Plot two horizontal bar charts sharing the same Y-axis in Python Matplotlib

To plot two horizontal bar charts sharing the same Y-axis in Python Matplotlib, we can use sharey=ax1 in the subplot() method and barh() for creating horizontal bars. This technique is useful for comparing two related datasets side by side.

Steps

• Create lists for data points
• Create a new figure using figure() method
• Add the first subplot using subplot() method at index 1
• Plot horizontal bar chart on the first axis using barh() method
• Add the second subplot at index 2, sharing the Y-axis with the first subplot
• Plot the second horizontal bar chart on the shared axis
• Display the figure using show() method

Example

Here's how to create two horizontal bar charts with a shared Y-axis ?

import matplotlib.pyplot as plt

# Set figure size and layout
plt.rcParams["figure.figsize"] = [7.50, 3.50]
plt.rcParams["figure.autolayout"] = True

# Sample data
y = [3, 1, 5]
x1 = [10, 7, 3]
x2 = [9, 5, 1]

# Create figure
fig = plt.figure()

# First subplot
ax1 = plt.subplot(121)
ax1.barh(y, x1, align='center', color='red', edgecolor='black')
ax1.set_title('Dataset 1')

# Second subplot sharing Y-axis with first
ax2 = plt.subplot(122, sharey=ax1)
ax2.barh(y, x2, align='center', color='green', edgecolor='black')
ax2.set_title('Dataset 2')

plt.show()

The output shows two horizontal bar charts positioned side by side, both using the same Y-axis scale ?

Two horizontal bar charts displayed side by side with shared Y-axis values [1, 3, 5]
Left chart: red bars with values [10, 7, 3]
Right chart: green bars with values [9, 5, 1]

Key Parameters

Benefits of Shared Y-Axis

Using sharey=ax1 ensures both charts use the same Y-axis scale, making it easier to compare values between the two datasets. The Y-axis labels appear only on the left chart to avoid duplication.

Conclusion

Shared Y-axis horizontal bar charts in Matplotlib allow effective side-by-side comparison of datasets. Use sharey parameter in subplot() and barh() method to create professional comparative visualizations.