lets_plot.geom_density¶
- 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)¶
Displays kernel density estimate, which is a smoothed version of the histogram.
- Parameters
- mappingFeatureSpec
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.
- samplingFeatureSpec
Result of the call to the sampling_xxx() function. Value None (or ‘none’) will disable sampling for this layer.
- tooltipslayer_tooltips
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’.
- 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.
- adjustfloat
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_args
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.
- Returns
- LayerSpec
Geom object specification.
Notes
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. Understands numbers between 0 and 1.
color (colour) : color of a 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. Defines line 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.
Examples
1import numpy as np 2from lets_plot import * 3LetsPlot.setup_html() 4np.random.seed(42) 5x = np.random.normal(size=1000) 6ggplot({'x': x}, aes(x='x')) + geom_density()