pandas.core.window.ewm.ExponentialMovingWindow.cov
- ExponentialMovingWindow.cov(other=None, pairwise=None, bias=False, **kwargs)[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.
- **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.
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.
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Licensed under the 3-clause BSD License.
https://pandas.pydata.org/pandas-docs/version/1.3.4/reference/api/pandas.core.window.ewm.ExponentialMovingWindow.cov.html