input: Write a python function to modify a dataframe by adding a new column 'new_value' which is the 'value' column incremented by 1, replacing values in the 'id' column by prefixing 'id_', and updating the 'value' column by doubling its values.
  assert edit_dataframe(pd.DataFrame({'id': [1, 2, 3], 'value': [10, 20, 30]})).equals(pd.DataFrame({'id': [1, 2, 3], 'value': [20, 40, 60], 'new_value': [11, 21, 31], 'new_id': ['id_1', 'id_2', 'id_3']}))
  assert edit_dataframe(pd.DataFrame({'id': [4, 5, 6], 'value': [40, 50, 60]})).equals(pd.DataFrame({'id': [4, 5, 6], 'value': [80, 100, 120], 'new_value': [41, 51, 61], 'new_id': ['id_4', 'id_5', 'id_6']}))
  assert edit_dataframe(pd.DataFrame({'id': [7, 8, 9], 'value': [70, 80, 90]})).equals(pd.DataFrame({'id': [7, 8, 9], 'value': [140, 160, 180], 'new_value': [71, 81, 91], 'new_id': ['id_7', 'id_8', 'id_9']}))
output: def edit_dataframe(df):
      df['new_value'] = df['value'] + 1
      df['new_id'] = 'id_' + df['id'].astype(str)
      df['value'] = df['value'] * 2
      return df