text: import pandas as pd
  from os import listdir
  from os.path import isfile, join
  
  
  class InputParser:
  
      @staticmethod
      def parse_input(folder_path, header='infer'):
          input_file_names = [f for f in listdir(folder_path) if isfile(join(folder_path, f))]
  
          incrementer = 0
          input_database = {}
          for file in input_file_names:
  
              if file.split('0')[0] != 'frame':
                  continue
  
              input_data = pd.read_csv(folder_path + file, header=header)
              input_database[incrementer] = input_data.as_matrix()
  
              incrementer += 1
  
          if header is not None:
              return input_database, list(input_data)
          else:
              return input_database
  
      @staticmethod
      def get_labels(filename):
          return list(pd.read_csv(filename))
  
      # @staticmethod
      # def parse_input_scene_analyzer(folder_path, header='infer'):
      #     input_file_names = [f for f in listdir(folder_path) if isfile(join(folder_path, f))]
      #
      #     incrementer = 0
      #     input_database = {}
      #     for file in input_file_names:
      #
      #         if file.split('.')[0] != 'vision_features':
      #             continue
      #
      #         input_data = pd.read_csv(folder_path + file, header=header)
      #         input_database[incrementer] = input_data.as_matrix()
      #
      #         incrementer += 1
      #
      #     if header is not None:
      #         return input_database, list(input_data)
      #     else:
      #         return input_database
      #
      # @staticmethod
      # def parse_input_fire_data(folder_path, header='infer'):
      #     input_file_names = [f for f in listdir(folder_path) if isfile(join(folder_path, f))]
      #
      #     incrementer = 0
      #     input_database = {}
      #     for file in input_file_names:
      #
      #         if file.split('.')[0] != 'features':
      #             continue
      #
      #         input_data = pd.read_csv(folder_path + file, header=header)
      #         input_database[incrementer] = input_data.as_matrix()
      #
      #         incrementer += 1
      #
      #     if header is not None:
      #         return input_database, list(input_data)
      #     else:
      #         return input_database
      #
      # @staticmethod
      # def parse_input_telco_data(folder_path, header='infer'):
      #     input_file_names = [f for f in listdir(folder_path) if isfile(join(folder_path, f))]
      #
      #     incrementer = 0
      #     input_database = {}
      #     for file in input_file_names:
      #
      #         if file.split('.')[0] != 'outage_feb9_features':
      #             continue
      #
      #         input_data = pd.read_csv(folder_path + file, header=header)
      #         input_database[incrementer] = input_data.as_matrix()
      #
      #         incrementer += 1
      #
      #     if header is not None:
      #         return input_database, list(input_data)
      #     else:
      #         return input_database
      #
      # @staticmethod
      # def parse_input_vicroads_bluetooth_travel_data(folder_path, filename, header='infer'):
      #     input_file_names = [f for f in listdir(folder_path) if isfile(join(folder_path, f))]
      #
      #     incrementer = 0
      #     input_database = {}
      #     for file in input_file_names:
      #
      #         if file.split('.')[0] != filename:
      #             continue
      #
      #         input_data = pd.read_csv(folder_path + file, header=header)
      #         del input_data['Unnamed: 0']
      #
      #         input_database[incrementer] = input_data.as_matrix()
      #
      #         incrementer += 1
      #
      #     if header is not None:
      #         return input_database, list(input_data)
      #     else:
      #         return input_database