How to scrape content from a website with no class or id specified in attribute with BeautifulSoup4

This one was a bit of a time consuming one! The webpage is not complete and it has less tags and identifiers. To add more on that they haven't even spell checked the content Eg. One place has a heading Scope of Services and another place has Scope of services and there are many more like that! So what I have done is a crude extraction and I'm sure it would help you if you have the idea of paginating also.

import requests
from bs4 import BeautifulSoup
import csv

page = requests.get('https://www.re-thinkingthefuture.com/top-architects/top-architecture-firms-in-india-part-1/')
soup = BeautifulSoup(page.text, 'lxml')

# there are many h2 tags but we want the one without any class name
h2 = soup.find_all('h2', class_= '')

headers = []
contents = []
header_len = []
a_tags = []

for i in h2:
    if i.find_next().name == 'a':             # to make sure we do not grab the wrong tag
        a_tags.append(i.find_next().text)
        p = i.find_next_sibling()
        contents.append(p.text)
        h =[j.text for j in  p.find_all('strong')]   #  some headings were bold in the website
        headers.append(h)
        header_len.append(len(h))

# since only some headings were in bold the max number of bold would give all headers
headers = headers[header_len.index(max(header_len))]

# removing the : from headings
headers = [i[:len(i)-1] for i in headers]

# inserted a new heading
headers.insert(0, 'Firm')

# n for traversing through headers list
# k for traversing through a_tags list
n =1
k =0

# this is the difficult part where the content will have all the details in one value including the heading like this
"""
Scope of services: Architecture, Interiors, Urban Design.Types of Built Projects: Residential, commercial, hospitality, offices, retail, healthcare, housing, InstitutionalLocations of Built Projects: New Delhi and nearby statesStyle of work: ContemporaryWebsite: 42mm.co.in
"""
# thus I am splitting it using the ':' and then splicing it from the start of the each heading

contents = [i.split(':') for i in contents]
for i in contents:
    for j in i:
        h = headers[n][:5]
        if i.index(j) == 0:
            i[i.index(j)] = a_tags[k]
            n+=1
            k+=1
        elif h in j:
            i[i.index(j)] = j[:j.index(h)]
            j = j[:j.index(h)]
            if n < len(headers)-1:
                n+=1
    n =1

    # merging those extra values in the list if any
    if len(i) == 7:
        i[3] = i[3] + ' ' + i[4]
        i.remove(i[4])

# writing into csv file
# if you don't want a line space between each row then add newline = '' argument in the open function below
with open('output.csv', 'w') as f:   
    writer = csv.writer(f)
    writer.writerow(headers)
    writer.writerows(contents)

This was the output:

If you want to paginate then just add the page number to the end of the url and you'll be good!

page_num = 1
while page_num <13:
    page = requests.get(f'https://www.re-thinkingthefuture.com/top-architects/top-architecture-firms-in-india-part-1/{page_num}/')

    # paste the above code starting from soup = BeautifulSoup(page.text, 'lxml')

    page_num +=1

Hope this helps, let me know if there's any error.

EDIT 1: I forgot to say the most important part sorry, if there is a tag with no class name then you can still get the tag with what I have used in the code above

h2 = soup.find_all('h2', class_= '')

This just says that give me all the h2 tags which does not have a class name. This itself can sometimes be a unique identifier as we are using this no class value to identify it.

You can use this example as a basis how to scrape the informations from that page:

import requests
import pandas as pd

url = "https://www.gov.uk/government/publications/endorsing-bodies-start-up/start-up"

soup = BeautifulSoup(requests.get(url).content, "html.parser")
parent = soup.select_one("div.govspeak")

mapping = {"sector": "sectors", "endorses businesses": "endorses businesses in"}

all_data = []
for h3 in parent.select("h3"):
    name = h3.text
    link = h3.a["href"] if h3.a else "-"

    ul = h3.find_next("ul")
    if ul and ul.find_previous("h3") == h3 and ul.parent == parent:
        li = [
            list(map(lambda x: mapping.get((i := x.strip()), i), v))
            for li in ul.select("li")
            if len(v := li.get_text(strip=True).split(":")) == 2
        ]
    else:
        li = []

    all_data.append({"name": name, "link": link, **dict(li)})


df = pd.DataFrame(all_data)
print(df)
df.to_csv("data.csv", index=False)

Creates data.csv (screenshot from LibreOffice):