This Python application read all data of real estate object directly from webpage. Then wrrite all the data into Panda dataframe and retrieve all the data on terminal.

ParseDataFromWebsiteWithPagination.py

import requests
from bs4 import BeautifulSoup as bc

l =[] #List
base_url = "http://www.pythonhow.com/real-estate/rock-springs-wy/LCWYROCKSPRINGS/"

r = requests.get(base_url)
c = r.content
soup = bc(c, "html.parser")

page_nr = soup.find_all("a",{"class":"Page"})[-1].text
print(page_nr)

for page in range(0,int(page_nr)*10,10):
    r = requests.get("http://www.pythonhow.com/real-estate/rock-springs-wy/LCWYROCKSPRINGS/t=0&s="+str(page)+".html")
    print ("http://www.pythonhow.com/real-estate/rock-springs-wy/LCWYROCKSPRINGS/t=0&s="+str(page)+".html")
    print(r)
    c = r.content
    soup = bc(c,"html.parser")
    all= soup.find_all("div",{"class":"propertyRow"})
    for item in all:
        d={}

        d["Address"] = item.find_all("span",{"class","propAddressCollapse"})[0].text
        try:
            d["Locality"] = item.find_all("span",{"class","propAddressCollapse"})[1].text
        except:
            d["Locality"] = None
        d["Price"] = item.find("h4", {"class": "propPrice"}).text.replace("\n", "").replace(" ", "")

        try:
            d["Beds"] = item.find("span",{"class","infoBed"}).find("b").text
        except:
            d["Beds"] = None

        try:
            d["Area"] = item.find("span",{"class","infoSqFt"}).find("b").text
        except:
            d["Area"] = None

        try:
            d["Full Baths"] = item.find("span",{"class","infoValueFullBath"}).find("b").text
        except:
            d["Full Baths"] = None

        try:
            d["Half Baths"] = item.find("span", {"class", "infoValueHalfBath"}).find("b").text
        except:
            d["Half Baths"] = None

        for column_group in item.find_all("div",{"class":"columnGroup"}):
            #print(column_group)
            for feature_group, feature_name in zip(column_group.find_all("span",{"class":"featureGroup"}), column_group.find_all("span",{"class":"featureName"})):
                #print feature_group.text, feature_name.text
                if "Lot Size" in feature_group.text:
                    d["Lot Size"] = feature_name.text
        l.append(d)

import pandas
df = pandas.DataFrame(l)

print(df)
df.to_csv("Output.csv")