def is_sparse(arr):
from pandas.core.arrays.sparse import SparseDtype<EOL>dtype = getattr(arr, '<STR_LIT>', arr)<EOL>return isinstance(dtype, SparseDtype)<EOL>
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