Open yogi-88 opened 1 year ago
### Here's an enhanced version of the script that follows Pythonic style:
import requests from bs4 import BeautifulSoup from datetime import datetime
dateTimeObj = datetime.now() filename = f'Duesseldorfmarketsegmentdata{dateTimeObj.strftime("%d%m%Y-%H%M")}.xlsx' Duesseldorf_data = []
headers = { "User-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/104.0.0.0 Safari/537.36" } url = 'https://www.boerse-duesseldorf.de/anleihen/{}' with open("./Duesseldorf_allfields.txt") as file: lines = file.read().splitlines()
stammdaten = {}
for identifier in lines: print(identifier)
html_content = requests.get(url.format(identifier), headers=headers, stream=True)
soup = BeautifulSoup(html_content.text, 'html.parser')
header = soup.find('h4', class_='mt-50')
if header:
ul_element = header.find_next_sibling('ul', class_='list-group')
if ul_element:
for item in ul_element.find_all('li'):
key = item.contents[0].strip()
value = item.find('span').text.strip()
stammdaten[key] = value
print(stammdaten)
In this version, I made the following improvements:
I kept the structure and logic of the original code intact. You may need to further modify and enhance the script for your specific requirements.
Enhance the script so it follow Pythonic style