# Create a Contour Plot in Seaborn

This discussion will introduce how to create a contour plot using the `kdeplot()`

function in Seaborn.

## Create a Contour Plot Using the `kdeplot()`

Function in Seaborn

Kernel density estimation allows us to estimate the probability density function from our finite data set. The `kdeplot()`

has the option of the bivariate plot; in this case, we can estimate the joint probability density function for data in two dimensions.

Seaborn’s `kdeplot()`

allows for creating contours representing the different density levels of your data so that you can estimate the joint PDF. Seaborn does not have a `contour`

function, so we need to use the `kdeplot()`

function to display the contour plot.

Let’s look at a Seaborn code to build a bivariate or two-dimensional plot using `kdeplot()`

. We will start by importing `pyplot`

and Seaborn and aliasing both libraries.

```
import seaborn as seaborn
import matplotlib.pyplot as plot
```

The next step is to load some data from Seaborn. We will be using a data set about cars, so we have different statistics about various cars.

The `dropna()`

function will drop all `null`

values from the data set.

Code:

```
data_set=seaborn.load_dataset('mpg').dropna()
data_set.head()
```

Output:

Now, we will use the `kdeplot()`

function and pass the `horsepower`

and the `mpg`

or miles per gallon.

Code:

```
import seaborn as seaborn
import matplotlib.pyplot as plot
data_set=seaborn.load_dataset('mpg').dropna()
seaborn.kdeplot(data_set.horsepower, data_set.mpg)
plot.show()
```

Output:

There are a couple of various options we can take advantage of here. The first one is having more rings or more different levels in this plot.

We can change that value by accessing the `n_levels`

parameter.

```
import seaborn as seaborn
import matplotlib.pyplot as plot
data_set=seaborn.load_dataset('mpg').dropna()
seaborn.kdeplot(data_set.horsepower, data_set.mpg,n_levels=20)
plot.show()
```

Output:

We can also switch it over to a shaded version using the `shade`

version.

```
import seaborn as seaborn
import matplotlib.pyplot as plot
data_set=seaborn.load_dataset('mpg').dropna()
seaborn.kdeplot(data_set.horsepower, data_set.mpg,shade=True)
plot.show()
```

Output:

We can also include a color bar using the `cbar`

parameter.

```
import seaborn as seaborn
import matplotlib.pyplot as plot
data_set=seaborn.load_dataset('mpg').dropna()
seaborn.kdeplot(data_set.horsepower, data_set.mpg,shade=True,cbar=True)
plot.show()
```

As you notice, you have two different categories in your data.

Output:

**Salman Mehmood**

Hello! I am Salman Bin Mehmood(Baum), a software developer and I help organizations, address complex problems. My expertise lies within back-end, data science and machine learning. I am a lifelong learner, currently working on metaverse, and enrolled in a course building an AI application with python. I love solving problems and developing bug-free software for people. I write content related to python and hot Technologies.

LinkedIn## Related Article - Seaborn Plot

- Seaborn Count Plot
- Seaborn Histogram Plot
- Create a 3D Plot Using Seaborn and Matplotlib
- Create Linear Regression in Seaborn
- Seaborn Joint Plot