Conditional means with observations

seaborn components used: set_theme(), load_dataset(), despine(), stripplot(), pointplot(), move_legend()

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

sns.set_theme(style="whitegrid")
iris = sns.load_dataset("iris")

# "Melt" the dataset to "long-form" or "tidy" representation
iris = pd.melt(iris, "species", var_name="measurement")

# Initialize the figure
f, ax = plt.subplots()
sns.despine(bottom=True, left=True)

# Show each observation with a scatterplot
sns.stripplot(
    data=iris, x="value", y="measurement", hue="species",
    dodge=True, alpha=.25, zorder=1, legend=False
)

# Show the conditional means, aligning each pointplot in the
# center of the strips by adjusting the width allotted to each
# category (.8 by default) by the number of hue levels
sns.pointplot(
    data=iris, x="value", y="measurement", hue="species",
    join=False, dodge=.8 - .8 / 3, palette="dark",
    markers="d", scale=.75, errorbar=None
)

# Improve the legend
sns.move_legend(
    ax, loc="lower right", ncol=3, frameon=True, columnspacing=1, handletextpad=0
)