pandas.core.window.rolling.Rolling.var
- Rolling. var ( ddof=1, *args, engine=None, engine_kwargs=None, **kwargs ) [source]
-
Calculate the rolling variance.
- Parameters
-
- ddof :int, default 1
-
Delta Degrees of Freedom. The divisor used in calculations is
N - ddof
, whereN
represents the number of elements. - *args
-
For NumPy compatibility and will not have an effect on the result.
- engine :str, default None
-
'cython'
: Runs the operation through C-extensions from cython.'numba'
: Runs the operation through JIT compiled code from numba.-
None
: Defaults to'cython'
or globally settingcompute.use_numba
New in version 1.4.0.
- engine_kwargs :dict, default None
-
For
'cython'
engine, there are no acceptedengine_kwargs
-
For
'numba'
engine, the engine can acceptnopython
,nogil
andparallel
dictionary keys. The values must either beTrue
orFalse
. The defaultengine_kwargs
for the'numba'
engine is{'nopython': True, 'nogil': False, 'parallel': False}
New in version 1.4.0.
- **kwargs
-
For NumPy compatibility and will not have an effect on the result.
- Returns
-
- Series or DataFrame
-
Return type is the same as the original object with
np.float64
dtype.
See also
-
numpy.var
-
Equivalent method for NumPy array.
-
pandas.Series.rolling
-
Calling rolling with Series data.
-
pandas.DataFrame.rolling
-
Calling rolling with DataFrames.
-
pandas.Series.var
-
Aggregating var for Series.
-
pandas.DataFrame.var
-
Aggregating var for DataFrame.
Notes
The default
ddof
of 1 used inSeries.var()
is different than the defaultddof
of 0 innumpy.var()
.A minimum of one period is required for the rolling calculation.
The implementation is susceptible to floating point imprecision as shown in the example below.
Examples
>>> s = pd.Series([5, 5, 6, 7, 5, 5, 5]) >>> s.rolling(3).var() 0 NaN 1 NaN 2 3.333333e-01 3 1.000000e+00 4 1.000000e+00 5 1.333333e+00 6 6.661338e-16 dtype: float64
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Licensed under the 3-clause BSD License.
https://pandas.pydata.org/pandas-docs/version/1.4.0/reference/api/pandas.core.window.rolling.Rolling.var.html