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 MultiIndex DataFrame in the case of DataFrame inputs. In the case of missing elements, only complete pairwise observations will be used. `bias : bool, default False` Use a standard estimation bias correction. **kwargs Keyword arguments to be passed into func.
Returns:	Series or DataFrame Return type is determined by the caller.

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 MultiIndex DataFrame in the case of DataFrame inputs. In the case of missing elements, only complete pairwise observations will be used.
bias : bool, default False: Use a standard estimation bias correction.
**kwargs: Keyword arguments to be passed into func.

Returns:

Title here