Count Plots

When to use count plots

Count plots answer:

• How many records are in each category?
• Is there imbalance?

Examples:

• Orders by status
• Users by plan
• Tickets by priority

diagram How countplot works mermaid

  flowchart LR
  A["Raw rows"] --> B["Group by category"]
  B --> C["Count rows
in each group"]
  C --> D["Draw one bar
per category"]

countplot tallies rows per category automatically — no separate aggregation step needed.

From the book

McKinney’s bar-plot recipe applies directly here: “a useful recipe for bar plots is to visualize a Series’s value frequency using value_counts: s.value_counts().plot.bar().” Seaborn’s countplot does that same job in one call, straight from the raw (non-aggregated) rows.

Basic countplot

countplot

import seaborn as sns
import matplotlib.pyplot as plt
 
tips = sns.load_dataset("tips")
 
plt.figure(figsize=(6, 4))
sns.countplot(data=tips, x="day")
plt.title("Count by day")
plt.tight_layout()
plt.show()

Sorted order

Sorted categories

import seaborn as sns
import matplotlib.pyplot as plt
 
tips = sns.load_dataset("tips")
 
order = tips["day"].value_counts().index
 
plt.figure(figsize=(6, 4))
sns.countplot(data=tips, x="day", order=order)
plt.title("Count by day (sorted)")
plt.tight_layout()
plt.show()

Count with hue

Countplot with hue

import seaborn as sns
import matplotlib.pyplot as plt
 
tips = sns.load_dataset("tips")
 
plt.figure(figsize=(7, 4))
sns.countplot(data=tips, x="day", hue="sex")
plt.title("Count by day split by sex")
plt.tight_layout()
plt.show()

Visualize it

Each bar’s height is simply “how many rows had this category” — nothing is averaged:

sketch Category counts as bars p5.js Pause Restart

Bar height is a raw tally of rows per category, unlike barplot which averages a numeric column.

Tips

• Use countplot when you just need frequencies, not an aggregated numeric column.
• Pass order= to control category order, especially for sorted or custom sequences.
• Combine with hue to compare two categorical columns at once.

Next

Continue to Heatmaps for Correlation to visualize relationships between several numeric columns at once.

🧪 Try It Yourself

Exercise 1 – Count Rows per Category

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Exercise 2 – Sort by Frequency

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Exercise 3 – countplot vs barplot

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