Visualization Basics

  • ID: DS-L05
  • Type: Lesson
  • Audience: Public
  • Theme: Foundational data visualization using reproducible Quarto workflows

Visualization is one of the most powerful tools in data science.

It allows you to:

As in previous chapters, all code runs inside this Quarto (.qmd) file and executes when you render the book.

Lesson Overview

By the end of this lesson, you will be able to:

  • Create histograms and boxplots
  • Build scatter plots
  • Visualize grouped comparisons
  • Customize plot titles and labels
  • Save figures reproducibly

Chapter Initialization

from cdi_viz.theme import cdi_notebook_init, show_and_save_mpl

cdi_notebook_init(chapter="05", title_x=0.5)

Load the Clean Dataset

import pandas as pd

df = pd.read_csv("data/iris_clean.csv")
df.head()
sepal_length sepal_width petal_length petal_width species
0 5.1 3.5 1.4 0.2 setosa
1 4.9 3.0 1.4 0.2 setosa
2 4.7 3.2 1.3 0.2 setosa
3 4.6 3.1 1.5 0.2 setosa
4 5.0 3.6 1.4 0.2 setosa

Histogram

Histograms help us understand the distribution of a variable.

import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(8, 5))
ax.hist(df["sepal_length"], bins=12)
ax.set_title("Distribution of Sepal Length")
ax.set_xlabel("sepal_length")
ax.set_ylabel("Frequency")
ax.grid(True, alpha=0.2)

show_and_save_mpl(fig)

Iris — Distribution of Sepal Length
'figures/05_001.png'

Boxplot

Boxplots highlight spread and potential outliers.

import seaborn as sns

fig, ax = plt.subplots(figsize=(8, 5))
sns.boxplot(data=df, x="species", y="sepal_length", ax=ax)
ax.set_title("Sepal Length by Species")
ax.grid(True, axis="y", alpha=0.2)

show_and_save_mpl(fig)

Iris — Sepal Length by Species
'figures/05_002.png'

Scatter Plot

Scatter plots reveal relationships between two variables.

fig, ax = plt.subplots(figsize=(8, 5))
sns.scatterplot(
    data=df,
    x="sepal_length",
    y="petal_length",
    hue="species",
    ax=ax
)
ax.set_title("Sepal Length vs Petal Length")
ax.grid(True, alpha=0.2)

show_and_save_mpl(fig)

Iris — Sepal Length vs Petal Length
'figures/05_003.png'

Pairplot Overview

Pairplots provide a quick overview of relationships between multiple variables.

g = sns.pairplot(df, hue="species", corner=True, plot_kws={"alpha": 0.7})
g.fig.suptitle("Iris — Pairplot Overview", y=1.02)

show_and_save_mpl(g.fig)

Iris — Pairplot Overview
'figures/05_004.png'

Visualization Best Practices

  • Label axes clearly
  • Use meaningful titles
  • Avoid unnecessary decoration
  • Keep plots readable
  • Maintain consistent styling

Good visualization supports interpretation.
Cluttered visualization hides insight.

Summary

  • You created histograms, boxplots, and scatter plots
  • You visualized grouped comparisons
  • You applied consistent styling
  • You saved figures reproducibly using the CDI visualization utilities

You are now prepared to generate structured insights from visual evidence.