Covariance and Correlation in Python

Covariance and Correlation in Python

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.

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 directions.

Correlation is a standardized statistical measure that expresses the extent to which two variables are linearly related (meaning how much they change together at a constant rate). The strength and directional association of the relationship between two variables are defined by correlation and it ranges from -1 to +1. Similar to covariance, a positive value denotes that both variables move in the same direction whereas a negative value tells us that they move in opposite directions.

Both covariance and correlation are vital tools used in data exploration for feature selection and multivariate analyses. For example, an investor looking to spread the risk of a portfolio might look for stocks with a high covariance, as it suggests that their prices move up at the same time. However, a similar movement is not enough on its own. The investor would then use the correlation metric to determine how strongly linked those stock prices are to each other.

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