pandas.core.window.ewm.ExponentialMovingWindow.cov#

ExponentialMovingWindow.cov(other=None, pairwise=None, bias=False, numeric_only=False)[source]#

Calculate the ewm (exponential weighted moment) sample covariance.

Parameters:

otherSeries or DataFrame , optional: If not supplied then will default to self and produce pairwise output.
pairwisebool, 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 MultiIndex DataFrame in the case of DataFrame inputs. In the case of missing elements, only complete pairwise observations will be used.
biasbool, default False: Use a standard estimation bias correction.
numeric_onlybool, default False: Include only float, int, boolean columns.

New in version 1.5.0.

Returns:

Series or DataFrame: Return type is the same as the original object with np.float64 dtype.

See also

pandas.Series.ewm: Calling ewm with Series data.
pandas.DataFrame.ewm: Calling ewm with DataFrames.
pandas.Series.cov: Aggregating cov for Series.
pandas.DataFrame.cov: Aggregating cov for DataFrame.

Examples

>>> ser1 = pd.Series([1, 2, 3, 4])
>>> ser2 = pd.Series([10, 11, 13, 16])
>>> ser1.ewm(alpha=.2).cov(ser2)
0         NaN
1    0.500000
2    1.524590
3    3.408836
dtype: float64