Define "KPI" function for dataframe grouped by columns "time" and "Volts" for mean data of columns "NEVs" and "MWht". Call "KPI" for "global_dataframe" as "gKPI". 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", "Volts"])[["NEVs", "MWht"]].mean()

gKPI = KPI(global_dataframe)

NVEI = len(gKPI.index)
NNewInd = len(gKPI.index[0])

RdataFrame = pd.DataFrame(
    {
        "absGap": [0.0] * Samples,
        **{
            "dG{}".format(i): [0.0] * NVEI for i in range(1, NVEI + 1)
        },
        **{
            "Id{}".format(i): [0.0] * NNewInd for i in range(1, NNewInd + 1)
        },
    }
)

for i in range(Samples):
    SDFrame = global_dataframe.sample(frac=0.1)
    sKPI = KPI(SDFrame)

    absGap = (sKPI - gKPI).