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').agg({'NEVs': 'mean', 'MWhT': 'mean'})
    KPI = KPI.reset_index()
    return KPI

gKPI = KPI(global_dataframe)

NNewInd = round(len(global_dataframe)*0.1)
NVEl = len(global_dataframe.Volt.unique())

RdataFrame = pd.DataFrame(columns= ['aGap'] + ['dG' + str(i) for i in range(1, NVEl+1)] + ['Id' + str(i) for i in range(1, NNewInd+1)])

Samples = 1000

for Sample in range(Samples):
    SDFrame = global_dataframe.sample(NNewInd)
    sKPI = KPI(SDFrame)
    Indexes = SDFrame.index.values.reshape(1