add Tick, BaseOffset to tslibs namespace (#34963)

Author: jbrockmendel

pandas/_libs/tslibs/__init__.py

@@ -16,13 +16,15 @@
     "Timestamp",
     "tz_convert_single",
     "to_offset",
+    "Tick",
+    "BaseOffset",
 ]
 
 from . import dtypes
 from .conversion import localize_pydatetime
 from .nattype import NaT, NaTType, iNaT, is_null_datetimelike, nat_strings
 from .np_datetime import OutOfBoundsDatetime
-from .offsets import to_offset
+from .offsets import BaseOffset, Tick, to_offset
 from .period import IncompatibleFrequency, Period
 from .resolution import Resolution
 from .timedeltas import Timedelta, delta_to_nanoseconds, ints_to_pytimedelta

pandas/compat/pickle_compat.py

@@ -9,9 +9,9 @@
 from typing import TYPE_CHECKING, Optional
 import warnings
 
-from pandas import Index
+from pandas._libs.tslibs import BaseOffset
 
-from pandas.tseries.offsets import DateOffset
+from pandas import Index
 
 if TYPE_CHECKING:
     from pandas import Series, DataFrame
@@ -42,7 +42,7 @@ def load_reduce(self):
                 return
             except TypeError:
                 pass
-        elif args and issubclass(args[0], DateOffset):
+        elif args and issubclass(args[0], BaseOffset):
             # TypeError: object.__new__(Day) is not safe, use Day.__new__()
             cls = args[0]
             stack[-1] = cls.__new__(*args)

pandas/core/arrays/_ranges.py

@@ -7,16 +7,14 @@
 
 import numpy as np
 
-from pandas._libs.tslibs import OutOfBoundsDatetime, Timedelta, Timestamp
-
-from pandas.tseries.offsets import DateOffset
+from pandas._libs.tslibs import BaseOffset, OutOfBoundsDatetime, Timedelta, Timestamp
 
 
 def generate_regular_range(
     start: Union[Timestamp, Timedelta],
     end: Union[Timestamp, Timedelta],
     periods: int,
-    freq: DateOffset,
+    freq: BaseOffset,
 ):
     """
     Generate a range of dates or timestamps with the spans between dates

pandas/core/arrays/datetimelike.py

@@ -7,10 +7,12 @@
 
 from pandas._libs import algos, lib
 from pandas._libs.tslibs import (
+    BaseOffset,
     NaT,
     NaTType,
     Period,
     Resolution,
+    Tick,
     Timestamp,
     delta_to_nanoseconds,
     iNaT,
@@ -62,7 +64,6 @@
 from pandas.core.ops.invalid import invalid_comparison, make_invalid_op
 
 from pandas.tseries import frequencies
-from pandas.tseries.offsets import DateOffset, Tick
 
 DTScalarOrNaT = Union[DatetimeLikeScalar, NaTType]
 
@@ -421,7 +422,7 @@ def _with_freq(self, freq):
         if freq is None:
             # Always valid
             pass
-        elif len(self) == 0 and isinstance(freq, DateOffset):
+        elif len(self) == 0 and isinstance(freq, BaseOffset):
             # Always valid.  In the TimedeltaArray case, we assume this
             #  is a Tick offset.
             pass
@@ -1398,7 +1399,7 @@ def __add__(self, other):
             result = self._add_nat()
         elif isinstance(other, (Tick, timedelta, np.timedelta64)):
             result = self._add_timedeltalike_scalar(other)
-        elif isinstance(other, DateOffset):
+        elif isinstance(other, BaseOffset):
             # specifically _not_ a Tick
             result = self._add_offset(other)
         elif isinstance(other, (datetime, np.datetime64)):
@@ -1454,7 +1455,7 @@ def __sub__(self, other):
             result = self._sub_nat()
         elif isinstance(other, (Tick, timedelta, np.timedelta64)):
             result = self._add_timedeltalike_scalar(-other)
-        elif isinstance(other, DateOffset):
+        elif isinstance(other, BaseOffset):
             # specifically _not_ a Tick
             result = self._add_offset(-other)
         elif isinstance(other, (datetime, np.datetime64)):
@@ -1778,7 +1779,7 @@ def maybe_infer_freq(freq):
         Whether we should inherit the freq of passed data.
     """
     freq_infer = False
-    if not isinstance(freq, DateOffset):
+    if not isinstance(freq, BaseOffset):
         # if a passed freq is None, don't infer automatically
         if freq != "infer":
             freq = to_offset(freq)

pandas/core/arrays/period.py

@@ -5,6 +5,7 @@
 import numpy as np
 
 from pandas._libs.tslibs import (
+    BaseOffset,
     NaT,
     NaTType,
     Timedelta,
@@ -48,8 +49,6 @@
 from pandas.core.arrays import datetimelike as dtl
 import pandas.core.common as com
 
-from pandas.tseries.offsets import DateOffset
-
 
 def _field_accessor(name: str, docstring=None):
     def f(self):
@@ -280,7 +279,7 @@ def dtype(self) -> PeriodDtype:
 
     # error: Read-only property cannot override read-write property  [misc]
     @property  # type: ignore
-    def freq(self) -> DateOffset:
+    def freq(self) -> BaseOffset:
         """
         Return the frequency object for this PeriodArray.
         """
@@ -656,7 +655,7 @@ def _addsub_int_array(
         res_values[self._isnan] = iNaT
         return type(self)(res_values, freq=self.freq)
 
-    def _add_offset(self, other: DateOffset):
+    def _add_offset(self, other: BaseOffset):
         assert not isinstance(other, Tick)
 
         if other.base != self.freq.base:
@@ -784,7 +783,7 @@ def raise_on_incompatible(left, right):
     # GH#24283 error message format depends on whether right is scalar
     if isinstance(right, (np.ndarray, ABCTimedeltaArray)) or right is None:
         other_freq = None
-    elif isinstance(right, (ABCPeriodIndex, PeriodArray, Period, DateOffset)):
+    elif isinstance(right, (ABCPeriodIndex, PeriodArray, Period, BaseOffset)):
         other_freq = right.freqstr
     else:
         other_freq = delta_to_tick(Timedelta(right)).freqstr

pandas/core/arrays/timedeltas.py

@@ -4,7 +4,7 @@
 import numpy as np
 
 from pandas._libs import lib, tslibs
-from pandas._libs.tslibs import NaT, Period, Timedelta, Timestamp, iNaT, to_offset
+from pandas._libs.tslibs import NaT, Period, Tick, Timedelta, Timestamp, iNaT, to_offset
 from pandas._libs.tslibs.conversion import precision_from_unit
 from pandas._libs.tslibs.fields import get_timedelta_field
 from pandas._libs.tslibs.timedeltas import array_to_timedelta64, parse_timedelta_unit
@@ -35,8 +35,6 @@
 from pandas.core.construction import extract_array
 from pandas.core.ops.common import unpack_zerodim_and_defer
 
-from pandas.tseries.offsets import Tick
-
 
 def _field_accessor(name, alias, docstring=None):
     def f(self):

pandas/core/generic.py

@@ -31,7 +31,7 @@
 from pandas._config import config
 
 from pandas._libs import lib
-from pandas._libs.tslibs import Timestamp, to_offset
+from pandas._libs.tslibs import Tick, Timestamp, to_offset
 from pandas._typing import (
     Axis,
     FilePathOrBuffer,
@@ -101,7 +101,6 @@
 from pandas.io.formats import format as fmt
 from pandas.io.formats.format import DataFrameFormatter, format_percentiles
 from pandas.io.formats.printing import pprint_thing
-from pandas.tseries.offsets import Tick
 
 if TYPE_CHECKING:
     from pandas.core.resample import Resampler

pandas/core/indexes/datetimelike.py

@@ -7,7 +7,7 @@
 import numpy as np
 
 from pandas._libs import NaT, Timedelta, iNaT, join as libjoin, lib
-from pandas._libs.tslibs import Resolution, timezones
+from pandas._libs.tslibs import BaseOffset, Resolution, Tick, timezones
 from pandas._libs.tslibs.parsing import DateParseError
 from pandas._typing import Label
 from pandas.compat.numpy import function as nv
@@ -44,8 +44,6 @@
 from pandas.core.sorting import ensure_key_mapped
 from pandas.core.tools.timedeltas import to_timedelta
 
-from pandas.tseries.offsets import DateOffset, Tick
-
 _index_doc_kwargs = dict(ibase._index_doc_kwargs)
 
 _T = TypeVar("_T", bound="DatetimeIndexOpsMixin")
@@ -91,7 +89,7 @@ class DatetimeIndexOpsMixin(ExtensionIndex):
     """
 
     _data: Union[DatetimeArray, TimedeltaArray, PeriodArray]
-    freq: Optional[DateOffset]
+    freq: Optional[BaseOffset]
     freqstr: Optional[str]
     _resolution_obj: Resolution
     _bool_ops: List[str] = []

pandas/core/indexes/interval.py

@@ -9,7 +9,7 @@
 
 from pandas._libs import lib
 from pandas._libs.interval import Interval, IntervalMixin, IntervalTree
-from pandas._libs.tslibs import Timedelta, Timestamp, to_offset
+from pandas._libs.tslibs import BaseOffset, Timedelta, Timestamp, to_offset
 from pandas._typing import AnyArrayLike, Label
 from pandas.errors import InvalidIndexError
 from pandas.util._decorators import Appender, Substitution, cache_readonly
@@ -56,8 +56,6 @@
 from pandas.core.indexes.timedeltas import TimedeltaIndex, timedelta_range
 from pandas.core.ops import get_op_result_name
 
-from pandas.tseries.offsets import DateOffset
-
 _VALID_CLOSED = {"left", "right", "both", "neither"}
 _index_doc_kwargs = dict(ibase._index_doc_kwargs)
 
@@ -1161,8 +1159,8 @@ def _is_type_compatible(a, b) -> bool:
     """
     Helper for interval_range to check type compat of start/end/freq.
     """
-    is_ts_compat = lambda x: isinstance(x, (Timestamp, DateOffset))
-    is_td_compat = lambda x: isinstance(x, (Timedelta, DateOffset))
+    is_ts_compat = lambda x: isinstance(x, (Timestamp, BaseOffset))
+    is_td_compat = lambda x: isinstance(x, (Timedelta, BaseOffset))
     return (
         (is_number(a) and is_number(b))
         or (is_ts_compat(a) and is_ts_compat(b))

pandas/core/indexes/period.py

@@ -5,7 +5,7 @@
 
 from pandas._libs import index as libindex
 from pandas._libs.lib import no_default
-from pandas._libs.tslibs import Period, Resolution
+from pandas._libs.tslibs import BaseOffset, Period, Resolution, Tick
 from pandas._libs.tslibs.parsing import DateParseError, parse_time_string
 from pandas._typing import DtypeObj, Label
 from pandas.errors import InvalidIndexError
@@ -43,8 +43,6 @@
 from pandas.core.indexes.numeric import Int64Index
 from pandas.core.ops import get_op_result_name
 
-from pandas.tseries.offsets import DateOffset, Tick
-
 _index_doc_kwargs = dict(ibase._index_doc_kwargs)
 _index_doc_kwargs.update(dict(target_klass="PeriodIndex or list of Periods"))
 
@@ -145,7 +143,7 @@ class PeriodIndex(DatetimeIndexOpsMixin, Int64Index):
     _is_numeric_dtype = False
 
     _data: PeriodArray
-    freq: DateOffset
+    freq: BaseOffset
 
     _engine_type = libindex.PeriodEngine
     _supports_partial_string_indexing = True
@@ -287,7 +285,7 @@ def _maybe_convert_timedelta(self, other):
                 # _check_timedeltalike_freq_compat will raise if incompatible
                 delta = self._data._check_timedeltalike_freq_compat(other)
                 return delta
-        elif isinstance(other, DateOffset):
+        elif isinstance(other, BaseOffset):
             if other.base == self.freq.base:
                 return other.n
 

pandas/core/window/rolling.py

@@ -10,7 +10,7 @@
 
 import numpy as np
 
-from pandas._libs.tslibs import to_offset
+from pandas._libs.tslibs import BaseOffset, to_offset
 import pandas._libs.window.aggregations as window_aggregations
 from pandas._typing import Axis, FrameOrSeries, Scalar
 from pandas.compat._optional import import_optional_dependency
@@ -55,8 +55,6 @@
 )
 from pandas.core.window.numba_ import generate_numba_apply_func
 
-from pandas.tseries.offsets import DateOffset
-
 
 def calculate_center_offset(window) -> int:
     """
@@ -1935,7 +1933,7 @@ def validate(self):
 
         # we allow rolling on a datetimelike index
         if (self.obj.empty or self.is_datetimelike) and isinstance(
-            self.window, (str, DateOffset, timedelta)
+            self.window, (str, BaseOffset, timedelta)
         ):
 
             self._validate_monotonic()

pandas/plotting/_matplotlib/timeseries.py

@@ -5,7 +5,7 @@
 
 import numpy as np
 
-from pandas._libs.tslibs import Period, to_offset
+from pandas._libs.tslibs import BaseOffset, Period, to_offset
 from pandas._libs.tslibs.dtypes import FreqGroup
 from pandas._typing import FrameOrSeriesUnion
 
@@ -22,7 +22,6 @@
     TimeSeries_TimedeltaFormatter,
 )
 from pandas.tseries.frequencies import get_period_alias, is_subperiod, is_superperiod
-from pandas.tseries.offsets import DateOffset
 
 if TYPE_CHECKING:
     from pandas import Series, Index  # noqa:F401
@@ -218,7 +217,7 @@ def _use_dynamic_x(ax, data: "FrameOrSeriesUnion") -> bool:
     return True
 
 
-def _get_index_freq(index: "Index") -> Optional[DateOffset]:
+def _get_index_freq(index: "Index") -> Optional[BaseOffset]:
     freq = getattr(index, "freq", None)
     if freq is None:
         freq = getattr(index, "inferred_freq", None)

pandas/tests/plotting/test_datetimelike.py

@@ -6,18 +6,17 @@
 import numpy as np
 import pytest
 
-from pandas._libs.tslibs import to_offset
+from pandas._libs.tslibs import BaseOffset, to_offset
 import pandas.util._test_decorators as td
 
 from pandas import DataFrame, Index, NaT, Series, isna
 import pandas._testing as tm
-from pandas.core.indexes.datetimes import bdate_range, date_range
+from pandas.core.indexes.datetimes import DatetimeIndex, bdate_range, date_range
 from pandas.core.indexes.period import Period, PeriodIndex, period_range
 from pandas.core.indexes.timedeltas import timedelta_range
-from pandas.core.resample import DatetimeIndex
 from pandas.tests.plotting.common import TestPlotBase
 
-from pandas.tseries.offsets import DateOffset, WeekOfMonth
+from pandas.tseries.offsets import WeekOfMonth
 
 
 @td.skip_if_no_mpl
@@ -1509,7 +1508,7 @@ def _check_plot_works(f, freq=None, series=None, *args, **kwargs):
         ax = kwargs.pop("ax", plt.gca())
         if series is not None:
             dfreq = series.index.freq
-            if isinstance(dfreq, DateOffset):
+            if isinstance(dfreq, BaseOffset):
                 dfreq = dfreq.rule_code
             if orig_axfreq is None:
                 assert ax.freq == dfreq

pandas/tests/tslibs/test_api.py

@@ -25,6 +25,7 @@ def test_namespace():
     ]
 
     api = [
+        "BaseOffset",
         "NaT",
         "NaTType",
         "iNaT",
@@ -34,6 +35,7 @@ def test_namespace():
         "Period",
         "IncompatibleFrequency",
         "Resolution",
+        "Tick",
         "Timedelta",
         "Timestamp",
         "delta_to_nanoseconds",