Matplotlib - Artists

Understanding Artists in Matplotlib

In Matplotlib, almost everything you see on a plot is an instance of an Artist, which are objects that represent various components of a plot. Whether it's a line representing data, a text label, or even the tick marks on an axis, everything in a Matplotlib plot is an Artist object.

Before exploring the Artist hierarchy, let’s observe the below image to understand the basic components of a figure in matplotlib −

In the figure, each element, such as lines and points that represent data, along with minor ticks and points associated text labels, can be identified as individual Artist objects.

Artist Hierarchy

In matplotlib's hierarchy, Artists can be broadly categorized into two types −

Primitive Artists − These are the basic building blocks of a plot. Examples include Line2D, Rectangle, Text, AxesImage, and more. These are the standard graphical objects that we want to paint onto our canvas.
Composite Artists − These are higher-level structures that contain other Artists. Examples include Figure, Axis, and Axes. A Figure is like a canvas that holds everything, while Axes represents a specific plot within the figure.

Creating and Accessing the Artists

Artists are typically created through plotting methods on an Axes object. These methods not only facilitate the creation of visual elements but also return specific artist instances corresponding to the plotted elements.

Below are some common plotting methods and the corresponding artists they generate −

annotate − Generates an Annotation artist for text annotations.
bar − Creates a Rectangle artist for bar charts.
errorbar − Produces Line2D and Rectangle artists for error bar plots.
fill − Generates a Polygon artist for shared areas.
hist − Creates Rectangle artists for histograms.
imshow − Generates an AxesImage artist for image data.
legend − Produces a Legend artist for Axes legends.
plot − Creates Line2D artists for XY plots. Returns a list when multiple datasets are plotted.
scatter − Generates a PolyCollection artist for scatter charts.
text − Produces a Text artist for displaying text on the plot.

Example

Let's see an example for creating and accessing the artists using the plot() method.

import matplotlib.pyplot as plt
import matplotlib.artist as martist
import numpy as np

# Create a subplot
fig, ax = plt.subplots()

# Generate random data
x, y = np.random.rand(2, 100)

# Use the plot method to create a Line2D artist
lines = ax.plot(x, y, '-', label='example')

# Accessing the Line2D artists created by the plot method
print('Line2D artists created by the plot method:', lines)

# Retrieve all Line2D artists associated with the Axes
lines_on_axes = ax.get_lines()

# Display the retrieved Line2D artists
print('Line2D artists obtained using get_lines():', lines_on_axes)

# Accessing the first (and in this case, the only) Line2D artist
print('Accessing the first Line2D artist', ax.get_lines()[0])

Output

On executing the above code we will get the following output −

Line2D artists created by the plot method: [<matplotlib.lines.Line2D object at 0x000001DB3666A620>]
Line2D artists obtained using get_lines(): 
Accessing the first Line2D artist Line2D(example)

Modifying the Artists Properties

Artists have various properties that define their appearance and behavior. These properties include color, linestyle, linewidth, and markers. Modifying these properties enables us to control the visual aspects of the plot.

Example

The following example demonstrates how to create a simple plot and then modify the properties of the associated Line2D artist.

import matplotlib.pyplot as plt
import numpy as np

# Creating a simple plot
fig, ax = plt.subplots(figsize=(7, 4))
x = np.linspace(0, 2 * np.pi, 100)
y = np.sin(x)

# Use the plot method to create a Line2D artist
lines = ax.plot(x, y)

# Displaying initial properties of the Line2D artist
print('Initial properties of the Line2D artist:')
print('Color:', ax.get_lines()[0].get_color())
print('Line Style:', ax.get_lines()[0].get_linestyle())
print('Marker Size:', ax.get_lines()[0].get_markersize())

# Modifying properties 
lines[0].set(color='green', linewidth=2)
lines[0].set_linestyle(':')         
lines[0].set_marker('s')            
lines[0].set_markersize(10)

# Accessing the modified properties of the Line2D artist
print('\nModified properties of the Line2D artist:')
print('Color:', ax.get_lines()[0].get_color())
print('Line Style:', ax.get_lines()[0].get_linestyle())
print('Marker Size:', ax.get_lines()[0].get_markersize())

Output

On executing the above code we will get the following output −

Initial properties of the Line2D artist:
Color: #1f77b4
Line Style: -
Marker Size: 6.0

Modified properties of the Line2D artist:
Color: green
Line Style: :
Marker Size: 10.0

Manipulating Artist Data

Beyond styling properties, artists may also contain data. For example, the Line2D object has a data property that can be modified using the set_data() method.

Example

Here is an example that manipulate artist data.

import matplotlib.pyplot as plt
import numpy as np

# Creating a simple plot 
fig, ax = plt.subplots(figsize=(7, 4))
x = np.linspace(0, 2 * np.pi, 100)

# Initial sinusoidal curve
y = np.sin(x)

# Use the plot method to create a Line2D artist a sinusoidal curve
lines = ax.plot(x, y)

# Modifying the artist data with the cosine 
lines[0].set_data([x, np.cos(x)])         

# Displaying the plot
plt.show()

Output

On executing the above code we will get the following output −

Manual Addition of Artists

While many artists have helper methods, there are scenarios where manual addition is necessary. For instance, adding patches like circles or rectangles directly to an Axes object can be possible by using the add_artist() method.

Example

In this example, we manually add circle patches to the axes.

import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as mpatches

# Creating a simple plot 
fig, ax = plt.subplots(figsize=(7, 4))
x = np.linspace(0, 2 * np.pi, 100)

# Initial sinusoidal curve
y = np.sin(x)

# Use the plot method to create a Line2D artist for a sinusoidal curve
lines = ax.plot(x, y)

# Adding a circle 
circle = mpatches.Circle((3, 0), 0.25, ec="none")
ax.add_artist(circle)

# Adding another clipped circle 
clipped_circle = mpatches.Circle((3, 1), 0.125, ec="none", facecolor='C1')
ax.add_artist(clipped_circle)
ax.set_aspect(1)

# Adding a title to the plot
plt.title('Manual Addition of Artists')

# Displaying the plot
plt.show()

Output

On executing the above code we will get the following output −

Removing Artists

Matplotlib provides a flexible way to remove specific artists from a plot. The remove() method of an artist allows for removing an Artist from its Axes list.

Example

Here is an example that demonstrates how to remove specific artists (Line2D and text) from the plot.

import numpy as np
import matplotlib.pyplot as plt

# Create the figure
fig, ax = plt.subplots(figsize=(7, 4))
x = np.linspace(-10, 10, 100)
y = np.sin(x)

# plot the scatter plot 
scat = ax.scatter(x, y)

# Assign the another Line2D artist to line_2
line_2 = plt.plot(x, np.cos(x), linestyle='dashed')  

plt.margins(0.2)
plt.title("After removing the text and line artist")

text = fig.text(0.5, 0.96, "$y=sin(x)$")

# Remove the line_2 artist
l = line_2.pop(0)
l.remove()

# Remove the text artist
text.remove()

# Displaying the plot
plt.show()

Output

On executing the above code you will get the following output −

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