API: make Timestamp/Timedelta _as_unit public as_unit (#48819)

* API: make Timestamp/Timedelta _as_unit public as_unit

* update test

* update test

* update tests

* fix pyi typo

* fixup

* fixup

doc/source/reference/arrays.rst

@@ -139,6 +139,7 @@ Properties
    Timestamp.second
    Timestamp.tz
    Timestamp.tzinfo
+   Timestamp.unit
    Timestamp.value
    Timestamp.week
    Timestamp.weekofyear
@@ -149,6 +150,7 @@ Methods
 .. autosummary::
    :toctree: api/
 
+   Timestamp.as_unit
    Timestamp.astimezone
    Timestamp.ceil
    Timestamp.combine
@@ -242,6 +244,7 @@ Properties
    Timedelta.nanoseconds
    Timedelta.resolution
    Timedelta.seconds
+   Timedelta.unit
    Timedelta.value
    Timedelta.view
 
@@ -250,6 +253,7 @@ Methods
 .. autosummary::
    :toctree: api/
 
+   Timedelta.as_unit
    Timedelta.ceil
    Timedelta.floor
    Timedelta.isoformat

pandas/_libs/tslib.pyx

@@ -551,7 +551,7 @@ cpdef array_to_datetime(
                             raise ValueError('Cannot mix tz-aware with '
                                              'tz-naive values')
                         if isinstance(val, _Timestamp):
-                            iresult[i] = val._as_unit("ns").value
+                            iresult[i] = val.as_unit("ns").value
                         else:
                             iresult[i] = pydatetime_to_dt64(val, &dts)
                             check_dts_bounds(&dts)
@@ -906,7 +906,7 @@ def array_to_datetime_with_tz(ndarray values, tzinfo tz):
                 else:
                     # datetime64, tznaive pydatetime, int, float
                     ts = ts.tz_localize(tz)
-                ts = ts._as_unit("ns")
+                ts = ts.as_unit("ns")
                 ival = ts.value
 
         # Analogous to: result[i] = ival

pandas/_libs/tslibs/nattype.pyi

@@ -127,3 +127,4 @@ class NaTType:
     __le__: _NatComparison
     __gt__: _NatComparison
     __ge__: _NatComparison
+    def as_unit(self, unit: str, round_ok: bool = ...) -> NaTType: ...

pandas/_libs/tslibs/nattype.pyx

@@ -1195,6 +1195,22 @@ default 'raise'
     def tzinfo(self) -> None:
         return None
 
+    def as_unit(self, str unit, bint round_ok=True) -> "NaTType":
+        """
+        Convert the underlying int64 representaton to the given unit.
+
+        Parameters
+        ----------
+        unit : {"ns", "us", "ms", "s"}
+        round_ok : bool, default True
+            If False and the conversion requires rounding, raise.
+
+        Returns
+        -------
+        Timestamp
+        """
+        return c_NaT
+
 
 c_NaT = NaTType()  # C-visible
 NaT = c_NaT        # Python-visible

pandas/_libs/tslibs/timedeltas.pyi

@@ -152,5 +152,5 @@ class Timedelta(timedelta):
     def to_numpy(self) -> np.timedelta64: ...
     def view(self, dtype: npt.DTypeLike = ...) -> object: ...
     @property
-    def _unit(self) -> str: ...
-    def _as_unit(self, unit: str, round_ok: bool = ...) -> Timedelta: ...
+    def unit(self) -> str: ...
+    def as_unit(self, unit: str, round_ok: bool = ...) -> Timedelta: ...

pandas/_libs/tslibs/timedeltas.pyx

@@ -339,7 +339,7 @@ cdef convert_to_timedelta64(object ts, str unit):
     elif isinstance(ts, _Timedelta):
         # already in the proper format
         if ts._creso != NPY_FR_ns:
-            ts = ts._as_unit("ns").asm8
+            ts = ts.as_unit("ns").asm8
         else:
             ts = np.timedelta64(ts.value, "ns")
     elif is_timedelta64_object(ts):
@@ -1081,6 +1081,10 @@ cdef class _Timedelta(timedelta):
         # TODO: add nanos/1e9?
         return self.days * 24 * 3600 + self.seconds + self.microseconds / 1_000_000
 
+    @property
+    def unit(self) -> str:
+        return npy_unit_to_abbrev(self._creso)
+
     def __hash__(_Timedelta self):
         if self._has_ns():
             # Note: this does *not* satisfy the invariance
@@ -1500,7 +1504,20 @@ cdef class _Timedelta(timedelta):
         # exposing as classmethod for testing
         return _timedelta_from_value_and_reso(value, reso)
 
-    def _as_unit(self, str unit, bint round_ok=True):
+    def as_unit(self, str unit, bint round_ok=True):
+        """
+        Convert the underlying int64 representaton to the given unit.
+
+        Parameters
+        ----------
+        unit : {"ns", "us", "ms", "s"}
+        round_ok : bool, default True
+            If False and the conversion requires rounding, raise.
+
+        Returns
+        -------
+        Timedelta
+        """
         dtype = np.dtype(f"m8[{unit}]")
         reso = get_unit_from_dtype(dtype)
         return self._as_creso(reso, round_ok=round_ok)

pandas/_libs/tslibs/timestamps.pyi

@@ -220,5 +220,5 @@ class Timestamp(datetime):
     @property
     def daysinmonth(self) -> int: ...
     @property
-    def _unit(self) -> str: ...
-    def _as_unit(self, unit: str, round_ok: bool = ...) -> Timestamp: ...
+    def unit(self) -> str: ...
+    def as_unit(self, unit: str, round_ok: bool = ...) -> Timestamp: ...

pandas/_libs/tslibs/timestamps.pyx

@@ -233,7 +233,7 @@ cdef class _Timestamp(ABCTimestamp):
     resolution = MinMaxReso("resolution")  # GH#21336, GH#21365
 
     @property
-    def _unit(self) -> str:
+    def unit(self) -> str:
         """
         The abbreviation associated with self._creso.
         """
@@ -993,7 +993,20 @@ cdef class _Timestamp(ABCTimestamp):
         value = convert_reso(self.value, self._creso, reso, round_ok=round_ok)
         return type(self)._from_value_and_reso(value, reso=reso, tz=self.tzinfo)
 
-    def _as_unit(self, str unit, bint round_ok=True):
+    def as_unit(self, str unit, bint round_ok=True):
+        """
+        Convert the underlying int64 representaton to the given unit.
+
+        Parameters
+        ----------
+        unit : {"ns", "us", "ms", "s"}
+        round_ok : bool, default True
+            If False and the conversion requires rounding, raise.
+
+        Returns
+        -------
+        Timestamp
+        """
         dtype = np.dtype(f"M8[{unit}]")
         reso = get_unit_from_dtype(dtype)
         try:

pandas/core/arrays/datetimelike.py

@@ -816,7 +816,7 @@ def isin(self, values) -> npt.NDArray[np.bool_]:
 
         if self.dtype.kind in ["m", "M"]:
             self = cast("DatetimeArray | TimedeltaArray", self)
-            values = values._as_unit(self._unit)
+            values = values.as_unit(self.unit)
 
         try:
             self._check_compatible_with(values)
@@ -1116,7 +1116,7 @@ def _add_datetimelike_scalar(self, other) -> DatetimeArray:
             # i.e. np.datetime64("NaT")
             # In this case we specifically interpret NaT as a datetime, not
             # the timedelta interpretation we would get by returning self + NaT
-            result = self._ndarray + NaT.to_datetime64().astype(f"M8[{self._unit}]")
+            result = self._ndarray + NaT.to_datetime64().astype(f"M8[{self.unit}]")
             # Preserve our resolution
             return DatetimeArray._simple_new(result, dtype=result.dtype)
 
@@ -1128,10 +1128,10 @@ def _add_datetimelike_scalar(self, other) -> DatetimeArray:
         result = checked_add_with_arr(
             self.asi8, other_i8, arr_mask=self._isnan, b_mask=o_mask
         )
-        res_values = result.view(f"M8[{self._unit}]")
+        res_values = result.view(f"M8[{self.unit}]")
 
-        dtype = tz_to_dtype(tz=other.tz, unit=self._unit)
-        res_values = result.view(f"M8[{self._unit}]")
+        dtype = tz_to_dtype(tz=other.tz, unit=self.unit)
+        res_values = result.view(f"M8[{self.unit}]")
         new_freq = self._get_arithmetic_result_freq(other)
         return DatetimeArray._simple_new(res_values, dtype=dtype, freq=new_freq)
 
@@ -1191,7 +1191,7 @@ def _sub_datetimelike(self, other: Timestamp | DatetimeArray) -> TimedeltaArray:
         res_values = checked_add_with_arr(
             self.asi8, -other_i8, arr_mask=self._isnan, b_mask=o_mask
         )
-        res_m8 = res_values.view(f"timedelta64[{self._unit}]")
+        res_m8 = res_values.view(f"timedelta64[{self.unit}]")
 
         new_freq = self._get_arithmetic_result_freq(other)
         return TimedeltaArray._simple_new(res_m8, dtype=res_m8.dtype, freq=new_freq)
@@ -1989,13 +1989,13 @@ def _creso(self) -> int:
         return get_unit_from_dtype(self._ndarray.dtype)
 
     @cache_readonly
-    def _unit(self) -> str:
+    def unit(self) -> str:
         # e.g. "ns", "us", "ms"
         # error: Argument 1 to "dtype_to_unit" has incompatible type
         # "ExtensionDtype"; expected "Union[DatetimeTZDtype, dtype[Any]]"
         return dtype_to_unit(self.dtype)  # type: ignore[arg-type]
 
-    def _as_unit(self: TimelikeOpsT, unit: str) -> TimelikeOpsT:
+    def as_unit(self: TimelikeOpsT, unit: str) -> TimelikeOpsT:
         dtype = np.dtype(f"{self.dtype.kind}8[{unit}]")
         new_values = astype_overflowsafe(self._ndarray, dtype, round_ok=True)
 
@@ -2017,9 +2017,9 @@ def _ensure_matching_resos(self, other):
         if self._creso != other._creso:
             # Just as with Timestamp/Timedelta, we cast to the higher resolution
             if self._creso < other._creso:
-                self = self._as_unit(other._unit)
+                self = self.as_unit(other.unit)
             else:
-                other = other._as_unit(self._unit)
+                other = other.as_unit(self.unit)
         return self, other
 
     # --------------------------------------------------------------

pandas/core/arrays/datetimes.py

@@ -351,9 +351,9 @@ def _from_sequence_not_strict(
         data_unit = np.datetime_data(subarr.dtype)[0]
         data_dtype = tz_to_dtype(tz, data_unit)
         result = cls._simple_new(subarr, freq=freq, dtype=data_dtype)
-        if unit is not None and unit != result._unit:
+        if unit is not None and unit != result.unit:
             # If unit was specified in user-passed dtype, cast to it here
-            result = result._as_unit(unit)
+            result = result.as_unit(unit)
 
         if inferred_freq is None and freq is not None:
             # this condition precludes `freq_infer`
@@ -843,7 +843,7 @@ def tz_convert(self, tz) -> DatetimeArray:
             )
 
         # No conversion since timestamps are all UTC to begin with
-        dtype = tz_to_dtype(tz, unit=self._unit)
+        dtype = tz_to_dtype(tz, unit=self.unit)
         return self._simple_new(self._ndarray, dtype=dtype, freq=self.freq)
 
     @dtl.ravel_compat
@@ -1018,8 +1018,8 @@ def tz_localize(
                 nonexistent=nonexistent,
                 creso=self._creso,
             )
-        new_dates = new_dates.view(f"M8[{self._unit}]")
-        dtype = tz_to_dtype(tz, unit=self._unit)
+        new_dates = new_dates.view(f"M8[{self.unit}]")
+        dtype = tz_to_dtype(tz, unit=self.unit)
 
         freq = None
         if timezones.is_utc(tz) or (len(self) == 1 and not isna(new_dates[0])):

pandas/core/arrays/timedeltas.py

@@ -268,10 +268,10 @@ def _generate_range(cls, start, end, periods, freq, closed=None):
             )
 
         if start is not None:
-            start = Timedelta(start)._as_unit("ns")
+            start = Timedelta(start).as_unit("ns")
 
         if end is not None:
-            end = Timedelta(end)._as_unit("ns")
+            end = Timedelta(end).as_unit("ns")
 
         left_closed, right_closed = validate_endpoints(closed)
 
@@ -298,7 +298,7 @@ def _unbox_scalar(self, value) -> np.timedelta64:
         if value is NaT:
             return np.timedelta64(value.value, "ns")
         else:
-            return value._as_unit(self._unit).asm8
+            return value.as_unit(self.unit).asm8
 
     def _scalar_from_string(self, value) -> Timedelta | NaTType:
         return Timedelta(value)

pandas/core/dtypes/common.py

@@ -931,7 +931,7 @@ def is_datetime64_ns_dtype(arr_or_dtype) -> bool:
         else:
             return False
     return tipo == DT64NS_DTYPE or (
-        isinstance(tipo, DatetimeTZDtype) and tipo._unit == "ns"
+        isinstance(tipo, DatetimeTZDtype) and tipo.unit == "ns"
     )
 
 

pandas/core/dtypes/dtypes.py

@@ -676,7 +676,7 @@ def na_value(self) -> NaTType:
     # error: Signature of "str" incompatible with supertype "PandasExtensionDtype"
     @cache_readonly
     def str(self) -> str:  # type: ignore[override]
-        return f"|M8[{self._unit}]"
+        return f"|M8[{self.unit}]"
 
     def __init__(self, unit: str_type | DatetimeTZDtype = "ns", tz=None) -> None:
         if isinstance(unit, DatetimeTZDtype):
@@ -720,7 +720,7 @@ def _creso(self) -> int:
             "ms": dtypes.NpyDatetimeUnit.NPY_FR_ms,
             "us": dtypes.NpyDatetimeUnit.NPY_FR_us,
             "ns": dtypes.NpyDatetimeUnit.NPY_FR_ns,
-        }[self._unit]
+        }[self.unit]
         return reso.value
 
     @property

pandas/core/window/ewm.py

@@ -123,7 +123,7 @@ def _calculate_deltas(
     """
     _times = np.asarray(times.view(np.int64), dtype=np.float64)
     # TODO: generalize to non-nano?
-    _halflife = float(Timedelta(halflife)._as_unit("ns").value)
+    _halflife = float(Timedelta(halflife).as_unit("ns").value)
     return np.diff(_times) / _halflife
 
 

pandas/tests/arrays/test_datetimes.py

@@ -215,12 +215,12 @@ def test_add_mismatched_reso_doesnt_downcast(self):
         # https://github.com/pandas-dev/pandas/pull/48748#issuecomment-1260181008
         td = pd.Timedelta(microseconds=1)
         dti = pd.date_range("2016-01-01", periods=3) - td
-        dta = dti._data._as_unit("us")
+        dta = dti._data.as_unit("us")
 
-        res = dta + td._as_unit("us")
+        res = dta + td.as_unit("us")
         # even though the result is an even number of days
         #  (so we _could_ downcast to unit="s"), we do not.
-        assert res._unit == "us"
+        assert res.unit == "us"
 
     @pytest.mark.parametrize(
         "scalar",
@@ -240,32 +240,32 @@ def test_add_timedeltalike_scalar_mismatched_reso(self, dta_dti, scalar):
         exp_reso = max(dta._creso, td._creso)
         exp_unit = npy_unit_to_abbrev(exp_reso)
 
-        expected = (dti + td)._data._as_unit(exp_unit)
+        expected = (dti + td)._data.as_unit(exp_unit)
         result = dta + scalar
         tm.assert_extension_array_equal(result, expected)
 
         result = scalar + dta
         tm.assert_extension_array_equal(result, expected)
 
-        expected = (dti - td)._data._as_unit(exp_unit)
+        expected = (dti - td)._data.as_unit(exp_unit)
         result = dta - scalar
         tm.assert_extension_array_equal(result, expected)
 
     def test_sub_datetimelike_scalar_mismatch(self):
         dti = pd.date_range("2016-01-01", periods=3)
-        dta = dti._data._as_unit("us")
+        dta = dti._data.as_unit("us")
 
-        ts = dta[0]._as_unit("s")
+        ts = dta[0].as_unit("s")
 
         result = dta - ts
-        expected = (dti - dti[0])._data._as_unit("us")
+        expected = (dti - dti[0])._data.as_unit("us")
         assert result.dtype == "m8[us]"
         tm.assert_extension_array_equal(result, expected)
 
     def test_sub_datetime64_reso_mismatch(self):
         dti = pd.date_range("2016-01-01", periods=3)
-        left = dti._data._as_unit("s")
-        right = left._as_unit("ms")
+        left = dti._data.as_unit("s")
+        right = left.as_unit("ms")
 
         result = left - right
         exp_values = np.array([0, 0, 0], dtype="m8[ms]")

pandas/tests/arrays/test_timedeltas.py

@@ -104,7 +104,7 @@ def test_add_pdnat(self, tda):
     def test_add_datetimelike_scalar(self, tda, tz_naive_fixture):
         ts = pd.Timestamp("2016-01-01", tz=tz_naive_fixture)
 
-        expected = tda._as_unit("ns") + ts
+        expected = tda.as_unit("ns") + ts
         res = tda + ts
         tm.assert_extension_array_equal(res, expected)
         res = ts + tda