Import pandas 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.

import pandas as pd

def get_dataframe():
  global_dataframe = pd.read_csv("Sum Data.csv")
  KPI = global_dataframe.groupby('Volt').mean().loc[:,['NEVs','MWhT']]
  gKPI = KPI.loc[global_dataframe["Volt"]]
  NNewInd = int(round(0.1*len(global_dataframe)))
  NVEI = len(global_dataframe["Volt"].unique())
  RdataFrame = pd.DataFrame(columns=["absGap"] + ["dG"+str(i) for i in range(1,NVEI+1)] + ["Id"+str(i) for i in range(1,NNewInd+1)])

  return global_dataframe, gKPI, NNewInd, NVEI, RdataFrame