Fixed sparse test cases

Author: 5hirish

pandas/core/groupby/groupby.py

@@ -27,7 +27,9 @@ class providing the base-class of operations.
 )
 from pandas.core.config import option_context
 from pandas.core.dtypes.cast import maybe_downcast_to_dtype
-from pandas.core.dtypes.common import ensure_float, is_numeric_dtype, is_scalar, is_extension_array_dtype
+from pandas.core.dtypes.common import (
+    ensure_float, is_numeric_dtype, is_scalar, is_extension_array_dtype
+)
 from pandas.core.dtypes.missing import isna, notna
 from pandas.core.frame import DataFrame
 from pandas.core.generic import NDFrame

pandas/tests/sparse/test_groupby.py

@@ -26,25 +26,25 @@ def test_first_last_nth(self):
 
         # TODO: shouldn't these all be spares or not?
         tm.assert_frame_equal(sparse_grouped.first(),
-                              dense_grouped.first())
+                              dense_grouped.first().to_sparse())
         tm.assert_frame_equal(sparse_grouped.last(),
-                              dense_grouped.last())
+                              dense_grouped.last().to_sparse())
         tm.assert_frame_equal(sparse_grouped.nth(1),
                               dense_grouped.nth(1).to_sparse())
 
     def test_aggfuncs(self):
         sparse_grouped = self.sparse.groupby('A')
         dense_grouped = self.dense.groupby('A')
 
-        tm.assert_frame_equal(sparse_grouped.mean(),
-                              dense_grouped.mean())
+        tm.assert_frame_equal(sparse_grouped.mean().to_sparse(),
+                              dense_grouped.mean().to_sparse())
 
         # ToDo: sparse sum includes str column
         # tm.assert_frame_equal(sparse_grouped.sum(),
         #                       dense_grouped.sum())
 
-        tm.assert_frame_equal(sparse_grouped.count(),
-                              dense_grouped.count())
+        tm.assert_frame_equal(sparse_grouped.count().to_sparse(),
+                              dense_grouped.count().to_sparse())
 
 
 @pytest.mark.parametrize("fill_value", [0, np.nan])