seaborn.lineplot(data=None, *, x=None, y=None, hue=None, size=None, style=None, units=None, weights=None, palette=None, hue_order=None, hue_norm=None, sizes=None, size_order=None, size_norm=None, dashes=True, markers=None, style_order=None, estimator='mean', errorbar=('ci', 95), n_boot=1000, seed=None, orient='x', sort=True, err_style='band', err_kws=None, legend='auto', ci='deprecated', ax=None, **kwargs)#

Draw a line plot with possibility of several semantic groupings.

The relationship between x and y can be shown for different subsets of the data using the hue, size, and style parameters. These parameters control what visual semantics are used to identify the different subsets. It is possible to show up to three dimensions independently by using all three semantic types, but this style of plot can be hard to interpret and is often ineffective. Using redundant semantics (i.e. both hue and style for the same variable) can be helpful for making graphics more accessible.

See the tutorial for more information.

The default treatment of the hue (and to a lesser extent, size) semantic, if present, depends on whether the variable is inferred to represent “numeric” or “categorical” data. In particular, numeric variables are represented with a sequential colormap by default, and the legend entries show regular “ticks” with values that may or may not exist in the data. This behavior can be controlled through various parameters, as described and illustrated below.

By default, the plot aggregates over multiple y values at each value of x and shows an estimate of the central tendency and a confidence interval for that estimate.

datapandas.DataFrame, numpy.ndarray, mapping, or sequence

Input data structure. Either a long-form collection of vectors that can be assigned to named variables or a wide-form dataset that will be internally reshaped.

x, yvectors or keys in data

Variables that specify positions on the x and y axes.

huevector or key in data

Grouping variable that will produce lines with different colors. Can be either categorical or numeric, although color mapping will behave differently in latter case.

sizevector or key in data

Grouping variable that will produce lines with different widths. Can be either categorical or numeric, although size mapping will behave differently in latter case.

stylevector or key in data

Grouping variable that will produce lines with different dashes and/or markers. Can have a numeric dtype but will always be treated as categorical.

unitsvector or key in data

Grouping variable identifying sampling units. When used, a separate line will be drawn for each unit with appropriate semantics, but no legend entry will be added. Useful for showing distribution of experimental replicates when exact identities are not needed.

weightsvector or key in data

Data values or column used to compute weighted estimation. Note that use of weights currently limits the choice of statistics to a ‘mean’ estimator and ‘ci’ errorbar.

palettestring, list, dict, or matplotlib.colors.Colormap

Method for choosing the colors to use when mapping the hue semantic. String values are passed to color_palette(). List or dict values imply categorical mapping, while a colormap object implies numeric mapping.

hue_ordervector of strings

Specify the order of processing and plotting for categorical levels of the hue semantic.

hue_normtuple or matplotlib.colors.Normalize

Either a pair of values that set the normalization range in data units or an object that will map from data units into a [0, 1] interval. Usage implies numeric mapping.

sizeslist, dict, or tuple

An object that determines how sizes are chosen when size is used. List or dict arguments should provide a size for each unique data value, which forces a categorical interpretation. The argument may also be a min, max tuple.


Specified order for appearance of the size variable levels, otherwise they are determined from the data. Not relevant when the size variable is numeric.

size_normtuple or Normalize object

Normalization in data units for scaling plot objects when the size variable is numeric.

dashesboolean, list, or dictionary

Object determining how to draw the lines for different levels of the style variable. Setting to True will use default dash codes, or you can pass a list of dash codes or a dictionary mapping levels of the style variable to dash codes. Setting to False will use solid lines for all subsets. Dashes are specified as in matplotlib: a tuple of (segment, gap) lengths, or an empty string to draw a solid line.

markersboolean, list, or dictionary

Object determining how to draw the markers for different levels of the style variable. Setting to True will use default markers, or you can pass a list of markers or a dictionary mapping levels of the style variable to markers. Setting to False will draw marker-less lines. Markers are specified as in matplotlib.


Specified order for appearance of the style variable levels otherwise they are determined from the data. Not relevant when the style variable is numeric.

estimatorname of pandas method or callable or None

Method for aggregating across multiple observations of the y variable at the same x level. If None, all observations will be drawn.

errorbarstring, (string, number) tuple, or callable

Name of errorbar method (either “ci”, “pi”, “se”, or “sd”), or a tuple with a method name and a level parameter, or a function that maps from a vector to a (min, max) interval, or None to hide errorbar. See the errorbar tutorial for more information.


Number of bootstraps to use for computing the confidence interval.

seedint, numpy.random.Generator, or numpy.random.RandomState

Seed or random number generator for reproducible bootstrapping.

orient“x” or “y”

Dimension along which the data are sorted / aggregated. Equivalently, the “independent variable” of the resulting function.


If True, the data will be sorted by the x and y variables, otherwise lines will connect points in the order they appear in the dataset.

err_style“band” or “bars”

Whether to draw the confidence intervals with translucent error bands or discrete error bars.

err_kwsdict of keyword arguments

Additional parameters to control the aesthetics of the error bars. The kwargs are passed either to matplotlib.axes.Axes.fill_between() or matplotlib.axes.Axes.errorbar(), depending on err_style.

legend“auto”, “brief”, “full”, or False

How to draw the legend. If “brief”, numeric hue and size variables will be represented with a sample of evenly spaced values. If “full”, every group will get an entry in the legend. If “auto”, choose between brief or full representation based on number of levels. If False, no legend data is added and no legend is drawn.

ciint or “sd” or None

Size of the confidence interval to draw when aggregating.

Deprecated since version 0.12.0: Use the new errorbar parameter for more flexibility.


Pre-existing axes for the plot. Otherwise, call matplotlib.pyplot.gca() internally.

kwargskey, value mappings

Other keyword arguments are passed down to matplotlib.axes.Axes.plot().


The matplotlib axes containing the plot.

See also


Plot data using points.


Plot point estimates and CIs using markers and lines.


The flights dataset has 10 years of monthly airline passenger data:

flights = sns.load_dataset("flights")
year month passengers
0 1949 Jan 112
1 1949 Feb 118
2 1949 Mar 132
3 1949 Apr 129
4 1949 May 121

To draw a line plot using long-form data, assign the x and y variables:

may_flights = flights.query("month == 'May'")
sns.lineplot(data=may_flights, x="year", y="passengers")

Pivot the dataframe to a wide-form representation:

flights_wide = flights.pivot(index="year", columns="month", values="passengers")
month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1949 112 118 132 129 121 135 148 148 136 119 104 118
1950 115 126 141 135 125 149 170 170 158 133 114 140
1951 145 150 178 163 172 178 199 199 184 162 146 166
1952 171 180 193 181 183 218 230 242 209 191 172 194
1953 196 196 236 235 229 243 264 272 237 211 180 201

To plot a single vector, pass it to data. If the vector is a pandas.Series, it will be plotted against its index:


Passing the entire wide-form dataset to data plots a separate line for each column:


Passing the entire dataset in long-form mode will aggregate over repeated values (each year) to show the mean and 95% confidence interval:

sns.lineplot(data=flights, x="year", y="passengers")

Assign a grouping semantic (hue, size, or style) to plot separate lines

sns.lineplot(data=flights, x="year", y="passengers", hue="month")

The same column can be assigned to multiple semantic variables, which can increase the accessibility of the plot:

sns.lineplot(data=flights, x="year", y="passengers", hue="month", style="month")

Use the orient parameter to aggregate and sort along the vertical dimension of the plot:

sns.lineplot(data=flights, x="passengers", y="year", orient="y")

Each semantic variable can also represent a different column. For that, we’ll need a more complex dataset:

fmri = sns.load_dataset("fmri")
subject timepoint event region signal
0 s13 18 stim parietal -0.017552
1 s5 14 stim parietal -0.080883
2 s12 18 stim parietal -0.081033
3 s11 18 stim parietal -0.046134
4 s10 18 stim parietal -0.037970

Repeated observations are aggregated even when semantic grouping is used:

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

Assign both hue and style to represent two different grouping variables:

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

When assigning a style variable, markers can be used instead of (or along with) dashes to distinguish the groups:

    x="timepoint", y="signal", hue="event", style="event",
    markers=True, dashes=False

Show error bars instead of error bands and extend them to two standard error widths:

    data=fmri, x="timepoint", y="signal", hue="event", err_style="bars", errorbar=("se", 2),

Assigning the units variable will plot multiple lines without applying a semantic mapping:

    data=fmri.query("region == 'frontal'"),
    x="timepoint", y="signal", hue="event", units="subject",
    estimator=None, lw=1,

Load another dataset with a numeric grouping variable:

dots = sns.load_dataset("dots").query("align == 'dots'")
align choice time coherence firing_rate
0 dots T1 -80 0.0 33.189967
1 dots T1 -80 3.2 31.691726
2 dots T1 -80 6.4 34.279840
3 dots T1 -80 12.8 32.631874
4 dots T1 -80 25.6 35.060487

Assigning a numeric variable to hue maps it differently, using a different default palette and a quantitative color mapping:

    data=dots, x="time", y="firing_rate", hue="coherence", style="choice",

Control the color mapping by setting the palette and passing a matplotlib.colors.Normalize object:

    data=dots.query("coherence > 0"),
    x="time", y="firing_rate", hue="coherence", style="choice",
     palette="flare", hue_norm=mpl.colors.LogNorm(),

Or pass specific colors, either as a Python list or dictionary:

palette = sns.color_palette("mako_r", 6)
    data=dots, x="time", y="firing_rate",
    hue="coherence", style="choice",

Assign the size semantic to map the width of the lines with a numeric variable:

    data=dots, x="time", y="firing_rate",
    size="coherence", hue="choice",

Pass a a tuple, sizes=(smallest, largest), to control the range of linewidths used to map the size semantic:

    data=dots, x="time", y="firing_rate",
    size="coherence", hue="choice",
    sizes=(.25, 2.5)

By default, the observations are sorted by x. Disable this to plot a line with the order that observations appear in the dataset:

x, y = np.random.normal(size=(2, 5000)).cumsum(axis=1)
sns.lineplot(x=x, y=y, sort=False, lw=1)

Use relplot() to combine lineplot() and FacetGrid. This allows grouping within additional categorical variables. Using relplot() is safer than using FacetGrid directly, as it ensures synchronization of the semantic mappings across facets:

    data=fmri, x="timepoint", y="signal",
    col="region", hue="event", style="event",