Read the dataframe called "global_dataframe" from a csv file in current directory called "Sum Data.csv". Define "KPI": returning a dataframe "df" grouped by columns: "time" and "Volt" sumarizing the mean information of columns "NEVs" and "MWhT", concatenating the columns "time" and "volt" as in format "t\sV\s". Calculate "NtVEl" as number of different elements of dataframe grouped by the columns "time" and "Volt" of "global_dataframe". Call "KPI" for "global_dataframe" as "gKPI". Calculate "NNewInd" as round the 10% of indexes of "global_dataframe". 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 read_csv():
  return pd.read_csv("Sum Data.csv")

def KPI(df):
  return df.groupby(["time", "Volt"]).mean()[["NEVs", "MWhT"]].reset_index().rename(columns={"time": "t", "Volt": "V"}).assign(KPI=lambda x: x.apply(lambda x: f"{x.t}\sV{x.V}\s", axis=1))

def NVEI(df):
  return df.groupby(["time", "Volt"]).mean().shape[0]

global_dataframe = read_csv()

gKPI = KPI(global_dataframe)

NVEI = NVEI(global_dataframe)

NNewInd = int(round(global_dataframe.shape[0]*0.1))

RdataFrame = pd.DataFrame(columns=["absGap", *[f"dG{i}" for i in range(1, N