currently working on a python-script that fetches all data from a Wiki-page: the contact data from the following wikipedia based list https://de.wikipedia.org/wiki/Liste_der_Genossenschaftsbanken_in_Deutschland

well i think that an appropriate method could be to make use of beautiful soup and pandas

in short: i think best would be to create a Python scraper working against the above mentioned Wikipedia-site: BS4, Pandas, in order to fetch a list of data from all the derived pages:

step 0: To fetch all the contact data from the Wikipedia page listing Genossenschaftsbanken i think i can use BeautifulSoup and Python. firstly i need to identify the table containing the contact information and then i can extract the data from it.

Here’s how i think i should go for it:

firstly: Inspect the Webpage: i think that all the important information – of a typical Wikipedia page we have in this little task: – and this could be a good approach for me – to dive into learing of python-scraper: so here is my start: ( https://de.wikipedia.org/wiki/Liste_der_Genossenschaftsbanken_in_Deutschland ) and on this page i firstly need to inspect the HTML structure to locate the table containing the contact information of the according banks:

so here we go

import requests
from bs4 import BeautifulSoup
import pandas as pd

# URL of the Wikipedia page
url = "https://de.wikipedia.org/wiki/Liste_der_Genossenschaftsbanken_in_Deutschland"

# Send a GET request to the URL
response = requests.get(url)

# Parse the HTML content
soup = BeautifulSoup(response.content, "html.parser")

# Find the table containing the bank data
table = soup.find("table", {"class": "wikitable"})

# Initialize lists to store data
banks = []
contacts = []
websites = []

# Extract data from the table
for row in table.find_all("tr")[1:]:
    cols = row.find_all("td")
    # Bank name is in the first column
    banks.append(cols[0].text.strip())
    # Contact information is in the second column
    contacts.append(cols[1].text.strip())
    # Check if there's a link in the contact cell (for the website)
    link = cols[1].find("a")
    if link:
        websites.append(link.get("href"))
    else:
        websites.append("")

# Create a DataFrame using pandas
bank_data = pd.DataFrame({"Bank": banks, "Contact": contacts, "Website": websites})

# Print the DataFrame
print(bank_data)

the output so far.

    Bank                                            Contact  
0   BWGV  Baden-Württembergischer Genossenschaftsverband...   
1    GVB                Genossenschaftsverband Bayern e. V.   
2     GV                                  Genoverband e. V.   
3   GVWE             Genossenschaftsverband Weser-Ems e. V.   
4  GPVMV  Genossenschaftlicher Prüfungsverband Mecklenbu...   
5    PDG     PDG Genossenschaftlicher Prüfungsverband e. V.   
6                           Verband der Sparda-Banken e. V.   
7                              Verband der PSD Banken e. V.   

                                             Website  
0  /wiki/Baden-W%C3%BCrttembergischer_Genossensch...  
1                /wiki/Genossenschaftsverband_Bayern  
2                                  /wiki/Genoverband  
3             /wiki/Genossenschaftsverband_Weser-Ems  
4                                                     
5                                                     
6                    /wiki/Sparda-Bank_(Deutschland)  
7                                     /wiki/PSD_Bank