Python Dataframe aggregate/groupby columns to do calculation

Hi team,

Below myWACC() is calculated in a country. I would like to apply this calculation groupby its column such as TRBCEcoSectorName. below coding is show one country as output. Would how to make it output aggregate and grouping as column "TRBCEcoSectorName"? Wonder it is possible? Thanks in advance.

import asyncio

import pandas as pd

import numpy as np

from datetime import date

import plotly.express as px

import refinitiv.dataplatform.eikon as ek

import ipywidgets

from ipywidgets import *

from plotly.subplots import make_subplots

import plotly.graph_objects as go

# by using stane along Jupyter Note book

ek.set_app_key('DEFAULT_CODE_BOOK_APP_KEY')

def my_WACC():

start_date='07/01/2021'

scale='6'

curn='USD'

Rics = country_screen = 'SCREEN(U(IN(Equity(active,public,primary))/*UNV:Public*/), IN(TR.ExchangeCountryCode,"HK"), IN(TR.InstrumentTypeCode,"ADR","BDR","CEDEAR","CHINDR","DRC","EDR","FULLPAID","GDR","INDIDR","INTERDR","ORD","PDR"),TOP(TR.CompanyMarketCap, 5000, nnumber), CURN=USD)'

df_main,err=ek.get_data(Rics,

['TR.CompanyMarketCap',

'Zav(Avail(TR.TotalDebtOutstanding(Period=FI0),TR.TotalDebtOutstanding(Period=FY0)))',

'Zav(Avail(TR.TtlPreferredSharesOut(Period=FI0),TR.TtlPreferredSharesOut(Period=FY0)))',

'TR.WACCCostofEquity',

'TR.WACCCostofDebt',

'TR.WACCCostofPreferred',

'TR.ExchangeCountryCode',

'TR.TRBCEconomicSector'],

{'Sdate':start_date,'Scale':scale,'Curn':curn})

# rename the column label

df_main.set_axis(['Instrument',

'Company_MarketCap',

'Total_debt',

'Total_PS',

'WACC_CostOfEuity',

'WACC_CostOfDebt',

'WACC_CostOfPS',

'ExchangeCode',

'TRBCEcoSectorName'],axis=1, inplace=True)

df_main['weight_mkc_country']=df_main['Company_MarketCap']/df_main['Company_MarketCap'].sum()

df_main['weight_ttd_country']=df_main['Total_debt']/df_main['Total_debt'].sum()

df_main['weight_ps_country']=df_main['Total_PS']/df_main['Total_PS'].sum()

df_main['wavg_cost_equity_country'] = df_main['WACC_CostOfEuity']*df_main['weight_mkc_country']

df_main['wavg_cost_debt_country'] = df_main['WACC_CostOfDebt']*df_main['weight_ttd_country']

df_main['wavg_cost_ps_country'] = df_main['WACC_CostOfPS']*df_main['weight_ps_country']

total_capital = df_main['Company_MarketCap'].sum()+df_main['Total_debt'].sum()+df_main['Total_PS'].sum()

weight_MktCap = df_main['Company_MarketCap'].sum()/total_capital

weight_TtlDebt = df_main['Total_debt'].sum()/total_capital

weight_ps = df_main['Total_PS'].sum()/total_capital

display(df_main)

WACC_country = weight_MktCap*(df_main['wavg_cost_equity_country'].sum()/100) + weight_TtlDebt*(df_main['wavg_cost_debt_country'].sum()/100) + weight_ps*(df_main['wavg_cost_ps_country'].sum()/100)