Seaborn is a popular data visualization library in Python, built on top of Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. However, one common issue that users might encounter when creating scatterplots with Seaborn is the appearance of white borders around the dots. This article will guide you through removing these white borders and customizing your scatterplot to achieve the desired look.

Introduction to Seaborn Scatterplots

Seaborn makes it easy to create scatterplots with just a few lines of code. A scatterplot displays values for two variables as a collection of points. For instance, if you have a dataset containing information about the height and weight of individuals, you can visualize the relationship between these variables using a scatterplot.

Here is a basic example of creating a scatterplot using Seaborn:

import seaborn as sns
import matplotlib.pyplot as plt

# Sample data
data = sns.load_dataset('tips')

# Basic scatterplot
sns.scatterplot(x='total_bill', y='tip', data=data)
plt.show()

This code generates a scatterplot of total bill versus tip from the tips dataset. However, you might notice that the points have a white border around them. While this default style can be useful for readability in some cases, you might want to remove it for a cleaner look.

Removing the White Border

To remove the white border around the dots, you can use the edgecolor parameter of the scatterplot function. Setting edgecolor to None or ‘none’ will eliminate the border. Here’s how you can do it:

# Scatterplot without white borders
sns.scatterplot(x='total_bill', y='tip', data=data, edgecolor='none')
plt.show()

In this example, setting edgecolor to ‘none’ removes the white borders, resulting in a cleaner scatterplot.

Customizing Dot Appearance

Besides removing the white border, you might want to further customize the appearance of the dots in your scatterplot. Seaborn provides several parameters for this purpose, including size, hue, and style.

Changing Dot Size

You can change the size of the dots using the s parameter:

# Scatterplot with larger dots
sns.scatterplot(x='total_bill', y='tip', data=data, s=100, edgecolor='none')
plt.show()

Coloring Dots by Category

To color the dots based on a categorical variable, use the hue parameter:

# Scatterplot with colored dots
sns.scatterplot(x='total_bill', y='tip', hue='sex', data=data, edgecolor='none')
plt.show()

Styling Dots by Category

Similarly, you can use the style parameter to differentiate the dots by category:

# Scatterplot with different styles
sns.scatterplot(x='total_bill', y='tip', hue='sex', style='time', data=data, edgecolor='none')
plt.show()

Combining Customizations

You can combine these customizations to create a more informative and visually appealing scatterplot:

# Custom scatterplot
sns.scatterplot(x='total_bill', y='tip', hue='sex', style='time', size='size', data=data, edgecolor='none', sizes=(20, 200))
plt.show()

In this example, the dots are colored by sex, styled by time, and sized by a variable named size in the dataset. Adjusting these parameters helps in better understanding the data and making your plots more insightful.

Conclusion

Seaborn scatterplots are a powerful tool for visualizing relationships between variables. While the default settings may include white borders around the dots, you can easily remove them by setting the edgecolor parameter to ‘none’. Additionally, Seaborn offers various options to customize the appearance of your scatterplots, allowing you to create clear and informative visualizations tailored to your specific needs.

Colab Link: https://colab.research.google.com/drive/17L4MhzvbgnyWmlJ1T5pZdEEDWnhLCicn?usp=sharing