Seaborn offers a variety of plots for showing the distribution of categorical variables. Let’s walk through some of them, from simplest to most detailed.

Setup

# Import libraries
import pandas as pd
import seaborn as sns

# Set plots size
sns.set(rc={'figure.figsize':(9,6)})

# Load sample data
df = sns.load_dataset('tips')
df.tail()

	total_bill	tip	sex	smoker	day	time	size
239	29.03	5.92	Male	No	Sat	Dinner	3
240	27.18	2.00	Female	Yes	Sat	Dinner	2
241	22.67	2.00	Male	Yes	Sat	Dinner	2
242	17.82	1.75	Male	No	Sat	Dinner	2
243	18.78	3.00	Female	No	Thur	Dinner	2

Boxplot

The classic. A number of parameters can be tuned to adjust proportions and outliers display.

# Boxplot
sns.boxplot(
		data=df, y='total_bill', x='day',
		whis=1,                   # Whiskers extent vs IQR
		showfliers=False,         # Hide outliers markers
		width=.5,                 # Boxes width
		color='cornflowerblue',   # Avoid rainbow effect
		linewidth=1               # Line width
);

Boxenplot

An “advanced” version of the boxplot, that displays a number of percentiles as small boxes, to show more detail about the distribution.

# Boxenplot
sns.boxenplot(
		data=df, y='total_bill', x='day',
		k_depth=3   # Fixed number of percentiles to draw
);

Violinplot

Violinplots combine boxplots and kernel density estimates, and are an interesting intermediary solution between simple boxplots and detailed stripplots.

# Violinplot
sns.violinplot(
		data=df, y='total_bill', x='day', 
		hue='sex', split=True,   # Split by gender  
		cut=0,                   # Do not extend density past extreme values
		inner='box',             # Inner plot type
		bw=.35                   # "Flexibility" of kernel bandwidth
);

Stripplot

Stripplots show every data point. It can be a good idea to combine them with more a simple representation like boxplots.

# Boxplot + stripplot
sns.boxplot(
		data=df, y='total_bill', x='day', 
		width=.5, showfliers=False, color='lightgray'
)
sns.stripplot(
		data=df, y='total_bill', x='day',
		size=4,      # Custom point radius
		jitter=.05   # Amount of jitter to avoid overlap
);

Swarmplot

Swarmplot are like stripplots, but with points adjustment to avoid overlapping.