Seaborn.JointGrid class

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The Seaborn.JointGrid class is used as a Grid for drawing a bivariate plot with marginal univariate plots.

Usually, many plots can already be drawn using a figure-level interface called jointplot(); however, we can use this class if we need the data visualization to be more flexible. This class, at the base, sets up a grid and stores the data internally for making the plotting easier

Following is the syntax of the seaborn.JointGrid

class seaborn.JointGrid(**kwargs)
__init__(self, *, x=None, y=None, data=None, height=6, ratio=5, space=0.2, dropna=False, xlim=None, ylim=None, size=None, marginal_ticks=False, hue=None, palette=None, hue_order=None, hue_norm=None)

Parameters of seaborn.JointGrid

Some of the parameters for this class are shown below.

Let us load the seaborn library and the dataset before moving on to the developing the plots.

Loading the seaborn library

To load or import the seaborn library the following line of code can be used.

Import seaborn as sns

Loading the dataset

In this article, we will make use of the Tips dataset inbuilt in the seaborn library. the following command is used to load the dataset.

tips=sns.load_dataset("tips")

The below mentioned command is used to view the first 5 rows in the dataset. This enables us to understand what variables can be used to plot a graph.

tips.head()

The below is the output for the above piece of code −

index,total_bill,tip,sex,smoker,day,time,size
0,16.99,1.01,Female,No,Sun,Dinner,2
1,10.34,1.66,Male,No,Sun,Dinner,3
2,21.01,3.5,Male,No,Sun,Dinner,3
3,23.68,3.31,Male,No,Sun,Dinner,2
4,24.59,3.61,Female,No,Sun,Dinner,4

Now that we have loaded the data, we will move on to plotting the data.

Example 1

In the below example, we will draw a simple JointGrid by passing it the tips dataset and x,y columns. This will generate a JointGrid empty facet.

import seaborn as sns
import matplotlib.pyplot as plt
tips=sns.load_dataset("tips")
tips.head()
sns.JointGrid(data=tips, x="total_bill", y="tip")
plt.show()

Output

the output plot produced is as follows,

Example 2

We will plot a scatterplot over the joint grid facet in this example. A scatter plot is drawn inside the facet and a kernel density estimate plot is drawn on the marginal of the facet.

import seaborn as sns
import matplotlib.pyplot as plt
tips=sns.load_dataset("tips")
tips.head()
g = sns.JointGrid(data=tips, x="total_bill", y="tip")
g.plot(sns.scatterplot, sns.kdeplot)
plt.show()

Output

the output plot is as shown,

Example 3

Similar to the above example, the plot() function is used to draw a scatterplot inside the joint grid facet and a histogram is drawn on the marginal of the facet. Along with these, a few other parameters are passed to the function such as edge color, line width and alpha.

Here, edge color is a special parameter hat takes the values, matplotlib color or “grey”. it is an optional argument. The hue of the lines around each spot are determined by this parameter. The color scheme used for the body of the points determines the brightness if you pass "grey."

Linewidth is a parameter that takes floating value and determines the width of lines that divide each cell. Next, the alpha parameter is passed. We can utilize the alpha argument in the plot function to modify the transparency of plots. Its value is by default set to 1. This option has a value between 0 and 1, and as the value approaches 0, the plot becomes more translucent and invisible.

import seaborn as sns
import matplotlib.pyplot as plt
tips=sns.load_dataset("tips")
tips.head()
g = sns.JointGrid(data=tips, x="total_bill", y="sex")
g.plot(sns.scatterplot, sns.histplot, alpha=.5, edgecolor=".5", linewidth=.5)
plt.show()

Output

now, the plot obtained is as follows,

Example 4

A categorical plot can also be drawn on the marginal of the JointGrid facet. In this example we will how a box plot can be drawn.

import seaborn as sns
import matplotlib.pyplot as plt
tips=sns.load_dataset("tips")
tips.head()
g = sns.JointGrid(data=tips, x="total_bill", y="tip")
g.plot(sns.regplot, sns.boxplot)
plt.show()

Output

The output obtained is as follows −