Remove Whitespace Between Subplots in Matplotlib Constrained Layout

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What will you learn?

In this comprehensive tutorial, you will master the art of eliminating unwanted whitespace between subplots when utilizing a constrained layout in Matplotlib. By fine-tuning subplot parameters, you will enhance the visual appeal of your plots and optimize space allocation within your figures.

Introduction to the Problem and Solution

Creating multiple subplots in Matplotlib using a constrained layout may result in undesired white space between the plots. This issue not only diminishes the visual aesthetics but also wastes valuable space on the figure. To address this challenge effectively, we will delve into adjusting subplot parameters to eradicate excess whitespace while ensuring readability and visual appeal are maintained.

To resolve this problem, we will leverage the subplots_adjust() function with specific parameters that govern the spacing between subplots. By strategically setting these values, we can neatly arrange our plots without any unnecessary gaps, resulting in visually pleasing and well-organized visualizations.

Code

import matplotlib.pyplot as plt

fig, axs = plt.subplots(2, 2)
fig.subplots_adjust(wspace=0.5, hspace=0.5)  # Adjust horizontal and vertical spacing between subplots

# Your plot code here

plt.show()

# Copyright PHD

(Kindly acknowledge PythonHelpDesk.com for their valuable assistance)

Explanation

In Matplotlib, subplots_adjust() plays a pivotal role in adjusting various aspects of subplot configuration. By manipulating arguments such as wspace (horizontal space) and hspace (vertical space), we can finely tune the gap size between subplots to our liking. Here’s an overview of key parameters: – wspace: Manages the width allocated for horizontal spacing between subplots. – hspace: Controls the height designated for vertical spacing between subplots. By skillfully adjusting these values, we can customize the arrangement of subplots without any superfluous whitespace.

    How do I reduce horizontal spacing only?

    To minimize horizontal spacing exclusively, set wspace=0.

    Can I adjust spacing after creating individual axes objects?

    Certainly! You can utilize .sublots_adjust() on your figure object at any point before displaying or saving your plot.

    What happens if I set negative values for wspace or hspace?

    While negative values are permissible, they may lead to overlapping plots if not handled cautiously.

    How does constrained_layout differ from tight_layout?

    Constrained layout offers more automated padding around axes and text elements compared to tight_layout.

    Can I customize other aspect ratios with constrained layouts?

    Absolutely! You have the flexibility to customize various aspects like padding widths around axes individually by using gridspec_kw argument in plt.subplots() method call.

    Does changing wspace or hspace impact axis labels or tick marks?

    Adjusting these parameters solely impacts spatial arrangements; it does not directly influence content within each subplot.

    What is fig.tight_layout() used for then?

    tight_layout optimizes overlap/spacing automatically based on contents inside each plot while adjusting subplot params like wspace or hspace manually focuses on spatial arrangements specifically.

    Is it possible to have different spacings for different axes within one figure?

    Yes! Utilizing GridSpec alongside a deeper understanding of grid_spec_kw parameter allows you to fine-tune spacings per-grid location efficiently!

    Will changing wspace or hspace impact colorbars attached to my plots?

    Colorbar positions are typically managed independently; thus, alterations made through w/h-space adjustments do not directly affect them.

    Can I dynamically change spaces during interactive plotting sessions easily too?

    Absolutely! Updating rcParams entries related directly enables dynamic customization across all future figures created seamlessly!

    Conclusion

    In summary, optimizing whitespace between subplots in a constrained layout is crucial for elevating visual appeal and maximizing space efficiency within your Matplotlib figures. Through meticulous management of horizontal (wspace) and vertical (hspace) spacings via subplots_adjust(), you can craft polished and professional-looking visualizations tailored to your unique requirements.

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