Define an interger variable "NVEI" with the value 128 Define an interger variable "NNewInd" with the value 16 Define the dataframe "RdataFrame" with columns: - "absGap" - "dG1", "dG2", "dG3",etc. With "NVEI" columns. - "Id1","Id2","Id3", etc. With "NNewInd" columns. For the first index of "RdataFrame", in the columns "dG1", "dG2", etc. Put the information of the column "time" of "gKPI" 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.

NVEI = 128
NNewInd = 16
RdataFrame = pd.DataFrame(columns=["absGap"] + ["dG" + str(i) for i in list(range(1,NVEI+1))] + ["Id" + str(i) for i in list(range(1,NNewInd+1))])
RdataFrame.loc[0] = [0] + [0]*NVEI + [0]*NNewInd
for i in range(1,NVEI+1):
    RdataFrame.loc[0,"dG" + str(i)] = gKPI.loc[:,"time"][i]
Samples = 1000    
for i in range(Samples):
    SDFrame = global_dataframe.sample(frac = 0.1, random_state = i)
    sKPI = KPI(SDFrame)
    absGap = abs(gKPI.loc[:,"mean"][0] - sKPI.loc[:,"mean"][0])
    detGap = [