How to Make Countplot or barplot with Seaborn Catplot?

How to Make a Countplot or Barplot with Seaborn Catplot

When working with categorical data in Python, visualizing distribution is essential. Seaborn’s catplot() (with kind='count') automatically tallies category frequencies and plots them as bars, saving you from manual aggregation.

Creating a Basic Countplot

Let's use the classic Titanic dataset included in Seaborn:

import seaborn as sns
import matplotlib.pyplot as plt

sns.set_style('darkgrid')
titanic = sns.load_dataset('titanic')

sns.catplot(x='sex', kind='count', data=titanic)
plt.xlabel('Gender')
plt.ylabel('Count')
plt.show()

This produces a clear bar chart that displays the total number of male and female passengers.

Adding a Second Category: Grouped Bars

You can split bars by another variable using hue. Here’s the survival status grouped by gender:

sns.catplot(x='sex', hue='survived', kind='count', data=titanic)
plt.xlabel('Gender')
plt.ylabel('Count')
plt.show()

This shows separate bars for survivors and non-survivors within each gender category—great for comparison.

Flipping Orientation: Horizontal Bars

Using a horizontal layout can improve readability when category names are long or numerous:

sns.catplot(y='sex', hue='survived', kind='count', data=titanic)
plt.xlabel('Count')
plt.ylabel('Gender')
plt.show()

Customizing Plot Dimensions

You can adjust the size and shape of your figure using height and aspect arguments:

sns.catplot(x='sex', hue='survived', kind='count',
               data=titanic, height=4, aspect=1.5)
plt.xlabel('Gender')
plt.ylabel('Count')
plt.show()

Example with a Custom Dataframe

For cases where your data isn't pre-counted, Seaborn handles the grouping internally. Here's an example using a fictional developer survey:

import pandas as pd

df = pd.DataFrame({
  'Education': ['Bachelor', 'Master', 'PhD', 'Bachelor', 'Master'],
  'Gender': ['M', 'F', 'M', 'F', 'F']
})

sns.catplot(x='Education', kind='count', data=df, height=5, aspect=1.2)
plt.xlabel('Education Level')
plt.ylabel('Count')
plt.show()

# Grouped by gender
sns.catplot(x='Education', hue='Gender', kind='count', data=df,
            height=5, aspect=1.2)
plt.xlabel('Education Level')
plt.ylabel('Count')
plt.show()

This effectively counts entries per category and optionally per subgroup.

Tips for Effective Counts & Barplots

• Use kind='count' so Seaborn computes frequencies for you.
• Apply hue to introduce a secondary categorical separation.
• Choose between vertical and horizontal layouts using x or y.
• Tweak figure dimensions with height and aspect for better visuals.
• Remember to label axes clearly using Matplotlib’s plt.xlabel() and plt.ylabel().

Conclusion

Seaborn’s catplot(kind='count') is a fast and intuitive way to visualize category frequencies. Between grouping with hue, switching orientation, and adjusting figure size, you can create polished, informative bar charts with minimal effort.