# begins with "606", as a list object. # I recommend using zip codes, as they seem to be the best option for catching # as many house listings as possible. If you want to use search terms other # than zip codes, simply skip running zipcodes_list() function below, and add # a line of code to manually assign values to object st, for example: # st = ["Chicago", "New Haven, CT", "77005", "Jacksonville, FL"] # Keep in mind that, for each search term, the number of listings scraped is # capped at 520, so in using a search term like "Chicago" the scraper would # end up missing most of the results. # Param st_items can be either a list of zipcode strings, or a single zipcode # string. #st = zl.zipcodes_list(st_items = ["100", "770"]) st = ["48104"] # Initialize the webdriver. driver = zl.init_driver(chromedriver_binary.chromedriver_filename) # Go to www.zillow.com/homes zl.navigate_to_website(driver, "http://www.zillow.com/homes") # Click the "buy" button. zl.click_buy_button(driver) # Get total number of search terms. num_search_terms = len(st) # Initialize list obj that will house all scraped data. output_data = [] # Start the scraping. for idx, term in enumerate(st):
# I recommend using zip codes, as they seem to be the best option for catching
# as many house listings as possible. If you want to use search terms other
# than zip codes, simply skip running zipcodes_list() function below, and add
# a line of code to manually assign values to object st, for example:
# st = ["Chicago", "New Haven, CT", "77005", "Jacksonville, FL"]
# Keep in mind that, for each search term, the number of listings scraped is
# capped at 520, so in using a search term like "Chicago" the scraper would
# end up missing most of the results.
# Param st_items can be either a list of zipcode strings, or a single zipcode
# string.
#st = zl.zipcodes_list(st_items = ["48103"])
st = ["Tuscon, AZ"]
# Initialize the webdriver.
driver = zl.init_driver("/Applications/chromedriver")
#os.environ["webdriver.chrome.driver"] = driver
# Go to www.zillow.com/homes/rs
#zl.navigate_to_website(driver, "http://www.zillow.com/homes/recently_sold/house_type/")
zl.navigate_to_website(driver, 'https://www.zillow.com/homes/tuscon-az_rb/')
# Click the "buy" button.
#zl.click_buy_button(driver)
# Get total number of search terms.
num_search_terms = len(st)
# Initialize list obj that will house all scraped data.
output_data = []
# "91319", "91320", "930", "9062", "9063", "90680"
import time
import pandas as pd
import zillow_functions as zl
from bs4 import BeautifulSoup
# Enter zipcode term here
st = zl.zipcodes_list(st_items=["91320"])
nm = str(st[0])
# Initialize the webdriver.
# Use the location of the chromedriver file in your machine
# in a PC would be somethign like
# driver = zl.init_driver("C:/Users/username/chromedriver.exe")
driver = zl.init_driver("/Users/bjaimes/Desktop/nu/chromedriver")
# Go to https://www.zillow.com/homes/recently_sold
zl.navigate_to_website(driver, "https://www.zillow.com/homes/recently_sold")
# Create 10 variables from the scrapped HTML data.
# These variables will make up the final output dataframe.
df = pd.DataFrame({
'address': [],
'city': [],
'state': [],
'zip': [],
'sqft': [],
'price_sqft': [],
'calculated_price': [],
'bathrooms': [],
ap.add_argument("-r",
"--resume",
required=False,
default=0,
type=int,
help="start line of zipcode table")
args = ap.parse_args()
zipcodes = pd.read_csv(args.zipcode, dtype={
"zipcode": object
}).iloc[args.resume:]
st = zipcodes.zipcode.values.tolist()
num_search_terms = len(st)
# Initialize the webdriver.
driver = zl.init_driver("/Users/mlangberg/venv3/bin/chromedriver")
# Go to www.zillow.com/homes
zl.navigate_to_website(driver, "http://www.zillow.com/homes")
# Click the "buy" button.
zl.click_buy_button(driver)
# Get total number of search terms.
num_search_terms = len(st)
columns = [
"address", "city", "state", "zip", "price", "sqft", "bedrooms",
"bathrooms", "days_on_zillow", "sale_type", "realtor_phone", "url"
]
st_items = []
zfile = open('z_ct.dat', 'r')
for z in zfile:
z = str(z.strip())
st_items.append(z)
st = zl.zipcodes_list(st_items[246:])
nm = str(st[0])
#print (nm)
# Initialize the webdriver.
# Use the location of the chromedriver file in your machine
# in a PC would be somethign like
#driver = zl.init_driver("C:/Users/username/chromedriver.exe")
driver = zl.init_driver("/Users/X/chromedriver")
# Go to https://www.zillow.com/homes/recently_sold
zl.navigate_to_website(driver, "https://www.zillow.com/homes/recently_sold")
# Create 10 variables from the scrapped HTML data.
# These variables will make up the final output dataframe.
df = pd.DataFrame({
'address': [],
'city': [],
'state': [],
'zip': [],
'sqft': [],
'price_sqft': [],
'calculated_price': [],
'bathrooms': [],
"92127",
"92128",
"92129",
"92130",
"92131", #"92132","92134",
"92135",
"92139",
"92140",
"92145",
"92147",
"92154",
"92173"
] # zl.zipcodes_list(st_items = ["100", "770"])
# Initialize the webdriver.
driver = zl.init_driver("chromedriver")
# Go to www.zillow.com/homes
zl.navigate_to_website(driver, "http://www.zillow.com/homes")
# Click the "buy" button.
zl.click_buy_button(driver)
# Create 11 variables from the scrapped HTML data.
# These variables will make up the final output dataframe.
df = pd.DataFrame({
'address': [],
'bathrooms': [],
'bedrooms': [],
'city': [],
'days_on_zillow': [],
# will yield every US zip code that begins with '10', begins with "11", or
# begins with "606" as a single list.
# I recommend using zip codes, as they seem to be the best option for catching
# as many house listings as possible. If you want to use search terms other
# than zip codes, simply skip running zipcodes_list() function below, and add
# a line of code to manually assign values to object st, for example:
# st = ['Chicago', 'New Haven, CT', '77005', 'Jacksonville, FL']
# Keep in mind that, for each search term, the number of listings scraped is
# capped at 520, so in using a search term like "Chicago" the scraper would
# end up missing most of the results.
#st = zl.zipcodes_list(st_items = ['Novi','48377','MI'])
st = ['Novi', '48377', 'MI']
# Initialize the webdriver.
driver = zl.init_driver()
# Go to www.zillow.com/homes
zl.navigate_to_website(driver, "http://www.zillow.com/homes")
# Click the "buy" button.
zl.click_buy_button(driver)
# Create 11 variables from the scrapped HTML data.
# These variables will make up the final output dataframe.
df = pd.DataFrame({
'address': [],
'city': [],
'state': [],
'zip': [],
'price': [],
# Use function zipcodes_list() to create a list of US zip codes that will be
# passed to the scraper. For example, st = zipcodes_list(['10', '11', '606'])
# will yield every US zip code that begins with '10', begins with "11", or
# begins with "606" as a single list.
# I recommend using zip codes, as they seem to be the best option for catching
# as many house listings as possible. If you want to use search terms other
# than zip codes, simply skip running zipcodes_list() function below, and add
# a line of code to manually assign values to object st, for example:
# st = ['Chicago', 'New Haven, CT', '77005', 'Jacksonville, FL']
# Keep in mind that, for each search term, the number of listings scraped is
# capped at 520, so in using a search term like "Chicago" the scraper would
# end up missing most of the results.
st = zl.zipcodes_list(st_items=['10', '11', '606'])
# Initialize the webdriver.
driver = zl.init_driver('C:/Users/username/My Documents/chromedriver')
# Go to www.zillow.com/homes
zl.navigate_to_website(driver, "http://www.zillow.com/homes")
# Click the "buy" button.
zl.click_buy_button(driver)
# Create 11 variables from the scrapped HTML data.
# These variables will make up the final output dataframe.
df = pd.DataFrame({
'address': [],
'city': [],
'state': [],
'zip': [],
'price': [],
import time
import zillow_functions as zl
from bs4 import BeautifulSoup
import boto
import pandas as pd
# List of zip codes
df = pd.read_csv('~/Programming/zillow/work_zip_codes1.csv')
# CHANGE THE S3 FOLDER NAME!!!
# state = df[df['zip'] == 95138]
temp = df.ix[:, 'zip'].tolist()
# zipcodes = [temp[(i + 1) * 20: (i + 2) * 20] for i in range(int(len(temp) / 20))]
# Initialize the webdriver.
driver = zl.init_driver("/anaconda/bin/chromedriver")
# Go to www.zillow.com/homes
zl.navigate_to_website(driver, "http://www.zillow.com/homes")
# Click the "buy" button.
zl.click_buy_button(driver)
def scrape_data(zc):
st = zc
conn = boto.connect_s3()
bucket = conn.get_bucket('zillowstreamjk')
# Create 11 variables from the scrapped HTML data.
# will yield every US zip code that begins with "10", begins with "11", or
# begins with "606", as a list object.
# I recommend using zip codes, as they seem to be the best option for catching
# as many house listings as possible. If you want to use search terms other
# than zip codes, simply skip running zipcodes_list() function below, and add
# a line of code to manually assign values to object st, for example:
# st = ["Chicago", "New Haven, CT", "77005", "Jacksonville, FL"]
# Keep in mind that, for each search term, the number of listings scraped is
# capped at 520, so in using a search term like "Chicago" the scraper would
# end up missing most of the results.
# Param st_items can be either a list of zipcode strings, or a single zipcode
# string.
st = zl.zipcodes_list(st_items=["98296", "98201"])
# Initialize the webdriver.
driver = zl.init_driver("/Users/gilliangoodman/Downloads/chromedriver")
# Go to www.zillow.com/homes
zl.navigate_to_website(driver, "http://www.zillow.com/homes")
# Click the "buy" button.
zl.click_buy_button(driver)
# Get total number of search terms.
num_search_terms = len(st)
# Initialize list obj that will house all scraped data.
output_data = []
# Start the scraping.
for idx, term in enumerate(st):
# begins with "606" as a single list.
# I recommend using zip codes, as they seem to be the best option for catching
# as many house listings as possible. If you want to use search terms other
# than zip codes, simply skip running zipcodes_list() function below, and add
# a line of code to manually assign values to object st, for example:
# st = ['Chicago', 'New Haven, CT', '77005', 'Jacksonville, FL']
# Keep in mind that, for each search term, the number of listings scraped is
# capped at 520, so in using a search term like "Chicago" the scraper would
# end up missing most of the results.
# Param st_items can be either a list of zipcode strings, or a single zipcode
# string.
st = zl.zipcodes_list(st_items=['900', '901', '902'])
# Initialize the webdriver.
driver = zl.init_driver(
"/Users/rossi/PycharmProjects/Introduction_to_Linear_Regression_Analysis/chromedriver"
)
# Go to www.zillow.com/homes
zl.navigate_to_website(driver, "http://www.zillow.com/homes")
# Click the "buy" button.
zl.click_buy_button(driver)
# Create 11 variables from the scrapped HTML data.
# These variables will make up the final output dataframe.
df = pd.DataFrame({
'bathrooms': [],
'bedrooms': [],
'city': [],
'price': [],
# than zip codes, simply skip running zipcodes_list() function below, and add
# a line of code to manually assign values to object st, for example:
# st = ["Chicago", "New Haven, CT", "77005", "Jacksonville, FL"]
# Keep in mind that, for each search term, the number of listings scraped is
# capped at 520, so in using a search term like "Chicago" the scraper would
# end up missing most of the results.
# Param st_items can be either a list of zipcode strings, or a single zipcode
# string.
st = zl.zipcodes_list(st_items = ["02108", "02109","02110","02111","02113","02114","02115","02116",
"02118","02119","02120","02121","02122","02124","02125","02126","02127",
"02128","02129","02130","02131","02132","02134","02135","02136","02151","02152",
"02163","02199","02203","02210","02215","02467"])
# st = "Boston, MA"
# Initialize the webdriver.
driver = zl.init_driver("/Users/maria/_CMU/_S18/15388/15388project/Zillow/chromedriver.exe")
# Go to www.zillow.com/homes
zl.navigate_to_website(driver, "http://www.zillow.com/homes/recently_sold/")
# Click the "buy" button.
#zl.click_buy_button(driver)
# Get total number of search terms.
num_search_terms = len(st)
print(num_search_terms)
# Initialize list obj that will house all scraped data.
output_data = []
# Start the scraping.
# will yield every US zip code that begins with "10", begins with "11", or
# begins with "606", as a list object.
# I recommend using zip codes, as they seem to be the best option for catching
# as many house listings as possible. If you want to use search terms other
# than zip codes, simply skip running zipcodes_list() function below, and add
# a line of code to manually assign values to object st, for example:
# st = ["Chicago", "New Haven, CT", "77005", "Jacksonville, FL"]
# Keep in mind that, for each search term, the number of listings scraped is
# capped at 520, so in using a search term like "Chicago" the scraper would
# end up missing most of the results.
# Param st_items can be either a list of zipcode strings, or a single zipcode
# string.
st = zl.zipcodes_list(st_items=["100", "770"])
# Initialize the webdriver.
driver = zl.init_driver("C:/Users/username/chromedriver.exe")
# Go to www.zillow.com/homes
zl.navigate_to_website(driver, "http://www.zillow.com/homes")
# Click the "buy" button.
zl.click_buy_button(driver)
# Get total number of search terms.
num_search_terms = len(st)
# Initialize list obj that will house all scraped data.
output_data = []
# Start the scraping.
for idx, term in enumerate(st):
import time
import pandas as pd
from bs4 import BeautifulSoup
import zillow_functions as zl
st = zl.zipcodes_list(st_items = ["93727"])
# Initialize the webdriver.
driver = zl.init_driver('/usr/local/share/chromedriver')
# Go to www.zillow.com/homes
zl.navigate_to_website(driver, "http://www.zillow.com/homes")
# Click the "buy" button.
zl.click_buy_button(driver)
# Get total number of search terms.
num_search_terms = len(st)
# Initialize list obj that will house all scraped data.
output_data = []
# Start the scraping.
for idx, term in enumerate(st):
# than zip codes, simply skip running zipcodes_list() function below, and add
# a line of code to manually assign values to object st, for example:
# st = ['Chicago', 'New Haven, CT', '77005', 'Jacksonville, FL']
# Keep in mind that, for each search term, the number of listings scraped is
# capped at 520, so in using a search term like "Chicago" the scraper would
# end up missing most of the results.
# Param st_items can be either a list of zipcode strings, or a single zipcode
# string.
st = zl.zipcodes_list(st_items = ["15232"])
#connection to MySQL
engine = create_engine('mysql://*****:*****@localhost:3306/zillowproject', echo=False)
# Initialize the webdriver.
driver = zl.init_driver("E:\Programs\ChromeWebDriver\chromedriver.exe")
# Go to www.zillow.com/homes
zl.navigate_to_website(driver, "http://www.zillow.com/homes")
# Click the "buy" button.
zl.click_buy_button(driver)
# Create 11 variables from the scrapped HTML data.
# These variables will make up the final output dataframe.
df = pd.DataFrame({'zpid': [],
'url' : [],
'zip' : [],
'address' : [],
'bathrooms' : [],
# string.
'''
01741 = Carlisle
01778 = Sudbury
01778 = Wayland
01890 = Winchester
02090 = Westwood
02493 = Weston
02420/1 = Lexington
'''
st = zl.zipcodes_list(st_items=[
"01778", "02493", "02090", "01776", "01741", "02420", "02421", "01890"
])
# Initialize the webdriver.
driver = zl.init_driver("/Users/clchang/chromedriver")
# Go to www.zillow.com/homes
zl.navigate_to_website(driver, "http://www.zillow.com/homes")
# Click the "buy" button.
zl.click_buy_button(driver)
# Get total number of search terms.
num_search_terms = len(st)
# Initialize list obj that will house all scraped data.
output_data = []
# Recently Sold Homes
zl.click_recently_sold_button(driver)
# begins with "606", as a list object.
# I recommend using zip codes, as they seem to be the best option for catching
# as many house listings as possible. If you want to use search terms other
# than zip codes, simply skip running zipcodes_list() function below, and add
# a line of code to manually assign values to object st, for example:
# st = ["Chicago", "New Haven, CT", "77005", "Jacksonville, FL"]
# Keep in mind that, for each search term, the number of listings scraped is
# capped at 520, so in using a search term like "Chicago" the scraper would
# end up missing most of the results.
# Param st_items can be either a list of zipcode strings, or a single zipcode
# string.
#st = ["85201"]
# Initialize the webdriver.
driver = zl.init_driver("C:\webdrivers\chromedriver.exe")
# Go to www.zillow.com/homes
zl.navigate_to_website(driver, "http://www.google.com")
print("Enter a zip code")
time.sleep(30.091)
# Initialize list obj that will house all scraped data.
output_data = []
raw_data = zl.get_html(driver)
print("%s pages of listings found" % str(len(raw_data)))
# Take the extracted HTML and split it up by individual home listings.
listings = zl.get_listings(raw_data)
def search(event):
# Create list of search terms.
# Function zipcodes_list() creates a list of US zip codes that will be
# passed to the scraper. For example, st = zipcodes_list(['10', '11', '606'])
# will yield every US zip code that begins with '10', begins with "11", or
# begins with "606" as a single list.
# I recommend using zip codes, as they seem to be the best option for catching
# as many house listings as possible. If you want to use search terms other
# than zip codes, simply skip running zipcodes_list() function below, and add
# a line of code to manually assign values to object st, for example:
# st = ['Chicago', 'New Haven, CT', '77005', 'Jacksonville, FL']
# Keep in mind that, for each search term, the number of listings scraped is
# capped at 520, so in using a search term like "Chicago" the scraper would
# end up missing most of the results.
# Param st_items can be either a list of zipcode strings, or a single zipcode
# string.
global list_of_zipcodes
if len(list_of_zipcodes) == 0:
print("No inputs given")
zipcodes_label['text'] = "No Inputs Given"
return
st = zl.zipcodes_list(st_items=list(list_of_zipcodes))
# Initialize the webdriver.
driver = zl.init_driver(
"/Users/jasontu/Projects/Real_Estate_Aggregator/Zillow/chromedriver")
# Go to www.zillow.com/homes
zl.navigate_to_website(driver, "http://www.zillow.com/homes")
# Click the "buy" button.
zl.click_buy_button(driver)
# Create 11 variables from the scrapped HTML data.
# These variables will make up the final output dataframe.
df = pd.DataFrame({
'address': [],
'bathrooms': [],
'bedrooms': [],
'city': [],
'days_on_zillow': [],
'price': [],
'sale_type': [],
'state': [],
'sqft': [],
'url': [],
'zip': []
})
# Get total number of search terms.
num_search_terms = len(st)
# Start the scraping.
for k in range(num_search_terms):
# Define search term (must be str object).
search_term = st[k]
# Enter search term and execute search.
if zl.enter_search_term(driver, search_term):
print("Entering search term number " + str(k + 1) + ": '" +
search_term + "' " + " out of " + str(num_search_terms))
else:
print("Search term " + str(k + 1) + ": '" + search_term + "' " +
" failed, moving onto next search term\n***")
continue
# Check to see if any results were returned from the search.
# If there were none, move onto the next search.
if zl.results_test(driver):
print("Search " + str(search_term) +
" returned zero results. Moving onto the next search\n***")
continue
# Pull the html for each page of search results. Zillow caps results at
# 20 pages, each page can contain 26 home listings, thus the cap on home
# listings per search is 520.
raw_data = zl.get_html(driver)
print(str(len(raw_data)) + " pages of listings found")
# Take the extracted HTML and split it up by individual home listings.
listings = zl.get_listings(raw_data)
# For each home listing, extract the 11 variables that will populate that
# specific observation within the output dataframe.
for n in range(len(listings)):
soup = BeautifulSoup(listings[n], "lxml")
new_obs = []
# List that contains number of beds, baths, and total sqft (and
# sometimes price as well).
card_info = zl.get_card_info(soup)
# Street Address
new_obs.append(zl.get_street_address(soup))
# Bathrooms
new_obs.append(zl.get_bathrooms(card_info))
# Bedrooms
new_obs.append(zl.get_bedrooms(card_info))
# City
new_obs.append(zl.get_city(soup))
# Days on the Market/Zillow
new_obs.append(zl.get_days_on_market(soup))
# Price
new_obs.append(zl.get_price(soup, card_info))
# Sale Type (House for Sale, New Construction, Foreclosure, etc.)
new_obs.append(zl.get_sale_type(soup))
# Sqft
new_obs.append(zl.get_sqft(card_info))
# State
new_obs.append(zl.get_state(soup))
# URL for each house listing
new_obs.append(zl.get_url(soup))
# Zipcode
new_obs.append(zl.get_zipcode(soup))
# Append new_obs to df as a new observation
if len(new_obs) == len(df.columns):
df.loc[len(df.index)] = new_obs
# Close the webdriver connection.
zl.close_connection(driver)
# Write df to CSV.
columns = [
'address', 'city', 'state', 'zip', 'price', 'sqft', 'bedrooms',
'bathrooms', 'days_on_zillow', 'sale_type', 'url'
]
df = df[columns]
dt = time.strftime("%Y-%m-%d") + "_" + time.strftime("%H%M%S")
file_name = str(dt) + ".csv"
df.to_csv(file_name, index=False)
zipcodes_label[
'text'] = "Scraping Complete. Review the following CSV file: \n" + str(
dt) + ".csv"
return
# will yield every US zip code that begins with "10", begins with "11", or # begins with "606", as a list object. # I recommend using zip codes, as they seem to be the best option for catching # as many house listings as possible. If you want to use search terms other # than zip codes, simply skip running zipcodes_list() function below, and add # a line of code to manually assign values to object st, for example: # st = ["Chicago", "New Haven, CT", "77005", "Jacksonville, FL"] # Keep in mind that, for each search term, the number of listings scraped is # capped at 520, so in using a search term like "Chicago" the scraper would # end up missing most of the results. # Param st_items can be either a list of zipcode strings, or a single zipcode # string. st = zl.zipcodes_list(st_items=["100", "770"]) # Initialize the webdriver. driver = zl.init_driver(r"C:\Users\mhuem\chromedriver.exe") # Go to www.zillow.com/homes zl.navigate_to_website(driver, "http://www.zillow.com/homes") # Click the "buy" button. zl.click_buy_button(driver) # Get total number of search terms. num_search_terms = len(st) # Initialize list obj that will house all scraped data. output_data = [] # Start the scraping. for idx, term in enumerate(st):
# will yield every US zip code that begins with "10", begins with "11", or
# begins with "606", as a list object.
# I recommend using zip codes, as they seem to be the best option for catching
# as many house listings as possible. If you want to use search terms other
# than zip codes, simply skip running zipcodes_list() function below, and add
# a line of code to manually assign values to object st, for example:
# st = ["Chicago", "New Haven, CT", "77005", "Jacksonville, FL"]
# Keep in mind that, for each search term, the number of listings scraped is
# capped at 520, so in using a search term like "Chicago" the scraper would
# end up missing most of the results.
# Param st_items can be either a list of zipcode strings, or a single zipcode
# string.
st = zl.zipcodes_list(st_items=["98107", "98117", "98115", "98125", "98122"])
# Initialize the webdriver.
driver = zl.init_driver(
"C:/Programming/RealEstate/Zillow-master/chromedriver.exe")
# Go to www.zillow.com/homes
zl.navigate_to_website(driver, "http://www.zillow.com/homes")
# Click the "buy" button.
zl.click_buy_button(driver)
# Get total number of search terms.
num_search_terms = len(st)
# Initialize list obj that will house all scraped data.
output_data = []
# Start the scraping.
for idx, term in enumerate(st):
# will yield every US zip code that begins with '10', begins with "11", or
# begins with "606" as a single list.
# I recommend using zip codes, as they seem to be the best option for catching
# as many house listings as possible. If you want to use search terms other
# than zip codes, simply skip running zipcodes_list() function below, and add
# a line of code to manually assign values to object st, for example:
# st = ['Chicago', 'New Haven, CT', '77005', 'Jacksonville, FL']
# Keep in mind that, for each search term, the number of listings scraped is
# capped at 520, so in using a search term like "Chicago" the scraper would
# end up missing most of the results.
# Param st_items can be either a list of zipcode strings, or a single zipcode
# string.
st = zl.zipcodes_list(st_items=["111"])
# Initialize the webdriver.
driver = zl.init_driver("chromedriver.exe")
# Go to www.zillow.com/homes
zl.navigate_to_website(driver, "http://www.zillow.com/homes")
# Click the "buy" button.
zl.click_buy_button(driver)
# Create 11 variables from the scrapped HTML data.
# These variables will make up the final output dataframe.
df = pd.DataFrame({
'address': [],
'bathrooms': [],
'bedrooms': [],
'city': [],
'days_on_zillow': [],
# I recommend using zip codes, as they seem to be the best option for catching
# as many house listings as possible. If you want to use search terms other
# than zip codes, simply skip running zipcodes_list() function below, and add
# a line of code to manually assign values to object st, for example:
# st = ['Chicago', 'New Haven, CT', '77005', 'Jacksonville, FL']
# Keep in mind that, for each search term, the number of listings scraped is
# capped at 520, so in using a search term like "Chicago" the scraper would
# end up missing most of the results.
st = zl.zipcodes_list(st_items = ['94102', '94103', '94104', '94105', '94107', '94108',
'94109', '94110', '94111', '94112', '94114', '94115',
'94116', '94117', '94118', '94121', '94122', '94123',
'94124', '94127', '94129', '94130', '94131', '94132',
'94133', '94134', '94158'])
# Initialize the webdriver.
driver = zl.init_driver('chromedriver')
# Go to www.zillow.com/homes
zl.navigate_to_website(driver, "http://www.zillow.com/homes")
# Click the "buy" button.
zl.click_buy_button(driver)
# Create 11 variables from the scrapped HTML data.
# These variables will make up the final output dataframe.
df = pd.DataFrame({'address' : [],
'city' : [],
'state' : [],
'zip' : [],
'price' : [],
'sqft' : [],
