REF: Simplify Datetimelike constructor dispatching

pandas/core/arrays/datetimelike.py

@@ -344,7 +344,8 @@ def _validate_frequency(cls, index, freq, **kwargs):
             # Frequency validation is not meaningful for Period Array/Index
             return None
 
-        # DatetimeArray may pass `ambiguous`, nothing else allowed
+        # DatetimeArray may pass `ambiguous`, nothing else will be accepted
+        # by cls._generate_range below
         assert all(key == 'ambiguous' for key in kwargs)
 
         inferred = index.inferred_freq

pandas/core/arrays/period.py

@@ -141,7 +141,7 @@ def __new__(cls, values, freq=None, dtype=None, **kwargs):
 
         elif is_object_dtype(values) or isinstance(values, (list, tuple)):
             # e.g. array([Period(...), Period(...), NaT])
-            values = np.array(values)
+            values = np.array(values, dtype=object)
             if freq is None:
                 freq = libperiod.extract_freq(values)
             values = libperiod.extract_ordinals(values, freq)

pandas/core/indexes/datetimelike.py

@@ -430,10 +430,7 @@ def min(self, axis=None, *args, **kwargs):
         --------
         numpy.ndarray.min
         """
-        if axis is not None and axis >= self.ndim:
-            raise ValueError("`axis` must be fewer than the number of "
-                             "dimensions ({ndim})".format(ndim=self.ndim))
-
+        _validate_minmax_axis(axis)
         nv.validate_min(args, kwargs)
 
         try:
@@ -462,10 +459,7 @@ def argmin(self, axis=None, *args, **kwargs):
         --------
         numpy.ndarray.argmin
         """
-        if axis is not None and axis >= self.ndim:
-            raise ValueError("`axis` must be fewer than the number of "
-                             "dimensions ({ndim})".format(ndim=self.ndim))
-
+        _validate_minmax_axis(axis)
         nv.validate_argmin(args, kwargs)
 
         i8 = self.asi8
@@ -486,10 +480,7 @@ def max(self, axis=None, *args, **kwargs):
         --------
         numpy.ndarray.max
         """
-        if axis is not None and axis >= self.ndim:
-            raise ValueError("`axis` must be fewer than the number of "
-                             "dimensions ({ndim})".format(ndim=self.ndim))
-
+        _validate_minmax_axis(axis)
         nv.validate_max(args, kwargs)
 
         try:
@@ -518,10 +509,7 @@ def argmax(self, axis=None, *args, **kwargs):
         --------
         numpy.ndarray.argmax
         """
-        if axis is not None and axis >= self.ndim:
-            raise ValueError("`axis` must be fewer than the number of "
-                             "dimensions ({ndim})".format(ndim=self.ndim))
-
+        _validate_minmax_axis(axis)
         nv.validate_argmax(args, kwargs)
 
         i8 = self.asi8
@@ -722,6 +710,25 @@ def _time_shift(self, periods, freq=None):
         return result
 
 
+def _validate_minmax_axis(axis):
+    """
+    Ensure that the axis argument passed to min, max, argmin, or argmax is
+    zero or None, as otherwise it will be incorrectly ignored.
+
+    Parameters
+    ----------
+    axis : int or None
+
+    Raises
+    ------
+    ValueError
+    """
+    ndim = 1  # hard-coded for Index
+    if axis is not None and axis >= ndim:
+        raise ValueError("`axis` must be fewer than the number of "
+                         "dimensions ({ndim})".format(ndim=ndim))
+
+
 def _ensure_datetimelike_to_i8(other, to_utc=False):
     """
     helper for coercing an input scalar or array to i8

pandas/core/indexes/datetimes.py

@@ -241,11 +241,11 @@ def __new__(cls, data=None,
 
         if data is None:
             # TODO: Remove this block and associated kwargs; GH#20535
-            out = cls._generate_range(start, end, periods,
-                                      freq=freq, tz=tz, normalize=normalize,
-                                      closed=closed, ambiguous=ambiguous)
-            out.name = name
-            return out
+            result = cls._generate_range(start, end, periods,
+                                         freq=freq, tz=tz, normalize=normalize,
+                                         closed=closed, ambiguous=ambiguous)
+            result.name = name
+            return result
 
         if not isinstance(data, (np.ndarray, Index, ABCSeries,
                                  DatetimeArrayMixin)):