DEPR: to_timedelta box kwarg (#30177)

Author: jbrockmendel

doc/source/whatsnew/v1.0.0.rst

@@ -541,7 +541,7 @@ or ``matplotlib.Axes.plot``. See :ref:`plotting.formatters` for more.
 - Removed the previously deprecated :meth:`Series.compound` and :meth:`DataFrame.compound` (:issue:`26405`)
 - Changed the the default value of `inplace` in :meth:`DataFrame.set_index` and :meth:`Series.set_axis`. It now defaults to ``False`` (:issue:`27600`)
 - Removed the previously deprecated :attr:`Series.cat.categorical`, :attr:`Series.cat.index`, :attr:`Series.cat.name` (:issue:`24751`)
-- :func:`to_datetime` no longer accepts "box" argument, always returns :class:`DatetimeIndex` or :class:`Index`, :class:`Series`, or :class:`DataFrame` (:issue:`24486`)
+- :func:`to_datetime` and :func:`to_timedelta` no longer accept "box" argument, always returns :class:`DatetimeIndex`, :class:`TimedeltaIndex`, :class:`Index`, :class:`Series`, or :class:`DataFrame` (:issue:`24486`)
 - :func:`to_timedelta`, :class:`Timedelta`, and :class:`TimedeltaIndex` no longer allow "M", "y", or "Y" for the "unit" argument (:issue:`23264`)
 - Removed the previously deprecated ``time_rule`` keyword from (non-public) :func:`offsets.generate_range`, which has been moved to :func:`core.arrays._ranges.generate_range` (:issue:`24157`)
 - :meth:`DataFrame.loc` or :meth:`Series.loc` with listlike indexers and missing labels will no longer reindex (:issue:`17295`)

pandas/core/dtypes/dtypes.py

@@ -669,7 +669,7 @@ def __init__(self, unit="ns", tz=None):
                 tz = result.tz
                 msg = (
                     "Passing a dtype alias like 'datetime64[ns, {tz}]' "
-                    "to DatetimeTZDtype is deprecated. Use "
+                    "to DatetimeTZDtype is no longer supported. Use "
                     "'DatetimeTZDtype.construct_from_string()' instead."
                 )
                 raise ValueError(msg)

pandas/core/indexes/datetimes.py

@@ -108,31 +108,6 @@ class DatetimeIndex(DatetimeIndexOpsMixin, Int64Index, DatetimeDelegateMixin):
         One of pandas date offset strings or corresponding objects. The string
         'infer' can be passed in order to set the frequency of the index as the
         inferred frequency upon creation.
-
-    start : starting value, datetime-like, optional
-        If data is None, start is used as the start point in generating regular
-        timestamp data.
-
-        .. deprecated:: 0.24.0
-
-    periods  : int, optional, > 0
-        Number of periods to generate, if generating index. Takes precedence
-        over end argument.
-
-        .. deprecated:: 0.24.0
-
-    end : end time, datetime-like, optional
-        If periods is none, generated index will extend to first conforming
-        time on or just past end argument.
-
-        .. deprecated:: 0.24.0
-
-    closed : str or None, default None
-        Make the interval closed with respect to the given frequency to
-        the 'left', 'right', or both sides (None).
-
-        .. deprecated:: 0.24. 0
-
     tz : pytz.timezone or dateutil.tz.tzfile
     ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'
         When clocks moved backward due to DST, ambiguous times may arise.
@@ -217,9 +192,6 @@ class DatetimeIndex(DatetimeIndexOpsMixin, Int64Index, DatetimeDelegateMixin):
     -----
     To learn more about the frequency strings, please see `this link
     <http://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
-
-    Creating a DatetimeIndex based on `start`, `periods`, and `end` has
-    been deprecated in favor of :func:`date_range`.
     """
 
     _typ = "datetimeindex"

pandas/core/indexes/multi.py

@@ -157,11 +157,6 @@ class MultiIndex(Index):
         Integers for each level designating which label at each location.
 
         .. versionadded:: 0.24.0
-    labels : sequence of arrays
-        Integers for each level designating which label at each location.
-
-        .. deprecated:: 0.24.0
-            Use ``codes`` instead
     sortorder : optional int
         Level of sortedness (must be lexicographically sorted by that
         level).

pandas/core/indexes/period.py

@@ -91,24 +91,6 @@ class PeriodIndex(DatetimeIndexOpsMixin, Int64Index, PeriodDelegateMixin):
         Make a copy of input ndarray
     freq : str or period object, optional
         One of pandas period strings or corresponding objects
-    start : starting value, period-like, optional
-        If data is None, used as the start point in generating regular
-        period data.
-
-        .. deprecated:: 0.24.0
-
-    periods : int, optional, > 0
-        Number of periods to generate, if generating index. Takes precedence
-        over end argument
-
-        .. deprecated:: 0.24.0
-
-    end : end value, period-like, optional
-        If periods is none, generated index will extend to first conforming
-        period on or just past end argument
-
-        .. deprecated:: 0.24.0
-
     year : int, array, or Series, default None
     month : int, array, or Series, default None
     quarter : int, array, or Series, default None
@@ -157,11 +139,6 @@ class PeriodIndex(DatetimeIndexOpsMixin, Int64Index, PeriodDelegateMixin):
     TimedeltaIndex : Index of timedelta64 data.
     period_range : Create a fixed-frequency PeriodIndex.
 
-    Notes
-    -----
-    Creating a PeriodIndex based on `start`, `periods`, and `end` has
-    been deprecated in favor of :func:`period_range`.
-
     Examples
     --------
     >>> idx = pd.PeriodIndex(year=year_arr, quarter=q_arr)

pandas/core/indexes/timedeltas.py

@@ -82,30 +82,6 @@ class TimedeltaIndex(
         inferred frequency upon creation.
     copy  : bool
         Make a copy of input ndarray.
-    start : starting value, timedelta-like, optional
-        If data is None, start is used as the start point in generating regular
-        timedelta data.
-
-        .. deprecated:: 0.24.0
-
-    periods  : int, optional, > 0
-        Number of periods to generate, if generating index. Takes precedence
-        over end argument.
-
-        .. deprecated:: 0.24.0
-
-    end : end time, timedelta-like, optional
-        If periods is none, generated index will extend to first conforming
-        time on or just past end argument.
-
-        .. deprecated:: 0.24. 0
-
-    closed : str or None, default None
-        Make the interval closed with respect to the given frequency to
-        the 'left', 'right', or both sides (None).
-
-        .. deprecated:: 0.24. 0
-
     name : object
         Name to be stored in the index.
 
@@ -140,9 +116,6 @@ class TimedeltaIndex(
     -----
     To learn more about the frequency strings, please see `this link
     <http://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
-
-    Creating a TimedeltaIndex based on `start`, `periods`, and `end` has
-    been deprecated in favor of :func:`timedelta_range`.
     """
 
     _typ = "timedeltaindex"

pandas/core/tools/timedeltas.py

@@ -6,16 +6,14 @@
 
 from pandas._libs.tslibs import NaT
 from pandas._libs.tslibs.timedeltas import Timedelta, parse_timedelta_unit
-from pandas.util._decorators import deprecate_kwarg
 
 from pandas.core.dtypes.common import is_list_like
 from pandas.core.dtypes.generic import ABCIndexClass, ABCSeries
 
 from pandas.core.arrays.timedeltas import sequence_to_td64ns
 
 
-@deprecate_kwarg(old_arg_name="box", new_arg_name=None)
-def to_timedelta(arg, unit="ns", box=True, errors="raise"):
+def to_timedelta(arg, unit="ns", errors="raise"):
     """
     Convert argument to timedelta.
 
@@ -36,15 +34,6 @@ def to_timedelta(arg, unit="ns", box=True, errors="raise"):
         'milli', 'millis', 'L', 'us', 'microseconds', 'microsecond',
         'micro', 'micros', 'U', 'ns', 'nanoseconds', 'nano', 'nanos',
         'nanosecond', 'N').
-    box : bool, default True
-        - If True returns a Timedelta/TimedeltaIndex of the results.
-        - If False returns a numpy.timedelta64 or numpy.darray of
-          values of dtype timedelta64[ns].
-
-        .. deprecated:: 0.25.0
-            Use :meth:`Series.to_numpy` or :meth:`Timedelta.to_timedelta64`
-            instead to get an ndarray of values or numpy.timedelta64,
-            respectively.
 
     errors : {'ignore', 'raise', 'coerce'}, default 'raise'
         - If 'raise', then invalid parsing will raise an exception.
@@ -86,11 +75,6 @@ def to_timedelta(arg, unit="ns", box=True, errors="raise"):
     >>> pd.to_timedelta(np.arange(5), unit='d')
     TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'],
                    dtype='timedelta64[ns]', freq=None)
-
-    Returning an ndarray by using the 'box' keyword argument:
-
-    >>> pd.to_timedelta(np.arange(5), box=False)
-    array([0, 1, 2, 3, 4], dtype='timedelta64[ns]')
     """
     unit = parse_timedelta_unit(unit)
 
@@ -106,32 +90,29 @@ def to_timedelta(arg, unit="ns", box=True, errors="raise"):
     if arg is None:
         return arg
     elif isinstance(arg, ABCSeries):
-        values = _convert_listlike(arg._values, unit=unit, box=False, errors=errors)
+        values = _convert_listlike(arg._values, unit=unit, errors=errors)
         return arg._constructor(values, index=arg.index, name=arg.name)
     elif isinstance(arg, ABCIndexClass):
-        return _convert_listlike(arg, unit=unit, box=box, errors=errors, name=arg.name)
+        return _convert_listlike(arg, unit=unit, errors=errors, name=arg.name)
     elif isinstance(arg, np.ndarray) and arg.ndim == 0:
         # extract array scalar and process below
         arg = arg.item()
     elif is_list_like(arg) and getattr(arg, "ndim", 1) == 1:
-        return _convert_listlike(arg, unit=unit, box=box, errors=errors)
+        return _convert_listlike(arg, unit=unit, errors=errors)
     elif getattr(arg, "ndim", 1) > 1:
         raise TypeError(
             "arg must be a string, timedelta, list, tuple, 1-d array, or Series"
         )
 
     # ...so it must be a scalar value. Return scalar.
-    return _coerce_scalar_to_timedelta_type(arg, unit=unit, box=box, errors=errors)
+    return _coerce_scalar_to_timedelta_type(arg, unit=unit, errors=errors)
 
 
-def _coerce_scalar_to_timedelta_type(r, unit="ns", box=True, errors="raise"):
+def _coerce_scalar_to_timedelta_type(r, unit="ns", errors="raise"):
     """Convert string 'r' to a timedelta object."""
 
     try:
         result = Timedelta(r, unit)
-        if not box:
-            # explicitly view as timedelta64 for case when result is pd.NaT
-            result = result.asm8.view("timedelta64[ns]")
     except ValueError:
         if errors == "raise":
             raise
@@ -144,7 +125,7 @@ def _coerce_scalar_to_timedelta_type(r, unit="ns", box=True, errors="raise"):
     return result
 
 
-def _convert_listlike(arg, unit="ns", box=True, errors="raise", name=None):
+def _convert_listlike(arg, unit="ns", errors="raise", name=None):
     """Convert a list of objects to a timedelta index object."""
 
     if isinstance(arg, (list, tuple)) or not hasattr(arg, "dtype"):
@@ -169,8 +150,7 @@ def _convert_listlike(arg, unit="ns", box=True, errors="raise", name=None):
             # like to surface it.
             raise
 
-    if box:
-        from pandas import TimedeltaIndex
+    from pandas import TimedeltaIndex
 
-        value = TimedeltaIndex(value, unit="ns", name=name)
+    value = TimedeltaIndex(value, unit="ns", name=name)
     return value

pandas/io/excel/_base.py

@@ -79,11 +79,6 @@
     is based on the subset.
 usecols : int, str, list-like, or callable default None
     * If None, then parse all columns.
-    * If int, then indicates last column to be parsed.
-
-      .. deprecated:: 0.24.0
-         Pass in a list of int instead from 0 to `usecols` inclusive.
-
     * If str, then indicates comma separated list of Excel column letters
       and column ranges (e.g. "A:E" or "A,C,E:F"). Ranges are inclusive of
       both sides.

pandas/tests/indexes/timedeltas/test_tools.py

@@ -3,35 +3,13 @@
 import numpy as np
 import pytest
 
-from pandas._libs.tslib import iNaT
-
 import pandas as pd
 from pandas import Series, TimedeltaIndex, isna, to_timedelta
 import pandas.util.testing as tm
 
 
 class TestTimedeltas:
     def test_to_timedelta(self):
-        def conv(v):
-            return v.astype("m8[ns]")
-
-        d1 = np.timedelta64(1, "D")
-
-        with tm.assert_produces_warning(FutureWarning):
-            assert to_timedelta("1 days 06:05:01.00003", box=False) == conv(
-                d1
-                + np.timedelta64(6 * 3600 + 5 * 60 + 1, "s")
-                + np.timedelta64(30, "us")
-            )
-
-        with tm.assert_produces_warning(FutureWarning):
-            assert to_timedelta("15.5us", box=False) == conv(
-                np.timedelta64(15500, "ns")
-            )
-
-            # empty string
-            result = to_timedelta("", box=False)
-            assert result.astype("int64") == iNaT
 
         result = to_timedelta(["", ""])
         assert isna(result).all()
@@ -41,12 +19,6 @@ def conv(v):
         expected = pd.Index(np.array([np.timedelta64(1, "s")]))
         tm.assert_index_equal(result, expected)
 
-        with tm.assert_produces_warning(FutureWarning):
-            # ints
-            result = np.timedelta64(0, "ns")
-            expected = to_timedelta(0, box=False)
-            assert result == expected
-
         # Series
         expected = Series([timedelta(days=1), timedelta(days=1, seconds=1)])
         result = to_timedelta(Series(["1d", "1days 00:00:01"]))
@@ -59,19 +31,6 @@ def conv(v):
         expected = to_timedelta([0, 10], unit="s")
         tm.assert_index_equal(result, expected)
 
-        with tm.assert_produces_warning(FutureWarning):
-            # single element conversion
-            v = timedelta(seconds=1)
-            result = to_timedelta(v, box=False)
-            expected = np.timedelta64(timedelta(seconds=1))
-            assert result == expected
-
-        with tm.assert_produces_warning(FutureWarning):
-            v = np.timedelta64(timedelta(seconds=1))
-            result = to_timedelta(v, box=False)
-            expected = np.timedelta64(timedelta(seconds=1))
-            assert result == expected
-
         # arrays of various dtypes
         arr = np.array([1] * 5, dtype="int64")
         result = to_timedelta(arr, unit="s")
@@ -98,28 +57,6 @@ def conv(v):
         expected = TimedeltaIndex([np.timedelta64(1, "D")] * 5)
         tm.assert_index_equal(result, expected)
 
-        with tm.assert_produces_warning(FutureWarning):
-            # Test with lists as input when box=false
-            expected = np.array(np.arange(3) * 1000000000, dtype="timedelta64[ns]")
-            result = to_timedelta(range(3), unit="s", box=False)
-            tm.assert_numpy_array_equal(expected, result)
-
-        with tm.assert_produces_warning(FutureWarning):
-            result = to_timedelta(np.arange(3), unit="s", box=False)
-            tm.assert_numpy_array_equal(expected, result)
-
-        with tm.assert_produces_warning(FutureWarning):
-            result = to_timedelta([0, 1, 2], unit="s", box=False)
-            tm.assert_numpy_array_equal(expected, result)
-
-        with tm.assert_produces_warning(FutureWarning):
-            # Tests with fractional seconds as input:
-            expected = np.array(
-                [0, 500000000, 800000000, 1200000000], dtype="timedelta64[ns]"
-            )
-            result = to_timedelta([0.0, 0.5, 0.8, 1.2], unit="s", box=False)
-            tm.assert_numpy_array_equal(expected, result)
-
     def test_to_timedelta_invalid(self):
 
         # bad value for errors parameter
@@ -208,13 +145,3 @@ def test_to_timedelta_float(self):
         result = pd.to_timedelta(arr, unit="s")
         expected_asi8 = np.arange(999990000, int(1e9), 1000, dtype="int64")
         tm.assert_numpy_array_equal(result.asi8, expected_asi8)
-
-    def test_to_timedelta_box_deprecated(self):
-        result = np.timedelta64(0, "ns")
-
-        # Deprecated - see GH24416
-        with tm.assert_produces_warning(FutureWarning):
-            to_timedelta(0, box=False)
-
-        expected = to_timedelta(0).to_timedelta64()
-        assert result == expected