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In the field of data visualization with Python, especially with the Seaborn library, stripplot() and swarmplot() are widely employed to showcase how data points are spread out across a categorical axis. Although they share similarities, these plots possess unique characteristics and applications that make them appropriate for various situations. This article examines the distinctions between stripplot() and swarmplot() and offers tips on their effective utilization.
Difference between stripplot() and swarmplot() in Python
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| Stripplot()
| Swarmplot()
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Purpose
| Visualize the distribution of a single variable
| Visualize the distribution of a single variable while avoiding overlap between points
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Visualization
| Points are placed on a number line with random positional adjustments to prevent clustering.
| Points are arranged on the number line in a way that ensures they do not intersect
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Overlap
| Points can overlap significantly
| Points do not overlap significantly
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Scalability
| Becomes less scalable with an increasing number of data points.
| More efficient with a higher quantity of data points.
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Overview of stripplot() The stripplot() function in Seaborn creates a scatter plot where each data point is plotted along one axis with some jitter (random noise) added to the other axis to prevent overlapping of points. This type of plot is particularly useful for visualizing the distribution of data points in a single dimension.
Main characteristics of stripplot()- Jittering: Adding jitter to the data points with stripplot() introduces random noise along the categorical axis. This aids in dispersing the points to enhance visibility, particularly in cases of overlapping data points.
- Personalization: The level of jitter can be managed through the jitter parameter. Adding jitter can be done by setting jitter=True for a default amount or jitter=0.2 for a specific amount.
- Density Indication: Although stripplot() can show data point clustering to indicate density, the random jitter may obscure precise point locations and density accuracy.
Python
import seaborn as sns
import matplotlib.pyplot as plt
# Sample data
data = sns.load_dataset("tips")
# Create a strip plot
sns.stripplot(x="day", y="total_bill", data=data, jitter=True)
plt.title("Strip Plot of Total Bill by Day")
plt.show()
Output
 Overview of swarmplot()The swarmplot() function in Seaborn, on the other hand, arranges the data points in a non-overlapping manner. Unlike stripplot(), swarmplot() ensures that points are adjusted along the categorical axis so that they do not overlap, giving a better sense of the density of points.
Main characteristics of swarmplot()- Non- overlapping arrangement: In contrast to stripplot(), swarmplot() guarantees no overlapping of data points. A packing algorithm is utilized to modify the positions of the points on the categorical axis while preserving their relative positions.
- Data visualization: It offers a more transparent visualization of data density by arranging points to prevent overlap. Groups of points show regions with increased concentration.
- Aesthetics: It stands out visually and is more user-friendly when dealing with datasets that have numerous overlapping points.
Python
import seaborn as sns
import matplotlib.pyplot as plt
# Sample data
data = sns.load_dataset("tips")
# Create a swarm plot
sns.swarmplot(x="day", y="total_bill", data=data)
plt.title("Swarm Plot of Total Bill by Day")
plt.show()
Output
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