Kernel density plot in seaborn with kdeplot

Basic kernel density plot in seaborn with 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)

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)

Bandwidth selection

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)

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")