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how can I scrape data beyond the page limit on Zillow?

I created a code to scrape the Zillow data and it works fine. The only problem I have is that it's limited to 20 pages even though there are many more results. Is there a way to get around this page limitation and scrap all the data ?
I also wanted to know if there is a general solution to this problem since I encounter it practically in every site that I want to scrape.
Thank you
from bs4 import BeautifulSoup
import requests
import lxml
import json
headers = {
"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36",
"Accept-Language": "en-US,en;q=0.9"
}
search_link = 'https://www.zillow.com/homes/Florida--/'
response = requests.get(url=search_link, headers=headers)
pages_number = 19
def OnePage():
soup = BeautifulSoup(response.text, 'lxml')
data = json.loads(
soup.select_one("script[data-zrr-shared-data-key]")
.contents[0]
.strip("!<>-")
)
all_data = data['cat1']['searchResults']['listResults']
home_info = []
result = []
for i in range(len(all_data)):
property_link = all_data[i]['detailUrl']
property_response = requests.get(url=property_link, headers=headers)
property_page_source = BeautifulSoup(property_response.text, 'lxml')
property_data_all = json.loads(json.loads(property_page_source.find('script', {'id': 'hdpApolloPreloadedData'}).get_text())['apiCache'])
zp_id = str(json.loads(property_page_source.find('script', {'id': 'hdpApolloPreloadedData'}).get_text())['zpid'])
property_data = property_data_all['ForSaleShopperPlatformFullRenderQuery{"zpid":'+zp_id+',"contactFormRenderParameter":{"zpid":'+zp_id+',"platform":"desktop","isDoubleScroll":true}}']["property"]
home_info["Broker Name"] = property_data['attributionInfo']['brokerName']
home_info["Broker Phone"] = property_data['attributionInfo']['brokerPhoneNumber']
result.append(home_info)
return result
data = pd.DataFrame()
all_page_property_info = []
for page in range(pages_number):
property_info_one_page = OnePage()
search_link = 'https://www.zillow.com/homes/Florida--/'+str(page+2)+'_p'
response = requests.get(url=search_link, headers=headers)
all_page_property_info = all_page_property_info+property_info_one_page
data = pd.DataFrame(all_page_property_info)
data.to_csv(f"/Users//Downloads/Zillow Search Result.csv", index=False)

Actually, you can't grab any data from zillow using bs4 because they are dynamically loaded by JS and bs4 can't render JS. Only 6 to 8 data items are static. All data are lying down in script tag with html comment as json format. How to pull the requied data? In this case you can follow the next example.
Thus way you can extract all the items. So to pull rest of data items, is your task or just add your data items here.
Zillow is one of the most famous and smart enough websites. So we should respect its terms and conditions.
Example:
import requests
import re
import json
import pandas as pd
url='https://www.zillow.com/fl/{page}_p/?searchQueryState=%7B%22usersSearchTerm%22%3A%22FL%22%2C%22mapBounds%22%3A%7B%22west%22%3A-94.21964006249998%2C%22east%22%3A-80.68448381249998%2C%22south%22%3A22.702203494269085%2C%22north%22%3A32.23788425255877%7D%2C%22regionSelection%22%3A%5B%7B%22regionId%22%3A14%2C%22regionType%22%3A2%7D%5D%2C%22isMapVisible%22%3Afalse%2C%22filterState%22%3A%7B%22sort%22%3A%7B%22value%22%3A%22days%22%7D%2C%22ah%22%3A%7B%22value%22%3Atrue%7D%7D%2C%22isListVisible%22%3Atrue%2C%22mapZoom%22%3A6%2C%22pagination%22%3A%7B%22currentPage%22%3A2%7D%7D'
lst=[]
for page in range(1,21):
r = requests.get(url.format(page=page),headers = {'User-Agent':'Mozilla/5.0'})
data = json.loads(re.search(r'!--(\{"queryState".*?)-->', r.text).group(1))
for item in data['cat1']['searchResults']['listResults']:
price= item['price']
lst.append({'price': price})
df = pd.DataFrame(lst).to_csv('out.csv',index=False)
print(df)
Output:
price
0 $354,900
1 $164,900
2 $155,000
3 $475,000
4 $245,000
.. ...
795 $295,000
796 $10,000
797 $385,000
798 $1,785,000
799 $1,550,000
[800 rows x 1 columns]

How to change the code to asynchronously iterate links and IDs for scrap web page?

I have the list of links, each link has an id that is in the Id list
How to change the code so that when iterating the link, the corresponding id is substituted into the string:
All code is below:
import pandas as pd
from bs4 import BeautifulSoup
import requests
HEADERS = {'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) '
'Chrome/81.0.4044.138 Safari/537.36 OPR/68.0.3618.125', 'accept': '*/*'}
links = ['https://www..ie', 'https://www..ch', 'https://www..com']
Id = ['164240372761e5178f0488d', '164240372661e5178e1b377', '164240365661e517481a1e6']
def get_html(url, params=None):
r = requests.get(url, headers=HEADERS, params=params)
def get_data_no_products(html):
data = []
soup = BeautifulSoup(html, 'html.parser')
items = soup.find_all('div', id= '') # How to iteration paste id???????
for item in items:
data.append({'pn': item.find('a').get('href')})
return print(data)
def parse():
for i in links:
html = get_html(i)
get_data_no_products(html.text)
parse()

Parametrise your code:
def get_data_no_products(html, id_):
data = []
soup = BeautifulSoup(html, 'html.parser')
items = soup.find_all('div', id=id_)
And then use zip():
for link, id_ in zip(links, ids):
get_data_no_producs(link, id_)
Note that there's a likely bug in your code: you return print(data) which will always be none. You likely just want to return data.
PS
There is another solution to this which you will frequently encounter from people beginning in python:
for i in range(len(links)):
link = links[i]
id_ = ids[i]
...
This... works. It might even be easier or more natural, if you are coming from e.g. C. (Then again I'd likely use pointers...). Style is very much personal, but if you're going to write in a high level language like python you might as well avoid thinking about things like 'the index of the current item' as much as possible. Just my £0.02.

Multiple Page BeautifulSoup Script only Pulling first value

New to screen scraping here and this is my first time posting on stackoverflow. Aplogies in advance for any formatting errors in this post. Attempting to extract data from multiple pages with URL:
https://www.landwatch.com/Michigan_land_for_sale/West_Central_Region/Page-' + str(page)
For instance, page 1 is:
https://www.landwatch.com/Michigan_land_for_sale/West_Central_Region/Page-1
Page 2:
https://www.landwatch.com/Michigan_land_for_sale/West_Central_Region/Page-2
and so on...
My script is running without errors. However, my Pandas exported csv only contains 1 row with the first extracted value. At the time of this posting, the first value is:
14.01 Acres   Vestaburg, Montcalm County, MI$275,000
My intent is to create a spreadsheet with hundreds of rows that pull the property description from the URLs.
Here is my code:
import requests
from requests import get
from bs4 import BeautifulSoup
headers = ({'User-Agent':
'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.36'
}
)
n_pages = 0
desc = []
for page in range(1,900):
n_pages += 1
sapo_url = 'https://www.landwatch.com/Michigan_land_for_sale/West_Central_Region/Page-' + str(page)
r=get(sapo_url, headers=headers)
page_html = BeautifulSoup(r.text, 'html.parser')
house_containers = page_html.find_all('div', class_="propName")
if house_containers != []:
for container in house_containers:
desc = container.getText(strip=True)
else:
break
print('you scraped {} pages containing {} Properties'.format(n_pages, len(desc)))
import pandas as pd
df = pd.DataFrame({'description': [desc]})
df.to_csv('test4.csv', encoding = 'utf-8')
I suspect the problem is with the line reading desc = container.getText(strip=True) and have tried changing the line but keep getting errors when running.
Any help is appreciated.

I believe the mistake is in the line:
desc = container.getText(strip=True)
Every time it loops, the value in desc is replaced, not added on. To add items into the list, do:
desc.append(container.getText(strip=True))
Also, since it is already a list, you can remove the brackets from the DataFrame creation like so:
df = pd.DataFrame({'description': desc})

The cause is that no data is being added in the loop, so only the final data is being saved. For testing purposes, this code is now on page 2, so please fix it.
import requests
from requests import get
from bs4 import BeautifulSoup
import pandas as pd
headers = ({'User-Agent':
'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.36'
}
)
n_pages = 0
desc = []
all_data = pd.DataFrame(index=[], columns=['description'])
for page in range(1,3):
n_pages += 1
sapo_url = 'https://www.landwatch.com/Michigan_land_for_sale/West_Central_Region/Page-' + str(page)
r=get(sapo_url, headers=headers)
page_html = BeautifulSoup(r.text, 'html.parser')
house_containers = page_html.find_all('div', class_="propName")
if house_containers != []:
for container in house_containers:
desc = container.getText(strip=True)
df = pd.DataFrame({'description': [desc]})
all_data = pd.concat([all_data, df], ignore_index=True)
else:
break
all_data.to_csv('test4.csv', encoding = 'utf-8')
print('you scraped {} pages containing {} Properties'.format(n_pages, len(desc)))

How do I fix the code to scrape Zomato website?

I wrote this code but got this as the error "IndexError: list index out of range" after running the last line. Please, how do I fix this?
import requests
from bs4 import BeautifulSoup
headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML,
like Gecko) Chrome/61.0.3163.100 Safari/537.36'}
response = requests.get("https://www.zomato.com/bangalore/top-restaurants",headers=headers)
content = response.content
soup = BeautifulSoup(content,"html.parser")
top_rest = soup.find_all("div",attrs={"class": "sc-bblaLu dOXFUL"})
list_tr = top_rest[0].find_all("div",attrs={"class": "sc-gTAwTn cKXlHE"})
list_rest =[]
for tr in list_tr:
dataframe ={}
dataframe["rest_name"] = (tr.find("div",attrs={"class": "res_title zblack bold nowrap"})).text.replace('\n', ' ')
dataframe["rest_address"] = (tr.find("div",attrs={"class": "nowrap grey-text fontsize5 ttupper"})).text.replace('\n', ' ')
dataframe["cuisine_type"] = (tr.find("div",attrs={"class":"nowrap grey-text"})).text.replace('\n', ' ')
list_rest.append(dataframe)
list_rest

You are receiving this error because top_rest is empty when you attempt to get the first element of it "top_rest[0]". The reason for that is because the first class your attempting to reference is dynamically named. You will notice if you refresh the page the same location of that div will not be named the same. So when you attempt to scrape you get empty results.
An alternative would be to scrape ALL divs, then narrow in on the elements you want, be mindful of the dynamic div naming schema so from one request to another you will get different results:
import requests
from bs4 import BeautifulSoup
headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.100 Safari/537.36'}
response = requests.get("https://www.zomato.com/bangalore/top-restaurants",headers=headers)
content = response.content
soup = BeautifulSoup(content,"html.parser")
top_rest = soup.find_all("div")
list_tr = top_rest[0].find_all("div",attrs={"class": "bke1zw-1 eMsYsc"})
list_tr

I recently did a project that made me research scraping the Zomato's website in Manila, Philippines. I used Geolibrary to get the longitude and latitude values of Manila City, then scraped the restaurants' details using this information.
ADD: You can get your own API key on zomato website to make up to 1000 calls in a day.
# Use geopy library to get the latitude and longitude values of Manila City.
from geopy.geocoders import Nominatim
address = 'Manila City, Philippines'
geolocator = Nominatim(user_agent = 'Makati_explorer')
location = geolocator.geocode(address)
latitude = location.lenter code hereatitude
longitude = location.longitude
print('The geographical coordinate of Makati City are {}, {}.'.format(latitude, longitude))
# Use Zomato's API to make call
headers = {'user-key': '617e6e315c6ec2ad5234e884957bfa4d'}
venues_information = []
for index, row in foursquare_venues.iterrows():
print("Fetching data for venue: {}".format(index + 1))
venue = []
url = ('https://developers.zomato.com/api/v2.1/search?q={}' +
'&start=0&count=1&lat={}&lon={}&sort=real_distance').format(row['name'], row['lat'], row['lng'])
try:
result = requests.get(url, headers = headers).json()
except:
print("There was an error...")
try:
if (len(result['restaurants']) > 0):
venue.append(result['restaurants'][0]['restaurant']['name'])
venue.append(result['restaurants'][0]['restaurant']['location']['latitude'])
venue.append(result['restaurants'][0]['restaurant']['location']['longitude'])
venue.append(result['restaurants'][0]['restaurant']['average_cost_for_two'])
venue.append(result['restaurants'][0]['restaurant']['price_range'])
venue.append(result['restaurants'][0]['restaurant']['user_rating']['aggregate_rating'])
venue.append(result['restaurants'][0]['restaurant']['location']['address'])
venues_information.append(venue)
else:
venues_information.append(np.zeros(6))
except:
pass
ZomatoVenues = pd.DataFrame(venues_information,
columns = ['venue', 'latitude',
'longitude', 'price_for_two',
'price_range', 'rating', 'address'])

Using Web Scraping Language I was able to write this:
GOTO https://www.zomato.com/bangalore/top-restaurants
EXTRACT {'rest_name': '//div[#class="res_title zblack bold nowrap"]',
'rest_address': '//div[#class="nowrap grey-text fontsize5 ttupper',
'cusine_type': '//div[#class="nowrap grey-text"]'} IN //div[#class="bke1zw-1 eMsYsc"]
This will iterate over each record element with class bke1zw-1 eMsYsc and pull
each restaurant information.

Web-scraping data from a graph

I am working with lobbying data from opensecrets.org, in particular industry data. I want to have a time series of lobby expenditures for each industry going back since the 90's.
I want to web-scrape the data automatically. Urls where the data is have the following format:
https://www.opensecrets.org/lobby/indusclient.php?id=H04&year=2019
which are pretty easy to embed in a loop, the problem is that the data I need is not in an easy format in the webpage. It is inside a bar graph, and when I inspect the graph I do not know how to get the data since it is not in the html code. I am familiar with web-scraping in python when the data is in the html code, but in this case I am not sure how to proceed.

If there is an API, that your best bet as mentioned above. But the data is able to be parsed anyway provided you get the right url/query parameters:
I've managed to iterate through it with the links for you to grab each table. I stored it in a dictionary with the key being the Firm name, and the value being the table/data. You can change it up to anyway you'd like. Maybe just store as json, or save each as csv.
Code:
import requests
from bs4 import BeautifulSoup
import pandas as pd
url = 'https://www.opensecrets.org/lobby/indusclient.php?id=H04&year=2019'
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.100 Safari/537.36'}
data = requests.get(url, headers=headers)
soup = BeautifulSoup(data.text, 'html.parser')
links = soup.find_all('a', href=True)
root_url = 'https://www.opensecrets.org/lobby/include/IMG_client_year_comp.php?'
links_dict = {}
for each in links:
if 'clientsum.php?' in each['href']:
w=1
firms = each.text
link = root_url + each['href'].split('?')[-1].split('&')[0].strip() + '&type=c'
links_dict[firms] = link
all_tables = {}
n=1
tot = len(links_dict)
for firms, link in links_dict.items():
print ('%s of %s ---- %s' %(n, tot, firms))
data = requests.get(link)
soup = BeautifulSoup(data.text, 'html.parser')
results = pd.DataFrame()
graph = soup.find_all('set')
for each in graph:
year = each['label']
total = each['value']
temp_df = pd.DataFrame([[year, total]], columns=['year','$mil'])
results = results.append(temp_df,sort=True).reset_index(drop=True)
all_tables[firms] = results
n+=1
*Output:**
Not going to print as there are 347 tables, but just so you see the structure: