seaborn.regplot(*, x=None, y=None, data=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, dropna=True, x_jitter=None, y_jitter=None, label=None, color=None, marker='o', scatter_kws=None, line_kws=None, ax=None)¶Plot data and a linear regression model fit.
There are a number of mutually exclusive options for estimating the regression model. See the tutorial for more information.
Input variables. If strings, these should correspond with column names
in data. When pandas objects are used, axes will be labeled with
the series name.
Tidy (“long-form”) dataframe where each column is a variable and each row is an observation.
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.
Label to apply to either the scatterplot or regression line (if
scatter is False) for use in a legend.
Color to apply to all plot elements; will be superseded by colors
passed in scatter_kws or line_kws.
Marker to use for the scatterplot glyphs.
Additional keyword arguments to pass to plt.scatter and
plt.plot.
Axes object to draw the plot onto, otherwise uses the current Axes.
The Axes object containing the plot.
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.
It’s also easy to combine combine regplot() and JointGrid or
PairGrid through the jointplot() and pairplot()
functions, although these do not directly accept all of regplot()’s
parameters.
Examples
Plot the relationship between two variables in a DataFrame:
>>> import seaborn as sns; sns.set_theme(color_codes=True)
>>> tips = sns.load_dataset("tips")
>>> ax = sns.regplot(x="total_bill", y="tip", data=tips)
Plot with two variables defined as numpy arrays; use a different color:
>>> import numpy as np; np.random.seed(8)
>>> mean, cov = [4, 6], [(1.5, .7), (.7, 1)]
>>> x, y = np.random.multivariate_normal(mean, cov, 80).T
>>> ax = sns.regplot(x=x, y=y, color="g")
Plot with two variables defined as pandas Series; use a different marker:
>>> import pandas as pd
>>> x, y = pd.Series(x, name="x_var"), pd.Series(y, name="y_var")
>>> ax = sns.regplot(x=x, y=y, marker="+")
Use a 68% confidence interval, which corresponds with the standard error of the estimate, and extend the regression line to the axis limits:
>>> ax = sns.regplot(x=x, y=y, ci=68, truncate=False)
Plot with a discrete x variable and add some jitter:
>>> ax = sns.regplot(x="size", y="total_bill", data=tips, x_jitter=.1)
Plot with a discrete x variable showing means and confidence intervals
for unique values:
>>> ax = sns.regplot(x="size", y="total_bill", data=tips,
... x_estimator=np.mean)
Plot with a continuous variable divided into discrete bins:
>>> ax = sns.regplot(x=x, y=y, x_bins=4)
Fit a higher-order polynomial regression:
>>> ans = sns.load_dataset("anscombe")
>>> ax = sns.regplot(x="x", y="y", data=ans.loc[ans.dataset == "II"],
... scatter_kws={"s": 80},
... order=2, ci=None)
Fit a robust regression and don’t plot a confidence interval:
>>> ax = sns.regplot(x="x", y="y", data=ans.loc[ans.dataset == "III"],
... scatter_kws={"s": 80},
... robust=True, ci=None)
Fit a logistic regression; jitter the y variable and use fewer bootstrap iterations:
>>> tips["big_tip"] = (tips.tip / tips.total_bill) > .175
>>> ax = sns.regplot(x="total_bill", y="big_tip", data=tips,
... logistic=True, n_boot=500, y_jitter=.03)
Fit the regression model using log(x):
>>> ax = sns.regplot(x="size", y="total_bill", data=tips,
... x_estimator=np.mean, logx=True)
