pandas.core.groupby.SeriesGroupBy.cov#

SeriesGroupBy.cov(other, min_periods=None, ddof=1)[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:
otherSeries

Series with which to compute the covariance.

min_periodsint, optional

Minimum number of observations needed to have a valid result.

ddofint, default 1

Delta degrees of freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.

Returns:
float

Covariance between Series and other normalized by N-1 (unbiased estimator).

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