Exporting and Reproducible Figure Workflows

  • ID: VISPY-009
  • Type: Lesson
  • Audience: Public
  • Theme: Reproducible figure export and publication discipline

Professional visualization is not only about creating figures. It is about exporting them consistently and reproducibly.

In this lesson you will:

All figures are exported to figures/ using CDI helpers.

Setup

import warnings
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px

from cdi_viz.theme import (
    cdi_notebook_init,
    cdi_theme,
    show_and_save_mpl,
    show_and_save_plotly,
)

warnings.filterwarnings("ignore")

cdi_notebook_init(chapter="09")

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

Static Export: Matplotlib (High Resolution)

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

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

ax.set_title("Study hours vs math score")
ax.set_xlabel("Study hours per week")
ax.set_ylabel("Math score")

fig.tight_layout()

show_and_save_mpl(fig, dpi=300)  # figures/09_001.png
Saved PNG → figures/09_001.png

Controlling Figure Size

fig, ax = plt.subplots(figsize=(9, 5))

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

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

fig.tight_layout()

show_and_save_mpl(fig, dpi=300)  # figures/09_002.png
Saved PNG → figures/09_002.png

Plotly: Static Snapshot Export

fig = px.scatter(
    df,
    x="study_hours",
    y="math_score",
    color="test_prep",
    title="Study hours vs math score (Plotly)",
)

cdi_theme(fig)

show_and_save_plotly(fig, show=False)  # figures/09_003.png
Saved PNG → figures/09_003.png

Project Structure Discipline

A clean visualization project should look like this:

project-root/ │ ├── data/ ├── figures/ ├── cdi_viz/ ├── 01-preface-and-setup.qmd ├── 02-story-driven-visualization.qmd …

Rules:

  • Never manually rename exported figures
  • Let the chapter counter manage numbering
  • Keep figure filenames deterministic

Key Takeaways

  • Professional visualization requires export discipline.
  • Use consistent figure sizes.
  • Control DPI intentionally.
  • Keep project structure clean.
  • Use CDI helpers to standardize outputs.

Exercises

  1. Export the same scatter plot at 150 DPI and 300 DPI. Compare clarity.
  2. Change figure size to (12, 6) and evaluate readability.
  3. Create a Plotly histogram and export a static snapshot.
  4. Inspect your figures/ folder and confirm numbering consistency.