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Correlation Matrix in R Programming
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Create a correlation Matrix using Python

Last Updated : 01 May, 2025
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A Correlation matrix is a table that shows how different variables are related to each other. Each cell in the table displays a number i.e. correlation coefficient which tells us how strongly two variables are together. It helps in quickly spotting patterns, understand relationships and making better decisions based on data.

A correlation matrix can be created using two libraries:

1. Using NumPy Library

NumPy provides a simple way to create a correlation matrix. We can use the np.corrcoef() function to find the correlation between two or more variables.

Example: A daily sales and temperature record is kept by an ice cream store. To determine the relationship between sales and temperature, we can utilize the NumPy library, where x is sales in dollars and y is the daily temperature.

Python 
import numpy as np x = [215, 325, 185, 332, 406, 522, 412,      614, 544, 421, 445, 408], y = [14.2, 16.4, 11.9, 15.2, 18.5, 22.1,       19.4, 25.1, 23.4, 18.1, 22.6, 17.2] matrix = np.corrcoef(x, y) print(matrix)

Output: 

[[1.                     0.95750662]   
[0.95750662             1.        ]]

2. Using Pandas library

Pandas is used to create a correlation matrix using its built-in corr() method. It helps in analyzing and interpreting relationships between different variables in a dataset.

Example: Let’s create a simple DataFrame with three variables and calculate correlation matrix.

Python 
import pandas as pd data = {     'x': [45, 37, 42, 35, 39],     'y': [38, 31, 26, 28, 33],     'z': [10, 15, 17, 21, 12] } dataframe = pd.DataFrame(data, columns=['x', 'y', 'z']) print("Dataframe is : ") print(dataframe) matrix = dataframe.corr() print("Correlation matrix is : ") print(matrix)

Output:

corelation1

Using Pandas

Example with Real Dataset (Iris Dataset)

In this example we will consider Iris dataset and find correlation between the features of the dataset.

  • dataset = datasets.load_iris(): Loads Iris dataset from sklearn which contains data on flowers’ features like petal and sepal length/width.
  • dataframe[“target”] = dataset.target: Adds target column which contains the species of the iris flowers to the DataFrame.
Python 
from sklearn import datasets   import pandas as pd  dataset = datasets. load_iris()   dataframe = pd.DataFrame(data = dataset.data,columns = dataset.feature_names)  dataframe["target"] = dataset.target   matrix = dataframe.corr() print(matrix)

Output:

correlation-2

Using IRIS dataset

By using libraries like NumPy and Pandas creating a correlation matrix in Python becomes easy and helps in understanding the hidden relationships between different variables in a dataset.

Related Articles:

  • Correlation: Meaning, Significance, Types and Degree of Correlation
  • Correlation Matrix in R Programming
  • How to Create a Correlation Matrix using Pandas?
  • Exploring Correlation in Python
  • Plotting Correlation Matrix using Python


Next Article
Correlation Matrix in R Programming

R
rohanchopra96
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Article Tags :
  • AI-ML-DS
  • Data Science
  • AI-ML-DS With Python
  • Python-numpy
  • Python-pandas

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