Constrained layout vs tight layout in Matplotlib - When to Use Which

The Big Idea

Discover how to make your charts look perfect without endless tweaking!

The Scenario

Imagine you create multiple charts in one figure using matplotlib. You want them to look neat and not overlap, so you try to adjust the spaces between them by hand.

The Problem

Manually adjusting spaces is slow and frustrating. You guess values, run the code, check if labels overlap, then tweak again. It's easy to miss overlaps or waste time on trial and error.

The Solution

Constrained layout and tight layout automatically adjust spaces between plots and labels. They save you from guessing and make your figure look clean and readable with minimal effort.

Before vs After

✗ Before

plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1)

✓ After

plt.tight_layout()  # or plt.subplots(constrained_layout=True)

What It Enables

You can focus on your data story while matplotlib handles the tricky spacing, making your visuals clear and professional.

Real Life Example

When preparing a report with multiple charts, using constrained or tight layout ensures all titles, labels, and legends fit nicely without overlap, saving hours of manual tweaking.

Key Takeaways

Manual spacing is slow and error-prone.

Constrained and tight layouts automate spacing for clean figures.

They help create professional visuals quickly.