Filter warnings in init_spmatrix

Closes pandas-dev#26777

Author: TomAugspurger

pandas/core/sparse/frame.py

@@ -211,32 +211,37 @@ def _init_spmatrix(self, data, index, columns, dtype=None,
         """
         Init self from scipy.sparse matrix.
         """
-        index, columns = SparseFrameAccessor._prep_index(data, index, columns)
-        data = data.tocoo()
-        N = len(index)
-
-        # Construct a dict of SparseSeries
-        sdict = {}
-        values = Series(data.data, index=data.row, copy=False)
-        for col, rowvals in values.groupby(data.col):
-            # get_blocks expects int32 row indices in sorted order
-            rowvals = rowvals.sort_index()
-            rows = rowvals.index.values.astype(np.int32)
-            blocs, blens = get_blocks(rows)
-
-            sdict[columns[col]] = SparseSeries(
-                rowvals.values, index=index,
-                fill_value=fill_value,
-                sparse_index=BlockIndex(N, blocs, blens))
-
-        # Add any columns that were empty and thus not grouped on above
-        sdict.update({column: SparseSeries(index=index,
-                                           fill_value=fill_value,
-                                           sparse_index=BlockIndex(N, [], []))
-                      for column in columns
-                      if column not in sdict})
-
-        return self._init_dict(sdict, index, columns, dtype)
+        with warnings.catch_warnings():
+            warnings.filterwarnings('ignore', 'SparseSeries', FutureWarning)
+            index, columns = SparseFrameAccessor._prep_index(data, index,
+                                                             columns)
+            data = data.tocoo()
+            N = len(index)
+
+            # Construct a dict of SparseSeries
+            sdict = {}
+            values = Series(data.data, index=data.row, copy=False)
+            for col, rowvals in values.groupby(data.col):
+                # get_blocks expects int32 row indices in sorted order
+                rowvals = rowvals.sort_index()
+                rows = rowvals.index.values.astype(np.int32)
+                blocs, blens = get_blocks(rows)
+
+                sdict[columns[col]] = SparseSeries(
+                    rowvals.values, index=index,
+                    fill_value=fill_value,
+                    sparse_index=BlockIndex(N, blocs, blens))
+
+            # Add any columns that were empty and thus not grouped on above
+            sdict.update({column: SparseSeries(index=index,
+                                               fill_value=fill_value,
+                                               sparse_index=BlockIndex(N,
+                                                                       [],
+                                                                       []))
+                          for column in columns
+                          if column not in sdict})
+
+            return self._init_dict(sdict, index, columns, dtype)
 
     @Appender(SparseFrameAccessor.to_coo.__doc__)
     def to_coo(self):

pandas/tests/sparse/frame/test_frame.py

@@ -6,6 +6,7 @@
 
 from pandas._libs.sparse import BlockIndex, IntIndex
 from pandas.errors import PerformanceWarning
+import pandas.util._test_decorators as td
 
 import pandas as pd
 from pandas import DataFrame, Series, bdate_range, compat
@@ -24,6 +25,18 @@ def test_deprecated():
         pd.SparseDataFrame({"A": [1, 2]})
 
 
+@td.skip_if_no_scipy
+def test_single_warning_from_spmatrix():
+    import scipy.sparse
+
+    arr = scipy.sparse.rand(10, 10, 0.5)
+    with tm.assert_produces_warning(FutureWarning,
+                                    check_stacklevel=False) as w:
+        pd.SparseDataFrame(arr)
+
+    assert len(w) == 1
+
+
 @pytest.mark.filterwarnings("ignore:Sparse:FutureWarning")
 class TestSparseDataFrame(SharedWithSparse):
     klass = SparseDataFrame