2266 行
71 KiB
Python
2266 行
71 KiB
Python
from __future__ import annotations
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import copy
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from datetime import timedelta
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from textwrap import dedent
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from typing import (
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TYPE_CHECKING,
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Callable,
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Hashable,
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Literal,
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final,
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no_type_check,
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)
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import warnings
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import numpy as np
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from pandas._libs import lib
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from pandas._libs.tslibs import (
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BaseOffset,
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IncompatibleFrequency,
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NaT,
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Period,
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Timedelta,
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Timestamp,
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to_offset,
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)
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from pandas._typing import (
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IndexLabel,
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NDFrameT,
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T,
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TimedeltaConvertibleTypes,
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TimestampConvertibleTypes,
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npt,
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)
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from pandas.compat.numpy import function as nv
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from pandas.errors import (
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AbstractMethodError,
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DataError,
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)
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from pandas.util._decorators import (
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Appender,
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Substitution,
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deprecate_nonkeyword_arguments,
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doc,
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)
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from pandas.util._exceptions import find_stack_level
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from pandas.core.dtypes.generic import (
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ABCDataFrame,
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ABCSeries,
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)
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import pandas.core.algorithms as algos
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from pandas.core.apply import ResamplerWindowApply
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from pandas.core.base import PandasObject
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import pandas.core.common as com
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from pandas.core.generic import (
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NDFrame,
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_shared_docs,
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)
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from pandas.core.groupby.generic import SeriesGroupBy
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from pandas.core.groupby.groupby import (
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BaseGroupBy,
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GroupBy,
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_pipe_template,
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get_groupby,
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)
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from pandas.core.groupby.grouper import Grouper
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from pandas.core.groupby.ops import BinGrouper
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from pandas.core.indexes.datetimes import (
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DatetimeIndex,
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date_range,
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)
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from pandas.core.indexes.period import (
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PeriodIndex,
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period_range,
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)
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from pandas.core.indexes.timedeltas import (
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TimedeltaIndex,
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timedelta_range,
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)
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from pandas.tseries.frequencies import (
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is_subperiod,
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is_superperiod,
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)
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from pandas.tseries.offsets import (
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DateOffset,
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Day,
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Nano,
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Tick,
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)
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if TYPE_CHECKING:
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from pandas import (
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DataFrame,
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Index,
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Series,
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)
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_shared_docs_kwargs: dict[str, str] = {}
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class Resampler(BaseGroupBy, PandasObject):
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"""
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Class for resampling datetimelike data, a groupby-like operation.
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See aggregate, transform, and apply functions on this object.
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It's easiest to use obj.resample(...) to use Resampler.
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Parameters
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----------
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obj : Series or DataFrame
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groupby : TimeGrouper
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axis : int, default 0
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kind : str or None
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'period', 'timestamp' to override default index treatment
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Returns
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-------
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a Resampler of the appropriate type
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Notes
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-----
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After resampling, see aggregate, apply, and transform functions.
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"""
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grouper: BinGrouper
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exclusions: frozenset[Hashable] = frozenset() # for SelectionMixin compat
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# to the groupby descriptor
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_attributes = [
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"freq",
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"axis",
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"closed",
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"label",
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"convention",
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"loffset",
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"kind",
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"origin",
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"offset",
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]
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def __init__(
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self,
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obj: DataFrame | Series,
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groupby: TimeGrouper,
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axis: int = 0,
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kind=None,
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*,
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group_keys: bool | lib.NoDefault = lib.no_default,
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selection=None,
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**kwargs,
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) -> None:
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self.groupby = groupby
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self.keys = None
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self.sort = True
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self.axis = axis
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self.kind = kind
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self.squeeze = False
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self.group_keys = group_keys
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self.as_index = True
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self.groupby._set_grouper(self._convert_obj(obj), sort=True)
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self.binner, self.grouper = self._get_binner()
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self._selection = selection
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if self.groupby.key is not None:
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self.exclusions = frozenset([self.groupby.key])
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else:
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self.exclusions = frozenset()
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@final
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def _shallow_copy(self, obj, **kwargs):
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"""
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return a new object with the replacement attributes
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"""
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if isinstance(obj, self._constructor):
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obj = obj.obj
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for attr in self._attributes:
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if attr not in kwargs:
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kwargs[attr] = getattr(self, attr)
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return self._constructor(obj, **kwargs)
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def __str__(self) -> str:
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"""
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Provide a nice str repr of our rolling object.
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"""
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attrs = (
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f"{k}={getattr(self.groupby, k)}"
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for k in self._attributes
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if getattr(self.groupby, k, None) is not None
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)
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return f"{type(self).__name__} [{', '.join(attrs)}]"
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def __getattr__(self, attr: str):
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if attr in self._internal_names_set:
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return object.__getattribute__(self, attr)
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if attr in self._attributes:
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return getattr(self.groupby, attr)
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if attr in self.obj:
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return self[attr]
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return object.__getattribute__(self, attr)
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# error: Signature of "obj" incompatible with supertype "BaseGroupBy"
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@property
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def obj(self) -> NDFrame: # type: ignore[override]
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# error: Incompatible return value type (got "Optional[Any]",
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# expected "NDFrameT")
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return self.groupby.obj # type: ignore[return-value]
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@property
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def ax(self):
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# we can infer that this is a PeriodIndex/DatetimeIndex/TimedeltaIndex,
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# but skipping annotating bc the overrides overwhelming
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return self.groupby.ax
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@property
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def _from_selection(self) -> bool:
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"""
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Is the resampling from a DataFrame column or MultiIndex level.
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"""
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# upsampling and PeriodIndex resampling do not work
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# with selection, this state used to catch and raise an error
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return self.groupby is not None and (
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self.groupby.key is not None or self.groupby.level is not None
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)
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def _convert_obj(self, obj: NDFrameT) -> NDFrameT:
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"""
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Provide any conversions for the object in order to correctly handle.
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Parameters
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----------
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obj : Series or DataFrame
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Returns
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-------
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Series or DataFrame
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"""
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return obj._consolidate()
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def _get_binner_for_time(self):
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raise AbstractMethodError(self)
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@final
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def _get_binner(self):
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"""
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Create the BinGrouper, assume that self.set_grouper(obj)
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has already been called.
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"""
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binner, bins, binlabels = self._get_binner_for_time()
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assert len(bins) == len(binlabels)
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bin_grouper = BinGrouper(bins, binlabels, indexer=self.groupby.indexer)
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return binner, bin_grouper
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@Substitution(
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klass="Resampler",
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examples="""
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>>> df = pd.DataFrame({'A': [1, 2, 3, 4]},
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... index=pd.date_range('2012-08-02', periods=4))
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>>> df
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A
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2012-08-02 1
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2012-08-03 2
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2012-08-04 3
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2012-08-05 4
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To get the difference between each 2-day period's maximum and minimum
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value in one pass, you can do
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>>> df.resample('2D').pipe(lambda x: x.max() - x.min())
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A
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2012-08-02 1
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2012-08-04 1""",
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)
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@Appender(_pipe_template)
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def pipe(
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self,
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func: Callable[..., T] | tuple[Callable[..., T], str],
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*args,
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**kwargs,
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) -> T:
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return super().pipe(func, *args, **kwargs)
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_agg_see_also_doc = dedent(
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"""
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See Also
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--------
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DataFrame.groupby.aggregate : Aggregate using callable, string, dict,
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or list of string/callables.
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DataFrame.resample.transform : Transforms the Series on each group
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based on the given function.
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DataFrame.aggregate: Aggregate using one or more
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operations over the specified axis.
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"""
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)
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_agg_examples_doc = dedent(
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"""
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Examples
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--------
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>>> s = pd.Series([1, 2, 3, 4, 5],
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... index=pd.date_range('20130101', periods=5, freq='s'))
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>>> s
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2013-01-01 00:00:00 1
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2013-01-01 00:00:01 2
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2013-01-01 00:00:02 3
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2013-01-01 00:00:03 4
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2013-01-01 00:00:04 5
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Freq: S, dtype: int64
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>>> r = s.resample('2s')
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>>> r.agg(np.sum)
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2013-01-01 00:00:00 3
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2013-01-01 00:00:02 7
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2013-01-01 00:00:04 5
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Freq: 2S, dtype: int64
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>>> r.agg(['sum', 'mean', 'max'])
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sum mean max
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2013-01-01 00:00:00 3 1.5 2
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2013-01-01 00:00:02 7 3.5 4
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2013-01-01 00:00:04 5 5.0 5
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>>> r.agg({'result': lambda x: x.mean() / x.std(),
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... 'total': np.sum})
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result total
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2013-01-01 00:00:00 2.121320 3
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2013-01-01 00:00:02 4.949747 7
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2013-01-01 00:00:04 NaN 5
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>>> r.agg(average="mean", total="sum")
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average total
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2013-01-01 00:00:00 1.5 3
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2013-01-01 00:00:02 3.5 7
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2013-01-01 00:00:04 5.0 5
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"""
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)
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@doc(
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_shared_docs["aggregate"],
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see_also=_agg_see_also_doc,
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examples=_agg_examples_doc,
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klass="DataFrame",
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axis="",
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)
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def aggregate(self, func=None, *args, **kwargs):
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result = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg()
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if result is None:
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how = func
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result = self._groupby_and_aggregate(how, *args, **kwargs)
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result = self._apply_loffset(result)
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return result
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agg = aggregate
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apply = aggregate
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def transform(self, arg, *args, **kwargs):
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"""
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Call function producing a like-indexed Series on each group.
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Return a Series with the transformed values.
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Parameters
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----------
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arg : function
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To apply to each group. Should return a Series with the same index.
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Returns
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-------
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transformed : Series
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Examples
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--------
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>>> s = pd.Series([1, 2],
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... index=pd.date_range('20180101',
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... periods=2,
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... freq='1h'))
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>>> s
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2018-01-01 00:00:00 1
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2018-01-01 01:00:00 2
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Freq: H, dtype: int64
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>>> resampled = s.resample('15min')
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>>> resampled.transform(lambda x: (x - x.mean()) / x.std())
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2018-01-01 00:00:00 NaN
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2018-01-01 01:00:00 NaN
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Freq: H, dtype: float64
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"""
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return self._selected_obj.groupby(self.groupby).transform(arg, *args, **kwargs)
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def _downsample(self, f, **kwargs):
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raise AbstractMethodError(self)
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def _upsample(self, f, limit=None, fill_value=None):
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raise AbstractMethodError(self)
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def _gotitem(self, key, ndim: int, subset=None):
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"""
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Sub-classes to define. Return a sliced object.
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Parameters
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----------
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key : string / list of selections
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ndim : {1, 2}
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requested ndim of result
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subset : object, default None
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subset to act on
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"""
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grouper = self.grouper
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if subset is None:
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subset = self.obj
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grouped = get_groupby(
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subset, by=None, grouper=grouper, axis=self.axis, group_keys=self.group_keys
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)
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# try the key selection
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try:
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return grouped[key]
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except KeyError:
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return grouped
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def _groupby_and_aggregate(self, how, *args, **kwargs):
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"""
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Re-evaluate the obj with a groupby aggregation.
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"""
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grouper = self.grouper
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if self._selected_obj.ndim == 1:
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obj = self._selected_obj
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else:
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# Excludes `on` column when provided
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obj = self._obj_with_exclusions
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grouped = get_groupby(
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obj, by=None, grouper=grouper, axis=self.axis, group_keys=self.group_keys
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)
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try:
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if isinstance(obj, ABCDataFrame) and callable(how):
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# Check if the function is reducing or not.
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result = grouped._aggregate_item_by_item(how, *args, **kwargs)
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else:
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result = grouped.aggregate(how, *args, **kwargs)
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except DataError:
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# got TypeErrors on aggregation
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result = grouped.apply(how, *args, **kwargs)
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except (AttributeError, KeyError):
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# we have a non-reducing function; try to evaluate
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# alternatively we want to evaluate only a column of the input
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# test_apply_to_one_column_of_df the function being applied references
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# a DataFrame column, but aggregate_item_by_item operates column-wise
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# on Series, raising AttributeError or KeyError
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# (depending on whether the column lookup uses getattr/__getitem__)
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result = grouped.apply(how, *args, **kwargs)
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except ValueError as err:
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if "Must produce aggregated value" in str(err):
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# raised in _aggregate_named
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# see test_apply_without_aggregation, test_apply_with_mutated_index
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pass
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else:
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raise
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# we have a non-reducing function
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# try to evaluate
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result = grouped.apply(how, *args, **kwargs)
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result = self._apply_loffset(result)
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return self._wrap_result(result)
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def _apply_loffset(self, result):
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"""
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If loffset is set, offset the result index.
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This is NOT an idempotent routine, it will be applied
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exactly once to the result.
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Parameters
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----------
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result : Series or DataFrame
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the result of resample
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"""
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# error: Cannot determine type of 'loffset'
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needs_offset = (
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isinstance(
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self.loffset, # type: ignore[has-type]
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(DateOffset, timedelta, np.timedelta64),
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)
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and isinstance(result.index, DatetimeIndex)
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and len(result.index) > 0
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)
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if needs_offset:
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# error: Cannot determine type of 'loffset'
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result.index = result.index + self.loffset # type: ignore[has-type]
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self.loffset = None
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return result
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def _get_resampler_for_grouping(self, groupby, key=None):
|
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"""
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Return the correct class for resampling with groupby.
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"""
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return self._resampler_for_grouping(self, groupby=groupby, key=key)
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|
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def _wrap_result(self, result):
|
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"""
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Potentially wrap any results.
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"""
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if isinstance(result, ABCSeries) and self._selection is not None:
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result.name = self._selection
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if isinstance(result, ABCSeries) and result.empty:
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obj = self.obj
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# When index is all NaT, result is empty but index is not
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result.index = _asfreq_compat(obj.index[:0], freq=self.freq)
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result.name = getattr(obj, "name", None)
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return result
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|
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def ffill(self, limit=None):
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"""
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Forward fill the values.
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|
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Parameters
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----------
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limit : int, optional
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Limit of how many values to fill.
|
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|
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Returns
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-------
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An upsampled Series.
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See Also
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--------
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Series.fillna: Fill NA/NaN values using the specified method.
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DataFrame.fillna: Fill NA/NaN values using the specified method.
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"""
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return self._upsample("ffill", limit=limit)
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|
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def pad(self, limit=None):
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"""
|
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Forward fill the values.
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|
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.. deprecated:: 1.4
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Use ffill instead.
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Parameters
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----------
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limit : int, optional
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Limit of how many values to fill.
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Returns
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-------
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An upsampled Series.
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"""
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warnings.warn(
|
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"pad is deprecated and will be removed in a future version. "
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"Use ffill instead.",
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FutureWarning,
|
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stacklevel=find_stack_level(),
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)
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return self.ffill(limit=limit)
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|
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def nearest(self, limit=None):
|
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"""
|
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Resample by using the nearest value.
|
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|
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When resampling data, missing values may appear (e.g., when the
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resampling frequency is higher than the original frequency).
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The `nearest` method will replace ``NaN`` values that appeared in
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the resampled data with the value from the nearest member of the
|
|
sequence, based on the index value.
|
|
Missing values that existed in the original data will not be modified.
|
|
If `limit` is given, fill only this many values in each direction for
|
|
each of the original values.
|
|
|
|
Parameters
|
|
----------
|
|
limit : int, optional
|
|
Limit of how many values to fill.
|
|
|
|
Returns
|
|
-------
|
|
Series or DataFrame
|
|
An upsampled Series or DataFrame with ``NaN`` values filled with
|
|
their nearest value.
|
|
|
|
See Also
|
|
--------
|
|
backfill : Backward fill the new missing values in the resampled data.
|
|
pad : Forward fill ``NaN`` values.
|
|
|
|
Examples
|
|
--------
|
|
>>> s = pd.Series([1, 2],
|
|
... index=pd.date_range('20180101',
|
|
... periods=2,
|
|
... freq='1h'))
|
|
>>> s
|
|
2018-01-01 00:00:00 1
|
|
2018-01-01 01:00:00 2
|
|
Freq: H, dtype: int64
|
|
|
|
>>> s.resample('15min').nearest()
|
|
2018-01-01 00:00:00 1
|
|
2018-01-01 00:15:00 1
|
|
2018-01-01 00:30:00 2
|
|
2018-01-01 00:45:00 2
|
|
2018-01-01 01:00:00 2
|
|
Freq: 15T, dtype: int64
|
|
|
|
Limit the number of upsampled values imputed by the nearest:
|
|
|
|
>>> s.resample('15min').nearest(limit=1)
|
|
2018-01-01 00:00:00 1.0
|
|
2018-01-01 00:15:00 1.0
|
|
2018-01-01 00:30:00 NaN
|
|
2018-01-01 00:45:00 2.0
|
|
2018-01-01 01:00:00 2.0
|
|
Freq: 15T, dtype: float64
|
|
"""
|
|
return self._upsample("nearest", limit=limit)
|
|
|
|
def bfill(self, limit=None):
|
|
"""
|
|
Backward fill the new missing values in the resampled data.
|
|
|
|
In statistics, imputation is the process of replacing missing data with
|
|
substituted values [1]_. When resampling data, missing values may
|
|
appear (e.g., when the resampling frequency is higher than the original
|
|
frequency). The backward fill will replace NaN values that appeared in
|
|
the resampled data with the next value in the original sequence.
|
|
Missing values that existed in the original data will not be modified.
|
|
|
|
Parameters
|
|
----------
|
|
limit : int, optional
|
|
Limit of how many values to fill.
|
|
|
|
Returns
|
|
-------
|
|
Series, DataFrame
|
|
An upsampled Series or DataFrame with backward filled NaN values.
|
|
|
|
See Also
|
|
--------
|
|
bfill : Alias of backfill.
|
|
fillna : Fill NaN values using the specified method, which can be
|
|
'backfill'.
|
|
nearest : Fill NaN values with nearest neighbor starting from center.
|
|
ffill : Forward fill NaN values.
|
|
Series.fillna : Fill NaN values in the Series using the
|
|
specified method, which can be 'backfill'.
|
|
DataFrame.fillna : Fill NaN values in the DataFrame using the
|
|
specified method, which can be 'backfill'.
|
|
|
|
References
|
|
----------
|
|
.. [1] https://en.wikipedia.org/wiki/Imputation_(statistics)
|
|
|
|
Examples
|
|
--------
|
|
Resampling a Series:
|
|
|
|
>>> s = pd.Series([1, 2, 3],
|
|
... index=pd.date_range('20180101', periods=3, freq='h'))
|
|
>>> s
|
|
2018-01-01 00:00:00 1
|
|
2018-01-01 01:00:00 2
|
|
2018-01-01 02:00:00 3
|
|
Freq: H, dtype: int64
|
|
|
|
>>> s.resample('30min').bfill()
|
|
2018-01-01 00:00:00 1
|
|
2018-01-01 00:30:00 2
|
|
2018-01-01 01:00:00 2
|
|
2018-01-01 01:30:00 3
|
|
2018-01-01 02:00:00 3
|
|
Freq: 30T, dtype: int64
|
|
|
|
>>> s.resample('15min').bfill(limit=2)
|
|
2018-01-01 00:00:00 1.0
|
|
2018-01-01 00:15:00 NaN
|
|
2018-01-01 00:30:00 2.0
|
|
2018-01-01 00:45:00 2.0
|
|
2018-01-01 01:00:00 2.0
|
|
2018-01-01 01:15:00 NaN
|
|
2018-01-01 01:30:00 3.0
|
|
2018-01-01 01:45:00 3.0
|
|
2018-01-01 02:00:00 3.0
|
|
Freq: 15T, dtype: float64
|
|
|
|
Resampling a DataFrame that has missing values:
|
|
|
|
>>> df = pd.DataFrame({'a': [2, np.nan, 6], 'b': [1, 3, 5]},
|
|
... index=pd.date_range('20180101', periods=3,
|
|
... freq='h'))
|
|
>>> df
|
|
a b
|
|
2018-01-01 00:00:00 2.0 1
|
|
2018-01-01 01:00:00 NaN 3
|
|
2018-01-01 02:00:00 6.0 5
|
|
|
|
>>> df.resample('30min').bfill()
|
|
a b
|
|
2018-01-01 00:00:00 2.0 1
|
|
2018-01-01 00:30:00 NaN 3
|
|
2018-01-01 01:00:00 NaN 3
|
|
2018-01-01 01:30:00 6.0 5
|
|
2018-01-01 02:00:00 6.0 5
|
|
|
|
>>> df.resample('15min').bfill(limit=2)
|
|
a b
|
|
2018-01-01 00:00:00 2.0 1.0
|
|
2018-01-01 00:15:00 NaN NaN
|
|
2018-01-01 00:30:00 NaN 3.0
|
|
2018-01-01 00:45:00 NaN 3.0
|
|
2018-01-01 01:00:00 NaN 3.0
|
|
2018-01-01 01:15:00 NaN NaN
|
|
2018-01-01 01:30:00 6.0 5.0
|
|
2018-01-01 01:45:00 6.0 5.0
|
|
2018-01-01 02:00:00 6.0 5.0
|
|
"""
|
|
return self._upsample("bfill", limit=limit)
|
|
|
|
def backfill(self, limit=None):
|
|
"""
|
|
Backward fill the values.
|
|
|
|
.. deprecated:: 1.4
|
|
Use bfill instead.
|
|
|
|
Parameters
|
|
----------
|
|
limit : int, optional
|
|
Limit of how many values to fill.
|
|
|
|
Returns
|
|
-------
|
|
Series, DataFrame
|
|
An upsampled Series or DataFrame with backward filled NaN values.
|
|
"""
|
|
warnings.warn(
|
|
"backfill is deprecated and will be removed in a future version. "
|
|
"Use bfill instead.",
|
|
FutureWarning,
|
|
stacklevel=find_stack_level(),
|
|
)
|
|
return self.bfill(limit=limit)
|
|
|
|
def fillna(self, method, limit=None):
|
|
"""
|
|
Fill missing values introduced by upsampling.
|
|
|
|
In statistics, imputation is the process of replacing missing data with
|
|
substituted values [1]_. When resampling data, missing values may
|
|
appear (e.g., when the resampling frequency is higher than the original
|
|
frequency).
|
|
|
|
Missing values that existed in the original data will
|
|
not be modified.
|
|
|
|
Parameters
|
|
----------
|
|
method : {'pad', 'backfill', 'ffill', 'bfill', 'nearest'}
|
|
Method to use for filling holes in resampled data
|
|
|
|
* 'pad' or 'ffill': use previous valid observation to fill gap
|
|
(forward fill).
|
|
* 'backfill' or 'bfill': use next valid observation to fill gap.
|
|
* 'nearest': use nearest valid observation to fill gap.
|
|
|
|
limit : int, optional
|
|
Limit of how many consecutive missing values to fill.
|
|
|
|
Returns
|
|
-------
|
|
Series or DataFrame
|
|
An upsampled Series or DataFrame with missing values filled.
|
|
|
|
See Also
|
|
--------
|
|
bfill : Backward fill NaN values in the resampled data.
|
|
ffill : Forward fill NaN values in the resampled data.
|
|
nearest : Fill NaN values in the resampled data
|
|
with nearest neighbor starting from center.
|
|
interpolate : Fill NaN values using interpolation.
|
|
Series.fillna : Fill NaN values in the Series using the
|
|
specified method, which can be 'bfill' and 'ffill'.
|
|
DataFrame.fillna : Fill NaN values in the DataFrame using the
|
|
specified method, which can be 'bfill' and 'ffill'.
|
|
|
|
References
|
|
----------
|
|
.. [1] https://en.wikipedia.org/wiki/Imputation_(statistics)
|
|
|
|
Examples
|
|
--------
|
|
Resampling a Series:
|
|
|
|
>>> s = pd.Series([1, 2, 3],
|
|
... index=pd.date_range('20180101', periods=3, freq='h'))
|
|
>>> s
|
|
2018-01-01 00:00:00 1
|
|
2018-01-01 01:00:00 2
|
|
2018-01-01 02:00:00 3
|
|
Freq: H, dtype: int64
|
|
|
|
Without filling the missing values you get:
|
|
|
|
>>> s.resample("30min").asfreq()
|
|
2018-01-01 00:00:00 1.0
|
|
2018-01-01 00:30:00 NaN
|
|
2018-01-01 01:00:00 2.0
|
|
2018-01-01 01:30:00 NaN
|
|
2018-01-01 02:00:00 3.0
|
|
Freq: 30T, dtype: float64
|
|
|
|
>>> s.resample('30min').fillna("backfill")
|
|
2018-01-01 00:00:00 1
|
|
2018-01-01 00:30:00 2
|
|
2018-01-01 01:00:00 2
|
|
2018-01-01 01:30:00 3
|
|
2018-01-01 02:00:00 3
|
|
Freq: 30T, dtype: int64
|
|
|
|
>>> s.resample('15min').fillna("backfill", limit=2)
|
|
2018-01-01 00:00:00 1.0
|
|
2018-01-01 00:15:00 NaN
|
|
2018-01-01 00:30:00 2.0
|
|
2018-01-01 00:45:00 2.0
|
|
2018-01-01 01:00:00 2.0
|
|
2018-01-01 01:15:00 NaN
|
|
2018-01-01 01:30:00 3.0
|
|
2018-01-01 01:45:00 3.0
|
|
2018-01-01 02:00:00 3.0
|
|
Freq: 15T, dtype: float64
|
|
|
|
>>> s.resample('30min').fillna("pad")
|
|
2018-01-01 00:00:00 1
|
|
2018-01-01 00:30:00 1
|
|
2018-01-01 01:00:00 2
|
|
2018-01-01 01:30:00 2
|
|
2018-01-01 02:00:00 3
|
|
Freq: 30T, dtype: int64
|
|
|
|
>>> s.resample('30min').fillna("nearest")
|
|
2018-01-01 00:00:00 1
|
|
2018-01-01 00:30:00 2
|
|
2018-01-01 01:00:00 2
|
|
2018-01-01 01:30:00 3
|
|
2018-01-01 02:00:00 3
|
|
Freq: 30T, dtype: int64
|
|
|
|
Missing values present before the upsampling are not affected.
|
|
|
|
>>> sm = pd.Series([1, None, 3],
|
|
... index=pd.date_range('20180101', periods=3, freq='h'))
|
|
>>> sm
|
|
2018-01-01 00:00:00 1.0
|
|
2018-01-01 01:00:00 NaN
|
|
2018-01-01 02:00:00 3.0
|
|
Freq: H, dtype: float64
|
|
|
|
>>> sm.resample('30min').fillna('backfill')
|
|
2018-01-01 00:00:00 1.0
|
|
2018-01-01 00:30:00 NaN
|
|
2018-01-01 01:00:00 NaN
|
|
2018-01-01 01:30:00 3.0
|
|
2018-01-01 02:00:00 3.0
|
|
Freq: 30T, dtype: float64
|
|
|
|
>>> sm.resample('30min').fillna('pad')
|
|
2018-01-01 00:00:00 1.0
|
|
2018-01-01 00:30:00 1.0
|
|
2018-01-01 01:00:00 NaN
|
|
2018-01-01 01:30:00 NaN
|
|
2018-01-01 02:00:00 3.0
|
|
Freq: 30T, dtype: float64
|
|
|
|
>>> sm.resample('30min').fillna('nearest')
|
|
2018-01-01 00:00:00 1.0
|
|
2018-01-01 00:30:00 NaN
|
|
2018-01-01 01:00:00 NaN
|
|
2018-01-01 01:30:00 3.0
|
|
2018-01-01 02:00:00 3.0
|
|
Freq: 30T, dtype: float64
|
|
|
|
DataFrame resampling is done column-wise. All the same options are
|
|
available.
|
|
|
|
>>> df = pd.DataFrame({'a': [2, np.nan, 6], 'b': [1, 3, 5]},
|
|
... index=pd.date_range('20180101', periods=3,
|
|
... freq='h'))
|
|
>>> df
|
|
a b
|
|
2018-01-01 00:00:00 2.0 1
|
|
2018-01-01 01:00:00 NaN 3
|
|
2018-01-01 02:00:00 6.0 5
|
|
|
|
>>> df.resample('30min').fillna("bfill")
|
|
a b
|
|
2018-01-01 00:00:00 2.0 1
|
|
2018-01-01 00:30:00 NaN 3
|
|
2018-01-01 01:00:00 NaN 3
|
|
2018-01-01 01:30:00 6.0 5
|
|
2018-01-01 02:00:00 6.0 5
|
|
"""
|
|
return self._upsample(method, limit=limit)
|
|
|
|
@deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "method"])
|
|
@doc(NDFrame.interpolate, **_shared_docs_kwargs)
|
|
def interpolate(
|
|
self,
|
|
method="linear",
|
|
axis=0,
|
|
limit=None,
|
|
inplace=False,
|
|
limit_direction="forward",
|
|
limit_area=None,
|
|
downcast=None,
|
|
**kwargs,
|
|
):
|
|
"""
|
|
Interpolate values according to different methods.
|
|
"""
|
|
result = self._upsample("asfreq")
|
|
return result.interpolate(
|
|
method=method,
|
|
axis=axis,
|
|
limit=limit,
|
|
inplace=inplace,
|
|
limit_direction=limit_direction,
|
|
limit_area=limit_area,
|
|
downcast=downcast,
|
|
**kwargs,
|
|
)
|
|
|
|
def asfreq(self, fill_value=None):
|
|
"""
|
|
Return the values at the new freq, essentially a reindex.
|
|
|
|
Parameters
|
|
----------
|
|
fill_value : scalar, optional
|
|
Value to use for missing values, applied during upsampling (note
|
|
this does not fill NaNs that already were present).
|
|
|
|
Returns
|
|
-------
|
|
DataFrame or Series
|
|
Values at the specified freq.
|
|
|
|
See Also
|
|
--------
|
|
Series.asfreq: Convert TimeSeries to specified frequency.
|
|
DataFrame.asfreq: Convert TimeSeries to specified frequency.
|
|
"""
|
|
return self._upsample("asfreq", fill_value=fill_value)
|
|
|
|
def std(
|
|
self,
|
|
ddof=1,
|
|
numeric_only: bool | lib.NoDefault = lib.no_default,
|
|
*args,
|
|
**kwargs,
|
|
):
|
|
"""
|
|
Compute standard deviation of groups, excluding missing values.
|
|
|
|
Parameters
|
|
----------
|
|
ddof : int, default 1
|
|
Degrees of freedom.
|
|
numeric_only : bool, default False
|
|
Include only `float`, `int` or `boolean` data.
|
|
|
|
.. versionadded:: 1.5.0
|
|
|
|
Returns
|
|
-------
|
|
DataFrame or Series
|
|
Standard deviation of values within each group.
|
|
"""
|
|
nv.validate_resampler_func("std", args, kwargs)
|
|
return self._downsample("std", ddof=ddof, numeric_only=numeric_only)
|
|
|
|
def var(
|
|
self,
|
|
ddof=1,
|
|
numeric_only: bool | lib.NoDefault = lib.no_default,
|
|
*args,
|
|
**kwargs,
|
|
):
|
|
"""
|
|
Compute variance of groups, excluding missing values.
|
|
|
|
Parameters
|
|
----------
|
|
ddof : int, default 1
|
|
Degrees of freedom.
|
|
|
|
numeric_only : bool, default False
|
|
Include only `float`, `int` or `boolean` data.
|
|
|
|
.. versionadded:: 1.5.0
|
|
|
|
Returns
|
|
-------
|
|
DataFrame or Series
|
|
Variance of values within each group.
|
|
"""
|
|
nv.validate_resampler_func("var", args, kwargs)
|
|
return self._downsample("var", ddof=ddof, numeric_only=numeric_only)
|
|
|
|
@doc(GroupBy.size)
|
|
def size(self):
|
|
result = self._downsample("size")
|
|
if not len(self.ax):
|
|
from pandas import Series
|
|
|
|
if self._selected_obj.ndim == 1:
|
|
name = self._selected_obj.name
|
|
else:
|
|
name = None
|
|
result = Series([], index=result.index, dtype="int64", name=name)
|
|
return result
|
|
|
|
@doc(GroupBy.count)
|
|
def count(self):
|
|
result = self._downsample("count")
|
|
if not len(self.ax):
|
|
if self._selected_obj.ndim == 1:
|
|
result = type(self._selected_obj)(
|
|
[], index=result.index, dtype="int64", name=self._selected_obj.name
|
|
)
|
|
else:
|
|
from pandas import DataFrame
|
|
|
|
result = DataFrame(
|
|
[], index=result.index, columns=result.columns, dtype="int64"
|
|
)
|
|
|
|
return result
|
|
|
|
def quantile(self, q=0.5, **kwargs):
|
|
"""
|
|
Return value at the given quantile.
|
|
|
|
Parameters
|
|
----------
|
|
q : float or array-like, default 0.5 (50% quantile)
|
|
|
|
Returns
|
|
-------
|
|
DataFrame or Series
|
|
Quantile of values within each group.
|
|
|
|
See Also
|
|
--------
|
|
Series.quantile
|
|
Return a series, where the index is q and the values are the quantiles.
|
|
DataFrame.quantile
|
|
Return a DataFrame, where the columns are the columns of self,
|
|
and the values are the quantiles.
|
|
DataFrameGroupBy.quantile
|
|
Return a DataFrame, where the coulmns are groupby columns,
|
|
and the values are its quantiles.
|
|
"""
|
|
return self._downsample("quantile", q=q, **kwargs)
|
|
|
|
|
|
def _add_downsample_kernel(
|
|
name: str, args: tuple[str, ...], docs_class: type = GroupBy
|
|
) -> None:
|
|
"""
|
|
Add a kernel to Resampler.
|
|
|
|
Arguments
|
|
---------
|
|
name : str
|
|
Name of the kernel.
|
|
args : tuple
|
|
Arguments of the method.
|
|
docs_class : type
|
|
Class to get kernel docstring from.
|
|
"""
|
|
assert args in (
|
|
("numeric_only", "min_count"),
|
|
("numeric_only",),
|
|
("ddof", "numeric_only"),
|
|
(),
|
|
)
|
|
|
|
# Explicitly provide args rather than args/kwargs for API docs
|
|
if args == ("numeric_only", "min_count"):
|
|
|
|
def f(
|
|
self,
|
|
numeric_only: bool | lib.NoDefault = lib.no_default,
|
|
min_count: int = 0,
|
|
*args,
|
|
**kwargs,
|
|
):
|
|
nv.validate_resampler_func(name, args, kwargs)
|
|
if numeric_only is lib.no_default and name != "sum":
|
|
# For DataFrameGroupBy, set it to be False for methods other than `sum`.
|
|
numeric_only = False
|
|
|
|
return self._downsample(
|
|
name, numeric_only=numeric_only, min_count=min_count
|
|
)
|
|
|
|
elif args == ("numeric_only",):
|
|
# error: All conditional function variants must have identical signatures
|
|
def f( # type: ignore[misc]
|
|
self, numeric_only: bool | lib.NoDefault = lib.no_default, *args, **kwargs
|
|
):
|
|
nv.validate_resampler_func(name, args, kwargs)
|
|
return self._downsample(name, numeric_only=numeric_only)
|
|
|
|
elif args == ("ddof", "numeric_only"):
|
|
# error: All conditional function variants must have identical signatures
|
|
def f( # type: ignore[misc]
|
|
self,
|
|
ddof: int = 1,
|
|
numeric_only: bool | lib.NoDefault = lib.no_default,
|
|
*args,
|
|
**kwargs,
|
|
):
|
|
nv.validate_resampler_func(name, args, kwargs)
|
|
return self._downsample(name, ddof=ddof, numeric_only=numeric_only)
|
|
|
|
else:
|
|
# error: All conditional function variants must have identical signatures
|
|
def f( # type: ignore[misc]
|
|
self,
|
|
*args,
|
|
**kwargs,
|
|
):
|
|
nv.validate_resampler_func(name, args, kwargs)
|
|
return self._downsample(name)
|
|
|
|
f.__doc__ = getattr(docs_class, name).__doc__
|
|
setattr(Resampler, name, f)
|
|
|
|
|
|
for method in ["sum", "prod", "min", "max", "first", "last"]:
|
|
_add_downsample_kernel(method, ("numeric_only", "min_count"))
|
|
for method in ["mean", "median"]:
|
|
_add_downsample_kernel(method, ("numeric_only",))
|
|
for method in ["sem"]:
|
|
_add_downsample_kernel(method, ("ddof", "numeric_only"))
|
|
for method in ["ohlc"]:
|
|
_add_downsample_kernel(method, ())
|
|
for method in ["nunique"]:
|
|
_add_downsample_kernel(method, (), SeriesGroupBy)
|
|
|
|
|
|
class _GroupByMixin(PandasObject):
|
|
"""
|
|
Provide the groupby facilities.
|
|
"""
|
|
|
|
_attributes: list[str] # in practice the same as Resampler._attributes
|
|
_selection: IndexLabel | None = None
|
|
|
|
def __init__(self, obj, parent=None, groupby=None, key=None, **kwargs) -> None:
|
|
# reached via ._gotitem and _get_resampler_for_grouping
|
|
|
|
if parent is None:
|
|
parent = obj
|
|
|
|
# initialize our GroupByMixin object with
|
|
# the resampler attributes
|
|
for attr in self._attributes:
|
|
setattr(self, attr, kwargs.get(attr, getattr(parent, attr)))
|
|
self._selection = kwargs.get("selection")
|
|
|
|
self.binner = parent.binner
|
|
self.key = key
|
|
|
|
self._groupby = groupby
|
|
self._groupby.mutated = True
|
|
self._groupby.grouper.mutated = True
|
|
self.groupby = copy.copy(parent.groupby)
|
|
|
|
@no_type_check
|
|
def _apply(self, f, *args, **kwargs):
|
|
"""
|
|
Dispatch to _upsample; we are stripping all of the _upsample kwargs and
|
|
performing the original function call on the grouped object.
|
|
"""
|
|
|
|
def func(x):
|
|
x = self._shallow_copy(x, groupby=self.groupby)
|
|
|
|
if isinstance(f, str):
|
|
return getattr(x, f)(**kwargs)
|
|
|
|
return x.apply(f, *args, **kwargs)
|
|
|
|
result = self._groupby.apply(func)
|
|
return self._wrap_result(result)
|
|
|
|
_upsample = _apply
|
|
_downsample = _apply
|
|
_groupby_and_aggregate = _apply
|
|
|
|
@final
|
|
def _gotitem(self, key, ndim, subset=None):
|
|
"""
|
|
Sub-classes to define. Return a sliced object.
|
|
|
|
Parameters
|
|
----------
|
|
key : string / list of selections
|
|
ndim : {1, 2}
|
|
requested ndim of result
|
|
subset : object, default None
|
|
subset to act on
|
|
"""
|
|
# create a new object to prevent aliasing
|
|
if subset is None:
|
|
# error: "GotItemMixin" has no attribute "obj"
|
|
subset = self.obj # type: ignore[attr-defined]
|
|
|
|
# we need to make a shallow copy of ourselves
|
|
# with the same groupby
|
|
kwargs = {attr: getattr(self, attr) for attr in self._attributes}
|
|
|
|
# Try to select from a DataFrame, falling back to a Series
|
|
try:
|
|
if isinstance(key, list) and self.key not in key:
|
|
key.append(self.key)
|
|
groupby = self._groupby[key]
|
|
except IndexError:
|
|
groupby = self._groupby
|
|
|
|
selection = None
|
|
if subset.ndim == 2 and (
|
|
(lib.is_scalar(key) and key in subset) or lib.is_list_like(key)
|
|
):
|
|
selection = key
|
|
|
|
new_rs = type(self)(
|
|
subset, groupby=groupby, parent=self, selection=selection, **kwargs
|
|
)
|
|
return new_rs
|
|
|
|
|
|
class DatetimeIndexResampler(Resampler):
|
|
@property
|
|
def _resampler_for_grouping(self):
|
|
return DatetimeIndexResamplerGroupby
|
|
|
|
def _get_binner_for_time(self):
|
|
|
|
# this is how we are actually creating the bins
|
|
if self.kind == "period":
|
|
return self.groupby._get_time_period_bins(self.ax)
|
|
return self.groupby._get_time_bins(self.ax)
|
|
|
|
def _downsample(self, how, **kwargs):
|
|
"""
|
|
Downsample the cython defined function.
|
|
|
|
Parameters
|
|
----------
|
|
how : string / cython mapped function
|
|
**kwargs : kw args passed to how function
|
|
"""
|
|
how = com.get_cython_func(how) or how
|
|
ax = self.ax
|
|
if self._selected_obj.ndim == 1:
|
|
obj = self._selected_obj
|
|
else:
|
|
# Excludes `on` column when provided
|
|
obj = self._obj_with_exclusions
|
|
|
|
if not len(ax):
|
|
# reset to the new freq
|
|
obj = obj.copy()
|
|
obj.index = obj.index._with_freq(self.freq)
|
|
assert obj.index.freq == self.freq, (obj.index.freq, self.freq)
|
|
return obj
|
|
|
|
# do we have a regular frequency
|
|
|
|
# error: Item "None" of "Optional[Any]" has no attribute "binlabels"
|
|
if (
|
|
(ax.freq is not None or ax.inferred_freq is not None)
|
|
and len(self.grouper.binlabels) > len(ax)
|
|
and how is None
|
|
):
|
|
|
|
# let's do an asfreq
|
|
return self.asfreq()
|
|
|
|
# we are downsampling
|
|
# we want to call the actual grouper method here
|
|
result = obj.groupby(self.grouper, axis=self.axis).aggregate(how, **kwargs)
|
|
|
|
result = self._apply_loffset(result)
|
|
return self._wrap_result(result)
|
|
|
|
def _adjust_binner_for_upsample(self, binner):
|
|
"""
|
|
Adjust our binner when upsampling.
|
|
|
|
The range of a new index should not be outside specified range
|
|
"""
|
|
if self.closed == "right":
|
|
binner = binner[1:]
|
|
else:
|
|
binner = binner[:-1]
|
|
return binner
|
|
|
|
def _upsample(self, method, limit=None, fill_value=None):
|
|
"""
|
|
Parameters
|
|
----------
|
|
method : string {'backfill', 'bfill', 'pad',
|
|
'ffill', 'asfreq'} method for upsampling
|
|
limit : int, default None
|
|
Maximum size gap to fill when reindexing
|
|
fill_value : scalar, default None
|
|
Value to use for missing values
|
|
|
|
See Also
|
|
--------
|
|
.fillna: Fill NA/NaN values using the specified method.
|
|
|
|
"""
|
|
if self.axis:
|
|
raise AssertionError("axis must be 0")
|
|
if self._from_selection:
|
|
raise ValueError(
|
|
"Upsampling from level= or on= selection "
|
|
"is not supported, use .set_index(...) "
|
|
"to explicitly set index to datetime-like"
|
|
)
|
|
|
|
ax = self.ax
|
|
obj = self._selected_obj
|
|
binner = self.binner
|
|
res_index = self._adjust_binner_for_upsample(binner)
|
|
|
|
# if we have the same frequency as our axis, then we are equal sampling
|
|
if (
|
|
limit is None
|
|
and to_offset(ax.inferred_freq) == self.freq
|
|
and len(obj) == len(res_index)
|
|
):
|
|
result = obj.copy()
|
|
result.index = res_index
|
|
else:
|
|
result = obj.reindex(
|
|
res_index, method=method, limit=limit, fill_value=fill_value
|
|
)
|
|
|
|
result = self._apply_loffset(result)
|
|
return self._wrap_result(result)
|
|
|
|
def _wrap_result(self, result):
|
|
result = super()._wrap_result(result)
|
|
|
|
# we may have a different kind that we were asked originally
|
|
# convert if needed
|
|
if self.kind == "period" and not isinstance(result.index, PeriodIndex):
|
|
result.index = result.index.to_period(self.freq)
|
|
return result
|
|
|
|
|
|
class DatetimeIndexResamplerGroupby(_GroupByMixin, DatetimeIndexResampler):
|
|
"""
|
|
Provides a resample of a groupby implementation
|
|
"""
|
|
|
|
@property
|
|
def _constructor(self):
|
|
return DatetimeIndexResampler
|
|
|
|
|
|
class PeriodIndexResampler(DatetimeIndexResampler):
|
|
@property
|
|
def _resampler_for_grouping(self):
|
|
return PeriodIndexResamplerGroupby
|
|
|
|
def _get_binner_for_time(self):
|
|
if self.kind == "timestamp":
|
|
return super()._get_binner_for_time()
|
|
return self.groupby._get_period_bins(self.ax)
|
|
|
|
def _convert_obj(self, obj: NDFrameT) -> NDFrameT:
|
|
obj = super()._convert_obj(obj)
|
|
|
|
if self._from_selection:
|
|
# see GH 14008, GH 12871
|
|
msg = (
|
|
"Resampling from level= or on= selection "
|
|
"with a PeriodIndex is not currently supported, "
|
|
"use .set_index(...) to explicitly set index"
|
|
)
|
|
raise NotImplementedError(msg)
|
|
|
|
if self.loffset is not None:
|
|
# Cannot apply loffset/timedelta to PeriodIndex -> convert to
|
|
# timestamps
|
|
self.kind = "timestamp"
|
|
|
|
# convert to timestamp
|
|
if self.kind == "timestamp":
|
|
obj = obj.to_timestamp(how=self.convention)
|
|
|
|
return obj
|
|
|
|
def _downsample(self, how, **kwargs):
|
|
"""
|
|
Downsample the cython defined function.
|
|
|
|
Parameters
|
|
----------
|
|
how : string / cython mapped function
|
|
**kwargs : kw args passed to how function
|
|
"""
|
|
# we may need to actually resample as if we are timestamps
|
|
if self.kind == "timestamp":
|
|
return super()._downsample(how, **kwargs)
|
|
|
|
how = com.get_cython_func(how) or how
|
|
ax = self.ax
|
|
|
|
if is_subperiod(ax.freq, self.freq):
|
|
# Downsampling
|
|
return self._groupby_and_aggregate(how, **kwargs)
|
|
elif is_superperiod(ax.freq, self.freq):
|
|
if how == "ohlc":
|
|
# GH #13083
|
|
# upsampling to subperiods is handled as an asfreq, which works
|
|
# for pure aggregating/reducing methods
|
|
# OHLC reduces along the time dimension, but creates multiple
|
|
# values for each period -> handle by _groupby_and_aggregate()
|
|
return self._groupby_and_aggregate(how)
|
|
return self.asfreq()
|
|
elif ax.freq == self.freq:
|
|
return self.asfreq()
|
|
|
|
raise IncompatibleFrequency(
|
|
f"Frequency {ax.freq} cannot be resampled to {self.freq}, "
|
|
"as they are not sub or super periods"
|
|
)
|
|
|
|
def _upsample(self, method, limit=None, fill_value=None):
|
|
"""
|
|
Parameters
|
|
----------
|
|
method : {'backfill', 'bfill', 'pad', 'ffill'}
|
|
Method for upsampling.
|
|
limit : int, default None
|
|
Maximum size gap to fill when reindexing.
|
|
fill_value : scalar, default None
|
|
Value to use for missing values.
|
|
|
|
See Also
|
|
--------
|
|
.fillna: Fill NA/NaN values using the specified method.
|
|
|
|
"""
|
|
# we may need to actually resample as if we are timestamps
|
|
if self.kind == "timestamp":
|
|
return super()._upsample(method, limit=limit, fill_value=fill_value)
|
|
|
|
ax = self.ax
|
|
obj = self.obj
|
|
new_index = self.binner
|
|
|
|
# Start vs. end of period
|
|
memb = ax.asfreq(self.freq, how=self.convention)
|
|
|
|
# Get the fill indexer
|
|
indexer = memb.get_indexer(new_index, method=method, limit=limit)
|
|
new_obj = _take_new_index(
|
|
obj,
|
|
indexer,
|
|
new_index,
|
|
axis=self.axis,
|
|
)
|
|
return self._wrap_result(new_obj)
|
|
|
|
|
|
class PeriodIndexResamplerGroupby(_GroupByMixin, PeriodIndexResampler):
|
|
"""
|
|
Provides a resample of a groupby implementation.
|
|
"""
|
|
|
|
@property
|
|
def _constructor(self):
|
|
return PeriodIndexResampler
|
|
|
|
|
|
class TimedeltaIndexResampler(DatetimeIndexResampler):
|
|
@property
|
|
def _resampler_for_grouping(self):
|
|
return TimedeltaIndexResamplerGroupby
|
|
|
|
def _get_binner_for_time(self):
|
|
return self.groupby._get_time_delta_bins(self.ax)
|
|
|
|
def _adjust_binner_for_upsample(self, binner):
|
|
"""
|
|
Adjust our binner when upsampling.
|
|
|
|
The range of a new index is allowed to be greater than original range
|
|
so we don't need to change the length of a binner, GH 13022
|
|
"""
|
|
return binner
|
|
|
|
|
|
class TimedeltaIndexResamplerGroupby(_GroupByMixin, TimedeltaIndexResampler):
|
|
"""
|
|
Provides a resample of a groupby implementation.
|
|
"""
|
|
|
|
@property
|
|
def _constructor(self):
|
|
return TimedeltaIndexResampler
|
|
|
|
|
|
def get_resampler(
|
|
obj, kind=None, **kwds
|
|
) -> DatetimeIndexResampler | PeriodIndexResampler | TimedeltaIndexResampler:
|
|
"""
|
|
Create a TimeGrouper and return our resampler.
|
|
"""
|
|
tg = TimeGrouper(**kwds)
|
|
return tg._get_resampler(obj, kind=kind)
|
|
|
|
|
|
get_resampler.__doc__ = Resampler.__doc__
|
|
|
|
|
|
def get_resampler_for_grouping(
|
|
groupby, rule, how=None, fill_method=None, limit=None, kind=None, on=None, **kwargs
|
|
):
|
|
"""
|
|
Return our appropriate resampler when grouping as well.
|
|
"""
|
|
# .resample uses 'on' similar to how .groupby uses 'key'
|
|
tg = TimeGrouper(freq=rule, key=on, **kwargs)
|
|
resampler = tg._get_resampler(groupby.obj, kind=kind)
|
|
return resampler._get_resampler_for_grouping(groupby=groupby, key=tg.key)
|
|
|
|
|
|
class TimeGrouper(Grouper):
|
|
"""
|
|
Custom groupby class for time-interval grouping.
|
|
|
|
Parameters
|
|
----------
|
|
freq : pandas date offset or offset alias for identifying bin edges
|
|
closed : closed end of interval; 'left' or 'right'
|
|
label : interval boundary to use for labeling; 'left' or 'right'
|
|
convention : {'start', 'end', 'e', 's'}
|
|
If axis is PeriodIndex
|
|
"""
|
|
|
|
_attributes = Grouper._attributes + (
|
|
"closed",
|
|
"label",
|
|
"how",
|
|
"loffset",
|
|
"kind",
|
|
"convention",
|
|
"origin",
|
|
"offset",
|
|
)
|
|
|
|
def __init__(
|
|
self,
|
|
freq="Min",
|
|
closed: Literal["left", "right"] | None = None,
|
|
label: Literal["left", "right"] | None = None,
|
|
how="mean",
|
|
axis=0,
|
|
fill_method=None,
|
|
limit=None,
|
|
loffset=None,
|
|
kind: str | None = None,
|
|
convention: Literal["start", "end", "e", "s"] | None = None,
|
|
base: int | None = None,
|
|
origin: str | TimestampConvertibleTypes = "start_day",
|
|
offset: TimedeltaConvertibleTypes | None = None,
|
|
group_keys: bool | lib.NoDefault = True,
|
|
**kwargs,
|
|
) -> None:
|
|
# Check for correctness of the keyword arguments which would
|
|
# otherwise silently use the default if misspelled
|
|
if label not in {None, "left", "right"}:
|
|
raise ValueError(f"Unsupported value {label} for `label`")
|
|
if closed not in {None, "left", "right"}:
|
|
raise ValueError(f"Unsupported value {closed} for `closed`")
|
|
if convention not in {None, "start", "end", "e", "s"}:
|
|
raise ValueError(f"Unsupported value {convention} for `convention`")
|
|
|
|
freq = to_offset(freq)
|
|
|
|
end_types = {"M", "A", "Q", "BM", "BA", "BQ", "W"}
|
|
rule = freq.rule_code
|
|
if rule in end_types or ("-" in rule and rule[: rule.find("-")] in end_types):
|
|
if closed is None:
|
|
closed = "right"
|
|
if label is None:
|
|
label = "right"
|
|
else:
|
|
# The backward resample sets ``closed`` to ``'right'`` by default
|
|
# since the last value should be considered as the edge point for
|
|
# the last bin. When origin in "end" or "end_day", the value for a
|
|
# specific ``Timestamp`` index stands for the resample result from
|
|
# the current ``Timestamp`` minus ``freq`` to the current
|
|
# ``Timestamp`` with a right close.
|
|
if origin in ["end", "end_day"]:
|
|
if closed is None:
|
|
closed = "right"
|
|
if label is None:
|
|
label = "right"
|
|
else:
|
|
if closed is None:
|
|
closed = "left"
|
|
if label is None:
|
|
label = "left"
|
|
|
|
self.closed = closed
|
|
self.label = label
|
|
self.kind = kind
|
|
self.convention = convention if convention is not None else "e"
|
|
self.how = how
|
|
self.fill_method = fill_method
|
|
self.limit = limit
|
|
self.group_keys = group_keys
|
|
|
|
if origin in ("epoch", "start", "start_day", "end", "end_day"):
|
|
self.origin = origin
|
|
else:
|
|
try:
|
|
self.origin = Timestamp(origin)
|
|
except (ValueError, TypeError) as err:
|
|
raise ValueError(
|
|
"'origin' should be equal to 'epoch', 'start', 'start_day', "
|
|
"'end', 'end_day' or "
|
|
f"should be a Timestamp convertible type. Got '{origin}' instead."
|
|
) from err
|
|
|
|
try:
|
|
self.offset = Timedelta(offset) if offset is not None else None
|
|
except (ValueError, TypeError) as err:
|
|
raise ValueError(
|
|
"'offset' should be a Timedelta convertible type. "
|
|
f"Got '{offset}' instead."
|
|
) from err
|
|
|
|
# always sort time groupers
|
|
kwargs["sort"] = True
|
|
|
|
# Handle deprecated arguments since v1.1.0 of `base` and `loffset` (GH #31809)
|
|
if base is not None and offset is not None:
|
|
raise ValueError("'offset' and 'base' cannot be present at the same time")
|
|
|
|
if base and isinstance(freq, Tick):
|
|
# this conversion handle the default behavior of base and the
|
|
# special case of GH #10530. Indeed in case when dealing with
|
|
# a TimedeltaIndex base was treated as a 'pure' offset even though
|
|
# the default behavior of base was equivalent of a modulo on
|
|
# freq_nanos.
|
|
self.offset = Timedelta(base * freq.nanos // freq.n)
|
|
|
|
if isinstance(loffset, str):
|
|
loffset = to_offset(loffset)
|
|
self.loffset = loffset
|
|
|
|
super().__init__(freq=freq, axis=axis, **kwargs)
|
|
|
|
def _get_resampler(self, obj, kind=None):
|
|
"""
|
|
Return my resampler or raise if we have an invalid axis.
|
|
|
|
Parameters
|
|
----------
|
|
obj : input object
|
|
kind : string, optional
|
|
'period','timestamp','timedelta' are valid
|
|
|
|
Returns
|
|
-------
|
|
a Resampler
|
|
|
|
Raises
|
|
------
|
|
TypeError if incompatible axis
|
|
|
|
"""
|
|
self._set_grouper(obj)
|
|
|
|
ax = self.ax
|
|
if isinstance(ax, DatetimeIndex):
|
|
return DatetimeIndexResampler(
|
|
obj, groupby=self, kind=kind, axis=self.axis, group_keys=self.group_keys
|
|
)
|
|
elif isinstance(ax, PeriodIndex) or kind == "period":
|
|
return PeriodIndexResampler(
|
|
obj, groupby=self, kind=kind, axis=self.axis, group_keys=self.group_keys
|
|
)
|
|
elif isinstance(ax, TimedeltaIndex):
|
|
return TimedeltaIndexResampler(
|
|
obj, groupby=self, axis=self.axis, group_keys=self.group_keys
|
|
)
|
|
|
|
raise TypeError(
|
|
"Only valid with DatetimeIndex, "
|
|
"TimedeltaIndex or PeriodIndex, "
|
|
f"but got an instance of '{type(ax).__name__}'"
|
|
)
|
|
|
|
def _get_grouper(self, obj, validate: bool = True):
|
|
# create the resampler and return our binner
|
|
r = self._get_resampler(obj)
|
|
return r.binner, r.grouper, r.obj
|
|
|
|
def _get_time_bins(self, ax: DatetimeIndex):
|
|
if not isinstance(ax, DatetimeIndex):
|
|
raise TypeError(
|
|
"axis must be a DatetimeIndex, but got "
|
|
f"an instance of {type(ax).__name__}"
|
|
)
|
|
|
|
if len(ax) == 0:
|
|
binner = labels = DatetimeIndex(data=[], freq=self.freq, name=ax.name)
|
|
return binner, [], labels
|
|
|
|
first, last = _get_timestamp_range_edges(
|
|
ax.min(),
|
|
ax.max(),
|
|
self.freq,
|
|
closed=self.closed,
|
|
origin=self.origin,
|
|
offset=self.offset,
|
|
)
|
|
# GH #12037
|
|
# use first/last directly instead of call replace() on them
|
|
# because replace() will swallow the nanosecond part
|
|
# thus last bin maybe slightly before the end if the end contains
|
|
# nanosecond part and lead to `Values falls after last bin` error
|
|
# GH 25758: If DST lands at midnight (e.g. 'America/Havana'), user feedback
|
|
# has noted that ambiguous=True provides the most sensible result
|
|
binner = labels = date_range(
|
|
freq=self.freq,
|
|
start=first,
|
|
end=last,
|
|
tz=ax.tz,
|
|
name=ax.name,
|
|
ambiguous=True,
|
|
nonexistent="shift_forward",
|
|
)
|
|
|
|
ax_values = ax.asi8
|
|
binner, bin_edges = self._adjust_bin_edges(binner, ax_values)
|
|
|
|
# general version, knowing nothing about relative frequencies
|
|
bins = lib.generate_bins_dt64(
|
|
ax_values, bin_edges, self.closed, hasnans=ax.hasnans
|
|
)
|
|
|
|
if self.closed == "right":
|
|
labels = binner
|
|
if self.label == "right":
|
|
labels = labels[1:]
|
|
elif self.label == "right":
|
|
labels = labels[1:]
|
|
|
|
if ax.hasnans:
|
|
binner = binner.insert(0, NaT)
|
|
labels = labels.insert(0, NaT)
|
|
|
|
# if we end up with more labels than bins
|
|
# adjust the labels
|
|
# GH4076
|
|
if len(bins) < len(labels):
|
|
labels = labels[: len(bins)]
|
|
|
|
return binner, bins, labels
|
|
|
|
def _adjust_bin_edges(self, binner, ax_values):
|
|
# Some hacks for > daily data, see #1471, #1458, #1483
|
|
|
|
if self.freq != "D" and is_superperiod(self.freq, "D"):
|
|
if self.closed == "right":
|
|
# GH 21459, GH 9119: Adjust the bins relative to the wall time
|
|
bin_edges = binner.tz_localize(None)
|
|
bin_edges = bin_edges + timedelta(1) - Nano(1)
|
|
bin_edges = bin_edges.tz_localize(binner.tz).asi8
|
|
else:
|
|
bin_edges = binner.asi8
|
|
|
|
# intraday values on last day
|
|
if bin_edges[-2] > ax_values.max():
|
|
bin_edges = bin_edges[:-1]
|
|
binner = binner[:-1]
|
|
else:
|
|
bin_edges = binner.asi8
|
|
return binner, bin_edges
|
|
|
|
def _get_time_delta_bins(self, ax: TimedeltaIndex):
|
|
if not isinstance(ax, TimedeltaIndex):
|
|
raise TypeError(
|
|
"axis must be a TimedeltaIndex, but got "
|
|
f"an instance of {type(ax).__name__}"
|
|
)
|
|
|
|
if not len(ax):
|
|
binner = labels = TimedeltaIndex(data=[], freq=self.freq, name=ax.name)
|
|
return binner, [], labels
|
|
|
|
start, end = ax.min(), ax.max()
|
|
|
|
if self.closed == "right":
|
|
end += self.freq
|
|
|
|
labels = binner = timedelta_range(
|
|
start=start, end=end, freq=self.freq, name=ax.name
|
|
)
|
|
|
|
end_stamps = labels
|
|
if self.closed == "left":
|
|
end_stamps += self.freq
|
|
|
|
bins = ax.searchsorted(end_stamps, side=self.closed)
|
|
|
|
if self.offset:
|
|
# GH 10530 & 31809
|
|
labels += self.offset
|
|
if self.loffset:
|
|
# GH 33498
|
|
labels += self.loffset
|
|
|
|
return binner, bins, labels
|
|
|
|
def _get_time_period_bins(self, ax: DatetimeIndex):
|
|
if not isinstance(ax, DatetimeIndex):
|
|
raise TypeError(
|
|
"axis must be a DatetimeIndex, but got "
|
|
f"an instance of {type(ax).__name__}"
|
|
)
|
|
|
|
freq = self.freq
|
|
|
|
if not len(ax):
|
|
binner = labels = PeriodIndex(data=[], freq=freq, name=ax.name)
|
|
return binner, [], labels
|
|
|
|
labels = binner = period_range(start=ax[0], end=ax[-1], freq=freq, name=ax.name)
|
|
|
|
end_stamps = (labels + freq).asfreq(freq, "s").to_timestamp()
|
|
if ax.tz:
|
|
end_stamps = end_stamps.tz_localize(ax.tz)
|
|
bins = ax.searchsorted(end_stamps, side="left")
|
|
|
|
return binner, bins, labels
|
|
|
|
def _get_period_bins(self, ax: PeriodIndex):
|
|
if not isinstance(ax, PeriodIndex):
|
|
raise TypeError(
|
|
"axis must be a PeriodIndex, but got "
|
|
f"an instance of {type(ax).__name__}"
|
|
)
|
|
|
|
memb = ax.asfreq(self.freq, how=self.convention)
|
|
|
|
# NaT handling as in pandas._lib.lib.generate_bins_dt64()
|
|
nat_count = 0
|
|
if memb.hasnans:
|
|
# error: Incompatible types in assignment (expression has type
|
|
# "bool_", variable has type "int") [assignment]
|
|
nat_count = np.sum(memb._isnan) # type: ignore[assignment]
|
|
memb = memb[~memb._isnan]
|
|
|
|
if not len(memb):
|
|
# index contains no valid (non-NaT) values
|
|
bins = np.array([], dtype=np.int64)
|
|
binner = labels = PeriodIndex(data=[], freq=self.freq, name=ax.name)
|
|
if len(ax) > 0:
|
|
# index is all NaT
|
|
binner, bins, labels = _insert_nat_bin(binner, bins, labels, len(ax))
|
|
return binner, bins, labels
|
|
|
|
freq_mult = self.freq.n
|
|
|
|
start = ax.min().asfreq(self.freq, how=self.convention)
|
|
end = ax.max().asfreq(self.freq, how="end")
|
|
bin_shift = 0
|
|
|
|
if isinstance(self.freq, Tick):
|
|
# GH 23882 & 31809: get adjusted bin edge labels with 'origin'
|
|
# and 'origin' support. This call only makes sense if the freq is a
|
|
# Tick since offset and origin are only used in those cases.
|
|
# Not doing this check could create an extra empty bin.
|
|
p_start, end = _get_period_range_edges(
|
|
start,
|
|
end,
|
|
self.freq,
|
|
closed=self.closed,
|
|
origin=self.origin,
|
|
offset=self.offset,
|
|
)
|
|
|
|
# Get offset for bin edge (not label edge) adjustment
|
|
start_offset = Period(start, self.freq) - Period(p_start, self.freq)
|
|
# error: Item "Period" of "Union[Period, Any]" has no attribute "n"
|
|
bin_shift = start_offset.n % freq_mult # type: ignore[union-attr]
|
|
start = p_start
|
|
|
|
labels = binner = period_range(
|
|
start=start, end=end, freq=self.freq, name=ax.name
|
|
)
|
|
|
|
i8 = memb.asi8
|
|
|
|
# when upsampling to subperiods, we need to generate enough bins
|
|
expected_bins_count = len(binner) * freq_mult
|
|
i8_extend = expected_bins_count - (i8[-1] - i8[0])
|
|
rng = np.arange(i8[0], i8[-1] + i8_extend, freq_mult)
|
|
rng += freq_mult
|
|
# adjust bin edge indexes to account for base
|
|
rng -= bin_shift
|
|
|
|
# Wrap in PeriodArray for PeriodArray.searchsorted
|
|
prng = type(memb._data)(rng, dtype=memb.dtype)
|
|
bins = memb.searchsorted(prng, side="left")
|
|
|
|
if nat_count > 0:
|
|
binner, bins, labels = _insert_nat_bin(binner, bins, labels, nat_count)
|
|
|
|
return binner, bins, labels
|
|
|
|
|
|
def _take_new_index(
|
|
obj: NDFrameT, indexer: npt.NDArray[np.intp], new_index: Index, axis: int = 0
|
|
) -> NDFrameT:
|
|
|
|
if isinstance(obj, ABCSeries):
|
|
new_values = algos.take_nd(obj._values, indexer)
|
|
# error: Incompatible return value type (got "Series", expected "NDFrameT")
|
|
return obj._constructor( # type: ignore[return-value]
|
|
new_values, index=new_index, name=obj.name
|
|
)
|
|
elif isinstance(obj, ABCDataFrame):
|
|
if axis == 1:
|
|
raise NotImplementedError("axis 1 is not supported")
|
|
new_mgr = obj._mgr.reindex_indexer(new_axis=new_index, indexer=indexer, axis=1)
|
|
# error: Incompatible return value type
|
|
# (got "DataFrame", expected "NDFrameT")
|
|
return obj._constructor(new_mgr) # type: ignore[return-value]
|
|
else:
|
|
raise ValueError("'obj' should be either a Series or a DataFrame")
|
|
|
|
|
|
def _get_timestamp_range_edges(
|
|
first: Timestamp,
|
|
last: Timestamp,
|
|
freq: BaseOffset,
|
|
closed: Literal["right", "left"] = "left",
|
|
origin="start_day",
|
|
offset: Timedelta | None = None,
|
|
) -> tuple[Timestamp, Timestamp]:
|
|
"""
|
|
Adjust the `first` Timestamp to the preceding Timestamp that resides on
|
|
the provided offset. Adjust the `last` Timestamp to the following
|
|
Timestamp that resides on the provided offset. Input Timestamps that
|
|
already reside on the offset will be adjusted depending on the type of
|
|
offset and the `closed` parameter.
|
|
|
|
Parameters
|
|
----------
|
|
first : pd.Timestamp
|
|
The beginning Timestamp of the range to be adjusted.
|
|
last : pd.Timestamp
|
|
The ending Timestamp of the range to be adjusted.
|
|
freq : pd.DateOffset
|
|
The dateoffset to which the Timestamps will be adjusted.
|
|
closed : {'right', 'left'}, default "left"
|
|
Which side of bin interval is closed.
|
|
origin : {'epoch', 'start', 'start_day'} or Timestamp, default 'start_day'
|
|
The timestamp on which to adjust the grouping. The timezone of origin must
|
|
match the timezone of the index.
|
|
If a timestamp is not used, these values are also supported:
|
|
|
|
- 'epoch': `origin` is 1970-01-01
|
|
- 'start': `origin` is the first value of the timeseries
|
|
- 'start_day': `origin` is the first day at midnight of the timeseries
|
|
offset : pd.Timedelta, default is None
|
|
An offset timedelta added to the origin.
|
|
|
|
Returns
|
|
-------
|
|
A tuple of length 2, containing the adjusted pd.Timestamp objects.
|
|
"""
|
|
if isinstance(freq, Tick):
|
|
index_tz = first.tz
|
|
if isinstance(origin, Timestamp) and (origin.tz is None) != (index_tz is None):
|
|
raise ValueError("The origin must have the same timezone as the index.")
|
|
elif origin == "epoch":
|
|
# set the epoch based on the timezone to have similar bins results when
|
|
# resampling on the same kind of indexes on different timezones
|
|
origin = Timestamp("1970-01-01", tz=index_tz)
|
|
|
|
if isinstance(freq, Day):
|
|
# _adjust_dates_anchored assumes 'D' means 24H, but first/last
|
|
# might contain a DST transition (23H, 24H, or 25H).
|
|
# So "pretend" the dates are naive when adjusting the endpoints
|
|
first = first.tz_localize(None)
|
|
last = last.tz_localize(None)
|
|
if isinstance(origin, Timestamp):
|
|
origin = origin.tz_localize(None)
|
|
|
|
first, last = _adjust_dates_anchored(
|
|
first, last, freq, closed=closed, origin=origin, offset=offset
|
|
)
|
|
if isinstance(freq, Day):
|
|
first = first.tz_localize(index_tz)
|
|
last = last.tz_localize(index_tz)
|
|
else:
|
|
first = first.normalize()
|
|
last = last.normalize()
|
|
|
|
if closed == "left":
|
|
first = Timestamp(freq.rollback(first))
|
|
else:
|
|
first = Timestamp(first - freq)
|
|
|
|
last = Timestamp(last + freq)
|
|
|
|
return first, last
|
|
|
|
|
|
def _get_period_range_edges(
|
|
first: Period,
|
|
last: Period,
|
|
freq: BaseOffset,
|
|
closed: Literal["right", "left"] = "left",
|
|
origin="start_day",
|
|
offset: Timedelta | None = None,
|
|
) -> tuple[Period, Period]:
|
|
"""
|
|
Adjust the provided `first` and `last` Periods to the respective Period of
|
|
the given offset that encompasses them.
|
|
|
|
Parameters
|
|
----------
|
|
first : pd.Period
|
|
The beginning Period of the range to be adjusted.
|
|
last : pd.Period
|
|
The ending Period of the range to be adjusted.
|
|
freq : pd.DateOffset
|
|
The freq to which the Periods will be adjusted.
|
|
closed : {'right', 'left'}, default "left"
|
|
Which side of bin interval is closed.
|
|
origin : {'epoch', 'start', 'start_day'}, Timestamp, default 'start_day'
|
|
The timestamp on which to adjust the grouping. The timezone of origin must
|
|
match the timezone of the index.
|
|
|
|
If a timestamp is not used, these values are also supported:
|
|
|
|
- 'epoch': `origin` is 1970-01-01
|
|
- 'start': `origin` is the first value of the timeseries
|
|
- 'start_day': `origin` is the first day at midnight of the timeseries
|
|
offset : pd.Timedelta, default is None
|
|
An offset timedelta added to the origin.
|
|
|
|
Returns
|
|
-------
|
|
A tuple of length 2, containing the adjusted pd.Period objects.
|
|
"""
|
|
if not all(isinstance(obj, Period) for obj in [first, last]):
|
|
raise TypeError("'first' and 'last' must be instances of type Period")
|
|
|
|
# GH 23882
|
|
first_ts = first.to_timestamp()
|
|
last_ts = last.to_timestamp()
|
|
adjust_first = not freq.is_on_offset(first_ts)
|
|
adjust_last = freq.is_on_offset(last_ts)
|
|
|
|
first_ts, last_ts = _get_timestamp_range_edges(
|
|
first_ts, last_ts, freq, closed=closed, origin=origin, offset=offset
|
|
)
|
|
|
|
first = (first_ts + int(adjust_first) * freq).to_period(freq)
|
|
last = (last_ts - int(adjust_last) * freq).to_period(freq)
|
|
return first, last
|
|
|
|
|
|
def _insert_nat_bin(
|
|
binner: PeriodIndex, bins: np.ndarray, labels: PeriodIndex, nat_count: int
|
|
) -> tuple[PeriodIndex, np.ndarray, PeriodIndex]:
|
|
# NaT handling as in pandas._lib.lib.generate_bins_dt64()
|
|
# shift bins by the number of NaT
|
|
assert nat_count > 0
|
|
bins += nat_count
|
|
bins = np.insert(bins, 0, nat_count)
|
|
|
|
# Incompatible types in assignment (expression has type "Index", variable
|
|
# has type "PeriodIndex")
|
|
binner = binner.insert(0, NaT) # type: ignore[assignment]
|
|
# Incompatible types in assignment (expression has type "Index", variable
|
|
# has type "PeriodIndex")
|
|
labels = labels.insert(0, NaT) # type: ignore[assignment]
|
|
return binner, bins, labels
|
|
|
|
|
|
def _adjust_dates_anchored(
|
|
first: Timestamp,
|
|
last: Timestamp,
|
|
freq: Tick,
|
|
closed: Literal["right", "left"] = "right",
|
|
origin="start_day",
|
|
offset: Timedelta | None = None,
|
|
) -> tuple[Timestamp, Timestamp]:
|
|
# First and last offsets should be calculated from the start day to fix an
|
|
# error cause by resampling across multiple days when a one day period is
|
|
# not a multiple of the frequency. See GH 8683
|
|
# To handle frequencies that are not multiple or divisible by a day we let
|
|
# the possibility to define a fixed origin timestamp. See GH 31809
|
|
origin_nanos = 0 # origin == "epoch"
|
|
if origin == "start_day":
|
|
origin_nanos = first.normalize().value
|
|
elif origin == "start":
|
|
origin_nanos = first.value
|
|
elif isinstance(origin, Timestamp):
|
|
origin_nanos = origin.value
|
|
elif origin in ["end", "end_day"]:
|
|
origin = last if origin == "end" else last.ceil("D")
|
|
sub_freq_times = (origin.value - first.value) // freq.nanos
|
|
if closed == "left":
|
|
sub_freq_times += 1
|
|
first = origin - sub_freq_times * freq
|
|
origin_nanos = first.value
|
|
origin_nanos += offset.value if offset else 0
|
|
|
|
# GH 10117 & GH 19375. If first and last contain timezone information,
|
|
# Perform the calculation in UTC in order to avoid localizing on an
|
|
# Ambiguous or Nonexistent time.
|
|
first_tzinfo = first.tzinfo
|
|
last_tzinfo = last.tzinfo
|
|
if first_tzinfo is not None:
|
|
first = first.tz_convert("UTC")
|
|
if last_tzinfo is not None:
|
|
last = last.tz_convert("UTC")
|
|
|
|
foffset = (first.value - origin_nanos) % freq.nanos
|
|
loffset = (last.value - origin_nanos) % freq.nanos
|
|
|
|
if closed == "right":
|
|
if foffset > 0:
|
|
# roll back
|
|
fresult_int = first.value - foffset
|
|
else:
|
|
fresult_int = first.value - freq.nanos
|
|
|
|
if loffset > 0:
|
|
# roll forward
|
|
lresult_int = last.value + (freq.nanos - loffset)
|
|
else:
|
|
# already the end of the road
|
|
lresult_int = last.value
|
|
else: # closed == 'left'
|
|
if foffset > 0:
|
|
fresult_int = first.value - foffset
|
|
else:
|
|
# start of the road
|
|
fresult_int = first.value
|
|
|
|
if loffset > 0:
|
|
# roll forward
|
|
lresult_int = last.value + (freq.nanos - loffset)
|
|
else:
|
|
lresult_int = last.value + freq.nanos
|
|
fresult = Timestamp(fresult_int)
|
|
lresult = Timestamp(lresult_int)
|
|
if first_tzinfo is not None:
|
|
fresult = fresult.tz_localize("UTC").tz_convert(first_tzinfo)
|
|
if last_tzinfo is not None:
|
|
lresult = lresult.tz_localize("UTC").tz_convert(last_tzinfo)
|
|
return fresult, lresult
|
|
|
|
|
|
def asfreq(
|
|
obj: NDFrameT,
|
|
freq,
|
|
method=None,
|
|
how=None,
|
|
normalize: bool = False,
|
|
fill_value=None,
|
|
) -> NDFrameT:
|
|
"""
|
|
Utility frequency conversion method for Series/DataFrame.
|
|
|
|
See :meth:`pandas.NDFrame.asfreq` for full documentation.
|
|
"""
|
|
if isinstance(obj.index, PeriodIndex):
|
|
if method is not None:
|
|
raise NotImplementedError("'method' argument is not supported")
|
|
|
|
if how is None:
|
|
how = "E"
|
|
|
|
new_obj = obj.copy()
|
|
new_obj.index = obj.index.asfreq(freq, how=how)
|
|
|
|
elif len(obj.index) == 0:
|
|
new_obj = obj.copy()
|
|
|
|
new_obj.index = _asfreq_compat(obj.index, freq)
|
|
else:
|
|
dti = date_range(obj.index.min(), obj.index.max(), freq=freq)
|
|
dti.name = obj.index.name
|
|
new_obj = obj.reindex(dti, method=method, fill_value=fill_value)
|
|
if normalize:
|
|
new_obj.index = new_obj.index.normalize()
|
|
|
|
return new_obj
|
|
|
|
|
|
def _asfreq_compat(index: DatetimeIndex | PeriodIndex | TimedeltaIndex, freq):
|
|
"""
|
|
Helper to mimic asfreq on (empty) DatetimeIndex and TimedeltaIndex.
|
|
|
|
Parameters
|
|
----------
|
|
index : PeriodIndex, DatetimeIndex, or TimedeltaIndex
|
|
freq : DateOffset
|
|
|
|
Returns
|
|
-------
|
|
same type as index
|
|
"""
|
|
if len(index) != 0:
|
|
# This should never be reached, always checked by the caller
|
|
raise ValueError(
|
|
"Can only set arbitrary freq for empty DatetimeIndex or TimedeltaIndex"
|
|
)
|
|
new_index: Index
|
|
if isinstance(index, PeriodIndex):
|
|
new_index = index.asfreq(freq=freq)
|
|
elif isinstance(index, DatetimeIndex):
|
|
new_index = DatetimeIndex([], dtype=index.dtype, freq=freq, name=index.name)
|
|
elif isinstance(index, TimedeltaIndex):
|
|
new_index = TimedeltaIndex([], dtype=index.dtype, freq=freq, name=index.name)
|
|
else: # pragma: no cover
|
|
raise TypeError(type(index))
|
|
return new_index
|