Read the dataframe called "global_dataframe" from a csv file in current directory called "Sum Data.csv". Define "KPI": Grouping the dataframe by column "Volt" and get mean information of columns "NEVs" and "MWhT". Call "KPI" for "global_dataframe" as "gKPI". Calculate "NNewInd" as round the 10% of indexes of "global_dataframe". Calculate "NVEl" as number of different elements in the column "Volts" of "global_dataframe". Define the dataframe "RdataFrame" with columns: - "absGap" - "dG1", "dG2", "dG3",etc. With "NVEI" columns. - "Id1","Id2","Id3", etc. With "NNewInd" columns. 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.

def func_read_from_csv(df_name, path):
  df_name = pd.read_csv(path)
  return df_name
  
def func_kpi(df):
  df_ag = df.groupby(['Volts']).agg({'NEVs':'mean', 'MWhT':'mean'})
  return df_ag
  
def func_calc_new_ind(df):
  new_ind = np.random.choice(df.index, round(len(df.index)*0.1), replace=False)
  new_ind = new_ind.tolist()
  return new_ind
  
def func_calc_NVEI(df):
  NVEI = df["Volts"].nunique()
  return NVEI
  
global_dataframe = func_read_from_csv(global_dataframe,"Sum Data.csv")
gKPI = func_kpi(global_dataframe)
NNewInd = func_calc_new_ind(global_dataframe