`kdeplot`

The `kdeplot`

function from seaborn calculates a kernel density estimate of the data and plots it. By default the function uses a gaussian kernel, 200 points as grid for the X-axis and a bandwidth factor of 1 for the Scott method.

```
import numpy as np
import seaborn as sns
# Data simulation
rng = np.random.RandomState(4)
x = rng.normal(0, 1, size = 100)
df = {'x': x}
# KDE plot
sns.kdeplot(x = x)
# Equivalent to:
sns.kdeplot(x = "x", data = df)
```

It is possible to modify the default color of the density with the `color`

argument of the function. Note that you could also modify the line type or width with `linestyle`

and `linewidth`

, respectively.

```
import numpy as np
import seaborn as sns
# Data simulation
rng = np.random.RandomState(4)
x = rng.normal(0, 1, size = 100)
# KDE plot
sns.kdeplot(x = x, color = "red")
```

Moreover, you can also fill the area under the density plot with `fill = True`

and specify the desired color and transparency with `color`

and `alpha`

, respectively.

```
import numpy as np
import seaborn as sns
# Data simulation
rng = np.random.RandomState(4)
x = rng.normal(0, 1, size = 100)
# KDE plot
sns.kdeplot(x = x,
fill = True, color = "green", alpha = 0.5)
```

As we pointed out before, the function uses a bandwidth factor of 1 by default for the Scott method, but you can override it with the `bw_adjust`

argument. You can also use the Silverman method instead of the Scott method with the `bw_method`

argument. See this documentation for more details.

```
import numpy as np
import seaborn as sns
# Data simulation
rng = np.random.RandomState(4)
x = rng.normal(0, 1, size = 100)
# KDE plot
sns.kdeplot(x = x,
bw_adjust = 0.5)
```

**Silverman method**

```
import numpy as np
import seaborn as sns
# Data simulation
rng = np.random.RandomState(4)
x = rng.normal(0, 1, size = 100)
# KDE plot
sns.kdeplot(x = x,
bw_method = "silverman")
```

Kernel selection was deprecated in 0.11.0, so the support of non-gaussian kernels was removed.

See also