Summary  
This chapter demonstrates how to compute the Pearson correlation coefficient between two numerical data sequences using NumPy’s `corrcoef` function and extract the resulting value.

General domain of usage  
Data analysis

**Correlation** is a statistical measure that quantifies the degree of association or relationship between two variables. In other words, it helps us understand how two variables tend to move in relation to each other. 

Definition

Correlation provides a straightforward way to examine the result. The correlation value falls within the range of `[-1, 1]`. Refer to the table below:

## Correlation with Python

To calculate correlation, use the `np.corrcoef()` function from `numpy`, which requires two parameters: the data sequences for which correlation is to be computed. Here's an example:

import pandas as pd
import numpy as np

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/update/Stores.csv')

# Calculating correlation 
corr = np.corrcoef(df['Store_Area'], df['Items_Available'])[0,1]

print(corr)

Here, we extracted the value at index `[0, 1]`, just like in the case of **covariance**. In the previous chapter, we obtained the value `74955.85`, and interpreting the result of the covariation function can be challenging. However, in this case, we can conclude that **the values are strongly related**.

What does correlation measure in statistics?

Learn core statistical concepts used in data analysis with Python. The course covers descriptive statistics, including mean, median, mode, variance, and standard deviation, as well as sampling, probability distributions, the Central Limit Theorem, and outlier detection.