Read the dataframe called "global_dataframe" from a csv file in current directory called "Sum Data.csv". Define "KPI": Grouping the dataframe by column "Volt" and get mean information of columns "NEVs" and "MWhT". Call "KPI" for "global_dataframe" as "gKPI". Calculate "NNewInd" as round the 10% of indexes of "global_dataframe". Calculate "NVEl" as number of different elements in the column "Volts" of "global_dataframe". Define the dataframe "RdataFrame" with columns: - "absGap" - "dG1", "dG2", "dG3",etc. With "NVEI" columns. - "Id1","Id2","Id3", etc. With "NNewInd" columns. Start a loop of 1000 "Sample" in "Samples", per each sample: - Get sub dataframe "SDFrame" componed by 10% of the indexes of "global_dataframe" choosen randomly, Store the indexes choosen transposed in the dataframe "RdataFrame" in the columns "Id1","Id2","Id3",etc. - Call "KPI" for "SDFrame" as "sKPI". - Calculate absolute error, "absGap", and detailed error, "detGap", between "sKPI" and "gKPI". - Store "absGap" and "detGap" in the dataframe "RdataFrame" in columns "aGap", "dG1", "dG2", "dG3",etc.

def main():
    global_dataframe = pd.read_csv('Sum Data.csv')
    KPI = lambda dataframe : dataframe.groupby(['Volts']).mean()[['NEVs', 'MWhT']]
    gKPI = KPI(global_dataframe)
    NNewInd = round(len(global_dataframe.index)*0.1)
    NVEI = len(global_dataframe['Volts'].unique())
    RdataFrame = pd.DataFrame(columns=["absGap"]+["dG"+str(x) for x in range (1,NVEI+1)]+["Id"+str(x) for x in range(1,NNewInd+1)])
    samples = 1000
    for sample in range (samples):    
        SDFrame = global_dataframe.sample(frac=0.1)
        RdataFrame.loc[sample,'Id1':'Id'+str(NNewInd)]= SDFrame.index.values
        sKPI = KPI(SD