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.

def RFrame(global_dataframe, NVEI, Sample, NNewInd):
    RdataFrame = pd.DataFrame(columns=['absGap', 'dG1', 'dG2', 'dG3', 'dG4'])
    gKPI = KPI(global_dataframe)
    l = 0
    for i in range(Sample):
        SDFrame = global_dataframe.sample(frac = 0.1)
        sKPI = KPI(SDFrame)
        Indexes = SDFrame.index.values.reshape(1,len(SDFrame.index.values))
        absGap = abs(gKPI['time'] - sKPI['time'])
        detGap = gKPI['time'] - sKPI['time']
        RdataFrame.loc[i] = [absGap] + [detGap[k] for k in range(NVEI)]
        RdataFrame.loc[i] = RdataFrame.loc[i].append(pd.Series(Indexes.transpose(),