Read the dataframe called "global_dataframe" from a csv file in current directory called "Sum Data.csv". Define "KPI": Grouping the dataframe by columns: "time" and "Volt" and get mean information of columns "NEVs" and "MWhT". 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. 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.

import pandas as pd
import numpy as np

def read_csv():
    return pd.read_csv("Sum Data.csv")
    
def KPI(df):
    return df.groupby(["time","Volt"]).agg(['mean'])[["NEVs","MWhT"]]
    
def NVEI(df):
    return len(df.groupby(["time","Volt"]))
    
def NNewInd(df):
    return round(len(df)*0.1)

def SampleDF(df):
    return df.sample(NNewInd(df))

def Indexes(df):
    return df.index.values.reshape(1,NNewInd(df))

def sKPI(df):
    return KPI(df)

def absGap(gKPI,sKPI):
    return abs(gKPI-sKPI)

def detGap(gKPI,sKPI):
    return gKPI-sKPI
    
def createR