seaborn.lmplot(data, *, x=None, y=None, hue=None, col=None, row=None, palette=None, col_wrap=None, height=5, aspect=1, markers='o', sharex=None, sharey=None, hue_order=None, col_order=None, row_order=None, legend=True, legend_out=None, x_estimator=None, x_bins=None, x_ci='ci', scatter=True, fit_reg=True, ci=95, n_boot=1000, units=None, seed=None, order=1, logistic=False, lowess=False, robust=False, logx=False, x_partial=None, y_partial=None, truncate=True, x_jitter=None, y_jitter=None, scatter_kws=None, line_kws=None, facet_kws=None)#

Plot data and regression model fits across a FacetGrid.

This function combines regplot() and FacetGrid. It is intended as a convenient interface to fit regression models across conditional subsets of a dataset.

When thinking about how to assign variables to different facets, a general rule is that it makes sense to use hue for the most important comparison, followed by col and row. However, always think about your particular dataset and the goals of the visualization you are creating.

There are a number of mutually exclusive options for estimating the regression model. See the tutorial for more information.

The parameters to this function span most of the options in FacetGrid, although there may be occasional cases where you will want to use that class and regplot() directly.


Tidy (“long-form”) dataframe where each column is a variable and each row is an observation.

x, ystrings, optional

Input variables; these should be column names in data.

hue, col, rowstrings

Variables that define subsets of the data, which will be drawn on separate facets in the grid. See the *_order parameters to control the order of levels of this variable.

palettepalette name, list, or dict

Colors to use for the different levels of the hue variable. Should be something that can be interpreted by color_palette(), or a dictionary mapping hue levels to matplotlib colors.


“Wrap” the column variable at this width, so that the column facets span multiple rows. Incompatible with a row facet.


Height (in inches) of each facet. See also: aspect.


Aspect ratio of each facet, so that aspect * height gives the width of each facet in inches.

markersmatplotlib marker code or list of marker codes, optional

Markers for the scatterplot. If a list, each marker in the list will be used for each level of the hue variable.

share{x,y}bool, ‘col’, or ‘row’ optional

If true, the facets will share y axes across columns and/or x axes across rows.

Deprecated since version 0.12.0: Pass using the facet_kws dictionary.

{hue,col,row}_orderlists, optional

Order for the levels of the faceting variables. By default, this will be the order that the levels appear in data or, if the variables are pandas categoricals, the category order.

legendbool, optional

If True and there is a hue variable, add a legend.


If True, the figure size will be extended, and the legend will be drawn outside the plot on the center right.

Deprecated since version 0.12.0: Pass using the facet_kws dictionary.

x_estimatorcallable that maps vector -> scalar, optional

Apply this function to each unique value of x and plot the resulting estimate. This is useful when x is a discrete variable. If x_ci is given, this estimate will be bootstrapped and a confidence interval will be drawn.

x_binsint or vector, optional

Bin the x variable into discrete bins and then estimate the central tendency and a confidence interval. This binning only influences how the scatterplot is drawn; the regression is still fit to the original data. This parameter is interpreted either as the number of evenly-sized (not necessary spaced) bins or the positions of the bin centers. When this parameter is used, it implies that the default of x_estimator is numpy.mean.

x_ci“ci”, “sd”, int in [0, 100] or None, optional

Size of the confidence interval used when plotting a central tendency for discrete values of x. If "ci", defer to the value of the ci parameter. If "sd", skip bootstrapping and show the standard deviation of the observations in each bin.

scatterbool, optional

If True, draw a scatterplot with the underlying observations (or the x_estimator values).

fit_regbool, optional

If True, estimate and plot a regression model relating the x and y variables.

ciint in [0, 100] or None, optional

Size of the confidence interval for the regression estimate. This will be drawn using translucent bands around the regression line. The confidence interval is estimated using a bootstrap; for large datasets, it may be advisable to avoid that computation by setting this parameter to None.

n_bootint, optional

Number of bootstrap resamples used to estimate the ci. The default value attempts to balance time and stability; you may want to increase this value for “final” versions of plots.

unitsvariable name in data, optional

If the x and y observations are nested within sampling units, those can be specified here. This will be taken into account when computing the confidence intervals by performing a multilevel bootstrap that resamples both units and observations (within unit). This does not otherwise influence how the regression is estimated or drawn.

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

Seed or random number generator for reproducible bootstrapping.

orderint, optional

If order is greater than 1, use numpy.polyfit to estimate a polynomial regression.

logisticbool, optional

If True, assume that y is a binary variable and use statsmodels to estimate a logistic regression model. Note that this is substantially more computationally intensive than linear regression, so you may wish to decrease the number of bootstrap resamples (n_boot) or set ci to None.

lowessbool, optional

If True, use statsmodels to estimate a nonparametric lowess model (locally weighted linear regression). Note that confidence intervals cannot currently be drawn for this kind of model.

robustbool, optional

If True, use statsmodels to estimate a robust regression. This will de-weight outliers. Note that this is substantially more computationally intensive than standard linear regression, so you may wish to decrease the number of bootstrap resamples (n_boot) or set ci to None.

logxbool, optional

If True, estimate a linear regression of the form y ~ log(x), but plot the scatterplot and regression model in the input space. Note that x must be positive for this to work.

{x,y}_partialstrings in data or matrices

Confounding variables to regress out of the x or y variables before plotting.

truncatebool, optional

If True, the regression line is bounded by the data limits. If False, it extends to the x axis limits.

{x,y}_jitterfloats, optional

Add uniform random noise of this size to either the x or y variables. The noise is added to a copy of the data after fitting the regression, and only influences the look of the scatterplot. This can be helpful when plotting variables that take discrete values.


Additional keyword arguments to pass to plt.scatter and plt.plot.


Dictionary of keyword arguments for FacetGrid.

See also


Plot data and a conditional model fit.


Subplot grid for plotting conditional relationships.


Combine regplot() and PairGrid (when used with kind="reg").


The regplot() and lmplot() functions are closely related, but the former is an axes-level function while the latter is a figure-level function that combines regplot() and FacetGrid.


See the regplot() docs for demonstrations of various options for specifying the regression model, which are also accepted here.

Plot a regression fit over a scatter plot:

sns.lmplot(data=penguins, x="bill_length_mm", y="bill_depth_mm")

Condition the regression fit on another variable and represent it using color:

sns.lmplot(data=penguins, x="bill_length_mm", y="bill_depth_mm", hue="species")

Condition the regression fit on another variable and split across subplots:

    data=penguins, x="bill_length_mm", y="bill_depth_mm",
    hue="species", col="sex", height=4,

Condition across two variables using both columns and rows:

    data=penguins, x="bill_length_mm", y="bill_depth_mm",
    col="species", row="sex", height=3,

Allow axis limits to vary across subplots:

    data=penguins, x="bill_length_mm", y="bill_depth_mm",
    col="species", row="sex", height=3,
    facet_kws=dict(sharex=False, sharey=False),