lets_plot.layer¶
- lets_plot.layer(geom=None, stat=None, data=None, mapping=None, position=None, **kwargs)¶
Create a new layer.
- Parameters
- geomstr
The geometric object to use to display the data.
- statstr, default=’identity’
The statistical transformation to use on the data for this layer, as a string. Supported transformations: ‘identity’ (leaves the data unchanged), ‘count’ (count number of points with same x-axis coordinate), ‘bin’ (count number of points with x-axis coordinate in the same bin), ‘smooth’ (perform smoothing - linear default), ‘density’ (compute and draw kernel density estimate).
- datadict or 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.
- mappingFeatureSpec
Set of aesthetic mappings created by aes() function. Aesthetic mappings describe the way that variables in the data are mapped to plot “aesthetics”.
- positionstr or FeatureSpec
Position adjustment, either as a string (‘identity’, ‘stack’, ‘dodge’, …), or the result of a call to a position adjustment function.
- kwargs:
Other arguments passed on to 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
A layer is a combination of data, stat and geom with a potential position adjustment. Usually layers are created using geom_* or stat_* calls but they can be created directly using this function.
Examples
1import numpy as np 2from lets_plot import * 3LetsPlot.setup_html() 4n = 50 5np.random.seed(42) 6x = np.random.uniform(-1, 1, size=n) 7y = 25 * x ** 2 + np.random.normal(size=n) 8ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + layer(geom='point')