Colorbars¶

Colorbars provide a visual legend mapping colors to data values, essential for interpreting heatmaps and other color-coded visualizations.

Basic Colorbar¶

Add a colorbar to a plot.

1. Simple Addition¶

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(42)
data = np.random.rand(10, 10)

fig, ax = plt.subplots()
im = ax.imshow(data)
plt.colorbar(im)
plt.show()

2. Specify Axes¶

fig, ax = plt.subplots()
im = ax.imshow(data)
plt.colorbar(im, ax=ax)

3. Using Figure Method¶

fig, ax = plt.subplots()
im = ax.imshow(data)
fig.colorbar(im, ax=ax)

Colorbar Label¶

Add descriptive labels to the colorbar.

1. Label Parameter¶

fig, ax = plt.subplots()
im = ax.imshow(data, cmap='viridis')
plt.colorbar(im, label='Intensity')
plt.show()

2. LaTeX Label¶

plt.colorbar(im, label=r'Temperature ($^\circ$C)')

3. Set Label After Creation¶

cbar = plt.colorbar(im)
cbar.set_label('Value', fontsize=12, rotation=270, labelpad=15)

Colorbar Position¶

Control where the colorbar appears.

1. Location Parameter¶

fig, ax = plt.subplots()
im = ax.imshow(data)

# Options: 'right', 'left', 'top', 'bottom'
plt.colorbar(im, location='right')
plt.show()

2. Horizontal Colorbar¶

fig, ax = plt.subplots()
im = ax.imshow(data)
plt.colorbar(im, orientation='horizontal')
plt.show()

3. Custom Position with cax¶

from mpl_toolkits.axes_grid1 import make_axes_locatable

fig, ax = plt.subplots()
im = ax.imshow(data)

divider = make_axes_locatable(ax)
cax = divider.append_axes('right', size='5%', pad=0.1)
plt.colorbar(im, cax=cax)
plt.show()

Colorbar Size¶

Adjust colorbar dimensions.

1. Shrink Parameter¶

fig, ax = plt.subplots()
im = ax.imshow(data)
plt.colorbar(im, shrink=0.8)  # 80% of default height
plt.show()

2. Aspect Ratio¶

plt.colorbar(im, aspect=30)  # Length / width ratio

3. Fraction and Pad¶

plt.colorbar(im, fraction=0.046, pad=0.04)

Tick Customization¶

Control colorbar tick positions and labels.

1. Set Ticks¶

fig, ax = plt.subplots()
im = ax.imshow(data, vmin=0, vmax=1)
cbar = plt.colorbar(im, ticks=[0, 0.25, 0.5, 0.75, 1])
plt.show()

2. Custom Labels¶

cbar = plt.colorbar(im, ticks=[0, 0.5, 1])
cbar.ax.set_yticklabels(['Low', 'Medium', 'High'])

3. Tick Parameters¶

cbar = plt.colorbar(im)
cbar.ax.tick_params(labelsize=10, length=5, width=1)

Discrete Colorbars¶

Create colorbars for categorical or binned data.

1. Boundaries and Values¶

import matplotlib.colors as mcolors

bounds = [0, 0.2, 0.4, 0.6, 0.8, 1.0]
norm = mcolors.BoundaryNorm(bounds, plt.cm.viridis.N)

fig, ax = plt.subplots()
im = ax.imshow(data, cmap='viridis', norm=norm)
plt.colorbar(im, boundaries=bounds, ticks=bounds)
plt.show()

2. Fixed Number of Colors¶

from matplotlib.colors import BoundaryNorm

n_colors = 5
cmap = plt.cm.get_cmap('viridis', n_colors)
bounds = np.linspace(0, 1, n_colors + 1)
norm = BoundaryNorm(bounds, cmap.N)

fig, ax = plt.subplots()
im = ax.imshow(data, cmap=cmap, norm=norm)
plt.colorbar(im, ticks=bounds[:-1] + 0.1)
plt.show()

3. Categorical Colorbar¶

categories = ['A', 'B', 'C', 'D']
cat_data = np.random.randint(0, 4, (10, 10))

cmap = plt.cm.get_cmap('Set1', len(categories))

fig, ax = plt.subplots()
im = ax.imshow(cat_data, cmap=cmap, vmin=-0.5, vmax=len(categories)-0.5)
cbar = plt.colorbar(im, ticks=range(len(categories)))
cbar.ax.set_yticklabels(categories)
plt.show()

Colorbar with Subplots¶

Handle colorbars in multi-plot layouts.

1. Individual Colorbars¶

fig, axes = plt.subplots(1, 3, figsize=(12, 4))

for ax in axes:
    data = np.random.rand(10, 10)
    im = ax.imshow(data)
    plt.colorbar(im, ax=ax, shrink=0.8)

plt.tight_layout()
plt.show()

2. Shared Colorbar¶

fig, axes = plt.subplots(1, 3, figsize=(12, 4))

vmin, vmax = 0, 1
for ax in axes:
    data = np.random.rand(10, 10)
    im = ax.imshow(data, vmin=vmin, vmax=vmax)

fig.colorbar(im, ax=axes, shrink=0.8, label='Shared Scale')
plt.tight_layout()
plt.show()

3. Colorbar Axes List¶

fig, axes = plt.subplots(2, 2, figsize=(8, 8))

for ax in axes.flat:
    data = np.random.rand(10, 10)
    im = ax.imshow(data, vmin=0, vmax=1)

fig.colorbar(im, ax=axes.ravel().tolist(), shrink=0.6)
plt.tight_layout()
plt.show()

Logarithmic Colorbar¶

Display data with logarithmic color scaling.

1. LogNorm¶

from matplotlib.colors import LogNorm

data_log = np.random.rand(10, 10) * 1000 + 1

fig, ax = plt.subplots()
im = ax.imshow(data_log, norm=LogNorm(vmin=1, vmax=1000))
plt.colorbar(im, label='Log Scale')
plt.show()

2. SymLogNorm for Negative Values¶

from matplotlib.colors import SymLogNorm

data_sym = np.random.randn(10, 10) * 100

fig, ax = plt.subplots()
im = ax.imshow(data_sym, cmap='RdBu', 
               norm=SymLogNorm(linthresh=1, vmin=-100, vmax=100))
plt.colorbar(im)
plt.show()

3. PowerNorm¶

from matplotlib.colors import PowerNorm

fig, ax = plt.subplots()
im = ax.imshow(data, norm=PowerNorm(gamma=0.5))
plt.colorbar(im)
plt.show()

Colorbar Styling¶

Customize colorbar appearance.

1. Outline¶

cbar = plt.colorbar(im)
cbar.outline.set_edgecolor('black')
cbar.outline.set_linewidth(2)

2. Extension Arrows¶

# Show arrows when data exceeds vmin/vmax
plt.colorbar(im, extend='both')  # 'min', 'max', 'both', 'neither'

3. Draw Edges¶

cbar = plt.colorbar(im, drawedges=True)

Complete Example¶

Create a polished heatmap with customized colorbar.

1. Setup Data¶

np.random.seed(42)
data = np.random.randn(12, 12)

2. Create Figure¶

fig, ax = plt.subplots(figsize=(8, 6))

im = ax.imshow(data, cmap='RdBu', vmin=-3, vmax=3)

ax.set_xticks(np.arange(12))
ax.set_yticks(np.arange(12))
ax.set_xticklabels([f'C{i}' for i in range(12)], rotation=45, ha='right')
ax.set_yticklabels([f'R{i}' for i in range(12)])
ax.set_title('Heatmap with Custom Colorbar')

3. Add Colorbar¶

cbar = plt.colorbar(im, shrink=0.8, aspect=30, pad=0.02)
cbar.set_label('Z-Score', fontsize=12)
cbar.set_ticks([-3, -2, -1, 0, 1, 2, 3])
cbar.ax.tick_params(labelsize=10)

plt.tight_layout()
plt.show()