pandas.core.window.rolling.Rolling.cov#

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

Calculate the rolling 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 MultiIndexed DataFrame in the case of DataFrame inputs. In the case of missing elements, only complete pairwise observations will be used.
ddofint, default 1: Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.
**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.