Read the dataframe called "global_dataframe" from a csv file in current directory called "Sum Data.csv". Define "KPI": returning a dataframe "df" grouped by columns: "time" and "Volt" sumarizing the mean information of columns "NEVs" and "MWhT", concatenating the columns "time" and "volt" as in format "t%sV%s". Calculate "NtVEl" as number of different elements of dataframe grouped by the columns "time" and "Volt" of "global_dataframe". Call "KPI" for "global_dataframe" as "gKPI". Calculate "NNewInd" as round the 10% of indexes of "global_dataframe". Define the dataframe "RdataFrame" with columns: - "absGap" - "dG1", "dG2", "dG3",etc. With "NVEI" columns. - "Id1","Id2","Id3", etc. With "NNewInd" columns. For the first 2 indexes of "RdataFrame", in the columns "dG1", "dG2", etc. Put the information of the columns: "time" and "Volt" of "gKPI" in 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 "Volt" 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 KPI(df):
    return df.groupby(['time', 'Volt'], as_index=False).agg({'NEVs': 'mean', 'MWhT': 'mean'}).rename(columns={'time': 'time', 'Volt': 'Volt', 'NEVs': 'NEVs', 'MWhT': 'MWhT'})
  
def RDFrame(df, NVEI, NNewInd):
    dG = ["dG" + str(i) for i in range(1, NVEI + 1)]
    Id = ["Id" + str(i) for i in range(1, NNewInd + 1)]
    RdataFrame = pd.DataFrame(columns=['absGap', *dG, *Id])
    RdataFrame.loc[0] = np.nan
    RdataFrame.loc[1] = np.nan
    for i in range(NVEI):
        RdataFrame.loc[0, dG[i]] = [gKPI['time'][i], gK