💡 Problem Formulation: When visualizing data in Python using matplotlib, analysts often create multiple subplots for comparative analysis. However, they may encounter a situation where each subplot requires varying heights for optimal presentation. For instance, if one is plotting a time series alongside its distribution, the time series plot might need more vertical space compared to the histogram of distribution. This article guides you through various methods to adjust individual subplot heights in matplotlib programmatically.
Method 1: Adjusting with subplots_adjust
Matplotlib provides the subplots_adjust function as part of the Figure class, which can be used to tweak the spacing between subplots. While this method does not directly set the height of each subplot, adjusting the vertical spacing effectively changes the relative heights when combined with an appropriate subplot grid layout.
Here’s an example:
import matplotlib.pyplot as plt
# Generate some data to plot
x = range(10)
y1 = range(10)
y2 = [i**2 for i in x]
# Create two subplots with different heights
fig, (ax1, ax2) = plt.subplots(2, 1, gridspec_kw={'height_ratios':[3, 1]})
# Plot the data
ax1.plot(x, y1)
ax2.plot(x, y2)
# Adjust subplot parameters
plt.subplots_adjust(hspace=0.5)
plt.show()The output of this code will display two vertically stacked plots with different amounts of vertical space allocated to each subplot.
This code example starts by importing the Matplotlib library. It then creates a figure with two subplots, arranging them vertically. By specifying the height_ratios parameter within gridspec_kw, it sets different heights for the two subplots. After plotting data on both axes, it uses plt.subplots_adjust to set the horizontal spacing between these plots, influencing their height ratio visually before showing the plot.
Method 2: Using GridSpec
The GridSpec class in Matplotlib offers fine-grained control over subplot layout, providing both flexibility and specificity for setting individual subplot dimensions within a figure. This method is particularly useful for complex subplot arrangements where precise adjustments are necessary.
Here’s an example:
import matplotlib.pyplot as plt from matplotlib.gridspec import GridSpec # Create 2x1 grid with custom height ratios gs = GridSpec(2, 1, height_ratios=[2, 1]) # Initialize figure fig = plt.figure() # Add subplots ax1 = fig.add_subplot(gs[0]) ax2 = fig.add_subplot(gs[1]) # Generate some data ax1.plot([1, 2, 3], [1, 4, 9]) ax2.bar([1, 2, 3], [2, 3, 5]) # Render the plot plt.show()
The output will reveal two subplots with different heights, the first being twice as tall as the second.
In this snippet, the GridSpec class is used to define a 2-row grid where the first row is twice the height of the second. We then create a new figure and add two subplots, specifying which row of our grid each should occupy. Finally, different types of plots are rendered on each subplot, and the figure is displayed.
Method 3: Using axes with a List of Rectangles
This method involves the direct creation of axes objects at specific locations by defining a list of rectangle coordinates. The coordinates specify the position and size of each subplot, offering precise control.
Here’s an example:
import matplotlib.pyplot as plt # Initialize figure fig = plt.figure() # Generate subplot coordinates (left, bottom, width, height) rect1 = [0.1, 0.5, 0.8, 0.4] # Top half of the figure rect2 = [0.1, 0.1, 0.8, 0.2] # Bottom quarter of the figure # Create subplots ax1 = fig.add_axes(rect1) ax2 = fig.add_axes(rect2) # Plot data ax1.plot([0, 1], [0, 1]) ax2.bar([0, 1], [1, 0.5]) # Render figure plt.show()
The output will display two subplots, with the top one covering half of the figure height, and the bottom one covering a quarter.
Here, the rectangle list contains coordinates that define the position and size for each subplot within the figure. The add_axes method uses these coordinates to create two axes, which are then used to plot data. This approach allows for specifying the exact height of each subplot individually.
Method 4: Dynamic Adjustment with axes.set_position
The set_position method of an axes object updates the size and position of the subplot dynamically after its creation. This can be particularly useful if subplot adjustments need to be made in response to user input or other runtime conditions.
Here’s an example:
import matplotlib.pyplot as plt # Create two subplots fig, (ax1, ax2) = plt.subplots(2) # Plot on first subplot ax1.plot([1, 3, 2]) # Dynamically update the position of the second subplot pos1 = ax2.get_position() # Get current position new_position = [pos1.x0, pos1.y0, pos1.width, pos1.height * 0.5] # Halve the height ax2.set_position(new_position) # Plot on the updated subplot ax2.plot([3, 1, 3]) # Render the plot plt.show()
The output will present two subplots where the second subplot’s height is adjusted to be half of its original size.
In this code example, after initially creating two subplots, we obtain the current position of the second subplot using the get_position method. We then modify the position, specifically the height, before updating the subplot’s position with set_position. This approach allows for modification of the subplot’s size after it’s been rendered.
Bonus One-Liner Method 5: Inline Height Adjustment with subplot2grid
The subplot2grid function is a utility that allows creating subplots in a grid with a one-liner, which also lets us define the rowspan or colspan of each subplot, indirectly adjusting its height by spanning across multiple grid rows.
Here’s an example:
import matplotlib.pyplot as plt # Initialize subplots with varying heights ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2) ax2 = plt.subplot2grid((3, 1), (2, 0)) # Generate sample data ax1.plot([1, 2]) ax2.bar([0, 1], [2, 1]) # Render the plot plt.show()
The output will display two subplots where the first subplot is twice as tall as the second.
Using subplot2grid, we create a grid of 3×1 and then add two subplots to it. The first subplot takes up two rows of the grid, making it twice as tall as the second subplot which occupies only one row. This method provides an easy and quick way to adjust the relative heights of subplots within a grid layout.
Summary/Discussion
- Method 1: subplots_adjust. Adjusts spacing and can be used for simple layouts. Indirectly affects height. Not suitable for complex scenarios.
- Method 2: GridSpec. Offers fine-grained control and works well with complex layouts. Requires a good understanding of matplotlib’s grid system.
- Method 3: List of Rectangles. Provides the most precise control over the subplot dimensions. May be less convenient when dealing with multiple subplots.
- Method 4: set_position. Allows for dynamic adjustments post-creation of subplots. Useful when subplot sizes need to be responsive to data or events.
- Method 5: subplot2grid. Quick and convenient for simpler adjustments. Limited to grid configurations and less flexible than other methods.