Mini Capstone: Building a Structured Visual Report

  • ID: VISPY-FREE-12
  • Type: Capstone
  • Audience: Public
  • Theme: From plots to structured visual reasoning

This mini capstone brings everything together.

You will build a short, structured visual report using:

The goal is not to produce many plots.

The goal is to produce a small number of clear, defensible figures.

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,
    set_cdi_theme,
    show_and_save_mpl,
    show_and_save_plotly,
)

warnings.filterwarnings("ignore")

cdi_notebook_init(chapter="12")

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

Question 1

Is test preparation associated with higher math scores?

Figure 1: Distribution comparison

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

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

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

fig.tight_layout()

show_and_save_mpl(fig)  # figures/12_001.png
Saved PNG → figures/12_001.png

Interpretation:

  • Compare medians.
  • Compare spread.
  • Look for overlap.

Question 2

Does study time relate to performance?

Figure 2: Scatter with reference line

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

sns.scatterplot(data=df, x="study_hours", y="math_score", alpha=0.6, ax=ax)

mean_score = df["math_score"].mean()
ax.axhline(mean_score, linestyle="--", linewidth=1)

ax.set_title("Higher study time is 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/12_002.png
Saved PNG → figures/12_002.png

Interpretation:

  • Is there visible trend structure?
  • Are there diminishing returns?
  • Are there extreme outliers?

Question 3

Do patterns differ by group?

Figure 3: Multi-panel comparison

groups = sorted(df["group"].unique())

fig, axes = plt.subplots(ncols=len(groups), figsize=(11, 4), sharey=True)

for ax, g in zip(axes, groups):
    sub = df[df["group"] == g]
    ax.scatter(sub["study_hours"], sub["math_score"], alpha=0.6)
    ax.set_title(f"Group {g}")
    ax.set_xlabel("Study hours")

axes[0].set_ylabel("Math score")
fig.suptitle("Study hours vs math score by group", y=1.02)
fig.tight_layout()

show_and_save_mpl(fig)  # figures/12_003.png
Saved PNG → figures/12_003.png

Interpretation:

  • Are slopes visually similar?
  • Does one group show greater spread?
  • Are patterns consistent?

Optional: Interactive Exploration (Development Only)

fig = px.scatter(
    df,
    x="study_hours",
    y="math_score",
    color="test_prep",
    facet_col="group",
    title="Interactive exploration of study patterns",
)

set_cdi_theme(fig)

show_and_save_plotly(fig, show=False)  # figures/12_004.png
Saved PNG → figures/12_004.png

Writing the Visual Summary

A structured visual report should:

  1. State the question.
  2. Show one focused figure.
  3. Provide short interpretation.
  4. Avoid overclaiming.

Example summary:

Test preparation is associated with higher median math scores, with moderate overlap between groups.
Study hours show a positive association with performance, though variability remains substantial.
Patterns appear broadly consistent across groups.

Capstone Checklist

  • Did you answer clear questions?
  • Did each figure support a claim?
  • Did you avoid unnecessary plots?
  • Are scales consistent?
  • Is interpretation cautious and evidence-based?

Key Takeaways

  • Professional visualization supports reasoning.
  • Fewer strong figures are better than many weak ones.
  • Structured reporting improves credibility.
  • Clarity is more important than complexity.

Final Exercise

Build your own three-question visual report using this dataset. Each question must: - use one primary figure - include a short interpretation - avoid unnecessary visual elements