I'm just learning web scraping & want to output the result of this website to a csv file https://www.avbuyer.com/aircraft/private-jets

but am struggling with parsing the next pages here is my code (with help of Amen Aziz) which only gives me the 1st page
I'm using Chrome so not sure if it makes any difference I'm running Python 3.8.12
Thank you in advance

import requests
from bs4 import BeautifulSoup
import pandas as pd
headers= {'User-Agent': 'Mozilla/5.0'}
response = requests.get('https://www.avbuyer.com/aircraft/private-jets')
soup = BeautifulSoup(response.content, 'html.parser')
postings = soup.find_all('div', class_ = 'listing-item premium')
temp=[]
for post in postings:link = post.find('a', class_ = 'more-info').get('href')link_full = 'https://www.avbuyer.com'+ linkplane = post.find('h2', class_ = 'item-title').textprice = post.find('div', class_ = 'price').textlocation = post.find('div', class_ = 'list-item-location').textdesc = post.find('div', class_ = 'list-item-para').texttry:tag = post.find('div', class_ = 'list-viewing-date').textexcept:tag = 'N/A'updated = post.find('div', class_ = 'list-update').textt=post.find_all('div',class_='list-other-dtl')for i in t:data=[tup.text for tup in i.find_all('li')]years=data[0]s=data[1]total_time=data[2]temp.append([plane,price,location,years,s,total_time,desc,tag,updated,link_full])df=pd.DataFrame(temp,columns=["plane","price","location","Year","S/N","Totaltime","Description","Tag","Last Updated","link"])next_page = soup.find('a', {'rel':'next'}).get('href')
next_page_full = 'https://www.avbuyer.com'+next_page
next_page_fullurl = next_page_full
page = requests.get(url)
soup = BeautifulSoup(page.text, 'lxml') df.to_csv('/Users/xxx/avbuyer.csv')

Try this: If you want cvs file then you finish the line print(df) and use df.to_csv("prod.csv") I have written in code to get csv file

import requests
from bs4 import BeautifulSoup
import pandas as pd
headers = {'User-Agent': 'Mozilla/5.0'}
temp=[]
for page in range(1, 20):response = requests.get("https://www.avbuyer.com/aircraft/private-jets/page-{page}".format(page=page),headers=headers,)soup = BeautifulSoup(response.content, 'html.parser')postings = soup.find_all('div', class_='grid-x list-content')for post in postings:plane = post.find('h2', class_='item-title').texttry:price = post.find('div', class_='price').textexcept:price=" "location = post.find('div', class_='list-item-location').textt=post.find_all('div',class_='list-other-dtl')for i in t:data=[tup.text for tup in i.find_all('li')]years=data[0]s=data[1]total_time=data[2]temp.append([plane,price,location,years,s,total_time])df=pd.DataFrame(temp,columns=["plane","price","location","Years","S/N","Totaltime"])
print(df)

output:

                      plane         price  ...             S/N         Totaltime
0            Gulfstream G280     Make offer  ...        S/N 2007   Total Time 2528
1    Dassault Falcon 2000LXS     Make offer  ...         S/N 377     Total Time 33
2       Cirrus Vision SF50 G1  Please call   ...        S/N 0080    Total Time 615
3              Gulfstream IV     Make offer  ...        S/N 1148   Total Time 6425
4            Gulfstream G280     Make offer  ...        S/N 2072   Total Time 1918
..                        ...           ...  ...             ...               ...
342       Embraer Phenom 100       Now Sold  ...    S/N 50000035   Total Time 3417
343          Gulfstream G200       Now Sold  ...         S/N 152   Total Time 7209
344     Cessna Citation XLS+       Now Sold  ...           S/N -      Total Time -
345    Cessna Citation Ultra       Now Sold  ...    S/N 560-0393  Total Time 12947
346    Cessna Citation Excel       Now Sold  ...  S/N 560XL-5253   Total Time 4850