- Series.cov(other: Series, min_periods: int | None = None, ddof: int | None = 1) float #
Compute covariance with Series, excluding missing values.
The two Series objects are not required to be the same length and will be aligned internally before the covariance is calculated.
other (Series) – Series with which to compute the covariance.
min_periods (int, optional) – Minimum number of observations needed to have a valid result.
ddof (int, default 1) – Delta degrees of freedom. The divisor used in calculations is
N - ddof, where
Nrepresents the number of elements.
Covariance between Series and other normalized by N-1 (unbiased estimator).
- Return type
Compute pairwise covariance of columns.
>>> s1 = pd.Series([0.90010907, 0.13484424, 0.62036035]) >>> s2 = pd.Series([0.12528585, 0.26962463, 0.51111198]) >>> s1.cov(s2) -0.01685762652715874
This method has not been implemented yet. Xorbits will try to execute it with pandas.
This docstring was copied from pandas.core.series.Series.