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", including a key column composed by information of columns "time" and "volt". 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.

import pandas as pd
import numpy as np
import random

global_dataframe = pd.read_csv('sum_data.csv')

def KPI(df):
    KPI = df.groupby(["time","Volt"])["NEVs","MWhT"].mean()
    return KPI
    
gKPI = KPI(global_dataframe)
NVEI = len(gKPI.index.unique())
NNewInd = int(round(len(global_dataframe.index)*0.1))


RdataFrame = pd.DataFrame(index=range(1000),
                          columns=["absGap","dG1","dG2","dG3","dG4","dG5","dG6","dG7","dG8","dG9","dG10","dG11","dG12","dG13","dG14","dG15","dG16","dG17","dG18","dG19","dG20","dG21","dG22","dG23","d