They could have changed something because my output when running the code is different. The reason why it was up to 25 years was because that was an approximate for 100 rows in the dataframe… now it is only going back to 2012 for me. If you want to scrape more than what Yahoo gives you back, you have to use multithreading or something similar. This is the code that I’m using to scrape dividends:

import math
import time
import numpy as np 
import pandas as pd
import concurrent.futures
from finance_python.scrapers import scraperclass historical_data():def __init__(self, symbol, start, end, fil='history'):
        '''
        :param symbol: example "AAPL, or TRI.TO"
        :param start: Start date as datetime object 
                      start = datetime.today() - timedelta(days=365)
        :param end: End date as datetime object 
                    end = datetime.today()
        :param fil: 'history' or 'div' for dividend
        '''
        self.symbol = symbol.upper()
        self.start = start
        self.end = end
        self.urls = self.__urls__(fil)
        self.history = self.scrape_threading()
        
    def scrape_history(self, url):
        ''':param url: URL location of stock price history
        :return: price history
        '''
        price_history = scraper(url).__table__()
        price_history = self.__clean_history__(price_history[0])
        time.sleep(0.25)
        return price_historydef __getStarts__(self):
        response = self.__check__(self.start, self.end)
        pages = self.__calc_pages__(response)
        starts = self.__starts__(pages, self.start, self.end)
        return startsdef __format_date__(self, date_datetime):
        date_timetuple = date_datetime.timetuple()
        date_mktime = time.mktime(date_timetuple)
        date_int = int(date_mktime)
        date_str = str(date_int)
        return date_strdef __clean_history__(self, price_history):
        price_history = price_history.drop(len(price_history) - 1)
        price_history = price_history.set_index('Date')
        return price_historydef __check__(self, s, e):
        if np.busday_count(np.datetime64(s, "D"), np.datetime64(e, "D")) > 100:
            response = True
        else:
            response = False
        return responsedef __calc_pages__(self, response):
        s, e = [self.start, self.end]
        if response:
            pages = math.ceil(np.busday_count(np.datetime64(s, "D"), np.datetime64(e, "D")) / 100)
        else:
            pages = 1
        return pagesdef __calc_start__(self, pages, s, e):
        calendar_days = (e - s) / pages
        for i in range(pages):
            s = s + calendar_days
            yield sdef __starts__(self, pages, s, e):
        if pages == 0 | pages==1:
            pass
        else:
            starts = [s for s in self.__calc_start__(pages, s, e)]
            starts.append(e)
        return startsdef __urls__(self, fil):
        '''Returns
        -------
        urls : a list of urls complete with start and end dates for each 100 trading day block'''
        starts = self.__getStarts__()
        symbol = self.symbol
        urls = []
        for d in range(len(starts) - 1):
            start = str(self.__format_date__(starts[d]))
            end = str(self.__format_date__(starts[d + 1]))
            url = "HTTP://finance.yahoo.com/quote/{0}/history?period1={1}&period2={2}&interval=1d&filter={3}&frequency=1d"
            url = url.format(symbol, start, end, fil)
            urls.append(url)
        return urlsdef scrape_threading(self,threads=None):
        if threads != None: 
            threads = min(100, len(self.urls))
        else:
            threads = threads
        
        with concurrent.futures.ThreadPoolExecutor(max_workers=threads) as executor:
            history = list(executor.map(self.scrape_history, self.urls))
        return pd.concat(history, sort=False)
        
if __name__ == "__main__":
    from datetime import datetime, timedelta
    start = datetime.today() - timedelta(days=365*30)
    end = datetime.today()
    aapl = historical_data('aapl', start, end, fil ='div')

I also have an article that applies to this: