xorbits.pandas.window.Rolling.cov#

Rolling.cov(**kwargs)[source]#

Calculate the rolling sample covariance.

Parameters

  • other (Series or DataFrame, optional (Not supported yet)) – If not supplied then will default to self and produce pairwise output.

  • pairwise (bool, default None (Not supported yet)) – 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 (Not supported yet)) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.

  • numeric_only (bool, default False (Not supported yet)) –

    Include only float, int, boolean columns.

    New in version 1.5.0(pandas).

Returns

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

Return type

Series or DataFrame

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    NaN
1    1.5
2    0.5
3    1.5
dtype: float64

This docstring was copied from pandas.core.window.rolling.Rolling.