Реализовать загрузку данных по дивидендам about poptimizer_old HOT 6 CLOSED

from bs4 import BeautifulSoup
import urllib.request

url = 'http://www.dohod.ru/ik/analytics/dividend/rtkmp'

# *requests* fails on SSL, using *urllib.request*
with urllib.request.urlopen(url) as response:
   html = response.read().decode("utf-8") 
   
# following https://gist.github.com/phillipsm/0ed98b2585f0ada5a769
#           https://www.crummy.com/software/BeautifulSoup/bs4/doc/#find-all
soup = BeautifulSoup(html, 'lxml') 

# идентифицировать нужную таблицу
table = soup.find_all('table')[2] 

# выдать строки
for html_row in table.find_all('tr'):
    row = [column.text.strip() for column in html_row.find_all('td')]    
    print (row)

# following https://gist.github.com/phillipsm/0ed98b2585f0ada5a769
#           https://www.crummy.com/software/BeautifulSoup/bs4/doc/#find-all

from bs4 import BeautifulSoup
import pandas as pd
import urllib.request
from pathlib import Path


def get_text(url):
    # *requests* fails on SSL, using *urllib.request*
    with urllib.request.urlopen(url) as response:
       return response.read().decode("utf-8") 

def yield_rows(table: str):
    for html_row in table.find_all('tr'):
        row = [column.text.strip() for column in html_row.find_all('td')]    
        try:
            yield dict(DATE=pd.to_datetime(row[0]), DIVIDEND=row[2])
        except (IndexError, ValueError) as e:
            print("Not parsed:", row)
 
def make_dataframe(table: str):        
    df = pd.DataFrame(columns=('DATE', 'DIVIDEND'))
    for row in yield_rows(table):
        df = df.append(row, ignore_index=True)
    return df.set_index('DATE').drop_duplicates()
   
if __name__ == '__main__':
    url = 'http://www.dohod.ru/ik/analytics/dividend/rtkmp'
    # dirty cache   
    cache = Path('temp.txt')
    if cache.exists():
       html = cache.read_text()
    else:
       html = get_text(url)  
       cache.write_text(html)      
    # identify table 
    soup = BeautifulSoup(html, 'lxml') 
    table = soup.find_all('table')[2] 
    df = make_dataframe(table)
    print(df)