Section: Displot | Data Visualization on Seaborn

Section outline

The displot() function allows you to display different types of distributions.

signature displot

Parameter name	Explanations	Required type	Example
data	Table you are planning to use	DataFrame, Series, dict, array, or list of arrays	data=tableau
x	Variable of the table for the x-axis	string corresponding to a variable of the table	x="weight"
y	Variable of the table for the y-axis	string corresponding to a variable of the table	y=”height”
hue	Variable of the table to distinguish with colors	string corresponding to a variable of the table	hue=”age”
row	Variable of the table that will be used to make different plots, in rows	string corresponding to a variable of the table	row=”category”
col	Variable of the table that will be used to make different plots, in columns	string corresponding to a variable of the table	col=”job”
kind	Type of plot desired	string, 3 choices	kind=”hist”,kind=”kde” or kind=”ecdf”
rug	Allows to see individual values on the 2 axes	Boolean	rug=True

 Importation Code
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
from pyodide.http import open_url

url = "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/penguins.csv"
data = pd.read_csv(open_url(url))
← Running

Here is an editable example code that allows you to create a histogram:

 Cell 2
sns.displot(data=data, x="bill_length_mm", rug=True, hue="sex", bins=20)
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If no data is specified for the y-axis, the y-axis will represent the number of occurrences. If kind is not specified, a histogram is displayed by default.

The bins argument controls the number of bars. The rug parameter allows you to visualize individual observations along the axes of the plot.

We can also use Kernel Density Estimation (KDE) to estimate a distribution. Here is an example of how to use it:

 Cell 3
sns.displot(data=data,x="bill_length_mm", rug=True, hue="sex", kind="kde")
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If a variable is specified for y:

 Cell 4
sns.displot(data=data,x="bill_length_mm", y="bill_depth_mm", rug=True, hue="sex", kind="kde")
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A plot of this type can be read like a contour map. Each line connects points with similar probability densities. The centers of the contours correspond to areas of higher density.

The last type of distribution available is the ECDF (Empirical Cumulative Distribution Function). The y parameter cannot be specified for this type of distribution, as it is univariate.

 Cell 5
sns.displot(data=data, x="body_mass_g", rug=True, hue="sex", kind="ecdf", row="species", col="sex", height=5)
← Running

The row parameter allows you to display even more plots based on another variable in the dataset. The data contains three penguin species, so there are three rows of plots. There are two sexes in the dataset, so there are two columns. height controls the height of the plots.