lets_plot.geom_tile(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, **other_args)

Display rectangles with x, y values mapped to the center of the tile.


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

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=’identity’

The statistical transformation to use on the data for this layer, as a string.

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. Value None (or ‘none’) will disable sampling for this layer.


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


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.


Understands the following aesthetics mappings:

  • x : x-axis coordinates of the center of rectangles.

  • y : y-axis coordinates of the center of rectangles.

  • 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.

  • width : width of a tile.

  • height : height of a tile.

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


 1import numpy as np
 2from scipy.stats import multivariate_normal
 3from lets_plot import *
 5n = 100
 6a, b = -1, 0
 7x = np.linspace(-3, 3, n)
 8y = np.linspace(-3, 3, n)
 9X, Y = np.meshgrid(x, y)
10Z = np.exp(-5 * np.abs(Y ** 2 - X ** 3 - a * X - b))
11data = {'x': X.flatten(), 'y': Y.flatten(), 'z': Z.flatten()}
12ggplot(data, aes(x='x', y='y', color='z', fill='z')) + geom_tile()