API: to_timedelta([bad_type]) TypeError instead of ValueError

doc/source/whatsnew/v2.0.0.rst

@@ -235,6 +235,7 @@ Other API changes
 - :func:`pandas.api.dtypes.is_string_dtype` now only returns ``True`` for array-likes with ``dtype=object`` when the elements are inferred to be strings (:issue:`15585`)
 - Passing a sequence containing ``datetime`` objects and ``date`` objects to :class:`Series` constructor will return with ``object`` dtype instead of ``datetime64[ns]`` dtype, consistent with :class:`Index` behavior (:issue:`49341`)
 - Passing strings that cannot be parsed as datetimes to :class:`Series` or :class:`DataFrame` with ``dtype="datetime64[ns]"`` will raise instead of silently ignoring the keyword and returning ``object`` dtype (:issue:`24435`)
+- Passing a sequence containing a type that cannot be converted to :class:`Timedelta` to :func:`to_timedelta` or to the :class:`Series` or :class:`DataFrame` constructor with ``dtype="timedelta64[ns]"`` or to :class:`TimedeltaIndex` now raises ``TypeError`` instead of ``ValueError`` (:issue:`49525`)
 -
 
 .. ---------------------------------------------------------------------------

pandas/_libs/tslibs/timedeltas.pyx

@@ -364,7 +364,7 @@ cdef convert_to_timedelta64(object ts, str unit):
     if PyDelta_Check(ts):
         ts = np.timedelta64(delta_to_nanoseconds(ts), "ns")
     elif not is_timedelta64_object(ts):
-        raise ValueError(f"Invalid type for timedelta scalar: {type(ts)}")
+        raise TypeError(f"Invalid type for timedelta scalar: {type(ts)}")
     return ts.astype("timedelta64[ns]")
 
 

pandas/core/indexes/base.py

@@ -41,7 +41,6 @@
     IncompatibleFrequency,
     OutOfBoundsDatetime,
     Timestamp,
-    is_unitless,
     tz_compare,
 )
 from pandas._typing import (
@@ -61,7 +60,6 @@
 from pandas.compat.numpy import function as nv
 from pandas.errors import (
     DuplicateLabelError,
-    IntCastingNaNError,
     InvalidIndexError,
 )
 from pandas.util._decorators import (
@@ -1016,11 +1014,6 @@ def astype(self, dtype, copy: bool = True):
 
         values = self._data
         if isinstance(values, ExtensionArray):
-            if isinstance(dtype, np.dtype) and dtype.kind == "M" and is_unitless(dtype):
-                # TODO(2.0): remove this special-casing once this is enforced
-                #  in DTA.astype
-                raise TypeError(f"Cannot cast {type(self).__name__} to dtype")
-
             with rewrite_exception(type(values).__name__, type(self).__name__):
                 new_values = values.astype(dtype, copy=copy)
 
@@ -1039,22 +1032,12 @@ def astype(self, dtype, copy: bool = True):
             new_values = cls._from_sequence(self, dtype=dtype, copy=copy)
 
         else:
-            try:
-                if dtype == str:
-                    # GH#38607
-                    new_values = values.astype(dtype, copy=copy)
-                else:
-                    # GH#13149 specifically use astype_nansafe instead of astype
-                    new_values = astype_nansafe(values, dtype=dtype, copy=copy)
-            except IntCastingNaNError:
-                raise
-            except (TypeError, ValueError) as err:
-                if dtype.kind == "u" and "losslessly" in str(err):
-                    # keep the message from _astype_float_to_int_nansafe
-                    raise
-                raise TypeError(
-                    f"Cannot cast {type(self).__name__} to dtype {dtype}"
-                ) from err
+            if dtype == str:
+                # GH#38607 see test_astype_str_from_bytes
+                new_values = values.astype(dtype, copy=copy)
+            else:
+                # GH#13149 specifically use astype_nansafe instead of astype
+                new_values = astype_nansafe(values, dtype=dtype, copy=copy)
 
         # pass copy=False because any copying will be done in the astype above
         if self._is_backward_compat_public_numeric_index:

pandas/tests/frame/test_constructors.py

@@ -141,13 +141,8 @@ def test_array_of_dt64_nat_with_td64dtype_raises(self, frame_or_series):
         if frame_or_series is DataFrame:
             arr = arr.reshape(1, 1)
 
-        msg = "|".join(
-            [
-                "Could not convert object to NumPy timedelta",
-                "Invalid type for timedelta scalar: <class 'numpy.datetime64'>",
-            ]
-        )
-        with pytest.raises(ValueError, match=msg):
+        msg = "Invalid type for timedelta scalar: <class 'numpy.datetime64'>"
+        with pytest.raises(TypeError, match=msg):
             frame_or_series(arr, dtype="m8[ns]")
 
     @pytest.mark.parametrize("kind", ["m", "M"])

pandas/tests/indexes/datetimes/methods/test_astype.py

@@ -214,6 +214,8 @@ def test_astype_raises(self, dtype):
         # GH 13149, GH 13209
         idx = DatetimeIndex(["2016-05-16", "NaT", NaT, np.NaN])
         msg = "Cannot cast DatetimeIndex to dtype"
+        if dtype == "datetime64":
+            msg = "Casting to unit-less dtype 'datetime64' is not supported"
         with pytest.raises(TypeError, match=msg):
             idx.astype(dtype)
 

pandas/tests/indexes/object/test_astype.py

@@ -19,6 +19,6 @@ def test_astype_invalid_nas_to_tdt64_raises():
     # GH#45722 don't cast np.datetime64 NaTs to timedelta64 NaT
     idx = Index([NaT.asm8] * 2, dtype=object)
 
-    msg = r"Cannot cast Index to dtype timedelta64\[ns\]"
+    msg = r"Invalid type for timedelta scalar: <class 'numpy.datetime64'>"
     with pytest.raises(TypeError, match=msg):
         idx.astype("m8[ns]")

pandas/tests/indexes/timedeltas/test_constructors.py

@@ -23,17 +23,19 @@ def test_array_of_dt64_nat_raises(self):
         nat = np.datetime64("NaT", "ns")
         arr = np.array([nat], dtype=object)
 
-        # TODO: should be TypeError?
         msg = "Invalid type for timedelta scalar"
-        with pytest.raises(ValueError, match=msg):
+        with pytest.raises(TypeError, match=msg):
             TimedeltaIndex(arr)
 
-        with pytest.raises(ValueError, match=msg):
+        with pytest.raises(TypeError, match=msg):
             TimedeltaArray._from_sequence(arr)
 
-        with pytest.raises(ValueError, match=msg):
+        with pytest.raises(TypeError, match=msg):
             sequence_to_td64ns(arr)
 
+        with pytest.raises(TypeError, match=msg):
+            to_timedelta(arr)
+
     @pytest.mark.parametrize("unit", ["Y", "y", "M"])
     def test_unit_m_y_raises(self, unit):
         msg = "Units 'M', 'Y', and 'y' are no longer supported"