Reduce to only row totalRevenue and rename the colunmn names in years using yahoo finance and pandas

Question

I try to scrape the yearly total revenues from yahoo finance using pandas and yahoo_fin by using the following code:

from yahoo_fin import stock_info as si
import yfinance as yf
import pandas as pd

tickers = ('AAPL', 'MSFT', 'IBM')

income_statements_yearly= [] #All numbers in thousands

for ticker in tickers:    
    income_statement = si.get_income_statement(ticker, yearly=True)    
    years = income_statement.columns
    income_statement.insert(loc=0, column='Ticker', value=ticker)
    for i in range(4):       
        #print(years[i].year)        
        income_statement.rename(columns = {years[i]:years[i].year}, inplace = True)
    income_statements_yearly.append(income_statement)
income_statements_yearly = pd.concat(income_statements_yearly)
income_statements_yearly

The result I get looks like:

I would like to create on that basis another dataframe revenues and reduce the dataframe to only the row totalRevenue instead of getting all rows and at the same time I would love to rename the columns 2021, 2020, 2019, 2018 to revenues_2021, revenues_2020, revenues_2019, revenues_2018.

The result shall look like:

df = pd.DataFrame({'Ticker': ['AAPL', 'MSFT', 'IBM'],
                   'revenues_2021': [365817000000, 168088000000, 57351000000],
                   'revenues_2020': [274515000000, 143015000000, 55179000000],
                   'revenues_2019': [260174000000, 125843000000, 57714000000],
                   'revenues_2018': [265595000000, 110360000000, 79591000000]})

How can I solve this in an easy and fast way?

Ty for your help in advance.

Answer 1

CODE

revenues = income_statements_yearly.loc["totalRevenue"].reset_index(drop=True)
revenues.columns = ["revenues_" + str(col) for col in revenues.columns]

OUTPUT

  revenues_Ticker revenues_2021 revenues_2020 revenues_2019 revenues_2018
0            AAPL  365817000000  274515000000  260174000000  265595000000
1            MSFT  168088000000  143015000000  125843000000  110360000000
2             IBM   57351000000   55179000000   57714000000   79591000000