lmplot(*, x=None, y=None, data=None, hue=None, col=None, row=None, palette=None, col_wrap=None, height=5, aspect=1, markers='o', sharex=True, sharey=True, hue_order=None, col_order=None, row_order=None, legend=True, legend_out=True, 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, size=None)¶
Plot data and regression model fits across a FacetGrid.
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
row. However, always think about
your particular dataset and the goals of the visualization you are
There are a number of mutually exclusive options for estimating the regression model. See the tutorial for more information.
Input variables; these should be column names in
Tidy (“long-form”) dataframe where each column is a variable and each row is an observation.
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.
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
Height (in inches) of each facet. See also:
Aspect ratio of each facet, so that
aspect * height gives the width
of each facet in inches.
Markers for the scatterplot. If a list, each marker in the list will be
used for each level of the
If true, the facets will share y axes across columns and/or x axes across rows.
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.
True and there is a
hue variable, add a legend.
True, the figure size will be extended, and the legend will be
drawn outside the plot on the center right.
Apply this function to each unique value of
x and plot the
resulting estimate. This is useful when
x is a discrete variable.
x_ci is given, this estimate will be bootstrapped and a
confidence interval will be drawn.
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
Size of the confidence interval used when plotting a central tendency
for discrete values of
"ci", defer to the value of the
ci parameter. If
"sd", skip bootstrapping and show the
standard deviation of the observations in each bin.
True, draw a scatterplot with the underlying observations (or
True, estimate and plot a regression model relating the
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.
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.
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.
Seed or random number generator for reproducible bootstrapping.
order is greater than 1, use
numpy.polyfit to estimate a
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.
statsmodels to estimate a nonparametric lowess
model (locally weighted linear regression). Note that confidence
intervals cannot currently be drawn for this kind of model.
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.
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.
Confounding variables to regress out of the
True, the regression line is bounded by the data limits. If
False, it extends to the
x axis limits.
Add uniform random noise of this size to either the
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
These examples focus on basic regression model plots to exhibit the
various faceting options; see the
regplot() docs for demonstrations
of the other options for plotting the data and models. There are also
other examples for how to manipulate plot using the returned object on
Plot a simple linear relationship between two variables:
>>> import seaborn as sns; sns.set_theme(color_codes=True) >>> tips = sns.load_dataset("tips") >>> g = sns.lmplot(x="total_bill", y="tip", data=tips)
Condition on a third variable and plot the levels in different colors:
>>> g = sns.lmplot(x="total_bill", y="tip", hue="smoker", data=tips)
Use different markers as well as colors so the plot will reproduce to black-and-white more easily:
>>> g = sns.lmplot(x="total_bill", y="tip", hue="smoker", data=tips, ... markers=["o", "x"])
Use a different color palette:
>>> g = sns.lmplot(x="total_bill", y="tip", hue="smoker", data=tips, ... palette="Set1")
hue levels to colors with a dictionary:
>>> g = sns.lmplot(x="total_bill", y="tip", hue="smoker", data=tips, ... palette=dict(Yes="g", No="m"))
Plot the levels of the third variable across different columns:
>>> g = sns.lmplot(x="total_bill", y="tip", col="smoker", data=tips)
Change the height and aspect ratio of the facets:
>>> g = sns.lmplot(x="size", y="total_bill", hue="day", col="day", ... data=tips, height=6, aspect=.4, x_jitter=.1)
Wrap the levels of the column variable into multiple rows:
>>> g = sns.lmplot(x="total_bill", y="tip", col="day", hue="day", ... data=tips, col_wrap=2, height=3)
Condition on two variables to make a full grid:
>>> g = sns.lmplot(x="total_bill", y="tip", row="sex", col="time", ... data=tips, height=3)
Use methods on the returned
FacetGrid instance to further tweak
>>> g = sns.lmplot(x="total_bill", y="tip", row="sex", col="time", ... data=tips, height=3) >>> g = (g.set_axis_labels("Total bill (US Dollars)", "Tip") ... .set(xlim=(0, 60), ylim=(0, 12), ... xticks=[10, 30, 50], yticks=[2, 6, 10]) ... .fig.subplots_adjust(wspace=.02))