cov(self, other: 'Series', min_periods: 'int | None' = None, ddof: 'int | None' = 1) -> 'float'

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 , where N represents the number of elements.

versionadded

Returns

float

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

Compute covariance with Series, excluding missing values.

See Also

DataFrame.cov

Compute pairwise covariance of columns.

Examples

This example is valid syntax, but we were not able to check execution

>>> s1 = pd.Series([0.90010907, 0.13484424, 0.62036035])
... s2 = pd.Series([0.12528585, 0.26962463, 0.51111198])
... s1.cov(s2)
-0.01685762652715874

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

pandas.core.window.expanding.Expanding.cov pandas.core.frame.DataFrame.cov pandas.core.window.ewm.ExponentialMovingWindow.cov pandas.core.window.rolling.Rolling.cov

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

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File: /pandas/core/series.py#2567
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