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Python – seaborn.boxenplot() method

  • Last Updated : 18 Aug, 2020
Geek Week

Prerequisite : Fundamentals of Seaborn

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.

seaborn.boxenplot()

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 :

Example 1:




# 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()

Output :

Example 2:




# 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()

Output :

Example 3:




# 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()

Output :

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