geom_density2df#
- geom_density2df(mapping=None, *, data=None, stat=None, position=None, show_legend=None, inherit_aes=None, manual_key=None, sampling=None, tooltips=None, kernel=None, adjust=None, bw=None, n=None, bins=None, binwidth=None, color_by=None, fill_by=None, **other_args)#
Fill density function contour.
By default, this geom uses coord_fixed(). However, this may not be the best choice when the values on the X/Y axis have significantly different magnitudes. In such cases, try using coord_cartesian().
- 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=’density2df’
The statistical transformation to use on the data for this layer, as a string.
- positionstr or FeatureSpec, default=’identity’
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.
- inherit_aesbool, default=True
False - do not combine the layer aesthetic mappings with the plot shared mappings.
- manual_keystr or layer_key
The key to show in the manual legend. Specify text for the legend label or advanced settings using the layer_key() function.
- 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. Set tooltips=’none’ to hide tooltips from the layer.
- 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 list of float
The method (or exact value) of bandwidth. Either a string (choose among ‘nrd0’ and ‘nrd’), or a float array of length 2.
- adjustfloat
Adjust the value of bandwidth by multiplying it. Change how smooth the frequency curve is.
- nlist of int
The number of sampled points for plotting the function (on x and y direction correspondingly).
- binsint
Number of levels.
- binwidthfloat
Distance between levels.
- 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:
..group.. : number of density estimate contour band.
..level.. : calculated value of the density estimate for given contour band.
geom_density2df() understands the following aesthetics mappings:
x : x-axis coordinates.
alpha : transparency level of a layer. Accept values between 0 and 1.
fill : fill color. For more info see Color and Fill.
weight : used by ‘density2df’ stat to compute weighted density.
‘density2df’ statistical transformation combined with parameter value contour=False could be used to draw heatmaps (see the example below).
Examples
1import numpy as np 2from lets_plot import * 3LetsPlot.setup_html() 4n = 1000 5np.random.seed(42) 6x = np.random.normal(size=n) 7y = np.random.normal(size=n) 8ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + \ 9 geom_density2df(aes(fill='..level..'))
1import numpy as np 2from lets_plot import * 3LetsPlot.setup_html() 4n = 1000 5np.random.seed(42) 6x = np.random.normal(size=n) 7y = np.random.normal(size=n) 8ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + \ 9 geom_density2df(aes(fill='..group..'), show_legend=False) + \ 10 scale_fill_brewer(type='seq', palette='GnBu', direction=-1)
1import numpy as np 2from lets_plot import * 3LetsPlot.setup_html() 4n = 1000 5np.random.seed(42) 6x = np.random.normal(size=n) 7y = np.random.normal(size=n) 8p = ggplot({'x': x, 'y': y}, aes(x='x', y='y')) 9bunch = GGBunch() 10for i, bw in enumerate([.2, .4]): 11 for j, n in enumerate([16, 256]): 12 bunch.add_plot(p + geom_density2df(kernel='epanechikov', bw=bw, n=n, \ 13 size=.5, color='white') + \ 14 ggtitle('bw={0}, n={1}'.format(bw, n)), 15 j * 400, i * 400, 400, 400) 16bunch.show()
1import numpy as np 2from lets_plot import * 3LetsPlot.setup_html() 4n = 1000 5np.random.seed(42) 6x = np.random.normal(size=n) 7y = np.random.normal(size=n) 8p = ggplot({'x': x, 'y': y}, aes(x='x', y='y')) 9bunch = GGBunch() 10for i, adjust in enumerate([1.5, 2.5]): 11 for j, bins in enumerate([5, 15]): 12 bunch.add_plot(p + geom_density2df(kernel='cosine', \ 13 size=.5, color='white', \ 14 adjust=adjust, bins=bins) + \ 15 ggtitle('adjust={0}, bins={1}'.format(adjust, bins)), 16 j * 400, i * 400, 400, 400) 17bunch.show()
1import numpy as np 2from lets_plot import * 3LetsPlot.setup_html() 4n = 1000 5np.random.seed(42) 6x = np.random.normal(size=n) 7y = np.random.normal(size=n) 8ggplot({'x': x, 'y': y}, aes('x', 'y')) + \ 9 geom_tile(aes(fill='..density..'), color='black', \ 10 stat='density2df', contour=False, n=50) + \ 11 scale_fill_gradient(low='#49006a', high='#fff7f3')
1import numpy as np 2from lets_plot import * 3LetsPlot.setup_html() 4n = 1000 5np.random.seed(42) 6data = {'x': 10 * np.random.normal(size=n) - 100, \ 7 'y': 3 * np.random.normal(size=n) + 40} 8ggplot(data, aes('x', 'y')) + geom_livemap() + \ 9 geom_density2df(aes(fill='..group..'), \ 10 alpha=.5, show_legend=False)