In this tutorial, we will learn how to calculate the **coefficient of correlation** in Python using two common methods: **Pearson’s correlation coefficient** and **Spearman’s rank correlation coefficient**. These coefficients help us understand the strength and direction of a linear relationship between two variables.

### Step 1: Loading the Dataset

First, let’s create a sample dataset consisting of two variables X and Y.

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X = [10, 20, 30, 40, 50] Y = [15, 25, 35, 45, 55] |

To calculate the coefficient of correlation, we will be using the `scipy`

library in Python. If you don’t have it installed, you can install it using the following command:

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!pip install scipy |

### Step 2: Calculating Pearson’s Correlation Coefficient

**Pearson’s correlation coefficient** is a measure of the linear relationship between two variables. It ranges from -1 (perfect negative correlation) to 1 (perfect positive correlation) with 0 being no correlation.

To calculate Pearson’s correlation coefficient in Python, we can use the `pearsonr`

function from the `scipy.stats`

module.

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from scipy.stats import pearsonr correlation_coefficient, p_value = pearsonr(X, Y) print("Pearson's correlation coefficient:", correlation_coefficient) |

### Step 3: Calculating Spearman’s Rank Correlation Coefficient

**Spearman’s rank correlation coefficient** is a non-parametric measure of the strength and direction of the association between two ranked variables. It ranges from -1 (perfect inverse relationship) to 1 (perfect positive relationship) with 0 being no correlation.

To calculate Spearman’s rank correlation coefficient in Python, we can use the `spearmanr`

function from the `scipy.stats`

module.

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from scipy.stats import spearmanr correlation_coefficient, p_value = spearmanr(X, Y) print("Spearman's rank correlation coefficient:", correlation_coefficient) |

## Full Code

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X = [10, 20, 30, 40, 50] Y = [15, 25, 35, 45, 55] from scipy.stats import pearsonr correlation_coefficient, p_value = pearsonr(X, Y) print("Pearson's correlation coefficient:", correlation_coefficient) from scipy.stats import spearmanr correlation_coefficient, p_value = spearmanr(X, Y) print("Spearman's rank correlation coefficient:", correlation_coefficient) |

## Output

Pearson's correlation coefficient: 1.0 Spearman's rank correlation coefficient: 1.0

## Conclusion

In this tutorial, we learned how to calculate the coefficient of correlation in Python using two common methods: Pearson’s correlation coefficient and Spearman’s rank correlation coefficient. We used the `scipy.stats`

module to calculate these coefficients and thus, analyze the relationship between two variables.