How to adjust padding with cutoff or overlapping labels

python

matplotlib

plotting

data-visualization

byAnton Shumikhin·Nov 17, 2024

Slay the dragon of cutoffs and overlaps in charts by taming the beast of matplotlib's layout settings:

import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [4, 5, 6])
plt.xlabel('X Axis', labelpad=20)  # More room for X label, not just for cats anymore
plt.ylabel('Y Axis', labelpad=20)  # More room for Y label, as spacious as your ex's heart
plt.title('Title', pad=20)         # More room for title, like a penthouse suite

plt.tight_layout()  # No more scrabble for space, let's tidy up!
plt.show()

Settle your wild space management worries with labelpad and pad. Keep the layout house clean and cutoff-free with the ever faithful plt.tight_layout().

When strolling through the layout maze, you might encounter winding paths and sneaky corners. Often, having the map plt.tight_layout() can guide you, but on other days, you might want to draw your own way. This is where plt.subplots_adjust() leaps in, offering unparalleled granular control over padding tweaks and ensuring every label, legend, and title has its own stage time.

Hand-stitched Extra Padding

plt.subplots_adjust(bottom=0.15, left=0.15)  # Carving out extra space, the kitchen needs to breathe!

iPadding (OS Compatibility)

Different Operating Systems can put out your plot differently, just like your kid's drawing of you. It's of utmost importance to test your visualizations across environments, especially when you meet head-scratching instances similar to OSX with matplotlib 1.0.0. At times, hopping to a newer matplotlib version (like 3.4.2) is the peace treaty that ensures cross-OS fittingness.

Subplotting the Story

Working with the naughty subplots? Make sure to coordinate the figure size and axes at creation using plt.subplots(). Then, bring home all elements snug and cozy with tight_layout().

fig, axs = plt.subplots(2, 2, figsize=(10, 8))
fig.tight_layout()

Autolayout: Just Magic

Craving for some consistency potion? Your magician's wand figure.autolayout in your matplotlibrc file can freely sprinkle the layout adjustment treats across your figures. For on-the-go charm, brew the rcParams at runtime:

plt.rcParams['figure.autolayout'] = True

A Step Further: Understanding Figures

Embracing the tight_layout()

tight_layout() can be your best friend, but did you know, it doesn't go to sleep when you save the figures? Keep it awake by using bbox_inches="tight" in plt.savefig(). Your saved figures will thank you for their clipping-free existence.

plt.savefig('plot.png', bbox_inches="tight")

A Smooth Ride with Text and Math Expressions

Roll out the red carpet for TeX expressions within matplotlib! Those vertical bars need extra space, and the combo of r(raw string) notation and labelpad is the limo for your math formulas:

plt.ylabel(r'$\frac{a}{b}$', labelpad=40)

Customizing plot scaling like a Maestro

When scaling seems off, a wave of the plt.autoscale() wand does the trick, adjusting axis limits and resize plot to fit the data like Cinderella's shoe:

plt.autoscale()

Tasting the Object-Oriented Approach

When the procedural style of matplotlib gets too mainstream, dive into the waters of object-oriented interface. One taste and you won't go back as you see yourself having direct control over the axes properties:

fig, ax = plt.subplots()
ax.set_title('Title', pad=20)

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