In this short guide, I’ll show you how to create a Correlation Matrix using Pandas. I’ll also review the steps to display the matrix using Seaborn and Matplotlib.
To start, here is a template that you can apply in order to create a correlation matrix using pandas:
df.corr()
Next, I’ll show you an example with the steps to create a correlation matrix for a given dataset.
Steps to Create a Correlation Matrix using Pandas
Step 1: Collect the Data
Firstly, collect the data that will be used for the correlation matrix.
For example, I collected the following data about 3 variables:
A | B | C |
45 | 38 | 10 |
37 | 31 | 15 |
42 | 26 | 17 |
35 | 28 | 21 |
39 | 33 | 12 |
Step 2: Create a DataFrame using Pandas
Next, create a DataFrame in order to capture the above dataset in Python:
import pandas as pd data = {'A': [45,37,42,35,39], 'B': [38,31,26,28,33], 'C': [10,15,17,21,12] } df = pd.DataFrame(data,columns=['A','B','C']) print (df)
Once you run the code, you’ll get the following DataFrame:
Step 3: Create a Correlation Matrix using Pandas
Now, create a correlation matrix using this template:
df.corr()
This is the complete Python code that you can use to create the correlation matrix for our example:
import pandas as pd data = {'A': [45,37,42,35,39], 'B': [38,31,26,28,33], 'C': [10,15,17,21,12] } df = pd.DataFrame(data,columns=['A','B','C']) corrMatrix = df.corr() print (corrMatrix)
Run the code in Python, and you’ll get the following matrix:
Step 4 (optional): Get a Visual Representation of the Correlation Matrix using Seaborn and Matplotlib
You can use the seaborn and matplotlib packages in order to get a visual representation of the correlation matrix.
First import the seaborn and matplotlib packages:
import seaborn as sn import matplotlib.pyplot as plt
Then, add the following syntax at the bottom of the code:
sn.heatmap(corrMatrix, annot=True) plt.show()
So the complete Python code would look like this:
import pandas as pd import seaborn as sn import matplotlib.pyplot as plt data = {'A': [45,37,42,35,39], 'B': [38,31,26,28,33], 'C': [10,15,17,21,12] } df = pd.DataFrame(data,columns=['A','B','C']) corrMatrix = df.corr() sn.heatmap(corrMatrix, annot=True) plt.show()
Run the code, and you’ll get the following correlation matrix:
That’s it! You may also want to review the following source that explains the steps to create a Confusion Matrix using Python. Alternatively, you may check this guide about creating a Covariance Matrix in Python.