code: def _parse_pandas_column(column_object):
      # There are two types of parsing we do here.
      # 1. We explicitly parse the types we know how to parse
      # 2. We try to partially match a type name to the column's type.
      #   - We do this because `datetime64` can match `datetime64[ns]` and `datetime64[ns, UTC]`
      #   - We do this because period can match `period[D]` and `period[2D]` etc.
      #   - There are just too many types to explicitly parse so we go by this heuristic
      # We have a default parser called `truncate_long_objects` which type casts any column to string
      # and truncates it to 30 characters.
      # If there is any form of TypeError or ValueError we set the column value to "Unsupported Type"
      # We also set columns which are have null values to "null" strings
      time_format = "%Y-%m-%dT%H:%M:%S%Z"
      truncate_long_objects = (
          lambda x: x.astype("string").str.slice(0, 30) + "..."
          if len(x) > 0 and x.astype("string").str.len().max() > 30
          else x.astype("string")
      )
      type_parser = {
          "int64": lambda x: x,
          "float64": lambda x: x,
          "bool": lambda x: x,
          "object": lambda x: truncate_long_objects(x.fillna("null")),
          "category": truncate_long_objects,
      }
  
      partial_type_name_match_parsers = {
          "complex": {
              "complex": lambda x: x.astype("string"),
          },
          "datetime": {
              "datetime64": lambda x: x.dt.strftime(time_format),
              "timedelta": lambda x: x.dt.total_seconds(),
          },
          "interval": {
              "interval": lambda x: x.astype("string"),
          },
          "period": {
              "period": lambda x: x.astype("string"),
          },
      }
  
      def _match_partial_type():
          col_type = column_object.dtype
          for (
              _,
              type_parsers,
          ) in partial_type_name_match_parsers.items():
              for type_name, parser in type_parsers.items():
                  if type_name in str(col_type):
                      return parser(column_object)
          return None
  
      try:
          col_type = str(column_object.dtype)
          if col_type in type_parser:
              return type_parser[col_type](column_object.fillna("null"))
          else:
              parsed_col = _match_partial_type()
              if parsed_col is not None:
                  return parsed_col.fillna("null")
          return truncate_long_objects(column_object.fillna("null"))
      except ValueError as e:
          return "Unsupported type: {0}".format(col_type)
      except TypeError as e:
          return "Unsupported type: {0}".format(col_type)
  
imports: import json
  import sys
  import base64
  import datetime
  from collections import namedtuple
label_desc: Use image node_id 25 for calling the TaskToDict obj's underlying member method code with example usage: obj._parse_pandas_column(column_object) and returns: None, truncate_long_objects, type_parser, str, str, parsed_col, parser