pandas read_html error: not finding the table I want

Question

I want to get the tables form this link: https://radarintermediacoes.com.br/compra-e-venda-de-negocios/estacionamento-no-centro-17/

I am trying to get the information through the following code:

import pandas as pd
import requests
    
url = "https://radarintermediacoes.com.br/compra-e-venda-de-negocios/estacionamento-no-centro-17/"
    
    
header = {
  "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.75 Safari/537.36",
  "X-Requested-With": "XMLHttpRequest"
}

r = requests.get(url, headers=header)

data = pd.read_html(r.text)
data

With this code, I manage to get only one of the tables I want, the table "DESPESAS FIXAS/VARIÁVEIS", but I also want the table "DETALHES DO NEGÓCIO".

I hope I can get some help or suggestions, thanks!

Answer 1

The data you're looking for isn't inside <table> tag, so Pandas doesn't see it. But you can parse the data with BeautifulSoup for example:

import requests
import pandas as pd
from bs4 import BeautifulSoup
  

url = "https://radarintermediacoes.com.br/compra-e-venda-de-negocios/estacionamento-no-centro-17/"
soup = BeautifulSoup(requests.get(url).content, 'html.parser')  

all_data = []
for li in soup.select('div.panel-heading:contains("Detalhes do Negócio") + div li'):
    a, v = li.strong.text, li.strong.find_next_sibling(text=True)
    all_data.append({'Attribute': a.strip(':'), 'Value': v})

df = pd.DataFrame(all_data)
print(df)

Prints:

                  Attribute                                  Value
0           TIPO DE NEGÓCIO                        Estacionamentos
1                    REGIÃO                                 Centro
2        FATURAMENTO MENSAL                           R$ 13.500,00
3             LUCRO LÍQUIDO                            R$ 3.000,00
4    NÚMERO DE FUNCIONÁRIOS                                      1
5         TEMPO DE CONTRATO                                 3 anos
6  HORÁRIO DE FUNCIONAMENTO      Segunda a sexta ds 07:00 as 19:00
7    CONDIÇÕES DE PAGAMENTO   50% de entrada e restante em 30 dias
8                     PREÇO                            R$50.000,00