Why Choose the Right Colormap?

• Nature of Data: Form vs. metric data
• Dataset Knowledge: Understanding data distribution
• Intuitive Scheme: Does the color scheme match human expectation?
• Field Standards: Common choices in your domain
• Perceptual Uniformity: Equal data steps appear equal visually

Categories of Colormaps

• Sequential: Ordered lightness changes using one hue
• Diverging: Two hues meeting at a neutral midpoint
• Cyclic: Wrap-around colors like in phase or time data
• Qualitative: Distinct colors for categories without order

Sequential Colormaps

Sequential colormaps increase in lightness—ideal for ordered data.

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

cmap_list = ['Greys', 'Purples', 'Blues', ...]  # Many more
gradient = np.linspace(0, 1, 256)
gradient = np.vstack((gradient, gradient))
fig, axs = plt.subplots(len(cmap_list)+1, figsize=(7, nrows*0.3))
for ax, name in zip(axs, cmap_list):
    ax.imshow(gradient, aspect='auto', cmap=mpl.colormaps[name])
    ax.text(-0.1, 0.5, name, transform=ax.transAxes)
    ax.set_axis_off()
plt.show()

Diverging Colormaps

Diverging palettes highlight deviation around a central value.

cmap_list = ['PiYG','PRGn','RdBu','coolwarm','seismic', ...]
# Similar plotting code as above to show diverging gradients

Cyclic Colormaps

For wrapping data like angles—start and end colors match.

cmap_list = ['twilight','twilight_shifted','hsv']
# Plotting code like above to display cyclic gradients

Qualitative Colormaps

Use distinct colors without order for categorical data.

cmap_list = ['Pastel1','Set1','tab10','Accent','Paired', ...]
# Plot to visualize each qualitative palette