code: def _process_pandas_column(
      X_col, categories, feature_type, min_unique_continuous
  ):
      if isinstance(X_col.dtype, np.dtype):
          if (
              issubclass(X_col.dtype.type, np.floating)
              or issubclass(X_col.dtype.type, np.integer)
              or X_col.dtype.type is np.bool_
          ):
              X_col = X_col.values
              return _process_ndarray(
                  X_col,
                  None,
                  categories,
                  feature_type,
                  min_unique_continuous,
              )
          elif X_col.dtype.type is np.object_:
              nonmissings = None
              if X_col.hasnans:
                  # if hasnans is true then there is definetly a real missing value in there and not just a mask
                  nonmissings = X_col.notna().values
                  X_col = X_col.dropna()
              X_col = X_col.values
              return _process_ndarray(
                  X_col,
                  nonmissings,
                  categories,
                  feature_type,
                  min_unique_continuous,
              )
      elif isinstance(X_col.dtype, pd.CategoricalDtype):
          # unlike other missing value types, we get back -1's for missing here, so no need to drop them
          X_col = X_col.values
          is_ordered = X_col.ordered
          pd_categories = X_col.categories.values.astype(
              dtype=np.unicode_, copy=False
          )
          X_col = X_col.codes
  
          if feature_type == "ignore":
              pd_categories = pd_categories.astype(dtype=np.object_)
              pd_categories = np.insert(pd_categories, 0, None)
              bad = pd_categories[X_col + 1]
              return None, None, bad, "ignore"
          else:
              if categories is None:
                  # called under: fit
                  (
                      X_col,
                      categories,
                  ) = _encode_pandas_categorical_initial(
                      X_col, pd_categories, is_ordered, feature_type
                  )
                  bad = None
              else:
                  # called under: predict
                  X_col, bad = _encode_pandas_categorical_existing(
                      X_col, pd_categories, categories
                  )
  
              return (
                  "ordinal" if is_ordered else "nominal",
                  X_col,
                  categories,
                  bad,
              )
      elif (
          issubclass(X_col.dtype.type, np.integer)
          or X_col.dtype.type is np.bool_
      ):
          # this handles Int8Dtype to Int64Dtype, UInt8Dtype to UInt64Dtype, and BooleanDtype
          nonmissings = None
          if X_col.hasnans:
              # if hasnans is true then there is definetly a real missing value in there and not just a mask
              nonmissings = X_col.notna().values
              X_col = X_col.dropna()
          X_col = X_col.values
          X_col = X_col.astype(dtype=X_col.dtype.type, copy=False)
          return _process_ndarray(
              X_col,
              nonmissings,
              categories,
              feature_type,
              min_unique_continuous,
          )
  
      # TODO: implement pd.SparseDtype
      # TODO: implement pd.StringDtype both the numpy and arrow versions
      # https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.StringDtype.html#pandas.StringDtype
      msg = f"{type(X_col.dtype)} not supported"
      _log.error(msg)
      raise TypeError(msg)
  
imports: from collections import Counter
  from itertools import count
  import numpy
  import numpy.ma
  import logging
label_desc: Use image node_id 10 for calling a global function with example usage: _process_pandas_column(X_col, categories, feature_type, min_unique_continuous) and returns: _process_ndarray, _process_ndarray, None,  None,  bad,  str, unknown,  X_col,  categories,  bad, _process_ndarray