1. Read the dataframe "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.

def build_data_frame(global_dataframe):
    KPI = global_dataframe.groupby('Volt').mean().reset_index()
    gKPI = KPI.set_index("Volt").loc[global_dataframe.Volt]
    NNewInd = round(global_dataframe.shape[0] * 0.1)
    NVEl = global_dataframe["Volt"].unique().shape[0]
    RdataFrame = pd.DataFrame({"absGap": np.abs(gKPI.NEVs - global_dataframe.NEVs),
                               "dG1": global_dataframe.NEVs - gKPI.NEVs.iloc[0],
                               "dG2": global_dataframe.NEVs - gKPI.NEVs.iloc[1],
                               "dG3": global_dataframe.NEVs - gKPI.NEVs.iloc[2],
                               "dG4": global_dataframe.NEVs - gKPI.NEVs.iloc[