# How to Create a Correlation Matrix using Pandas

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.