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Sharing Axes Across Subplots
Introduction to Sharing Axes
Sharing axes across subplots in Matplotlib is a powerful feature that allows for synchronized zooming and panning of plots. This is particularly useful when comparing data across multiple plots, as it ensures consistency in axis scaling and makes differences more apparent.
Benefits of Sharing Axes
When axes are shared, any change in the view limits of one subplot will automatically be applied to the others. This is beneficial for comparative analysis, ensuring that the viewer can focus on data trends rather than adjusting axes manually.
Sharing X-Axis
Sharing X-Axis Across Subplots
By sharing the x-axis, you ensure that all subplots have the same x-axis limits, which is helpful when the x-axis represents a common variable like time or categories.
import matplotlib.pyplot as plt
fig, (ax1, ax2) = plt.subplots(2, sharex=True)
ax1.plot([1, 2, 3], [1, 4, 9])
ax2.plot([1, 2, 3], [9, 4, 1])
plt.show()
Explanation
In this example, two subplots share the x-axis. The x-axis limits and ticks are synchronized, making it easier to compare the y-values across the two plots.
Sharing Y-Axis
Sharing Y-Axis Across Subplots
Sharing the y-axis ensures that all subplots have the same y-axis limits, which is crucial when comparing datasets with similar y-values.
import matplotlib.pyplot as plt
fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.plot([1, 2, 3], [1, 4, 9])
ax2.plot([1, 2, 3], [1, 2, 3])
plt.show()
Explanation
Here, the y-axis is shared between two subplots. This is particularly useful for comparing the shape and trend of two datasets with a common y-scale.
Sharing Both Axes
Full Axis Sharing
Sharing both axes across subplots is useful when you need to maintain a consistent scale across multiple plots, helping to highlight differences and similarities.
import matplotlib.pyplot as plt
fig, axs = plt.subplots(2, 2, sharex=True, sharey=True)
axs[0, 0].plot([1, 2, 3], [1, 4, 9])
axs[0, 1].plot([1, 2, 3], [9, 4, 1])
axs[1, 0].plot([1, 2, 3], [1, 2, 3])
axs[1, 1].plot([1, 2, 3], [3, 2, 1])
plt.show()
Explanation
In this example, both the x and y axes are shared across a 2x2 grid of subplots. This setup is ideal for comparing multiple datasets with similar ranges.
Independent Axes with Shared Axes
Combining Shared and Independent Axes
Sometimes, you may want to share axes only across specific subplots while keeping others independent. This can be achieved by selectively sharing axes.
import matplotlib.pyplot as plt
fig, axs = plt.subplots(2, 2, sharex='col')
axs[0, 0].plot([1, 2, 3], [1, 4, 9])
axs[0, 1].plot([1, 2, 3], [9, 4, 1])
axs[1, 0].plot([1, 2, 3], [1, 2, 3])
axs[1, 1].plot([1, 2, 3], [3, 2, 1])
plt.show()
Explanation
In this case, the x-axis is shared across columns, allowing for comparison within each column while maintaining independent y-axis scales.
Sharing Axes in Complex Layouts
Advanced Axis Sharing
In complex subplot layouts, sharing axes can help maintain a coherent visual structure, making the data presentation more intuitive.
import matplotlib.pyplot as plt
fig = plt.figure()
ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(222, sharey=ax1)
ax3 = fig.add_subplot(223, sharex=ax1)
ax4 = fig.add_subplot(224)
ax1.plot([1, 2, 3], [1, 4, 9])
ax2.plot([1, 2, 3], [9, 4, 1])
ax3.plot([1, 2, 3], [1, 2, 3])
ax4.plot([1, 2, 3], [3, 2, 1])
plt.show()
Explanation
This example demonstrates how to share axes in a more complex layout using the `add_subplot` method. Axes are shared selectively to enhance data comparison while maintaining individual plot characteristics.
