Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://www.geeksforgeeks.org/data-visualization-with-pairplot-seaborn-and-pandas/ below:

Data visualization with Seaborn Pairplot

Last Updated : 24 Jul, 2025

Data Visualization is the presentation of data in pictorial format. It is extremely important for Data Analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Seaborn is one of those packages that can make analyzing data much easier.

In this article, we will use Pairplot Seaborn to analyze data and, using the sns.pairplot() function.

Pairplot in Seaborn is a data visualization tool that creates a matrix of scatterplots, showing pairwise relationships between variables in a dataset, aiding in visualizing correlations and distributions.

To plot multiple pairwise bivariate distributions in a dataset, you can use the pairplot() function. This shows the relationship for (n, 2) combination of variable in a DataFrame as a matrix of plots and the diagonal plots are the univariate plots.

Syntax:

seaborn.pairplot( data, \*\*kwargs )

Parameter:

• data: Tidy (long-form) dataframe where each column is a variable and  each row is an observation.
• hue: Variable in “data“ to map plot aspects to different colors.
• palette: dict or seaborn color palette
• {x, y}_vars: lists of variable names, optional
• dropna: boolean, optional

First of all, We see Upload seaborn librarry 'tips' using pandas. Then, we will visualize data with seaborn.

# importing packages 
import seaborn 
import matplotlib.pyplot as plt 
# loading dataset using seaborn 
df = seaborn.load_dataset('tips')
df.head() 

Output:

   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  3.50    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

Let's plot pairplot using seaborn:

We will simply plot a pairplot with tips data frame.

seaborn.pairplot(df)
plt.show() 

Output:

Each combination of variables is represented by a scatter plot, and the diagonal plots show the distribution of each individual variable.

• The relationship between total_bill and tip is stronger for larger parties. 
• There is a positive correlation between tip and size. 
• The relationship between tip and size is stronger for larger total bill amounts.
• There is a weak positive correlation between total_bill and size. This means that larger parties tend to have larger bills, but the relationship is not very strong.
• There is no clear relationship between sex and tip or total_bill.
• There is a weak positive correlation between day and total_bill. This means that bills tend to be slightly larger on Saturdays and Fridays than on other days of the week.
• There is no clear relationship between time and tip or total_bill.
• There is a weak positive correlation between smoker and tip. This means that smokers tend to tip slightly more than non-smokers.

import seaborn as sns
import matplotlib.pyplot as plt

df = sns.load_dataset('tips')
selected_vars = ['total_bill', 'tip']
sns.pairplot(df, vars=selected_vars)
plt.show()

Output:

The plots on the diagonal show the distribution of each individual variable. For example, the top left plot shows the distribution of total bills, and the bottom right plot shows the distribution of tips.

The off-diagonal plots show the relationship between two variables. For example, the top right plot shows the relationship between total bill and tip. There is a positive correlation between these two variables, which means that larger bills tend to have larger tips.

import seaborn 
import matplotlib.pyplot as plt 
df = seaborn.load_dataset('tips')
seaborn.pairplot(df,hue ='size') 
plt.show() 

Output:

The points in this scatter plot are colored by the value of size, so you can see how the relationship between total_bill and tip varies depending on the size of the party.

• There is a positive correlation between total_bill and tip. This means that, in general, larger bills tend to have larger tips.
• There is a positive correlation between tip and size. This means that, in general, larger parties tend to have larger tips.
• The relationship between tip and size is stronger for larger total bill amounts.

import seaborn as sns
import matplotlib.pyplot as plt

df = sns.load_dataset('tips')
sns.pairplot(df, hue="size", palette="husl")
plt.show

Output:

In Seaborn's Pairplot, the 'diag_kind' parameter specifies the type of plot to display along the diagonal axis, representing the univariate distribution of each variable. Options include 'hist' for histograms, 'kde' for kernel density estimates, and 'scatter' for scatterplots. Choose based on the nature of the data and analysis goals. Here, let's plot with kernel density estimates.

import seaborn as sns
import matplotlib.pyplot as plt

df = sns.load_dataset('tips')
sns.pairplot(df,diag_kind = 'kde')
plt.show

Output:

The kind parameter allows to change the type of plot used for the off-diagonal plots. You can choose any like scatter, kde, or reg (regression).

sns.pairplot(df, kind='reg')
plt.show()

Output:

The markers parameter allows you to specify different markers for different categories.

sns.pairplot(df, hue='sex', markers=["o", "s"])
plt.show()

Output:

If you are interested in only a subset of the variables, you can specify them using the vars parameter.

sns.pairplot(df, hue='sex', vars=['total_bill', 'tip', 'size'])
plt.show()

Output:

For advanced customization, you can access the underlying FacetGrid object and modify it further.

g = sns.pairplot(df, hue='day')
g.fig.suptitle("Pairplot of Tips Dataset", y=1.02)  # Add a title
g.set(xticks=[], yticks=[])  # Remove tick labels
plt.show()

Output:

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4