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.