5 Best Ways to Create a Standard Colorbar for a Series of Plots in Python

Rate this post

πŸ’‘ Problem Formulation: When generating multiple plots with similar data ranges in Python, it’s important to have a standard colorbar that consistently represents the data values across the plots. This article will demonstrate how to create a standardized colorbar that can be used across various plots to maintain consistency and ease of comparison. For instance, given a set of temperature data visualized through heatmaps, the goal is to have a uniform colorbar that indicates the temperature scale applied to all maps.

Method 1: Utilize Matplotlib’s Norm and Colormap

This method involves the use of Matplotlib’s normalization and colormap functionalities. Creating a custom normalization object (Normalize) which is then applied to a colormap (Colormap), enables a standard color scale over multiple plots. It’s specifically useful when you need to control the range and distribution of your color mapping.

Here’s an example:

import matplotlib.pyplot as plt
import matplotlib.colors as colors

# Create a shared normalization object
norm = colors.Normalize(vmin=10, vmax=100)

# Define the colormap
cmap = plt.cm.viridis

fig, ax = plt.subplots()

# Apply the same norm and colormap to each plot
sc = ax.scatter(data[:, 0], data[:, 1], c=data[:, 2], norm=norm, cmap=cmap)

# Create a colorbar with the same normalization
cbar = plt.colorbar(sc, ax=ax, cmap=cmap, norm=norm)
cbar.set_label('Temperature (Β°C)')

The output will be a scatter plot with a colorbar showing the temperature range between 10 and 100 Β°C.

The code snippet above initializes a shared normalization ranging from 10 to 100, which corresponds to our data’s limits. The colorbar is then created from the scatter plot object, ensuring it follows the same norm and colormap.

Method 2: Fixed Colorbar with Matplotlib’s ColorbarBase

By creating a colorbar independently using ColorbarBase class of Matplotlib, one can design a color scale that is not only uniform across different plots but can also be positioned and customized manually. This is handy when the plots are not necessarily drawn within the same figure or when the colorbar needs to be displayed separately.

Here’s an example:

import matplotlib.pyplot as plt
from matplotlib.colorbar import ColorbarBase
import matplotlib.colors as colors

fig, ax = plt.subplots()
cmap = plt.cm.viridis
norm = colors.Normalize(vmin=10, vmax=100)

# Assuming ax contains multiple plots...

# Create a new axis for the colorbar
cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
ColorbarBase(cbar_ax, cmap=cmap, norm=norm, orientation='vertical')

The output displays a series of plots alongside a vertical colorbar on their right-hand side, indicating the same temperature scale.

This snippet adds a new axis to the figure specifically for the colorbar, using the ColorbarBase class. The colorbar shares the norm and cmap with the actual plots to ensure uniformity.

Method 3: Standardize Using Seaborn’s Unified Color Palette

Seaborn, a statistical plotting library built on top of Matplotlib, provides a convenient approach to standardize colorbars using predefined or custom color palettes. Seaborn ensures that the colorbar is consistent across multiple plots by harnessing the internal mapping to these palettes.

Here’s an example:

import seaborn as sns

# Establish a color palette
cmap = sns.color_palette("coolwarm", as_cmap=True)

sns.heatmap(data1, cmap=cmap)
sns.heatmap(data2, cmap=cmap)

# Ensure consistent color mapping with cbar_kws
sns.heatmap(data1, cmap=cmap, cbar_kws={"shrink": .5, "ticks":[0, 50, 100]})

The output will be a heatmap with a colorbar reflecting the color palette ‘coolwarm’ applied to each map.

In this example, Seaborn’s color_palette function is used to define a colormap which is consistently used across multiple heatmaps. The cbar_kws argument is used to ensure that the colorbar maintains consistent scale and formatting.

Method 4: Normalize Across Plots with Shared Colorbar

For cases where multiple subplots need a shared colorbar, it’s crucial to normalize the data across all subplots first, then create a single colorbar that reflects this common scale. This approach ensures that all subplots are comparable to each other.

Here’s an example:

import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
import numpy as np

# Simulate data
data1 = np.random.rand(10, 10) * 100
data2 = np.random.rand(10, 10) * 100

# Get the min and max of all your data
vmin = min(data1.min(), data2.min())
vmax = max(data1.max(), data2.max())

norm = Normalize(vmin=vmin, vmax=vmax)
cmap = plt.cm.viridis

fig, (ax1, ax2) = plt.subplots(1, 2)
img1 = ax1.imshow(data1, norm=norm, cmap=cmap)
img2 = ax2.imshow(data2, norm=norm, cmap=cmap)

# Add colorbar to the figure, rather than the individual axes
cbar = fig.colorbar(img1, ax=[ax1, ax2], fraction=0.046, pad=0.04)

The output consists of two plots with a single colorbar shared between them, showing the range of data values.

This example simulates two random datasets, finds the collective minimum and maximum values for normalization, and applies a single colormap. The primary benefit of this method is that it represents data on a common scale, making comparisons straightforward.

Bonus One-Liner Method 5: Use of Matplotlib’s AxesGrid1 Toolkit

Matplotlib’s AxesGrid1 toolkit facilitates the inclusion of a common colorbar for multiple subplots with minimal code. This is particularly useful when creating a quick uniform colorbar for a grid of subplots without manually calculating ranges or creating normalization instances.

Here’s an example:

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

fig, ax = plt.subplots()
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.1)

plt.colorbar(im, cax=cax)

The output results in a series of plots, each sharing the same sidebar colorbar.

This compact example shows how to leverage the make_axes_locatable helper function to quickly append a colorbar to a plot, sharing the same aesthetic properties as the subplots.


  • Method 1: Utilize Matplotlib’s Norm and Colormap. Offers precise control over color scaling, ideal for customized color mapping. However, it requires explicit setting of the normalization and colormap for each plot.
  • Method 2: Fixed Colorbar with Matplotlib’s ColorbarBase. Allows for high customization and separate positioning of the colorbar. Might be less intuitive than other methods due to manual setup of colorbar axes.
  • Method 3: Standardize Using Seaborn’s Unified Color Palette. Simplifies the creation of a consistent color scale across multiple plots using built-in palettes. Relies on Seaborn, which might not offer as much detailed control as Matplotlib.
  • Method 4: Normalize Across Plots with Shared Colorbar. Ensures that multiple subplots are directly comparable. The setup might be more complex if many plots with different data ranges are involved.
  • Bonus One-Liner Method 5: Use of Matplotlib’s AxesGrid1 Toolkit. A quick method for a shared colorbar but may offer less flexibility in complex layout scenarios.