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 random_dataframe(df, I1, I2, NVEI, NNewInd):
    d1 = {'absGap': [0.0] * 1}
    for i in range(NVEI):
        d1['dG' + str(i + 1)] = [0.0] * 1
    for i in range(NNewInd):
        d1['Id' + str(i + 1)] = [0.0] * 1

    RdataFrame = pd.DataFrame(data=d1)
    for i in range(2):
        RdataFrame.at[i, ['dG1', 'dG2', 'dG3']] = (gKPI.loc[[0, 1], 'Volt':'time'])
    # RdataFrame.loc[0:1, ['dG1', 'dG2', 'dG3']] = (gKPI.loc[[0, 1], 'Volt':'time'])  # WRONG!

    Samples = 1000
    for Sample in range(Samples