Matplotlib.pyplot.annotate() in Python

The matplotlib.pyplot.annotate() function in the pyplot module of matplotlib enables users to add text to a figure at a specific point. Annotations are useful for highlighting specific points or adding additional information to plots.

Let us understand what Annotate is. Annotate means to label something. For instance, if you draw a graph for corona statistics, you label the x-axis as the year and the y-axis as no.of people affected. Matplotlib Annotate helps us label graphs created with the help of matplotlib. To understand it better, let's explore this in the article.

Syntax

Here is the basic syntax for annotate() is as follows:

• text: The text of the annotation.
• xy: The point being annotated (x, y).
• xytext: The position (x, y) to place the text. If None defaults to xy.
• arrowprops: A dictionary of arrow properties that define the arrow's style. If None, no arrow is drawn.
• **kwargs: Additional keyword arguments for controlling the appearance of the text.

Example

Let's take a simple example to demonstrate how to use annotate().

Program

Output:

Explanation

We first imported the pyplot module and then defined the instances to plot the x-values and y-values. Afterwards, we used the annotated function to add a specific point to the figure. Within the annotated function, we defined the text and position in the figure. Additionally, we customized the appearance of the arrow by providing additional properties in the arrowprops dictionary. This allowed us to enhance the visualization of the text.

Sine Wave

Example

Here is an example program to demonstrate adding the annotations on the sine waves.

Program

Output:

Explanation

imported the matplotlib and numpy values, then with the help of the np.array() function, created the x values and y values. We've created a sine wave using NumPy and plotted it. We then added annotations to highlight the maximum and minimum values of the sine wave. The annotate() function places text annotations at specific points on the plot, and arrows connect the annotated points with the specified text positions.

Bounding Box Around the Annotated Text

The bbox (bounding box) is a parameter available in Matplotlib that lets you specify a rectangular or customized area around text or other elements in a plot. This parameter is frequently used to highlight or enclose specific information. You can find the bbox parameter in many text-related plotting functions such as text(), annotate(), and others.

• boxstyle: The following attribute defines the form of the box that encloses an object.
• edgecolor: Edgecolor used to define the colour of the border
• facecolor: The colour of the box's background or face.
• ha (horizontal alignment), and va (vertical alignment)
• pad: The padding refers to the space between the text and the bounding box's edge.

Example

Let us consider an example program to bind the box around the annotated text.

Program

Output:

Explanation

We first specified the x and y points, then defined the arrow properties in different style. Using the annotate function, we added annotations to the plot by specifying the axes points. To draw the text around the label, we utilized the bbox property and customized its appearance.

Bounding Circle Around the Annotated Text

To create a circle around annotated text, change the bbox property boxstyle to "circle" instead of writing a new function.

Example

Let us consider the code to bind the text with a circle

Program

Output:

Explanation

We have annotated using the annotate function, defined the annotation text, and points to get the text at the specified position. In bbox properties, we defined the boxstyle as a circle to get the circle around the annotated text.

Conclusion

Annotations in Python, especially within the Matplotlib library, are a powerful tool to improve the interpretability and clarity of data visualizations. Whether you use the `annotate()` function or incorporate bounding boxes, circles, or arrows, annotations enable you to emphasize specific data points and areas of interest or convey additional information in a visually appealing way. Customization options such as text properties, colors, and shapes provide flexibility to meet diverse visualization requirements. As a result, effectively using annotations helps communicate insights and significantly enhances the communicative impact of your data visualizations, making them more informative and accessible to your audience.