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pandas.core.window.EWM.cov

EWM.cov(other=None, pairwise=None, bias=False, **kwargs) [source]

exponential weighted sample covariance

Parameters:

Parameters:	other : Series, DataFrame, or ndarray, 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 Panel in the case of DataFrame inputs. In the case of missing elements, only complete pairwise observations will be used. bias : boolean, default False Use a standard estimation bias correction
Returns:	same type as input

other : Series, DataFrame, or ndarray, 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 Panel in the case of DataFrame inputs. In the case of missing elements, only complete pairwise observations will be used.

bias : boolean, default False

Use a standard estimation bias correction

Returns:

same type as input