Using Seaborn Markers in Python Data Visualization

Markers allow you to distinguish data points, highlight trends, and provide a deeper understanding of the data.

In this tutorial, you’ll learn how to use markers in Seaborn, including different marker types, customizing markers, and marker mapping.

 

 

Marker Types

Built-in Markers

Seaborn supports a wide array of built-in markers.

Code:

import seaborn as sns
import matplotlib.pyplot as plt
data = {"Category": ["A", "B", "C", "D"], "Value": [10, 15, 7, 10]}

# Using circle marker
sns.scatterplot(x="Category", y="Value", data=data, marker='o')
plt.title("Scatter Plot with Circle Markers")
plt.show()

# Using square marker
sns.scatterplot(x="Category", y="Value", data=data, marker='s')
plt.title("Scatter Plot with Square Markers")
plt.show()

Output:

The first plot uses circle markers, while the second one uses square markers.

The built-in markers available in Seaborn are:

1. 'o': Circle
2. '.': Point
3. ',': Pixel
4. 'x': X
5. 'X': X (filled)
6. '+': Plus
7. 'P': Plus (filled)
8. 's': Square
9. 'd': Thin diamond
10. 'D': Diamond
11. 'p': Pentagon
12. 'H': Hexagon
13. 'h': Hexagon
14. 'v': Triangle down
15. '^': Triangle up
16. '<': Triangle left
17. '>': Triangle right
18. '1': Tri down
19. '2': Tri up
20. '3': Tri left
21. '4': Tri right
22. '8': Octagon
23. '_': Horizontal line
24. '|': Vertical line
25. 'None', ' ' or '': Nothing
26. A tuple (verts, 0): The first element of the tuple is the marker path vertices, and the second element is the marker path codes.

Custom Markers

You can create custom markers using strings, paths, or lists of vertices.

Code:

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.markers import MarkerStyle
from matplotlib.path import Path
data = {"Category": ["A", "B", "C", "D"], "Value": [10, 15, 7, 10]}
df = pd.DataFrame(data)
star = MarkerStyle(marker=Path.unit_regular_star(5))

# Here, we use the index of the DataFrame as the x-coordinate for the scatter plot
sns.scatterplot(x=range(len(df['Category'])), y='Value', data=df, marker=star, s=200)
plt.xticks(range(len(df['Category'])), df['Category'])
plt.xlabel('Category')
plt.ylabel('Value')
plt.show()

Output:

This code shows how to create a custom star-shaped marker using matplotlib Path.

 

Markers in Line Plot

You can add markers to line plots to highlight each data point.

Code:

import seaborn as sns
import matplotlib.pyplot as plt
data = {"Month": ["Jan", "Feb", "Mar", "Apr"], "Sales": [200, 220, 250, 300]}
sns.lineplot(x="Month", y="Sales", data=data, marker='o')
plt.title("Monthly Sales with Markers")
plt.show()

Output:

 

Customize Marker Size

You can use the s parameter to adjust the marker size.

Code:

import seaborn as sns
import matplotlib.pyplot as plt
data = {"Category": ["A", "B", "C", "D"], "Value": [10, 15, 7, 10]}

# Scatter plot with varying marker sizes
sns.scatterplot(x="Category", y="Value", data=data, s=200)
plt.title("Scatter Plot with Custom Marker Sizes")
plt.show()

Output:

In this example, the size of the markers is 200.

 

Marker Mapping

To create marker mapping in Seaborn, you can use the markers parameter in the sns.scatterplot() function.

Code:

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
data = pd.DataFrame({
    'Value': [10, 20, 15, 30, 25, 35, 40, 45, 30, 50],
    'Category': ['A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B'],
    'Group': ['X', 'X', 'Y', 'Y', 'Z', 'Z', 'X', 'X', 'Y', 'Y']
})

# Define marker mapping
marker_map = {'X': 'o', 'Y': '^', 'Z': 's'}
sns.scatterplot(x='Category', y='Value', data=data, hue='Group', style='Group', markers=marker_map)
plt.title("Scatter Plot with Marker Mapping")
plt.show()

Output:

We define a dictionary marker_map that maps the values in the 'Group' column to specific markers (‘o’ for circle, ‘^’ for triangle, and ‘s’ for square).

The markers parameter is used to specify the marker mapping defined.