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 run(n):
  global_dataframe = pd.read_csv("Sum Data.csv")
  KPI = global_dataframe.groupby("Volt").agg({"NEVs": np.mean, "MWhT": np.mean})
  gKPI = KPI.agg(np.mean)
  NNewInd = round(len(global_dataframe) * 0.1)
  NVEI = len(global_dataframe["Volt"].unique())
  RdataFrame= pd.DataFrame(index=np.arange(0, n), columns=["absGap", "dG1", "dG2", "dG3", "dG4", "dG5", "dG6", "dG7", "dG8","dG9", "dG10", "dG11", "dG12", "dG13", "dG14", "dG15", "dG16", "dG17", "dG18", "dG19", "dG20", "dG21", "d