pandas.core.window.Rolling.cov

Rolling.cov(self, other=None, pairwise=None, ddof=1, **kwargs) [source]

Calculate the rolling 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 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. **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 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.
**kwargs: Keyword arguments to be passed into func.

Returns:

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