code: def _convert_data(
      self,
      data: Mapping[Hashable, np.ndarray],
  ) -> Mapping[Hashable, ArrayLike]:
      # apply converters
      clean_conv = self._clean_mapping(self.converters)
      clean_dtypes = self._clean_mapping(self.dtype)
  
      # Apply NA values.
      clean_na_values = {}
      clean_na_fvalues = {}
  
      if isinstance(self.na_values, dict):
          for col in self.na_values:
              na_value = self.na_values[col]
              na_fvalue = self.na_fvalues[col]
  
              if isinstance(col, int) and col not in self.orig_names:
                  col = self.orig_names[col]
  
              clean_na_values[col] = na_value
              clean_na_fvalues[col] = na_fvalue
      else:
          clean_na_values = self.na_values
          clean_na_fvalues = self.na_fvalues
  
      return self._convert_to_ndarrays(
          data,
          clean_na_values,
          clean_na_fvalues,
          self.verbose,
          clean_conv,
          clean_dtypes,
      )
  
imports: from __future__ import annotations
  from collections import abc, defaultdict
  import csv
  from io import StringIO
  import re
  from typing import IO, TYPE_CHECKING, DefaultDict, Literal, cast
  import warnings
  import numpy
  from pandas._libs import lib
  from pandas.errors import EmptyDataError, ParserError, ParserWarning
  from pandas.util._decorators import cache_readonly
  from pandas.util._exceptions import find_stack_level
  from pandas.core.dtypes.common import is_bool_dtype, is_integer, is_numeric_dtype
  from pandas.core.dtypes.inference import is_dict_like
  from pandas.io.common import dedup_names, is_potential_multi_index
  from pandas.io.parsers.base_parser import ParserBase, parser_defaults
label_desc: Use image node_id 7 for calling the PythonParser obj's underlying member method code with example usage: obj._convert_data(data) and returns: self