lets_plot.geom_smooth(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, orientation=None, method=None, n=None, se=None, level=None, span=None, deg=None, seed=None, max_n=None, **other_args)

Add a smoothed conditional mean.


Set of aesthetic mappings created by aes() function. Aesthetic mappings describe the way that variables in the data are mapped to plot “aesthetics”.

datadict or DataFrame or polars.DataFrame

The data to be displayed in this layer. If None, the default, the data is inherited from the plot data as specified in the call to ggplot.

statstr, default=’smooth’

The statistical transformation to use on the data for this layer, as a string. Supported transformations: ‘identity’ (leaves the data unchanged), ‘count’ (counts number of points with same x-axis coordinate), ‘bin’ (counts number of points with x-axis coordinate in the same bin), ‘smooth’ (performs smoothing - linear default), ‘density’ (computes and draws kernel density estimate).

positionstr or FeatureSpec

Position adjustment, either as a string (‘identity’, ‘stack’, ‘dodge’, …), or the result of a call to a position adjustment function.

show_legendbool, default=True

False - do not show legend for this layer.


Result of the call to the sampling_xxx() function. Value None (or ‘none’) will disable sampling for this layer.


Result of the call to the layer_tooltips() function. Specifies appearance, style and content.

orientationstr, default=’x’

Specifies the axis that the layer’ stat and geom should run along. Possible values: ‘x’, ‘y’.

methodstr, default=’lm’

Smoothing method: ‘lm’ (Linear Model) or ‘loess’ (Locally Estimated Scatterplot Smoothing).


Number of points to evaluate smoother at.

sebool, default=True

Display confidence interval around smooth.

levelfloat, default=0.95

Level of confidence interval to use.

spanfloat, default=0.5

Only for ‘loess’ method. The fraction of source points closest to the current point is taken into account for computing a least-squares regression. A sensible value is usually 0.25 to 0.5.

degint, default=1

Degree of polynomial for linear regression model.


Random seed for ‘loess’ sampling.

max_nint, default=1000

Maximum number of data-points for ‘loess’ method. If this quantity exceeded random sampling is applied to data.


Other arguments passed on to the layer. These are often aesthetics settings used to set an aesthetic to a fixed value, like color=’red’, fill=’blue’, size=3 or shape=21. They may also be parameters to the paired geom/stat.


Geom object specification.


geom_smooth() aids the eye in seeing patterns in the presence of overplotting.

Computed variables:

  • ..y.. : predicted (smoothed) value.

  • ..ymin.. : lower pointwise confidence interval around the mean.

  • ..ymax.. : upper pointwise confidence interval around the mean.

  • ..se.. : standard error.

geom_smooth() understands the following aesthetics mappings:

  • x : x-axis value.

  • y : y-axis value.

  • alpha : transparency level of a layer. Understands numbers between 0 and 1.

  • color (colour) : color of a geometry. Can be continuous or discrete. For continuous value this will be a color gradient between two colors.

  • linetype : type of the line of conditional mean line. Codes and names: 0 = ‘blank’, 1 = ‘solid’, 2 = ‘dashed’, 3 = ‘dotted’, 4 = ‘dotdash’, 5 = ‘longdash’, 6 = ‘twodash.

  • size : lines width. Defines line width for conditional mean and confidence bounds lines.


1import numpy as np
2from lets_plot import *
5n = 50
6x = np.arange(n)
7y = x + np.random.normal(scale=10, size=n)
8ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + \
9    geom_point() + geom_smooth()