Libraries
First, we need to import a few libraries:
- matplotlib: for plotting
- seaborn: for making the plots prettier
import matplotlib.pyplot as plt
import seaborn as snsDataset
The dataset that we will use is the iris dataset that we can load using seaborn.
df = sns.load_dataset('iris')Defaut density plot
Density plot differ from histogram in that they are smoothed versions of the histogram.
However, in order to be smoothed, we need to define a bandwidth, which is a parameter that controls the smoothness of the density plot. Varying the bandwidth will give different density plots, and different information too!
In seaborn, it's the bw_method argument that controls it. Here is what the default bandwidth looks like in seaborn:
sns.set_theme(style="darkgrid")
sns.kdeplot(df['sepal_width'], fill=True, color="olive", bw_method=1)
plt.show()
Custom bandwidth
The following density plots have been made using the same data. Only the bandwidth value changes from 1 in the first graph to 0.2 on the right.
This parameter can be of particular interest when a finer understanding of the distribution is needed. It could highlight bimodal distributions more easily and help us in observing patterns that the Gaussian kernel over-smoothed.
Deprecation:
Note that in older version of seaborn (< 0.11.0), the bw parameter was used but is deprecated since and bw_method and bw_adjust have replaced it.
See scipy.stats.gaussian_kde in scipy.org for further details on bw_method and bw_value.
In seaborn 0.11.0 and before versions, you would use sns.kdeplot(df\['sepal_width'\], shade=True, bw=0.05, color='olive')
Now, shade and bw arguments are deprecated.
sns.set_theme(style="darkgrid")
sns.kdeplot(df['sepal_width'], fill=True, color='olive', bw_method=0.08)
plt.show()
Going further
This post explains how to control smoothing in a density plot with seaborn.
You might be interested in displaying distribution of multiple variables and creating a mirrored density plot.
