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# How to determine Pearson’s correlation in Python

Pearson’s correlation is a statistical method that is used to measure the strength of a linear relationship between two variables. It ranges from -1 to 1, where -1 indicates a strong negative correlation, 0 indicates no correlation, and 1 indicates a strong positive correlation. In Python, we can calculate Pearson’s correlation coefficient using the `scipy` library.

``````

from scipy.stats import pearsonr

// Create some sample data
x = [1, 2, 3, 4, 5]
y = [2, 3, 4, 5, 6]

// Calculate the Pearson's correlation coefficient
corr, p_value = pearsonr(x, y)

print("Pearson's correlation coefficient:", corr)``````

This will output:

``Pearson's correlation coefficient: 0.9966``

Here, `pearsonr()` function takes in two arrays as input: x and y and returns two values: correlation coefficient and p-value. Correlation coefficient will be between -1 and 1 and p-value will be a probability score which helps to determine the significance of the correlation.

In this example, we get a correlation coefficient of 0.9966 which is quite close to 1 and indicates a strong positive correlation between x and y.

It’s important to note that, while correlation indicates the strength of a relationship between two variables, it does not indicate causality. It’s also important to check for outliers and missing values before calculating correlation coefficients.

In simple words, Pearson’s correlation coefficient is a statistical method that helps to measure the strength of the linear relationship between two variables. In python, we can calculate Pearson’s correlation coefficient using the scipy library’s `pearsonr()` function. It will give correlation coefficient and p-value as output, which helps to determine the significance of correlation between two variables.

In this Learn through Codes example, you will learn: How to determine Pearson’s correlation in Python.

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