Bar plots

Seaborn basics

2 min read

Published Oct 7 2025

ChartsGraphsMatplotlibNumPyPandasPythonSeabornVisualisation

seaborn.barplot() is used to visualise the mean (or another aggregate) of a numerical variable for different categories.

It automatically computes and plots:

The central tendency (by default, the mean) of each category
Error bars showing uncertainty (by default, the 95% confidence interval)

In other words:

Each bar shows the average of y for each category of x.

Syntax:

sns.barplot(

data=None,

x=None,

y=None,

hue=None,

estimator='mean',

ci='auto',

palette=None,

order=None,

hue_order=None,

orient=None,

errorbar=('ci', 95),

width=0.8,

dodge=True,

**kwargs

)

Parameters:

data = DataFrame with data
x, y = Variables for categories (x) and values (y)
hue = Adds subcategories (grouped bars)
estimator = Function for aggregation (default = mean)
ci / errorbar = Confidence interval or error bars (None, 'sd', 'ci', or numeric)
palette = Colour palette
order / hue_order = Specify order of categories
orient = "v" (vertical) or "h" (horizontal)
width = Bar width
dodge = Separate bars for hue categories (True/False)

Basic example

import seaborn as sns

import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

sns.barplot(data=tips, x="day", y="total_bill")

plt.show()

This shows the average total bill for each day of the week.

Add hue (subgroups)

import seaborn as sns

import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

sns.barplot(data=tips, x="day", y="total_bill", hue="sex")

plt.show()

Each day shows two bars, one for each sex.
Bars are placed side by side (because dodge=True by default).

Change estimator (aggregation function)

import seaborn as sns

import matplotlib.pyplot as plt

import numpy as np

tips = sns.load_dataset("tips")

sns.barplot(data=tips, x="day", y="total_bill", estimator=np.median)

plt.show()

Now, each bar shows the median total bill per day instead of the mean.

seaborn bar plot change estimator example

Customise error bars

Remove error bars:

import seaborn as sns

import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

sns.barplot(data=tips, x="day", y="total_bill", ci=None)

plt.show()

Or show standard deviation:

import seaborn as sns

import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

sns.barplot(data=tips, x="day", y="total_bill", errorbar='sd')

plt.show()

Horizontal bars

import seaborn as sns

import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

sns.barplot(data=tips, x="total_bill", y="day", orient="h")

plt.show()

Simply swap x and y and set orient="h" to flip orientation.

Customise colours and style

import seaborn as sns

import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

sns.barplot(

data=tips,

x="day",

y="total_bill",

hue="sex",

palette="coolwarm",

edgecolor="black",

width=0.7

)

plt.show()

palette="coolwarm" → colourful gradient
edgecolor="black" → outline bars
width=0.7 → slightly thinner bars

Control category order

import seaborn as sns

import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

sns.barplot(

data=tips,

x="day",

y="total_bill",

order=["Sun", "Sat", "Fri", "Thur"]

)

plt.show()

Controls the order of categories on the x-axis.

Combine with `hue` and `estimator`

import seaborn as sns

import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

sns.barplot(

data=tips,

x="day",

y="tip",

hue="smoker",

estimator=sum,

palette="pastel"

)

plt.show()

Bars now show the total tips (not average) by day and smoker status.

Bar plots

Seaborn basics

2 min read