Python 如何抓取具有相同类名的两个表

这是我第一次浏览网站.问题是两个不同的表具有相同的类名.到目前为止，我已经了解到，要找到数据，我必须通过HTML标记的类名来找到它.

import bs4 as bs
from urllib.request import Request, urlopen
import pandas as pd
from pyparsing import col


req = Request('https://www.worldometers.info/world-population/albania-population/',
              headers={'User-Agent': 'Mozilla/5.0'})

webpage = urlopen(req).read()

soup = bs.BeautifulSoup(webpage, 'html5lib')


# albania population
pupulation = soup.find(class_='col-md-8 country-pop-description')
for i in pupulation.find_all('strong')[1]:
    print()
    # print(i.text, end=" ")

# getting all city populattion
city_population = soup.find(
    class_='table table-hover table-condensed table-list')
# print(city_population.text, end=" ")


# the first table
# population of albania(historical)
df = pd.DataFrame(columns=['Year', 'Population' 'Yearly Change %', 'Yearly Change', 'Migrants (net)', 'Median Age', 'Fertility Rate',
                  'Density(P/Km2)', 'Urban Pop %', 'Urban Population', "Countrys Share of Population", 'World Population', 'Albania Global Rank'])

hisoric_population = soup.find('table',
                               class_='table table-striped table-bordered table-hover table-condensed table-list')


for row in hisoric_population.tbody.find_all('tr'):
    columns = row.find_all('td')

    if (columns != []):
        Year = columns[0].text.strip()
        Population = columns[1].text.strip()
        YearlyChange_percent = columns[2].text.strip('&0')
        YearlyChange = columns[3].text.strip()
        Migrants_net = columns[4].text.strip()
        MedianAge = columns[5].text.strip('&0')
        FertilityRate = columns[6].text.strip('&0')
        Density_P_Km2 = columns[7].text.strip()
        UrbanPop_percent = columns[8].text.strip('&0')
        Urban_Population = columns[9].text.strip()
        Countrys_Share_of_Population = columns[10].text.strip('&0')
        World_Population = columns[11].text.strip()
        Albania_Global_Rank = columns[12].text.strip()

        df = df.append({'Year': Year, 'Population': Population, 'Yearly Change %': YearlyChange_percent, 'Yearly Change': YearlyChange, 'Migrants (net)': Migrants_net, 'Median Age': MedianAge, 'Fertility Rate': FertilityRate,
                        'Density(P/Km2)': Density_P_Km2, 'Urban Pop %': UrbanPop_percent, 'Countrys Share of Population': Countrys_Share_of_Population, 'World Population': World_Population, 'Albania Global Rank': Albania_Global_Rank}, ignore_index=True)
df.head()
# print(df)

#the second table
# Albania Population Forecast

forecast_population = soup.find(
    'table', class_='table table-striped table-bordered table-hover table-condensed table-list')

for row in hisoric_population.tbody.find_all('tr'):
    columns = row.find_all('td')
    print(columns)