5 Best Ways to Make Colorbar Orientation Horizontal in Python Using Matplotlib

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💡 Problem Formulation: When visualizing data in Python using Matplotlib, it is often necessary to include a colorbar to represent the color scale of a heatmap or a similar plot. By default, colorbars in Matplotlib are vertical, but certain layouts and designs might require a horizontal colorbar for better aesthetic or functional integration. This article explores different methods to change a colorbar’s orientation to horizontal in Matplotlib, along with full code examples and output illustrations.

Method 1: Using the orientation Parameter Within colorbar()

This method involves direct utilization of Matplotlib’s colorbar() function with the orientation parameter set to ‘horizontal’. Not only is this approach straightforward, but it is also the most common and the preferred way to create horizontal colorbars in matplotlib.

Here’s an example:

import matplotlib.pyplot as plt
import numpy as np

# Generating some data
data = np.random.rand(10, 10)

plt.imshow(data, cmap='Spectral')
cbar = plt.colorbar(orientation='horizontal')
plt.show()

The code will output a heatmap with a horizontal colorbar at the bottom of the plot.

This code snippet begins by importing the necessary modules and then creates a random 10×10 data set. After calling imshow() to generate the heatmap, the colorbar() function is invoked with the orientation parameter set to ‘horizontal’ to display the colorbar in the desired orientation.

Method 2: Using the orientation Parameter with figure.colorbar()

Another way to achieve a horizontal colorbar is by using the colorbar method associated with a figure object. Invoking figure.colorbar() provides additional control over the colorbar properties by associating it with a specific figure, which is particularly beneficial when dealing with subplots.

Here’s an example:

import matplotlib.pyplot as plt
import numpy as np

# Generating some data for the subplot
data = np.random.rand(10, 10)
fig, ax = plt.subplots()

cax = ax.imshow(data, cmap='viridis')
fig.colorbar(cax, ax=ax, orientation='horizontal')
plt.show()

The code produces a plot with a horizontal colorbar placed relative to the subplot.

After preparing the data and creating a subplot, this snippet proceeds with displaying the data using imshow(). The colorbar() is then added to the figure with respect to an axis object (ax) while setting the orientation parameter to ‘horizontal’, thus creating a horizontal colorbar associated with the particular subplot.

Method 3: Adjusting with make_axes() Function

Creating a horizontal colorbar can also be achieved by custom placement using the make_axes() function from the Matplotlib colorbar kit. This method grants greater flexibility regarding the colorbar’s positioning and size.

Here’s an example:

import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.axes_grid1 import make_axes_locatable

# Generating random data
data = np.random.rand(10, 10)
fig, ax = plt.subplots()

im = ax.imshow(data, cmap='plasma')
divider = make_axes_locatable(ax)
cax = divider.append_axes("bottom", size="5%", pad=0.05)
plt.colorbar(im, cax=cax, orientation='horizontal')
plt.show()

The output is a plot with a horizontal colorbar, perfectly aligned with the bottom of the plot.

In this approach, the make_axes_locatable() function is utilized along with append_axes() to append a new axis for the colorbar at the bottom of the plot. Specifying the percentage of the axis size and the padding gives direct control over the appearance of the horizontal colorbar.

Method 4: Custom Colorbar Axis Using inset_axes()

For scenarios that require an embedded colorbar within the plot axis, the inset_axes() method is an ideal option. It allows for a neat, coherent design of the colorbar within the plot’s axes despite potentially requiring fine-tuning of positional arguments for optimal layout.

Here’s an example:

import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.axes_grid1.inset_locator import inset_axes

# Generating some data
data = np.random.rand(10, 10)
fig, ax = plt.subplots()
im = ax.imshow(data, cmap='coolwarm')

# Creating a colorbar in the plot's inset
cax = inset_axes(ax, width="50%", height="3%", loc='lower center')
fig.colorbar(im, cax=cax, orientation='horizontal')
plt.show()

The output displays a plot with a horizontal colorbar placed inside the lower center of the axis.

This effective method appends an inset axis using the inset_axes() method from the mpl_toolkits.axes_grid1.inset_locator module. The colorbar is placed within these inset axes, and its width, height, and location are specified for creating the desired horizontal colorbar layout.

Bonus One-Liner Method 5: Short-form syntax using plt.colorbar()

For those who value conciseness, there is a one-liner approach to add a horizontal colorbar to a plot. This method utilizes the axes-level plt.colorbar() function provided by Matplotlib, delivering speed and simplicity.

Here’s an example:

import matplotlib.pyplot as plt
import numpy as np

# Generating some data
data = np.random.rand(10, 10)

plt.imshow(data, cmap='autumn')
plt.colorbar(plt.imshow(data), orientation='horizontal')
plt.show()

This results in a colorful heatmap with a horizontal colorbar.

Succinct and to the point, this snippet reveals how a single line of code can be used to both display the heatmap and add a horizontal colorbar right away with minimal fuss, thanks to the plt.colorbar() function.

Summary/Discussion

  • Method 1: Direct use of colorbar() with orientation parameter. Strengths: Simple and straightforward. Weaknesses: Less control over positioning and resizing.
  • Method 2: Use of figure.colorbar() for specific figures or subplots. Strengths: Good control over the colorbar relating to its figure. Weaknesses: Slightly more verbose.
  • Method 3: Using make_axes() for customizable placement. Strengths: Flexible control of colorbar positioning and sizing. Weaknesses: Requires more setup and potentially complex adjustments.
  • Method 4: The inset_axes() method for an embedded colorbar. Strengths: Clean and integrated design within the axis. Weaknesses: Requires careful adjustment of dimensions and position.
  • Method 5: One-liner shorthand. Strengths: Quick and easy. Weaknesses: Limited customization options.