Covariance and Correlation in Python
Introduction
Working with variables in data analysis always drives the question: How are the variables dependent, linked, and varying against each other? Covariance and Correlation measures aid in establishing this.
Covariance brings about the variation across variables. We use covariance to measure how much two variables change with each other. Correlation reveals the relation between the variables. We use correlation to determine how strongly linked two variables are to each other.
In this article, we’ll learn how to calculate the covariance and correlation in Python.
Covariance and Correlation – In Simple Terms
Both covariance and correlation are about the relationship between the variables. Covariance defines the directional association between the variables. Covariance values range from -inf to +inf where a positive value denotes that both the variables move in the same direction and a negative value denotes that both the variables move in opposite