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Pandas is a powerful data analysis library in Python, but it does not have a built-in parser for HTML. However, you can use the Beautiful Soup library along with Pandas to parse HTML and create a Pandas dataframe.

Here is an example of how to do it:

Install the required libraries:

pip install pandas beautifulsoup4 lxml

Import the required libraries:

import pandas as pd
from bs4 import BeautifulSoup
import requests

Use requests library to get the HTML content of the webpage you want to parse:

url = "https://www.example.com"
page = requests.get(url)
soup = BeautifulSoup(page.content, 'html.parser')

Find the table you want to extract data from. You can use the find_all() method to find all tables on the page, and then select the one you want:

table = soup.find_all('table')[0] # assuming the table you want is the first one on the page

Use Pandas’ read_html() function to read the table into a dataframe:

df = pd.read_html(str(table))[0]

The read_html() function takes a string of HTML as input, so we need to convert the table object to a string using the str() method.

Now you have a Pandas dataframe with the data from the HTML table. You can perform any data analysis or manipulation you want on the dataframe.

Note that this method assumes that the HTML table is well-formed and can be easily parsed by Beautiful Soup. If the HTML is more complex or contains nested elements, you may need to use more advanced techniques to extract the data.