from __future__ import annotations import copy from datetime import timedelta from textwrap import dedent from typing import ( TYPE_CHECKING, Callable, Hashable, Literal, final, no_type_check, ) import warnings import numpy as np from pandas._libs import lib from pandas._libs.tslibs import ( BaseOffset, IncompatibleFrequency, NaT, Period, Timedelta, Timestamp, to_offset, ) from pandas._typing import ( IndexLabel, NDFrameT, T, TimedeltaConvertibleTypes, TimestampConvertibleTypes, npt, ) from pandas.compat.numpy import function as nv from pandas.errors import ( AbstractMethodError, DataError, ) from pandas.util._decorators import ( Appender, Substitution, deprecate_nonkeyword_arguments, doc, ) from pandas.util._exceptions import find_stack_level from pandas.core.dtypes.generic import ( ABCDataFrame, ABCSeries, ) import pandas.core.algorithms as algos from pandas.core.apply import ResamplerWindowApply from pandas.core.base import PandasObject import pandas.core.common as com from pandas.core.generic import ( NDFrame, _shared_docs, ) from pandas.core.groupby.generic import SeriesGroupBy from pandas.core.groupby.groupby import ( BaseGroupBy, GroupBy, _pipe_template, get_groupby, ) from pandas.core.groupby.grouper import Grouper from pandas.core.groupby.ops import BinGrouper from pandas.core.indexes.datetimes import ( DatetimeIndex, date_range, ) from pandas.core.indexes.period import ( PeriodIndex, period_range, ) from pandas.core.indexes.timedeltas import ( TimedeltaIndex, timedelta_range, ) from pandas.tseries.frequencies import ( is_subperiod, is_superperiod, ) from pandas.tseries.offsets import ( DateOffset, Day, Nano, Tick, ) if TYPE_CHECKING: from pandas import ( DataFrame, Index, Series, ) _shared_docs_kwargs: dict[str, str] = {} class Resampler(BaseGroupBy, PandasObject): """ Class for resampling datetimelike data, a groupby-like operation. See aggregate, transform, and apply functions on this object. It's easiest to use obj.resample(...) to use Resampler. Parameters ---------- obj : Series or DataFrame groupby : TimeGrouper axis : int, default 0 kind : str or None 'period', 'timestamp' to override default index treatment Returns ------- a Resampler of the appropriate type Notes ----- After resampling, see aggregate, apply, and transform functions. """ grouper: BinGrouper exclusions: frozenset[Hashable] = frozenset() # for SelectionMixin compat # to the groupby descriptor _attributes = [ "freq", "axis", "closed", "label", "convention", "loffset", "kind", "origin", "offset", ] def __init__( self, obj: DataFrame | Series, groupby: TimeGrouper, axis: int = 0, kind=None, *, group_keys: bool | lib.NoDefault = lib.no_default, selection=None, **kwargs, ) -> None: self.groupby = groupby self.keys = None self.sort = True self.axis = axis self.kind = kind self.squeeze = False self.group_keys = group_keys self.as_index = True self.groupby._set_grouper(self._convert_obj(obj), sort=True) self.binner, self.grouper = self._get_binner() self._selection = selection if self.groupby.key is not None: self.exclusions = frozenset([self.groupby.key]) else: self.exclusions = frozenset() @final def _shallow_copy(self, obj, **kwargs): """ return a new object with the replacement attributes """ if isinstance(obj, self._constructor): obj = obj.obj for attr in self._attributes: if attr not in kwargs: kwargs[attr] = getattr(self, attr) return self._constructor(obj, **kwargs) def __str__(self) -> str: """ Provide a nice str repr of our rolling object. """ attrs = ( f"{k}={getattr(self.groupby, k)}" for k in self._attributes if getattr(self.groupby, k, None) is not None ) return f"{type(self).__name__} [{', '.join(attrs)}]" def __getattr__(self, attr: str): if attr in self._internal_names_set: return object.__getattribute__(self, attr) if attr in self._attributes: return getattr(self.groupby, attr) if attr in self.obj: return self[attr] return object.__getattribute__(self, attr) # error: Signature of "obj" incompatible with supertype "BaseGroupBy" @property def obj(self) -> NDFrame: # type: ignore[override] # error: Incompatible return value type (got "Optional[Any]", # expected "NDFrameT") return self.groupby.obj # type: ignore[return-value] @property def ax(self): # we can infer that this is a PeriodIndex/DatetimeIndex/TimedeltaIndex, # but skipping annotating bc the overrides overwhelming return self.groupby.ax @property def _from_selection(self) -> bool: """ Is the resampling from a DataFrame column or MultiIndex level. """ # upsampling and PeriodIndex resampling do not work # with selection, this state used to catch and raise an error return self.groupby is not None and ( self.groupby.key is not None or self.groupby.level is not None ) def _convert_obj(self, obj: NDFrameT) -> NDFrameT: """ Provide any conversions for the object in order to correctly handle. Parameters ---------- obj : Series or DataFrame Returns ------- Series or DataFrame """ return obj._consolidate() def _get_binner_for_time(self): raise AbstractMethodError(self) @final def _get_binner(self): """ Create the BinGrouper, assume that self.set_grouper(obj) has already been called. """ binner, bins, binlabels = self._get_binner_for_time() assert len(bins) == len(binlabels) bin_grouper = BinGrouper(bins, binlabels, indexer=self.groupby.indexer) return binner, bin_grouper @Substitution( klass="Resampler", examples=""" >>> df = pd.DataFrame({'A': [1, 2, 3, 4]}, ... index=pd.date_range('2012-08-02', periods=4)) >>> df A 2012-08-02 1 2012-08-03 2 2012-08-04 3 2012-08-05 4 To get the difference between each 2-day period's maximum and minimum value in one pass, you can do >>> df.resample('2D').pipe(lambda x: x.max() - x.min()) A 2012-08-02 1 2012-08-04 1""", ) @Appender(_pipe_template) def pipe( self, func: Callable[..., T] | tuple[Callable[..., T], str], *args, **kwargs, ) -> T: return super().pipe(func, *args, **kwargs) _agg_see_also_doc = dedent( """ See Also -------- DataFrame.groupby.aggregate : Aggregate using callable, string, dict, or list of string/callables. DataFrame.resample.transform : Transforms the Series on each group based on the given function. DataFrame.aggregate: Aggregate using one or more operations over the specified axis. """ ) _agg_examples_doc = dedent( """ Examples -------- >>> s = pd.Series([1, 2, 3, 4, 5], ... index=pd.date_range('20130101', periods=5, freq='s')) >>> s 2013-01-01 00:00:00 1 2013-01-01 00:00:01 2 2013-01-01 00:00:02 3 2013-01-01 00:00:03 4 2013-01-01 00:00:04 5 Freq: S, dtype: int64 >>> r = s.resample('2s') >>> r.agg(np.sum) 2013-01-01 00:00:00 3 2013-01-01 00:00:02 7 2013-01-01 00:00:04 5 Freq: 2S, dtype: int64 >>> r.agg(['sum', 'mean', 'max']) sum mean max 2013-01-01 00:00:00 3 1.5 2 2013-01-01 00:00:02 7 3.5 4 2013-01-01 00:00:04 5 5.0 5 >>> r.agg({'result': lambda x: x.mean() / x.std(), ... 'total': np.sum}) result total 2013-01-01 00:00:00 2.121320 3 2013-01-01 00:00:02 4.949747 7 2013-01-01 00:00:04 NaN 5 >>> r.agg(average="mean", total="sum") average total 2013-01-01 00:00:00 1.5 3 2013-01-01 00:00:02 3.5 7 2013-01-01 00:00:04 5.0 5 """ ) @doc( _shared_docs["aggregate"], see_also=_agg_see_also_doc, examples=_agg_examples_doc, klass="DataFrame", axis="", ) def aggregate(self, func=None, *args, **kwargs): result = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg() if result is None: how = func result = self._groupby_and_aggregate(how, *args, **kwargs) result = self._apply_loffset(result) return result agg = aggregate apply = aggregate def transform(self, arg, *args, **kwargs): """ Call function producing a like-indexed Series on each group. Return a Series with the transformed values. Parameters ---------- arg : function To apply to each group. Should return a Series with the same index. Returns ------- transformed : Series 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 >>> resampled = s.resample('15min') >>> resampled.transform(lambda x: (x - x.mean()) / x.std()) 2018-01-01 00:00:00 NaN 2018-01-01 01:00:00 NaN Freq: H, dtype: float64 """ return self._selected_obj.groupby(self.groupby).transform(arg, *args, **kwargs) def _downsample(self, f, **kwargs): raise AbstractMethodError(self) def _upsample(self, f, limit=None, fill_value=None): raise AbstractMethodError(self) def _gotitem(self, key, ndim: int, 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 """ grouper = self.grouper if subset is None: subset = self.obj grouped = get_groupby( subset, by=None, grouper=grouper, axis=self.axis, group_keys=self.group_keys ) # try the key selection try: return grouped[key] except KeyError: return grouped def _groupby_and_aggregate(self, how, *args, **kwargs): """ Re-evaluate the obj with a groupby aggregation. """ grouper = self.grouper if self._selected_obj.ndim == 1: obj = self._selected_obj else: # Excludes `on` column when provided obj = self._obj_with_exclusions grouped = get_groupby( obj, by=None, grouper=grouper, axis=self.axis, group_keys=self.group_keys ) try: if isinstance(obj, ABCDataFrame) and callable(how): # Check if the function is reducing or not. result = grouped._aggregate_item_by_item(how, *args, **kwargs) else: result = grouped.aggregate(how, *args, **kwargs) except DataError: # got TypeErrors on aggregation result = grouped.apply(how, *args, **kwargs) except (AttributeError, KeyError): # we have a non-reducing function; try to evaluate # alternatively we want to evaluate only a column of the input # test_apply_to_one_column_of_df the function being applied references # a DataFrame column, but aggregate_item_by_item operates column-wise # on Series, raising AttributeError or KeyError # (depending on whether the column lookup uses getattr/__getitem__) result = grouped.apply(how, *args, **kwargs) except ValueError as err: if "Must produce aggregated value" in str(err): # raised in _aggregate_named # see test_apply_without_aggregation, test_apply_with_mutated_index pass else: raise # we have a non-reducing function # try to evaluate result = grouped.apply(how, *args, **kwargs) result = self._apply_loffset(result) return self._wrap_result(result) def _apply_loffset(self, result): """ If loffset is set, offset the result index. This is NOT an idempotent routine, it will be applied exactly once to the result. Parameters ---------- result : Series or DataFrame the result of resample """ # error: Cannot determine type of 'loffset' needs_offset = ( isinstance( self.loffset, # type: ignore[has-type] (DateOffset, timedelta, np.timedelta64), ) and isinstance(result.index, DatetimeIndex) and len(result.index) > 0 ) if needs_offset: # error: Cannot determine type of 'loffset' result.index = result.index + self.loffset # type: ignore[has-type] self.loffset = None return result def _get_resampler_for_grouping(self, groupby, key=None): """ Return the correct class for resampling with groupby. """ return self._resampler_for_grouping(self, groupby=groupby, key=key) def _wrap_result(self, result): """ Potentially wrap any results. """ if isinstance(result, ABCSeries) and self._selection is not None: result.name = self._selection if isinstance(result, ABCSeries) and result.empty: obj = self.obj # When index is all NaT, result is empty but index is not result.index = _asfreq_compat(obj.index[:0], freq=self.freq) result.name = getattr(obj, "name", None) return result def ffill(self, limit=None): """ Forward fill the values. Parameters ---------- limit : int, optional Limit of how many values to fill. Returns ------- An upsampled Series. See Also -------- Series.fillna: Fill NA/NaN values using the specified method. DataFrame.fillna: Fill NA/NaN values using the specified method. """ return self._upsample("ffill", limit=limit) def pad(self, limit=None): """ Forward fill the values. .. deprecated:: 1.4 Use ffill instead. Parameters ---------- limit : int, optional Limit of how many values to fill. Returns ------- An upsampled Series. """ warnings.warn( "pad is deprecated and will be removed in a future version. " "Use ffill instead.", FutureWarning, stacklevel=find_stack_level(), ) return self.ffill(limit=limit) def nearest(self, limit=None): """ Resample by using the nearest value. When resampling data, missing values may appear (e.g., when the resampling frequency is higher than the original frequency). The `nearest` method will replace ``NaN`` values that appeared in 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