这是我第一次抓取网站。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,要查找数据,我必须通过 HTML 标记的类名来查找它。 该代码可以从第一个表中抓取数据,但我也想对第二个表执行此操作。
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)
最佳答案
如上所述,使用.find_all()
。当您使用 .find()
时,它只会返回找到的第一个实例。 find_all() 会将其找到的所有实例返回到列表中。然后,您需要通过索引值计算出您想要的具体索引。
另一方面,为什么不使用 pandas
来解析表格。它在底层使用了 BeautifulSoup。
import requests
import pandas as pd
url = 'https://www.worldometers.info/world-population/albania-population/'
response = requests.get(url)
dfs = pd.read_html(response.text, attrs={'class':'table table-striped table-bordered table-hover table-condensed table-list'})
historic_population = dfs[0]
forecast_population = dfs[1]
输出:
print(historic_population)
Year Population ... World Population AlbaniaGlobal Rank
0 2020 2877797 ... 7794798739 140
1 2019 2880917 ... 7713468100 140
2 2018 2882740 ... 7631091040 140
3 2017 2884169 ... 7547858925 140
4 2016 2886438 ... 7464022049 141
5 2015 2890513 ... 7379797139 141
6 2010 2948023 ... 6956823603 138
7 2005 3086810 ... 6541907027 134
8 2000 3129243 ... 6143493823 131
9 1995 3112936 ... 5744212979 130
10 1990 3286073 ... 5327231061 125
11 1985 2969672 ... 4870921740 125
12 1980 2682690 ... 4458003514 125
13 1975 2411732 ... 4079480606 126
14 1970 2150707 ... 3700437046 125
15 1965 1896171 ... 3339583597 127
16 1960 1636090 ... 3034949748 124
17 1955 1419994 ... 2773019936 127
[18 rows x 13 columns]
print(forecast_population)
Year Population ... World Population AlbaniaGlobal Rank
0 NaN NaN ... NaN NaN
1 2020.0 2877797.0 ... 7.794799e+09 140.0
2 2025.0 2840464.0 ... 8.184437e+09 141.0
3 2030.0 2786974.0 ... 8.548487e+09 143.0
4 2035.0 2721082.0 ... 8.887524e+09 145.0
5 2040.0 2634384.0 ... 9.198847e+09 146.0
6 2045.0 2533645.0 ... 9.481803e+09 147.0
7 2050.0 2424061.0 ... 9.735034e+09 148.0
[8 rows x 13 columns]
关于python - 如何抓取两个具有相同类名的表?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/71661653/