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

Calculate the rolling sample covariance.

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.

numeric_onlybool, default False

Include only float, int, boolean columns.

New in version 1.5.0.

Series or DataFrame

Return type is the same as the original object with np.float64 dtype.

See also


Calling rolling with Series data.


Calling rolling with DataFrames.


Aggregating cov for Series.


Aggregating cov for DataFrame.


>>> ser1 = pd.Series([1, 2, 3, 4])
>>> ser2 = pd.Series([1, 4, 5, 8])
>>> ser1.rolling(2).cov(ser2)
0    NaN
1    1.5
2    0.5
3    1.5
dtype: float64