Categorical Plots (barplot, countplot)

Categorical plots allow you to visualize how data is distributed across categories — such as weekdays, product types, or survey answers.

In Seaborn, two of the most commonly used categorical plots are:

• barplot() – Displays the average value of a numeric variable for each category.
• countplot() – Displays how often each category occurs in the dataset.

A bar plot is ideal for comparing average values across different categories. By default, Seaborn’s barplot() computes the mean of the numeric variable for each group.

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import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

sns.barplot(data=tips, x="day", y="total_bill")
plt.title("Average Total Bill by Day")
plt.show()

A count plot is best for showing how frequently each category appears in the dataset. Unlike a bar plot, countplot() doesn’t need a numeric column — it automatically counts the number of records in each category.

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sns.countplot(data=tips, x="day")
plt.title("Count of Records by Day")
plt.show()

• Use barplot() to compare average values or other aggregations across categories.
• Use countplot() to display frequency counts or category distributions.