Create a DataFrame with ordered data from a scraped table with Beautiful Soup

I am trying to create a DataFrame with the CPI data scraped from a website with Beautiful Soup, sorted by year and Country. The DF would have the following structure:

I am able to get all the needed data from the following script:

import requests
import pandas as pd
from bs4 import BeautifulSoup as bs


list_countries=['australia','canada','brazil','italy','japan','mexico','new-zealand','france','germany','philippines','india','korea','russia','singapore','switzerland','uk','usa']

url = 'https://www.rateinflation.com/consumer-price-index/australia-historical-cpi/'
r = requests.get(url)
soup = bs(r.text, 'html.parser')
cpi_get = soup.find('table', class_='css-8rh80p eyyd7td0')

for year in cpi_get.find_all('tr'):
    quarters = year.find_all('td')
    print(quarters)

The problem is that the output has the following structure, for each iteration:

[<td>2021</td>, <td>117.9</td>, <td>118.8</td>, <td>119.7</td>, <td>121.3</td>, <td>119.4</td>]

Having the first td as the year, the second q1,… and consecutively up to year. As it does not have any class or extra info apart of the position, I don´t know how to create the table from that.

Does anyone know how to get the desired output from that?

>Solution :

Try pd.read_html:

import pandas as pd

url = "https://www.rateinflation.com/consumer-price-index/australia-historical-cpi/"

df = pd.read_html(url)[0]
df.rename(
    columns={
        "mar": "q1",
        "jun": "q2",
        "sep": "q3",
        "dec": "q4",
    },
    inplace=True,
)
df["Country"] = "Australia"

# print first 10 rows:
print(df.head(10).to_markdown())

Prints:

EDIT:

list_countries = ["australia", "canada"]
url = "https://www.rateinflation.com/consumer-price-index/{}-historical-cpi/"

dfs = []
for country in list_countries:
    df = pd.read_html(url.format(country))[0]
    df["Country"] = country.capitalize()
    dfs.append(df)

df = pd.concat(dfs)
print(df)