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keshe/.venv/Lib/site-packages/pandas/core/resample.py

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from __future__ import annotations
import copy
from textwrap import dedent
from typing import (
TYPE_CHECKING,
Callable,
Literal,
cast,
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._libs.tslibs.dtypes import freq_to_period_freqstr
from pandas._typing import NDFrameT
from pandas.compat.numpy import function as nv
from pandas.errors import AbstractMethodError
from pandas.util._decorators import (
Appender,
Substitution,
doc,
)
from pandas.util._exceptions import (
find_stack_level,
rewrite_warning,
)
from pandas.core.dtypes.dtypes import ArrowDtype
from pandas.core.dtypes.generic import (
ABCDataFrame,
ABCSeries,
)
import pandas.core.algorithms as algos
from pandas.core.apply import (
ResamplerWindowApply,
warn_alias_replacement,
)
from pandas.core.arrays import ArrowExtensionArray
from pandas.core.base import (
PandasObject,
SelectionMixin,
)
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,
_apply_groupings_depr,
_pipe_template,
get_groupby,
)
from pandas.core.groupby.grouper import Grouper
from pandas.core.groupby.ops import BinGrouper
from pandas.core.indexes.api import MultiIndex
from pandas.core.indexes.base import Index
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 (
Day,
Tick,
)
if TYPE_CHECKING:
from collections.abc import Hashable
from pandas._typing import (
AnyArrayLike,
Axis,
AxisInt,
Frequency,
IndexLabel,
InterpolateOptions,
T,
TimedeltaConvertibleTypes,
TimeGrouperOrigin,
TimestampConvertibleTypes,
npt,
)
from pandas import (
DataFrame,
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
_timegrouper: TimeGrouper
binner: DatetimeIndex | TimedeltaIndex | PeriodIndex # depends on subclass
exclusions: frozenset[Hashable] = frozenset() # for SelectionMixin compat
_internal_names_set = set({"obj", "ax", "_indexer"})
# to the groupby descriptor
_attributes = [
"freq",
"axis",
"closed",
"label",
"convention",
"kind",
"origin",
"offset",
]
def __init__(
self,
obj: NDFrame,
timegrouper: TimeGrouper,
axis: Axis = 0,
kind=None,
*,
gpr_index: Index,
group_keys: bool = False,
selection=None,
include_groups: bool = True,
) -> None:
self._timegrouper = timegrouper
self.keys = None
self.sort = True
self.axis = obj._get_axis_number(axis)
self.kind = kind
self.group_keys = group_keys
self.as_index = True
self.include_groups = include_groups
self.obj, self.ax, self._indexer = self._timegrouper._set_grouper(
self._convert_obj(obj), sort=True, gpr_index=gpr_index
)
self.binner, self._grouper = self._get_binner()
self._selection = selection
if self._timegrouper.key is not None:
self.exclusions = frozenset([self._timegrouper.key])
else:
self.exclusions = frozenset()
@final
def __str__(self) -> str:
"""
Provide a nice str repr of our rolling object.
"""
attrs = (
f"{k}={getattr(self._timegrouper, k)}"
for k in self._attributes
if getattr(self._timegrouper, k, None) is not None
)
return f"{type(self).__name__} [{', '.join(attrs)}]"
@final
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._timegrouper, attr)
if attr in self.obj:
return self[attr]
return object.__getattribute__(self, attr)
@final
@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._timegrouper is not None and (
self._timegrouper.key is not None or self._timegrouper.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._indexer)
return binner, bin_grouper
@final
@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("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': "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
"""
)
@final
@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)
return result
agg = aggregate
apply = aggregate
@final
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
-------
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._timegrouper).transform(
arg, *args, **kwargs
)
def _downsample(self, f, **kwargs):
raise AbstractMethodError(self)
def _upsample(self, f, limit: int | None = 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
if key is not None:
subset = subset[key]
else:
# reached via Apply.agg_dict_like with selection=None and ndim=1
assert subset.ndim == 1
if ndim == 1:
assert subset.ndim == 1
grouped = get_groupby(
subset, by=None, grouper=grouper, axis=self.axis, group_keys=self.group_keys
)
return grouped
def _groupby_and_aggregate(self, how, *args, **kwargs):
"""
Re-evaluate the obj with a groupby aggregation.
"""
grouper = self._grouper
# 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 callable(how):
# TODO: test_resample_apply_with_additional_args fails if we go
# through the non-lambda path, not clear that it should.
func = lambda x: how(x, *args, **kwargs)
result = grouped.aggregate(func)
else:
result = grouped.aggregate(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 = _apply(
grouped, how, *args, include_groups=self.include_groups, **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 = _apply(
grouped, how, *args, include_groups=self.include_groups, **kwargs
)
return self._wrap_result(result)
@final
def _get_resampler_for_grouping(
self, groupby: GroupBy, key, include_groups: bool = True
):
"""
Return the correct class for resampling with groupby.
"""
return self._resampler_for_grouping(
groupby=groupby, key=key, parent=self, include_groups=include_groups
)
def _wrap_result(self, result):
"""
Potentially wrap any results.
"""
# GH 47705
obj = self.obj
if (
isinstance(result, ABCDataFrame)
and len(result) == 0
and not isinstance(result.index, PeriodIndex)
):
result = result.set_index(
_asfreq_compat(obj.index[:0], freq=self.freq), append=True
)
if isinstance(result, ABCSeries) and self._selection is not None:
result.name = self._selection
if isinstance(result, ABCSeries) and result.empty:
# 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)
if self._timegrouper._arrow_dtype is not None:
result.index = result.index.astype(self._timegrouper._arrow_dtype)
return result
@final
def ffill(self, limit: int | None = 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.
Examples
--------
Here we only create a ``Series``.
>>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
... ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
>>> ser
2023-01-01 1
2023-01-15 2
2023-02-01 3
2023-02-15 4
dtype: int64
Example for ``ffill`` with downsampling (we have fewer dates after resampling):
>>> ser.resample('MS').ffill()
2023-01-01 1
2023-02-01 3
Freq: MS, dtype: int64
Example for ``ffill`` with upsampling (fill the new dates with
the previous value):
>>> ser.resample('W').ffill()
2023-01-01 1
2023-01-08 1
2023-01-15 2
2023-01-22 2
2023-01-29 2
2023-02-05 3
2023-02-12 3
2023-02-19 4
Freq: W-SUN, dtype: int64
With upsampling and limiting (only fill the first new date with the
previous value):
>>> ser.resample('W').ffill(limit=1)
2023-01-01 1.0
2023-01-08 1.0
2023-01-15 2.0
2023-01-22 2.0
2023-01-29 NaN
2023-02-05 3.0
2023-02-12 NaN
2023-02-19 4.0
Freq: W-SUN, dtype: float64
"""
return self._upsample("ffill", limit=limit)
@final
def nearest(self, limit: int | None = 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: 15min, 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: 15min, dtype: float64
"""
return self._upsample("nearest", limit=limit)
@final
def bfill(self, limit: int | None = 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: 30min, 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: 15min, 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)
@final
def fillna(self, method, limit: int | None = 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: 30min, 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: 30min, 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: 15min, 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: 30min, 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: 30min, 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: 30min, 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: 30min, 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: 30min, 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
"""
warnings.warn(
f"{type(self).__name__}.fillna is deprecated and will be removed "
"in a future version. Use obj.ffill(), obj.bfill(), "
"or obj.nearest() instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
return self._upsample(method, limit=limit)
@final
def interpolate(
self,
method: InterpolateOptions = "linear",
*,
axis: Axis = 0,
limit: int | None = None,
inplace: bool = False,
limit_direction: Literal["forward", "backward", "both"] = "forward",
limit_area=None,
downcast=lib.no_default,
**kwargs,
):
"""
Interpolate values between target timestamps according to different methods.
The original index is first reindexed to target timestamps
(see :meth:`core.resample.Resampler.asfreq`),
then the interpolation of ``NaN`` values via :meth:`DataFrame.interpolate`
happens.
Parameters
----------
method : str, default 'linear'
Interpolation technique to use. One of:
* 'linear': Ignore the index and treat the values as equally
spaced. This is the only method supported on MultiIndexes.
* 'time': Works on daily and higher resolution data to interpolate
given length of interval.
* 'index', 'values': use the actual numerical values of the index.
* 'pad': Fill in NaNs using existing values.
* 'nearest', 'zero', 'slinear', 'quadratic', 'cubic',
'barycentric', 'polynomial': Passed to
`scipy.interpolate.interp1d`, whereas 'spline' is passed to
`scipy.interpolate.UnivariateSpline`. These methods use the numerical
values of the index. Both 'polynomial' and 'spline' require that
you also specify an `order` (int), e.g.
``df.interpolate(method='polynomial', order=5)``. Note that,
`slinear` method in Pandas refers to the Scipy first order `spline`
instead of Pandas first order `spline`.
* 'krogh', 'piecewise_polynomial', 'spline', 'pchip', 'akima',
'cubicspline': Wrappers around the SciPy interpolation methods of
similar names. See `Notes`.
* 'from_derivatives': Refers to
`scipy.interpolate.BPoly.from_derivatives`.
axis : {{0 or 'index', 1 or 'columns', None}}, default None
Axis to interpolate along. For `Series` this parameter is unused
and defaults to 0.
limit : int, optional
Maximum number of consecutive NaNs to fill. Must be greater than
0.
inplace : bool, default False
Update the data in place if possible.
limit_direction : {{'forward', 'backward', 'both'}}, Optional
Consecutive NaNs will be filled in this direction.
If limit is specified:
* If 'method' is 'pad' or 'ffill', 'limit_direction' must be 'forward'.
* If 'method' is 'backfill' or 'bfill', 'limit_direction' must be
'backwards'.
If 'limit' is not specified:
* If 'method' is 'backfill' or 'bfill', the default is 'backward'
* else the default is 'forward'
raises ValueError if `limit_direction` is 'forward' or 'both' and
method is 'backfill' or 'bfill'.
raises ValueError if `limit_direction` is 'backward' or 'both' and
method is 'pad' or 'ffill'.
limit_area : {{`None`, 'inside', 'outside'}}, default None
If limit is specified, consecutive NaNs will be filled with this
restriction.
* ``None``: No fill restriction.
* 'inside': Only fill NaNs surrounded by valid values
(interpolate).
* 'outside': Only fill NaNs outside valid values (extrapolate).
downcast : optional, 'infer' or None, defaults to None
Downcast dtypes if possible.
.. deprecated:: 2.1.0
``**kwargs`` : optional
Keyword arguments to pass on to the interpolating function.
Returns
-------
DataFrame or Series
Interpolated values at the specified freq.
See Also
--------
core.resample.Resampler.asfreq: Return the values at the new freq,
essentially a reindex.
DataFrame.interpolate: Fill NaN values using an interpolation method.
Notes
-----
For high-frequent or non-equidistant time-series with timestamps
the reindexing followed by interpolation may lead to information loss
as shown in the last example.
Examples
--------
>>> start = "2023-03-01T07:00:00"
>>> timesteps = pd.date_range(start, periods=5, freq="s")
>>> series = pd.Series(data=[1, -1, 2, 1, 3], index=timesteps)
>>> series
2023-03-01 07:00:00 1
2023-03-01 07:00:01 -1
2023-03-01 07:00:02 2
2023-03-01 07:00:03 1
2023-03-01 07:00:04 3
Freq: s, dtype: int64
Upsample the dataframe to 0.5Hz by providing the period time of 2s.
>>> series.resample("2s").interpolate("linear")
2023-03-01 07:00:00 1
2023-03-01 07:00:02 2
2023-03-01 07:00:04 3
Freq: 2s, dtype: int64
Downsample the dataframe to 2Hz by providing the period time of 500ms.
>>> series.resample("500ms").interpolate("linear")
2023-03-01 07:00:00.000 1.0
2023-03-01 07:00:00.500 0.0
2023-03-01 07:00:01.000 -1.0
2023-03-01 07:00:01.500 0.5
2023-03-01 07:00:02.000 2.0
2023-03-01 07:00:02.500 1.5
2023-03-01 07:00:03.000 1.0
2023-03-01 07:00:03.500 2.0
2023-03-01 07:00:04.000 3.0
Freq: 500ms, dtype: float64
Internal reindexing with ``asfreq()`` prior to interpolation leads to
an interpolated timeseries on the basis the reindexed timestamps (anchors).
Since not all datapoints from original series become anchors,
it can lead to misleading interpolation results as in the following example:
>>> series.resample("400ms").interpolate("linear")
2023-03-01 07:00:00.000 1.0
2023-03-01 07:00:00.400 1.2
2023-03-01 07:00:00.800 1.4
2023-03-01 07:00:01.200 1.6
2023-03-01 07:00:01.600 1.8
2023-03-01 07:00:02.000 2.0
2023-03-01 07:00:02.400 2.2
2023-03-01 07:00:02.800 2.4
2023-03-01 07:00:03.200 2.6
2023-03-01 07:00:03.600 2.8
2023-03-01 07:00:04.000 3.0
Freq: 400ms, dtype: float64
Note that the series erroneously increases between two anchors
``07:00:00`` and ``07:00:02``.
"""
assert downcast is lib.no_default # just checking coverage
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,
)
@final
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.
Examples
--------
>>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
... ['2023-01-01', '2023-01-31', '2023-02-01', '2023-02-28']))
>>> ser
2023-01-01 1
2023-01-31 2
2023-02-01 3
2023-02-28 4
dtype: int64
>>> ser.resample('MS').asfreq()
2023-01-01 1
2023-02-01 3
Freq: MS, dtype: int64
"""
return self._upsample("asfreq", fill_value=fill_value)
@final
def sum(
self,
numeric_only: bool = False,
min_count: int = 0,
*args,
**kwargs,
):
"""
Compute sum of group values.
Parameters
----------
numeric_only : bool, default False
Include only float, int, boolean columns.
.. versionchanged:: 2.0.0
numeric_only no longer accepts ``None``.
min_count : int, default 0
The required number of valid values to perform the operation. If fewer
than ``min_count`` non-NA values are present the result will be NA.
Returns
-------
Series or DataFrame
Computed sum of values within each group.
Examples
--------
>>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
... ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
>>> ser
2023-01-01 1
2023-01-15 2
2023-02-01 3
2023-02-15 4
dtype: int64
>>> ser.resample('MS').sum()
2023-01-01 3
2023-02-01 7
Freq: MS, dtype: int64
"""
maybe_warn_args_and_kwargs(type(self), "sum", args, kwargs)
nv.validate_resampler_func("sum", args, kwargs)
return self._downsample("sum", numeric_only=numeric_only, min_count=min_count)
@final
def prod(
self,
numeric_only: bool = False,
min_count: int = 0,
*args,
**kwargs,
):
"""
Compute prod of group values.
Parameters
----------
numeric_only : bool, default False
Include only float, int, boolean columns.
.. versionchanged:: 2.0.0
numeric_only no longer accepts ``None``.
min_count : int, default 0
The required number of valid values to perform the operation. If fewer
than ``min_count`` non-NA values are present the result will be NA.
Returns
-------
Series or DataFrame
Computed prod of values within each group.
Examples
--------
>>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
... ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
>>> ser
2023-01-01 1
2023-01-15 2
2023-02-01 3
2023-02-15 4
dtype: int64
>>> ser.resample('MS').prod()
2023-01-01 2
2023-02-01 12
Freq: MS, dtype: int64
"""
maybe_warn_args_and_kwargs(type(self), "prod", args, kwargs)
nv.validate_resampler_func("prod", args, kwargs)
return self._downsample("prod", numeric_only=numeric_only, min_count=min_count)
@final
def min(
self,
numeric_only: bool = False,
min_count: int = 0,
*args,
**kwargs,
):
"""
Compute min value of group.
Returns
-------
Series or DataFrame
Examples
--------
>>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
... ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
>>> ser
2023-01-01 1
2023-01-15 2
2023-02-01 3
2023-02-15 4
dtype: int64
>>> ser.resample('MS').min()
2023-01-01 1
2023-02-01 3
Freq: MS, dtype: int64
"""
maybe_warn_args_and_kwargs(type(self), "min", args, kwargs)
nv.validate_resampler_func("min", args, kwargs)
return self._downsample("min", numeric_only=numeric_only, min_count=min_count)
@final
def max(
self,
numeric_only: bool = False,
min_count: int = 0,
*args,
**kwargs,
):
"""
Compute max value of group.
Returns
-------
Series or DataFrame
Examples
--------
>>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
... ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
>>> ser
2023-01-01 1
2023-01-15 2
2023-02-01 3
2023-02-15 4
dtype: int64
>>> ser.resample('MS').max()
2023-01-01 2
2023-02-01 4
Freq: MS, dtype: int64
"""
maybe_warn_args_and_kwargs(type(self), "max", args, kwargs)
nv.validate_resampler_func("max", args, kwargs)
return self._downsample("max", numeric_only=numeric_only, min_count=min_count)
@final
@doc(GroupBy.first)
def first(
self,
numeric_only: bool = False,
min_count: int = 0,
skipna: bool = True,
*args,
**kwargs,
):
maybe_warn_args_and_kwargs(type(self), "first", args, kwargs)
nv.validate_resampler_func("first", args, kwargs)
return self._downsample(
"first", numeric_only=numeric_only, min_count=min_count, skipna=skipna
)
@final
@doc(GroupBy.last)
def last(
self,
numeric_only: bool = False,
min_count: int = 0,
skipna: bool = True,
*args,
**kwargs,
):
maybe_warn_args_and_kwargs(type(self), "last", args, kwargs)
nv.validate_resampler_func("last", args, kwargs)
return self._downsample(
"last", numeric_only=numeric_only, min_count=min_count, skipna=skipna
)
@final
@doc(GroupBy.median)
def median(self, numeric_only: bool = False, *args, **kwargs):
maybe_warn_args_and_kwargs(type(self), "median", args, kwargs)
nv.validate_resampler_func("median", args, kwargs)
return self._downsample("median", numeric_only=numeric_only)
@final
def mean(
self,
numeric_only: bool = False,
*args,
**kwargs,
):
"""
Compute mean of groups, excluding missing values.
Parameters
----------
numeric_only : bool, default False
Include only `float`, `int` or `boolean` data.
.. versionchanged:: 2.0.0
numeric_only now defaults to ``False``.
Returns
-------
DataFrame or Series
Mean of values within each group.
Examples
--------
>>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
... ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
>>> ser
2023-01-01 1
2023-01-15 2
2023-02-01 3
2023-02-15 4
dtype: int64
>>> ser.resample('MS').mean()
2023-01-01 1.5
2023-02-01 3.5
Freq: MS, dtype: float64
"""
maybe_warn_args_and_kwargs(type(self), "mean", args, kwargs)
nv.validate_resampler_func("mean", args, kwargs)
return self._downsample("mean", numeric_only=numeric_only)
@final
def std(
self,
ddof: int = 1,
numeric_only: bool = False,
*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
.. versionchanged:: 2.0.0
numeric_only now defaults to ``False``.
Returns
-------
DataFrame or Series
Standard deviation of values within each group.
Examples
--------
>>> ser = pd.Series([1, 3, 2, 4, 3, 8],
... index=pd.DatetimeIndex(['2023-01-01',
... '2023-01-10',
... '2023-01-15',
... '2023-02-01',
... '2023-02-10',
... '2023-02-15']))
>>> ser.resample('MS').std()
2023-01-01 1.000000
2023-02-01 2.645751
Freq: MS, dtype: float64
"""
maybe_warn_args_and_kwargs(type(self), "std", args, kwargs)
nv.validate_resampler_func("std", args, kwargs)
return self._downsample("std", ddof=ddof, numeric_only=numeric_only)
@final
def var(
self,
ddof: int = 1,
numeric_only: bool = False,
*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
.. versionchanged:: 2.0.0
numeric_only now defaults to ``False``.
Returns
-------
DataFrame or Series
Variance of values within each group.
Examples
--------
>>> ser = pd.Series([1, 3, 2, 4, 3, 8],
... index=pd.DatetimeIndex(['2023-01-01',
... '2023-01-10',
... '2023-01-15',
... '2023-02-01',
... '2023-02-10',
... '2023-02-15']))
>>> ser.resample('MS').var()
2023-01-01 1.0
2023-02-01 7.0
Freq: MS, dtype: float64
>>> ser.resample('MS').var(ddof=0)
2023-01-01 0.666667
2023-02-01 4.666667
Freq: MS, dtype: float64
"""
maybe_warn_args_and_kwargs(type(self), "var", args, kwargs)
nv.validate_resampler_func("var", args, kwargs)
return self._downsample("var", ddof=ddof, numeric_only=numeric_only)
@final
@doc(GroupBy.sem)
def sem(
self,
ddof: int = 1,
numeric_only: bool = False,
*args,
**kwargs,
):
maybe_warn_args_and_kwargs(type(self), "sem", args, kwargs)
nv.validate_resampler_func("sem", args, kwargs)
return self._downsample("sem", ddof=ddof, numeric_only=numeric_only)
@final
@doc(GroupBy.ohlc)
def ohlc(
self,
*args,
**kwargs,
):
maybe_warn_args_and_kwargs(type(self), "ohlc", args, kwargs)
nv.validate_resampler_func("ohlc", args, kwargs)
ax = self.ax
obj = self._obj_with_exclusions
if len(ax) == 0:
# GH#42902
obj = obj.copy()
obj.index = _asfreq_compat(obj.index, self.freq)
if obj.ndim == 1:
obj = obj.to_frame()
obj = obj.reindex(["open", "high", "low", "close"], axis=1)
else:
mi = MultiIndex.from_product(
[obj.columns, ["open", "high", "low", "close"]]
)
obj = obj.reindex(mi, axis=1)
return obj
return self._downsample("ohlc")
@final
@doc(SeriesGroupBy.nunique)
def nunique(
self,
*args,
**kwargs,
):
maybe_warn_args_and_kwargs(type(self), "nunique", args, kwargs)
nv.validate_resampler_func("nunique", args, kwargs)
return self._downsample("nunique")
@final
@doc(GroupBy.size)
def size(self):
result = self._downsample("size")
# If the result is a non-empty DataFrame we stack to get a Series
# GH 46826
if isinstance(result, ABCDataFrame) and not result.empty:
result = result.stack(future_stack=True)
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
@final
@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
@final
def quantile(self, q: float | list[float] | AnyArrayLike = 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 columns are groupby columns,
and the values are its quantiles.
Examples
--------
>>> ser = pd.Series([1, 3, 2, 4, 3, 8],
... index=pd.DatetimeIndex(['2023-01-01',
... '2023-01-10',
... '2023-01-15',
... '2023-02-01',
... '2023-02-10',
... '2023-02-15']))
>>> ser.resample('MS').quantile()
2023-01-01 2.0
2023-02-01 4.0
Freq: MS, dtype: float64
>>> ser.resample('MS').quantile(.25)
2023-01-01 1.5
2023-02-01 3.5
Freq: MS, dtype: float64
"""
return self._downsample("quantile", q=q, **kwargs)
class _GroupByMixin(PandasObject, SelectionMixin):
"""
Provide the groupby facilities.
"""
_attributes: list[str] # in practice the same as Resampler._attributes
_selection: IndexLabel | None = None
_groupby: GroupBy
_timegrouper: TimeGrouper
def __init__(
self,
*,
parent: Resampler,
groupby: GroupBy,
key=None,
selection: IndexLabel | None = None,
include_groups: bool = False,
) -> None:
# reached via ._gotitem and _get_resampler_for_grouping
assert isinstance(groupby, GroupBy), type(groupby)
# parent is always a Resampler, sometimes a _GroupByMixin
assert isinstance(parent, Resampler), type(parent)
# initialize our GroupByMixin object with
# the resampler attributes
for attr in self._attributes:
setattr(self, attr, getattr(parent, attr))
self._selection = selection
self.binner = parent.binner
self.key = key
self._groupby = groupby
self._timegrouper = copy.copy(parent._timegrouper)
self.ax = parent.ax
self.obj = parent.obj
self.include_groups = include_groups
@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._resampler_cls(x, timegrouper=self._timegrouper, gpr_index=self.ax)
if isinstance(f, str):
return getattr(x, f)(**kwargs)
return x.apply(f, *args, **kwargs)
result = _apply(self._groupby, func, include_groups=self.include_groups)
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:
subset = self.obj
if key is not None:
subset = subset[key]
else:
# reached via Apply.agg_dict_like with selection=None, ndim=1
assert subset.ndim == 1
# Try to select from a DataFrame, falling back to a Series
try:
if isinstance(key, list) and self.key not in key and self.key is not None:
key.append(self.key)
groupby = self._groupby[key]
except IndexError:
groupby = self._groupby
selection = self._infer_selection(key, subset)
new_rs = type(self)(
groupby=groupby,
parent=cast(Resampler, self),
selection=selection,
)
return new_rs
class DatetimeIndexResampler(Resampler):
ax: DatetimeIndex
@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._timegrouper._get_time_period_bins(self.ax)
return self._timegrouper._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
"""
orig_how = how
how = com.get_cython_func(how) or how
if orig_how != how:
warn_alias_replacement(self, orig_how, how)
ax = self.ax
# 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
if self.axis == 0:
result = obj.groupby(self._grouper).aggregate(how, **kwargs)
else:
# test_resample_axis1
result = obj.T.groupby(self._grouper).aggregate(how, **kwargs).T
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: int | None = 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:
if method == "asfreq":
method = None
result = obj.reindex(
res_index, method=method, limit=limit, fill_value=fill_value
)
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):
if isinstance(result.index, MultiIndex):
# GH 24103 - e.g. groupby resample
if not isinstance(result.index.levels[-1], PeriodIndex):
new_level = result.index.levels[-1].to_period(self.freq)
result.index = result.index.set_levels(new_level, level=-1)
else:
result.index = result.index.to_period(self.freq)
return result
# error: Definition of "ax" in base class "_GroupByMixin" is incompatible
# with definition in base class "DatetimeIndexResampler"
class DatetimeIndexResamplerGroupby( # type: ignore[misc]
_GroupByMixin, DatetimeIndexResampler
):
"""
Provides a resample of a groupby implementation
"""
@property
def _resampler_cls(self):
return DatetimeIndexResampler
class PeriodIndexResampler(DatetimeIndexResampler):
# error: Incompatible types in assignment (expression has type "PeriodIndex", base
# class "DatetimeIndexResampler" defined the type as "DatetimeIndex")
ax: PeriodIndex # type: ignore[assignment]
@property
def _resampler_for_grouping(self):
warnings.warn(
"Resampling a groupby with a PeriodIndex is deprecated. "
"Cast to DatetimeIndex before resampling instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
return PeriodIndexResamplerGroupby
def _get_binner_for_time(self):
if self.kind == "timestamp":
return super()._get_binner_for_time()
return self._timegrouper._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)
# 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)
orig_how = how
how = com.get_cython_func(how) or how
if orig_how != how:
warn_alias_replacement(self, orig_how, 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: int | None = 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
if method == "asfreq":
method = None
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)
# error: Definition of "ax" in base class "_GroupByMixin" is incompatible with
# definition in base class "PeriodIndexResampler"
class PeriodIndexResamplerGroupby( # type: ignore[misc]
_GroupByMixin, PeriodIndexResampler
):
"""
Provides a resample of a groupby implementation.
"""
@property
def _resampler_cls(self):
return PeriodIndexResampler
class TimedeltaIndexResampler(DatetimeIndexResampler):
# error: Incompatible types in assignment (expression has type "TimedeltaIndex",
# base class "DatetimeIndexResampler" defined the type as "DatetimeIndex")
ax: TimedeltaIndex # type: ignore[assignment]
@property
def _resampler_for_grouping(self):
return TimedeltaIndexResamplerGroupby
def _get_binner_for_time(self):
return self._timegrouper._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
# error: Definition of "ax" in base class "_GroupByMixin" is incompatible with
# definition in base class "DatetimeIndexResampler"
class TimedeltaIndexResamplerGroupby( # type: ignore[misc]
_GroupByMixin, TimedeltaIndexResampler
):
"""
Provides a resample of a groupby implementation.
"""
@property
def _resampler_cls(self):
return TimedeltaIndexResampler
def get_resampler(obj: Series | DataFrame, kind=None, **kwds) -> Resampler:
"""
Create a TimeGrouper and return our resampler.
"""
tg = TimeGrouper(obj, **kwds) # type: ignore[arg-type]
return tg._get_resampler(obj, kind=kind)
get_resampler.__doc__ = Resampler.__doc__
def get_resampler_for_grouping(
groupby: GroupBy,
rule,
how=None,
fill_method=None,
limit: int | None = None,
kind=None,
on=None,
include_groups: bool = True,
**kwargs,
) -> Resampler:
"""
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, include_groups=include_groups, 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",
"kind",
"convention",
"origin",
"offset",
)
origin: TimeGrouperOrigin
def __init__(
self,
obj: Grouper | None = None,
freq: Frequency = "Min",
key: str | None = None,
closed: Literal["left", "right"] | None = None,
label: Literal["left", "right"] | None = None,
how: str = "mean",
axis: Axis = 0,
fill_method=None,
limit: int | None = None,
kind: str | None = None,
convention: Literal["start", "end", "e", "s"] | None = None,
origin: Literal["epoch", "start", "start_day", "end", "end_day"]
| TimestampConvertibleTypes = "start_day",
offset: TimedeltaConvertibleTypes | None = None,
group_keys: bool = False,
**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`")
if (
key is None
and obj is not None
and isinstance(obj.index, PeriodIndex) # type: ignore[attr-defined]
or (
key is not None
and obj is not None
and getattr(obj[key], "dtype", None) == "period" # type: ignore[index]
)
):
freq = to_offset(freq, is_period=True)
else:
freq = to_offset(freq)
end_types = {"ME", "YE", "QE", "BME", "BYE", "BQE", "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
self._arrow_dtype: ArrowDtype | None = None
if origin in ("epoch", "start", "start_day", "end", "end_day"):
# error: Incompatible types in assignment (expression has type "Union[Union[
# Timestamp, datetime, datetime64, signedinteger[_64Bit], float, str],
# Literal['epoch', 'start', 'start_day', 'end', 'end_day']]", variable has
# type "Union[Timestamp, Literal['epoch', 'start', 'start_day', 'end',
# 'end_day']]")
self.origin = origin # type: ignore[assignment]
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
super().__init__(freq=freq, key=key, axis=axis, **kwargs)
def _get_resampler(self, obj: NDFrame, kind=None) -> Resampler:
"""
Return my resampler or raise if we have an invalid axis.
Parameters
----------
obj : Series or DataFrame
kind : string, optional
'period','timestamp','timedelta' are valid
Returns
-------
Resampler
Raises
------
TypeError if incompatible axis
"""
_, ax, _ = self._set_grouper(obj, gpr_index=None)
if isinstance(ax, DatetimeIndex):
return DatetimeIndexResampler(
obj,
timegrouper=self,
kind=kind,
axis=self.axis,
group_keys=self.group_keys,
gpr_index=ax,
)
elif isinstance(ax, PeriodIndex) or kind == "period":
if isinstance(ax, PeriodIndex):
# GH#53481
warnings.warn(
"Resampling with a PeriodIndex is deprecated. "
"Cast index to DatetimeIndex before resampling instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
else:
warnings.warn(
"Resampling with kind='period' is deprecated. "
"Use datetime paths instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
return PeriodIndexResampler(
obj,
timegrouper=self,
kind=kind,
axis=self.axis,
group_keys=self.group_keys,
gpr_index=ax,
)
elif isinstance(ax, TimedeltaIndex):
return TimedeltaIndexResampler(
obj,
timegrouper=self,
axis=self.axis,
group_keys=self.group_keys,
gpr_index=ax,
)
raise TypeError(
"Only valid with DatetimeIndex, "
"TimedeltaIndex or PeriodIndex, "
f"but got an instance of '{type(ax).__name__}'"
)
def _get_grouper(
self, obj: NDFrameT, validate: bool = True
) -> tuple[BinGrouper, NDFrameT]:
# create the resampler and return our binner
r = self._get_resampler(obj)
return r._grouper, cast(NDFrameT, 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, dtype=ax.dtype
)
return binner, [], labels
first, last = _get_timestamp_range_edges(
ax.min(),
ax.max(),
self.freq,
unit=ax.unit,
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",
unit=ax.unit,
)
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: DatetimeIndex, ax_values: npt.NDArray[np.int64]
) -> tuple[DatetimeIndex, npt.NDArray[np.int64]]:
# Some hacks for > daily data, see #1471, #1458, #1483
if self.freq.name in ("BME", "ME", "W") or self.freq.name.split("-")[0] in (
"BQE",
"BYE",
"QE",
"YE",
"W",
):
# If the right end-point is on the last day of the month, roll forwards
# until the last moment of that day. Note that we only do this for offsets
# which correspond to the end of a super-daily period - "month start", for
# example, is excluded.
if self.closed == "right":
# GH 21459, GH 9119: Adjust the bins relative to the wall time
edges_dti = binner.tz_localize(None)
edges_dti = (
edges_dti
+ Timedelta(days=1, unit=edges_dti.unit).as_unit(edges_dti.unit)
- Timedelta(1, unit=edges_dti.unit).as_unit(edges_dti.unit)
)
bin_edges = edges_dti.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 isinstance(self.freq, Tick):
# GH#51896
raise ValueError(
"Resampling on a TimedeltaIndex requires fixed-duration `freq`, "
f"e.g. '24h' or '3D', not {self.freq}"
)
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
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 len(ax) == 0:
binner = labels = PeriodIndex(
data=[], freq=freq, name=ax.name, dtype=ax.dtype
)
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 _set_grouper(
self, obj: NDFrameT, sort: bool = False, *, gpr_index: Index | None = None
) -> tuple[NDFrameT, Index, npt.NDArray[np.intp] | None]:
obj, ax, indexer = super()._set_grouper(obj, sort, gpr_index=gpr_index)
if isinstance(ax.dtype, ArrowDtype) and ax.dtype.kind in "Mm":
self._arrow_dtype = ax.dtype
ax = Index(
cast(ArrowExtensionArray, ax.array)._maybe_convert_datelike_array()
)
return obj, ax, indexer
def _take_new_index(
obj: NDFrameT, indexer: npt.NDArray[np.intp], new_index: Index, axis: AxisInt = 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)
return obj._constructor_from_mgr(new_mgr, axes=new_mgr.axes)
else:
raise ValueError("'obj' should be either a Series or a DataFrame")
def _get_timestamp_range_edges(
first: Timestamp,
last: Timestamp,
freq: BaseOffset,
unit: str,
closed: Literal["right", "left"] = "left",
origin: TimeGrouperOrigin = "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.")
if 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, unit=unit
)
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: TimeGrouperOrigin = "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, unit="ns", 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: TimeGrouperOrigin = "start_day",
offset: Timedelta | None = None,
unit: str = "ns",
) -> 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
first = first.as_unit(unit)
last = last.as_unit(unit)
if offset is not None:
offset = offset.as_unit(unit)
freq_value = Timedelta(freq).as_unit(unit)._value
origin_timestamp = 0 # origin == "epoch"
if origin == "start_day":
origin_timestamp = first.normalize()._value
elif origin == "start":
origin_timestamp = first._value
elif isinstance(origin, Timestamp):
origin_timestamp = origin.as_unit(unit)._value
elif origin in ["end", "end_day"]:
origin_last = last if origin == "end" else last.ceil("D")
sub_freq_times = (origin_last._value - first._value) // freq_value
if closed == "left":
sub_freq_times += 1
first = origin_last - sub_freq_times * freq
origin_timestamp = first._value
origin_timestamp += 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_timestamp) % freq_value
loffset = (last._value - origin_timestamp) % freq_value
if closed == "right":
if foffset > 0:
# roll back
fresult_int = first._value - foffset
else:
fresult_int = first._value - freq_value
if loffset > 0:
# roll forward
lresult_int = last._value + (freq_value - 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_value - loffset)
else:
lresult_int = last._value + freq_value
fresult = Timestamp(fresult_int, unit=unit)
lresult = Timestamp(lresult_int, unit=unit)
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"
if isinstance(freq, BaseOffset):
if hasattr(freq, "_period_dtype_code"):
freq = freq_to_period_freqstr(freq.n, freq.name)
else:
raise ValueError(
f"Invalid offset: '{freq.base}' for converting time series "
f"with PeriodIndex."
)
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:
unit = None
if isinstance(obj.index, DatetimeIndex):
# TODO: should we disallow non-DatetimeIndex?
unit = obj.index.unit
dti = date_range(obj.index.min(), obj.index.max(), freq=freq, unit=unit)
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
def maybe_warn_args_and_kwargs(cls, kernel: str, args, kwargs) -> None:
"""
Warn for deprecation of args and kwargs in resample functions.
Parameters
----------
cls : type
Class to warn about.
kernel : str
Operation name.
args : tuple or None
args passed by user. Will be None if and only if kernel does not have args.
kwargs : dict or None
kwargs passed by user. Will be None if and only if kernel does not have kwargs.
"""
warn_args = args is not None and len(args) > 0
warn_kwargs = kwargs is not None and len(kwargs) > 0
if warn_args and warn_kwargs:
msg = "args and kwargs"
elif warn_args:
msg = "args"
elif warn_kwargs:
msg = "kwargs"
else:
return
warnings.warn(
f"Passing additional {msg} to {cls.__name__}.{kernel} has "
"no impact on the result and is deprecated. This will "
"raise a TypeError in a future version of pandas.",
category=FutureWarning,
stacklevel=find_stack_level(),
)
def _apply(
grouped: GroupBy, how: Callable, *args, include_groups: bool, **kwargs
) -> DataFrame:
# GH#7155 - rewrite warning to appear as if it came from `.resample`
target_message = "DataFrameGroupBy.apply operated on the grouping columns"
new_message = _apply_groupings_depr.format("DataFrameGroupBy", "resample")
with rewrite_warning(
target_message=target_message,
target_category=DeprecationWarning,
new_message=new_message,
):
result = grouped.apply(how, *args, include_groups=include_groups, **kwargs)
return result
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