seaborn.
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=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, size=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.
Input variables; these should be column names in data
.
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 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.
Markers for the scatterplot. If a list, each marker in the list will be
used for each level of the hue
variable.
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.
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.
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.
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.
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
.
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.
If True
, draw a scatterplot with the underlying observations (or
the x_estimator
values).
If True
, estimate and plot a regression model relating the x
and y
variables.
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.
data
, optionalIf 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.
Seed or random number generator for reproducible bootstrapping.
If order
is greater than 1, use numpy.polyfit
to estimate a
polynomial regression.
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.
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.
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.
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.
data
or matricesConfounding variables to regress out of the x
or y
variables
before plotting.
If 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 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
Notes
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
.
Examples
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
the FacetGrid
docs.
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")
Map 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
the plot:
>>> 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))