5 Best Ways to Use factorplot in Seaborn to Visualize Data in Python

Rate this post

πŸ’‘ Problem Formulation: When working with statistical data in Python, it often becomes necessary to visualize complex categorical relationships. Seaborn’s factorplot is a powerful way to create charts that can show these relationships, using various plot kinds like bar plots, box plots, violin plots, etc. For example, given a dataset of car features, one might want to visualize how fuel efficiency varies across different car body types and manufacturers.

Method 1: Basic Factorplot Visualization

This method involves creating a simple, straightforward visualization using factorplot. The function is versatile, allowing customization of kind, palette, and several other plot properties. It is specifically designed to make it easy to separate plots by categorical classes.

Here’s an example:

import seaborn as sns
import matplotlib.pyplot as plt

# Load a sample dataset
tips = sns.load_dataset('tips')

# Create a factorplot
sns.factorplot(x='day', y='total_bill', hue='smoker', data=tips, kind='bar')

Factorplot Visualization

In this code snippet, the factorplot function creates a bar chart using the ‘tips’ dataset bundled with Seaborn. It differentiates between smoker and non-smoker bills across different days of the week. The output clearly shows the difference in total bills for smokers and non-smokers throughout the week.

Method 2: Customizing the Factorplot Appearance

Customizing the appearance of a factorplot can be done through parameters such as palette, aspect, and scale. This adds clarity and visual appeal, which can help in interpreting the data more effectively.

Here’s an example:

sns.factorplot(x='day', y='total_bill', hue='sex', data=tips,
              palette='BuGn_r', aspect=0.6, scale=1.5)

Customized Factorplot

By modifying the parameters palette, aspect, and scale, the resultant plot has a different color scheme, aspect ratio, and size for the bars. These customizations allow the plot to adhere to specific aesthetic requirements or to emphasize certain aspects of the data.

Method 3: Using Different Plot Kinds

The factorplot function allows users to specify the type of plot to render through the kind parameter. This includes options like ‘box’, ‘violin’, ‘point’, etc., enabling the user to choose the most appropriate visualization method for their data.

Here’s an example:

sns.factorplot(x='day', y='total_bill', hue='time', data=tips, kind='violin')

Violin Plot with Factorplot

This code snippet utilizes a violin plot to compare the distribution of total bills throughout different days, separated by whether the meal was lunch or dinner. The violin plot is particularly useful for visualizing the distribution density and the multiple peaks.

Method 4: Faceting on Additional Categorical Variables

Faceting is a powerful feature of factorplot that allows for the creation of a grid of plots based on a categorizing variable, helping to distinguish complex datasets by subcategories.

Here’s an example:

sns.factorplot(x='day', y='total_bill', hue='time', col='sex', data=tips, kind='bar')

Faceted Factorplot

The above example demonstrates how faceting can be used to create separate bar charts for each sex, comparing the total bill for lunch and dinner throughout the week. It instantly provides a comparative visualization of the data across an additional category, in this case, the gender of the diner.

Bonus One-Liner Method 5: One-Liner Plot

A quick one-liner plot with factorplot is useful for an instant visualization without the need for customization or faceting.

Here’s an example:

sns.factorplot('sex', 'tip', data=tips)

One-Liner Factorplot

This line of code produces a default plot showing the relationship between diner’s sex and tipping behavior. It’s a straightforward way to get an initial look at your dataset.


  • Method 1: Basic Visualization. Easy to implement. May be too simple for complex datasets.
  • Method 2: Custom Appearance. Enhances readability and aesthetic appeal. May require trial and error to find the best settings.
  • Method 3: Different Plot Kinds. Versatility in choosing the plot style. The appropriate kind should be chosen based on the data’s nature.
  • Method 4: Faceting Variables. Allows for comparison across another dimension. Could result in a complex plot that’s hard to interpret if overused.
  • Method 5: One-Liner Plot. Quick and straightforward. Limited customization and detail.