Pandas: Series - cov() function
Compute covariance with Pandas Series
The cov() function is used to compute covariance with Series, excluding missing values.
Syntax:
Series.cov(self, other, min_periods=None)
Parameters:
| Name | Description | Type/Default Value | Required / Optional |
|---|---|---|---|
| other | Series with which to compute the covariance. | Series | Required |
| min_periods | Minimum number of observations needed to have a valid result. | int | optional |
Returns: float
Covariance between Series and other normalized by N-1 (unbiased estimator).
Example:
Python-Pandas Code:
import numpy as np
import pandas as pd
s1 = pd.Series([0.90011105, 0.13494030, 0.62026040])
s2 = pd.Series([0.12630090, 0.26100470, 0.41111190])
s1.cov(s2)
Output:
-0.01832098497271333
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