API: Disallow dtypes w/o frequency when casting (pandas-dev#23392)

Previously deprecated for Series constructor
and the `.astype` method. Now being enforced.

xref pandas-devgh-15987.

Author: gfyoung

doc/source/whatsnew/v0.24.0.txt

@@ -942,6 +942,7 @@ Removal of prior version deprecations/changes
 - Removal of the previously deprecated module ``pandas.core.datetools`` (:issue:`14105`, :issue:`14094`)
 - Strings passed into :meth:`DataFrame.groupby` that refer to both column and index levels will raise a ``ValueError`` (:issue:`14432`)
 - :meth:`Index.repeat` and :meth:`MultiIndex.repeat` have renamed the ``n`` argument to ``repeats`` (:issue:`14645`)
+- The ``Series`` constructor and ``.astype`` method will now raise a ``ValueError`` if timestamp dtypes are passed in without a frequency (e.g. ``np.datetime64``) for the ``dtype`` parameter (:issue:`15987`)
 - Removal of the previously deprecated ``as_indexer`` keyword completely from ``str.match()`` (:issue:`22356`, :issue:`6581`)
 - Removed the ``pandas.formats.style`` shim for :class:`pandas.io.formats.style.Styler` (:issue:`16059`)
 - :func:`pandas.pnow`, :func:`pandas.match`, :func:`pandas.groupby`, :func:`pd.get_store`, ``pd.Expr``, and ``pd.Term`` have been removed (:issue:`15538`, :issue:`15940`)

pandas/core/dtypes/cast.py

@@ -3,7 +3,6 @@
 from datetime import datetime, timedelta
 
 import numpy as np
-import warnings
 
 from pandas._libs import tslib, lib, tslibs
 from pandas._libs.tslibs import iNaT, OutOfBoundsDatetime, Period
@@ -664,6 +663,11 @@ def astype_nansafe(arr, dtype, copy=True, skipna=False):
         e.g. the item sizes don't align.
     skipna: bool, default False
         Whether or not we should skip NaN when casting as a string-type.
+
+    Raises
+    ------
+    ValueError
+        The dtype was a datetime /timedelta dtype, but it had no frequency.
     """
 
     # dispatch on extension dtype if needed
@@ -745,12 +749,9 @@ def astype_nansafe(arr, dtype, copy=True, skipna=False):
             return astype_nansafe(to_timedelta(arr).values, dtype, copy=copy)
 
     if dtype.name in ("datetime64", "timedelta64"):
-        msg = ("Passing in '{dtype}' dtype with no frequency is "
-               "deprecated and will raise in a future version. "
+        msg = ("The '{dtype}' dtype has no frequency. "
                "Please pass in '{dtype}[ns]' instead.")
-        warnings.warn(msg.format(dtype=dtype.name),
-                      FutureWarning, stacklevel=5)
-        dtype = np.dtype(dtype.name + "[ns]")
+        raise ValueError(msg.format(dtype=dtype.name))
 
     if copy or is_object_dtype(arr) or is_object_dtype(dtype):
         # Explicit copy, or required since NumPy can't view from / to object.
@@ -1019,16 +1020,14 @@ 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. "
+            # Force the dtype if needed.
+            msg = ("The '{dtype}' dtype has no frequency. "
                    "Please pass in '{dtype}[ns]' instead.")
 
             if is_datetime64 and not is_dtype_equal(dtype, _NS_DTYPE):
                 if dtype.name in ('datetime64', 'datetime64[ns]'):
                     if dtype.name == 'datetime64':
-                        warnings.warn(msg.format(dtype=dtype.name),
-                                      FutureWarning, stacklevel=5)
+                        raise ValueError(msg.format(dtype=dtype.name))
                     dtype = _NS_DTYPE
                 else:
                     raise TypeError("cannot convert datetimelike to "
@@ -1044,8 +1043,7 @@ def maybe_cast_to_datetime(value, dtype, errors='raise'):
             elif is_timedelta64 and not is_dtype_equal(dtype, _TD_DTYPE):
                 if dtype.name in ('timedelta64', 'timedelta64[ns]'):
                     if dtype.name == 'timedelta64':
-                        warnings.warn(msg.format(dtype=dtype.name),
-                                      FutureWarning, stacklevel=5)
+                        raise ValueError(msg.format(dtype=dtype.name))
                     dtype = _TD_DTYPE
                 else:
                     raise TypeError("cannot convert timedeltalike to "

pandas/tests/series/test_constructors.py

@@ -1192,32 +1192,26 @@ def test_constructor_cast_object(self, index):
         exp = Series(index).astype(object)
         tm.assert_series_equal(s, exp)
 
-    def test_constructor_generic_timestamp_deprecated(self):
-        # see gh-15524
-
-        with tm.assert_produces_warning(FutureWarning):
-            dtype = np.timedelta64
-            s = Series([], dtype=dtype)
-
-            assert s.empty
-            assert s.dtype == 'm8[ns]'
-
-        with tm.assert_produces_warning(FutureWarning):
-            dtype = np.datetime64
-            s = Series([], dtype=dtype)
+    @pytest.mark.parametrize("dtype", [
+        np.datetime64,
+        np.timedelta64,
+    ])
+    def test_constructor_generic_timestamp_no_frequency(self, dtype):
+        # see gh-15524, gh-15987
+        msg = "dtype has no frequency. Please pass in"
 
-            assert s.empty
-            assert s.dtype == 'M8[ns]'
+        with tm.assert_raises_regex(ValueError, msg):
+            Series([], dtype=dtype)
 
-        # These timestamps have the wrong frequencies,
-        # so an Exception should be raised now.
-        msg = "cannot convert timedeltalike"
-        with tm.assert_raises_regex(TypeError, msg):
-            Series([], dtype='m8[ps]')
+    @pytest.mark.parametrize("dtype,msg", [
+        ("m8[ps]", "cannot convert timedeltalike"),
+        ("M8[ps]", "cannot convert datetimelike"),
+    ])
+    def test_constructor_generic_timestamp_bad_frequency(self, dtype, msg):
+        # see gh-15524, gh-15987
 
-        msg = "cannot convert datetimelike"
         with tm.assert_raises_regex(TypeError, msg):
-            Series([], dtype='M8[ps]')
+            Series([], dtype=dtype)
 
     @pytest.mark.parametrize('dtype', [None, 'uint8', 'category'])
     def test_constructor_range_dtype(self, dtype):

pandas/tests/series/test_dtypes.py

@@ -3,7 +3,6 @@
 
 import string
 import sys
-import warnings
 from datetime import datetime, timedelta
 
 import numpy as np
@@ -21,7 +20,7 @@
 from pandas.compat import lrange, range, u
 
 
-class TestSeriesDtypes():
+class TestSeriesDtypes(object):
 
     def test_dt64_series_astype_object(self):
         dt64ser = Series(date_range('20130101', periods=3))
@@ -396,40 +395,30 @@ def test_astype_categoricaldtype_with_args(self):
         with pytest.raises(TypeError):
             s.astype(type_, categories=['a', 'b'], ordered=False)
 
-    def test_astype_generic_timestamp_deprecated(self):
-        # see gh-15524
+    @pytest.mark.parametrize("dtype", [
+        np.datetime64,
+        np.timedelta64,
+    ])
+    def test_astype_generic_timestamp_no_frequency(self, dtype):
+        # see gh-15524, gh-15987
         data = [1]
+        s = Series(data)
 
-        with tm.assert_produces_warning(FutureWarning,
-                                        check_stacklevel=False):
-            s = Series(data)
-            dtype = np.datetime64
-            result = s.astype(dtype)
-            expected = Series(data, dtype=dtype)
-            tm.assert_series_equal(result, expected)
-
-        with tm.assert_produces_warning(FutureWarning,
-                                        check_stacklevel=False):
-            s = Series(data)
-            dtype = np.timedelta64
-            result = s.astype(dtype)
-            expected = Series(data, dtype=dtype)
-            tm.assert_series_equal(result, expected)
+        msg = "dtype has no frequency. Please pass in"
+        with tm.assert_raises_regex(ValueError, msg):
+            s.astype(dtype)
 
     @pytest.mark.parametrize("dtype", np.typecodes['All'])
     def test_astype_empty_constructor_equality(self, dtype):
         # see gh-15524
 
-        if dtype not in ('S', 'V'):  # poor support (if any) currently
-            with warnings.catch_warnings(record=True):
-                if dtype in ('M', 'm'):
-                    # Generic timestamp dtypes ('M' and 'm') are deprecated,
-                    # but we test that already in series/test_constructors.py
-                    warnings.simplefilter("ignore", FutureWarning)
-
-                init_empty = Series([], dtype=dtype)
-                as_type_empty = Series([]).astype(dtype)
-                tm.assert_series_equal(init_empty, as_type_empty)
+        if dtype not in (
+            "S", "V",  # poor support (if any) currently
+            "M", "m"   # Generic timestamps raise a ValueError. Already tested.
+        ):
+            init_empty = Series([], dtype=dtype)
+            as_type_empty = Series([]).astype(dtype)
+            tm.assert_series_equal(init_empty, as_type_empty)
 
     def test_complex(self):
         # see gh-4819: complex access for ndarray compat