Matplotlib – Colorbars

Understanding Colorbars in Matplotlib

A colorbar is a visual representation of the color scale used in a plot. It displays color variation from the minimum to maximum values, helping you interpret data visually.

Colorbars in Matplotlib

The matplotlib.colorbar module provides functionality for creating and customizing colorbars. Common functions used include Figure.colorbar() or pyplot.colorbar(), which work with ScalarMappable objects, often generated using imshow().

Example 1: Basic Horizontal Colorbar

import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(6, 1), constrained_layout=True)
cmap = mpl.cm.cool
norm = mpl.colors.Normalize(vmin=5, vmax=10)
scalar_mappable = mpl.cm.ScalarMappable(norm=norm, cmap=cmap)
colorbar = fig.colorbar(scalar_mappable, cax=ax, orientation='horizontal', label='Some Units')
plt.title('Basic Colorbar')
plt.show()

Example 2: Image with Colorbar

import matplotlib.pyplot as plt
import numpy as np

data = np.random.random((10, 10))
fig, ax = plt.subplots(figsize=(7,4))
im = ax.imshow(data, cmap='viridis')
cbar = plt.colorbar(im, ax=ax)
plt.show()

Automatic Colorbar Placement

Each subplot can have its own colorbar automatically positioned for clarity.

import matplotlib.pyplot as plt
import numpy as np

fig, axs = plt.subplots(1, 2, figsize=(7,3))
cmaps = ['magma', 'coolwarm']

for col in range(2):
   ax = axs[col]
   pcm = ax.pcolormesh(np.random.random((20, 20)) * (col + 1), cmap=cmaps[col])
   fig.colorbar(pcm, ax=ax, pad=0.03)

plt.show()

Manual Colorbar Placement

When you want precise control over layout, you can manually position the colorbar.

import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt

npoints = 1000
x, y = np.random.normal(10, 2, (2, npoints))
fig, ax = plt.subplots(figsize=(7,4))
plt.title('Manual Colorbar Placement')
hexbin_artist = ax.hexbin(x, y, gridsize=20, cmap='gray_r', edgecolor='white')
cax = fig.add_axes([0.8, 0.15, 0.05, 0.3])
colorbar = fig.colorbar(hexbin_artist, cax=cax)
plt.show()

Customizing Colorbars

Colorbars can be customized using set_ticks, set_ticklabels, orientation, label formatting, and color styling.

Example 1: Horizontal Colorbar with Custom Tick Labels

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots(figsize=(7, 4))
data = np.random.normal(size=(250, 250))
data = np.clip(data, -1, 1)
cax = ax.imshow(data, cmap='afmhot')
ax.set_title('Horizontal Colorbar with Customizing Tick Labels')
cbar = fig.colorbar(cax, orientation='horizontal', label='A colorbar label')
cbar.set_ticks(ticks=[-1, 0, 1])
cbar.set_ticklabels(['Low', 'Medium', 'High'])
plt.show()

Example 2: Customized Appearance

import numpy as np
from matplotlib import pyplot as plt

plt.rcParams["figure.figsize"] = [7.00, 3.50]
plt.rcParams["figure.autolayout"] = True

data = np.random.randn(4, 4)
im = plt.imshow(data, interpolation='nearest', cmap="PuBuGn")
clb = plt.colorbar(im, shrink=0.9, pad=0.05)
clb.ax.set_title('Color Bar Title')
clb.ax.set_yticks([0, 1.5, 3, 4.5], labels=["A", "B", "C", "D"])
clb.ax.tick_params(labelcolor='red', labelsize=20)
plt.show()

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