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.

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