lets_plot.geom_contour(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, bins=None, binwidth=None, color_by=None, **other_args)#

Display contours of a 3d surface in 2d.


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=’contour’

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.


Result of the call to the sampling_xxx() function. To prevent any sampling for this layer pass value “none” (string “none”).


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


Number of levels.


Distance between levels.

color_by{‘fill’, ‘color’, ‘paint_a’, ‘paint_b’, ‘paint_c’}, default=’color’

Define the color aesthetic for the geometry.


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.


geom_contour() displays contours of a 3d surface in 2d.

Computed variables:

  • ..level.. : height of a contour.

geom_contour() understands the following aesthetics mappings:

  • x : x-axis coordinates of the center of rectangles, forming a tessellation.

  • y : y-axis coordinates of the center of rectangles, forming a tessellation.

  • z : value at point (x, y).

  • 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”).

  • size : lines width.

  • linetype : type of the line. 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 *
 6n = 25
 7x = np.linspace(-1, 1, n)
 8y = np.linspace(-1, 1, n)
 9X, Y = np.meshgrid(x, y)
10mean = np.zeros(2)
11cov = [[1, .5],
12       [.5, 1]]
13rv = multivariate_normal(mean, cov)
14Z = rv.pdf(np.dstack((X, Y)))
15data = {'x': X.flatten(), 'y': Y.flatten(), 'z': Z.flatten()}
16ggplot(data, aes(x='x', y='y', z='z')) + geom_contour()

 1import numpy as np
 2from lets_plot import *
 4n = 100
 5a, b = -1, 0
 6x = np.linspace(-3, 3, n)
 7y = np.linspace(-3, 3, n)
 8X, Y = np.meshgrid(x, y)
 9Z = np.exp(-5 * np.abs(Y ** 2 - X ** 3 - a * X - b))
10data = {'x': X.flatten(), 'y': Y.flatten(), 'z': Z.flatten()}
11ggplot(data, aes(x='x', y='y', z='z')) + \
12    geom_contour(aes(color='..level..'), bins=3, size=1) + \
13    scale_color_gradient(low='#dadaeb', high='#3f007d')

1import numpy as np
2from lets_plot import *
4n = 1000
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_contour(stat='density2d')