How to Fix Second Y-Axis Label Getting Cut Off in Matplotlib Twinx Bar Graph

Matplotlib is a powerful data visualization library in Python, widely used for creating static, animated, and interactive plots. A common requirement in data visualization is to compare two datasets with different scales on the same plot. Matplotlib’s twinx() function enables this by creating a secondary Y-axis sharing the same X-axis. However, a frequent frustration among users is that the label of the second (right-side) Y-axis often gets cut off, making the plot incomplete or misleading.

This blog post dives deep into why this issue occurs and provides step-by-step solutions to resolve it. Whether you’re a beginner or an experienced Matplotlib user, you’ll learn practical techniques to ensure your dual-axis bar graphs are clear, professional, and fully visible.

Table of Contents#

  1. Understanding the Problem
  2. Why Does the Second Y-Axis Label Get Cut Off?
  3. Step-by-Step Solutions
  4. Advanced Tips for Dynamic Adjustments
  5. Conclusion
  6. References

Understanding the Problem#

Let’s start by reproducing the issue with a simple example. Suppose we want to plot two datasets: monthly sales (in thousands of dollars) and monthly website visitors (in millions) for a company. We’ll use twinx() to create a secondary Y-axis for visitors.

Example Code (with the Issue):#

import matplotlib.pyplot as plt
import numpy as np
 
# Sample data
months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun"]
sales = [150, 220, 180, 300, 250, 320]  # Sales (thousands $)
visitors = [2.1, 3.5, 2.8, 4.2, 3.9, 5.1]  # Visitors (millions)
 
# Create figure and primary axis
fig, ax1 = plt.subplots(figsize=(10, 6))
 
# Plot sales on primary axis (left Y-axis)
ax1.bar(months, sales, color='skyblue', label='Sales (thousands $)')
ax1.set_xlabel('Month', fontsize=12)
ax1.set_ylabel('Sales (thousands $)', fontsize=12, color='blue')
ax1.tick_params(axis='y', labelcolor='blue')
 
# Create secondary axis (right Y-axis) for visitors
ax2 = ax1.twinx()
ax2.bar(months, visitors, color='salmon', alpha=0.6, label='Visitors (millions)')
ax2.set_ylabel('Visitors (millions)', fontsize=12, color='red')  # This label may get cut off
ax2.tick_params(axis='y', labelcolor='red')
 
# Add legend
fig.legend(loc='upper left')
 
plt.title('Monthly Sales vs. Website Visitors', fontsize=14)
plt.show()

Output:#

When you run this code, the plot will display both datasets, but the label for the secondary Y-axis (Visitors (millions)) on the right side may be partially or fully cut off. This happens because Matplotlib’s default layout engine does not always account for the secondary axis label when calculating the plot boundaries.

Why Does the Second Y-Axis Label Get Cut Off?#

The root cause lies in how Matplotlib manages plot layout. By default, Matplotlib uses a layout engine that optimizes spacing for the primary axes (left Y-axis and X-axis) but often overlooks the secondary axes created with twinx(). The label of the secondary Y-axis extends beyond the figure’s right boundary because:

  • Default Margins: The figure’s right margin is too narrow to accommodate the secondary label.
  • Tight Layout Limitations: The tight_layout() function (used to automatically adjust subplot parameters) prioritizes the primary axes and may not account for the secondary axis label’s width.
  • Axes Overlap: The secondary axis (ax2) shares the same X-axis as ax1 but is rendered on top, and its label may extend beyond the figure’s bounding box.

Step-by-Step Solutions#

Below are four proven solutions to fix the cut-off second Y-axis label. We’ll build on the example above and modify it to resolve the issue.

Solution 1: Use tight_layout() with the rect Parameter#

tight_layout() automatically adjusts subplot parameters to prevent overlapping elements. By default, it uses the entire figure area (rect=(0, 0, 1, 1)), but we can shrink the bounding box to leave extra space on the right for the secondary label.

Modified Code:#

# ... (previous code: data, ax1, ax2 setup) ...
 
# Add tight_layout with adjusted rect to leave space on the right
plt.tight_layout(rect=(0, 0, 0.9, 1))  # (left, bottom, right, top) in figure coordinates
 
plt.title('Monthly Sales vs. Website Visitors', fontsize=14)
plt.show()

Explanation:#

  • The rect parameter defines the bounding box for the subplots, where values range from 0 (left/bottom) to 1 (right/top) in normalized figure coordinates.
  • rect=(0, 0, 0.9, 1) reduces the right boundary to 0.9 (90% of the figure width), leaving 10% extra space on the right for the secondary label.

Solution 2: Adjust Margins with subplots_adjust()#

plt.subplots_adjust() manually adjusts the spacing between subplots. By reducing the right parameter, we increase the right margin, creating space for the secondary label.

Modified Code:#

# ... (previous code: data, ax1, ax2 setup) ...
 
# Adjust right margin to 85% of the figure width (leave 15% space on the right)
plt.subplots_adjust(right=0.85)  # Default right=0.9
 
plt.title('Monthly Sales vs. Website Visitors', fontsize=14)
plt.show()

Explanation:#

  • right=0.85 means the subplot will occupy 85% of the figure’s width, leaving 15% of the figure’s right side empty. This extra space accommodates the secondary Y-axis label.
  • Tune the right value (e.g., 0.8 or 0.9) based on your label’s length.

Solution 3: Manually Resize the Axes Position#

For precise control, you can manually adjust the position of the secondary axis (ax2). The get_position() method returns the current axes position, and set_position() lets you modify it.

Modified Code:#

# ... (previous code: data, ax1, ax2 setup) ...
 
# Get current position of ax2 (secondary axis)
pos = ax2.get_position()
 
# Shrink the width of ax2 to leave space on the right (e.g., reduce width by 10%)
new_pos = [pos.x0, pos.y0, pos.width * 0.9, pos.height]  # [left, bottom, width, height]
ax2.set_position(new_pos)
 
plt.title('Monthly Sales vs. Website Visitors', fontsize=14)
plt.show()

Explanation:#

  • ax2.get_position() returns a Bbox object with the axes’ coordinates (normalized to [0,1] for the figure).
  • By reducing the width of ax2 (e.g., pos.width * 0.9), we shrink the secondary axis horizontally, creating space for its label on the right.

Solution 4: Use constrained_layout (Matplotlib 3.3+)#

Matplotlib 3.3 introduced constrained_layout, a more advanced layout engine that dynamically adjusts spacing for all axes, including secondary axes created with twinx(). It is more robust than tight_layout() for complex plots.

Modified Code:#

# Enable constrained_layout when creating the figure
fig, ax1 = plt.subplots(figsize=(10, 6), constrained_layout=True)  # Key change
 
# ... (rest of the code: ax1, ax2 setup, plotting, labels) ...
 
plt.title('Monthly Sales vs. Website Visitors', fontsize=14)
plt.show()

Explanation:#

  • constrained_layout=True ensures the figure dynamically adjusts spacing for all axes, including ax2. It accounts for the secondary Y-axis label and reserves enough space on the right.
  • This is the recommended solution for Matplotlib versions ≥3.3, as it requires minimal manual adjustment.

Advanced Tips for Dynamic Adjustments#

For more complex scenarios (e.g., long labels, multiple subplots, or dynamic data), use these advanced techniques:

1. Combine subplots_adjust() with tight_layout()#

For maximum control, use subplots_adjust() to set a base margin, then tight_layout() to fine-tune:

plt.subplots_adjust(right=0.85)
plt.tight_layout()

2. Dynamically Adjust Based on Label Length#

If the secondary label’s length varies (e.g., in automated reports), calculate the label width and adjust margins programmatically. Use fig.canvas.get_renderer() to measure text width:

from matplotlib.text import Text
 
# Measure the width of the secondary label
label = ax2.get_ylabel()
text_width = Text(0, 0, label, fontsize=12).get_window_extent(fig.canvas.get_renderer()).width
fig_width = fig.get_window_extent().width
 
# Adjust right margin based on text width
right_margin = 1 - (text_width / fig_width) - 0.05  # 0.05 for extra padding
plt.subplots_adjust(right=right_margin)

3. Use GridSpec for Complex Subplots#

For plots with multiple subplots, use matplotlib.gridspec.GridSpec to define custom grid layouts with adjustable margins:

import matplotlib.gridspec as gridspec
 
gs = gridspec.GridSpec(1, 1, right=0.85)  # 1 row, 1 column, right margin=0.85
fig = plt.figure(figsize=(10, 6))
ax1 = fig.add_subplot(gs[0])
ax2 = ax1.twinx()

Conclusion#

The second Y-axis label cut-off issue in Matplotlib twinx() bar graphs is a common but solvable problem. The best approach depends on your Matplotlib version and use case:

  • For Matplotlib ≥3.3: Use constrained_layout=True when creating the figure (simplest and most robust).
  • Quick Fix: Use plt.subplots_adjust(right=0.85) to manually widen the right margin.
  • Precise Control: Manually resize the secondary axis with ax2.set_position().

By implementing these solutions, you’ll ensure your dual-axis plots are clear, professional, and fully readable.

References#

2026-03