How to change a data frame in order to plot the data in Python

I want to scrape multiple stocks from Yahoo finance, and convert the stocks to euro, if the stock is for instance in USD dollar of GBP. I have a code which converts the historical stocks to euro, with the date, but now I have to make the df that it is possible to plot the data correctly. This is code I have now.

 import yfinance as yf
 import pandas as pd
 import numpy as np
    
 start = '2013-01-01'
 end = '2021-01-01'
    
 rates = ['USDEUR=X', 'GBPEUR=X']
 tickers = yf.Tickers(' '.join(rates))
    
 exchange_rates = []
for i in range(len(tickers.tickers)):
    exchange_rates.append(tickers.tickers[i].history(start=start, end=end).Close)

ex_df = pd.DataFrame(exchange_rates).T
ex_df.columns = rates
ex_df['EUREUR=X'] = 1.0

assets = {'^AEX':'EUR', 'AAPL':'USD', 'AZN.L':'GBP', 'AD.AS':'EUR', 'ASML.AS':'EUR'}

  stock_df = pd.DataFrame()

for k,v in assets.items():
    data = yf.download(k, start=start, end=end, progress=False).Close.to_frame()
    data['ticker'] = k
    if v[:3] == 'EUR':
        data['rating'] = ex_df['EUREUR=X']
        data['price'] = data['Close'] / data['rating'] 
    elif v[:3] == 'USD':
        data['rating'] = ex_df['USDEUR=X']
        data['price'] = data['Close'] / data['rating'] 
    elif v[:3] == 'GBP':
        data['rating'] = ex_df['GBPEUR=X']
        data['price'] = data['Close'] / data['rating'] 
    else:
        data['rating'] = np.NaN
        data['price'] = np.NaN
stocks_df = pd.concat([stock_df, data], axis=0)

I will repost the full version of the code again. It is the same for both df.fillna(). I also replaced the graph image. Good luck.

import yfinance as yf
import pandas as pd
import numpy as np

start = '2020-07-01'
end = '2021-01-01'

rates = ['USDEUR=X', 'GBPEUR=X']
tickers = yf.Tickers(' '.join(rates))

exchange_rates = []
for i in range(len(tickers.tickers)):
    exchange_rates.append(tickers.tickers[i].history(start=start, end=end).Close)

ex_df = pd.DataFrame(exchange_rates).T
ex_df.columns = rates
ex_df['EUREUR=X'] = 1.0

assets = {'^AEX':'EUR', 'AAPL':'USD', 'AZN.L':'GBP', 'AD.AS':'EUR', 'ASML.AS':'EUR'}

stock_df = pd.DataFrame()
for k,v in assets.items():
    data = yf.download(k, start=start, end=end, progress=False).Close.to_frame()
    data['ticker'] = k
    if v[:3] == 'EUR':
        data['rating'] = ex_df['EUREUR=X']
        data['price'] = data['Close'] / data['rating'] 
    elif v[:3] == 'USD':
        data['rating'] = ex_df['USDEUR=X']
        data['price'] = data['Close'] / data['rating'] 
    elif v[:3] == 'GBP':
        data['rating'] = ex_df['GBPEUR=X']
        data['price'] = data['Close'] / data['rating'] 
    else:
        data['rating'] = np.NaN
        data['price'] = np.NaN

    stock_df = pd.concat([stock_df, data], axis=0)

my_stocks = stock_df.pivot(columns='ticker', values='price')
my_stocks.head()

ticker  AAPL    AD.AS   ASML.AS     AZN.L   ^AEX
Date                    
2020-06-30  102.574478  24.260000   326.899994  7694.486885     559.729980
2020-07-01  102.278087  23.879999   327.149994  7665.306388     562.669983
2020-07-02  102.418480  24.129999   332.750000  7758.488549     570.750000
2020-07-03  102.418480  23.870001   333.950012  7626.217088     568.630005
2020-07-06  105.119162  23.900000   343.549988  7810.244933     578.520020

import matplotlib.pyplot as plt

for c in my_stocks.columns.values[1:]:
    plt.plot(my_stocks.index, my_stocks[c], label=c)

plt.title('title')
plt.xlabel('Date',fontsize=18)
plt.ylabel('Adj. Price € (EUR)',fontsize=18)

plt.legend(my_stocks.columns.values[1:], loc='center right')
plt.show()