allow coercing casting

Author: jreback

pandas/core/arrays/sparse.py

@@ -1906,8 +1906,20 @@ def make_sparse(arr, kind='block', fill_value=None, dtype=None, copy=False):
 
     index = _make_index(length, indices, kind)
     sparsified_values = arr[mask]
+
+    # careful about casting here
+    # as we could easily specify a type that cannot hold the resulting values
+    # e.g. integer when we have floats
     if dtype is not None:
-        sparsified_values = astype_nansafe(sparsified_values, dtype=dtype)
+        try:
+            sparsified_values = astype_nansafe(
+                sparsified_values, dtype=dtype, casting='same_kind')
+        except TypeError:
+            dtype = 'float64'
+            sparsified_values = astype_nansafe(
+                sparsified_values, dtype=dtype, casting='unsafe')
+
+
     # TODO: copy
     return sparsified_values, index, fill_value
 

pandas/core/series.py

@@ -765,10 +765,7 @@ def __array_wrap__(self, result: np.ndarray,
 
         # we try to cast extension array types back to the original
         if is_extension_array_dtype(self):
-            result = result.astype(self.dtype,
-                                   copy=False,
-                                   errors='ignore',
-                                   casting='same_kind')
+            result = result.astype(self.dtype, copy=False)
 
         return result.__finalize__(self)
 

pandas/tests/sparse/frame/test_analytics.py

@@ -42,14 +42,18 @@ def test_quantile_multi():
     tm.assert_sp_frame_equal(result, sparse_expected)
 
 
+@pytest.mark.parametrize(
+    'data, dtype',
+    [([1, np.nan, 3], SparseDtype('float64', np.nan)),
+     ([1, 2, 3], SparseDtype('int'))])
 @pytest.mark.parametrize('func', [np.exp, np.sqrt], ids=str)
-def test_ufunc(func):
+def test_ufunc(data, dtype, func):
     # GH 23743
     # assert we preserve the incoming dtype on ufunc operation
     df = DataFrame(
-        {'A': Series([1, np.nan, 3], dtype=SparseDtype('float64', np.nan))})
+        {'A': Series(data, dtype=dtype)})
     result = func(df)
     expected = DataFrame(
-        {'A': Series(func([1, np.nan, 3]),
-                     dtype=SparseDtype('float64', np.nan))})
+        {'A': Series(func(data),
+                     dtype=dtype)})
     tm.assert_frame_equal(result, expected)

pandas/tests/sparse/series/test_analytics.py

@@ -5,12 +5,16 @@
 from pandas.util import testing as tm
 
 
+@pytest.mark.parametrize(
+    'data, dtype',
+    [([1, np.nan, 3], SparseDtype('float64', np.nan)),
+     ([1, 2, 3], SparseDtype('int'))])
 @pytest.mark.parametrize('func', [np.exp, np.sqrt], ids=str)
-def test_ufunc(func):
+def test_ufunc(data, dtype, func):
     # GH 23743
     # assert we preserve the incoming dtype on ufunc operation
-    s = Series([1, np.nan, 3], dtype=SparseDtype('float64', np.nan))
+    s = Series(data, dtype=dtype)
     result = func(s)
-    expected = Series(func([1, np.nan, 3]),
-                      dtype=SparseDtype('float64', np.nan))
+    expected = Series(func(data),
+                      dtype=dtype)
     tm.assert_series_equal(result, expected)