Box plots

Seaborn basics

2 min read

Published Oct 7 2025, updated Aug 17 2026

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seaborn.boxplot() is used to visualise the distribution of a numerical variable and compare it across categories.

A box plot (a.k.a. box-and-whisker plot) shows:

The median (middle value)
The quartiles (Q1, Q3) — 25th and 75th percentiles
The interquartile range (IQR) — Q3 − Q1
Whiskers — roughly 1.5×IQR beyond the box
Outliers — data points outside whiskers

It’s great for spotting spread, symmetry, and outliers in your data.

Syntax:

sns.boxplot(    data=None,    x=None,    y=None,    hue=None,    order=None,    hue_order=None,    orient=None,    color=None,    palette=None,    width=0.8,    dodge=True,    showcaps=True,    boxprops=None,    whiskerprops=None,    flierprops=None,    medianprops=None,    notch=False,    **kwargs)

Parameters:

data = DataFrame containing the data
x, y = Variables for categories and values
hue = Adds subcategories (side-by-side boxes)
order / hue_order = Specify category order
palette = Colour scheme
orient = "v" (vertical) or "h" (horizontal)
width = Width of boxes
dodge = Separate hue boxes side by side
notch = Draw notches to show confidence around median
flierprops = Customise outlier points
medianprops = Customise median line style

Basic example

import seaborn as snsimport matplotlib.pyplot as plttips = sns.load_dataset("tips")sns.boxplot(data=tips, x="day", y="total_bill")plt.show()

Shows the distribution of total bills for each day.

Add hue (subgroups)

import seaborn as snsimport matplotlib.pyplot as plttips = sns.load_dataset("tips")sns.boxplot(data=tips, x="day", y="total_bill", hue="sex")plt.show()

Each day has two boxes, one per sex.
Allows you to compare distributions between men and women for each day.

Horizontal boxes

import seaborn as snsimport matplotlib.pyplot as plttips = sns.load_dataset("tips")sns.boxplot(data=tips, x="total_bill", y="day", orient="h")plt.show()

Flips the boxes horizontally — good for long category labels.

Customise colors and style

import seaborn as snsimport matplotlib.pyplot as plttips = sns.load_dataset("tips")sns.boxplot(    data=tips,    x="day",    y="total_bill",    hue="sex",    palette="pastel",    width=0.6,    boxprops={"edgecolor": "black"},    medianprops={"color": "red", "linewidth": 2})plt.show()

palette="pastel" → soft colour theme
medianprops → style the median line
boxprops → outline colour

seaborn box plot customise colour example

Show or hide outliers

import seaborn as snsimport matplotlib.pyplot as plttips = sns.load_dataset("tips")sns.boxplot(data=tips, x="day", y="total_bill", showfliers=False)plt.show()

Removes outlier points (dots beyond whiskers).

Add notches for median confidence

import seaborn as snsimport matplotlib.pyplot as plttips = sns.load_dataset("tips")sns.boxplot(data=tips, x="day", y="total_bill", notch=True)plt.show()

Adds notches to indicate an approximate 95% confidence interval around the median.

Control category order

import seaborn as snsimport matplotlib.pyplot as plttips = sns.load_dataset("tips")sns.boxplot(    data=tips,    x="day",    y="total_bill",    order=["Sun", "Sat", "Fri", "Thur"])plt.show()

Controls the order of categories on the x-axis.

Using hue for subgroups

import seaborn as snsimport matplotlib.pyplot as plttips = sns.load_dataset("tips")sns.boxplot(    data=tips,    x="smoker",    y="total_bill",    hue="sex",    palette="coolwarm")plt.show()

Compares distributions of total bill across smokers/non-smokers for each sex.