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lmZ d Z G d d « Z G d de« Z G d de« Z G d de« Z G d de« Z G d de« Z G d de« Z G d de« Zy) zLIndexer objects for computing start/end window bounds for rolling operationsé )Ú annotations)Ú timedeltaN)Ú
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BaseOffset)Ú calculate_variable_window_bounds)ÚAppender)Úensure_platform_int)Ú DatetimeIndex)ÚNanoa¾
|
Computes the bounds of a window.
|
|
Parameters
|
----------
|
num_values : int, default 0
|
number of values that will be aggregated over
|
window_size : int, default 0
|
the number of rows in a window
|
min_periods : int, default None
|
min_periods passed from the top level rolling API
|
center : bool, default None
|
center passed from the top level rolling API
|
closed : str, default None
|
closed passed from the top level rolling API
|
step : int, default None
|
step passed from the top level rolling API
|
.. versionadded:: 1.5
|
win_type : str, default None
|
win_type passed from the top level rolling API
|
|
Returns
|
-------
|
A tuple of ndarray[int64]s, indicating the boundaries of each
|
window
|
c óf e Zd ZdZ d ddZ ee« d dd« Zy) Ú BaseIndexeraà
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Base class for window bounds calculations.
|
|
Examples
|
--------
|
>>> from pandas.api.indexers import BaseIndexer
|
>>> class CustomIndexer(BaseIndexer):
|
... def get_window_bounds(self, num_values, min_periods, center, closed, step):
|
... start = np.empty(num_values, dtype=np.int64)
|
... end = np.empty(num_values, dtype=np.int64)
|
... for i in range(num_values):
|
... start[i] = i
|
... end[i] = i + self.window_size
|
... return start, end
|
>>> df = pd.DataFrame({"values": range(5)})
|
>>> indexer = CustomIndexer(window_size=2)
|
>>> df.rolling(indexer).sum()
|
values
|
0 1.0
|
1 3.0
|
2 5.0
|
3 7.0
|
4 4.0
|
Nc ój || _ || _ |j « D ] \ }}t | ||« y ©N)Ú index_arrayÚ window_sizeÚitemsÚsetattr)Úselfr r ÚkwargsÚkeyÚvalues úOH:\Change_password\venv_build\Lib\site-packages\pandas/core/indexers/objects.pyÚ__init__zBaseIndexer.__init__H s9 ð 'ÔØ&Ôà ,,.ò &JCÜ D#uÕ %ñ &ó c ó t r )ÚNotImplementedError©r Ú
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ô "Ð!r )Nr )r únp.ndarray | Noner ÚintÚreturnÚNone©r NNNN© r r$ r ú
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'ò"ó %ñ"r r c óP e Zd ZdZ ee« d dd« Zy)ÚFixedWindowIndexerz3Creates window boundaries that are of fixed length.Nc ó8 |s| j dk( r| j dz
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}|dv r|dz }|dv r|dz }t j |d|« }t j |d|« }||fS )Nr é é Úint64©Údtype©ÚleftÚboth)r9 Úneither)r ÚnpÚarangeÚclip) r r r r r r! ÚoffsetÚendÚstarts r r" z$FixedWindowIndexer.get_window_bounds` s± ñ T×%Ñ%¨Ò*Ø×&Ñ&¨Ñ*¨qÑ0FàFäiiF
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'òó %ñr r1 c óP e Zd ZdZ ee« d dd« Zy)ÚVariableWindowIndexerzNCreates window boundaries that are of variable length, namely for time series.Nc óJ t || j |||| j « S r )r r r r s r r" z'VariableWindowIndexer.get_window_bounds~ s/ ô 0Ø Ø × Ñ Ø Ø Ø Ø × Ñ ó
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r rD c ó e Zd ZdZ d d fd Z ee« d dd« Z xZS )ÚVariableOffsetWindowIndexeraP
|
Calculate window boundaries based on a non-fixed offset such as a BusinessDay.
|
|
Examples
|
--------
|
>>> from pandas.api.indexers import VariableOffsetWindowIndexer
|
>>> df = pd.DataFrame(range(10), index=pd.date_range("2020", periods=10))
|
>>> offset = pd.offsets.BDay(1)
|
>>> indexer = VariableOffsetWindowIndexer(index=df.index, offset=offset)
|
>>> df
|
0
|
2020-01-01 0
|
2020-01-02 1
|
2020-01-03 2
|
2020-01-04 3
|
2020-01-05 4
|
2020-01-06 5
|
2020-01-07 6
|
2020-01-08 7
|
2020-01-09 8
|
2020-01-10 9
|
>>> df.rolling(indexer).sum()
|
0
|
2020-01-01 0.0
|
2020-01-02 1.0
|
2020-01-03 2.0
|
2020-01-04 3.0
|
2020-01-05 7.0
|
2020-01-06 12.0
|
2020-01-07 6.0
|
2020-01-08 7.0
|
2020-01-09 8.0
|
2020-01-10 9.0
|
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'òJó %ôJr rG c óP e Zd ZdZ ee« d dd« Zy)ÚExpandingIndexerz;Calculate expanding window bounds, mimicking df.expanding()Nc ó t j |t j ¬« t j d|dz t j ¬« fS )Nr6 r3 )r< Úzerosr5 r= r s r r" z"ExpandingIndexer.get_window_bounds s8 ô HHZ¤r§x¡xÔ 0Ü IIa a ¬r¯x©xÔ 8ð
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r rb c óP e Zd ZdZ ee« d dd« Zy)ÚFixedForwardWindowIndexera¿
|
Creates window boundaries for fixed-length windows that include the current row.
|
|
Examples
|
--------
|
>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
|
>>> df
|
B
|
0 0.0
|
1 1.0
|
2 2.0
|
3 NaN
|
4 4.0
|
|
>>> indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=2)
|
>>> df.rolling(window=indexer, min_periods=1).sum()
|
B
|
0 1.0
|
1 3.0
|
2 2.0
|
3 4.0
|
4 4.0
|
Nc óÜ |r t d« | t d« |d}t j d||d¬« }|| j z }| j rt j |d|« }||fS )Nz.Forward-looking windows can't have center=TruezAForward-looking windows don't support setting the closed argumentr3 r r5 r6 )rK r< r= r r> )r r r r r r! rA r@ s r r" z+FixedForwardWindowIndexer.get_window_boundsB s~ ñ Ü Ð MÓNÐ NØ Ð Ü ØSóð ð <ØDä !Z¨°WÔ=Ød×&Ñ&Ñ&Ø × Ò Ü''#q *Ó-CàczÐr r' r( rB r/ r r rf rf ) sl ññ0 Ð#Ó$ð Ø"&Ø "Ø !Ø ð àðð ðð ð ð
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'òó %ñr rf c ó e Zd ZdZdddedf d fd Z ee« d dd« Z xZ S ) ÚGroupbyIndexerzMCalculate bounds to compute groupby rolling, mimicking df.groupby().rolling()Nr c ó´ |xs i | _ || _ |r|j « ni | _ t | d|| j j d|« d|¤ y)a4
|
Parameters
|
----------
|
index_array : np.ndarray or None
|
np.ndarray of the index of the original object that we are performing
|
a chained groupby operation over. This index has been pre-sorted relative to
|
the groups
|
window_size : int or BaseIndexer
|
window size during the windowing operation
|
groupby_indices : dict or None
|
dict of {group label: [positional index of rows belonging to the group]}
|
window_indexer : BaseIndexer
|
BaseIndexer class determining the start and end bounds of each group
|
indexer_kwargs : dict or None
|
Custom kwargs to be passed to window_indexer
|
**kwargs :
|
keyword arguments that will be available when get_window_bounds is called
|
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'ò2ó %ô2r ri c óP e Zd ZdZ ee« d dd« Zy)Ú ExponentialMovingWindowIndexerz/Calculate ewm window bounds (the entire window)Nc ó t j dgt j ¬« t j |gt j ¬« fS )Nr r6 )r< rv r5 r s r r" z0ExponentialMovingWindowIndexer.get_window_bounds¼ s1 ô xx¤2§8¡8Ô,¬b¯h©h¸
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