Seaborn.stripplot() method

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The seaborn.stripplot() method is used to draw a scatter plot where one of variables passed as argument is a categorical variable. This plot can be drawn on its own but usually it is also used to complement other plots such as box plot where, along with depicting all the observations some underlying distributions are also needed to be shown.

This method takes input in different formats such as array, long-from data, wide-form data or list of vectors.

Syntax

Following is the syntax of the seaborn.stripplot() method

seaborn.stripplot(*, x=None, y=None, hue=None, data=None, order=None, hue_order=None, jitter=True, dodge=False, orient=None, color=None, palette=None, size=5, edgecolor='gray', linewidth=0, ax=None, **kwargs

Parameters

Some of the parameters of this method are as follows −

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 Titanic dataset inbuilt in the seaborn library. the following command is used to load the dataset.

titanic=sns.load_dataset("titanic")

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.

titanic.head()

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

index,survived,pclass,sex,age,sibsp,parch,fare,embarked,class,who,adult_male,deck,embark_town,alive,alone
0,0,3,male,22.0,1,0,7.25,S,Third,man,true,NaN,Southampton,no,false
1,1,1,female,38.0,1,0,71.2833,C,First,woman,false,C,Cherbourg,yes,false
2,1,3,female,26.0,0,0,7.925,S,Third,woman,false,NaN,Southampton,yes,true

Now that we have loaded the dataset, we will explore a few examples.

Example 1

we will understand a simple shortcut that can be used to plot a stripplot(). instead of passing the data and x as separate parameters , they can be combined and be passed as a single argument in the x parameter. The following line of code can be used to do so.

import seaborn as sns
import matplotlib.pyplot as plt
titanic=sns.load_dataset("titanic")
titanic.head()
sns.stripplot(x=titanic["age"])
plt.show()

Output

The output produced by the above line of code is as follows,

Example 2

now, we will pass the hue parameter along with the x,y parameters and see the change in the plot obtained. Since, we are using the titanic dataset, we will pass the alive variable to hue and also pass age and sex to x an dy parameters respectively. The required plot can be produced by using the below mentioned line of code.

import seaborn as sns
import matplotlib.pyplot as plt
titanic=sns.load_dataset("titanic")
titanic.head()
sns.stripplot(x="age", y="who", hue="alive",data=titanic)
plt.show()

Output

The plot produced can be seen below.

Example 3

There are many parameters in the seaborn.stripplot() method and we will see the working of another parameter called jitter. jitter parameter is used to space out the categorical variables data and it works only on data of categorical kind. it takes floating values as input.

The below code snippet depicts its usage and the plot thus produced can also be viewed below.

import seaborn as sns
import matplotlib.pyplot as plt
titanic=sns.load_dataset("titanic")
titanic.head()
#jitter spaces out categorical data and works only on this type of data.
sns.stripplot(x="age", y="who", data=titanic,jitter=0.5)
plt.show()

Output

Example 4

there are many parameters in the seaborn.stripplot() method and we will see the working of another parameter called jitter and linewidth. jitter parameter is used to space out the categorical variables data and it works only on data of categorical kind. it takes floating values as input. linewidth parameter takes floating values and determines the width of the gray lines that frame the elements in the plot.

the below code snipper depicts its usage and the plot thus produced can also be viewed below.

import seaborn as sns
import matplotlib.pyplot as plt
titanic=sns.load_dataset("titanic")
titanic.head()
sns.stripplot(x="age", y="who", hue="alive",data=titanic,jitter=0.5,linewidth=1)
plt.show()

Output

the output plot can be seen below.

Example 5

in this example we will understand how to use the dodge parameter along with the parameters used above. This parameter takes a boolean value. if we use hue nesting, passing true to this parameter will separate the strips for different hue levels. If False is passed, the points for each level will be plotted on top of each other. the below line of code can be used to plot a graph using the dodge parameter.

import seaborn as sns
import matplotlib.pyplot as plt
titanic=sns.load_dataset("titanic")
titanic.head()
sns.stripplot(y="fare", x="who", hue="alive",data=titanic,linewidth=1,dodge=True)
plt.show()

Output

the output thus obtained is as follows,