How To Use Seaborn Color Palette to Color Boxplot?

Seaborn is a popular Python library that provides a high-level interface for creating informative and aesthetically pleasing visualizations. One of the key features of Seaborn is its ability to customize the color palette of plots, allowing users to highlight specific aspects of the data. In this article, we will explore how to use Seaborn's color palette to color boxplots effectively.

Understanding Boxplots

Before understanding the customization options, it is essential to have a basic understanding of boxplots. A boxplot is a standardized way of displaying the distribution of a dataset, providing information about the median, quartiles, and potential outliers. It consists of a box that represents the interquartile range (IQR), with a line inside marking the median value. Whiskers extend from the box to show the range of the data, while individual points beyond the whiskers indicate outliers.

Importing Seaborn and Loading Data

Firstly, we need to import the necessary libraries and load a dataset to work with. Seaborn is usually imported as sns, and we can use the built-in dataset "tips" for our examples.

import seaborn as sns
import matplotlib.pyplot as plt

# Load the "tips" dataset
tips = sns.load_dataset("tips")

Creating a Basic Boxplot

Let's start by creating a basic boxplot using Seaborn's default color palette.By specifying the variables x and y as columns from the "tips" dataset, we can create a boxplot that displays the distribution of the total bill amount across different days of the week. The resulting plot will use Seaborn's default color palette.

Example

import seaborn as sns
import matplotlib.pyplot as plt

# Load the "tips" dataset
tips = sns.load_dataset("tips")

sns.boxplot(x="day", y="total_bill", data=tips)
plt.show()

Output

Customizing the Color Palette

Seaborn provides various ways to customize the color palette of plots, including boxplots. Let's implement some of these options.

Using Built-in Palettes: Seaborn provides several built-in color palettes that can be accessed through the color_palette() function. These palettes provide a range of visually appealing color schemes. For example, we can use the "Blues" palette.

import seaborn as sns
import matplotlib.pyplot as plt

# Load the "tips" dataset
tips = sns.load_dataset("tips")

sns.boxplot(x="day", y="total_bill", data=tips, palette="Blues")
plt.show()

Output

import seaborn as sns
import matplotlib.pyplot as plt

# Load the "tips" dataset
tips = sns.load_dataset("tips")

sns.boxplot(x="day", y="total_bill", data=tips, palette="Blues")
plt.show()

Using Sequential Palettes: Sequential palettes are well-suited for representing data that varies continuously. One of the popular sequential palettes in Seaborn is "viridis." Below is the implementation of the sequential palettes. By setting the palette parameter to "viridis," we create a boxplot that uses a sequential color palette ranging from light to dark.

import seaborn as sns
import matplotlib.pyplot as plt

# Load the "tips" dataset
tips = sns.load_dataset("tips")

sns.boxplot(x="day", y="total_bill", data=tips, palette="viridis")
plt.show()

Output

Using Categorical Palettes: Categorical palettes are designed to distinguish between different categories effectively. One such palette is "Set3." Let's use it to color our boxplot. By specifying palette="Set3", our boxplot will display distinct colors for each category, making it easier to differentiate between the days of the week.

import seaborn as sns
import matplotlib.pyplot as plt

# Load the "tips" dataset
tips = sns.load_dataset("tips")

sns.boxplot(x="day", y="total_bill", data=tips, palette="Set3")
plt.show()

Output

Creating Custom Palettes: Seaborn allows users to create custom color palettes by specifying a list of colors in various formats (e.g., RGB, HTML color names). Let's create a custom palette with three colors. By providing the custom_palette list to the palette parameter, the boxplot will be colored using the specified colors in the order they appear in the list

import seaborn as sns
import matplotlib.pyplot as plt

# Load the "tips" dataset
tips = sns.load_dataset("tips")

custom_palette = ["#FF7F50", "#87CEEB", "#7FFF00"]
sns.boxplot(x="day", y="total_bill", data=tips, palette=custom_palette)
plt.show()

Output

Conclusion

In this article, we discussed how we can use the seaborn color Palette to color boxplots and customize the appearance of boxplots. We explored various ways to customize the color palette using Seaborn's built-in palettes, sequential palettes, categorical palettes, and even creating custom palettes. By effectively utilizing color, we can enhance the visual representation of our data and improve its interpretability.