You’ve used vibrant crayons to make an exquisite painting. Wouldn’t it be interesting to add some text now that the picture meaning is clear? With Seaborn, we’re going to accomplish just that ! Seaborn is similar to a magical instrument that enables us to use data to produce incredible graphs or visuals. Additionally, adding text comments to our image is similar to adding such entertaining phrases to enhance its interest and educational value.

What are Annotations / Text Annotations?

Annotations, are essentially brief comments or labels that we apply to our graph in order to provide clarification. They assist us by highlighting noteworthy details, or offering more details.

The technique of adding text labels to certain plot locations is known as text annotation. The values, classifications and any other pertinent information about the data points can be found on these labels , or annotations. Your plots can be made more comprehensible and instructive by adding text comments.

Why Use Text Annotations?

To add descriptive text to particular story points, utilize text annotations. They facilitate understanding of the plot by emphasizing important points and providing context for the data. For instance, you may like to indicate which point in your data is the highest or give more context to specific data points.

Adding Annotations to the Plot

Let’s break down the process of adding text annotations to your Seaborn plots:

1. Importing Necessary Libraries

Before we start, we need to import Seaborn and other necessary libraries.

import seaborn as sns
import matplotlib.pyplot as plt

We need these libraries to create and display the plots. Using the import statement, these libraries are now available for use in our code.

2. Creating a Basic Plot

We’ll start by creating a simple plot using Seaborn. For this example, let’s use the tips dataset that comes with Seaborn.

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

# Create a scatter plot
sns.scatterplot(data=tips, x="total_bill", y="tip")
plt.show()

3. Adding Text Annotations

To add text annotations, we use the plt.text() function from Matplotlib. This function allows us to specify the position and content of the text.

# Create a scatter plot
sns.scatterplot(data=tips, x="total_bill", y="tip")

# Add a text annotation
plt.text(x=20, y=5, s="Highest tip", fontsize=12, color='red')

# Show the plot
plt.show()

• The coordinates for the text placement are x=20, y=5.
• s=”Highest tip”: The text to display.
• fontsize=12 : The size of the text.
• color=’red’: The color of the text.

In this instance the word “Highest tip”, was added to the plot at coordinates (20, 5). The x and y coordinates may be changed to position the text precisely where you like.

Example Code with Detailed Explanation

This is an example that goes through all the steps, step-by-step, of importing libraries, making a plot and adding several annotations:

import seaborn as sns
import matplotlib.pyplot as plt

# Load the penguins dataset from Seaborn's repository
penguins = sns.load_dataset("penguins")

# Create a scatter plot
sns.set(style="whitegrid")
plt.figure(figsize=(10, 6))
sns.scatterplot(data=penguins, x="bill_length_mm", y="bill_depth_mm", hue="species", palette="deep")

# Add text annotations
# Adding text at (58, 18) with the text "Largest Bill Length", font size 12, bold text, and a white background box
plt.text(x=58, y=18, s="Largest Bill Length", fontsize=12, color='black', weight='bold', bbox=dict(facecolor='white', alpha=0.5))
# Adding text at (32, 13) with the text "Smallest Bill Length", font size 12, bold text, and a white background box
plt.text(x=32, y=13, s="Smallest Bill Length", fontsize=12, color='black', weight='bold', bbox=dict(facecolor='white', alpha=0.5))

# Customizing plot with title, labels, and legend
plt.title("Penguin Bill Length vs. Bill Depth with Annotations")
plt.xlabel("Bill Length (mm)")
plt.ylabel("Bill Depth (mm)")
plt.legend(title="Species")

# Show the plot
plt.show()

Output:

Importing Libraries:

• import seaborn as sns: To create the plot import Seaborn.
• import matplotlib.pyplot as plt: To access more plot features , use Matplotlib.

Loading the Dataset:

• penguins = sns.load_dataset(“penguins”): Load the penguins dataset into a variable called penguins.

Creating the Plot:

• sns.set(style=”whitegrid”): The sns.set(style=”whitegrid”) array Plot style should be set to “whitegrid” for improved visual appeal.
• plt.figure(figsize=(10, 6)): Create a figure with a specified size.
• sns.scatterplot(data=penguins, x=”bill_length_mm”, y=”bill_depth_mm” , hue=”species”, palette=”deep”): Using bill_length_mm on the x-axis and bill_depth_mm on the y-axis, colored by species, create a scatter plot using sns.scatterplot(data=penguins, x=”bill_length_mm”, y=”bill_depth_mm”, hue=”species”, palette=”deep”).

Adding Text Annotations:

• plt.text(x=58, y=18, s=”Largest Bill Length”, fontsize=12, color=’black’, weight=’bold’, bbox=dict(facecolor=’white’, alpha=0.5)): Add a text annotation at coordinates (58, 18) saying “Largest Bill Length” with specified font size, color, weight, and background box.
• plt.text(x=32, y=13, s=”Smallest Bill Length”, fontsize=12, color=’black’, weight=’bold’, bbox=dict(facecolor=’white’, alpha=0.5)): plt.text At coordinates (32, 13), add an additional text annotation that reads “Smallest Bill Length” , and includes the font size, color, weight and backdrop box specifications.

Customizing Plot:

• plt.title(“Penguin Bill Length vs. Bill Depth with Annotations”): Add a title to the plot.
• plt.xlabel(“Bill Length (mm)”): Label the x-axis.
• `plt.ylabel(“Bill Depth (mm)”)

Customizing Your Annotations

You can customize the appearance of your annotations using various parameters:

1. color: Change the text color.
2. size: Adjust the text size.
3. fontweight: Make the text bold or light.
4. horizontalalignment and verticalalignment: Control text positioning.

Best Practices for Adding Annotations

• Maintain Simplicity: Refrain from packing too many annotations into your plot. Emphasize only the most crucial details.
• Choose readable fonts: Make sure your annotations can be read against the plot background, in terms of both color and text size.
• Positioning: Make sure the annotations don’t cross over into other narrative parts.

Conclusion

One effective technique to improve your data visualizations is to add text annotations to your Seaborn charts. It facilitates the development of more comprehensible, and informative graphs. You may begin producing annotated plots, that clearly convey your data insights by following the guidelines and best practices provided in this blog article.

Remember that practice makes perfect. Continue experimenting with various annotation styles and adapt them to meet your unique requirements. Happy plotting!