To create a *correlation matrix* using Pandas:

df.corr()

Next, you’ll see 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 illustration, let’s use 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) print(df)

Once you run the code, you’ll get the following DataFrame:

```
A B C
0 45 38 10
1 37 31 15
2 42 26 17
3 35 28 21
4 39 33 12
```

### 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) corr_matrix = df.corr() print(corr_matrix)

Run the code in Python, and you’ll get the following matrix:

```
A B C
A 1.000000 0.518457 -0.701886
B 0.518457 1.000000 -0.860941
C -0.701886 -0.860941 1.000000
```

### Step 4 (optional): Get a Visual Representation of the Correlation Matrix using Seaborn and Matplotlib

You may 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(corr_matrix, 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) corr_matrix = df.corr() sn.heatmap(corr_matrix, annot=True) plt.show()

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.