Skip to main content

Python Seaborn Categorical distribution plots: Boxen Plot

Boxen Plot is used to draw an enhanced version of the box plot for larger datasets.  This style of the plot was named "letter value" because it displays a large number of quantiles that are known as "letter value". By plotting more quartiles it becomes more informative about the shape of the distribution particularly in the tails.


seaborn.boxenplot(*, x=None, y=None, hue=None, data=None, order=None, 
hue_order=None, orient=None, color=None, palette=None, saturation=0.75, 
width=0.8, dodge=True, k_depth='tukey', linewidth=None, scale='exponential', 
outlier_prop=0.007, trust_alpha=0.05, showfliers=True, ax=None, **kwargs


  • x,y: It is input for plotting long-from data
  • data: It is used for input of Dataset for plotting.
  • orient: It is used for the orientation of the plot (vertical or horizontal).
  • color: It is the color for all of the elements.
  • palette: It is the colors to use for the different levels of the hue variable.
  • saturation: It is used as the proportion of the original saturation to draw colors at.
  • dodge : When hue nesting is used, whether elements should be shifted along the categorical axis.
  • outlier_prop : It is the proportion of data believed to be outliers.
  • showfliers : It is a boolean value. If False, it will suppress the plotting of outliers.


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
data = sns.load_dataset("taxis")

Creating a simple boxen plot 




Creating a boxen plot for all the numerical variables present in the dataframe




Creating a boxen plot for a numerical value and two categorical variables




Creating boxen plot by using orientation which will be horizontally

sns.boxenplot(x="total",y='payment',hue='color',data=data, orient ="h")



Submitted by devanshi.srivastava on March 5, 2021

Devanshi, is working as a Data Scientist with iVagus. She has expertise in Python, NumPy, Pandas and other data science technologies.


At ProgramsBuzz, you can learn, share and grow with millions of techie around the world from different domain like Data Science, Software Development, QA and Digital Marketing. You can ask doubt and get the answer for your queries from our experts.