Read the dataframe called "global_dataframe" from a csv file in current directory called "Sum Data.csv". Define "KPI": Grouping the dataframe by column "Volt" and get mean information of columns "NEVs" and "MWhT". Call "KPI" for "global_dataframe" as "gKPI". Calculate "NNewInd" as round the 10% of indexes of "global_dataframe". Calculate "NVEl" as number of different elements in the column "Volt" of "global_dataframe". Define the dataframe "RdataFrame" with columns: - "absGap" - "dG1", "dG2", "dG3",etc. With "NVEI" columns. - "Id1","Id2","Id3", etc. With "NNewInd" columns. 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.

global_dataframe = pd.read_csv("Sum Data.csv")

def KPI(df):
  KPI = df.groupby("Volt").mean()[["NEVs", "MWhT"]]
  return KPI

gKPI = KPI(global_dataframe)

NNewInd = int(round(len(global_dataframe.index)*0.1, 0))
NVEI = len(global_dataframe["Volt"].unique())

RdataFrame = pd.DataFrame(index=range(0,1000), columns=["absGap"] + ["dG" + str(x+1) for x in range(0, NVEI)] + ["Id" + str(x+1) for x in range(0, NNewInd)])

for Sample in range(0, 1000):
  Indexes = list(np.random.choice(global_dataframe.index, NNewInd, replace=False))
  Indexes = [Indexes]
  SDFrame = global_dataframe.loc[Indexes,