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l,m-Z-m.Z.m/Z/ d d l0m1Z1 d d l2m3Z3m4Z4 d dl5m6 m7Z8 d d l9m:Z: d dl;m<Z< d dl=m6 m>Z? d dl@mAZAmBZB d dlCmDZD d dlEmFZFmGZGmHZHmIZI d dlJmKZK d dlLmMZM d dlNmOZOmPZP d dlQmRZRmSZS d dlTmUZUmVZV d dlWmXZXmYZY d dlZm[Z[m\Z\ erød dl]m^Z^m_Z_m`Z` i Zadebd< G d d d eFe<ZcG d d d e<ZdG d d! d!ecZeG d"d# d#edeeZfG d$d% d%eeZgG d&d' d'edegZhG d(d) d)eeZiG d*d+ d+edeiZjdhd,d d-d.d/Zkecjlek_ldid0d d1d2d3ZmG d4d5 d5eKZndjd6d7d8d9d6d:d;d<Zodkd?d?d@dAdBdCdDdEdFdGdHZpdldIdId@dBdCdDdJdKdLdMZqdNdOdNdPdQdRdSdTZrdmd?d?dWdBdCdDdAdEdXdYdZZsdnd6d\d6d]d^d_Ztd`dadbdcZudAdddedfdgZvdS )oé )Ú annotationsN)Údedent)Ú TYPE_CHECKINGÚCallableÚHashableÚLiteralÚcastÚfinalÚ no_type_check)Úlib)Ú
|
BaseOffsetÚIncompatibleFrequencyÚNaTÚPeriodÚ TimedeltaÚ TimestampÚ to_offset) Ú AnyArrayLikeÚAxisÚAxisIntÚ FrequencyÚ
|
IndexLabelÚNDFrameTÚQuantileInterpolationÚTÚTimedeltaConvertibleTypesÚTimeGrouperOriginÚTimestampConvertibleTypesÚnpt)Úfunction©ÚAbstractMethodError)ÚAppenderÚ SubstitutionÚdoc)Úfind_stack_level)Ú ABCDataFrameÚ ABCSeries)ÚResamplerWindowApply)Ú PandasObject)ÚNDFrameÚ _shared_docs)Ú SeriesGroupBy)Ú BaseGroupByÚGroupByÚ_pipe_templateÚ get_groupby)ÚGrouper)Ú
|
BinGrouper)Ú DatetimeIndexÚ
|
date_range)Ú PeriodIndexÚ period_range)ÚTimedeltaIndexÚtimedelta_range)Ú is_subperiodÚis_superperiod)ÚDayÚTick)Ú DataFrameÚIndexÚSerieszdict[str, str]Ú_shared_docs_kwargsc s, e Zd ZU dZded< ded< ded< e Zded < ed
|
d d hZd dddddddgZ ddddddddd d d d d Z
|
d!d"d#d$Z d!d%d&d'Z e d d"d(d)Zd*d*d+d,d-Zd.d/ Zed0d1 Zed d2d3eed4d5d6 fd7d8 Zed9Zed:Zeed; eed<d=d>dd?d@Z e Z e Z dAdB Z dCdD Z ddEdFZ!ddGdHdIdJZ"dKdL Z#dMdNdOdPZ$dQdR Z%ddSdTZ&ddUdVZ'ddWdXZ(ddYdZZ)ee*j+fe,ddddd\ddd]d^dd d_d`dadbZ+ddcddZ-dd dGdedfdgZ.ee/j0dd dGdedhdiZ0dd dGdedjdkZ1dd dGdedldmZ2ee/j3dd dGdedndoZ3ee/j4dd dGdedpdqZ4ee/j5dd drdsdtZ5dd drdudvZ6ddGd dxdydzZ7ddGd dxd{d|Z8ee/j9d dGd dxd}d~Z9ee/j:dd Z:ee;j<dd Z<ee/j=dd Z=ee/j>d
d Z>d¡ddddZ? Z@S )¢Ú Resamplera/
|
Class for resampling datetimelike data, a groupby-like operation.
|
See aggregate, transform, and apply functions on this object.
|
|
It's easiest to use obj.resample(...) to use Resampler.
|
|
Parameters
|
----------
|
obj : Series or DataFrame
|
groupby : TimeGrouper
|
axis : int, default 0
|
kind : str or None
|
'period', 'timestamp' to override default index treatment
|
|
Returns
|
-------
|
a Resampler of the appropriate type
|
|
Notes
|
-----
|
After resampling, see aggregate, apply, and transform functions.
|
r2 ÚgrouperÚ TimeGrouperÚ _timegrouperz,DatetimeIndex | TimedeltaIndex | PeriodIndexÚbinnerzfrozenset[Hashable]Ú
|
exclusionsÚobjÚaxÚ_indexerÚfreqÚaxisÚclosedÚlabelÚ
|
conventionÚkindÚoriginÚoffsetr NF)Ú
|
group_keysÚ selectionr* r r> ÚboolÚNone)rG Ú timegrouperrK Ú gpr_indexrR Úreturnc C s || _ d | _d| _| |¡| _|| _|| _d| _| j j| |¡d|d\| _
|
| _ | _ | ¡ \| _| _|| _| j jd k rt| j jg| _nt | _d S )NT)ÚsortrW )rD ÚkeysrY Z_get_axis_numberrK rO rR Zas_indexÚ _set_grouperÚ _convert_objrG rH rI Ú _get_binnerrE rB Ú
|
_selectionÚkeyÚ frozensetrF )ÚselfrG rV rK rO rW rR rS © rb úKd:\z\workplace\vscode\pyvenv\venv\Lib\site-packages\pandas/core/resample.pyÚ__init__ s" ÿ zResampler.__init__Ústr)rX c s0 fdd j D }t j dd |¡ dS )z@
|
Provide a nice str repr of our rolling object.
|
c 3 s6 | ].}t j|d d k r| dt j| V qd S )Nú=)ÚgetattrrD )Ú.0Úk©ra rb rc Ú <genexpr>´ s þz$Resampler.__str__.<locals>.<genexpr>z [z, ú])Ú _attributesÚtypeÚ__name__Újoin)ra Úattrsrb rj rc Ú__str__° s
|
þzResampler.__str__)Úattrc C sJ || j krt | |¡S || jkr,t| j|S || jkr>| | S t | |¡S ©N)Ú_internal_names_setÚobjectÚ__getattribute__rm rg rD rG )ra rs rb rb rc Ú __getattr__» s
|
|
|
zResampler.__getattr__c C s" | j dk o | j jdk p | j jdk S )zP
|
Is the resampling from a DataFrame column or MultiIndex level.
|
N)rD r_ Úlevelrj rb rb rc Ú_from_selectionÅ s
|
ÿzResampler._from_selectionr ©rG rX c C s | ¡ S )zÜ
|
Provide any conversions for the object in order to correctly handle.
|
|
Parameters
|
----------
|
obj : Series or DataFrame
|
|
Returns
|
-------
|
Series or DataFrame
|
)Z _consolidate)ra rG rb rb rc r\ Ð s zResampler._convert_objc C s t | d S rt r rj rb rb rc Ú_get_binner_for_timeÞ s z Resampler._get_binner_for_timec C s: | ¡ \}}}t|t|ks"tt||| jd}||fS )zk
|
Create the BinGrouper, assume that self.set_grouper(obj)
|
has already been called.
|
)Úindexer)r| ÚlenÚAssertionErrorr2 rI )ra rE ÚbinsÚ binlabelsZ bin_grouperrb rb rc r] á s zResampler._get_binnera¼
|
>>> 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)ÚklassÚexamplesz/Callable[..., T] | tuple[Callable[..., T], str]r )ÚfuncrX c s t j|f||S rt )ÚsuperÚpipe)ra r ÚargsÚkwargs©Ú __class__rb rc r ì s zResampler.pipea[
|
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.
|
a
|
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
|
Ú aggregater= Ú )Zsee_alsor r rK c O s6 t | |||d ¡ }|d kr2|}| j|f||}|S )N)r r )r( ÚaggÚ_groupby_and_aggregate)ra r r r ÚresultÚhowrb rb rc r A s
|
zResampler.aggregatec O s | j | j¡j|f||S )aw
|
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
|
)Ú _selected_objÚgroupbyrD Ú transform)ra Úargr r rb rb rc r S s ÿÿÿzResampler.transformc K s t | d S rt r )ra Úfr rb rb rc Ú _downsamplew s zResampler._downsamplec C s t | d S rt r )ra r ÚlimitÚ
|
fill_valuerb rb rc Ú _upsamplez s zResampler._upsampleÚint)Úndimc C sd | j }|dkr4| j}|dk r&|| }n|jdks4t|dkrJ|jdksJtt|d|| j| jd}|S )á
|
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
|
Né ©ZbyrB rK rR )rB rG r r r0 rK rR )ra r_ r ÚsubsetrB Úgroupedrb rb rc Ú_gotitem} s
|
ÿzResampler._gotitemc
|
|
sè | j }| jjdkr| j}n| j}t|d|| j| jd}z:tr\ fdd}| |¡}n|jf }W nl t t
|
fk
|
r |j f }Y nD t k
|
rÜ } z&dt | kr¸n |j f }W 5 d} ~ X Y nX | |¡S )zA
|
Re-evaluate the obj with a groupby aggregation.
|
r Nr c s | f S rt rb ©Úx©r r r rb rc Ú<lambda>¬ ó z2Resampler._groupby_and_aggregate.<locals>.<lambda>z Must produce aggregated value)rB r r Ú_obj_with_exclusionsr0 rK rR Úcallabler ÚAttributeErrorÚKeyErrorÚapplyÚ
|
ValueErrorre Ú _wrap_result)
|
ra r r r rB rG r r r Úerrrb r¤ rc r s0 ÿ $z Resampler._groupby_and_aggregater. )r c C s | j ||| dS )zG
|
Return the correct class for resampling with groupby.
|
)r r_ Úparent)Ú_resampler_for_grouping)ra r r_ rb rb rc Ú_get_resampler_for_groupingÈ s z%Resampler._get_resampler_for_groupingc C s¢ | j }t|trJt|dkrJt|jtsJ|jt|jdd
| jddd}t|t rf| j
|
dk rf| j
|
|_ t|t r|j rt|jdd
| jd|_t |dd|_ |S )z/
|
Potentially wrap any results.
|
r N©rJ T)ÚappendÚname)rG Ú
|
isinstancer& r~ Úindexr5 Z set_indexÚ_asfreq_compatrJ r' r^ r´ Úemptyrg )ra r rG rb rb rc r Î s" ÿ
|
þ
|
ý ÿzResampler._wrap_resultc C s | j d|dS )a
|
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.
|
Úffill©r ©r ©ra r rb rb rc r¹ ç s zResampler.ffillc C s | j d|dS )a
|
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
|
Únearestrº r» r¼ rb rb rc r½ û s :zResampler.nearestc C s | j d|dS )a
|
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
|
Úbfillrº r» r¼ rb rb rc r¾ 7 s dzResampler.bfillc C s | j ||dS )a
|
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
|
rº r» )ra Úmethodr rb rb rc Úfillna s zResampler.fillnaÚlinearZforward)rK r ÚinplaceÚlimit_directionÚ
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limit_areaÚdowncastr z(Literal[('forward', 'backward', 'both')])r¿ rK r rà c
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K s* | d¡} | jf |||||||d|S )zD
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Interpolate values according to different methods.
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Úasfreq)r¿ rK r r rà rÄ rÅ )r Ú interpolate)
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ra r¿ rK r r rà rÄ rÅ r r rb rb rc rÇ = s
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ùøzResampler.interpolatec C s | j d|dS )a6
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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.
|
rÆ )r r» )ra r rb rb rc rÆ Y s zResampler.asfreq©Ú numeric_onlyÚ min_countc O s0 t t| d|| t d||¡ | jd||dS )NÚsumrÈ ©Úmaybe_warn_args_and_kwargsrn ÚnvZvalidate_resampler_funcr ©ra rÉ rÊ r r rb rb rc rË o s z Resampler.sumc O s0 t t| d|| t d||¡ | jd||dS )NÚprodrÈ rÌ rÏ rb rb rc rÐ z s zResampler.prodc O s0 t t| d|| t d||¡ | jd||dS )NÚminrÈ rÌ rÏ rb rb rc rÑ s z Resampler.minc O s0 t t| d|| t d||¡ | jd||dS )NÚmaxrÈ rÌ rÏ rb rb rc rÒ s z Resampler.maxc O s0 t t| d|| t d||¡ | jd||dS )NÚfirstrÈ rÌ rÏ rb rb rc rÓ s zResampler.firstc O s0 t t| d|| t d||¡ | jd||dS )NÚlastrÈ rÌ rÏ rb rb rc rÔ ¨ s zResampler.last©rÉ c O s. t t| d|| t d||¡ | jd|dS )NÚmedianrÕ rÌ ©ra rÉ r r rb rb rc rÖ ´ s zResampler.medianc O s. t t| d|| t d||¡ | jd|dS )a
|
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.
|
ÚmeanrÕ rÌ r× rb rb rc rØ º s zResampler.meanr ©ÚddofrÉ c O s0 t t| d|| t d||¡ | jd||dS )a
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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.
|
ÚstdrÙ rÌ ©ra rÚ rÉ r r rb rb rc rÛ Õ s z Resampler.stdc O s0 t t| d|| t d||¡ | jd||dS )a
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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.
|
ÚvarrÙ rÌ rÜ rb rb rc rÝ õ s z Resampler.varc O s0 t t| d|| t d||¡ | jd||dS )NÚsemrÙ rÌ rÜ rb rb rc rÞ s z Resampler.semc O s* t t| d|| t d||¡ | d¡S )NÚohlcrÌ ©ra r r rb rb rc rß " s zResampler.ohlcc O s* t t| d|| t d||¡ | d¡S )NÚnuniquerÌ rà rb rb rc rá , s zResampler.nuniquec C sh | d¡}t|tr"|js"| ¡ }t| jsdddlm} | j j
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dkrN| j j }nd }|g |j d|d}|S )NÚsizer )r? r Úint64©r¶ Údtyper´ ) r rµ r& r¸ Ústackr~ rH Úpandasr? r r r´ r¶ )ra r r? r´ rb rb rc râ 6 s
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ÿ ÿzResampler.countç à?zfloat | AnyArrayLike)Úqc K s | j dd|i|S )aÁ
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Return value at the given quantile.
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Parameters
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----------
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q : float or array-like, default 0.5 (50% quantile)
|
|
Returns
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-------
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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
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Return a DataFrame, where the columns are groupby columns,
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and the values are its quantiles.
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S )ÚDatetimeIndexResamplerc C s t S rt )Ú DatetimeIndexResamplerGroupbyrj rb rb rc r° â s z.DatetimeIndexResampler._resampler_for_groupingc C s& | j dkr| j | j¡S | j | j¡S ©NÚperiod)rO rD Ú_get_time_period_binsrH Ú_get_time_binsrj rb rb rc r| æ s
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dk s|j dk rªt| j jt|krª|dkrª| ¡ S |j| j | jdj|f|}| |¡S )úÀ
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Downsample the cython defined function.
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Parameters
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how : string / cython mapped function
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**kwargs : kw args passed to how function
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r N©rK )ÚcomÚget_cython_funcrH r r r§ r~ r÷ r¶ Z
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_with_freqrJ r Ú inferred_freqrB r rÆ r rK r r )ra r r rH rG r rb rb rc r ì s* ÿÿþý z"DatetimeIndexResampler._downsamplec C s( | j dkr|dd
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Adjust our binner when upsampling.
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The range of a new index should not be outside specified range
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Úrightr Néÿÿÿÿ)rL ©ra rE rb rb rc Ú_adjust_binner_for_upsample s
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z2DatetimeIndexResampler._adjust_binner_for_upsampleNc C s | j rtd| jr td| j}| j}| j}| |¡}|dkrpt|j | j
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krpt |t |krp| ¡ }||_ n|j||||d}| |¡S )aª
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Parameters
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----------
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method : string {'backfill', 'bfill', 'pad',
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'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.
|
|
zaxis must be 0zvUpsampling from level= or on= selection is not supported, use .set_index(...) to explicitly set index to datetime-likeN)r¿ r r )rK r rz r¬ rH r rE r r r rJ r~ r÷ r¶ Úreindexr ) ra r¿ r r rH rG rE Z res_indexr rb rb rc r ! s2 ÿ
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z!PeriodIndexResampler._convert_objc s¤ | j dkrt j|f|S t |¡p&|}| j}t|j| jrJ| j|f|S t |j| jrr|dkrj| |¡S |
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Frequency z cannot be resampled to z&, as they are not sub or super periodsN) rO r
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ÿz PeriodIndexResampler._downsampleNc
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sl | j dkr t j|||dS | j}| j}| j}|j| j| jd}|j |||d}t
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|||| j d} | | ¡S )a
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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.
|
|
r )r r r )r¿ r r ) rO r
r rH rG rE rÆ rJ rN Z get_indexerÚ_take_new_indexrK r )
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ra r¿ r r rH rG Ú new_indexÚmembr} Únew_objr rb rc r ¢ s
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üz PeriodIndexResampler._upsample)NN)
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ro rí rî rò r° r| r\ r r ró rb rb r rc r b s
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$r c @ s e Zd ZdZedd ZdS )r ú:
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Provides a resample of a groupby implementation.
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c C s t S rt )r rj rb rb rc rø Í s z*PeriodIndexResamplerGroupby._resampler_clsNr rb rb rb rc r È s r c @ s( e Zd Zedd Zdd Zdd ZdS )ÚTimedeltaIndexResamplerc C s t S rt )Ú TimedeltaIndexResamplerGroupbyrj rb rb rc r° Ó s z/TimedeltaIndexResampler._resampler_for_groupingc C s | j | j¡S rt )rD Ú_get_time_delta_binsrH rj rb rb rc r| × s z,TimedeltaIndexResampler._get_binner_for_timec C s |S )zÇ
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Adjust our binner when upsampling.
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|
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
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rb r rb rb rc r Ú s z3TimedeltaIndexResampler._adjust_binner_for_upsampleN)ro rí rî rò r° r| r rb rb rb rc r Ò s
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r c @ s e Zd ZdZedd ZdS )r r c C s t S rt )r rj rb rb rc rø é s z-TimedeltaIndexResamplerGroupby._resampler_clsNr rb rb rb rc r ä s r zSeries | DataFramer{ c K s t f |}|j| |dS )z8
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Create a TimeGrouper and return our resampler.
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r Úruler Ú fill_methodr rO Úonr r# Z resamplerrb rb rc Úget_resampler_for_groupingù s r( c sÀ e Zd ZU dZejd Zded< d4d d d d dddddddd fdd Zd5ddddd Zd6d dd d d!d"Z d#d$d%d&Z
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d#d'd(d)d*d+Z d,d$d-d.Z d#d$d/d0Z d1d$d2d3Z ZS )7rC ah
|
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
|
)rL rM r rO rN rP rQ r rP ÚMinNrØ r Ú start_dayFr z!Literal[('left', 'right')] | Nonere r z
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str | Nonez*Literal[('start', 'end', 'e', 's')] | NonezVLiteral[('epoch', 'start', 'start_day', 'end', 'end_day')] | TimestampConvertibleTypesz TimedeltaConvertibleTypes | NonerT rU ) rJ rL rM r rK rO rN rP rQ rR rX c
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ÿýzTimeGrouper.__init__r* rA r{ c C s | j |dd\}}}t|tr6t|| || j| j|dS t|tsH|dkr`t|| || j| j|dS t|trt || | j| j|dS t
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Return my resampler or raise if we have an invalid axis.
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|
Parameters
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----------
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obj : Series or DataFrame
|
kind : string, optional
|
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|
Returns
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-------
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Resampler
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|
Raises
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------
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TypeError if incompatible axis
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|
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Parameters
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----------
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first : pd.Timestamp
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The beginning Timestamp of the range to be adjusted.
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last : pd.Timestamp
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The ending Timestamp of the range to be adjusted.
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freq : pd.DateOffset
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The dateoffset to which the Timestamps will be adjusted.
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closed : {'right', 'left'}, default "left"
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Which side of bin interval is closed.
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origin : {'epoch', 'start', 'start_day'} or Timestamp, default 'start_day'
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The timestamp on which to adjust the grouping. The timezone of origin must
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- 'start_day': `origin` is the first day at midnight of the timeseries
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offset : pd.Timedelta, default is None
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last : pd.Period
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freq : pd.DateOffset
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The freq to which the Periods will be adjusted.
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closed : {'right', 'left'}, default "left"
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Which side of bin interval is closed.
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cls : type
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Class to warn about.
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kernel : str
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Operation name.
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args : tuple or None
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