lets_plot.geom_violin#
- lets_plot.geom_violin(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, orientation=None, show_half=None, quantiles=None, quantile_lines=None, scale=None, trim=None, tails_cutoff=None, kernel=None, bw=None, adjust=None, n=None, fs_max=None, color_by=None, fill_by=None, **other_args)#
A violin plot is a mirrored density plot with an additional grouping as for a boxplot.
- 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 Pandas 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=’ydensity’
The statistical transformation to use on the data for this layer, as a string.
- positionstr or FeatureSpec, default=’dodge’
Position adjustment. Either a position adjustment name: ‘dodge’, ‘dodgev’, ‘jitter’, ‘nudge’, ‘jitterdodge’, ‘fill’, ‘stack’ or ‘identity’, or the result of calling a position adjustment function (e.g., position_dodge() etc.).
- show_legendbool, default=True
False - do not show legend for this layer.
- samplingFeatureSpec
Result of the call to the sampling_xxx() function. To prevent any sampling for this layer pass value “none” (string “none”).
- tooltipslayer_tooltips
Result of the call to the layer_tooltips() function. Specify appearance, style and content.
- orientationstr
Specify the axis that the layer’s stat and geom should run along. The default value (None) automatically determines the orientation based on the aesthetic mapping. If the automatic detection doesn’t work, it can be set explicitly by specifying the ‘x’ or ‘y’ orientation.
- show_halffloat, default=0
If -1, only half of each violin is drawn. If 1, another half is drawn. If 0, violins look as usual.
- quantileslist of float, default=[0.25, 0.5, 0.75]
Draw horizontal lines at the given quantiles of the density estimate.
- quantile_linesbool, default=False
Show the quantile lines.
- scale{‘area’, ‘count’, ‘width’}, default=’area’
If ‘area’, all violins have the same area. If ‘count’, areas are scaled proportionally to the number of observations. If ‘width’, all violins have the same maximum width.
- trimbool, default=True
Trim the tails of the violins to the range of the data.
- tails_cutofffloat, default=3.0
Extend domain of each violin on tails_cutoff * bw if trim=False.
- 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. Change 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.
- color_by{‘fill’, ‘color’, ‘paint_a’, ‘paint_b’, ‘paint_c’}, default=’color’
Define the color aesthetic for the geometry.
- fill_by{‘fill’, ‘color’, ‘paint_a’, ‘paint_b’, ‘paint_c’}, default=’fill’
Define the fill aesthetic for the geometry.
- 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:
..violinwidth.. : density scaled for the violin plot, according to area, counts or to a constant maximum width (mapped by default).
..density.. : density estimate.
..count.. : density * number of points.
..scaled.. : density estimate, scaled to maximum of 1.
..quantile.. : quantile estimate.
geom_violin() understands the following aesthetics mappings:
x : x-axis coordinates.
y : y-axis coordinates.
alpha : transparency level of a layer. Accept values between 0 and 1.
color (colour) : color of the geometry lines. String in the following formats: RGB/RGBA (e.g. “rgb(0, 0, 255)”); HEX (e.g. “#0000FF”); color name (e.g. “red”); role name (“pen”, “paper” or “brush”).
fill : fill color. String in the following formats: RGB/RGBA (e.g. “rgb(0, 0, 255)”); HEX (e.g. “#0000FF”); color name (e.g. “red”); role name (“pen”, “paper” or “brush”).
size : lines width.
linetype : type of the line of border. Codes and names: 0 = ‘blank’, 1 = ‘solid’, 2 = ‘dashed’, 3 = ‘dotted’, 4 = ‘dotdash’, 5 = ‘longdash’, 6 = ‘twodash’.
weight : used by ‘ydensity’ stat to compute weighted density.
quantile : quantile values to draw quantile lines and fill quantiles of the geometry by color.
Examples
1import numpy as np 2from lets_plot import * 3LetsPlot.setup_html() 4n = 100 5np.random.seed(42) 6x = np.random.choice(['a', 'b', 'c'], size=n) 7y = np.random.normal(size=n) 8ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + \ 9 geom_violin()
1import numpy as np 2from lets_plot import * 3LetsPlot.setup_html() 4n = 100 5np.random.seed(42) 6x = np.random.choice(['a', 'b', 'b', 'c'], size=n) 7y = np.random.normal(size=n) 8ggplot({'x': x, 'y': y}, aes('x', 'y')) + \ 9 geom_violin(aes(fill='..quantile..'), scale='count', \ 10 quantiles=[.02, .25, .5, .75, .98], quantile_lines=True)
1import numpy as np 2from lets_plot import * 3LetsPlot.setup_html() 4n = 3 5np.random.seed(42) 6x = ['a'] * n + ['b'] * n + ['c'] * n 7y = 3 * list(range(n)) 8vw = np.random.uniform(size=3*n) 9ggplot({'x': x, 'y': y, 'vw': vw}, aes('x', 'y')) + \ 10 geom_violin(aes(violinwidth='vw', fill='x'), stat='identity')
1import numpy as np 2import pandas as pd 3from lets_plot import * 4LetsPlot.setup_html() 5n, m = 100, 5 6np.random.seed(42) 7df = pd.DataFrame({'x%s' % i: np.random.normal(size=n) \ 8 for i in range(1, m + 1)}) 9ggplot(df.melt(), aes('variable', 'value')) + \ 10 geom_violin(aes(color='variable', fill='variable'), \ 11 size=2, alpha=.5, scale='width') + \ 12 geom_boxplot(aes(fill='variable'), width=.2)
1import numpy as np 2from lets_plot import * 3LetsPlot.setup_html() 4n = 100 5np.random.seed(42) 6x = np.random.choice(["a", "b", "c", "d"], size=n) 7y1 = np.random.normal(size=n) 8y2 = np.random.normal(size=n) 9ggplot({'x': x, 'y1': y1, 'y2': y2}) + \ 10 geom_violin(aes('x', 'y1'), show_half=-1, \ 11 trim=False, fill='#ffffb2') + \ 12 geom_violin(aes('x', 'y2'), show_half=1, \ 13 trim=False, fill='#74c476')