Basic beeswarm plots using Seaborn

Beeswarm definition

Imagine you want to know how your friends' height is distributed.

To do this, you can use a swarm plot, which is a visual way of seeing individual data points (in this case, the height of your friends) and how they are distributed.

Circles are slightly shifted to avoid overlaps. It ends up in a neat organic shape that is visually attracting and avoids to hide information. It allows you to quickly understand the extent and distribution of data without losing any information.

You can read more about beeswarm in the dedicated section of the gallery.

Libraries

First, you need to install the following librairies:

• seaborn is used for creating the chart witht the swarmplot() function
• matplotlib is used for plot customization purposes
• numpy is used to generate some data

Don't forget to install seaborn if you haven't already done so with the pip install seaborn command.

# Libraries
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

plt.rcParams["figure.dpi"] = 300

Dataset

Since beeswarm plots are meant to represent continuous variables, let's generate a sample of 100 randomly distributed observations using numpy and its random.normal() function. Our sample is generated with a mean of 10 and a standard deviation of 5.

my_variable = np.random.normal(loc=10, scale=5, size=1000)

Basic beeswarm plot

fig, ax = plt.subplots(figsize=(10, 10))

sns.swarmplot(y=my_variable, ax=ax)

plt.show()

Color and orientation

Modify the colors

The following code uses the color, edgecolor and linewidth arguments to modify the style of points.

• color defines the point color
• edgecolor defines the color of the edge color
• linewidth defines the the edge size. The edgecolor will not appear if you don't explicit the latter argument since its default value is 0.

Use another orientation

fig, ax = plt.subplots(figsize=(10, 10))

sns.swarmplot(
    x=my_variable,
    ax=ax,
    color="red",  # Point color
    edgecolor="black",  # Edge color
    linewidth=0.9,  # Edge size
)
plt.show()

Beeswarm with multiple groups

Dataset

First, we need to create data with 2 groups. To do this, we take the following steps:

• Define the sample size per group. Given that we have two groups, there will be 100 people in each, for a total of 200.
• Create the data for each group (here, we give them a different mean with loc=0 VS loc=2, in order to have sufficiently different groups)
• Create the list containing the group name for each observation

sample_size = 500  # Define the size of the random data samples.

data_group1 = np.random.normal(
    loc=2, scale=2, size=sample_size
)  # Generate data points for 'Group 1'
data_group2 = np.random.normal(
    loc=5, scale=2, size=sample_size
)  # Generate data points for 'Group 2'
data_combined = np.concatenate(
    [data_group1, data_group2]
)  # Concatenate the data to create a combined dataset

category_feature = ["Group 1"] * sample_size + [
    "Group 2"
] * sample_size  # List that indicates the category for each data point

Plot

fig, ax = plt.subplots(figsize=(10, 10))

sns.swarmplot(
    x=category_feature,  # Group labels
    ax=ax,
    y=data_combined,  # Numeric variable
    palette="Set2",  # Color set used
    hue=category_feature,  # Add a legend
)

plt.xlabel("Category")
plt.ylabel("Data")
plt.show()

Customize palette

from pypalettes import load_palette

fig, ax = plt.subplots(figsize=(10, 10))

# load a palette
palette = load_palette("classic")

sns.swarmplot(
    x=category_feature,  # Group labels
    ax=ax,
    y=data_combined,  # Numeric variable
    palette=palette,  # Color set used
    hue=category_feature,  # Add a legend
    size=7,
)

plt.xlabel("Category")
plt.ylabel("Data")
plt.show()

Going further

That's it for a quick introduction to beeswarm plot with seaborn.

Please check the beeswarm section of the gallery to see many more examples with higher levels of customization.