Swarm Plot
Seaborn swarmplot is probably similar to stripplot, only the points are adjusted so it won’t get overlap to each other as it helps to represent the better representation of the distribution of values. A swarm plot can be drawn on its own, but it is also a good complement to a box, preferable because the associated names will be used to annotate the axes. This type of plot sometimes known as “beeswarm”.
Syntax: seaborn.swarmplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None, dodge=False, orient=None, color=None, palette=None, size=5, edgecolor=’gray’, linewidth=0, ax=None, **kwargs)
Parameters:
x, y, hue: Inputs for plotting long-form data.
data: Dataset for plotting.
color: Color for all of the elements
size: Radius of the markers, in points.
Example 1: Basic visualization of “fmri” dataset using swarmplot()
Python3
import seabornseaborn.set(style='whitegrid')fmri = seaborn.load_dataset("fmri")seaborn.swarmplot(x="timepoint", y="signal", data=fmri) |
Output:
Example 2: Grouping data points on the basis of category, here as region and event.
Python3
import seabornseaborn.set(style='whitegrid')fmri = seaborn.load_dataset("fmri")seaborn.swarmplot(x="timepoint", y="signal", hue="region", data=fmri) |
Output:
Example 3: Basic visualization of “tips” dataset using swarmplot()
Python3
import seabornseaborn.set(style='whitegrid')tip = seaborn.load_dataset('tips')seaborn.swarmplot(x='day', y='tip', data=tip) |
Output:
Swarmplot using Seaborn in Python
Seaborn is an amazing visualization library for statistical graphics plotting in Python. It provides beautiful default styles and color palettes to make statistical plots more attractive. It is built on the top of the matplotlib library and also closely integrated into the data structures from pandas.
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