Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://www.geeksforgeeks.org/python/python-seaborn-boxenplot-method/ below:

Python - seaborn.boxenplot() method - GeeksforGeeks

Last Updated : 15 Jul, 2025

Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. There is just something extraordinary about a well-designed visualization. The colors stand out, the layers blend nicely together, the contours flow throughout, and the overall package not only has a nice aesthetic quality, but it provides meaningful insights to us as well.

Draw an enhanced box plot for larger datasets. This style of plot was originally named a "letter value" plot because it shows a large number of quantiles that are defined as "letter values".  It is similar to a box plot in plotting a nonparametric representation of a distribution in which all features correspond to actual observations. By plotting more quantiles, it provides more information about the shape of the distribution, particularly in the tails.

Syntax : seaborn.boxenplot(parameters) Parameters :

• x, y, hue : Inputs for plotting long-form data.
• data : Dataset for plotting.
• order, hue_order : Order to plot the categorical levels in, otherwise the levels are inferred from the data objects.
• orient : Orientation of the plot (vertical or horizontal).
• color : Color for all of the elements, or seed for a gradient palette.
• palette : Colors to use for the different levels of the hue variable.
• saturation : Proportion of the original saturation to draw colors at.
• width : Width of a full element when not using hue nesting, or width of all the elements for one level of the major grouping variable.
• dodge : When hue nesting is used, whether elements should be shifted along the categorical axis.
• k_depth : The number of boxes, and by extension number of percentiles, to draw.
• linewidth : Width of the gray lines that frame the plot elements.
• scale : Method to use for the width of the letter value boxes.
• outlier_prop : Proportion of data believed to be outliers.
• showfliers : If False, suppress the plotting of outliers.
• ax : Axes object to draw the plot onto, otherwise uses the current Axes.
• kwargs : Other keyword arguments

Returns : Returns the Axes object with the plot drawn onto it.

Below is the implementation of above method with some examples :

# importing packages
import seaborn as sns
import matplotlib.pyplot as plt

# loading dataset
data = sns.load_dataset("tips")

# plot the boxenplot
sns.boxenplot(x = "day", y = "total_bill", 
              data = data)
plt.show()

# importing packages
import seaborn as sns
import matplotlib.pyplot as plt

# loading dataset
data = sns.load_dataset("tips")

# plot the boxenplot
# hue by sex
# width of 0.8
sns.boxenplot(x ="day", y = "total_bill", hue = "sex", 
              data = data, width = 0.8)
plt.show()

# importing packages
import seaborn as sns
import matplotlib.pyplot as plt

# loading dataset
data = sns.load_dataset("tips")

# plot the boxenplot
# orient to horizontal
sns.boxenplot(x = "total_bill", y = "size", 
              data = data, orient ="h")
plt.show()

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4