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db« ddcdd¬« dw dx fde « Z& xZ'S )yÚ Expandingaï
|
Provide expanding window calculations.
|
|
Parameters
|
----------
|
min_periods : int, default 1
|
Minimum number of observations in window required to have a value;
|
otherwise, result is ``np.nan``.
|
|
axis : int or str, default 0
|
If ``0`` or ``'index'``, roll across the rows.
|
|
If ``1`` or ``'columns'``, roll across the columns.
|
|
For `Series` this parameter is unused and defaults to 0.
|
|
method : str {'single', 'table'}, default 'single'
|
Execute the rolling operation per single column or row (``'single'``)
|
or over the entire object (``'table'``).
|
|
This argument is only implemented when specifying ``engine='numba'``
|
in the method call.
|
|
.. versionadded:: 1.3.0
|
|
Returns
|
-------
|
pandas.api.typing.Expanding
|
|
See Also
|
--------
|
rolling : Provides rolling window calculations.
|
ewm : Provides exponential weighted functions.
|
|
Notes
|
-----
|
See :ref:`Windowing Operations <window.expanding>` for further usage details
|
and examples.
|
|
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
|
|
**min_periods**
|
|
Expanding sum with 1 vs 3 observations needed to calculate a value.
|
|
>>> df.expanding(1).sum()
|
B
|
0 0.0
|
1 1.0
|
2 3.0
|
3 3.0
|
4 7.0
|
>>> df.expanding(3).sum()
|
B
|
0 NaN
|
1 NaN
|
2 3.0
|
3 3.0
|
4 7.0
|
)Ú min_periodsÚaxisÚmethodz list[str]Ú _attributesé c ó. t | |||||¬« y )N)Úobjr! r" r# Ú selection)ÚsuperÚ__init__)Úselfr' r! r" r# r( Ú __class__s úOH:\Change_password\venv_build\Lib\site-packages\pandas/core/window/expanding.pyr* zExpanding.__init__| s& ø ô ÑØØ#ØØØ ð õ
|
ó c ó t « S )z[
|
Return an indexer class that will compute the window start and end bounds
|
)r )r+ s r- Ú_get_window_indexerz Expanding._get_window_indexer s ô Ó!Ð!r. Ú aggregatez£
|
See Also
|
--------
|
pandas.DataFrame.aggregate : Similar DataFrame method.
|
pandas.Series.aggregate : Similar Series method.
|
a
|
Examples
|
--------
|
>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
|
>>> df
|
A B C
|
0 1 4 7
|
1 2 5 8
|
2 3 6 9
|
|
>>> df.ewm(alpha=0.5).mean()
|
A B C
|
0 1.000000 4.000000 7.000000
|
1 1.666667 4.666667 7.666667
|
2 2.428571 5.428571 8.428571
|
zSeries/DataframeÚ )Úsee_alsoÚexamplesÚklassr" c ó* t | |g|¢i |¤S )N)r) r1 )r+ ÚfuncÚargsÚkwargsr, s r- r1 zExpanding.aggregate s ø ô@ wÑ Ð7¨Ò7°Ñ7Ð7r. ÚReturnszSee AlsoÚExampleszÍ >>> ser = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
|
>>> ser.expanding().count()
|
a 1.0
|
b 2.0
|
c 3.0
|
d 4.0
|
dtype: float64
|
Ú expandingz count of non NaN observationsÚcount)Ú window_methodÚaggregation_descriptionÚ
|
agg_methodc ó$ t | |¬« S ©N)Ú numeric_only)r) r= ©r+ rC r, s r- r= zExpanding.count¶ s ø ô. w}¨,}Ó7Ð7r. Ú
|
Parameterszì >>> ser = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
|
>>> ser.expanding().apply(lambda s: s.max() - 2 * s.min())
|
a -1.0
|
b 0.0
|
c 1.0
|
d 2.0
|
dtype: float64
|
zcustom aggregation functionÚapplyc ó. t | ||||||¬« S )N)ÚrawÚengineÚ engine_kwargsr8 r9 )r) rF )r+ r7 rH rI rJ r8 r9 r, s r- rF zExpanding.applyÏ s. ø ôB w}Ø ØØØ'ØØð ó
|
ð
|
r. ÚNoteszÏ >>> ser = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
|
>>> ser.expanding().sum()
|
a 1.0
|
b 3.0
|
c 6.0
|
d 10.0
|
dtype: float64
|
Úsumc ó( t | |||¬« S ©N)rC rI rJ )r) rL ©r+ rC rI rJ r, s r- rL z Expanding.sumù ó% ø ôB w{Ø%ØØ'ð ó
|
ð
|
r. zË >>> ser = pd.Series([3, 2, 1, 4], index=['a', 'b', 'c', 'd'])
|
>>> ser.expanding().max()
|
a 3.0
|
b 3.0
|
c 3.0
|
d 4.0
|
dtype: float64
|
ÚmaximumÚmaxc ó( t | |||¬« S rN )r) rR rO s r- rR z Expanding.max rP r. zË >>> ser = pd.Series([2, 3, 4, 1], index=['a', 'b', 'c', 'd'])
|
>>> ser.expanding().min()
|
a 2.0
|
b 2.0
|
c 2.0
|
d 1.0
|
dtype: float64
|
ÚminimumÚminc ó( t | |||¬« S rN )r) rU rO s r- rU z Expanding.minG rP r. zÌ >>> ser = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
|
>>> ser.expanding().mean()
|
a 1.0
|
b 1.5
|
c 2.0
|
d 2.5
|
dtype: float64
|
Úmeanc ó( t | |||¬« S rN )r) rW rO s r- rW zExpanding.meann s% ø ôB w|Ø%ØØ'ð ó
|
ð
|
r. zÎ >>> ser = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
|
>>> ser.expanding().median()
|
a 1.0
|
b 1.5
|
c 2.0
|
d 2.5
|
dtype: float64
|
Úmedianc ó( t | |||¬« S rN )r) rY rO s r- rY zExpanding.median s% ø ôB w~Ø%ØØ'ð ó
|
ð
|
r. z¼
|
ddof : int, default 1
|
Delta Degrees of Freedom. The divisor used in calculations
|
is ``N - ddof``, where ``N`` represents the number of elements.
|
|
ú
|
z1.4z/numpy.std : Equivalent method for NumPy array.
|
zÛ
|
The default ``ddof`` of 1 used in :meth:`Series.std` is different
|
than the default ``ddof`` of 0 in :func:`numpy.std`.
|
|
A minimum of one period is required for the rolling calculation.
|
|
a
|
>>> s = pd.Series([5, 5, 6, 7, 5, 5, 5])
|
|
>>> s.expanding(3).std()
|
0 NaN
|
1 NaN
|
2 0.577350
|
3 0.957427
|
4 0.894427
|
5 0.836660
|
6 0.786796
|
dtype: float64
|
zstandard deviationÚstdc ó* t | ||||¬« S ©N)ÚddofrC rI rJ )r) r\ ©r+ r_ rC rI rJ r, s r- r\ z Expanding.std¼ ó( ø ôj w{ØØ%ØØ'ð ó
|
ð
|
r. z/numpy.var : Equivalent method for NumPy array.
|
zÛ
|
The default ``ddof`` of 1 used in :meth:`Series.var` is different
|
than the default ``ddof`` of 0 in :func:`numpy.var`.
|
|
A minimum of one period is required for the rolling calculation.
|
|
a
|
>>> s = pd.Series([5, 5, 6, 7, 5, 5, 5])
|
|
>>> s.expanding(3).var()
|
0 NaN
|
1 NaN
|
2 0.333333
|
3 0.916667
|
4 0.800000
|
5 0.700000
|
6 0.619048
|
dtype: float64
|
ÚvarianceÚvarc ó* t | ||||¬« S r^ )r) rc r` s r- rc z Expanding.varø ra r. z:A minimum of one period is required for the calculation.
|
|
zÁ
|
>>> s = pd.Series([0, 1, 2, 3])
|
|
>>> s.expanding().sem()
|
0 NaN
|
1 0.707107
|
2 0.707107
|
3 0.745356
|
dtype: float64
|
zstandard error of meanÚsemc ó& t | ||¬« S )N)r_ rC )r) re )r+ r_ rC r, s r- re z Expanding.sem4 s ø ôF w{ °<{Ó@Ð@r. z:scipy.stats.skew : Third moment of a probability density.
|
zEA minimum of three periods is required for the rolling calculation.
|
|
a >>> ser = pd.Series([-1, 0, 2, -1, 2], index=['a', 'b', 'c', 'd', 'e'])
|
>>> ser.expanding().skew()
|
a NaN
|
b NaN
|
c 0.935220
|
d 1.414214
|
e 0.315356
|
dtype: float64
|
zunbiased skewnessÚskewc ó$ t | |¬« S rB )r) rg rD s r- rg zExpanding.skewY s ø ô: w|¨ |Ó6Ð6r. z/scipy.stats.kurtosis : Reference SciPy method.
|
z<A minimum of four periods is required for the calculation.
|
|
a[
|
The example below will show a rolling calculation with a window size of
|
four matching the equivalent function call using `scipy.stats`.
|
|
>>> arr = [1, 2, 3, 4, 999]
|
>>> import scipy.stats
|
>>> print(f"{{scipy.stats.kurtosis(arr[:-1], bias=False):.6f}}")
|
-1.200000
|
>>> print(f"{{scipy.stats.kurtosis(arr, bias=False):.6f}}")
|
4.999874
|
>>> s = pd.Series(arr)
|
>>> s.expanding(4).kurt()
|
0 NaN
|
1 NaN
|
2 NaN
|
3 -1.200000
|
4 4.999874
|
dtype: float64
|
z,Fisher's definition of kurtosis without biasÚkurtc ó$ t | |¬« S rB )r) ri rD s r- ri zExpanding.kurtx s ø ôL w|¨ |Ó6Ð6r. aæ
|
quantile : float
|
Quantile to compute. 0 <= quantile <= 1.
|
|
.. deprecated:: 2.1.0
|
This will be renamed to 'q' in a future version.
|
interpolation : {{'linear', 'lower', 'higher', 'midpoint', 'nearest'}}
|
This optional parameter specifies the interpolation method to use,
|
when the desired quantile lies between two data points `i` and `j`:
|
|
* linear: `i + (j - i) * fraction`, where `fraction` is the
|
fractional part of the index surrounded by `i` and `j`.
|
* lower: `i`.
|
* higher: `j`.
|
* nearest: `i` or `j` whichever is nearest.
|
* midpoint: (`i` + `j`) / 2.
|
a >>> ser = pd.Series([1, 2, 3, 4, 5, 6], index=['a', 'b', 'c', 'd', 'e', 'f'])
|
>>> ser.expanding(min_periods=4).quantile(.25)
|
a NaN
|
b NaN
|
c NaN
|
d 1.75
|
e 2.00
|
f 2.25
|
dtype: float64
|
ÚquantileÚq)Ú old_arg_nameÚ new_arg_namec ó( t | |||¬« S )N)rl Ú interpolationrC )r) rk )r+ rl rp rC r, s r- rk zExpanding.quantile s& ø ôh wÑ ØØ'Ø%ð ó
|
ð
|
r. z.. versionadded:: 1.4.0
|
|
a
|
method : {{'average', 'min', 'max'}}, default 'average'
|
How to rank the group of records that have the same value (i.e. ties):
|
|
* average: average rank of the group
|
* min: lowest rank in the group
|
* max: highest rank in the group
|
|
ascending : bool, default True
|
Whether or not the elements should be ranked in ascending order.
|
pct : bool, default False
|
Whether or not to display the returned rankings in percentile
|
form.
|
a+
|
>>> s = pd.Series([1, 4, 2, 3, 5, 3])
|
>>> s.expanding().rank()
|
0 1.0
|
1 2.0
|
2 2.0
|
3 3.0
|
4 5.0
|
5 3.5
|
dtype: float64
|
|
>>> s.expanding().rank(method="max")
|
0 1.0
|
1 2.0
|
2 2.0
|
3 3.0
|
4 5.0
|
5 4.0
|
dtype: float64
|
|
>>> s.expanding().rank(method="min")
|
0 1.0
|
1 2.0
|
2 2.0
|
3 3.0
|
4 5.0
|
5 3.0
|
dtype: float64
|
Úrankc ó* t | ||||¬« S )N)r# Ú ascendingÚpctrC )r) rq )r+ r# rs rt rC r, s r- rq zExpanding.rankÚ s( ø ôH w|ØØ ØØ%ð ó
|
ð
|
r. a
|
other : Series or DataFrame, optional
|
If not supplied then will default to self and produce pairwise
|
output.
|
pairwise : bool, default None
|
If False then only matching columns between self and other will be
|
used and the output will be a DataFrame.
|
If True then all pairwise combinations will be calculated and the
|
output will be a MultiIndexed DataFrame in the case of DataFrame
|
inputs. In the case of missing elements, only complete pairwise
|
observations will be used.
|
ddof : int, default 1
|
Delta Degrees of Freedom. The divisor used in calculations
|
is ``N - ddof``, where ``N`` represents the number of elements.
|
a0 >>> ser1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
|
>>> ser2 = pd.Series([10, 11, 13, 16], index=['a', 'b', 'c', 'd'])
|
>>> ser1.expanding().cov(ser2)
|
a NaN
|
b 0.500000
|
c 1.500000
|
d 3.333333
|
dtype: float64
|
zsample covarianceÚcovc ó* t | ||||¬« S ©N)ÚotherÚpairwiser_ rC )r) ru ©r+ rx ry r_ rC r, s r- ru z Expanding.cov% s( ø ôb w{ØØ ØØ%ð ó
|
ð
|
r. aN
|
other : Series or DataFrame, optional
|
If not supplied then will default to self and produce pairwise
|
output.
|
pairwise : bool, default None
|
If False then only matching columns between self and other will be
|
used and the output will be a DataFrame.
|
If True then all pairwise combinations will be calculated and the
|
output will be a MultiIndexed DataFrame in the case of DataFrame
|
inputs. In the case of missing elements, only complete pairwise
|
observations will be used.
|
z
|
cov : Similar method to calculate covariance.
|
numpy.corrcoef : NumPy Pearson's correlation calculation.
|
ao
|
This function uses Pearson's definition of correlation
|
(https://en.wikipedia.org/wiki/Pearson_correlation_coefficient).
|
|
When `other` is not specified, the output will be self correlation (e.g.
|
all 1's), except for :class:`~pandas.DataFrame` inputs with `pairwise`
|
set to `True`.
|
|
Function will return ``NaN`` for correlations of equal valued sequences;
|
this is the result of a 0/0 division error.
|
|
When `pairwise` is set to `False`, only matching columns between `self` and
|
`other` will be used.
|
|
When `pairwise` is set to `True`, the output will be a MultiIndex DataFrame
|
with the original index on the first level, and the `other` DataFrame
|
columns on the second level.
|
|
In the case of missing elements, only complete pairwise observations
|
will be used.
|
|
a1 >>> ser1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
|
>>> ser2 = pd.Series([10, 11, 13, 16], index=['a', 'b', 'c', 'd'])
|
>>> ser1.expanding().corr(ser2)
|
a NaN
|
b 1.000000
|
c 0.981981
|
d 0.975900
|
dtype: float64
|
Ú correlationÚcorrc ó* t | ||||¬« S rw )r) r| rz s r- r| zExpanding.corr] s( ø ôX w|ØØ ØØ%ð ó
|
ð
|
r. )r% r ÚsingleN)
|
r' r r! Úintr" r r# ÚstrÚreturnÚNone)r r )F)rC Úbool)FNNNN) r7 zCallable[..., Any]rH r rI ú!Literal['cython', 'numba'] | NonerJ údict[str, bool] | Noner8 ztuple[Any, ...] | Noner9 zdict[str, Any] | None)FNN)rC r rI r rJ r
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|
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