Read the dataframe called "global_dataframe" from a csv file in current directory called "Sum Data.csv". Define "KPI": Grouping the dataframe by column "Volts" 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 list "Indexes" of indexes choosen transposed. - Call "KPI" for "SDFrame" as "sKPI". - Calculate absolute error, "absGap", and detailed error per "Volts" value, "detGap", between "sKPI" and "gKPI". - Store "absGap" and "detGap" in the dataframe "RdataFrame" in columns "aGap", "dG1", "dG2", "dG3",etc. - Store "Indexes" in the dataframe "RdataFrame" in the columns "Id1","Id2","Id3",etc.

def main():
    global_dataframe = pd.read_csv("Sum Data.csv")
    KPI = global_dataframe.groupby("Volts").mean()[["NEVs","MWhT"]]
    gKPI = KPI
    NNewInd = int(len(global_dataframe.index)*0.1)
    NVEl = len(global_dataframe["Volts"].unique())
    RdataFrame = pd.DataFrame(columns=["absGap"]+["dG"+str(i) for i in range(1,NVEl+1)]+["Id"+str(i) for i in range(1,NNewInd+1)])
    for Sample in range(1000):
        SDFrame = global_dataframe.sample(n=NNewInd)
        Indexes = SDFrame.index.tolist()
        sKPI = SDFrame.groupby("Volts").mean()[["NEVs","MWhT"]]
        absGap = abs(sKPI.sum().sum() -