Line charts

Seaborn basics

1 min read

Published Oct 7 2025, updated Aug 17 2026

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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 snsimport matplotlib.pyplot as pltdata = 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 snsimport matplotlib.pyplot as pltdata = 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 snsimport matplotlib.pyplot as pltdata = 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 snsimport matplotlib.pyplot as pltdata = 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 snsimport matplotlib.pyplot as pltdata = 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 snsimport matplotlib.pyplot as pltimport numpy as npdata = 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 snsimport matplotlib.pyplot as pltdata = 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()