使用yahoo finance在python中测试股息率

我正在尝试测试CSV文件中提供的股息率。其目的是检查CSV中给定日期各股票的股息率。因此，我试图使用yfinance library从yahoo finance收集股息率，目的是在csv文件中提供的利率旁边添加一列我收集的利率，以便我可以看到任何差异

这是我的代码示例：

import pandas as pd
import yfinance as yf
from datetime import datetime,date


## Creating a dividend CSV file example
tickers = ['ABP.AX', 'ABP.AX','AZJ.AX','AZJ.AX','BEN.AX','CIP.AX','CIP.AX',
           'CIP.AX','CIP.AX','CMW.AX','CMW.AX','CMW.AX','CMW.AX']

Dates = ['12/30/2019 0:00','6/29/2020 0:00','2/24/2020 0:00','8/24/2020 0:00',
         '3/6/2020 0:00','12/30/2019 0:00','3/30/2020 0:00','6/29/2020 0:00',
         '9/29/2020 0:00','12/30/2019 0:00','3/30/2020 0:00','6/29/2020 0:00','9/29/2020 0:00']

rates = [0.094500,0.090500, 0.137000,0.137000,0.310000,0.046269,0.046269,
         0.046269,0.042500,0.018750,0.018750,0.018750, 0.018750]

tickers = pd.Series(tickers)
Dates = pd.Series(Dates)
rates = pd.Series(rates)

frame = {"symbols" : tickers, 'Dates': Dates, "Amounts":rates}
dividends_csv = pd.DataFrame(frame)


## Now starting my Testing Process 

stocks = (dividends_csv.symbols)
stocks = stocks.unique()

# My Desired Dates Range
start = '2019-10-1'
end = '2020-10-30'


data = pd.DataFrame()

## Collecting data from yahoo finance 
for i in stocks:
    series= yf.Ticker(i).dividends.loc[start:end]
    data = pd.concat([data, series], axis=1)


data.columns = stocks
data.fillna(0)

# Transposing the data 
data1 = data.T

## To have better view of the data set cearting MultiIndex data frame
data2= data1.stack()
data2 = data2.to_frame()

## Assigning Names to Indices 
data2.index.names = ['symbol','Date']

# Name the column
data2.columns = ['checked']

# Adding columns using indices - This is additional work assuming may be columns will  simplify than using multiIndex
data2['symbols'] = data2.index.get_level_values('symbol')
data2['Dates'] = data2.index.get_level_values('Date')

## Some of the answer on stackoverflow suggested sorting
dividends_csv.sort_values(by=['symbols', 'Dates'], inplace=True)
data2.sort_values(by=['symbols','Dates'], inplace=True)

# This through an error
dividends_csv.loc[ (dividends_csv['symbols'] == data2['symbols'] ) &
          (dividends_csv['Dates'] == data2['Dates']), 'AuditRate3' ] = data2['checked']

## Merging - Merging is suplicating values
df = dividends_csv.merge(data2, on =["symbols"])

如果我运行这个：

# This through an error
dividends_csv.loc[ (dividends_csv['symbols'] == data2['symbols'] ) &
          (dividends_csv['Dates'] == data2['Dates']), 'AuditRate3' ] = data2['checked']

我得到这个错误

raceback (most recent call last):
  File "<input>", line 2, in <module>
  File "H:\Education\Arrow\Arrow_reports\PortfolioApp\venv\lib\site-packages\pandas\core\ops\common.py", line 65, in new_method
    return method(self, other)
  File "H:\Education\Arrow\Arrow_reports\PortfolioApp\venv\lib\site-packages\pandas\core\ops\__init__.py", line 365, in wrapper
    raise ValueError("Can only compare identically-labeled Series objects")
ValueError: Can only compare identically-labeled Series objects

我得到了重复的值

df
   symbols          Dates_x   Amounts   checked    Dates_y
0   ABP.AX  12/30/2019 0:00  0.094500  0.094500 2019-12-30
1   ABP.AX  12/30/2019 0:00  0.094500  0.090500 2020-06-29
2   ABP.AX   6/29/2020 0:00  0.090500  0.094500 2019-12-30
3   ABP.AX   6/29/2020 0:00  0.090500  0.090500 2020-06-29
4   AZJ.AX   2/24/2020 0:00  0.137000  0.137000 2020-02-24
5   AZJ.AX   2/24/2020 0:00  0.137000  0.137000 2020-08-24
6   AZJ.AX   8/24/2020 0:00  0.137000  0.137000 2020-02-24
7   AZJ.AX   8/24/2020 0:00  0.137000  0.137000 2020-08-24

我将感谢任何帮助

谢谢

df
   symbols          Dates_x   Amounts   checked    Dates_y
0   ABP.AX  12/30/2019 0:00  0.094500  0.094500 2019-12-30
1   ABP.AX  12/30/2019 0:00  0.094500  0.090500 2020-06-29
2   ABP.AX   6/29/2020 0:00  0.090500  0.094500 2019-12-30
3   ABP.AX   6/29/2020 0:00  0.090500  0.090500 2020-06-29
4   AZJ.AX   2/24/2020 0:00  0.137000  0.137000 2020-02-24
5   AZJ.AX   2/24/2020 0:00  0.137000  0.137000 2020-08-24
6   AZJ.AX   8/24/2020 0:00  0.137000  0.137000 2020-02-24
7   AZJ.AX   8/24/2020 0:00  0.137000  0.137000 2020-08-24