Bishop Elementary School': 0.7, 'Columbia M...  \n",
       "..             ...                                                ...  \n",
       "711             56  [{'Valley View Elementary School': 1.0, 'Pleas...  \n",
       "712             21  [{'Lydiksen Elementary School': 0.8, 'Thomas S...  \n",
       "713             21  [{'Lydiksen Elementary School': 0.8, 'Thomas S...  \n",
       "714             21  [{'Lydiksen Elementary School': 0.8, 'Thomas S...  \n",
       "715             21  [{'Lydiksen Elementary School': 0.8, 'Thomas S...  \n",
       "\n",
       "[716 rows x 22 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "details_df =pd.DataFrame(columns = ['List_price', 'Address', 'Beds', 'Baths',\n",
    "                    'Living_sqft','Status', 'Time on Redfin', 'Property Type', 'HOA Dues',\n",
    "                   'Year Built', 'Est. Mo. Payment', 'Price/Sq.Ft.', 'Drought_Score', \n",
    "                'Walk_score', \n",
    "                'Neighbourhood_Homes',\n",
    "                'Transit_score',\n",
    "                'Groceries_stores', \n",
    "                'Services',\n",
    "                'Emergency', \n",
    "                'Shopping',\n",
    "                'Food_and_Drink',\n",
    "                'Schools',\n",
    "                'Competitive_Score',\n",
    "            'page_view_count',\n",
    "            'page_fav_count_30',\n",
    "            'page_fav_all_time_count'e,\n",
    "            'has_supercenter',\n",
    "            'has_major_indian_grocery',\n",
    "            'has_major_entertainment',\n",
    "            'has_indian_restaurant',\n",
    "            'has_chinese_restaurant',\n",
    "            'has_mexican_restaurant',\n",
    "            'has_boba',\n",
    "            'has_starbucks',\n",
    "            'has_healthcare_support',\n",
    "            'has_mall'])\n",
    "for i in range((int(3*len(property_url_list)/4)),len(property_url_list)):\n",
    "    house_dict = house_details(house_doc(property_url_list[i]))\n",
    "    details_df.loc[len(details_df)] = house_dict\n",
    "    details_df.to_csv('Records3.csv', index = False)\n",
    "    print(i)\n",
    "#     time.sleep(3)\n",
    "details_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2864, 22)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = pd.concat([pd.read_csv(\"Records.csv\"),pd.read_csv(\"Records1.csv\"), \n",
    "                      pd.read_csv(\"Records2.csv\"), pd.read_csv(\"Records3.csv\")], axis = 0)\n",
    "final_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.to_csv(\"input_new.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "List_price                2\n",
       "Address                   2\n",
       "Beds                     74\n",
       "Baths                    75\n",
       "Living_sqft              63\n",
       "Status                 1441\n",
       "Time on Redfin         1485\n",
       "Property Type             0\n",
       "HOA Dues               1758\n",
       "Year Built              184\n",
       "Est. Mo. Payment          7\n",
       "Price/Sq.Ft.             69\n",
       "Drought_Score          1552\n",
       "Walk_score                0\n",
       "Neighbourhood_Homes     186\n",
       "Transit_score          1058\n",
       "Groceries_stores          0\n",
       "Services                  0\n",
       "Emergency                 0\n",
       "Shopping                  0\n",
       "Food_and_Drink            0\n",
       "Schools                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATA CLEANING AND PRE PROCESSING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"input_new.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>List_price</th>\n",
       "      <th>Address</th>\n",
       "      <th>Beds</th>\n",
       "      <th>Baths</th>\n",
       "      <th>Living_sqft</th>\n",
       "      <th>Status</th>\n",
       "      <th>Time on Redfin</th>\n",
       "      <th>Property Type</th>\n",
       "      <th>HOA Dues</th>\n",
       "      <th>Year Built</th>\n",
       "      <th>...</th>\n",
       "      <th>has_major_indian_grocery</th>\n",
       "      <th>has_major_entertainment</th>\n",
       "      <th>has_indian_restaurant</th>\n",
       "      <th>has_chinese_restaurant</th>\n",
       "      <th>has_mexican_restaurant</th>\n",
       "      <th>has_boba</th>\n",
       "      <th>has_starbucks</th>\n",
       "      <th>property_url</th>\n",
       "      <th>has_healthcare_support</th>\n",
       "      <th>has_mall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15,99,000</td>\n",
       "      <td>40519 Blacow Rd,Fremont, CA 94538</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1763.0</td>\n",
       "      <td>Coming</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>https://www.redfin.com//CA/Fremont/40519-Blaco...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7,99,000</td>\n",
       "      <td>39951 Fremont Blvd #325,Fremont, CA 94538</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1465.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Condo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>https://www.redfin.com//CA/Fremont/39951-Fremo...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14,80,000</td>\n",
       "      <td>40219 Laiolo Rd,FREMONT, CA 94538</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1169.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1959.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>https://www.redfin.com//CA/Fremont/40219-Laiol...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15,90,000</td>\n",
       "      <td>5428 Twilight Cmn,Fremont, CA 94555</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1658.0</td>\n",
       "      <td>New</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>$50/month</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>https://www.redfin.com//CA/Fremont/5428-Twilig...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6,05,000</td>\n",
       "      <td>39975 Cedar Blvd #143,Newark, CA 94560</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1071.0</td>\n",
       "      <td>New</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Condo</td>\n",
       "      <td>$467/month</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>https://www.redfin.com//CA/Newark/39975-Cedar-...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  List_price                                    Address  Beds  Baths  \\\n",
       "0  15,99,000          40519 Blacow Rd,Fremont, CA 94538   4.0    3.0   \n",
       "1   7,99,000  39951 Fremont Blvd #325,Fremont, CA 94538   2.0    2.0   \n",
       "2  14,80,000          40219 Laiolo Rd,FREMONT, CA 94538   3.0    2.0   \n",
       "3  15,90,000        5428 Twilight Cmn,Fremont, CA 94555   3.0    2.5   \n",
       "4   6,05,000     39975 Cedar Blvd #143,Newark, CA 94560   2.0    2.0   \n",
       "\n",
       "   Living_sqft  Status  Time on Redfin Property Type    HOA Dues  Year Built  \\\n",
       "0       1763.0  Coming            16.0        Single         NaN      1960.0   \n",
       "1       1465.0     NaN             NaN         Condo         NaN      1987.0   \n",
       "2       1169.0  Active             7.0        Single         NaN      1959.0   \n",
       "3       1658.0     New            10.0        Single   $50/month      1989.0   \n",
       "4       1071.0     New            14.0         Condo  $467/month      1985.0   \n",
       "\n",
       "   ... has_major_indian_grocery has_major_entertainment  \\\n",
       "0  ...                        0                       0   \n",
       "1  ...                        1                       0   \n",
       "2  ...                        0                       1   \n",
       "3  ...                        0                       0   \n",
       "4  ...                        1                       1   \n",
       "\n",
       "   has_indian_restaurant  has_chinese_restaurant  has_mexican_restaurant  \\\n",
       "0                      1                       1                       1   \n",
       "1                      1                       1                       1   \n",
       "2                      1                       1                       1   \n",
       "3                      1                       1                       1   \n",
       "4                      1                       1                       1   \n",
       "\n",
       "   has_boba  has_starbucks                                       property_url  \\\n",
       "0         1              1  https://www.redfin.com//CA/Fremont/40519-Blaco...   \n",
       "1         1              1  https://www.redfin.com//CA/Fremont/39951-Fremo...   \n",
       "2         1              1  https://www.redfin.com//CA/Fremont/40219-Laiol...   \n",
       "3         1              1  https://www.redfin.com//CA/Fremont/5428-Twilig...   \n",
       "4         1              1  https://www.redfin.com//CA/Newark/39975-Cedar-...   \n",
       "\n",
       "   has_healthcare_support  has_mall  \n",
       "0                       0         1  \n",
       "1                       0         0  \n",
       "2                       0         0  \n",
       "3                       0         0  \n",
       "4                       0         1  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2864, 37)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "List_price                   object\n",
       "Address                      object\n",
       "Beds                        float64\n",
       "Baths                       float64\n",
       "Living_sqft                 float64\n",
       "Status                       object\n",
       "Time on Redfin              float64\n",
       "Property Type                object\n",
       "HOA Dues                     object\n",
       "Year Built                  float64\n",
       "Est. Mo. Payment             object\n",
       "Price/Sq.Ft.                 object\n",
       "Drought_Score               float64\n",
       "Walk_score                    int64\n",
       "Neighbourhood_Homes         float64\n",
       "Transit_score               float64\n",
       "Groceries_stores              int64\n",
       "Services                      int64\n",
       "Emergency                     int64\n",
       "Shopping                      int64\n",
       "Food_and_Drink                int64\n",
       "Schools                      object\n",
       "Competitive_Score           float64\n",
       "page_view_count              object\n",
       "page_fav_count_30           float64\n",
       "page_fav_all_time_count      object\n",
       "has_supercenter               int64\n",
       "has_major_indian_grocery      int64\n",
       "has_major_entertainment       int64\n",
       "has_indian_restaurant         int64\n",
       "has_chinese_restaurant        int64\n",
       "has_mexican_restaurant        int64\n",
       "has_boba                      int64\n",
       "has_starbucks                 int64\n",
       "property_url                 object\n",
       "has_healthcare_support        int64\n",
       "has_mall                      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## List_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        15,99,000\n",
       "1         7,99,000\n",
       "2        14,80,000\n",
       "3        15,90,000\n",
       "4         6,05,000\n",
       "           ...    \n",
       "2859    3,699,995+\n",
       "2860    1,868,995+\n",
       "2861    1,852,995+\n",
       "2862    1,926,995+\n",
       "2863    1,904,995+\n",
       "Name: List_price, Length: 2864, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.List_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['List_price'] = df['List_price'].replace(\"[+]\", \"\", regex = True)\n",
    "df['List_price'] = df['List_price'].replace(\",\",\"\", regex = True)\n",
    "df['List_price'] = df['List_price'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(subset = ['List_price'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "List_price                     0\n",
       "Address                        0\n",
       "Beds                          72\n",
       "Baths                         73\n",
       "Living_sqft                   61\n",
       "Status                      1440\n",
       "Time on Redfin              1484\n",
       "Property Type                  0\n",
       "HOA Dues                    1756\n",
       "Year Built                   182\n",
       "Est. Mo. Payment               5\n",
       "Price/Sq.Ft.                  67\n",
       "Drought_Score               1552\n",
       "Walk_score                     0\n",
       "Neighbourhood_Homes          186\n",
       "Transit_score               1058\n",
       "Groceries_stores               0\n",
       "Services                       0\n",
       "Emergency                      0\n",
       "Shopping                       0\n",
       "Food_and_Drink                 0\n",
       "Schools                        0\n",
       "Competitive_Score            380\n",
       "page_view_count             1477\n",
       "page_fav_count_30           1477\n",
       "page_fav_all_time_count     1477\n",
       "has_supercenter                0\n",
       "has_major_indian_grocery       0\n",
       "has_major_entertainment        0\n",
       "has_indian_restaurant          0\n",
       "has_chinese_restaurant         0\n",
       "has_mexican_restaurant         0\n",
       "has_boba                       0\n",
       "has_starbucks                  0\n",
       "property_url                   0\n",
       "has_healthcare_support         0\n",
       "has_mall                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Changes in Living_sqft versus List_price')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=\"Living_sqft\",\n",
    "                    y=\"List_price\",\n",
    "                    data=df)\n",
    "# sns.regplot('Living_sqft','List_price',data=df)\n",
    "plt.title('Changes in %s versus %s' %('Living_sqft','List_price'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 14000000.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, ax1 = plt.subplots()\n",
    "ax1.set_title('Box Plot')\n",
    "#fig1.suptitle('Box Plot')\n",
    "ax1.boxplot(df['List_price'])\n",
    "ax1.set_ylim([0, 14000000]) #function boxplot returns to a dictionary, contains the following keys. \n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2862, 37)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Living_sqft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Comparison over Age Group'}>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Living_sqft'].plot.box(title = 'Comparison over Age Group', figsize = (4,6), fontsize = 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df['Living_sqft']> 7000).sum() # No. of rows with outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing outliers \n",
    "df.drop(df[df['Living_sqft']> 7000].index, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2837, 37)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 16060796.693677261)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.regplot(x = 'Living_sqft',y = 'List_price',data=df, color = 'green')\n",
    "plt.title('Changes in %s versus %s' %('Living_sqft','List_price'))\n",
    "g.set_xlim(0, 3600)\n",
    "g.set_ylim(0,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Beds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# df.boxplot(column = ['Baths'], fontsize = 15)\n",
    "# df['Baths'].plot(kind = 'box')\n",
    "# ax = df['Baths'].plot.box()\n",
    "fig, ax = plt.subplots()\n",
    "box = df.loc[:, ['Beds']].boxplot()\n",
    "# ax.margins(y=0.05)\n",
    "ax.set_ylim([0, 20])# the y_limit is set to 20 for the whiskers to be visisble on the plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(index = list(df[(df['Beds'] > 7)].index), inplace = True) \n",
    "#finding the rows with beds greater than 7 (deleting the outliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Beds.isna().sum() & df.Baths.isna().sum() #records have null values in both beds and baths "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(subset=['Beds','Baths'],how='all', inplace = True) #dropping records have null values in both beds and baths "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Changes in Beds versus List_price')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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332C1WlmzZg2zZ8/m9ttv57PPPvM6bV8IIUTzMfTSFdJkJGVpqQKlLIFSDpCyQAttMhJCCNHySEIQQggBSEIQQgjhIglBCCEEIAlBCCGEiyQEIYQQgCQEIUSAC8JMzNFw2Aoxx8IJksteneQnI4QIWEGYiX4vnKBfWWAUBPW3aMdy6fNKfipCiIAVddSKabIJSlwPlIBpsomoo1Zd42qpJCEIIQKW6Uy1ZFCpBExnTbrE09JJQhBCBCzVUUFYjQfDQLU37Io9fiUJQQgRsAo6l6LWVEsKYaDWKAqSSnWNq6XSZT8EIYRoDhU4OZ9aTFSKlaBzFioSHRQklVKBU+/QWiRJCEKIgFaBk9ykYmw9o8jNKtY7nBZNmoyEEEIAkhCEEEK4SEIQQggBSEIQQgjhIglBCCEEIAlBCCGEiyQEIYQQgCQEIYQQLpIQhBBCAJIQhBBCuEhCEEIIAUhCEEII4SIJQQghBCAJQQghhIskBCGEEIAkBCGEEC5+TQiFhYWkpqZy+vTpOp/zwQcf0L9/f3+GIYQQogH8lhC+/vpr7r77bo4fP17nc3766Seefvppf4UghBCiEfyWELZs2cK8efOIj4+v8zmzZ89mypQp/gpBCCFEI/htT+WFCxfWe37Dhg1cccUVXHXVVf4KQQghRCP4LSHU58iRI+zevZtXXnmFc+fO+fx94uIifX6tzRbl82tbGilLyxQoZQmUcoCU5efokhB27txJVlYWd9xxB3a7nczMTMaNG8frr7/eqO+TnV2I06ka/f42WxRZWQWNfl1LJGVpmQKlLIFSDpCyAJjNpnpvpHVJCFOnTmXq1KkAnD59mvHjxzc6GQghhLiwmnUeQlpaGt98801zvqUQQogG8nsNYe/eve7/r1u3rtb5Tp06eTxHCCGEPmSmshBCCEASghBCCBdJCEIIIQBJCEIIIVwkIQghhAAkIQghhHCRhCCEEAKQhCCEEMJFEoIQQghAEoIQQggXSQhCCCEASQhCCCFcJCEIIYQAJCEIIYRwkYQghBACkIQghBDCRRKCEEIIQBKCEEIIF0kIQgghAEkIQgghXCQhCCGEACQhCCGEcJGEIIQQApCEIIQQwkUSghBCCEASghBCCBdJCEIIIQBJCEIIIVwkIQghhAAkIQghhHCRhCCEEALwc0IoLCwkNTWV06dP1zr397//neHDhzNs2DAmTZpEXl6eP0MRQgjxM/yWEL7++mvuvvtujh8/XutcYWEh8+fPZ+3atbz77rskJyezatUqf4UihBCiAfyWELZs2cK8efOIj4+vdc5utzNv3jwSEhIASE5O5uzZs/4KRQghRAOYlFLKn2/Qv39/NmzYQKdOnbyeLy0tZdy4cdxzzz2MHDnSn6EIIYSoR5Ceb15QUMDkyZNJSUnxKRlkZxfidDY+n9lsUWRlFTT6dS2RlKVlCpSyBEo5QMoCYDabiIuLrPt8U4JqiszMTMaNG0dycjILFy7UKwwhhBAuutQQHA4HEydOZMiQIUyaNEmPEIQQQtTQrAkhLS2NqVOncu7cOb799lscDge7du0C4Morr5SaghBC6MjvCWHv3r3u/69btw6A7t27c+jQIX+/tRBCiEaQmcpCCCEASQhCCCFcJCEIIYQAJCEIIYRwkYQghBACkIQghBDCRRKCEEIIQBKCEEIIF0kIQgghAEkIQgghXCQhCCGEACQhCCGEcJGEIIQQApCEIIQQwkUSghBCCEASghBCCBdJCEIIIQBJCEIIIVwkIQghhAAkIQghhHCRhCCEEAKQhCCEEMJFEoIQQghAEoIQQggXSQhCCCEASQhCCCFcJCEIIYQAJCEIIYRwkYQghBACkIQghBDCxa8JobCwkNTUVE6fPl3r3MGDBxk1ahSDBg1i1qxZVFRU+DMUIYQQP8NvCeHrr7/m7rvv5vjx417PP/roo8ydO5ddu3ahlGLLli3+CkUIIUQD+C0hbNmyhXnz5hEfH1/r3I8//khpaSlXX301AKNGjWLnzp3+CkUIIUQDBPnrGy9cuLDOc5mZmdhsNvexzWYjIyOj0e8RFxfpU2zae0b5/NqWRsrSMgVKWQKlHCBl+Tl+Swj1cTqdmEwm97FSyuO4obKzC3E6VaNfZ7NFkZVV0OjXtURSlpYpUMoSKOUAKQuA2Wyq90Zal1FGiYmJZGVluY9/+uknr01LQgghmo8uCaFjx46Ehoby5ZdfArB9+3b69OmjRyhCCCFcmjUhpKWl8c033wCwbNkyFi9ezODBgykuLmb8+PHNGYoQopUIwkzM0XDYCjHHwgmS6Vd1MimlGt8I30JIH4KUpaUKlLIYvRxBmIl+LxzTZBOUAGGg1ijOpxZTgVPv8HwWUH0IQgjRHKKOWquSAUAJmCabiDpq1TWulkoSghCilmAsxB2OgC0QdySCYCx6h+QT05lqyaBSCZjONn5UY2sgCUEI4SEYC213hGEeaIa7wHyrmbY7wgyZFFRHBWE1HgwD1d6wLeV+JQlBCOGhzWErpik1mlmmmGhz2HjNLAWdS1FrqiUFVx9CQVKprnG1VLpMTBNCtGBn8NrMwhkgufnDaYoKnJxPLSYqxUrQOQsViQ4KkkoN3aHsT5IQhBCeOqLdUVdPCmFAB33CaaoKnOQmFWPrGUVuVrHe4bRo0mQkhPCQ36UUtbpGM8tqRX6yNLMEOqkhCCE8OHDi7KCwrDdBIRAJzrYKhzSzBDxJCEIIDxFnQrCMMns0GVnCzESkh1DQoUy/wITfSZOREMKDOcPstVPZnCmXi0Ann7AQwoMz0el17L4zXpqMAp0kBCGEh6L25VSsd3h0Klesd1DUoVzXuHwli9s1nPQhCCE8OFHk9S8hIj0Ea24IpTHlFHUox4nxZvfWXNwuKMxC9Jpwwy9u5y+SKoUQtThRWgfyLVDQocyQyQBkcbvGkoQghAhYsrhd40hCEEIELFncrnEalBCcTicvvvgiM2bMoLCwkD//+c84HA5/xyaEEE0ii9s1ToM6lZcuXUpOTo57+8uPPvqIrKwsZs+e7dfghBCiKWRxu8ZpUA0hPT2dJUuWEBoaSmRkJC+//DKffPKJv2MTQogmq1zcjpGQmySji+rToIQQFBSE2Vz11JCQEIKCZMSqEEIEkgZd1bt06cLGjRtxOBx8//33vPLKK6SkpPg7NiGEEM2oQTWEWbNmceDAAbKzsxk3bhzFxcXMnDnT37EJIYRoRg2qIURGRvLggw+yaNEiCgsLOXnyJDExMf6OTQihkyDM2uStjyGmfTgFnaUjtjVoUA3hL3/5C5MmTQIgNzeXhx56iDfffNOvgQkh9FG53EPQrywwCoL6W7RjmbYU8Br0CW/evJk33ngDgF/84hds27aNDRs2+DUwIYQ+ZLmH1qtBCcHhcBAZGek+joqKwmSSqd9CBCJZ7qH1alBCuOyyy1i2bBmnTp3i1KlTrFixgksuucTPoQkh9CDLPbReDUoIf/jDHzh+/DgjRoxg9OjRHD9+nPnz5/s5NCGEHmS5h9arQaOM2rVrx+rVq/0dixCiBZDlHlqvehPCwoULmTVrFhMnTvR6/oUXXvBLUEIIfVUu92DrGUVuVrHe4YhmUm9C6NmzJwCDBg3y6Zvv2LGD559/noqKCu69915+/etfe5w/cOAAc+fOxW630759e5555hnatGnj03sJIYRomnoTQv/+/QHYtm0br776aqO+cUZGBsuXL+edd94hJCSEsWPHcsMNN5CUlOR+zsKFC5k6dSp9+/ZlyZIlvPTSS0yfPt2HYgghhGiqBnUqFxQUUFzcuGrjvn37uPHGG4mOjiY8PJxBgwaxc+dOj+c4nU6KiooAKCkpwWqVcc5CCKGXBnUqh4WF0a9fP5KTkwkPD3c/Xl8fQmZmJjabzX0cHx/P/v37PZ7z+OOPc//997No0SLCwsLYsmVLY+MXQghxgTQoIYwePbrR39jpdHpMXlNKeRyXlpYya9YsXnnlFXr06MH69euZMWMGa9eubfB7xMVF/vyT6mCzRfn82pZGytIyBUpZAqUcIGX5OQ1KCCNHjiQ3N5cvvvgCs9nML3/5S6Ki6g8mMTGRL774wn2clZVFfHy8+/jIkSOEhobSo0cPAO666y5WrFjRqOCzswtxOhs/WcZmiyIrq6DRr2uJpCwtU6CUJVDKAVIWALPZVO+NdIP6EN5//30GDhzIq6++yosvvsitt97Kp59+Wu9revXqRXp6Ojk5OZSUlLB792769OnjPn/xxRdz7tw5vv/+ewD27NlD9+7dGxKOEEIIP2hQDWH58uW89tprJCcnA9pw0dmzZ7N169Y6X5OQkMD06dMZP348drud0aNH06NHD9LS0pg6dSrdu3dn8eLFTJs2DaUUcXFxLFq06MKUSgghRKM1KCFYrVZ3MgDo1q1bgxa3Gzp0KEOHDvV4bN26de7/9+3bl759+zY0ViGEEH7UoCajPn36sHbtWoqLiykrK2Pz5s1cfvnl5OXlcf78eT+HKIQQojk0qIawbt06HA4Hzz77rMfj27dvx2QycfDgQb8EJ4QQovk0KCEcOHCgznM7duy4YMGI1ikYC20OW+EfENcxgvwupdhx6B2WEK1Ok/fEe/nlly9EHKKVCsZC2x1hmAea4S4w32qm7Y4wgrHoHZoQrU6TE4JSsmmG8F2bw1ZMU2ps1zjFpNUYhBDNqskJQbbSFE1yBq/bNXJGh1iEaOWanBCEaJKOeN2ukQ46xCJEKycJQegqv0spanWN7RpXK/KTZbtGIZpbg0YZ1Uf6EERT2HGQN7SENl2smM+acbZ3kp8so4yE0EODagh///vfaz22bds2gFozkYVoLDsOspOLYAxkJxdJMhBCJ/XWEPbu3UtFRQVLly7F6azaYLuiooJVq1YxYsQIHnjgAb8HKYQQwv/qTQgHDx7k008/JTs7m7/85S9VLwoKYsKECf6OTQghRDOqNyFMnjyZyZMns3HjRn796183V0xCCCF00KA+hIEDB7Jnzx4Ali1bxr333suhQ4f8GpgQQojm1aCE8MQTT3Dq1CnS09P55z//yfDhw3nqqaf8HZsQQohm1KCEcP78eSZMmMA///lPUlNTGTVqFCUlNaeXCiGEMLIGJQS73Y7dbuejjz6iV69elJSUUFxc7O/YhBBCNKMGJYRf/epX9OzZk5iYGK688krGjBlDamqqv2MTQgjRjBo0U3nq1KnceeedJCQkAFrHckpKil8DE0II0bzqTQjbt29n+PDhrF+/vta59PR07rvvPr8FJoQQonnVmxBOnDgBwJEjR2qdKyoqkoQghBABpN6EMHXqVAAWL15c69y1117rn4iEEOICCiGIqEOhsFfborUguYxyKvQOq0XyeflrWeVUCNHShRBEmx1WzIPMMBbMA8202WElpOkLPQcknxOC7JQmhGjpog6Fet2iNepQqK5xtVSyQY4QInDJFq2NUm+96ZprrvFaE1BKUVoqO1oJUVMwFtoctsI/tPbq/C6y2Y+uKrdorZ4UZIvWOtWbEN57773mikMIwwvGQtv/C8M0SWuiMIeZaftcGHm3l0hS0ElBchltVlurmo1cW7QWpJTpHVqLVG9C6NixY3PFIYThtTlqdScDQGuvnmSizR4r2UlFusbWWpVTQeHQMiKSQrQtWjs4KUopl1FGdZA+BCEulLN4b68+p0MsAoAgzES+F4p5sGuU0SDtOEgufV7JT0WICyUSrX26ujAgXIdYBABRR62YJteotU02EXXUqmtcLZVfE8KOHTu47bbbGDhwIBs3bqx1/vvvv+eee+5h2LBhPPDAA+Tl5fkzHCH8qy0wl6qkEOY6jtYrIGE6Y/JaazOdlWHz3vgtIWRkZLB8+XJef/11tm3bxubNmzl69Kj7vFKKBx98kLS0NN599126du3K2rVr/RWOEH5XcFkZKlnBeuBFYD2oZEXBZdKBqRfVUXmttan2MrHWG78lhH379nHjjTcSHR1NeHg4gwYNYufOne7zBw4cIDw8nD59+gAwceJE2bdZGJoTJxQA9wG/cX0tcD0udFHa2Y5arTxqbWq1ojTJrmtcLZXf5m9nZmZis9ncx/Hx8ezfv999fPLkSdq1a8fMmTM5ePAgl112GXPmzPFXOEL4XdRRK6aJNdqrJ5qI2mMlN0k2lNJD+MEQTOtN8DJQBESCabWJ8KQQSrpKUqjJbwnB6XR6TGpTSnkcV1RU8K9//YvXXnuN7t2786c//YklS5awZMmSBr9HXFykz/HZbFE+v7alkbK0EB/jtb066JwFW0/jlsvQn8k+4DbgftzzEJgD5vNmY5cL/3wufksIiYmJfPHFF+7jrKws4uPj3cc2m42LL76Y7t27A5CamupeXbWhsrMLcTob3xZos0WRlVXQ6Ne1RFKWliOmfThBYZZas2IrEh3kZhmzhmD0zyQuJgLzk2aPWhtPgnOnk+ws484N8fVzMZtN9d5I+60PoVevXqSnp5OTk0NJSQm7d+929xeAtixGTk4Ohw4dAmDv3r1069bNX+EI4XcFnUtRa2q0V69RFCTJMi+6ycH73JBcHWIxAL/VEBISEpg+fTrjx4/HbrczevRoevToQVpaGlOnTqV79+6sWbOG2bNnU1JSQmJiIkuXLvVXOEL4XQVOilLLCO/smhXb3klx13IqDNipbMZExJkQOABRsaEUtS/HifFG5jgTFWYvaxk5441XluZgUgbe2ECajKQsLUkowUTtCK29bs7QMsowTgemGRNt94YRdJ/FXY6K9Q7y+pcYLikEYSb6vfCqyWmuWtv51GJDJupK/moykl0ihLhAIg+GeF17PzIphDIDjWiJOBNC0GYLvANkAzYIesFCREoIBR2MNaeiAid5qSW0udzqrrXlJ5caOhn4kyQEobuAWTL6LBAHjAcqB9S96nq8q15BNZ651Ay3A6OoGpmzCsxlxlvpxoyJyL2hmO8zu1egjVwfasjaTnOQhCB0FYyFtjvC3HfW5jAzbVeHkTfUgEtGXwJMBhZQdSGdC1ysY0w+CCozw0N4jsx5CIJ2Gi8hRJwJqWr6Am0Y8H0WItKNV9tpDsb7hEVAaXPY6rWZpc1hAy4+5qAqGeD6ugAM1zoRQKu2mjPMXstizpRLnzfyUxH6CqQtDn/Ee1l+1CGWpqjcZay6MKC9DrE0kTPR6bUsznijZenmIQlB6Kuui48RtzhMxHtZEnSIpQkKksu8rv9jxF3GitqXU7He4VGWivUOijqU6xpXSyV9CEJX+V1Kabs6rNZQzfxkA07mygfmAE/isUwChXoG1XjlVJA/tJSoy0PdI3MKUsoMucuYE0Ve/xIi0kOw5oZQGlNOUQdjzqloDpIQhK7sOMgbWkKbLp7DAg3XoQzQBm0Rteloo4yU6/glPYPyTTkVZKdUYLs5ytBLPICWFAo6lGG9KoSCLOPVcpqTJAShOzsOspOLsPU29sVHFaItolajhqCMuYyRaIUkIQhxgagY5bWGoDZI84QwBkkIQlwgBZ1LiZ5Ze5kEWdxOGIUkBCEukAqcFKSWEVm5uF0HJ4UpxlzcLgizthH9x9qy3gWdZbmH1kCGnQpxgQRjIWpHKObBZhgL5kFmonaEEoxF79AapXJBuKBfWWAUBPW3aMdyuQh48gkLcYEEyqzrqKPWqmYv0Mox2aTVGERAkyYjIS6UM3hf3O4MkKxXUI1nOmPyOuPadNYESbqEJJqJJASDkjbeFugS4H/RFrSrHHa6AMMtbqc6umYp19hURrWX0VKBTpqMDEjaeFsmBVXJANfXuaBMdb6kRZKtQFsvqSEYUJ1tvClWcpNkFpReTKfx3tRyGrhUh4B8VIGT86nFRKVYCTpnoSLRQUGS1EBbA7mlNKB623iFfqLxvrhd2+YPpakqcGo3FyMhN8nY202KhpOEYEDuNt7qpI1Xf+FoTUbVmlqYC0ToFpEQjSJNRgZU0LmU6DUyI7bFOQeswXPpijVo22d21jEuIRpIEoIBVeCkMLWMiMoZse2dFHU15ozYgBKHtin9omqPhQEx+oQjRGNJk5EBhRBEZPUZsYPNRO4IJUTyu75KgRV4NhmtAGTFZWEQcgUxoKhDoV5nxEZdHkp2ivE2MQkYocBSPJuMlmLI/RDMmIg4EwIHICo2lKL2sqlMayAJwYjq2gT9LJDS/OEIl0zgKJ5NRpWPd23+cHxlxkTbvWEE3WeBErCGhRC03kJe/xJDJgWZxNlwkhCMKB6vM0mJ1ycc4fILvH8unfQJx1cRZ0IImm2pqukAQbMtRLwVQkEHY7V/BWEm+q/hmB7UatRBYRainw/n/G0ylNYbSQgGZE90ELrCrC2TULlEwgqwJxhw28lAooCngSzACViAdrpG5BNLrtnrzm+W82booG9sjRX1vdWdDACtefVBE1H/sJJ7mUzirEkSggEVxpUR9AsLlvVmbQP3SHC0dVLYzlh3bwEnA61jeRlVF9I/oDUZGWjYqcVirkoGuL4+CZZdxhuDYv7R+yRO848muEyXkFo0SQgG5ERx/rpiIs6EYM0NoTSmnKIO0umnuxhgHp4X0nnATt0i8k023vuocnSIpana4L0ZL0qfcFo646V8AWhJoaBDGdwCBR3KJBm0ACoXrxdSdV6HYJoiDu9LcBhwPoUzRnmdPe6Mlb8Xb/yaEHbs2MFtt93GwIED2bhxY53P++CDD+jfv78/QxHC71RCHUuK2Ax28ckB5uB5EZ0DnNcrIN+VXGRHXazgEWAW8AioixUlF9n1Dq1F8ltCyMjIYPny5bz++uts27aNzZs3c/To0VrP++mnn3j66af9FYYwgFCCiTsYAZsh7lAEoQTrHZJPCjuXoV6osWz0C4rCJIP17cQBx4D3gNeB/3MdG7CGEHEoBNNzJrgCbcXZK8D0nImIQyF6h9Yi+S0h7Nu3jxtvvJHo6GjCw8MZNGgQO3fWbkydPXs2U6ZM8VcYooULJdjrPsRGTAph+cGYQkwed6OmEBNh+cYqi72jA3oBqcA44HagF9g7GXAUWw4wBvgW+AE46DrO1TOolstvncqZmZnYbDb3cXx8PPv37/d4zoYNG7jiiiu46qqr/BWGaOEiD4Z4nXUdmRRCWVdjVeuDz1m0xewmA0VAJLAagpdYtM5Ngwg+aYHFeMxDYDEEd7Zod9pGEo+WBKqP/JoL2Op7Uevlt4TgdDoxmarW51dKeRwfOXKE3bt388orr3Du3Dmf3iMuLrJxLygFPgf2gq1jFPwPEAD7httsBh4y8QHehwVmmLH1MVi5vkbbT/l+POaHmEvMxvqMzuN1HoI5z2DlAO0zWYDnyK8FYN5hwLLU4I/4/ZYQEhMT+eKLL9zHWVlZxMdXTaXduXMnWVlZ3HHHHdjtdjIzMxk3bhyvv/56g98jO7sQp7NhHXYhBNFmh7XqbjQM1GpF/tBSyjHu+j82WxRZWQV6h+GzuMQIzGHmWsMCnQlOsrOKdIvLF3EhEZj/1+x58flfcO40VlniYiIwP2muNQ/BaOUAiMuPwFxSo2W8BJx5xitLdb7+3ZvNpnpvpP3Wh9CrVy/S09PJycmhpKSE3bt306dPH/f5qVOnsmvXLrZv387atWuJj49vVDJorDoXhDsU6rf3FA1Q1wqh5bpF5LsMvI/fz9QhlqY4j9axPBOtL2SW6zhPx5h85OyoIAnPsiS5Hhe1+K2GkJCQwPTp0xk/fjx2u53Ro0fTo0cP0tLSmDp1Kt27d/fXW3t3Bu9/rGeQBeH0FEArhJJIYKwxlYDWD1LZ1FLZ7m60cgDFl9mJesJcq2Wg+DJj9U81F5NSyrCpsjFNRnGHIzAP9NI0sctJdkrrqzq2FHEfRmAeU7ui6nzTSXZfY30ucf+JwPyFuVbbu/N6J9lXGacscd9EYE718rfynpPs7sYpB9Tzd7/bSXayscpSnb+ajFrN0hUFyWW0WV27D6EgxWBjxANNe7zfVSfqE06TWIGX8aztvAzcpGdQPjiP99r0+WaPpOnqaxlIbv5wWrpWkxDKqSB/aClRl4e6t50sSCkzdIdyIMjvUkrbl8MwfWtyrxCquirykw24P3QOWjI4h1aWINfxeR1j8kUCgdH0BdAR72Ux2KqtzaXVJATQkkJ2SgW2m6MMPcIg4NRcIfQ5fcPxWSe0slQfRR2H8S4+ZVTNp6j8TNYABmx2D6gbjmbQqhKCaHnaHLZiWmjymARlWmiiTZLVeG28Cm02bM1JUAZa+hqAYLSmrpfRJthFoCWEhXoG5RuF0hJc9c/kedfjohZJCEJfOXidBGXIpZZz8ToJiuuAi/UKqvEcJQrzx8DHNR4vM95FNOpoHRvk7LGSmyQb5NQky18LfcXgdTMWIy6kRj7eOzDzdYilCVRiHau2xhsvIZjOeN8gx3TW5PX5rZ0kBKErR4nT6x+so9SA+91WzkOoLgytk9ZAgorMWs2m+mTBBRBUbLzLhepYR3Jrb7zk1hykyUjoypHoJNjLKBBHvAETQjDactFlaFubRgEhrn9Gko22VPT/oXWQt3c9lo0269dACjqXEr0mHNPkasPN1ygKkqRT2RtJCEJXRe3LCVpvIeg+i/sPtmK9g6IOBly7IhSt72MKVf0hq9EurkbSEfgKuIeqcqwCrtUzKN9U4OR8ajFRKVaCzlmoSHRQkFRKBQa84WgGxqsDioDiRFHc345zpxM2aTPHi/vbjbklaD5VyQDX1ykYrg+BfOAhPMvxEMYrh0sFTq0DeSTkJhVLMqiHJAShqxCCvG6QE2LEymt9s2KN5Bzey5GhQyyiWbWqhBCEmZij4bAVYo6FE9S6it8iBdQqtB2AkcDfgNeAXa7j9noG5YPK2b3VhWG8cohGazVXxCDMRL8XTtCvLDAKgvpbtOPW8yNomQLlrhooTylHpSoYBfw/YASoVEV5V2P1hxQll6NW19gberWiKMVY5RCNZ8B6uW+ijlqrRhqAdic62URUijEnqARhJuqoFT6GmPbhFHQ2aEdZR7S76N+ijWKxAS9gvOUegJA6tgMNSQoBA20HqkD7XLYCP6F9Jgoj9uqIRmo1CaHeCSoGG0pXWdupTHBBYRai14RzPtWAHWYd0DZzH4XnyJyOegblo7ra3s8BXZs/HF9Ffh+C6d8mj/0QTHNNREaHUCb7CAS0VpMQ3BNUaox3N+IElYCq7ZzG+8icnRjqIgoEzlLe+WgbFFVbX4qX0JbgEAGt1TSgF3QuRa2p0S5q0AkqATUd/yze76rP6hBLU1WgjdevPsN3FeDQLSLf2NHWl1qOtqDds65jqRwEvFZTQwikCSqBVNuhA97vqo04osUMvELtVUKf0jEmX7TB+/pSu3WLqEkCpr+tGbSahABVE1RsPaPIzTJY00o1ATUdvx1an0HN2b02PYPyUR7aCqEfe3ncSHLwXmsz4Aq0AdXf1gxaVUIIFIFU2yEL+BZt7P45tPb2HWgd/e10jMsXbfFe22mrTzg+C5RyEGD9bc2g1fQhBJqAmY5fgra37RBgrOtrMrXvUI0gAliJZx/CSiBct4h8k4e2J0X1cszBeDUdwJzpvb/NnGXA/rZmIDUEoa8wYANV7e6RaE1GBtydi0LgaapG5yjX8Yt6BuWDWLTPo3o5KndQM5pYvNd2onWJpsWThCD0VQqMRxvFUtmHsAJtCWmjyQWOAou8PG4kZuAx4H/x/Ewsegblo1y02k3NHfkMWNtpDpIQDCqcYMIOhsBeiOsQQUlKOcVGHBdoperCg+vr/6LNQzCaCKA32ub01Ws7NdcFaukygbV4jpb6IzAbuFzHuHwRg/fazkt6BtVySUIwoHCCCd9RtSicOcxM+OpQGIrxkkIGda+sabSJaTa0mk712s4aIF7PoHwQAxwA7q72mFGbWc7jfc9ugy7l7W+SEAworI41c8KSQig20Jo5gHYR9dbGa8Rhp8VozUXVZ/guQpubYCRFwBK0dYycaE1FcWjlM5q6+kPW6xlUyyUJwYjqm91rtLvqOLSROFOpuoNbifaHbDS5eL8bNVofQjhQDiyjqhwL0Jr3DCa/SyltnwiruoFyrdyan2zAOTvNQBKCEQXS7N5StLb2R9DuRs2uYyN2KkfhfYbv33SLyDdBwFw8yzEXQ85UtuMgb2gJbbpYMZ8142zvJD+5FLvh1hNpHpIQDKgkpZzw1aG17npKDLbuPgAFVLW5VwoD3tMnnCYpwHvNrUCHWJoiUMrhYsdBdnIRtt5RZGcV6R1OiyYJwYCKscNQCEsKcd/1lHQ16CijPLxffIzY6dcG7zW3NvqE47NIvJcjQp9wRBV/r8vk15nKO3bs4LbbbmPgwIFs3Lix1vm///3vDB8+nGHDhjFp0iTy8mRwcEMVYye7axHcBdldi4yZDEC7+CQBM4FZrn9JGPPiUznmveYM3/N6BeSjQClHgGmOXR/9VkPIyMhg+fLlvPPOO4SEhDB27FhuuOEGkpK03WgKCwuZP38+b7/9NgkJCaxYsYJVq1Yxe/Zsf4UkWqIYvE+CitYxJl/VNebdaDOVo4HPgXfQdrFrB/wZ6KVjTE0QRTAh1ebslKeUU2DAG6jmWJfJbzWEffv2ceONNxIdHU14eDiDBg1i586q2UZ2u5158+aRkJAAQHJyMmfPGnERfNEk5XifmGa8v1etU38p0AO4FLjKdWy07UAjqNrF7v+hbXGailabM5goggndEYp5sBnGgnmQmdAdoUQRrHdojdYc+6D4LSFkZmZis1UNJo+PjycjI8N9HBMTw6233gpAaWkpa9euZcCAAf4KR7RUmXjvQ8jSIZamOo8W+wHgB9fXEozX1FKE913sDNgfW+c+1wdDdI3LF+59UKq7wPug+K3JyOl0YjJVZS6llMdxpYKCAiZPnkxKSgojR45s1HvExfl+y2KzRfn82hahGPgC2Au2DlHa9oZGW1UTtOYJLx2Y5rZm431GR4GTeI7fnwvmSw1Wlr14XyH0rBlbHwOVAwKrLDHAc8Akqn6/noOg6y3Ygi5MWfyWEBITE/niiy/cx1lZWcTHe87hz8zM5IEHHuDGG29k5syZjX6P7OxCnM7GZ0ebLYqsLIOOoQOsBBO5o/aw08KhZZQarK0lrjgC8xJzrVmxzhKn4YYIxhVGYF5g9ryzXgDO7cYqS1yHCMxh5lpJ2tneWOWAwCoLQNDtZqL21NgHJbfho4zMZlO9N9J+azLq1asX6enp5OTkUFJSwu7du+nTp4/7vMPhYOLEiQwZMoRZs2Z5rT0I7yIOea8GRxwyXjWYGKpmxS4EnnEdG3AzFgrx3vxVqEMsTdEGz0X5KnexM+JnYsL7PtcG3QnG3/ug+K2GkJCQwPTp0xk/fjx2u53Ro0fTo0cP0tLSmDp1KufOnePbb7/F4XCwa9cuAK688koWLjTiQvjNrL6lK1KaP5wmcQLbqb0fQk89g/JRNIGx01gB2sTAmqOMuugZlI9C0K5y69EScyTa7Hjj9Sk3C5NSyoA7s2sa22QUjIU2h62Yz5hxdnSS38WYU9jjDkRgfs0M49BWBW0PvAbO/+cku5uxqsFxX0VgPm2Gh6hqF10Fzk5Osq81WFmORmD+xFy17INrDSDnTU6yk4xTlrh/RWD+1gzzqCrHH8B5hZPsXxqnHACx34dj6WeplaQd/3CQc5kRV+vT+Nrs/XNNRq1mpnIwFtruCPNYMrrt6jDyhpYYLyn8AvgV8AHaHfYh13EnHWPyVSTayprVVwhdgvFWCAVt1nU4nusyhWO8zViiqEoGuL7Ow5h7VJz2PlSTH01wmR4BtWytJiG0OWz12u7epouV7GRj3fWQBZyg1mgWLsV4zRPn8b5CqNEuoqCtBrocuBMtuTlcx+v0DMoH2Xi/iOboEEsTqU5Km/le+ZkAbHYN4RS1tJqEwBm8/5KfQdvU3Ujy0HZ8qn5X/RJwjW4R+S4C7yuE/lW3iHxXgfdZ1xV6BuWDutYyMuKwZivePxMDLuXdHFpPQuiI919yo80iBe3OcwZa1b7Q9fUS1+NGk4v3RG20PQRAu8gsxXPpiqUYb3P6KLQa5wI8a6BGW6QPMGeavM6EN283GfNv389aTULI71JK29UBslFGAlr1/QBVY/cvx3hbNYIWs7dEbcSynEebnLaoxuNGa/4KQuuPqt4X0gljXi0CbClvfzPoaNzGs+OgbGgZzp1O2ATOXU7KhpYZr0MZtM1jav5CF6CN3zcaE97HvBvxNzMcr0sLGK6p5Qe0ZpUr0PqlrnAd/6BnUD5KxPtnkqhDLBdAEGZijobDVog5dmFXOtW+fytRuchV9VFGoa6N6Q238qEd7a6zZqeywYoBaO3r66k9D8GI01FC0JpZagw7xWjzBRPRap93V3ssDK1majD5l5fS9rkwTJOqtQw8p8i/3HgtA5XLX1eueBoUZiF6TTjnUy/cBLVWkxDqXOQqKQSMtjG9He+dyv+jW0S+Ow/cRtWuaUZee78Q7eJfvaklBOPNVA5FqxEEQEesHQd5t5fQZrfxt9BsjuWvW01CCKiN6e14H6ppsLwGaMNkA2EfYtBGTD1O7f6QXfqE47MfgbVU1doigD+ibV50qY5x+ShQttCsd/nrpAvzHq0nIQTSxvR1DdU04sShurbQNFpHLGizxr2VJQNjLSkSgfcmo5pt8aJZuZe/rnENu5DLXxux684n5SnlqNXKo/NSrVaUG3Fj+iygG/AG2m5cm1zHRtxDoC3eO/0MOMSRBLyXxWgjpiLQ+kGqd/TPxZAb5ASSgs6lqDU1rmFrFAVJF64/pNXUEEIOhmBabPIYI25abNA+hEvQ1kSv3u6+BrhYx5h8VYzW3FWz+avmnbYRVOC97d1gzdXOSIXZy7BTZ6TM7tVTBU7OpxYTlVJj+esLuOJpq0kInMP7GPFzGK8PoRyYjGeT0WRgj24R+c6G932IX9czKB8FARvwbHtfg+FGTBV3sBP1vRlTN5N7hVAVoijuYLAbpwBUufy1rWcUuVkXfnG+1pMQ2uO9D8GI45HP4b2t+hwXrHOp2diB31F7qKbRlnsAbXb1GOBbqiYMjsFwI6YiD4dg+rVnB6YpzETk7hDKkiUpBLLWkxACpDoPQCzek1usPuE0yUm0z6F6DWEFcBHGS25xwBE854cswHifSyCt+yUapdV0KhOMNpTxHeA1YKvr2IgpMQTvnX5GmwAF2pII1ce3m1zHRlxnxkRVTQfX17kY76+sct2v6oy67pdoFCNeDn1TBAwBRuFZQzDiHhmn0dqmq99VrwE6Y7y7agveV6M04m9mJt7vrLPQ1poyiIBa90s0ihH/7Hxjxeuqh4acAJWI97tqAy4tQAGeK4TiOn5Ot4h8F01AbKFpx0He0BLadDH+7F7ROK0nIRTh/e7NiDWEutbdN+LfawXeZ10bsVO5hIAZQhsos3tF47SehFA5Aarm3VuUPuE0iQXvtR0jzlSOwvusa6Mt9wA4YxVmL0Nona/J+H1hDK0nIeTh/e7NiOuiZ1F3W7XR5lTUtdxDJsZa7gEouKyU6NnhmB6s1vb+vKLgMml7F8bQehJCMNq2jDUnDRlxhdDKJRICYVOZOLyXJU6fcJqiAifnbysmao//ZpIK4U9GGxDnuwhgPFp79W9cX8e7HjcaB96HnRrxuhMGrMKzLNWPDaZyJikjITfpwq1TL0RzaD01BAfe290N2FbNT3gfdtpFz6B8VAEswbMsS9A2zRFCNKvWkxACaTN3G5CN57pMYUA7fcJpkiy8rzGVrUMsQrRyrafJqA2Bs8xyBLASz2aWlRiz+SvAZsX6e89bIfyp9fy2FqKNKqp+EZ2D1sFsNA602sB6tP0Q1ruOjTgPIQTvfQgGXIajcs/boF9ZYBQE9bdox63oz0wYW+tpMmqD92WWX9IzKB850eZVWNC2AG2PlgyMmBDO470P4UUMV0tojj1vhfCn1pMQIoD5wGG0C2qQ69iIzSwKrd295kzlK/UMykeZeO9DyASuaP5wmqI59rwVwp9aTV22/KJylF1pSxMvBJ4BZVeUX2TALTTL8D5iqky3iHwXKNtOUm3P2+ou8J63QviTXxPCjh07uO222xg4cCAbN26sdf7gwYOMGjWKQYMGMWvWLCoq/LeATcjBEEyTalTnJ5kIOWjAxur6ZiobjR2tdlO9D2EFhlzLqDn2vBXCn/yWEDIyMli+fDmvv/4627ZtY/PmzRw9etTjOY8++ihz585l165dKKXYsmWLv8LR2tq9XUTP+u8t/aYD3u+q2+sQS1OFU7Xt5Iuurxsw5MS0yj1vK/Y44B2o2OPQjmVymjAIvyWEffv2ceONNxIdHU14eDiDBg1i586q1dd+/PFHSktLufrqqwEYNWqUx/kLLpAuonHAajzvqldjyHkIzhAF9+A5g/wecIYas5lFZioLI/Nbp3JmZiY2m819HB8fz/79++s8b7PZyMjI8Fc4lKaUE7Y6tNamH6VdjdeHkB9XStvLwzD9zaTto5wIyqzIjzNe00TBRaVEHwvH9IhJ6+w3g4pWFFxkvLIIYXR+SwhOpxOTyeQ+Vkp5HP/c+YaIi4tsXFBj0EZ7uIZqmq4xER5lJdxjtxmDaAt8jlbHCwLT9SaiQ8J1DspHo4BL0XaC66R9LjFBRhz+5clmM+La6rUFSjlAyvJz/JYQEhMT+eKLL9zHWVlZxMfHe5zPyqrqBf3pp588zjdEdnYhTmcjmxa6gq1PFFlZBVCK9s+ouoDtJldZ8vQOpokuAdv1rrIYcTmRGmw2V1kMLlDKAVIWALPZVO+NtN/6EHr16kV6ejo5OTmUlJSwe/du+vTp4z7fsWNHQkND+fLLLwHYvn27x3khhBDNy28JISEhgenTpzN+/HhGjBhBamoqPXr0IC0tjW+++QaAZcuWsXjxYgYPHkxxcTHjx4/3VzhCCCF+hkkpZczhHPjYZIRUHVsqKUvLEyjlACkL6NhkJIQQwlgkIQghhAAkIQghhHCRhCCEEAKQhCCEEMJFEoIQQgjA4BvkmM2NW+riQr22pZGytEyBUpZAKQdIWX7uNYaehyCEEOLCkSYjIYQQgCQEIYQQLpIQhBBCAJIQhBBCuEhCEEIIAUhCEEII4SIJQQghBCAJQQghhIskBCGEEIDBl67wRWFhIWPHjuWFF16gU6dOeofjs9WrV/O3v/0NgL59+/LYY4/pHJHvVqxYwa5duzCZTIwePZr77rtP75Ca7OmnnyY3N5clS5boHYrP7rnnHnJycggK0i4TCxYs4KqrrtI5qsbbu3cvq1evpqSkhJtuuonZs2frHZJP3nzzTV577TX38enTpxk+fDhz5869cG+iWpH//Oc/KjU1VXXr1k2dOnVK73B89sknn6i77rpLlZWVqfLycjV+/Hi1e/duvcPyyWeffabGjh2r7Ha7KikpUf369VPHjh3TO6wm2bdvn7rhhhvUjBkz9A7FZ06nU/Xu3VvZ7Xa9Q2mSkydPqt69e6uzZ8+q8vJydffdd6sPPvhA77Ca7MiRI+rWW29V2dnZF/T7tqomoy1btjBv3jzi4+P1DqVJbDYbjz/+OCEhIQQHB9O5c2fOnDmjd1g++eUvf8mGDRsICgoiOzsbh8NBeHi43mH57Pz58yxfvpyJEyfqHUqTfP/99wDcf//9DBs2zOPO1Ejef/99brvtNhITEwkODmb58uWGrOXUNH/+fKZPn05sbOwF/b6tqslo4cKFeodwQVx++eXu/x8/fpy//e1vvPHGGzpG1DTBwcGsXLmSl19+mcGDB5OQkKB3SD6bO3cu06dP5+zZs3qH0iT5+fn07NmTOXPmYLfbGT9+PJdeeik33XST3qE1yokTJwgODmbixImcPXuWW265hWnTpukdVpPs27eP0tJShgwZcsG/d6uqIQSa7777jvvvv5/HHnuMSy65RO9wmmTq1Kmkp6dz9uxZtmzZonc4PnnzzTdp3749PXv21DuUJrvmmmtYunQpUVFRxMbGMnr0aD788EO9w2o0h8NBeno6ixYtYvPmzezfv5+tW7fqHVaTbNq0yW/9bJIQDOrLL79kwoQJ/P73v2fkyJF6h+OzY8eOcfDgQQDCwsIYOHAghw8f1jkq3/z1r3/lk08+Yfjw4axcuZK9e/eyaNEivcPyyRdffEF6err7WCnl7lw2knbt2tGzZ09iY2OxWq0MGDCA/fv36x2Wz8rLy/n888/p37+/X76/JAQDOnv2LJMnT2bZsmXcfvvteofTJKdPn2b27NmUl5dTXl7Onj17uO666/QOyyfr16/nvffeY/v27UydOpX+/fszc+ZMvcPySUFBAUuXLqWsrIzCwkK2bt3KrbfeqndYjdavXz8+/vhj8vPzcTgcfPTRR3Tr1k3vsHx2+PBhLrnkEr/1sxkv5QteeuklysrKPIY0jh07lrvvvlvHqHzTt29f9u/fz4gRI7BYLAwcONDwSS4Q9OvXj6+//poRI0bgdDoZN24c11xzjd5hNdpVV13Fb37zG8aNG4fdbuemm27ijjvu0Dssn506dYrExES/fX/ZMU0IIQQgTUZCCCFcJCEIIYQAJCEIIYRwkYQghBACkIQghBDCRYadCvEzTp8+za233kqXLl0AcDqdWK1WHn/88UbNmViwYAExMTE89NBD/gpViCaRhCBEA1itVrZv3+4+/utf/8oTTzzB7t27dYxKiAtLEoIQPjh//jw2mw3Q1tt//vnnsdvtWK1WZsyYwTXXXENhYSGzZs3i0KFDxMfHY7FY3DWK119/nU2bNhEcHExoaCgLFiwgKSlJzyIJIQlBiIYoLS1l+PDhgLYSaFZWFmvWrOH48eMsX76cDRs2EBMTw3fffcd9993H7t27WblyJVarlZ07d5Kbm8vIkSO57rrrcDgcLFq0iL179xIfH8+2bdv48ssvJSEI3UlCEKIBajYZ7du3j8mTJ/PII4+QmZnJhAkT3OdMJhMnT54kPT2dmTNnYjKZiI2Nda8FZLFYGDx4MGPHjuWWW26hd+/e9O3bt7mLJEQtkhCE8EGvXr246KKLyM3NpWfPnvzpT39ynzt79qx7E6bqK8NYLBb3/5ctW8aRI0fYt28fa9euZfv27axYsaLZ4hfCGxl2KoQPfvjhB3788UcGDBjAJ598wrFjxwD48MMPGTZsGKWlpdx888289dZbOJ1O8vLy2LNnDwA5OTn07duX6OhoJkyYwLRp0/jmm2/0LI4QgNQQhGiQ6n0IoA09XbBgASkpKSxYsICHH37YvWfA888/T0REBA899BDz5s1jyJAhxMbGuoetxsbG8uCDDzJhwgSsVisWi4WnnnpKr6IJ4SarnQohhACkyUgIIYSLJAQhhBCAJAQhhBAukhCEEEIAkhCEEEK4SEIQQggBSEIQQgjhIglBCCEEAP8fwQ6do4HgOe4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc = {'figure.figsize':(6,6)})\n",
    "sns.scatterplot(x= 'Beds',y = 'List_price',data=df, color = 'magenta')\n",
    "plt.title('Changes in %s versus %s' %('Beds','List_price'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0    1004\n",
       "2.5     597\n",
       "3.0     342\n",
       "1.0     278\n",
       "3.5     234\n",
       "4.0      88\n",
       "1.5      85\n",
       "4.5      59\n",
       "5.0      19\n",
       "5.5      16\n",
       "6.0      10\n",
       "6.5       5\n",
       "7.0       4\n",
       "7.5       2\n",
       "Name: Baths, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#after deleting the outliers of Beds column the baths outliers are automatically deleted. \n",
    "#Hence fill the missing values baths with median value\n",
    "df['Baths'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2745, 37)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Baths\"] = df[\"Baths\"].fillna(value = df['Baths'].median())\n",
    "print(df.shape)\n",
    "df.Baths.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(index = list(df[(df['Baths'] >5)].index), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Changes in Baths versus List_price')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc = {'figure.figsize':(6,6)})\n",
    "sns.scatterplot(x= 'Baths',y = 'List_price',data=df, color = 'darkblue')\n",
    "plt.title('Changes in %s versus %s' %('Baths','List_price'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "box = df.loc[:, ['Baths']].boxplot()\n",
    "# ax.margins(y=0.05)\n",
    "ax.set_ylim([0, 20])# the y_limit is set to 20 for the whiskers to be visisble on the plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drought Score "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66.0    1198\n",
       "71.0      11\n",
       "61.0       7\n",
       "60.0       5\n",
       "64.0       4\n",
       "65.0       4\n",
       "67.0       2\n",
       "62.0       2\n",
       "Name: Drought_Score, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Drought_Score.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Drought_Score'] = df.Drought_Score.fillna(method = 'ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Changes in Drought_Score versus List_price')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc = {'figure.figsize':(6,6)})\n",
    "sns.scatterplot(x= 'Drought_Score',y = 'List_price',data=df, color = 'orange')\n",
    "plt.title('Changes in %s versus %s' %('Drought_Score','List_price'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Walk score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52    82\n",
       "42    70\n",
       "58    55\n",
       "46    52\n",
       "53    51\n",
       "      ..\n",
       "89     5\n",
       "92     4\n",
       "96     3\n",
       "4      3\n",
       "91     3\n",
       "Name: Walk_score, Length: 97, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Walk_score.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transit Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52.0    151\n",
       "51.0    138\n",
       "58.0    100\n",
       "63.0     94\n",
       "54.0     92\n",
       "53.0     88\n",
       "56.0     87\n",
       "65.0     86\n",
       "60.0     85\n",
       "66.0     84\n",
       "64.0     82\n",
       "55.0     81\n",
       "68.0     78\n",
       "57.0     78\n",
       "50.0     77\n",
       "59.0     76\n",
       "62.0     74\n",
       "61.0     66\n",
       "69.0     63\n",
       "67.0     52\n",
       "Name: Transit_score, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Transit_score.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Transit_score'] = df.Transit_score.fillna(method = 'ffill')\n",
    "df.Transit_score.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Changes in Drought_Score versus List_price')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc = {'figure.figsize':(6,6)})\n",
    "sns.scatterplot(x= 'Drought_Score',y = 'List_price',data=df, color = 'darkred')\n",
    "plt.title('Changes in %s versus %s' %('Drought_Score','List_price'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neighbourhood_Homes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0     450\n",
       "3.0     335\n",
       "5.0     231\n",
       "4.0     188\n",
       "2.0     183\n",
       "6.0     125\n",
       "8.0     100\n",
       "9.0      85\n",
       "28.0     78\n",
       "7.0      73\n",
       "11.0     73\n",
       "20.0     65\n",
       "21.0     55\n",
       "58.0     55\n",
       "54.0     52\n",
       "13.0     48\n",
       "15.0     45\n",
       "10.0     41\n",
       "30.0     31\n",
       "29.0     30\n",
       "12.0     28\n",
       "14.0     28\n",
       "75.0     24\n",
       "35.0     22\n",
       "24.0     22\n",
       "43.0     20\n",
       "32.0     17\n",
       "49.0     14\n",
       "18.0     14\n",
       "16.0     14\n",
       "19.0     12\n",
       "36.0     11\n",
       "74.0     10\n",
       "17.0      8\n",
       "27.0      8\n",
       "40.0      6\n",
       "47.0      4\n",
       "33.0      3\n",
       "23.0      2\n",
       "Name: Neighbourhood_Homes, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Neighbourhood_Homes.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(subset=['Neighbourhood_Homes','Year Built'],how='all', inplace = True) \n",
    "#dropping records which have null values in both 'Neighbourhood_Homes' and'Year Built' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filling the remaining null records of \"Neighbourhood_Homes\" with median values. \n",
    "df.Neighbourhood_Homes.fillna(df.Neighbourhood_Homes.median(), inplace = True)\n",
    "df.Neighbourhood_Homes.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "box = df.loc[:, ['Neighbourhood_Homes']].boxplot()\n",
    "# ax.margins(y=0.05)\n",
    "ax.set_ylim([0, 30])# the y_limit is set to 20 for the whiskers to be visisble on the plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "269"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df['Neighbourhood_Homes']> 29).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[df['Neighbourhood_Homes']> 29].index, inplace = True) # dropping outliers of nbhd_homes sold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Est. Mo. Payment "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(subset = ['Est. Mo. Payment'],inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Est. Mo. Payment'] = df['Est. Mo. Payment'].replace(\"\\$\",\"\", regex = True)\n",
    "df['Est. Mo. Payment'] = df['Est. Mo. Payment'].replace(\",\",\"\", regex = True)\n",
    "df['Est. Mo. Payment'] = df['Est. Mo. Payment'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Est. Mo. Payment'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2416, 37)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropped columns - HOA Dues, Time on Redfin, status, page_fav_all_time_count, page_fav_count_30, page_view_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping HOA dues and Time on redfin as the missing values are more and status column because it does not \n",
    "#add any business value\n",
    "df.drop(columns = ['HOA Dues', 'Time on Redfin', 'Status','page_fav_all_time_count',\n",
    "                   'page_fav_count_30', 'page_view_count'], inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['List_price', 'Address', 'Beds', 'Baths', 'Living_sqft',\n",
       "       'Property Type', 'Year Built', 'Est. Mo. Payment', 'Price/Sq.Ft.',\n",
       "       'Drought_Score', 'Walk_score', 'Neighbourhood_Homes', 'Transit_score',\n",
       "       'Groceries_stores', 'Services', 'Emergency', 'Shopping',\n",
       "       'Food_and_Drink', 'Schools', 'Competitive_Score', 'has_supercenter',\n",
       "       'has_major_indian_grocery', 'has_major_entertainment',\n",
       "       'has_indian_restaurant', 'has_chinese_restaurant',\n",
       "       'has_mexican_restaurant', 'has_boba', 'has_starbucks', 'property_url',\n",
       "       'has_healthcare_support', 'has_mall'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Year Built"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping the missing value records\n",
    "df.dropna(subset = ['Year Built'],inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2327, 31)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Address"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting the address column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       94538\n",
       "1       94538\n",
       "2       94538\n",
       "3       94555\n",
       "4       94560\n",
       "        ...  \n",
       "2846    94566\n",
       "2847    94588\n",
       "2848    94566\n",
       "2850    94566\n",
       "2855    94588\n",
       "Name: Zipcode, Length: 2327, dtype: object"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Zipcode'] = df[\"Address\"].str[-5:]\n",
    "df.Zipcode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Zipcode'].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Price_Sq_Ft_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Price/Sq.Ft.'] = df['Price/Sq.Ft.'].replace(\"\\$\",\"\", regex = True)\n",
    "df['Price/Sq.Ft.'] = df['Price/Sq.Ft.'].replace(\",\",\"\", regex = True)\n",
    "df['Price/Sq.Ft.'] = df['Price/Sq.Ft.'].astype('float')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Schools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Schools.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    [{'Fred E. Weibel Elementary School': '7', 'Jo...\n",
       "11    [{'Oliveira Elementary School': '7', 'Thornton...\n",
       "12                                                 [{}]\n",
       "13    [{'Tom Kitayama Elementary School': '7', 'Cesa...\n",
       "14    [{'Ardenwood Elementary School': '7', 'Thornto...\n",
       "15    [{'John F. Kennedy Elementary School': '6', 'N...\n",
       "16    [{'Ardenwood Elementary School': '7', 'Thornto...\n",
       "17    [{'Cabrillo Elementary School': '5', 'Centervi...\n",
       "18    [{'Fred E. Weibel Elementary School': '7', 'Ja...\n",
       "19    [{'Niles Elementary School': '8', 'Centerville...\n",
       "Name: Schools, dtype: object"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Schools.iloc[10:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " nan,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " nan,\n",
       " 3,\n",
       " 3,\n",
       " nan,\n",
       " 3,\n",
       " nan,\n",
       " 3,\n",
       " 3,\n",
       " nan,\n",
       " 3,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " 3,\n",
       " nan,\n",
       " nan,\n",
       " 3,\n",
       " nan,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " 9,\n",
       " nan,\n",
       " nan,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " 5,\n",
       " nan,\n",
       " 3,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " 5,\n",
       " nan,\n",
       " 3,\n",
       " nan,\n",
       " nan,\n",
       " 6,\n",
       " 9,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " 3,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " 5,\n",
       " 3,\n",
       " 3,\n",
       " 4,\n",
       " nan,\n",
       " nan,\n",
       " 4,\n",
       " 3,\n",
       " 3,\n",
       " nan,\n",
       " nan,\n",
       " 3,\n",
       " 7,\n",
       " 3,\n",
       " nan,\n",
       " nan,\n",
       " 3,\n",
       " nan,\n",
       " 3,\n",
       " 3,\n",
       " 5,\n",
       " ...]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_schools = []\n",
    "\n",
    "for i in range(len(df.Schools)):\n",
    "    try:\n",
    "        x = df.Schools[i][1:-1].replace(\"\\'\", \"\\\"\") #using escape sequence to convert single quotes to double quotes in a string\n",
    "        y = json.loads(x)\n",
    "        num_schools.append(len(list(y.keys()))) \n",
    "    except:\n",
    "        num_schools.append(np.nan)                   \n",
    "num_schools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "[6.666666666666667,\n",
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       " 6.0,\n",
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       " 9.0,\n",
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       " 6.333333333333333,\n",
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       " 5.0,\n",
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       " 5.0,\n",
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       " 6.333333333333333,\n",
       " 5.333333333333333,\n",
       " 7.0,\n",
       " 7.333333333333333,\n",
       " 7.0,\n",
       " 6.666666666666667,\n",
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       " 6.666666666666667,\n",
       " 7.333333333333333,\n",
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       " 7.666666666666667,\n",
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       " 7.0,\n",
       " 6.333333333333333,\n",
       " 7.0,\n",
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       " 8.666666666666666,\n",
       " 7.0,\n",
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       " 7.0,\n",
       " 6.333333333333333,\n",
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       " 7.666666666666667,\n",
       " 8.0,\n",
       " 7.666666666666667,\n",
       " 6.333333333333333,\n",
       " 7.0,\n",
       " nan,\n",
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       " 7.666666666666667,\n",
       " 6.666666666666667,\n",
       " 6.333333333333333,\n",
       " 7.666666666666667,\n",
       " 6.0,\n",
       " 8.0,\n",
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       " 6.0,\n",
       " 7.333333333333333,\n",
       " nan,\n",
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       " 6.333333333333333,\n",
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       " 7.666666666666667,\n",
       " 5.0,\n",
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       " 6.333333333333333,\n",
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       " 5.0,\n",
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       " 9.333333333333334,\n",
       " 8.666666666666666,\n",
       " 9.0,\n",
       " 8.666666666666666,\n",
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       " 9.0,\n",
       " 8.666666666666666,\n",
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       " 9.0,\n",
       " 8.666666666666666,\n",
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       " 8.333333333333334,\n",
       " 7.666666666666667,\n",
       " 9.0,\n",
       " 9.333333333333334,\n",
       " 7.666666666666667,\n",
       " 9.0,\n",
       " 8.333333333333334,\n",
       " 7.666666666666667,\n",
       " nan,\n",
       " 9.0,\n",
       " 7.666666666666667,\n",
       " 8.666666666666666,\n",
       " 9.0,\n",
       " 9.0,\n",
       " nan,\n",
       " 9.0,\n",
       " 9.0,\n",
       " 7.666666666666667,\n",
       " 9.333333333333334,\n",
       " 8.666666666666666,\n",
       " 9.0,\n",
       " 9.0,\n",
       " 8.666666666666666,\n",
       " 8.666666666666666,\n",
       " 9.0,\n",
       " 8.666666666666666,\n",
       " 7.666666666666667,\n",
       " 8.0,\n",
       " 7.666666666666667,\n",
       " 7.666666666666667,\n",
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       " 8.0,\n",
       " 7.666666666666667,\n",
       " 7.666666666666667,\n",
       " 7.666666666666667,\n",
       " 8.0,\n",
       " 8.0,\n",
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       " 8.0,\n",
       " 9.0,\n",
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       " 8.0,\n",
       " 7.666666666666667,\n",
       " 7.666666666666667,\n",
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       " 7.666666666666667,\n",
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       " 8.0,\n",
       " 7.666666666666667,\n",
       " 7.666666666666667,\n",
       " 7.666666666666667,\n",
       " 8.666666666666666,\n",
       " 8.0,\n",
       " 7.666666666666667,\n",
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       " 8.0,\n",
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       " 7.666666666666667,\n",
       " 8.0,\n",
       " 9.0,\n",
       " 7.666666666666667,\n",
       " nan,\n",
       " nan,\n",
       " 7.666666666666667,\n",
       " 7.666666666666667,\n",
       " 8.666666666666666,\n",
       " 7.666666666666667,\n",
       " nan,\n",
       " nan,\n",
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       " 7.666666666666667,\n",
       " 9.0,\n",
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       " nan,\n",
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       " 7.666666666666667,\n",
       " 7.666666666666667,\n",
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       " 9.0,\n",
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       " 8.0,\n",
       " 8.0,\n",
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       " 7.666666666666667,\n",
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       " 7.666666666666667,\n",
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       " 8.0,\n",
       " 8.0,\n",
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       " 5.0,\n",
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       " 6.333333333333333,\n",
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       " 6.333333333333333,\n",
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       " 6.333333333333333,\n",
       " 6.333333333333333,\n",
       " nan,\n",
       " 6.666666666666667,\n",
       " 5.333333333333333,\n",
       " 5.666666666666667,\n",
       " 6.333333333333333,\n",
       " 7.0,\n",
       " 6.333333333333333,\n",
       " 6.333333333333333,\n",
       " 8.333333333333334,\n",
       " 6.333333333333333,\n",
       " 6.333333333333333,\n",
       " 6.333333333333333,\n",
       " 6.0,\n",
       " 6.333333333333333,\n",
       " 6.0,\n",
       " 5.0,\n",
       " nan,\n",
       " nan,\n",
       " 4.666666666666667,\n",
       " 7.333333333333333,\n",
       " 6.333333333333333,\n",
       " 4.666666666666667,\n",
       " 4.666666666666667,\n",
       " 4.666666666666667,\n",
       " 6.333333333333333,\n",
       " 8.333333333333334,\n",
       " nan,\n",
       " 7.666666666666667,\n",
       " 6.333333333333333,\n",
       " 7.666666666666667,\n",
       " 4.666666666666667,\n",
       " 6.333333333333333,\n",
       " 6.333333333333333,\n",
       " 7.666666666666667,\n",
       " 5.0,\n",
       " 6.333333333333333,\n",
       " 6.333333333333333,\n",
       " 6.0,\n",
       " nan,\n",
       " 6.333333333333333,\n",
       " 5.333333333333333,\n",
       " 4.666666666666667,\n",
       " 6.333333333333333,\n",
       " 6.0,\n",
       " 4.666666666666667,\n",
       " 7.666666666666667,\n",
       " 4.666666666666667,\n",
       " 6.333333333333333,\n",
       " 7.666666666666667,\n",
       " 7.333333333333333,\n",
       " 4.666666666666667,\n",
       " 6.333333333333333,\n",
       " 6.333333333333333,\n",
       " 7.666666666666667,\n",
       " 5.0,\n",
       " 6.333333333333333,\n",
       " 6.333333333333333,\n",
       " 6.0,\n",
       " 6.0,\n",
       " 6.333333333333333,\n",
       " nan,\n",
       " 7.333333333333333,\n",
       " 7.666666666666667,\n",
       " 6.333333333333333,\n",
       " 4.666666666666667,\n",
       " 5.333333333333333,\n",
       " 4.666666666666667,\n",
       " 6.333333333333333,\n",
       " 7.666666666666667,\n",
       " 4.666666666666667,\n",
       " 7.333333333333333,\n",
       " 6.0,\n",
       " 6.333333333333333,\n",
       " 6.333333333333333,\n",
       " 4.666666666666667,\n",
       " 6.0,\n",
       " 6.0,\n",
       " 6.0,\n",
       " 6.333333333333333,\n",
       " 6.0,\n",
       " 4.666666666666667,\n",
       " nan,\n",
       " nan,\n",
       " 4.666666666666667,\n",
       " 7.333333333333333,\n",
       " 6.0,\n",
       " 7.333333333333333,\n",
       " 5.0,\n",
       " 6.333333333333333,\n",
       " 7.0,\n",
       " nan,\n",
       " 5.333333333333333,\n",
       " 6.333333333333333,\n",
       " nan,\n",
       " 7.0,\n",
       " 6.333333333333333,\n",
       " 7.666666666666667,\n",
       " nan,\n",
       " 4.666666666666667,\n",
       " 6.0,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " 6.333333333333333,\n",
       " 7.666666666666667,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " 6.333333333333333,\n",
       " 4.666666666666667,\n",
       " 7.0,\n",
       " 6.333333333333333,\n",
       " 7.666666666666667,\n",
       " 6.333333333333333,\n",
       " 7.666666666666667,\n",
       " 6.333333333333333,\n",
       " 8.333333333333334,\n",
       " 4.666666666666667,\n",
       " 7.666666666666667,\n",
       " 6.333333333333333,\n",
       " 6.333333333333333,\n",
       " 6.666666666666667,\n",
       " 6.333333333333333,\n",
       " 6.0,\n",
       " 6.333333333333333,\n",
       " 6.0,\n",
       " 6.0,\n",
       " 5.0,\n",
       " 6.0,\n",
       " nan,\n",
       " nan,\n",
       " 4.666666666666667,\n",
       " 7.333333333333333,\n",
       " 6.333333333333333,\n",
       " 6.333333333333333,\n",
       " 4.666666666666667,\n",
       " 6.0,\n",
       " 6.0,\n",
       " 4.666666666666667,\n",
       " 4.666666666666667,\n",
       " 4.666666666666667,\n",
       " 6.333333333333333,\n",
       " 4.666666666666667,\n",
       " 6.0,\n",
       " 7.666666666666667,\n",
       " 8.333333333333334,\n",
       " nan,\n",
       " 6.0,\n",
       " 7.0,\n",
       " nan,\n",
       " nan,\n",
       " 7.333333333333333,\n",
       " 7.0,\n",
       " 7.0,\n",
       " nan,\n",
       " nan,\n",
       " 7.0,\n",
       " 7.0,\n",
       " nan,\n",
       " 7.333333333333333,\n",
       " 7.333333333333333,\n",
       " 7.333333333333333,\n",
       " 7.0,\n",
       " 7.333333333333333,\n",
       " 7.333333333333333,\n",
       " nan,\n",
       " 7.333333333333333,\n",
       " nan,\n",
       " nan,\n",
       " 7.0,\n",
       " 7.0,\n",
       " nan,\n",
       " nan,\n",
       " 7.0,\n",
       " nan,\n",
       " 8.666666666666666,\n",
       " 8.666666666666666,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " 7.0,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " 7.0,\n",
       " 7.0,\n",
       " nan,\n",
       " 7.333333333333333,\n",
       " 4.333333333333333,\n",
       " nan,\n",
       " 2.3333333333333335,\n",
       " 4.333333333333333,\n",
       " 4.666666666666667,\n",
       " 4.333333333333333,\n",
       " 3.3333333333333335,\n",
       " 4.333333333333333,\n",
       " 2.3333333333333335,\n",
       " 4.333333333333333,\n",
       " 2.3333333333333335,\n",
       " 3.3333333333333335,\n",
       " 2.3333333333333335,\n",
       " 4.0,\n",
       " 3.3333333333333335,\n",
       " nan,\n",
       " 3.6666666666666665,\n",
       " 2.3333333333333335,\n",
       " 4.333333333333333,\n",
       " 4.333333333333333,\n",
       " 3.3333333333333335,\n",
       " 4.666666666666667,\n",
       " 2.3333333333333335,\n",
       " 4.0,\n",
       " 4.666666666666667,\n",
       " 2.6666666666666665,\n",
       " 4.0,\n",
       " nan,\n",
       " 4.666666666666667,\n",
       " 2.3333333333333335,\n",
       " 4.333333333333333,\n",
       " 5.333333333333333,\n",
       " 3.0,\n",
       " 4.333333333333333,\n",
       " 5.333333333333333,\n",
       " 3.3333333333333335,\n",
       " nan,\n",
       " 4.333333333333333,\n",
       " 4.333333333333333,\n",
       " 4.0,\n",
       " 3.3333333333333335,\n",
       " nan,\n",
       " 4.333333333333333,\n",
       " nan,\n",
       " 4.0,\n",
       " 4.333333333333333,\n",
       " 4.0,\n",
       " 4.333333333333333,\n",
       " 4.666666666666667,\n",
       " 3.6666666666666665,\n",
       " 4.333333333333333,\n",
       " 4.333333333333333,\n",
       " 4.0,\n",
       " 4.333333333333333,\n",
       " 3.3333333333333335,\n",
       " 4.0,\n",
       " 3.0,\n",
       " 4.333333333333333,\n",
       " 2.3333333333333335,\n",
       " 3.3333333333333335,\n",
       " 4.333333333333333,\n",
       " 4.333333333333333,\n",
       " 2.3333333333333335,\n",
       " 4.333333333333333,\n",
       " 4.0,\n",
       " 4.0,\n",
       " 2.3333333333333335,\n",
       " nan,\n",
       " nan,\n",
       " 4.0,\n",
       " 4.333333333333333,\n",
       " 4.333333333333333,\n",
       " 4.333333333333333,\n",
       " 4.333333333333333,\n",
       " 4.333333333333333,\n",
       " nan,\n",
       " 4.0,\n",
       " 2.6666666666666665,\n",
       " 4.333333333333333,\n",
       " 2.3333333333333335,\n",
       " 4.333333333333333,\n",
       " 2.3333333333333335,\n",
       " 4.333333333333333,\n",
       " 4.333333333333333,\n",
       " 4.0,\n",
       " 4.666666666666667,\n",
       " 3.0,\n",
       " 4.666666666666667,\n",
       " 3.3333333333333335,\n",
       " 3.0,\n",
       " 4.0,\n",
       " nan,\n",
       " 4.0,\n",
       " 2.6666666666666665,\n",
       " 4.333333333333333,\n",
       " nan,\n",
       " 4.333333333333333,\n",
       " 2.3333333333333335,\n",
       " nan,\n",
       " 3.3333333333333335,\n",
       " nan,\n",
       " 2.3333333333333335,\n",
       " 4.0,\n",
       " nan,\n",
       " 2.3333333333333335,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " 2.6666666666666665,\n",
       " nan,\n",
       " nan,\n",
       " 3.3333333333333335,\n",
       " nan,\n",
       " 4.333333333333333,\n",
       " 2.6666666666666665,\n",
       " 4.0,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " 8.0,\n",
       " 5.0,\n",
       " 7.666666666666667,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " 4.2,\n",
       " nan,\n",
       " 8.0,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " 6.0,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " 6.666666666666667,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " nan,\n",
       " 4.0,\n",
       " 5.666666666666667,\n",
       " 6.5,\n",
       " nan,\n",
       " nan,\n",
       " 6.5,\n",
       " 5.0,\n",
       " 5.333333333333333,\n",
       " nan,\n",
       " nan,\n",
       " 6.666666666666667,\n",
       " nan,\n",
       " 9.333333333333334,\n",
       " nan,\n",
       " nan,\n",
       " 5.0,\n",
       " nan,\n",
       " 3.3333333333333335,\n",
       " 3.6666666666666665,\n",
       " nan,\n",
       " ...]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_rating = []\n",
    "for i in range(len(df.Schools)):\n",
    "    try:\n",
    "        x = df.Schools[i][1:-1].replace(\"\\'\", \"\\\"\") #using escape sequence to convert single quotes to double quotes in a string\n",
    "        y = json.loads(x)\n",
    "        values = list(y.values())\n",
    "        float_lst = [float(item) for item in values]\n",
    "        avg_rating.append(sum(list(float_lst))/len(list(float_lst)))\n",
    "    except:\n",
    "        avg_rating.append(np.nan)                   \n",
    "avg_rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "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>List_price</th>\n",
       "      <th>Address</th>\n",
       "      <th>Beds</th>\n",
       "      <th>Baths</th>\n",
       "      <th>Living_sqft</th>\n",
       "      <th>Property Type</th>\n",
       "      <th>Year Built</th>\n",
       "      <th>Est. Mo. Payment</th>\n",
       "      <th>Price/Sq.Ft.</th>\n",
       "      <th>Drought_Score</th>\n",
       "      <th>...</th>\n",
       "      <th>has_chinese_restaurant</th>\n",
       "      <th>has_mexican_restaurant</th>\n",
       "      <th>has_boba</th>\n",
       "      <th>has_starbucks</th>\n",
       "      <th>property_url</th>\n",
       "      <th>has_healthcare_support</th>\n",
       "      <th>has_mall</th>\n",
       "      <th>Zipcode</th>\n",
       "      <th>No_of_Schools</th>\n",
       "      <th>Average_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1599000.0</td>\n",
       "      <td>40519 Blacow Rd,Fremont, CA 94538</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1763.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>8947.0</td>\n",
       "      <td>907.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>https://www.redfin.com//CA/Fremont/40519-Blaco...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>94538</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>799000.0</td>\n",
       "      <td>39951 Fremont Blvd #325,Fremont, CA 94538</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1465.0</td>\n",
       "      <td>Condo</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>4471.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>https://www.redfin.com//CA/Fremont/39951-Fremo...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94538</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   List_price                                    Address  Beds  Baths  \\\n",
       "0   1599000.0          40519 Blacow Rd,Fremont, CA 94538   4.0    3.0   \n",
       "1    799000.0  39951 Fremont Blvd #325,Fremont, CA 94538   2.0    2.0   \n",
       "\n",
       "   Living_sqft Property Type  Year Built  Est. Mo. Payment  Price/Sq.Ft.  \\\n",
       "0       1763.0        Single      1960.0            8947.0         907.0   \n",
       "1       1465.0         Condo      1987.0            4471.0         545.0   \n",
       "\n",
       "   Drought_Score  ...  has_chinese_restaurant  has_mexican_restaurant  \\\n",
       "0           66.0  ...                       1                       1   \n",
       "1           66.0  ...                       1                       1   \n",
       "\n",
       "   has_boba  has_starbucks                                       property_url  \\\n",
       "0         1              1  https://www.redfin.com//CA/Fremont/40519-Blaco...   \n",
       "1         1              1  https://www.redfin.com//CA/Fremont/39951-Fremo...   \n",
       "\n",
       "   has_healthcare_support  has_mall  Zipcode No_of_Schools  Average_rating  \n",
       "0                       0         1    94538           3.0        6.666667  \n",
       "1                       0         0    94538           3.0        6.333333  \n",
       "\n",
       "[2 rows x 34 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.assign(No_of_Schools= num_schools)\n",
    "df = df.assign(Average_rating= avg_rating)\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['List_price', 'Address', 'Beds', 'Baths', 'Living_sqft',\n",
       "       'Property Type', 'Year Built', 'Est. Mo. Payment', 'Price/Sq.Ft.',\n",
       "       'Drought_Score', 'Walk_score', 'Neighbourhood_Homes', 'Transit_score',\n",
       "       'Groceries_stores', 'Services', 'Emergency', 'Shopping',\n",
       "       'Food_and_Drink', 'Schools', 'Competitive_Score', 'has_supercenter',\n",
       "       'has_major_indian_grocery', 'has_major_entertainment',\n",
       "       'has_indian_restaurant', 'has_chinese_restaurant',\n",
       "       'has_mexican_restaurant', 'has_boba', 'has_starbucks', 'property_url',\n",
       "       'has_healthcare_support', 'has_mall', 'Zipcode', 'No_of_Schools',\n",
       "       'Average_rating'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "2846     True\n",
       "2847    False\n",
       "2848    False\n",
       "2850    False\n",
       "2855    False\n",
       "Name: No_of_Schools, Length: 2327, dtype: bool"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['No_of_Schools'].isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.No_of_Schools.fillna(method = 'ffill', inplace = True) # filling null values using forward fill method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Average_rating.fillna(method = 'ffill', inplace = True) # filling null values using forward fill method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "List_price                    0\n",
       "Address                       0\n",
       "Beds                          0\n",
       "Baths                         0\n",
       "Living_sqft                   0\n",
       "Property Type                 0\n",
       "Year Built                    0\n",
       "Est. Mo. Payment              0\n",
       "Price/Sq.Ft.                  0\n",
       "Drought_Score                 0\n",
       "Walk_score                    0\n",
       "Neighbourhood_Homes           0\n",
       "Transit_score                 0\n",
       "Groceries_stores              0\n",
       "Services                      0\n",
       "Emergency                     0\n",
       "Shopping                      0\n",
       "Food_and_Drink                0\n",
       "Schools                       0\n",
       "Competitive_Score           262\n",
       "has_supercenter               0\n",
       "has_major_indian_grocery      0\n",
       "has_major_entertainment       0\n",
       "has_indian_restaurant         0\n",
       "has_chinese_restaurant        0\n",
       "has_mexican_restaurant        0\n",
       "has_boba                      0\n",
       "has_starbucks                 0\n",
       "property_url                  0\n",
       "has_healthcare_support        0\n",
       "has_mall                      0\n",
       "Zipcode                       0\n",
       "No_of_Schools                 0\n",
       "Average_rating                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns = ['Schools'], inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Competetive Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Competitive_Score.fillna(method = 'ffill', inplace = True) # filling null values using forward fill method beacuse \n",
    "#all values are within the \n",
    "#range of 90 and 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2327, 33)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2327\n",
       "Name: has_healthcare_support, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.has_healthcare_support.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.has_healthcare_support.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping the has_healthcare_support because all values are zeros, no influence on the list price. \n",
    "df.drop(columns = ['has_healthcare_support'], inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Property type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Single          1407\n",
       "Condo            573\n",
       "Townhouse        233\n",
       "Double-Wide       55\n",
       "Duplex            20\n",
       "Single-Wide       15\n",
       "Multi-Family       6\n",
       "Fourplex           5\n",
       "5+                 4\n",
       "Triplex            3\n",
       "Townhome           2\n",
       "Triple-Wide        2\n",
       "Other              2\n",
       "Name: Property Type, dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Property Type'].value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_out = df[(df['Property Type'] == 'Single') | (df['Property Type'] == 'Condo') | (df['Property Type'] == 'Townhouse')] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_out.to_csv(\"Cleaned_Draft1.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1= pd.read_csv('Cleaned_Draft1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2213, 32)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['List_price', 'Address', 'Beds', 'Baths', 'Living_sqft',\n",
       "       'Property Type', 'Year Built', 'Est. Mo. Payment', 'Price/Sq.Ft.',\n",
       "       'Drought_Score', 'Walk_score', 'Neighbourhood_Homes', 'Transit_score',\n",
       "       'Groceries_stores', 'Services', 'Emergency', 'Shopping',\n",
       "       'Food_and_Drink', 'Competitive_Score', 'has_supercenter',\n",
       "       'has_major_indian_grocery', 'has_major_entertainment',\n",
       "       'has_indian_restaurant', 'has_chinese_restaurant',\n",
       "       'has_mexican_restaurant', 'has_boba', 'has_starbucks', 'property_url',\n",
       "       'has_mall', 'Zipcode', 'No_of_Schools', 'Average_rating'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": false
   },
   "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>List_price</th>\n",
       "      <th>Address</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Beds</th>\n",
       "      <th>Baths</th>\n",
       "      <th>Living_sqft</th>\n",
       "      <th>Property Type</th>\n",
       "      <th>Year Built</th>\n",
       "      <th>Est. Mo. Payment</th>\n",
       "      <th>...</th>\n",
       "      <th>has_indian_restaurant</th>\n",
       "      <th>has_chinese_restaurant</th>\n",
       "      <th>has_mexican_restaurant</th>\n",
       "      <th>has_boba</th>\n",
       "      <th>has_starbucks</th>\n",
       "      <th>property_url</th>\n",
       "      <th>has_mall</th>\n",
       "      <th>Zipcode</th>\n",
       "      <th>No_of_Schools</th>\n",
       "      <th>Average_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1599000.0</td>\n",
       "      <td>40519 Blacow Rd</td>\n",
       "      <td>Fremont</td>\n",
       "      <td>CA 94538</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1763.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>8947.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>https://www.redfin.com//CA/Fremont/40519-Blaco...</td>\n",
       "      <td>1</td>\n",
       "      <td>94538</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>799000.0</td>\n",
       "      <td>39951 Fremont Blvd #325</td>\n",
       "      <td>Fremont</td>\n",
       "      <td>CA 94538</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1465.0</td>\n",
       "      <td>Condo</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>4471.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>https://www.redfin.com//CA/Fremont/39951-Fremo...</td>\n",
       "      <td>0</td>\n",
       "      <td>94538</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1480000.0</td>\n",
       "      <td>40219 Laiolo Rd</td>\n",
       "      <td>FREMONT</td>\n",
       "      <td>CA 94538</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1169.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>1959.0</td>\n",
       "      <td>8282.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>https://www.redfin.com//CA/Fremont/40219-Laiol...</td>\n",
       "      <td>0</td>\n",
       "      <td>94538</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   List_price                  Address     City      State  Beds  Baths  \\\n",
       "0   1599000.0          40519 Blacow Rd  Fremont   CA 94538   4.0    3.0   \n",
       "1    799000.0  39951 Fremont Blvd #325  Fremont   CA 94538   2.0    2.0   \n",
       "2   1480000.0          40219 Laiolo Rd  FREMONT   CA 94538   3.0    2.0   \n",
       "\n",
       "   Living_sqft Property Type  Year Built  Est. Mo. Payment  ...  \\\n",
       "0       1763.0        Single      1960.0            8947.0  ...   \n",
       "1       1465.0         Condo      1987.0            4471.0  ...   \n",
       "2       1169.0        Single      1959.0            8282.0  ...   \n",
       "\n",
       "   has_indian_restaurant  has_chinese_restaurant  has_mexican_restaurant  \\\n",
       "0                      1                       1                       1   \n",
       "1                      1                       1                       1   \n",
       "2                      1                       1                       1   \n",
       "\n",
       "   has_boba  has_starbucks                                       property_url  \\\n",
       "0         1              1  https://www.redfin.com//CA/Fremont/40519-Blaco...   \n",
       "1         1              1  https://www.redfin.com//CA/Fremont/39951-Fremo...   \n",
       "2         1              1  https://www.redfin.com//CA/Fremont/40219-Laiol...   \n",
       "\n",
       "   has_mall  Zipcode  No_of_Schools  Average_rating  \n",
       "0         1    94538            3.0        6.666667  \n",
       "1         0    94538            3.0        6.333333  \n",
       "2         0    94538            3.0        6.000000  \n",
       "\n",
       "[3 rows x 34 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting the address\n",
    "Address = []\n",
    "City = []\n",
    "State = []\n",
    "for i in range(0,len(df1)):\n",
    "    try:\n",
    "        a = df1[\"Address\"][i].split(\",\")\n",
    "        Address.append(a[0])\n",
    "    except:\n",
    "        Address.append(np.nan)\n",
    "    try:\n",
    "        City.append(a[-2])\n",
    "    except:    \n",
    "        City.append(np.nan)\n",
    "    try:\n",
    "        State.append(a[-1])\n",
    "    except:\n",
    "        State.append(np.nan)\n",
    "df1['Address'] = Address\n",
    "df1.insert(2, 'City', City)  \n",
    "df1.insert(3, 'State', State)\n",
    "df1.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Fremont', 'SAN JOSE', 'SUNNYVALE', 'Hayward', 'SANTA CLARA', 'FREMONT',\n",
       "       'CUPERTINO', 'PALO ALTO', 'Pleasanton', 'Dublin', 'San Ramon',\n",
       "       'HAYWARD', 'Newark', 'Santa Clara', 'Sunnyvale', 'San Jose',\n",
       "       'PLEASANTON', 'CAMPBELL', 'DUBLIN', 'LOS ALTOS', 'Palo Alto',\n",
       "       'Union City', 'SARATOGA', 'MOUNTAIN VIEW', 'MENLO PARK', 'Cupertino',\n",
       "       'LOS GATOS', 'SAN RAMON', 'MILPITAS', 'LOS ALTOS HILLS', 'Danville',\n",
       "       'ALVISO', 'Milpitas', 'UNION CITY', 'NEWARK', 'EAST PALO ALTO'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.City.value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "santa_clara_county = ['SAN JOSE', 'SUNNYVALE', 'SANTA CLARA', \n",
    "                       'PALO ALTO', 'CUPERTINO', 'San Jose',  \n",
    "                       'Santa Clara', 'Sunnyvale', 'LOS ALTOS', \n",
    "                       'LOS ALTOS HILLS', 'CAMPBELL', 'Palo Alto',\n",
    "                      'MOUNTAIN VIEW', 'SARATOGA', 'Cupertino', \n",
    "                       'LOS GATOS', 'MILPITAS', 'EAST PALO ALTO', 'Milpitas']\n",
    "\n",
    "def county(x):\n",
    "    if x in santa_clara_county:\n",
    "        return 'Santa Clara'\n",
    "    else:\n",
    "        return 'Alameda'\n",
    "\n",
    "df1['County'] = df1['City'].apply(lambda x: county(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Alameda\n",
       "1       Alameda\n",
       "2       Alameda\n",
       "3       Alameda\n",
       "4       Alameda\n",
       "         ...   \n",
       "2208    Alameda\n",
       "2209    Alameda\n",
       "2210    Alameda\n",
       "2211    Alameda\n",
       "2212    Alameda\n",
       "Name: County, Length: 2213, dtype: object"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['County']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " CA 94539         193\n",
       " CA 94536         168\n",
       " CA 95014         140\n",
       " CA 95051         123\n",
       " CA 94538         114\n",
       "                 ... \n",
       " CA 94588-4849      2\n",
       " CA 94555-3513      1\n",
       " CA 94544-8714      1\n",
       " CA 94541-3940      1\n",
       " CA 94538-2838      1\n",
       "Name: State, Length: 76, dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.State.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.State = 'CA'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA    2213\n",
       "Name: State, dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.State.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>List_price</th>\n",
       "      <th>Address</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Beds</th>\n",
       "      <th>Baths</th>\n",
       "      <th>Living_sqft</th>\n",
       "      <th>Property Type</th>\n",
       "      <th>Year Built</th>\n",
       "      <th>Est. Mo. Payment</th>\n",
       "      <th>...</th>\n",
       "      <th>has_chinese_restaurant</th>\n",
       "      <th>has_mexican_restaurant</th>\n",
       "      <th>has_boba</th>\n",
       "      <th>has_starbucks</th>\n",
       "      <th>property_url</th>\n",
       "      <th>has_mall</th>\n",
       "      <th>Zipcode</th>\n",
       "      <th>No_of_Schools</th>\n",
       "      <th>Average_rating</th>\n",
       "      <th>County</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1599000.0</td>\n",
       "      <td>40519 Blacow Rd</td>\n",
       "      <td>Fremont</td>\n",
       "      <td>CA</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1763.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>8947.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>https://www.redfin.com//CA/Fremont/40519-Blaco...</td>\n",
       "      <td>1</td>\n",
       "      <td>94538</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>Alameda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>799000.0</td>\n",
       "      <td>39951 Fremont Blvd #325</td>\n",
       "      <td>Fremont</td>\n",
       "      <td>CA</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1465.0</td>\n",
       "      <td>Condo</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>4471.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>https://www.redfin.com//CA/Fremont/39951-Fremo...</td>\n",
       "      <td>0</td>\n",
       "      <td>94538</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>Alameda</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   List_price                  Address     City State  Beds  Baths  \\\n",
       "0   1599000.0          40519 Blacow Rd  Fremont    CA   4.0    3.0   \n",
       "1    799000.0  39951 Fremont Blvd #325  Fremont    CA   2.0    2.0   \n",
       "\n",
       "   Living_sqft Property Type  Year Built  Est. Mo. Payment  ...  \\\n",
       "0       1763.0        Single      1960.0            8947.0  ...   \n",
       "1       1465.0         Condo      1987.0            4471.0  ...   \n",
       "\n",
       "   has_chinese_restaurant  has_mexican_restaurant  has_boba  has_starbucks  \\\n",
       "0                       1                       1         1              1   \n",
       "1                       1                       1         1              1   \n",
       "\n",
       "                                        property_url  has_mall  Zipcode  \\\n",
       "0  https://www.redfin.com//CA/Fremont/40519-Blaco...         1    94538   \n",
       "1  https://www.redfin.com//CA/Fremont/39951-Fremo...         0    94538   \n",
       "\n",
       "   No_of_Schools  Average_rating   County  \n",
       "0            3.0        6.666667  Alameda  \n",
       "1            3.0        6.333333  Alameda  \n",
       "\n",
       "[2 rows x 35 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # finding the county and adding column to data frame\n",
    "# County = []\n",
    "# for i in range(0,len(df1)):\n",
    "#     try:\n",
    "#         location = geolocator.geocode('{},{}'.format(df1[\"City\"][i],df1[\"State\"][i]),language='en')\n",
    "#         loc_dict = location.raw\n",
    "# #         print (loc_dict['display_name'].split(',')[1].split()[0])\n",
    "#         county = loc_dict['display_name'].split(',')[1].split()[0]\n",
    "#         County.append(county)\n",
    "#     except:\n",
    "#         County.append(np.nan)\n",
    "#     print(i)\n",
    "# County"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Santa Clara    1133\n",
       "Alameda        1080\n",
       "Name: County, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.County.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['City'] = df1.City.str.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          FREMONT\n",
       "1          FREMONT\n",
       "2          FREMONT\n",
       "3          FREMONT\n",
       "4           NEWARK\n",
       "           ...    \n",
       "2208    PLEASANTON\n",
       "2209    PLEASANTON\n",
       "2210    PLEASANTON\n",
       "2211    PLEASANTON\n",
       "2212    PLEASANTON\n",
       "Name: City, Length: 2213, dtype: object"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['City']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "List_price                  float64\n",
       "Address                      object\n",
       "City                         object\n",
       "State                        object\n",
       "Beds                        float64\n",
       "Baths                       float64\n",
       "Living_sqft                 float64\n",
       "Property Type                object\n",
       "Year Built                  float64\n",
       "Est. Mo. Payment            float64\n",
       "Price/Sq.Ft.                float64\n",
       "Drought_Score               float64\n",
       "Walk_score                    int64\n",
       "Neighbourhood_Homes         float64\n",
       "Transit_score               float64\n",
       "Groceries_stores              int64\n",
       "Services                      int64\n",
       "Emergency                     int64\n",
       "Shopping                      int64\n",
       "Food_and_Drink                int64\n",
       "Competitive_Score           float64\n",
       "has_supercenter               int64\n",
       "has_major_indian_grocery      int64\n",
       "has_major_entertainment       int64\n",
       "has_indian_restaurant         int64\n",
       "has_chinese_restaurant        int64\n",
       "has_mexican_restaurant        int64\n",
       "has_boba                      int64\n",
       "has_starbucks                 int64\n",
       "property_url                 object\n",
       "has_mall                      int64\n",
       "Zipcode                      object\n",
       "No_of_Schools               float64\n",
       "Average_rating              float64\n",
       "County                       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modified column titles with no space and one word for titles:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['List_price', 'Address', 'City', 'State', 'Beds', 'Baths',\n",
       "       'Living_sqft', 'Property_Type', 'Year_Built', 'Est__Mo__Payment',\n",
       "       'Price_Sq_Ft_', 'Drought_Score', 'Walk_score', 'Neighbourhood_Homes',\n",
       "       'Transit_score', 'Groceries_stores', 'Services', 'Emergency',\n",
       "       'Shopping', 'Food_and_Drink', 'Competitive_Score', 'has_supercenter',\n",
       "       'has_major_indian_grocery', 'has_major_entertainment',\n",
       "       'has_indian_restaurant', 'has_chinese_restaurant',\n",
       "       'has_mexican_restaurant', 'has_boba', 'has_starbucks', 'property_url',\n",
       "       'has_mall', 'Zipcode', 'No_of_Schools', 'Average_rating', 'County'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We therefore strip trailing spaces and replace the remaining spaces \n",
    "# with an underscore _. Instead of using the `rename` method, we \n",
    "# create a modified copy of `columns` and assign to the `columns` \n",
    "# field of the dataframe.\n",
    "print('Modified column titles with no space and one word for titles:')\n",
    "df1.columns = [s.strip().replace(' ', '_') for s in df1.columns]\n",
    "df1.columns = [s.strip().replace('/', '_') for s in df1.columns]\n",
    "df1.columns = [s.strip().replace('.', '_') for s in df1.columns]\n",
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['List_price', 'Address', 'City', 'State', 'Beds', 'Baths',\n",
       "       'Living_sqft', 'Property_Type', 'Year_Built', 'Est__Mo__Payment',\n",
       "       'Price_Sq_Ft_', 'Drought_Score', 'Walk_score', 'Neighbourhood_Homes',\n",
       "       'Transit_score', 'Groceries_stores', 'Services', 'Emergency',\n",
       "       'Shopping', 'Food_and_Drink', 'Competitive_Score', 'has_supercenter',\n",
       "       'has_major_indian_grocery', 'has_major_entertainment',\n",
       "       'has_indian_restaurant', 'has_chinese_restaurant',\n",
       "       'has_mexican_restaurant', 'has_boba', 'has_starbucks', 'property_url',\n",
       "       'has_mall', 'Zipcode', 'No_of_Schools', 'Average_rating', 'County'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df1.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.to_csv('Cleaned_data.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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