seaborn.residplot(x, y, data=None, lowess=False, x_partial=None, y_partial=None, order=1, robust=False, dropna=True, label=None, color=None, scatter_kws=None, line_kws=None, ax=None)

Plot the residuals of a linear regression.

This function will regress y on x (possibly as a robust or polynomial regression) and then draw a scatterplot of the residuals. You can optionally fit a lowess smoother to the residual plot, which can help in determining if there is structure to the residuals.


x : vector or string

Data or column name in data for the predictor variable.

y : vector or string

Data or column name in data for the response variable.

data : DataFrame, optional

DataFrame to use if x and y are column names.

lowess : boolean, optional

Fit a lowess smoother to the residual scatterplot.

{x, y}_partial : matrix or string(s) , optional

Matrix with same first dimension as x, or column name(s) in data. These variables are treated as confounding and are removed from the x or y variables before plotting.

order : int, optional

Order of the polynomial to fit when calculating the residuals.

robust : boolean, optional

Fit a robust linear regression when calculating the residuals.

dropna : boolean, optional

If True, ignore observations with missing data when fitting and plotting.

label : string, optional

Label that will be used in any plot legends.

color : matplotlib color, optional

Color to use for all elements of the plot.

{scatter, line}_kws : dictionaries, optional

Additional keyword arguments passed to scatter() and plot() for drawing the components of the plot.

ax : matplotlib axis, optional

Plot into this axis, otherwise grab the current axis or make a new one if not existing.


ax: matplotlib axes

Axes with the regression plot.

See also

Plot a simple linear regression model.
marginal distrbutions.