3.4.1 Visualization Roadmap: Choose the Chart Before the Style

Visualization is not decoration. It turns an analysis result into something another person can understand quickly.

Look at the Chart Choice Map First

Use this decision first:

What you want to show	Start with
change over time	line chart
category comparison	bar chart
distribution	histogram or box plot
relationship between two numbers	scatter plot
correlation matrix	heatmap

After the chart type is right, then polish title, axes, legend, colors, and annotation.

Run One Chart Once

Create visual_first_loop.py and run it after installing pandas and matplotlib.

import pandas as pd
import matplotlib.pyplot as plt

sales = pd.DataFrame(
    {
        "month": ["2026-01", "2026-02", "2026-03", "2026-04"],
        "amount": [120, 180, 160, 220],
    }
)

ax = sales.plot(x="month", y="amount", marker="o", legend=False)
ax.set_title("Monthly sales")
ax.set_xlabel("Month")
ax.set_ylabel("Amount")
plt.tight_layout()
plt.savefig("sales_trend.png", dpi=150)

print("saved: sales_trend.png")

Expected output:

saved: sales_trend.png

Open the image and check one thing: can a reader see the trend within three seconds?

Learn in This Order

Order	Read	What to practice
1	3.4.2 Matplotlib Basics	Figure, Axes, line/bar/scatter
2	3.4.3 Seaborn Statistical Visualization	faster exploratory charts
3	3.4.5 Visualization Best Practices	chart choice, labels, color, misleading charts
4	3.4.4 Plotly Interactive Visualization	interactive charts when the project needs them

Evidence to Keep

Keep this page’s proof of learning as a small evidence card:

Question

what comparison, distribution, trend, or relationship the chart answers

Chart Choice

line, bar, scatter, histogram, box, heatmap, or interactive dashboard

Artifact

saved chart image/html plus the data slice used

Failure Check

misleading scale, overloaded chart, wrong aggregation, or missing labels

Expected Output

chart artifact with one sentence explaining the insight

Pass Check

You pass this subchapter when you can create 4 useful charts from one dataset and explain why each chart type was chosen.

Check reasoning and explanation

1. A passing answer starts from the question, identifies the table/DataFrame or query needed, and keeps the cleaning step reproducible.
2. The evidence should include a small output sample, a plot or SQL result when relevant, and one sentence interpreting what changed.
3. A good self-check names one data-quality risk such as missing values, duplicate rows, wrong joins, misleading aggregation, or an unreadable chart.