Line charts

Seaborn basics

1 min read

Published Oct 7 2025

ChartsGraphsMatplotlibNumPyPandasPythonSeabornVisualisation

seaborn.lineplot() draws a line plot — showing the relationship between two continuous variables, often used to display trends over time or aggregated relationships.

Syntax:

sns.lineplot(

data=None,

x=None,

y=None,

hue=None,

style=None,

size=None,

palette=None,

markers=False,

dashes=True,

ci='auto',

estimator='mean',

**kwargs

)

Parameters:

data = DataFrame containing data
x, y = Columns to plot
hue = Colours (categorical/numerical grouping)
style = Line style or marker shape for groups
size = Line thickness based on a variable
palette = Colour palette for hue
markers = Add point markers (True or column name)
dashes = Control line dash patterns
ci = Confidence interval ('sd', 'auto', None)
estimator = Function to aggregate data (default = mean)

Basic example

import seaborn as sns

import matplotlib.pyplot as plt

data = sns.load_dataset("fmri")

sns.lineplot(data=data, x="timepoint", y="signal")

plt.show()

Plots the average signal value at each timepoint.

Adding hue (colour)

import seaborn as sns

import matplotlib.pyplot as plt

data = sns.load_dataset("fmri")

sns.lineplot(data=data, x="timepoint", y="signal", hue="event")

plt.show()

Lines coloured by event category.

Multiple dimensions (hue, style, size)

import seaborn as sns

import matplotlib.pyplot as plt

data = sns.load_dataset("fmri")

sns.lineplot(

data=data,

x="timepoint",

y="signal",

hue="event",

style="region",

size="region"

)

plt.show()

hue → colour
style → different line types (solid, dashed)
size → line thickness

seaborn line plot hue size style example

Show data points with markers

import seaborn as sns

import matplotlib.pyplot as plt

data = sns.load_dataset("fmri")

sns.lineplot(

data=data,

x="timepoint",

y="signal",

hue="event",

style="event",

markers=True,

dashes=False

)

plt.show()

Adds markers for data points, disables dashed lines.

seaborn line plot datapoint marker example

Control confidence intervals

import seaborn as sns

import matplotlib.pyplot as plt

data = sns.load_dataset("fmri")

sns.lineplot(data=data, x="timepoint", y="signal", hue="event", ci=None)

plt.show()

ci=None removes shaded confidence intervals.
ci='sd' shows standard deviation bands.

Use a custom estimator

import seaborn as sns

import matplotlib.pyplot as plt

import numpy as np

data = sns.load_dataset("fmri")

sns.lineplot(data=data, x="timepoint", y="signal", hue="event", estimator=np.median)

plt.show()

Uses median instead of mean for aggregation.

seaborn line plot custom estimator example

Customise appearance

import seaborn as sns

import matplotlib.pyplot as plt

data = sns.load_dataset("fmri")

sns.lineplot(

data=data,

x="timepoint",

y="signal",

hue="event",

palette="coolwarm",

linewidth=2.5,

markers=True,

dashes=False

)

plt.show()

Line charts

Seaborn basics

1 min read

Published Oct 7 2025

Guide Sections

Guide Comments

Basic example

Adding hue (colour)

Multiple dimensions (hue, style, size)

Show data points with markers

Control confidence intervals

Use a custom estimator

Customise appearance

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