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I would like to present individual data points (NMSE values for a number of experiments in, let's say, two categories) similar to a box plot, and I want to show all individual data points. Assuming the main direction of the data is upside-down, a jittered (or scattered) dot plot is a nice way to slightly move data points sideways to avoid overlapping dots. For a visualization of this idea, check Google Image Search or this article.

I have found and read Adding a scatter of points to a boxplot using matplotlib, but these solution involve adding random noise irrespective of the data, so one has to fine tune parameters and maybe reiterate until one has a nice solution. And then you try to reproduce these a couple of months later ;)

So I would like an automated solution that creates figures such as the ones shown here. Are there solutions to do this with Matplotlib?

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You're looking for a "swarmplot", which is nicely implemented in seaborn

import pandas as pd import seaborn as sns sns.set(style="whitegrid", palette="muted") # Load the example iris dataset iris = sns.load_dataset("iris") # "Melt" the dataset to "long-form" or "tidy" representation iris = pd.melt(iris, "species", var_name="measurement") # Draw a categorical scatterplot to show each observation sns.swarmplot(x="measurement", y="value", hue="species", data=iris)

enter image description here

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Great solution. And for those slightly intimidated by the DataFrame format and pd.melt, rest assured: "Input data can be passed in a variety of formats, including (...) Anything accepted by plt.boxplot." Awesome.

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