How to remove axis, legends, and white padding

python

plotting

matplotlib

image-processing

byAlex Kataev·Dec 24, 2024

Eliminate any unnecessary noise from your matplotlib plot via:

import matplotlib.pyplot as plt

# Your plot code goes here...
# It's like Bob Ross painting, but with less hair and more brackets

# Introducing the no-chill method. Say 'Bye Felicia' to axis, legend, and white space
plt.gca().set_axis_off()
plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
plt.margins(0,0)
plt.gca().xaxis.set_major_locator(plt.NullLocator())
plt.gca().yaxis.set_major_locator(plt.NullLocator())
# Save it like your high school photo; no extra fluff
plt.savefig('figure.png', bbox_inches='tight', pad_inches=0, transparent=True)

You're left with a pure plot, tightly trimmed for a sharp visual representation.

Detailed guide to beautifying your output image

Peach up your presentations

To enhance your figure's visibility, consider these:

dpi parameter: Dictates the resolution of your plot. It's like the gym; the higher your dpi, the more resolution is buffed up.
aspect parameter: Proportional scaling for charming symmetry.
Colormaps: A sprinkle of vibrant colors for tastier data delivery.

Direct save? Yes, please!

If you need a straightforward, no-nonsense image saving, here's your friend:

plt.imsave(): The quick-and-dirty approach when your plot is ready to hit the image runway without any trims or embellishments.

More power to you

Upping your game for the non-standard requirements? Turn to:

plt.Axes(): This gives you the celebrity treatment with custom padding and frames for scenarios where attention to detail is key.
make_image(): Your personal stylist for perfect dimensions, ensuring your image never oversteps its boundaries.

Prepping up perfection: Quality assurance and troubleshooting

Verify before you apply

Before you transform your data into an image:

Examine your data: High-quality input, high-quality output.
Authenticate the format of your data: Invalid formats or non-numeric values can sabotage your visual masterpiece.

Perfecting the save

Time to save? Here's what you need in your favor:

A meaningful filename: Organize and retrieve the file with ease.
The appropriate file extensions: 'png' for crisp quality across platforms.

Enhanced cleanup methods

To achieve the refined and professional-looking plot:

Juggle with bbox_inches, pad_inches, and supplementary subplots_adjust parameters. They're your magic sauce for the perfect professional finish.

explain-codes / Python / How to remove axis, legends, and white padding

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