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", concatenating the columns "time" and "volt" as in format "t%sV%s". 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

#Reading data
global_dataframe = pd.read_csv('Sum Data.csv')

#Defining KPI function
def KPI(df):
    df = df.groupby(["Time", "Volt"]).agg({"NEVs": np.mean, "MWhT": np.mean})
    df.reset_index(level=["Time", "Volt"], inplace=True)
    df["Time"] = "t" + df["Time"]
    df["Volt"] = "V" + df["Volt"]
    df["Time"] = df["Time"] + df["Volt"]
    df = df.drop(columns=["Volt"])
    return df

#Calculating NVEI 
NVEI = len(KPI(global_dataframe).index)

#Calculating NNewInd
NNewInd = int(0.1*len(global_dataframe))

#Defining RdataFrame
RdataFrame = pd.DataFrame