COMPAT: unique() should preserve the dtype of the input (#27874)

doc/source/whatsnew/v1.0.0.rst

@@ -151,6 +151,7 @@ Other API changes
 
 - :meth:`pandas.api.types.infer_dtype` will now return "integer-na" for integer and ``np.nan`` mix (:issue:`27283`)
 - :meth:`MultiIndex.from_arrays` will no longer infer names from arrays if ``names=None`` is explicitly provided (:issue:`27292`)
+- The returned dtype of ::func:`pd.unique` now matches the input dtype. (:issue:`27874`)
 -
 
 .. _whatsnew_1000.api.documentation:

pandas/core/algorithms.py

@@ -180,13 +180,13 @@ def _reconstruct_data(values, dtype, original):
     if is_extension_array_dtype(dtype):
         values = dtype.construct_array_type()._from_sequence(values)
     elif is_bool_dtype(dtype):
-        values = values.astype(dtype)
+        values = values.astype(dtype, copy=False)
 
         # we only support object dtypes bool Index
         if isinstance(original, ABCIndexClass):
-            values = values.astype(object)
+            values = values.astype(object, copy=False)
     elif dtype is not None:
-        values = values.astype(dtype)
+        values = values.astype(dtype, copy=False)
 
     return values
 
@@ -396,7 +396,7 @@ def unique(values):
 
     table = htable(len(values))
     uniques = table.unique(values)
-    uniques = _reconstruct_data(uniques, dtype, original)
+    uniques = _reconstruct_data(uniques, original.dtype, original)
     return uniques
 
 

pandas/tests/test_base.py

@@ -159,8 +159,8 @@ def test_memory_usage(self):
 class Ops:
     def _allow_na_ops(self, obj):
         """Whether to skip test cases including NaN"""
-        if isinstance(obj, Index) and (obj.is_boolean() or not obj._can_hold_na):
-            # don't test boolean / int64 index
+        if (isinstance(obj, Index) and obj.is_boolean()) or not obj._can_hold_na:
+            # don't test boolean / integer dtypes
             return False
         return True
 
@@ -187,7 +187,24 @@ def setup_method(self, method):
         types = ["bool", "int", "float", "dt", "dt_tz", "period", "string", "unicode"]
         self.indexes = [getattr(self, "{}_index".format(t)) for t in types]
         self.series = [getattr(self, "{}_series".format(t)) for t in types]
-        self.objs = self.indexes + self.series
+
+        # To test narrow dtypes, we use narrower *data* elements, not *index* elements
+        index = self.int_index
+        self.float32_series = Series(arr.astype(np.float32), index=index, name="a")
+
+        arr_int = np.random.choice(10, size=10, replace=False)
+        self.int8_series = Series(arr_int.astype(np.int8), index=index, name="a")
+        self.int16_series = Series(arr_int.astype(np.int16), index=index, name="a")
+        self.int32_series = Series(arr_int.astype(np.int32), index=index, name="a")
+
+        self.uint8_series = Series(arr_int.astype(np.uint8), index=index, name="a")
+        self.uint16_series = Series(arr_int.astype(np.uint16), index=index, name="a")
+        self.uint32_series = Series(arr_int.astype(np.uint32), index=index, name="a")
+
+        nrw_types = ["float32", "int8", "int16", "int32", "uint8", "uint16", "uint32"]
+        self.narrow_series = [getattr(self, "{}_series".format(t)) for t in nrw_types]
+
+        self.objs = self.indexes + self.series + self.narrow_series
 
     def check_ops_properties(self, props, filter=None, ignore_failures=False):
         for op in props:
@@ -385,6 +402,7 @@ def test_value_counts_unique_nunique(self):
             if isinstance(o, Index):
                 assert isinstance(result, o.__class__)
                 tm.assert_index_equal(result, orig)
+                assert result.dtype == orig.dtype
             elif is_datetime64tz_dtype(o):
                 # datetimetz Series returns array of Timestamp
                 assert result[0] == orig[0]
@@ -396,6 +414,7 @@ def test_value_counts_unique_nunique(self):
                 )
             else:
                 tm.assert_numpy_array_equal(result, orig.values)
+                assert result.dtype == orig.dtype
 
             assert o.nunique() == len(np.unique(o.values))
 
@@ -904,7 +923,7 @@ def test_fillna(self):
 
                 expected = [fill_value] * 2 + list(values[2:])
 
-                expected = klass(expected)
+                expected = klass(expected, dtype=orig.dtype)
                 o = klass(values)
 
                 # check values has the same dtype as the original