xorbits.pandas.Series.cov#
- Series.cov(other: Series, min_periods: int | None = None, ddof: int | None = 1) float [source]#
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.
- Parameters
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
, whereN
represents the number of elements.
- Returns
Covariance between Series and other normalized by N-1 (unbiased estimator).
- Return type
float
See also
DataFrame.cov
Compute pairwise covariance of columns.
Examples
>>> s1 = pd.Series([0.90010907, 0.13484424, 0.62036035]) >>> s2 = pd.Series([0.12528585, 0.26962463, 0.51111198]) >>> s1.cov(s2) -0.01685762652715874
Warning
This method has not been implemented yet. Xorbits will try to execute it with pandas.
This docstring was copied from pandas.core.series.Series.