What is the Difference between stripplot() and swarmplot()?

What is Swarmplot() and Stripplot?

In python seaborn, the swarmplot() positions the points using a technique called "beeswarm" that adjusts the points to avoid overlap. This results in a plot where the points are spread out and are easier to distinguish, but the relative positions of the points within a category are not preserved. Whereas, stripplot() positions the points on a categorical axis, with one category per tick. The points are not adjusted to avoid overlap, so they may overlap if many points are in the same category.

Feature	stripplot()	swarmplot()
Purpose	Display the distribution of a single variable	Display the distribution of a single variable while avoiding overlap between points
Visualization	Points are plotted along a number line with jittered positional offsets to avoid overlap	Points are positioned on the number line such that they don't overlap with each other
Usefulness	Useful for showing the distribution of a single variable, especially in cases where the number of data points is large and overlap between points is significant	Useful for showing the distribution of a single variable while avoiding overlap between points and preserving the individual data points' positional information
Overlap	Points can overlap significantly	Points do not overlap significantly
Scalability	Less scalable as the number of data points increases	More scalable as the number of data points increases

stripplot() and swarmplot() are both functions in the Seaborn library in Python that visualize the distribution of a numerical variable for different categories.

Strip Plot

The distribution of several distinct one-dimensional values is visualized using a strip plot, which is a single-axis scatter plot. The values are shown as dots along a single axis, and identically sized dots may overlap. The color or opacity of the dots can be altered to represent overlapping values, or a jitter plot or counts plot can be used in their place. To examine the distributions of data points across various values, groups, or ranges, many strip plots are typically displayed side by side.

Example 1

import pandas as p
import matplotlib.pyplot as plt
import seaborn as sns

df = pd.DataFrame({"Quantity": [15,26,17,18,15,36,27,18,25,16,17,28,15,16,17,28],

"Price":[1900,1000,1500,1600,1300,1400,1500,1800,1100,1200,1400,1500,1600,1700,1800,1900],
   "Month" : [2,3,2,3,2,3,2,3,4,4,4,5,5,5,4,3],
   "Merchandise":['X','X','X','X','Z','Z','Z','Z',
   'Y','Y','Y','Y','X','X','Z','Z']})

sns.swarmplot(data = df, y = "Price", x = "Quantity")
plt.show()

Code explanation

We incorporate the packages Pandas as pd, matplotlib.pyplot as plt, and Seaborn as sns at the beginning of the code. The DataFrame() function is then used to specify data collection. The Pandas module is connected to this function. Four separate arrays are made. The quantity of the sold-out goods is shown in the first array. The prices of the goods are displayed in the second array. The record of the months is kept in the third array. The product names are listed in the final array.

We use the swarmplot() method in the next step to generate the swarm graph. Finally, we use matplotlib.pyplot library's display() function.

Example 2

The statistical category plane has a specified region for each "hue" parameter dimension. When we use the "hue" option and set the "dodge" parameter to True, the objects are isolated for different hue variations. The "palette" property represents the multiple hue attribute shades.

import seaborn
import matplotlib.pyplot as plt
seaborn.set(style="whitegrid")
tips = seaborn.load_dataset("tips")

seaborn.swarmplot(x="day", y="total_bill", hue="smoker",
   data=tips, palette="Set2", dodge=True)

plt.show()

Code Explanation

We invoke the set() method from the Seaborn package after adding the matplotlib.pyplot and Seaborn libraries. To this function, we supply the style as an argument. We set the style parameter's value to "white grid." It displays the graph's background color.

We now utilize the loaded dataset() method to acquire the built-in data frame. This function takes the input "tips" and is derived from the Seaborn header file. Next, we construct the swarm chart using the swarmplot() function. Here, the function's arguments are the title of both axes, the color value, the data, the palette, and the dodge. While the y-axis displays the record of the total bill, the x-axis displays the record of the days.

Swarm Plot

When you wish to display all observations combined with a depiction of the underlying distribution, a swarm plot can be created on its own or as a supplement to a box or violin plot.

To arrange the points appropriately, a precise transformation between the data and the point coordinates is necessary. As a result, non-default axis limitations must be established before the plot may be created.

Various formats can be used to pass input data, including −

• list, numpy, or pandas representations of data vectors directly to the x, y, and/or hue parameters passed series objects.

• a "long-form" DataFrame, in which case the data plotting is controlled by the x, y, and hue variables.

• a "wide-form" DataFrame that plots each numerical column.

• A collection or array of vectors.

Example 1

Draw a single horizontal swarm plot −

import seaborn as sns
sns.set(style="whitegrid")
tips = sns.load_dataset("tips")
ax = sns.swarmplot(x=tips["total_bill"])

Example 2

Grouping data points based on category, here as region and event.

import seaborn

seaborn.set(style='whitegrid')
fmri = seaborn.load_dataset("fmri")
 
seaborn.swarmplot(x="timepoint",
   y="signal",
   hue="region",
   data=fmri)

Conclusion

In conclusion, stripplot() and swarmplot() are functions in the Python library Seaborn used to create scatterplots. These plots visualize the relationship between two variables and the data distribution.

Stripplot() is a function that plots a scatterplot of the data with the points spread along the x-axis. It allows you to specify the x and y variables, the data, and various customization options such as the points' color, size, and style. Stripplot() is useful for visualizing the distribution of a continuous variable within each category of a categorical variable.

Swarmplot() is a function that plots a scatterplot of the data with the points spread out along the x-axis to avoid overlap. It allows you to specify the x and y variables, the data, and various customization options such as the points' color, size, and style. Swarmplot() is useful for visualizing the distribution of a continuous variable within each category of a categorical variable, particularly when the number of points is large.