text: import numpy as np
  # from sklearn.preprocessing import normalize
  
  def normalize_numeric_col(df, skip_cols = [], print_process=False):
      numeric_columns = df.select_dtypes([np.number]).columns.tolist()
      drop_col = []
      for column in numeric_columns:
          if column in skip_cols:
              continue
  
          min = df[column].values.min()
          max = df[column].values.max()
  
          if max-min > 0:
              if print_process:
                  print("Normalizing column: ", column)
              df[column] = (df[column].values - min)/(max-min)
          else:
              drop_col.append(column)
  
      for column in drop_col:
          print("Drop Column with all Zeros: ", column)
          numeric_columns.remove(column)
          del df[column]
  
      return df, numeric_columns
  