DEPR: Deprecated generic timestamp dtypes

We only use the nanosecond frequency, and numpy
doesn't even handle generic timestamp dtypes well.

xref pandas-devgh-15524 (comment).

Author: gfyoung

doc/source/whatsnew/v0.20.0.txt

@@ -1204,6 +1204,7 @@ Deprecations
 - ``SparseArray.to_dense()`` has deprecated the ``fill`` parameter, as that parameter was not being respected (:issue:`14647`)
 - ``SparseSeries.to_dense()`` has deprecated the ``sparse_only`` parameter (:issue:`14647`)
 - ``Series.repeat()`` has deprecated the ``reps`` parameter in favor of ``repeats`` (:issue:`12662`)
+- The ``Series`` constructor and ``.astype`` method have deprecated accepting timestamp dtypes without a frequency (e.g. ``np.datetime64``) for the ``dtype`` parameter (:issue:`15524`)
 - ``Index.repeat()`` and ``MultiIndex.repeat()`` have deprecated the ``n`` parameter in favor of ``repeats`` (:issue:`12662`)
 - ``Categorical.searchsorted()`` and ``Series.searchsorted()`` have deprecated the ``v`` parameter in favor of ``value`` (:issue:`12662`)
 - ``TimedeltaIndex.searchsorted()``, ``DatetimeIndex.searchsorted()``, and ``PeriodIndex.searchsorted()`` have deprecated the ``key`` parameter in favor of ``value`` (:issue:`12662`)

pandas/tests/series/test_constructors.py

@@ -839,3 +839,27 @@ def test_constructor_cast_object(self):
         s = Series(date_range('1/1/2000', periods=10), dtype=object)
         exp = Series(date_range('1/1/2000', periods=10))
         tm.assert_series_equal(s, exp)
+
+    def test_constructor_generic_timestamp(self):
+        # see gh-15524
+        dtype = np.timedelta64
+        s = Series([], dtype=dtype)
+
+        assert s.empty
+        assert s.dtype == 'm8[ns]'
+
+        dtype = np.datetime64
+        s = Series([], dtype=dtype)
+
+        assert s.empty
+        assert s.dtype == 'M8[ns]'
+
+        # These timestamps have the wrong frequencies,
+        # so an Exception should be raised now.
+        msg = "cannot convert timedeltalike"
+        with tm.assertRaisesRegexp(TypeError, msg):
+            Series([], dtype='m8[ps]')
+
+        msg = "cannot convert datetimelike"
+        with tm.assertRaisesRegexp(TypeError, msg):
+            Series([], dtype='M8[ps]')

pandas/tests/series/test_dtypes.py

@@ -153,6 +153,38 @@ def test_astype_dict(self):
         self.assertRaises(KeyError, s.astype, {'abc': str, 'def': str})
         self.assertRaises(KeyError, s.astype, {0: str})
 
+    def test_astype_generic_timestamp(self):
+        # see gh-15524
+        data = [1]
+
+        s = Series(data)
+        dtype = np.datetime64
+        result = s.astype(dtype)
+        expected = Series(data, dtype=dtype)
+        assert_series_equal(result, expected)
+
+        s = Series(data)
+        dtype = np.timedelta64
+        result = s.astype(dtype)
+        expected = Series(data, dtype=dtype)
+        assert_series_equal(result, expected)
+
+    def test_astype_empty_constructor_equality(self):
+        # see gh-15524
+
+        for dtype in np.typecodes['All']:
+            if dtype not in ('S', 'V'):  # poor support (if any) currently
+                init_empty = Series([], dtype=dtype)
+                astype_empty = Series([]).astype(dtype)
+
+                try:
+                    assert_series_equal(init_empty, astype_empty)
+                except AssertionError as e:
+                    name = np.dtype(dtype).name
+                    msg = "{dtype} failed: ".format(dtype=name) + str(e)
+
+                    raise AssertionError(msg)
+
     def test_complexx(self):
         # GH4819
         # complex access for ndarray compat

pandas/types/cast.py

@@ -1,7 +1,10 @@
 """ routings for casting """
 
 from datetime import datetime, timedelta
+
 import numpy as np
+import warnings
+
 from pandas._libs import tslib, lib
 from pandas._libs.tslib import iNaT
 from pandas.compat import string_types, text_type, PY3
@@ -620,6 +623,14 @@ def astype_nansafe(arr, dtype, copy=True):
         # work around NumPy brokenness, #1987
         return lib.astype_intsafe(arr.ravel(), dtype).reshape(arr.shape)
 
+    if dtype.name in ("datetime64", "timedelta64"):
+        msg = ("Passing in '{dtype}' dtype with no frequency is "
+               "deprecated and will raise in a future version. "
+               "Please pass in '{dtype}[ns]' instead.")
+        warnings.warn(msg.format(dtype=dtype.name),
+                      FutureWarning, stacklevel=2)
+        dtype = np.dtype(dtype.name + "[ns]")
+
     if copy:
         return arr.astype(dtype)
     return arr.view(dtype)
@@ -871,8 +882,15 @@ def maybe_cast_to_datetime(value, dtype, errors='raise'):
         if is_datetime64 or is_datetime64tz or is_timedelta64:
 
             # force the dtype if needed
+            msg = ("Passing in '{dtype}' dtype with no frequency is "
+                   "deprecated and will raise in a future version. "
+                   "Please pass in '{dtype}[ns]' instead.")
+
             if is_datetime64 and not is_dtype_equal(dtype, _NS_DTYPE):
-                if dtype.name == 'datetime64[ns]':
+                if dtype.name in ('datetime64', 'datetime64[ns]'):
+                    if dtype.name == 'datetime64':
+                        warnings.warn(msg.format(dtype=dtype.name),
+                                      FutureWarning, stacklevel=2)
                     dtype = _NS_DTYPE
                 else:
                     raise TypeError("cannot convert datetimelike to "
@@ -886,7 +904,10 @@ def maybe_cast_to_datetime(value, dtype, errors='raise'):
                     value = [value]
 
             elif is_timedelta64 and not is_dtype_equal(dtype, _TD_DTYPE):
-                if dtype.name == 'timedelta64[ns]':
+                if dtype.name in ('timedelta64', 'timedelta64[ns]'):
+                    if dtype.name == 'timedelta64':
+                        warnings.warn(msg.format(dtype=dtype.name),
+                                      FutureWarning, stacklevel=2)
                     dtype = _TD_DTYPE
                 else:
                     raise TypeError("cannot convert timedeltalike to "