Seaborn.barplot() method

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Seaborn.barplot() method is used to show point estimates and confidence intervals as rectangular bars. With the height of each rectangle representing an estimate of central tendency for a numerical variable, a bar plot also shows the degree of uncertainty surrounding that estimate via error bars.

When 0 is a meaningful value for the quantitative variable and you want to make comparisons against it, bar plots which include 0 in the quantitative axis range are an excellent option. A point plot will enable you to concentrate on disparities between levels of one or more categorical variables in datasets where 0 is not a meaningful value.

Syntax

Following is the syntax of seaborn.barplot() method −

seaborn.barplot(*, x=None, y=None, hue=None, data=None, order=None, hue_order=None, estimator=<function mean at 0x7ff320f315e0>, ci=95, n_boot=1000, units=None, seed=None, orient=None, color=None, palette=None, saturation=0.75, errcolor='.26', errwidth=None, capsize=None, dodge=True, ax=None, **kwargs

Parameters

Some of the parameters of the seaborn.barplot() method are as follows −

Loading the seaborn library

Let us load the Seaborn library and the dataset before moving on to developing the plots. 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 as few examples.

Example 1

To plot a basic barplot, passing the arguments x,y and the dataset to the data argument should suffice. Here, we are using the titanic dataset and the columns class and fare are passed to x,y respectively. Since barplot is also a categorical plot, it will need one categorical variable to plot a graph. The following line of code can be referred to for plotting a simple barplot().

import seaborn as sns
import matplotlib.pyplot as plt
titanic=sns.load_dataset("titanic")
titanic.head()
sns.barplot(x="class", y="fare", data=titanic)
plt.show()

Output

The output obtained is as follows −

Example 2

capsize parameter is a very useful parameter in the barplot() method. It takes floating point values and determines the width of the cap on error bars. In this example, we will pass the capsize parameter to the method and observe the changes in the graph produced. The hue parameter is also passed in the below example since the graph will be a lot clearer.

import seaborn as sns
import matplotlib.pyplot as plt
titanic=sns.load_dataset("titanic")
titanic.head()
sns.barplot(x="class", y="fare",hue="who",capsize=0.2,data=titanic)
plt.show()

Output

The output obtained is as follows,

Example 3

In this example, we will understand the working of the order parameter. This parameter takes values as a list of strings. These list of strings are the order in which the categorical levels are plotted. Since the order for the class variable in the dataset is First, Second and Third, the order passed in the below line of code is the reverse to the current order.

import seaborn as sns
import matplotlib.pyplot as plt
titanic=sns.load_dataset("titanic")
titanic.head()
sns.barplot(x="class",y="fare",hue="who", data=titanic,order=["Third", "Second","First"])
plt.show()

Output

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

Example 4

Central tendency is a parameter that has a lot of uses and is an optional parameter that takes the values, float, or none. It is an optional parameter. To be drawn around estimated values are confidence intervals of varying sizes. If "sd," value is passed, bootstrapping is performed and the plots contain the observed standard deviation instead. If None, error bars won't be displayed and no bootstrapping will be done.

import seaborn as sns
import matplotlib.pyplot as plt
titanic=sns.load_dataset("titanic")
titanic.head()
sns.barplot(x="class", y="fare", ci="sd",data=titanic)
plt.show()

Output

The output obtained is as follows −