lets_plot.geom_density(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, orientation=None, trim=None, kernel=None, adjust=None, bw=None, n=None, fs_max=None, **other_args)

Display kernel density estimate, which is a smoothed version of the histogram.


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=’density’

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. To prevent any sampling for this layer pass value “none” (string “none”).


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

orientationstr, default=’x’

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

trimbool, default=False

If False, each density is computed on the full range of the data. If True, each density is computed over the range of that group.

kernelstr, default=’gaussian’

The kernel we use to calculate the density function. Choose among ‘gaussian’, ‘cosine’, ‘optcosine’, ‘rectangular’ (or ‘uniform’), ‘triangular’, ‘biweight’ (or ‘quartic’), ‘epanechikov’ (or ‘parabolic’).

bwstr or float

The method (or exact value) of bandwidth. Either a string (choose among ‘nrd0’ and ‘nrd’), or a float.


Adjust the value of bandwidth by multiplying it. Changes how smooth the frequency curve is.

nint, default=512

The number of sampled points for plotting the function.

fs_maxint, default=500

Maximum size of data to use density computation with ‘full scan’. For bigger data, less accurate but more efficient density computation is applied.


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.


Computed variables:

  • ..density.. : density estimate (mapped by default).

  • ..count.. : density * number of points.

  • ..scaled.. : density estimate, scaled to maximum of 1.

geom_density() understands the following aesthetics mappings:

  • x : x-axis coordinates.

  • alpha : transparency level of a layer. Accept values between 0 and 1.

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

  • fill : color of geometry filling.

  • size : lines width.

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

  • weight : used by ‘density’ stat to compute weighted density.


1import numpy as np
2from lets_plot import *
5x = np.random.normal(size=1000)
6ggplot({'x': x}, aes(x='x')) + geom_density()

 1import numpy as np
 2from lets_plot import *
 4n = 300
 6x = np.random.normal(size=n)
 7c = np.random.choice(['a', 'b', 'c'], size=n)
 8ggplot({'x': x, 'c': c}, aes(x='x')) + \
 9    geom_density(aes(group='c', color='c', fill='c'), alpha=.2, \
10                 tooltips=layer_tooltips().format('..density..', '.3f')\
11                                          .line('density|@..density..')\
12                                          .format('..count..', '.1f')\
13                                          .line('count|@..count..')\
14                                          .format('..scaled..', '.2f')\
15                                          .line('scaled|@..scaled..'))

 1import numpy as np
 2from lets_plot import *
 5x = np.random.normal(size=1000)
 6p = ggplot({'x': x}, aes(x='x'))
 7bunch = GGBunch()
 8for i, bw in enumerate([.1, .2, .4]):
 9    for j, n in enumerate([16, 64, 256]):
10        bunch.add_plot(p + geom_density(kernel='epanechikov', bw=bw, n=n) + \
11                           ggtitle('bw={0}, n={1}'.format(bw, n)),
12                       j * 300, i * 200, 300, 200)

 1import numpy as np
 2from lets_plot import *
 5x = np.random.normal(size=1000)
 6y = np.sign(x)
 7p = ggplot({'x': x, 'y': y}, aes(x='x'))
 8bunch = GGBunch()
 9for i, adjust in [(i, .5 * (1 + i)) for i in range(3)]:
10    bunch.add_plot(p + geom_density(aes(weight='y'), kernel='cosine', \
11                                    adjust=adjust) + \
12                       ggtitle('adjust={0}'.format(adjust)),
13                   i * 300, 0, 300, 200)