seaborn.regplot(x, y, data=None, x_estimator=None, x_bins=None, x_ci='ci', scatter=True, fit_reg=True, ci=95, n_boot=1000, units=None, order=1, logistic=False, lowess=False, robust=False, logx=False, x_partial=None, y_partial=None, truncate=False, 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: order, logistic, lowess, robust, and logx. See the parameter docs for more information on these options.


x, y: string, series, or vector array

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.

data : DataFrame

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

x_estimator : callable 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 not None, this estimate will be bootstrapped and a confidence interval will be drawn.

x_bins : int 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”, 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.

scatter : bool, optional

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

fit_reg : bool, optional

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

ci : int 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_boot : int, 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.

units : variable 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.

order : int, optional

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

logistic : bool, 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.

lowess : bool, 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.

robust : bool, 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.

logx : bool, 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}_partial : strings in data or matrices

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

truncate : bool, optional

By default, the regression line is drawn to fill the x axis limits after the scatterplot is drawn. If truncate is True, it will instead by bounded by the data limits.

{x,y}_jitter : floats, 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.

label : string

Label to apply to ether the scatterplot or regression line (if scatter is False) for use in a legend.

color : matplotlib color

Color to apply to all plot elements; will be superseded by colors passed in scatter_kws or line_kws.

marker : matplotlib marker code

Marker to use for the scatterplot glyphs.

{scatter,line}_kws : dictionaries

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

ax : matplotlib Axes, optional

Axes object to draw the plot onto, otherwise uses the current Axes.


ax : matplotlib Axes

The Axes object containing the plot.

See also

Combine regplot() and FacetGrid to plot multiple linear relationships in a dataset.
Combine regplot() and JointGrid (when used with kind="reg").
Combine regplot() and PairGrid (when used with kind="reg").
Plot the residuals of a linear regression model.
Plot a two-way interaction between continuous variables


Understanding the difference between regplot() and lmplot() can be a bit tricky. In fact, they are closely related, as lmplot() uses regplot() internally and takes most of its parameters. However, regplot() is an axes-level function, so it draws directly onto an axes (either the currently active axes or the one provided by the ax parameter), while lmplot() is a figure-level function and creates its own figure, which is managed through a FacetGrid. This has a few consequences, namely that regplot() can happily coexist in a figure with other kinds of plots and will follow the global matplotlib color cycle. In contrast, lmplot() needs to occupy an entire figure, and the size and color cycle are controlled through function parameters, ignoring the global defaults.

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.


Plot the relationship between two variables in a DataFrame:

>>> import seaborn as sns; sns.set(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:

>>> ax = sns.regplot(x=x, y=y, ci=68)

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 and truncate the model prediction:

>>> 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, truncate=True)

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) and truncate the model prediction:

>>> ax = sns.regplot(x="size", y="total_bill", data=tips,
...                  x_estimator=np.mean, logx=True, truncate=True)