Is there a way to webscrapp a site where everything has the same name?

Hi ! I’m new to Beautifulsoup, I was trying to webscrapp the info from this website:

The problem is that when I try to inspect the elements on the website everything is called "td" and class"sch1". Therefore when I try to import I get a big mess. How can I import this information in a way that can be readible and usable, maybe I’ll try build a dataframe with this.

import requests
import pandas as pd
from bs4 import BeautifulSoup

url = "https://feeds.donbest.com/schedulemembers/getRotation.html?bookType=1&eventDate=20230129"
get_url = requests.get(url).content
soup = BeautifulSoup(get_url,"html.parser")

title = soup.find_all("td","schtop1")
 rotation = soup.find_all("td","sch1")

 title_list = []
 rotation_list = []

 for mainT in title:
     title_list.append(mainT.text)
 print(title_list)

 for rot in rotation:
     rotation_list.append(rot.text)
print(rotation_list)

Output:
[‘NFL CONFERENCE CHAMPIONSHIPS’, ‘SUNDAY, JANUARY 29, 2023’]
[‘321’, ‘SAN FRANCISCO 49ERS’, ”, ‘P: Sun Jan 29 12:00:00 PST 2023\xa0\n C: Sun Jan 29 14:00:00 PST 2023\xa0\n E: Sun Jan 29 15:00:00 PST 2023’, ‘322’, ‘PHILADELPHIA EAGLES’, ‘323’, ‘CINCINNATI BENGALS’, ”, ‘P: Sun Jan 29 15:30:00 PST 2023\xa0\n C: Sun Jan 29 17:30:00 PST 2023\xa0\n E: Sun Jan 29 18:30:00 PST 2023’, ‘324’, ‘KANSAS CITY CHIEFS’]

I need to be able to use this information to build a pandas dataframe that looks like this:

I think I can build the dataframe if I can get the data in a more useful format.

>Solution :

import pandas as pd


df = pd.read_html(
    'https://feeds.donbest.com/schedulemembers/getRotation.html?bookType=1&eventDate=20230129/')[0]
print(df)

Output:

0                       NFL CONFERENCE CHAMPIONSHIPS  ...  NFL CONFERENCE CHAMPIONSHIPS
1                           SUNDAY, JANUARY 29, 2023  ...      SUNDAY, JANUARY 29, 2023
2  321  SAN FRANCISCO 49ERS  P: Sun Jan 29 12:00:...  ...                           NaN
3  323  CINCINNATI BENGALS  P: Sun Jan 29 15:30:0...  ...                           NaN

[4 rows x 7 columns]