def answer_one():
    import pandas as pd
    import numpy as np

    x = pd.ExcelFile('Energy Indicators.xls')
    energy = x.parse(skiprows=17,skip_footer=(38))
    energy = energy[['Unnamed: 1','Petajoules','Gigajoules','%']]
    energy.columns = ['Country', 'Energy Supply', 'Energy Supply per Capita', '% Renewable']
    energy[['Energy Supply', 'Energy Supply per Capita', '% Renewable']] =  energy[['Energy Supply', 'Energy Supply per Capita', '% Renewable']].replace('...',np.NaN).apply(pd.to_numeric)
    energy['Energy Supply'] = energy['Energy Supply']*1000000
    energy['Country'] = energy['Country'].replace({'China, Hong Kong Special Administrative Region':'Hong Kong','United Kingdom of Great Britain and Northern Ireland':'United Kingdom','Republic of Korea':'South Korea','United States of America':'United States','Iran (Islamic Republic of)':'Iran'})
    energy['Country'] = energy['Country'].str.replace(r" \(.*\)","")

    GDP = pd.read_csv('world_bank.csv',skiprows=4)
    GDP['Country Name'] = GDP['Country Name'].replace('Korea, Rep.','South Korea')
    GDP['Country Name'] = GDP['Country Name'].replace('Iran, Islamic Rep.','Iran')
    GDP['Country Name'] = GDP['Country Name'].replace('Hong Kong SAR, China','Hong Kong')
    GDP = GDP[['Country Name','2006','2007','2008','2009','2010','2011','2012','2013','2014','2015']]
    GDP.columns = ['Country','2006','2007','2008','2009','2010','2011','2012','2013','2014','2015']

    ScimEn = pd.read_excel(io='scimagojr-3.xlsx')
    ScimEn_m = ScimEn[:15]

    df = pd.merge(ScimEn_m,energy,how='inner',left_on='Country',right_on='Country')
    final_df = pd.merge(df,GDP,how='inner',left_on='Country',right_on='Country')
    final_df = final_df.set_index('Country')

    return final_df

answer_one()