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

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

Calculate the rolling sample covariance.

Parameters:

otherSeries or DataFrame, optional

If not supplied then will default to self and produce pairwiseoutput.

pairwisebool, default None

If False then only matching columns between self and other will beused and the output will be a DataFrame.If True then all pairwise combinations will be calculated and theoutput will be a MultiIndexed DataFrame in the case of DataFrameinputs. In the case of missing elements, only complete pairwiseobservations will be used.

ddofint, default 1

Delta Degrees of Freedom. The divisor used in calculationsisN-ddof, whereN represents the number of elements.

numeric_onlybool, default False

Include only float, int, boolean columns.

Added in version 1.5.0.

Returns:

Series or DataFrame

Return type is the same as the original object withnp.float64 dtype.

See also

pandas.Series.rolling

Calling rolling with Series data.

pandas.DataFrame.rolling

Calling rolling with DataFrames.

pandas.Series.cov

Aggregating cov for Series.

pandas.DataFrame.cov

Aggregating cov for DataFrame.

Examples

>>>ser1=pd.Series([1,2,3,4])>>>ser2=pd.Series([1,4,5,8])>>>ser1.rolling(2).cov(ser2)0    NaN1    1.52    0.53    1.5dtype: float64