cov(self, other: 'DataFrame | Series | None' = None, pairwise: 'bool | None' = None, ddof: 'int' = 1, **kwargs)
If not supplied then will default to self and produce pairwise output.
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.
Delta Degrees of Freedom. The divisor used in calculations is N - ddof
, where N
represents the number of elements.
For NumPy compatibility and will not have an effect on the result.
Return type is the same as the original object with np.float64
dtype.
Calculate the rolling sample covariance.
pandas.DataFrame.cov
Aggregating cov for DataFrame.
pandas.DataFrame.rolling
Calling rolling with DataFrames.
pandas.Series.cov
Aggregating cov for Series.
pandas.Series.rolling
Calling rolling with Series data.
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.window.rolling.Rolling.corr
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