ENH: Series constructor supports 1d scipy.sparse.spmatrix input

Author: kernc

pandas/core/sparse/series.py

@@ -9,7 +9,7 @@
 import warnings
 
 from pandas.core.dtypes.missing import isnull, notnull
-from pandas.core.dtypes.common import is_scalar
+from pandas.core.dtypes.common import is_scalar, is_scipy_sparse
 from pandas.core.common import _values_from_object, _maybe_match_name
 
 from pandas.compat.numpy import function as nv
@@ -90,7 +90,7 @@ class SparseSeries(Series):
 
     Parameters
     ----------
-    data : {array-like, Series, SparseSeries, dict}
+    data : {array-like, Series, SparseSeries, dict, scipy.sparse.spmatrix}
     kind : {'block', 'integer'}
     fill_value : float
         Code for missing value. Defaults depends on dtype.
@@ -128,6 +128,10 @@ def __init__(self, data=None, index=None, sparse_index=None, kind='block',
             if isinstance(data, Series) and name is None:
                 name = data.name
 
+            if is_scipy_sparse(data):
+                data = SparseArray(data, dtype=dtype, kind=kind,
+                                   fill_value=fill_value)
+
             if isinstance(data, SparseArray):
                 if index is not None:
                     assert (len(index) == len(data))

pandas/tests/sparse/test_series.py

@@ -142,6 +142,24 @@ def test_constructor_preserve_attr(self):
         assert s.dtype == np.int64
         assert s.fill_value == 0
 
+    def test_constructor_spmatrix(self):
+        # GH-15634
+        tm.skip_if_no_package('scipy')
+        from scipy.sparse import csr_matrix
+
+        spm = csr_matrix(np.eye(5)[:, 2])
+
+        arr = SparseSeries(spm)
+        assert arr.dtype == spm.dtype
+        assert arr.fill_value == 0
+
+        arr = SparseSeries(spm, kind='block', dtype=float, fill_value=np.nan)
+        assert arr.dtype == float
+        assert np.isnan(arr.fill_value)
+
+        tm.assert_raises_regex(ValueError, '1D',
+                               lambda: SparseSeries(csr_matrix(np.eye(3))))
+
     def test_series_density(self):
         # GH2803
         ts = Series(np.random.randn(10))