content: import pandas as pd
  
  
  class DataFormatter():
      def __init__(self, dataloc):
          self.dataloc = dataloc
  
      def aggJsonToCsv(self):
          return "CSV file"
  
      def expTimestamp(self):
          return "Expand metric timestamp"
  
      def window(self):
          return "Window metrics"
  
      def pivot(self):
          return "Pivot values"
  
      def addID(self):
          return "Add new ID as index"
  
      def removeID(self):
          return "Remove selected column as index"
  
      def renameHeader(self):
          return "Rename headers"
  
      def normalize(self):
          return "Normalize data"
  
      def denormalize(self):
          return "Denormalize data"
  
  input_table = pd.read_csv("metrics.csv")
  
  
  for index, row in input_table.iterrows():
      input_table = input_table.append([row]*9)
  
  input_table = input_table.sort_values(['row ID'])
  input_table = input_table.reset_index(drop=True)
  
  for index, rows in input_table.iterrows():
  
      if int(index) > 59:
          print "Index to big!"
      time = rows[0].split(", ", 1) #In Knime row for timestamp is row(55) last one
      timeHour = time[1].split(":", 2)
      timeHourSeconds = timeHour[2].split(".", 1)
      timeHourSecondsDecimal = timeHour[2].split(".", 1)
      timeHourSecondsDecimal[0] = str(index)
      if len(timeHourSecondsDecimal[0]) == 1:
          timeHourSecondsDecimal[0] = '0%s' %timeHourSecondsDecimal[0]
      decimal = '.'.join(timeHourSecondsDecimal)
      timeHour[2] = decimal
      timenew = ':'.join(timeHour)
      time[1] = timenew
      finalString = ', '.join(time)
      input_table.set_value(index, 'row ID', finalString)
  
  input_table.to_csv('out.csv')
  
  
  