code: def describe_categorical_1d(
      data: Series,
      percentiles_ignored: Sequence[float],
  ) -> Series:
      """Describe series containing categorical data.
  
      Parameters
      ----------
      data : Series
          Series to be described.
      percentiles_ignored : list-like of numbers
          Ignored, but in place to unify interface.
      """
      names = ["count", "unique", "top", "freq"]
      objcounts = data.value_counts()
      count_unique = len(objcounts[objcounts != 0])
      if count_unique > 0:
          top, freq = objcounts.index[0], objcounts.iloc[0]
          dtype = None
      else:
          # If the DataFrame is empty, set 'top' and 'freq' to None
          # to maintain output shape consistency
          top, freq = np.nan, np.nan
          dtype = "object"
  
      result = [data.count(), count_unique, top, freq]
  
      from pandas import Series
  
      return Series(result, index=names, name=data.name, dtype=dtype)
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from typing import TYPE_CHECKING, Any, Callable, Hashable, Sequence, cast
  import numpy
  from pandas._libs.tslibs import Timestamp
  from pandas._typing import DtypeObj, NDFrameT, npt
  from pandas.util._validators import validate_percentile
  from pandas.core.dtypes.common import is_bool_dtype, is_complex_dtype, is_extension_array_dtype, is_numeric_dtype
  from pandas.core.dtypes.dtypes import DatetimeTZDtype
  from pandas.core.arrays.arrow.dtype import ArrowDtype
  from pandas.core.arrays.floating import Float64Dtype
  from pandas.core.reshape.concat import concat
  from pandas.io.formats.format import format_percentiles
label_desc: Use image node_id 4 for calling a global function with example usage: describe_categorical_1d(data, percentiles_ignored) and returns: Series