DEPR: deprecate units 'w', 'd', 'MS', 'US', 'NS' for Timedelta in favor of 'W', 'D', 'ms', 'us', 'ns'

doc/source/user_guide/timedeltas.rst

@@ -35,7 +35,7 @@ You can construct a ``Timedelta`` scalar through various arguments, including `I
    pd.Timedelta(days=1, seconds=1)
 
    # integers with a unit
-   pd.Timedelta(1, unit="d")
+   pd.Timedelta(1, unit="D")
 
    # from a datetime.timedelta/np.timedelta64
    pd.Timedelta(datetime.timedelta(days=1, seconds=1))
@@ -94,7 +94,7 @@ is numeric:
 .. ipython:: python
 
    pd.to_timedelta(np.arange(5), unit="s")
-   pd.to_timedelta(np.arange(5), unit="d")
+   pd.to_timedelta(np.arange(5), unit="D")
 
 .. warning::
     If a string or array of strings is passed as an input then the ``unit`` keyword

doc/source/whatsnew/v0.13.0.rst

@@ -523,13 +523,25 @@ Enhancements
   Using the new top-level ``to_timedelta``, you can convert a scalar or array from the standard
   timedelta format (produced by ``to_csv``) into a timedelta type (``np.timedelta64`` in ``nanoseconds``).
 
-  .. ipython:: python
+  .. code-block:: ipython
+
+     In [53]: pd.to_timedelta('1 days 06:05:01.00003')
+     Out[53]: Timedelta('1 days 06:05:01.000030')
+
+     In [54]: pd.to_timedelta('15.5us')
+     Out[54]: Timedelta('0 days 00:00:00.000015500')
+
+     In [55]: pd.to_timedelta(['1 days 06:05:01.00003', '15.5us', 'nan'])
+     Out[55]: TimedeltaIndex(['1 days 06:05:01.000030', '0 days 00:00:00.000015500', NaT], dtype='timedelta64[ns]', freq=None)
+
+     In [56]: pd.to_timedelta(np.arange(5), unit='s')
+     Out[56]:
+     TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:01', '0 days 00:00:02',
+                     '0 days 00:00:03', '0 days 00:00:04'],
+                    dtype='timedelta64[ns]', freq=None)
 
-     pd.to_timedelta('1 days 06:05:01.00003')
-     pd.to_timedelta('15.5us')
-     pd.to_timedelta(['1 days 06:05:01.00003', '15.5us', 'nan'])
-     pd.to_timedelta(np.arange(5), unit='s')
-     pd.to_timedelta(np.arange(5), unit='d')
+     In [57]: pd.to_timedelta(np.arange(5), unit='d')
+     Out[57]: TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'], dtype='timedelta64[ns]', freq=None)
 
   A Series of dtype ``timedelta64[ns]`` can now be divided by another
   ``timedelta64[ns]`` object, or astyped to yield a ``float64`` dtyped Series. This

doc/source/whatsnew/v3.0.0.rst

@@ -273,6 +273,7 @@ Other Deprecations
 - Deprecated allowing non-keyword arguments in :meth:`Series.to_string` except ``buf``. (:issue:`57280`)
 - Deprecated behavior of :meth:`Series.dt.to_pytimedelta`, in a future version this will return a :class:`Series` containing python ``datetime.timedelta`` objects instead of an ``ndarray`` of timedelta; this matches the behavior of other :meth:`Series.dt` properties. (:issue:`57463`)
 - Deprecated parameter ``method`` in :meth:`DataFrame.reindex_like` / :meth:`Series.reindex_like` (:issue:`58667`)
+- Deprecated strings ``w``, ``d``, ``MIN``, ``MS``, ``US`` and ``NS`` denoting units in :class:`Timedelta` in favour of ``W``, ``D``, ``min``, ``ms``, ``us`` and ``ns`` (:issue:`59051`)
 - Deprecated using ``epoch`` date format in :meth:`DataFrame.to_json` and :meth:`Series.to_json`, use ``iso`` instead. (:issue:`57063`)
 
 .. ---------------------------------------------------------------------------

pandas/_libs/tslibs/dtypes.pxd

@@ -15,6 +15,7 @@ cdef dict c_OFFSET_TO_PERIOD_FREQSTR
 cdef dict c_PERIOD_TO_OFFSET_FREQSTR
 cdef dict c_OFFSET_RENAMED_FREQSTR
 cdef dict c_DEPR_ABBREVS
+cdef dict c_DEPR_UNITS
 cdef dict c_PERIOD_AND_OFFSET_DEPR_FREQSTR
 cdef dict attrname_to_abbrevs
 cdef dict npy_unit_to_attrname

pandas/_libs/tslibs/dtypes.pyx

@@ -346,6 +346,17 @@ cdef dict c_DEPR_ABBREVS = {
     "S": "s",
 }
 
+cdef dict c_DEPR_UNITS = {
+    "w": "W",
+    "d": "D",
+    "H": "h",
+    "MIN": "min",
+    "S": "s",
+    "MS": "ms",
+    "US": "us",
+    "NS": "ns",
+}
+
 cdef dict c_PERIOD_AND_OFFSET_DEPR_FREQSTR = {
     "w": "W",
     "MIN": "min",

pandas/_libs/tslibs/timedeltas.pyx

@@ -43,7 +43,7 @@ from pandas._libs.tslibs.conversion cimport (
     precision_from_unit,
 )
 from pandas._libs.tslibs.dtypes cimport (
-    c_DEPR_ABBREVS,
+    c_DEPR_UNITS,
     get_supported_reso,
     is_supported_unit,
     npy_unit_to_abbrev,
@@ -719,15 +719,15 @@ cpdef inline str parse_timedelta_unit(str unit):
         return "ns"
     elif unit == "M":
         return unit
-    elif unit in c_DEPR_ABBREVS:
+    elif unit in c_DEPR_UNITS:
         warnings.warn(
             f"\'{unit}\' is deprecated and will be removed in a "
-            f"future version. Please use \'{c_DEPR_ABBREVS.get(unit)}\' "
+            f"future version. Please use \'{c_DEPR_UNITS.get(unit)}\' "
             f"instead of \'{unit}\'.",
             FutureWarning,
             stacklevel=find_stack_level(),
         )
-        unit = c_DEPR_ABBREVS[unit]
+        unit = c_DEPR_UNITS[unit]
     try:
         return timedelta_abbrevs[unit.lower()]
     except KeyError:
@@ -1811,6 +1811,11 @@ class Timedelta(_Timedelta):
             Values `H`, `T`, `S`, `L`, `U`, and `N` are deprecated in favour
             of the values `h`, `min`, `s`, `ms`, `us`, and `ns`.
 
+        .. deprecated:: 3.0.0
+
+            Allowing the values `w`, `d`, `MIN`, `MS`, `US` and `NS` to denote units
+            are deprecated in favour of the values `W`, `D`, `min`, `ms`, `us` and `ns`.
+
     **kwargs
         Available kwargs: {days, seconds, microseconds,
         milliseconds, minutes, hours, weeks}.

pandas/core/arrays/timedeltas.py

@@ -746,7 +746,7 @@ def total_seconds(self) -> npt.NDArray[np.float64]:
         --------
         **Series**
 
-        >>> s = pd.Series(pd.to_timedelta(np.arange(5), unit="d"))
+        >>> s = pd.Series(pd.to_timedelta(np.arange(5), unit="D"))
         >>> s
         0   0 days
         1   1 days
@@ -765,7 +765,7 @@ def total_seconds(self) -> npt.NDArray[np.float64]:
 
         **TimedeltaIndex**
 
-        >>> idx = pd.to_timedelta(np.arange(5), unit="d")
+        >>> idx = pd.to_timedelta(np.arange(5), unit="D")
         >>> idx
         TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'],
                        dtype='timedelta64[ns]', freq=None)
@@ -809,7 +809,7 @@ def to_pytimedelta(self) -> npt.NDArray[np.object_]:
     --------
     For Series:
 
-    >>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='d'))
+    >>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='D'))
     >>> ser
     0   1 days
     1   2 days

pandas/core/indexes/accessors.py

@@ -459,7 +459,7 @@ def to_pytimedelta(self) -> np.ndarray:
 
         Examples
         --------
-        >>> s = pd.Series(pd.to_timedelta(np.arange(5), unit="d"))
+        >>> s = pd.Series(pd.to_timedelta(np.arange(5), unit="D"))
         >>> s
         0   0 days
         1   1 days

pandas/core/tools/timedeltas.py

@@ -170,7 +170,7 @@ def to_timedelta(
     TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:01', '0 days 00:00:02',
                     '0 days 00:00:03', '0 days 00:00:04'],
                    dtype='timedelta64[ns]', freq=None)
-    >>> pd.to_timedelta(np.arange(5), unit="d")
+    >>> pd.to_timedelta(np.arange(5), unit="D")
     TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'],
                    dtype='timedelta64[ns]', freq=None)
     """

pandas/tests/frame/indexing/test_mask.py

@@ -122,15 +122,15 @@ def test_mask_stringdtype(frame_or_series):
 
 def test_mask_where_dtype_timedelta():
     # https://github.com/pandas-dev/pandas/issues/39548
-    df = DataFrame([Timedelta(i, unit="d") for i in range(5)])
+    df = DataFrame([Timedelta(i, unit="D") for i in range(5)])
 
     expected = DataFrame(np.full(5, np.nan, dtype="timedelta64[ns]"))
     tm.assert_frame_equal(df.mask(df.notna()), expected)
 
     expected = DataFrame(
         [np.nan, np.nan, np.nan, Timedelta("3 day"), Timedelta("4 day")]
     )
-    tm.assert_frame_equal(df.where(df > Timedelta(2, unit="d")), expected)
+    tm.assert_frame_equal(df.where(df > Timedelta(2, unit="D")), expected)
 
 
 def test_mask_return_dtype():

pandas/tests/frame/methods/test_astype.py

@@ -149,7 +149,7 @@ def test_astype_str(self):
         # see GH#9757
         a = Series(date_range("2010-01-04", periods=5))
         b = Series(date_range("3/6/2012 00:00", periods=5, tz="US/Eastern"))
-        c = Series([Timedelta(x, unit="d") for x in range(5)])
+        c = Series([Timedelta(x, unit="D") for x in range(5)])
         d = Series(range(5))
         e = Series([0.0, 0.2, 0.4, 0.6, 0.8])
 

pandas/tests/frame/methods/test_reset_index.py

@@ -600,8 +600,8 @@ def test_reset_index_with_drop(
                 {"a": [pd.NaT, Timestamp("2020-01-01")], "b": [1, 2], "x": [11, 12]},
             ),
             (
-                [(pd.NaT, 1), (pd.Timedelta(123, "d"), 2)],
-                {"a": [pd.NaT, pd.Timedelta(123, "d")], "b": [1, 2], "x": [11, 12]},
+                [(pd.NaT, 1), (pd.Timedelta(123, "D"), 2)],
+                {"a": [pd.NaT, pd.Timedelta(123, "D")], "b": [1, 2], "x": [11, 12]},
             ),
         ],
     )

pandas/tests/groupby/test_groupby.py

@@ -148,8 +148,8 @@ def test_len_nan_group():
 
 def test_groupby_timedelta_median():
     # issue 57926
-    expected = Series(data=Timedelta("1d"), index=["foo"])
-    df = DataFrame({"label": ["foo", "foo"], "timedelta": [pd.NaT, Timedelta("1d")]})
+    expected = Series(data=Timedelta("1D"), index=["foo"])
+    df = DataFrame({"label": ["foo", "foo"], "timedelta": [pd.NaT, Timedelta("1D")]})
     gb = df.groupby("label")["timedelta"]
     actual = gb.median()
     tm.assert_series_equal(actual, expected, check_names=False)

pandas/tests/groupby/test_reductions.py

@@ -982,7 +982,7 @@ def test_groupby_sum_timedelta_with_nat():
     df = DataFrame(
         {
             "a": [1, 1, 2, 2],
-            "b": [pd.Timedelta("1d"), pd.Timedelta("2d"), pd.Timedelta("3d"), pd.NaT],
+            "b": [pd.Timedelta("1D"), pd.Timedelta("2D"), pd.Timedelta("3D"), pd.NaT],
         }
     )
     td3 = pd.Timedelta(days=3)

pandas/tests/indexes/timedeltas/methods/test_shift.py

@@ -37,7 +37,7 @@ def test_tdi_shift_minutes(self):
 
     def test_tdi_shift_int(self):
         # GH#8083
-        tdi = pd.to_timedelta(range(5), unit="d")
+        tdi = pd.to_timedelta(range(5), unit="D")
         trange = tdi._with_freq("infer") + pd.offsets.Hour(1)
         result = trange.shift(1)
         expected = TimedeltaIndex(
@@ -54,7 +54,7 @@ def test_tdi_shift_int(self):
 
     def test_tdi_shift_nonstandard_freq(self):
         # GH#8083
-        tdi = pd.to_timedelta(range(5), unit="d")
+        tdi = pd.to_timedelta(range(5), unit="D")
         trange = tdi._with_freq("infer") + pd.offsets.Hour(1)
         result = trange.shift(3, freq="2D 1s")
         expected = TimedeltaIndex(

pandas/tests/indexes/timedeltas/test_constructors.py

@@ -168,7 +168,7 @@ def test_constructor_coverage(self):
         # NumPy string array
         strings = np.array(["1 days", "2 days", "3 days"])
         result = TimedeltaIndex(strings)
-        expected = to_timedelta([1, 2, 3], unit="d")
+        expected = to_timedelta([1, 2, 3], unit="D")
         tm.assert_index_equal(result, expected)
 
         from_ints = TimedeltaIndex(expected.asi8)
@@ -239,3 +239,28 @@ def test_from_categorical(self):
         ci = pd.CategoricalIndex(tdi)
         result = TimedeltaIndex(ci)
         tm.assert_index_equal(result, tdi)
+
+    @pytest.mark.parametrize(
+        "unit,unit_depr",
+        [
+            ("W", "w"),
+            ("D", "d"),
+            ("min", "MIN"),
+            ("s", "S"),
+            ("h", "H"),
+            ("ms", "MS"),
+            ("us", "US"),
+        ],
+    )
+    def test_unit_deprecated(self, unit, unit_depr):
+        # GH#52536, GH#59051
+        msg = f"'{unit_depr}' is deprecated and will be removed in a future version."
+
+        expected = TimedeltaIndex([f"1{unit}", f"2{unit}"])
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = TimedeltaIndex([f"1{unit_depr}", f"2{unit_depr}"])
+        tm.assert_index_equal(result, expected)
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            tdi = to_timedelta([1, 2], unit=unit_depr)
+        tm.assert_index_equal(tdi, expected)

pandas/tests/indexes/timedeltas/test_delete.py

@@ -44,7 +44,7 @@ def test_delete_slice(self):
 
         # reset freq to None
         expected_3_5 = TimedeltaIndex(
-            ["1 d", "2 d", "3 d", "7 d", "8 d", "9 d", "10d"], freq=None, name="idx"
+            ["1 D", "2 D", "3 D", "7 D", "8 D", "9 D", "10D"], freq=None, name="idx"
         )
 
         cases = {

pandas/tests/indexes/timedeltas/test_indexing.py

@@ -20,8 +20,10 @@
 
 class TestGetItem:
     def test_getitem_slice_keeps_name(self):
-        # GH#4226
-        tdi = timedelta_range("1d", "5d", freq="h", name="timebucket")
+        # GH#4226, GH#59051
+        msg = "'d' is deprecated and will be removed in a future version."
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            tdi = timedelta_range("1d", "5d", freq="h", name="timebucket")
         assert tdi[1:].name == tdi.name
 
     def test_getitem(self):
@@ -230,7 +232,7 @@ def test_take_invalid_kwargs(self):
 
     def test_take_equiv_getitem(self):
         tds = ["1day 02:00:00", "1 day 04:00:00", "1 day 10:00:00"]
-        idx = timedelta_range(start="1d", end="2d", freq="h", name="idx")
+        idx = timedelta_range(start="1D", end="2D", freq="h", name="idx")
         expected = TimedeltaIndex(tds, freq=None, name="idx")
 
         taken1 = idx.take([2, 4, 10])
@@ -337,8 +339,10 @@ def test_contains_nonunique(self):
 
     def test_contains(self):
         # Checking for any NaT-like objects
-        # GH#13603
-        td = to_timedelta(range(5), unit="d") + offsets.Hour(1)
+        # GH#13603, GH#59051
+        msg = "'d' is deprecated and will be removed in a future version."
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            td = to_timedelta(range(5), unit="d") + offsets.Hour(1)
         for v in [NaT, None, float("nan"), np.nan]:
             assert v not in td
 

pandas/tests/indexes/timedeltas/test_setops.py

@@ -42,7 +42,10 @@ def test_union_sort_false(self):
         tm.assert_index_equal(result, expected)
 
     def test_union_coverage(self):
-        idx = TimedeltaIndex(["3d", "1d", "2d"])
+        # GH#59051
+        msg = "'d' is deprecated and will be removed in a future version."
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            idx = TimedeltaIndex(["3d", "1d", "2d"])
         ordered = TimedeltaIndex(idx.sort_values(), freq="infer")
         result = ordered.union(idx)
         tm.assert_index_equal(result, ordered)
@@ -70,7 +73,7 @@ def test_union_bug_1745(self):
         tm.assert_index_equal(result, exp)
 
     def test_union_bug_4564(self):
-        left = timedelta_range("1 day", "30d")
+        left = timedelta_range("1 day", "30D")
         right = left + pd.offsets.Minute(15)
 
         result = left.union(right)

pandas/tests/indexing/test_categorical.py

@@ -511,13 +511,13 @@ def test_loc_and_at_with_categorical_index(self):
             # pandas scalars
             [Interval(1, 4), Interval(4, 6), Interval(6, 9)],
             [Timestamp(2019, 1, 1), Timestamp(2019, 2, 1), Timestamp(2019, 3, 1)],
-            [Timedelta(1, "d"), Timedelta(2, "d"), Timedelta(3, "D")],
+            [Timedelta(1, "D"), Timedelta(2, "D"), Timedelta(3, "D")],
             # pandas Integer arrays
             *(pd.array([1, 2, 3], dtype=dtype) for dtype in tm.ALL_INT_EA_DTYPES),
             # other pandas arrays
             pd.IntervalIndex.from_breaks([1, 4, 6, 9]).array,
             pd.date_range("2019-01-01", periods=3).array,
-            pd.timedelta_range(start="1d", periods=3).array,
+            pd.timedelta_range(start="1D", periods=3).array,
         ],
     )
     def test_loc_getitem_with_non_string_categories(self, idx_values, ordered):

pandas/tests/io/sas/test_sas7bdat.py

@@ -30,9 +30,9 @@ def data_test_ix(request, dirpath):
     fname = os.path.join(dirpath, f"test_sas7bdat_{i}.csv")
     df = pd.read_csv(fname)
     epoch = datetime(1960, 1, 1)
-    t1 = pd.to_timedelta(df["Column4"], unit="d")
+    t1 = pd.to_timedelta(df["Column4"], unit="D")
     df["Column4"] = (epoch + t1).astype("M8[s]")
-    t2 = pd.to_timedelta(df["Column12"], unit="d")
+    t2 = pd.to_timedelta(df["Column12"], unit="D")
     df["Column12"] = (epoch + t2).astype("M8[s]")
     for k in range(df.shape[1]):
         col = df.iloc[:, k]

pandas/tests/reshape/merge/test_merge.py

@@ -1367,8 +1367,8 @@ def test_merge_two_empty_df_no_division_error(self):
                 ),
             ),
             (
-                TimedeltaIndex(["1d", "2d", "3d"]),
-                TimedeltaIndex(["1d", "2d", "3d", pd.NaT, pd.NaT, pd.NaT]),
+                TimedeltaIndex(["1D", "2D", "3D"]),
+                TimedeltaIndex(["1D", "2D", "3D", pd.NaT, pd.NaT, pd.NaT]),
             ),
         ],
     )