I want to scrap information on different pages of the same site, societe.com and I have several questions.
first of all here is the code that I managed to do, I am a bit of a novice I admit it
I only put 2 URLs to see if the loop worked and some information, I can add some when everything works
urls = ["https://www.societe.com/societe/decathlon-france-500569405.html","https://www.societe.com/societe/go-sport-312193899.html"]
for url in urls:
response = requests.get(url, headers = {'User-agent': 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/64.0.3282.186 Safari/537.36'})
soup = BeautifulSoup(response.text, "html.parser")
numrcs = soup.find("td", class_="numdisplay")
nomcommercial = soup.find("td", class_="break-word")
print(nomcommercial.text)
print(numrcs.text.strip())
numsiret = soup.select('div[id^=siret_number]')
for div in numsiret:
print(div.text.strip())
formejuri = soup.select('div[id^=catjur-histo-description]')
for div in formejuri:
print(div.text.strip())
infosend = {
'numrcs': numrcs,
'nomcommercial':nomcommercial,
'numsiret':numsiret,
'formejuri':formejuri
}
tableau.append(infosend)
print(tableau)
my_infos = ['Numéro RCS', 'Numéro Siret ','Forme Juridique']
my_columns = [
np.tile(np.array(my_infos), len(nomcommercial))
]
df = pd.DataFrame( tableau,index=nomcommercial, columns=my_columns)
df
When I run the loop I have the right information coming out, like for example
DECATHLON FRANCE
Lille Metropole B 500569405
50056940503239
SASU Société par actions simplifiée à associé unique
but I would like to put all this information in a table but I can't really, only the last company appears and the data makes no sense I tried to follow a tutorial without success.
if you can help me i would be really happy
CodePudding user response:
To get data about the companies you can use next example:
import requests
import pandas as pd
from bs4 import BeautifulSoup
urls = [
"https://www.societe.com/societe/decathlon-france-500569405.html",
"https://www.societe.com/societe/go-sport-312193899.html",
]
headers = {
"User-agent": "Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/64.0.3282.186 Safari/537.36"
}
data = []
for url in urls:
soup = BeautifulSoup(
requests.get(url, headers=headers).content, "html.parser"
)
title = soup.select_one("#identite_deno").get_text(strip=True)
rcs = soup.select_one('td:-soup-contains("Numéro RCS") td').get_text(
strip=True
)
siret_number = soup.select_one("#siret_number").get_text(strip=True)
form = soup.select_one("#catjur-histo-description").get_text(strip=True)
data.append([title, url, rcs, siret_number, form])
df = pd.DataFrame(
data,
columns=["Title", "URL", "Numéro RCS", "Numéro Siret", "Forme Juridique"],
)
print(df.to_markdown())
Prints:
Title | URL | Numéro RCS | Numéro Siret | Forme Juridique | |
---|---|---|---|---|---|
0 | DECATHLON FRANCE (DECATHLON DIRECTION GENERALE FRANCE) | https://www.societe.com/societe/decathlon-france-500569405.html | Lille Metropole B 500569405 | 50056940503239 | SASU Société par actions simplifiée à associé unique |
1 | GO SPORT | https://www.societe.com/societe/go-sport-312193899.html | Grenoble B 312193899 | 31219389900191 | Société par actions simplifiée |