Merge branch 'master' of https://github.com/pandas-dev/pandas into cl…

…n-wrap_reduction
pandas-dev · jreback · Nov 8, 2020 · Oct 26, 2020 · Oct 26, 2020 · Oct 26, 2020
commit 297b2786fa7239e46fc6ad51968618d865622cea
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
@@ -1267,10 +1267,16 @@ def min(self, axis=None, skipna=True, *args, **kwargs):
         nv.validate_minmax_axis(axis, self.ndim)
 
         if is_period_dtype(self.dtype):
-            result = self.to_timestamp("S").min(axis=axis, skipna=skipna)
-            if result is not NaT:
-                result = result.to_period(self.freq)
-            return result
+            # pass datetime64 values to nanops to get correct NaT semantics
+            result = nanops.nanmin(
+                self._ndarray.view("M8[ns]"), axis=axis, skipna=skipna
+            )
+            if result is NaT:
+                return NaT
+            result = result.view("i8")
+            if axis is None or self.ndim == 1:
+                return self._box_func(result)
+            return self._from_backing_data(result)
 
         result = nanops.nanmin(self._ndarray, axis=axis, skipna=skipna)
         if lib.is_scalar(result):
@@ -1294,10 +1300,16 @@ def max(self, axis=None, skipna=True, *args, **kwargs):
         nv.validate_minmax_axis(axis, self.ndim)
 
         if is_period_dtype(self.dtype):
-            result = self.to_timestamp("S").max(axis=axis, skipna=skipna)
-            if result is not NaT:
-                result = result.to_period(self.freq)
-            return result
+            # pass datetime64 values to nanops to get correct NaT semantics
+            result = nanops.nanmax(
+                self._ndarray.view("M8[ns]"), axis=axis, skipna=skipna
+            )
+            if result is NaT:
+                return result
+            result = result.view("i8")
+            if axis is None or self.ndim == 1:
+                return self._box_func(result)
+            return self._from_backing_data(result)
 
         result = nanops.nanmax(self._ndarray, axis=axis, skipna=skipna)
         if lib.is_scalar(result):

diff --git a/pandas/tests/frame/test_constructors.py b/pandas/tests/frame/test_constructors.py
@@ -2697,6 +2697,19 @@ def test_frame_ctor_datetime64_column(self):
         df = DataFrame({"A": np.random.randn(len(rng)), "B": dates})
         assert np.issubdtype(df["B"].dtype, np.dtype("M8[ns]"))
 
+    def test_dataframe_constructor_infer_multiindex(self):
+        index_lists = [["a", "a", "b", "b"], ["x", "y", "x", "y"]]
+
+        multi = DataFrame(
+            np.random.randn(4, 4),
+            index=[np.array(x) for x in index_lists],
+        )
+        assert isinstance(multi.index, MultiIndex)
+        assert not isinstance(multi.columns, MultiIndex)
+
+        multi = DataFrame(np.random.randn(4, 4), columns=index_lists)
+        assert isinstance(multi.columns, MultiIndex)
+
     @pytest.mark.parametrize(
         "input_vals",
         [