"""
|
Base and utility classes for tseries type pandas objects.
|
"""
|
from __future__ import annotations
|
|
from abc import (
|
ABC,
|
abstractmethod,
|
)
|
from datetime import datetime
|
from typing import (
|
TYPE_CHECKING,
|
Any,
|
Callable,
|
Sequence,
|
TypeVar,
|
cast,
|
final,
|
)
|
|
import numpy as np
|
|
from pandas._libs import (
|
NaT,
|
Timedelta,
|
lib,
|
)
|
from pandas._libs.tslibs import (
|
BaseOffset,
|
Resolution,
|
Tick,
|
parsing,
|
to_offset,
|
)
|
from pandas._typing import (
|
Axis,
|
npt,
|
)
|
from pandas.compat.numpy import function as nv
|
from pandas.errors import NullFrequencyError
|
from pandas.util._decorators import (
|
Appender,
|
cache_readonly,
|
doc,
|
)
|
|
from pandas.core.dtypes.common import (
|
is_categorical_dtype,
|
is_dtype_equal,
|
is_integer,
|
is_list_like,
|
)
|
from pandas.core.dtypes.concat import concat_compat
|
|
from pandas.core.arrays import (
|
DatetimeArray,
|
ExtensionArray,
|
PeriodArray,
|
TimedeltaArray,
|
)
|
from pandas.core.arrays.datetimelike import DatetimeLikeArrayMixin
|
import pandas.core.common as com
|
import pandas.core.indexes.base as ibase
|
from pandas.core.indexes.base import (
|
Index,
|
_index_shared_docs,
|
)
|
from pandas.core.indexes.extension import NDArrayBackedExtensionIndex
|
from pandas.core.indexes.range import RangeIndex
|
from pandas.core.tools.timedeltas import to_timedelta
|
|
if TYPE_CHECKING:
|
from pandas import CategoricalIndex
|
|
_index_doc_kwargs = dict(ibase._index_doc_kwargs)
|
|
_T = TypeVar("_T", bound="DatetimeIndexOpsMixin")
|
_TDT = TypeVar("_TDT", bound="DatetimeTimedeltaMixin")
|
|
|
class DatetimeIndexOpsMixin(NDArrayBackedExtensionIndex, ABC):
|
"""
|
Common ops mixin to support a unified interface datetimelike Index.
|
"""
|
|
_can_hold_strings = False
|
_data: DatetimeArray | TimedeltaArray | PeriodArray
|
|
@doc(DatetimeLikeArrayMixin.mean)
|
def mean(self, *, skipna: bool = True, axis: int | None = 0):
|
return self._data.mean(skipna=skipna, axis=axis)
|
|
@property
|
def freq(self) -> BaseOffset | None:
|
return self._data.freq
|
|
@freq.setter
|
def freq(self, value) -> None:
|
# error: Property "freq" defined in "PeriodArray" is read-only [misc]
|
self._data.freq = value # type: ignore[misc]
|
|
@property
|
def asi8(self) -> npt.NDArray[np.int64]:
|
return self._data.asi8
|
|
@property
|
@doc(DatetimeLikeArrayMixin.freqstr)
|
def freqstr(self) -> str | None:
|
return self._data.freqstr
|
|
@cache_readonly
|
@abstractmethod
|
def _resolution_obj(self) -> Resolution:
|
...
|
|
@cache_readonly
|
@doc(DatetimeLikeArrayMixin.resolution)
|
def resolution(self) -> str:
|
return self._data.resolution
|
|
# ------------------------------------------------------------------------
|
|
@cache_readonly
|
def hasnans(self) -> bool:
|
return self._data._hasna
|
|
def equals(self, other: Any) -> bool:
|
"""
|
Determines if two Index objects contain the same elements.
|
"""
|
if self.is_(other):
|
return True
|
|
if not isinstance(other, Index):
|
return False
|
elif other.dtype.kind in ["f", "i", "u", "c"]:
|
return False
|
elif not isinstance(other, type(self)):
|
should_try = False
|
inferable = self._data._infer_matches
|
if other.dtype == object:
|
should_try = other.inferred_type in inferable
|
elif is_categorical_dtype(other.dtype):
|
other = cast("CategoricalIndex", other)
|
should_try = other.categories.inferred_type in inferable
|
|
if should_try:
|
try:
|
other = type(self)(other)
|
except (ValueError, TypeError, OverflowError):
|
# e.g.
|
# ValueError -> cannot parse str entry, or OutOfBoundsDatetime
|
# TypeError -> trying to convert IntervalIndex to DatetimeIndex
|
# OverflowError -> Index([very_large_timedeltas])
|
return False
|
|
if not is_dtype_equal(self.dtype, other.dtype):
|
# have different timezone
|
return False
|
|
return np.array_equal(self.asi8, other.asi8)
|
|
@Appender(Index.__contains__.__doc__)
|
def __contains__(self, key: Any) -> bool:
|
hash(key)
|
try:
|
self.get_loc(key)
|
except (KeyError, TypeError, ValueError):
|
return False
|
return True
|
|
def _convert_tolerance(self, tolerance, target):
|
tolerance = np.asarray(to_timedelta(tolerance).to_numpy())
|
return super()._convert_tolerance(tolerance, target)
|
|
# --------------------------------------------------------------------
|
# Rendering Methods
|
|
def format(
|
self,
|
name: bool = False,
|
formatter: Callable | None = None,
|
na_rep: str = "NaT",
|
date_format: str | None = None,
|
) -> list[str]:
|
"""
|
Render a string representation of the Index.
|
"""
|
header = []
|
if name:
|
header.append(
|
ibase.pprint_thing(self.name, escape_chars=("\t", "\r", "\n"))
|
if self.name is not None
|
else ""
|
)
|
|
if formatter is not None:
|
return header + list(self.map(formatter))
|
|
return self._format_with_header(header, na_rep=na_rep, date_format=date_format)
|
|
def _format_with_header(
|
self, header: list[str], na_rep: str = "NaT", date_format: str | None = None
|
) -> list[str]:
|
# matches base class except for whitespace padding and date_format
|
return header + list(
|
self._format_native_types(na_rep=na_rep, date_format=date_format)
|
)
|
|
@property
|
def _formatter_func(self):
|
return self._data._formatter()
|
|
def _format_attrs(self):
|
"""
|
Return a list of tuples of the (attr,formatted_value).
|
"""
|
attrs = super()._format_attrs()
|
for attrib in self._attributes:
|
# iterating over _attributes prevents us from doing this for PeriodIndex
|
if attrib == "freq":
|
freq = self.freqstr
|
if freq is not None:
|
freq = repr(freq) # e.g. D -> 'D'
|
attrs.append(("freq", freq))
|
return attrs
|
|
@Appender(Index._summary.__doc__)
|
def _summary(self, name=None) -> str:
|
result = super()._summary(name=name)
|
if self.freq:
|
result += f"\nFreq: {self.freqstr}"
|
|
return result
|
|
# --------------------------------------------------------------------
|
# Indexing Methods
|
|
@final
|
def _can_partial_date_slice(self, reso: Resolution) -> bool:
|
# e.g. test_getitem_setitem_periodindex
|
# History of conversation GH#3452, GH#3931, GH#2369, GH#14826
|
return reso > self._resolution_obj
|
# NB: for DTI/PI, not TDI
|
|
def _parsed_string_to_bounds(self, reso: Resolution, parsed):
|
raise NotImplementedError
|
|
def _parse_with_reso(self, label: str):
|
# overridden by TimedeltaIndex
|
try:
|
if self.freq is None or hasattr(self.freq, "rule_code"):
|
freq = self.freq
|
except NotImplementedError:
|
freq = getattr(self, "freqstr", getattr(self, "inferred_freq", None))
|
|
freqstr: str | None
|
if freq is not None and not isinstance(freq, str):
|
freqstr = freq.rule_code
|
else:
|
freqstr = freq
|
|
if isinstance(label, np.str_):
|
# GH#45580
|
label = str(label)
|
|
parsed, reso_str = parsing.parse_datetime_string_with_reso(label, freqstr)
|
reso = Resolution.from_attrname(reso_str)
|
return parsed, reso
|
|
def _get_string_slice(self, key: str):
|
# overridden by TimedeltaIndex
|
parsed, reso = self._parse_with_reso(key)
|
try:
|
return self._partial_date_slice(reso, parsed)
|
except KeyError as err:
|
raise KeyError(key) from err
|
|
@final
|
def _partial_date_slice(
|
self,
|
reso: Resolution,
|
parsed: datetime,
|
):
|
"""
|
Parameters
|
----------
|
reso : Resolution
|
parsed : datetime
|
|
Returns
|
-------
|
slice or ndarray[intp]
|
"""
|
if not self._can_partial_date_slice(reso):
|
raise ValueError
|
|
t1, t2 = self._parsed_string_to_bounds(reso, parsed)
|
vals = self._data._ndarray
|
unbox = self._data._unbox
|
|
if self.is_monotonic_increasing:
|
if len(self) and (
|
(t1 < self[0] and t2 < self[0]) or (t1 > self[-1] and t2 > self[-1])
|
):
|
# we are out of range
|
raise KeyError
|
|
# TODO: does this depend on being monotonic _increasing_?
|
|
# a monotonic (sorted) series can be sliced
|
left = vals.searchsorted(unbox(t1), side="left")
|
right = vals.searchsorted(unbox(t2), side="right")
|
return slice(left, right)
|
|
else:
|
lhs_mask = vals >= unbox(t1)
|
rhs_mask = vals <= unbox(t2)
|
|
# try to find the dates
|
return (lhs_mask & rhs_mask).nonzero()[0]
|
|
def _maybe_cast_slice_bound(self, label, side: str):
|
"""
|
If label is a string, cast it to scalar type according to resolution.
|
|
Parameters
|
----------
|
label : object
|
side : {'left', 'right'}
|
|
Returns
|
-------
|
label : object
|
|
Notes
|
-----
|
Value of `side` parameter should be validated in caller.
|
"""
|
if isinstance(label, str):
|
try:
|
parsed, reso = self._parse_with_reso(label)
|
except ValueError as err:
|
# DTI -> parsing.DateParseError
|
# TDI -> 'unit abbreviation w/o a number'
|
# PI -> string cannot be parsed as datetime-like
|
self._raise_invalid_indexer("slice", label, err)
|
|
lower, upper = self._parsed_string_to_bounds(reso, parsed)
|
return lower if side == "left" else upper
|
elif not isinstance(label, self._data._recognized_scalars):
|
self._raise_invalid_indexer("slice", label)
|
|
return label
|
|
# --------------------------------------------------------------------
|
# Arithmetic Methods
|
|
def shift(self: _T, periods: int = 1, freq=None) -> _T:
|
"""
|
Shift index by desired number of time frequency increments.
|
|
This method is for shifting the values of datetime-like indexes
|
by a specified time increment a given number of times.
|
|
Parameters
|
----------
|
periods : int, default 1
|
Number of periods (or increments) to shift by,
|
can be positive or negative.
|
freq : pandas.DateOffset, pandas.Timedelta or string, optional
|
Frequency increment to shift by.
|
If None, the index is shifted by its own `freq` attribute.
|
Offset aliases are valid strings, e.g., 'D', 'W', 'M' etc.
|
|
Returns
|
-------
|
pandas.DatetimeIndex
|
Shifted index.
|
|
See Also
|
--------
|
Index.shift : Shift values of Index.
|
PeriodIndex.shift : Shift values of PeriodIndex.
|
"""
|
raise NotImplementedError
|
|
# --------------------------------------------------------------------
|
|
@doc(Index._maybe_cast_listlike_indexer)
|
def _maybe_cast_listlike_indexer(self, keyarr):
|
try:
|
res = self._data._validate_listlike(keyarr, allow_object=True)
|
except (ValueError, TypeError):
|
if not isinstance(keyarr, ExtensionArray):
|
# e.g. we don't want to cast DTA to ndarray[object]
|
res = com.asarray_tuplesafe(keyarr)
|
# TODO: com.asarray_tuplesafe shouldn't cast e.g. DatetimeArray
|
else:
|
res = keyarr
|
return Index(res, dtype=res.dtype)
|
|
|
class DatetimeTimedeltaMixin(DatetimeIndexOpsMixin, ABC):
|
"""
|
Mixin class for methods shared by DatetimeIndex and TimedeltaIndex,
|
but not PeriodIndex
|
"""
|
|
_data: DatetimeArray | TimedeltaArray
|
_comparables = ["name", "freq"]
|
_attributes = ["name", "freq"]
|
|
# Compat for frequency inference, see GH#23789
|
_is_monotonic_increasing = Index.is_monotonic_increasing
|
_is_monotonic_decreasing = Index.is_monotonic_decreasing
|
_is_unique = Index.is_unique
|
|
_join_precedence = 10
|
|
@property
|
def unit(self) -> str:
|
return self._data.unit
|
|
def as_unit(self: _TDT, unit: str) -> _TDT:
|
"""
|
Convert to a dtype with the given unit resolution.
|
|
Parameters
|
----------
|
unit : {'s', 'ms', 'us', 'ns'}
|
|
Returns
|
-------
|
same type as self
|
"""
|
arr = self._data.as_unit(unit)
|
return type(self)._simple_new(arr, name=self.name)
|
|
def _with_freq(self, freq):
|
arr = self._data._with_freq(freq)
|
return type(self)._simple_new(arr, name=self._name)
|
|
@property
|
def values(self) -> np.ndarray:
|
# NB: For Datetime64TZ this is lossy
|
return self._data._ndarray
|
|
@doc(DatetimeIndexOpsMixin.shift)
|
def shift(self: _TDT, periods: int = 1, freq=None) -> _TDT:
|
if freq is not None and freq != self.freq:
|
if isinstance(freq, str):
|
freq = to_offset(freq)
|
offset = periods * freq
|
return self + offset
|
|
if periods == 0 or len(self) == 0:
|
# GH#14811 empty case
|
return self.copy()
|
|
if self.freq is None:
|
raise NullFrequencyError("Cannot shift with no freq")
|
|
start = self[0] + periods * self.freq
|
end = self[-1] + periods * self.freq
|
|
# Note: in the DatetimeTZ case, _generate_range will infer the
|
# appropriate timezone from `start` and `end`, so tz does not need
|
# to be passed explicitly.
|
result = self._data._generate_range(
|
start=start, end=end, periods=None, freq=self.freq
|
)
|
return type(self)._simple_new(result, name=self.name)
|
|
@cache_readonly
|
@doc(DatetimeLikeArrayMixin.inferred_freq)
|
def inferred_freq(self) -> str | None:
|
return self._data.inferred_freq
|
|
# --------------------------------------------------------------------
|
# Set Operation Methods
|
|
@cache_readonly
|
def _as_range_index(self) -> RangeIndex:
|
# Convert our i8 representations to RangeIndex
|
# Caller is responsible for checking isinstance(self.freq, Tick)
|
freq = cast(Tick, self.freq)
|
tick = freq.delta._value
|
rng = range(self[0]._value, self[-1]._value + tick, tick)
|
return RangeIndex(rng)
|
|
def _can_range_setop(self, other):
|
return isinstance(self.freq, Tick) and isinstance(other.freq, Tick)
|
|
def _wrap_range_setop(self, other, res_i8):
|
new_freq = None
|
if not len(res_i8):
|
# RangeIndex defaults to step=1, which we don't want.
|
new_freq = self.freq
|
elif isinstance(res_i8, RangeIndex):
|
new_freq = to_offset(Timedelta(res_i8.step))
|
|
# TODO(GH#41493): we cannot just do
|
# type(self._data)(res_i8.values, dtype=self.dtype, freq=new_freq)
|
# because test_setops_preserve_freq fails with _validate_frequency raising.
|
# This raising is incorrect, as 'on_freq' is incorrect. This will
|
# be fixed by GH#41493
|
res_values = res_i8.values.view(self._data._ndarray.dtype)
|
result = type(self._data)._simple_new(
|
res_values, dtype=self.dtype, freq=new_freq
|
)
|
return self._wrap_setop_result(other, result)
|
|
def _range_intersect(self, other, sort):
|
# Dispatch to RangeIndex intersection logic.
|
left = self._as_range_index
|
right = other._as_range_index
|
res_i8 = left.intersection(right, sort=sort)
|
return self._wrap_range_setop(other, res_i8)
|
|
def _range_union(self, other, sort):
|
# Dispatch to RangeIndex union logic.
|
left = self._as_range_index
|
right = other._as_range_index
|
res_i8 = left.union(right, sort=sort)
|
return self._wrap_range_setop(other, res_i8)
|
|
def _intersection(self, other: Index, sort: bool = False) -> Index:
|
"""
|
intersection specialized to the case with matching dtypes and both non-empty.
|
"""
|
other = cast("DatetimeTimedeltaMixin", other)
|
|
if self._can_range_setop(other):
|
return self._range_intersect(other, sort=sort)
|
|
if not self._can_fast_intersect(other):
|
result = Index._intersection(self, other, sort=sort)
|
# We need to invalidate the freq because Index._intersection
|
# uses _shallow_copy on a view of self._data, which will preserve
|
# self.freq if we're not careful.
|
# At this point we should have result.dtype == self.dtype
|
# and type(result) is type(self._data)
|
result = self._wrap_setop_result(other, result)
|
return result._with_freq(None)._with_freq("infer")
|
|
else:
|
return self._fast_intersect(other, sort)
|
|
def _fast_intersect(self, other, sort):
|
# to make our life easier, "sort" the two ranges
|
if self[0] <= other[0]:
|
left, right = self, other
|
else:
|
left, right = other, self
|
|
# after sorting, the intersection always starts with the right index
|
# and ends with the index of which the last elements is smallest
|
end = min(left[-1], right[-1])
|
start = right[0]
|
|
if end < start:
|
result = self[:0]
|
else:
|
lslice = slice(*left.slice_locs(start, end))
|
result = left._values[lslice]
|
|
return result
|
|
def _can_fast_intersect(self: _T, other: _T) -> bool:
|
# Note: we only get here with len(self) > 0 and len(other) > 0
|
if self.freq is None:
|
return False
|
|
elif other.freq != self.freq:
|
return False
|
|
elif not self.is_monotonic_increasing:
|
# Because freq is not None, we must then be monotonic decreasing
|
return False
|
|
# this along with matching freqs ensure that we "line up",
|
# so intersection will preserve freq
|
# Note we are assuming away Ticks, as those go through _range_intersect
|
# GH#42104
|
return self.freq.n == 1
|
|
def _can_fast_union(self: _T, other: _T) -> bool:
|
# Assumes that type(self) == type(other), as per the annotation
|
# The ability to fast_union also implies that `freq` should be
|
# retained on union.
|
freq = self.freq
|
|
if freq is None or freq != other.freq:
|
return False
|
|
if not self.is_monotonic_increasing:
|
# Because freq is not None, we must then be monotonic decreasing
|
# TODO: do union on the reversed indexes?
|
return False
|
|
if len(self) == 0 or len(other) == 0:
|
# only reached via union_many
|
return True
|
|
# to make our life easier, "sort" the two ranges
|
if self[0] <= other[0]:
|
left, right = self, other
|
else:
|
left, right = other, self
|
|
right_start = right[0]
|
left_end = left[-1]
|
|
# Only need to "adjoin", not overlap
|
return (right_start == left_end + freq) or right_start in left
|
|
def _fast_union(self: _TDT, other: _TDT, sort=None) -> _TDT:
|
# Caller is responsible for ensuring self and other are non-empty
|
|
# to make our life easier, "sort" the two ranges
|
if self[0] <= other[0]:
|
left, right = self, other
|
elif sort is False:
|
# TDIs are not in the "correct" order and we don't want
|
# to sort but want to remove overlaps
|
left, right = self, other
|
left_start = left[0]
|
loc = right.searchsorted(left_start, side="left")
|
right_chunk = right._values[:loc]
|
dates = concat_compat((left._values, right_chunk))
|
result = type(self)._simple_new(dates, name=self.name)
|
return result
|
else:
|
left, right = other, self
|
|
left_end = left[-1]
|
right_end = right[-1]
|
|
# concatenate
|
if left_end < right_end:
|
loc = right.searchsorted(left_end, side="right")
|
right_chunk = right._values[loc:]
|
dates = concat_compat([left._values, right_chunk])
|
# The can_fast_union check ensures that the result.freq
|
# should match self.freq
|
dates = type(self._data)(dates, freq=self.freq)
|
result = type(self)._simple_new(dates)
|
return result
|
else:
|
return left
|
|
def _union(self, other, sort):
|
# We are called by `union`, which is responsible for this validation
|
assert isinstance(other, type(self))
|
assert self.dtype == other.dtype
|
|
if self._can_range_setop(other):
|
return self._range_union(other, sort=sort)
|
|
if self._can_fast_union(other):
|
result = self._fast_union(other, sort=sort)
|
# in the case with sort=None, the _can_fast_union check ensures
|
# that result.freq == self.freq
|
return result
|
else:
|
return super()._union(other, sort)._with_freq("infer")
|
|
# --------------------------------------------------------------------
|
# Join Methods
|
|
def _get_join_freq(self, other):
|
"""
|
Get the freq to attach to the result of a join operation.
|
"""
|
freq = None
|
if self._can_fast_union(other):
|
freq = self.freq
|
return freq
|
|
def _wrap_joined_index(
|
self, joined, other, lidx: npt.NDArray[np.intp], ridx: npt.NDArray[np.intp]
|
):
|
assert other.dtype == self.dtype, (other.dtype, self.dtype)
|
result = super()._wrap_joined_index(joined, other, lidx, ridx)
|
result._data._freq = self._get_join_freq(other)
|
return result
|
|
def _get_engine_target(self) -> np.ndarray:
|
# engine methods and libjoin methods need dt64/td64 values cast to i8
|
return self._data._ndarray.view("i8")
|
|
def _from_join_target(self, result: np.ndarray):
|
# view e.g. i8 back to M8[ns]
|
result = result.view(self._data._ndarray.dtype)
|
return self._data._from_backing_data(result)
|
|
# --------------------------------------------------------------------
|
# List-like Methods
|
|
def _get_delete_freq(self, loc: int | slice | Sequence[int]):
|
"""
|
Find the `freq` for self.delete(loc).
|
"""
|
freq = None
|
if self.freq is not None:
|
if is_integer(loc):
|
if loc in (0, -len(self), -1, len(self) - 1):
|
freq = self.freq
|
else:
|
if is_list_like(loc):
|
# error: Incompatible types in assignment (expression has
|
# type "Union[slice, ndarray]", variable has type
|
# "Union[int, slice, Sequence[int]]")
|
loc = lib.maybe_indices_to_slice( # type: ignore[assignment]
|
np.asarray(loc, dtype=np.intp), len(self)
|
)
|
if isinstance(loc, slice) and loc.step in (1, None):
|
if loc.start in (0, None) or loc.stop in (len(self), None):
|
freq = self.freq
|
return freq
|
|
def _get_insert_freq(self, loc: int, item):
|
"""
|
Find the `freq` for self.insert(loc, item).
|
"""
|
value = self._data._validate_scalar(item)
|
item = self._data._box_func(value)
|
|
freq = None
|
if self.freq is not None:
|
# freq can be preserved on edge cases
|
if self.size:
|
if item is NaT:
|
pass
|
elif loc in (0, -len(self)) and item + self.freq == self[0]:
|
freq = self.freq
|
elif (loc == len(self)) and item - self.freq == self[-1]:
|
freq = self.freq
|
else:
|
# Adding a single item to an empty index may preserve freq
|
if isinstance(self.freq, Tick):
|
# all TimedeltaIndex cases go through here; is_on_offset
|
# would raise TypeError
|
freq = self.freq
|
elif self.freq.is_on_offset(item):
|
freq = self.freq
|
return freq
|
|
@doc(NDArrayBackedExtensionIndex.delete)
|
def delete(self, loc) -> DatetimeTimedeltaMixin:
|
result = super().delete(loc)
|
result._data._freq = self._get_delete_freq(loc)
|
return result
|
|
@doc(NDArrayBackedExtensionIndex.insert)
|
def insert(self, loc: int, item):
|
result = super().insert(loc, item)
|
if isinstance(result, type(self)):
|
# i.e. parent class method did not cast
|
result._data._freq = self._get_insert_freq(loc, item)
|
return result
|
|
# --------------------------------------------------------------------
|
# NDArray-Like Methods
|
|
@Appender(_index_shared_docs["take"] % _index_doc_kwargs)
|
def take(
|
self,
|
indices,
|
axis: Axis = 0,
|
allow_fill: bool = True,
|
fill_value=None,
|
**kwargs,
|
):
|
nv.validate_take((), kwargs)
|
indices = np.asarray(indices, dtype=np.intp)
|
|
result = NDArrayBackedExtensionIndex.take(
|
self, indices, axis, allow_fill, fill_value, **kwargs
|
)
|
|
maybe_slice = lib.maybe_indices_to_slice(indices, len(self))
|
if isinstance(maybe_slice, slice):
|
freq = self._data._get_getitem_freq(maybe_slice)
|
result._data._freq = freq
|
return result
|
