Plotting graph using Seaborn | Python

This article will introduce you to graphing in Python with Seaborn, which is the most popular statistical visualization library in Python.

Installation: The easiest way to install seaborn is to use pip. Type following command in terminal:  

pip install seaborn

OR, you can download it from here and install it manually.  

Plotting categorical scatter plots with Seaborn



Stripplot




# Python program to illustrate
# Plotting categorical scatter
# plots with Seaborn
 
# importing the required module
import matplotlib.pyplot as plt
import seaborn as sns
 
# x axis values
x =['sun', 'mon', 'fri', 'sat', 'tue', 'wed', 'thu']
 
# y axis values
y =[5, 6.7, 4, 6, 2, 4.9, 1.8]
 
# plotting strip plot with seaborn
ax = sns.stripplot(x, y);
 
# giving labels to x-axis and y-axis
ax.set(xlabel ='Days', ylabel ='Amount_spend')
 
# giving title to the plot
plt.title('My first graph');
 
# function to show plot
plt.show()

Output:

Explanation: This is the one kind of scatter plot of categorical data with the help of seaborn.  

Stripplot using inbuilt data-set given in seaborn : 




# Python program to illustrate
# Stripplot using inbuilt data-set
# given in seaborn
 
# importing the required module
import matplotlib.pyplot as plt
import seaborn as sns
 
# use to set style of background of plot
sns.set(style="whitegrid")
 
# loading data-set
iris = sns.load_dataset('iris')
 
# plotting strip plot with seaborn
# deciding the attributes of dataset on
# which plot should be made
ax = sns.stripplot(x='species', y='sepal_length', data=iris)
 
# giving title to the plot
plt.title('Graph')
 
# function to show plot
plt.show()

Output: 



Explanation:

Swarmplot 




# Python program to illustrate
# plotting using Swarmplot
 
# importing the required module
import matplotlib.pyplot as plt
import seaborn as sns
 
# use to set style of background of plot
sns.set(style="whitegrid")
 
# loading data-set
iris = sns.load_dataset('iris')
 
# plotting strip plot with seaborn
# deciding the attributes of dataset on
# which plot should be made
ax = sns.swarmplot(x='species', y='sepal_length', data=iris)
 
# giving title to the plot
plt.title('Graph')
 
# function to show plot
plt.show()

Output: 

Explanation: 
This is very much similar to stripplot but the only difference is that it does not allow overlapping of markers. It causes jittering in the markers of the plot so that graph can easily be read without information loss as seen in the above plot. 
 

Note: If we want we can also change the representation of data on a particular axis. 

Example : 




# importing the required module
import matplotlib.pyplot as plt
import seaborn as sns
 
# use to set style of background of plot
sns.set(style="whitegrid")
 
# loading data-set
iris = sns.load_dataset('iris')
 
# plotting strip plot with seaborn
# deciding the attributes of dataset on
# which plot should be made
ax = sns.swarmplot(x='sepal_length', y='species', data=iris)
 
 
# giving title to the plot
plt.title('Graph')
 
# function to show plot
plt.show()

Output: 



The same can be done in striplot. At last, we can say that Seaborn is an extended version of matplotlib which tries to make a well-defined set of hard things easy.

Barplot

A barplot is basically used to aggregate the categorical data according to some methods and by default it’s the mean. It can also be understood as a visualization of the group by action. To use this plot we choose a categorical column for the x-axis and a numerical column for the y-axis, and we see that it creates a plot taking a mean per categorical column.

Syntax:

barplot([x, y, hue, data, order, hue_order, …])




# import the seaborn library
import seaborn as sns
 
# reading the dataset
df = sns.load_dataset('tips')
 
# change the estimator from mean to
# standard deviation
sns.barplot(x ='sex', y ='total_bill', data = df, 
            palette ='plasma')

Output:

Explanation:
Looking at the plot we can say that the average total_bill for the male is more than compared to the female.

Countplot

A countplot basically counts the categories and returns a count of their occurrences. It is one of the simplest plots provided by the seaborn library.

Syntax:

countplot([x, y, hue, data, order, …])





# import the seaborn library
import seaborn as sns
 
# reading the dataset
df = sns.load_dataset('tips')
 
sns.countplot(x ='sex', data = df)

Output:

Explanation:

Looking at the plot we can say that the number of males is more than the number of females in the dataset. As it only returns the count based on a categorical column, we need to specify only the x parameter.

Boxplot

Box Plot is the visual representation of the depicting groups of numerical data through their quartiles. Boxplot is also used to detect the outlier in the data set.

Syntax:

boxplot([x, y, hue, data, order, hue_order, …])




# import the seaborn library
import seaborn as sns
 
# reading the dataset
df = sns.load_dataset('tips')
 
sns.boxplot(x='day', y='total_bill', data=df, hue='smoker')

Output:

Explanation:



x takes the categorical column and y is a numerical column. Hence we can see the total bill spent each day.” hue” parameter is used to further add a categorical separation. By looking at the plot we can say that the people who do not smoke had a higher bill on Friday as compared to the people who smoked.

Violinplot

It is similar to the boxplot except that it provides a higher, more advanced visualization and uses the kernel density estimation to give a better description about the data distribution.

Syntax:

violinplot([x, y, hue, data, order, …])




# import the seaborn library
import seaborn as sns
 
# reading the dataset
df = sns.load_dataset('tips')
sns.violinplot(x='day', y='total_bill', data=df,
               hue='sex', split=True)

Output:

Explanation:

Stripplot

It basically creates a scatter plot based on the category.

Syntax:

stripplot([x, y, hue, data, order, …])




# import the seaborn library
import seaborn as sns
 
# reading the dataset
df = sns.load_dataset('tips')
sns.stripplot(x='day', y='total_bill', data=df,
              jitter=True, hue='smoker', dodge=True)

Output:

Explanation:




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