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. 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.

from pandas import read_csv
from pandas import DataFrame
from pandas import concat
from pandas import Series
import pandas as pd
import numpy as np
from numpy import mean
from numpy import std
from numpy import where
from numpy import array
from numpy import random
import math
from math import floor
from math import sqrt
from math import pow
from math import isnan
from random import sample

def KPI(df):
    df = df.groupby(['time','Volt'])['NEVs','MWhT'].mean()
    NVEI = len(set(df.index.labels[1]))
    return df,NVEI

global_dataframe = read_csv('Sum Data.csv')
gKPI,NVEI = KPI(global_dataframe)

NNewInd = floor(len(global_dataframe)*0.1)
RdataFrame = DataFrame(columns=['absGap']+['dG'+str(i) for i in range(1,N