class_docstr: Extension array for string data.
  
  .. warning::
  
     StringArray is considered experimental. The implementation and
     parts of the API may change without warning.
  
  Parameters
  ----------
  values : array-like
      The array of data.
  
      .. warning::
  
         Currently, this expects an object-dtype ndarray
         where the elements are Python strings
         or nan-likes (``None``, ``np.nan``, ``NA``).
         This may change without warning in the future. Use
         :meth:`pandas.array` with ``dtype="string"`` for a stable way of
         creating a `StringArray` from any sequence.
  
      .. versionchanged:: 1.5.0
  
         StringArray now accepts array-likes containing
         nan-likes(``None``, ``np.nan``) for the ``values`` parameter
         in addition to strings and :attr:`pandas.NA`
  
  copy : bool, default False
      Whether to copy the array of data.
  
  Attributes
  ----------
  None
  
  Methods
  -------
  None
  
  See Also
  --------
  :func:`pandas.array`
      The recommended function for creating a StringArray.
  Series.str
      The string methods are available on Series backed by
      a StringArray.
  
  Notes
  -----
  StringArray returns a BooleanArray for comparison methods.
  
  Examples
  --------
  >>> pd.array(['This is', 'some text', None, 'data.'], dtype="string")
  <StringArray>
  ['This is', 'some text', <NA>, 'data.']
  Length: 4, dtype: string
  
  Unlike arrays instantiated with ``dtype="object"``, ``StringArray``
  will convert the values to strings.
  
  >>> pd.array(['1', 1], dtype="object")
  <PandasArray>
  ['1', 1]
  Length: 2, dtype: object
  >>> pd.array(['1', 1], dtype="string")
  <StringArray>
  ['1', '1']
  Length: 2, dtype: string
  
  However, instantiating StringArrays directly with non-strings will raise an error.
  
  For comparison methods, `StringArray` returns a :class:`pandas.BooleanArray`:
  
  >>> pd.array(["a", None, "c"], dtype="string") == "a"
  <BooleanArray>
  [True, <NA>, False]
  Length: 3, dtype: boolean
code: def _str_map(
      self,
      f,
      na_value=None,
      dtype: Dtype | None = None,
      convert: bool = True,
  ):
      from pandas.arrays import BooleanArray
  
      if dtype is None:
          dtype = StringDtype(storage="python")
      if na_value is None:
          na_value = self.dtype.na_value
  
      mask = isna(self)
      arr = np.asarray(self)
  
      if is_integer_dtype(dtype) or is_bool_dtype(dtype):
          constructor: type[IntegerArray] | type[BooleanArray]
          if is_integer_dtype(dtype):
              constructor = IntegerArray
          else:
              constructor = BooleanArray
  
          na_value_is_na = isna(na_value)
          if na_value_is_na:
              na_value = 1
          result = lib.map_infer_mask(
              arr,
              f,
              mask.view("uint8"),
              convert=False,
              na_value=na_value,
              # error: Argument 1 to "dtype" has incompatible type
              # "Union[ExtensionDtype, str, dtype[Any], Type[object]]"; expected
              # "Type[object]"
              dtype=np.dtype(dtype),  # type: ignore[arg-type]
          )
  
          if not na_value_is_na:
              mask[:] = False
  
          return constructor(result, mask)
  
      elif is_string_dtype(dtype) and not is_object_dtype(dtype):
          # i.e. StringDtype
          result = lib.map_infer_mask(
              arr,
              f,
              mask.view("uint8"),
              convert=False,
              na_value=na_value,
          )
          return StringArray(result)
      else:
          # This is when the result type is object. We reach this when
          # -> We know the result type is truly object (e.g. .encode returns bytes
          #    or .findall returns a list).
          # -> We don't know the result type. E.g. `.get` can return anything.
          return lib.map_infer_mask(arr, f, mask.view("uint8"))
  
imports: from __future__ import annotations
  from typing import TYPE_CHECKING, Literal
  import numpy
  from pandas._config import get_option
  from pandas._libs import lib, missing
  from pandas._libs.arrays import NDArrayBacked
  from pandas._typing import AxisInt, Dtype, Scalar, npt, type_t
  from pandas.compat import pa_version_under7p0
  from pandas.compat.numpy import function
  from pandas.util._decorators import doc
  from pandas.core.dtypes.base import ExtensionDtype, StorageExtensionDtype, register_extension_dtype
  from pandas.core.dtypes.common import is_array_like, is_bool_dtype, is_dtype_equal, is_integer_dtype, is_object_dtype, is_string_dtype, pandas_dtype
  from pandas.core import ops
  from pandas.core.array_algos import masked_reductions
  from pandas.core.arrays import ExtensionArray, FloatingArray, IntegerArray
  from pandas.core.arrays.floating import FloatingDtype
  from pandas.core.arrays.integer import IntegerDtype
  from pandas.core.arrays.numpy_ import PandasArray
  from pandas.core.construction import extract_array
  from pandas.core.indexers import check_array_indexer
  from pandas.core.missing import isna
label_desc: Use image node_id 18 for calling the StringArray obj's underlying member method code with example usage: obj._str_map(f, na_value, dtype, convert) and returns: constructor, StringArray, lib