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