code: def is_sparse(arr):
      
      from pandas.core.arrays.sparse import SparseDtype
  
      dtype = getattr(arr, 'dtype', arr)
      return isinstance(dtype, SparseDtype)
docs: Check whether an array-like is a 1-D pandas sparse array.
  
      Check that the one-dimensional array-like is a pandas sparse array.
      Returns True if it is a pandas sparse array, not another type of
      sparse array.
  
      Parameters
      ----------
      arr : array-like
          Array-like to check.
  
      Returns
      -------
      bool
          Whether or not the array-like is a pandas sparse array.
  
      See Also
      --------
      DataFrame.to_sparse : Convert DataFrame to a SparseDataFrame.
      Series.to_sparse : Convert Series to SparseSeries.
      Series.to_dense : Return dense representation of a Series.
  
      Examples
      --------
      Returns `True` if the parameter is a 1-D pandas sparse array.
  
      >>> is_sparse(pd.SparseArray([0, 0, 1, 0]))
      True
      >>> is_sparse(pd.SparseSeries([0, 0, 1, 0]))
      True
  
      Returns `False` if the parameter is not sparse.
  
      >>> is_sparse(np.array([0, 0, 1, 0]))
      False
      >>> is_sparse(pd.Series([0, 1, 0, 0]))
      False
  
      Returns `False` if the parameter is not a pandas sparse array.
  
      >>> from scipy.sparse import bsr_matrix
      >>> is_sparse(bsr_matrix([0, 1, 0, 0]))
      False
  
      Returns `False` if the parameter has more than one dimension.
  
      >>> df = pd.SparseDataFrame([389., 24., 80.5, np.nan],
                                  columns=['max_speed'],
                                  index=['falcon', 'parrot', 'lion', 'monkey'])
      >>> is_sparse(df)
      False
      >>> is_sparse(df.max_speed)
      True