Visual Design Principles and Color Discipline

  • ID: VISPY-FREE-10
  • Type: Lesson
  • Audience: Public
  • Theme: Color, hierarchy, and visual restraint

Good visualization is not decoration.

It is controlled hierarchy.

In this lesson you will learn:

Setup

import warnings
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

from cdi_viz.theme import (
    cdi_notebook_init,
    show_and_save_mpl,
)

warnings.filterwarnings("ignore")

cdi_notebook_init(chapter="10")

df = pd.read_csv("data/cdi-student-outcomes.csv")

print("First rows:")
print(df.head())
First rows:
     group  test_prep  study_hours  math_score  reading_score  writing_score
0  Group B  completed          3.9          58             64             51
1  Group A       none          7.7          67             85             61
2  Group A       none          9.3          83             65             73
3  Group A       none          3.9          60             67             48
4  Group A       none          8.3          68             63             47

1. Visual Hierarchy

A figure should guide the eye:

  1. Title (main message)
  2. Data
  3. Axis labels
  4. Annotations (if necessary)

If everything is bold, nothing stands out.

fig, ax = plt.subplots(figsize=(7.6, 4.6))

ax.scatter(df["study_hours"], df["math_score"], alpha=0.6)

ax.set_title("More study hours are associated with higher math scores")
ax.set_xlabel("Study hours per week")
ax.set_ylabel("Math score")

fig.tight_layout()

show_and_save_mpl(fig)  # figures/10_001.png
Saved PNG → figures/10_001.png

Notice the title answers a question. It does not simply repeat variable names.

2. Color Discipline

Color should encode meaning, not decorate.

Bad practice: - Random colors - Too many categories - Saturated palettes without purpose

Good practice: - Neutral base - One highlight color - Consistent mapping across plots

palette = {
    "completed": "#036281",
    "not_completed": "#9CA3AF",
}

df["prep_label"] = df["test_prep"].map({
    "completed": "completed",
    "none": "not_completed"
})

fig, ax = plt.subplots(figsize=(7.6, 4.6))

sns.boxplot(
    data=df,
    x="prep_label",
    y="math_score",
    palette=palette,
    ax=ax
)

ax.set_title("Test preparation is associated with higher math scores")
ax.set_xlabel("Test preparation status")
ax.set_ylabel("Math score")

fig.tight_layout()

show_and_save_mpl(fig)  # figures/10_002.png
Saved PNG → figures/10_002.png

Two colors. Clear meaning.

3. Reduce Visual Noise

Remove unnecessary elements:

  • Excess grid lines
  • Thick borders
  • Redundant legends
  • Background shading
fig, ax = plt.subplots(figsize=(7.6, 4.6))

sns.histplot(df["math_score"], bins=25, color="#036281", ax=ax)

ax.set_title("Distribution of math scores")
ax.set_xlabel("Math score")
ax.set_ylabel("Count")

ax.grid(False)

fig.tight_layout()

show_and_save_mpl(fig)  # figures/10_003.png
Saved PNG → figures/10_003.png

Simple is stronger.

4. Consistent Mapping Across Figures

If blue means “completed”, it must always mean “completed”.

Never change color meaning across lessons or reports.

Consistency builds trust.

5. Common Design Mistakes

Avoid:

  • 3D plots for 2D data
  • Excessive annotation
  • Rainbow colormaps for categorical data
  • Titles that restate axes
  • Legends inside data region

Design Checklist

Before exporting a figure:

  • Does the title communicate insight?
  • Is color used intentionally?
  • Is anything unnecessary?
  • Are categories consistently mapped?
  • Would this look appropriate in a professional report?

Key Takeaways

  • Hierarchy guides attention.
  • Color encodes meaning.
  • Simplicity increases clarity.
  • Consistency builds credibility.
  • Professional plots are restrained.

Exercises

  1. Redesign a previous plot using only one highlight color.
  2. Rewrite a neutral title into an insight-driven title.
  3. Remove unnecessary grid lines and compare readability.
  4. Create a two-color comparison plot with consistent mapping.