1. Read the dataframe called "global_dataframe" from a csv file in current directory called "Sum Data.csv". 2. Define "KPI": Grouping the dataframe by column "Volt" and get mean information of columns "NEVs" and "MWhT". 3. Execute "KPI" for "global_dataframe" as "gKPI". 4. Calculate "NNewInd" as round the 10% of indexes of "global_dataframe". 5. Calculate "NVEl" as number of different elements in the column "Volts" of "global_dataframe". 6. Define the dataframe "RdataFrame" with columns: 6.1."absGap" 6.2. "dG1", "dG2", "dG3",etc. With "NVEI" columns. 6.3. "Id1","Id2","Id3", etc. With "NNewInd" columns. 7. Start a loop of 1000 "Sample" in "Samples", per each sample: 7.1. Get sub dataframe "SDFrame" componed by 10% of the indexes of "global_dataframe" choosen randomly, Store the indexes choosen transposed in the dataframe "RdataFrame" in the columns "Id1","Id2","Id3",etc. 7.2. Execute "KPI" for "SDFrame" as "sKPI". 7.3. Calculate absolute error, "absGap", and detailed error, "detGap", between "sKPI" and "gKPI". 7.4. Store "absGap" and "detGap" in the dataframe "RdataFrame" in columns "aGap", "dG1", "dG2", "dG3",etc.

import random
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

def samp(global_dataframe, NNewInd,NVEI):
    #1. Read the dataframe called "global_dataframe" from a csv file in current directory called "Sum Data.csv".
    global_dataframe = pd.read_csv('Sum Data.csv')
    #2. Define "KPI": Grouping the dataframe by column "Volt" and get mean information of columns "NEVs" and "MWhT".
    KPI = global_dataframe.groupby('Volt', as_index=False).agg({'NEVs': 'mean', 'MWhT': 'mean'})
    #3. Execute "KPI" for "global_dataframe" as  "gKPI".
    gKPI = global_dataframe.groupby('Volt', as_index=False).agg({'NEVs': 'mean', 'MWhT': 'mean'})
    #4