pandas.core.window.ewm.ExponentialMovingWindow.cov

ExponentialMovingWindow.cov(other=None, pairwise=None, bias=False, numeric_only=False)[source]

Calculate the ewm (exponential weighted moment) sample covariance.

Parameters:

other:Series or DataFrame , 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 MultiIndex DataFrame in the case of DataFrame inputs. In the case of missing elements, only complete pairwise observations will be used.

bias:bool, default False

Use a standard estimation bias correction.

numeric_only:bool, 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 with np.float64 dtype.

See also

pandas.Series.ewm

Calling ewm with Series data.

pandas.DataFrame.ewm

Calling ewm 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([10, 11, 13, 16])
>>> ser1.ewm(alpha=.2).cov(ser2)
0         NaN
1    0.500000
2    1.524590
3    3.408836
dtype: float64