code: def _process_scipy_column(
      X_col, categories, feature_type, min_unique_continuous
  ):
      X_col = X_col.toarray().reshape(-1)
  
      nonmissings = None
      if X_col.dtype.type is np.object_:
          if _pandas_installed:
              # pandas also has the pd.NA value that indicates missing.  If Pandas is available though
              # we can use it's function that checks for pd.NA, np.nan, and None
              nonmissings = pd.notna(X_col)
          else:
              # X_col == X_col is a check for nan that works even with mixed types, since nan != nan
              nonmissings = np.logical_and(
                  X_col != _none_ndarray, X_col == X_col
              )
  
          if nonmissings.all():
              nonmissings = None
          else:
              X_col = X_col[nonmissings]
  
      return _process_ndarray(
          X_col,
          nonmissings,
          categories,
          feature_type,
          min_unique_continuous,
      )
  
imports: from collections import Counter
  from itertools import count
  import numpy
  import numpy.ma
  import logging
label_desc: Use image node_id 11 for calling a global function with example usage: _process_scipy_column(X_col, categories, feature_type, min_unique_continuous) and returns: _process_ndarray