Rotate label text in seaborn

The quest for the perfect angle

To avoid label congestion and opt for legibility, the rotation angle forms the essence. Ranging from something subtle to an orthogonal spin, the angle has got your axis covered:

• Short labels? A gentle angle between 30° and 45° usually does the trick.
• Dealing with long labels? An orthogonal or near-orthogonal rotation (90°) resolves your space and readability conundrum.
• Working with a seaborn.FacetGrid object (e.g., from seaborn.catplot()), rotate labels like this:

g = sns.catplot(...)
# Spin it like a DJ at 45 degrees!
g.set_xticklabels(rotation=45) 
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• Multiple graphs on a groove? Rotate labels only for the base plot to avoid duplicating transformations.

Aesthetics pleasing to the eyes, and mind

The context of your visual presentation matters. An unnecessary rotation can end up stealing the show, drawing attention away from what is essential. What matters is that your labels compliment the narrative of your data, not distract from it.

Dialing in your plots

While plt.xticks() seems like a great starting point, plotting in seaborn might require flexibility. You might need to consider other functions depending on your specific seaborn plot:

• In case of a factorplot, the following can work like a charm

(ax,) = g.facet_axis(0, 0)
# Abracadabra! Rotate at 90 degrees!
ax.tick_params(axis='x', rotation=90) 
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• For a clustermap, try this

sns.clustermap(data, row_cluster=False)
# You spin my labels right round, like a record baby, right round!
plt.setp(ax.xticklabels, rotation=90)
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• A sea of subplots? No worries! With a simple loop, you can rotate them all!

for ax in g.fig.axes:
    # Rotate labels of all subplots, because why not?
    plt.setp(ax.get_xticklabels(), rotation=45)
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Overcoming potential pitfalls

• When a non-standard plot or custom seaborn chart decides to play a villain with g.set_xticklabels(rotation=30), it's high time you switch your superhero robe for lower-level Matplotlib commands.
• Quite often, size does matter. Adjusting the figure size could potentially bring down the need for rotation. With larger plots, you might as well lay back and let the horizontal labels take over!
• For plots sharing axes, ensuring rotations are applied to each relevant axis is a prerequisite.
• Lastly, recall the principle of less is more. Rotate judiciously, only when it enhances data comprehension.

Getting hands dirty with examples

Why not roll up your sleeves and dive into seaborn's load_dataset feature for real-world data? This hands-on approach will allow you to witness and appreciate the immediacy of label rotation.

Striking the aesthetics-utility balance

The rotation of x-axis labels is much of an art as it is a science. The goal is to find that elegant equilibrium, where beauty meets functionality, and each label clearly and effectively contributes to the overarching data story.