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In statistics,Â **correlationÂ **refers to the strength and direction of a relationship between two variables. The value of a correlation coefficient can range from -1 to 1, with the following interpretations:

**-1:Â**a perfect negative relationship between two variables**0:Â**no relationship between two variables**1:Â**aÂ perfect positive relationship between two variables

One special type of correlation is calledÂ **Spearman Rank Correlation**, which is used to measure the correlation between two ranked variables. (e.g. rank of a studentâ€™s math exam score vs. rank of their science exam score in a class).

This tutorial explains how to calculate the Spearman rank correlation between two variables in Python

**Example: Spearman Rank Correlation in Python**

Suppose we have the following pandas DataFrame that containsÂ the math exam score and science exam score of 10 students in a particular class:

import pandas as pd #create DataFrame df = pd.DataFrame({'student': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'], 'math': [70, 78, 90, 87, 84, 86, 91, 74, 83, 85], 'science': [90, 94, 79, 86, 84, 83, 88, 92, 76, 75]})

To calculate the Spearman Rank correlation between the math and science scores, we can use the spearmanr() function fromÂ **scipy.stats**:

from scipy.stats import spearmanr #calculate Spearman Rank correlation and corresponding p-value rho, p = spearmanr(df['math'], df['science']) #print Spearman rank correlation and p-value print(rho) -0.41818181818181815 print(p) 0.22911284098281892

From the output we can see that the Spearman rank correlation is **-0.41818** and the corresponding p-value isÂ **0.22911**.

This indicates that there is a negative correlation between the science and math exam scores.

However, since the p-value of the correlation is not less than 0.05, the correlation is not statistically significant.

Note that we could also use the following syntax to just extract the correlation coefficient or the p-value:

#extract Spearman Rank correlation coefficient spearmanr(df['math'], df['science'])[0] -0.41818181818181815 #extract p-value of Spearman Rank correlation coefficient spearmanr(df['math'], df['science'])[1] 0.22911284098281892

**Additional Resources**

How to Calculate Spearman Rank Correlation in R

How to Calculate Spearman Rank Correlation in Excel

How to Calculate Spearman Rank Correlation in Stata