geom_bin2d#

geom_bin2d(mapping=None, *, data=None, stat=None, position=None, show_legend=None, inherit_aes=None, manual_key=None, sampling=None, tooltips=None, bins=None, binwidth=None, drop=None, color_by=None, fill_by=None, **other_args)#

Apply a rectangular grid to the plane, count observations in each cell (bin) of the grid, and map the count to the fill color of the cell (tile).

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=’bin2d’

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.

binslist of int, default=[30, 30]

Number of bins in both directions, vertical and horizontal. Overridden by binwidth.

binwidthlist of float

The width of the bins in both directions, vertical and horizontal. Override bins. The default is to use bin widths that cover the entire range of the data.

dropbool, default=True

Specify whether to remove all bins with 0 counts.

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:

  • ..count.. : number of points with coordinates in the same bin.

geom_bin2d() understands the following aesthetics mappings:

  • x : x-axis value.

  • y : y-axis value.

  • alpha : transparency level of a layer. Accept values between 0 and 1.

  • color (colour) : color of the geometry lines. For more info see Color and Fill.

  • fill : fill color. For more info see Color and Fill.

  • size : line width, default=0 (i.e. tiles outline initially is not visible).

  • weight : used by ‘bin’ stat to compute weighted sum instead of simple count.

To hide axis tooltips, set ‘blank’ or the result of element_blank() to the axis_tooltip, axis_tooltip_x or axis_tooltip_y parameter of the theme().

Examples

1import numpy as np
2from lets_plot import *
3LetsPlot.setup_html()
4np.random.seed(42)
5mean = np.zeros(2)
6cov = np.eye(2)
7x, y = np.random.multivariate_normal(mean, cov, 1000).T
8ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + geom_bin2d()


 1import numpy as np
 2from lets_plot import *
 3LetsPlot.setup_html()
 4np.random.seed(42)
 5n = 5000
 6x = np.random.uniform(-2, 2, size=n)
 7y = np.random.normal(scale=.5, size=n)
 8ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + \
 9    geom_bin2d(aes(fill='..density..'), binwidth=[.25, .24], \
10               tooltips=layer_tooltips().format('@x', '.2f')
11                       .format('@y', '.2f').line('(@x, @y)')
12                       .line('count|@..count..')
13                       .format('@..density..', '.3f')
14                       .line('density|@..density..')) + \
15    scale_fill_gradient(low='black', high='red')


 1import numpy as np
 2from lets_plot import *
 3LetsPlot.setup_html()
 4np.random.seed(42)
 5mean = np.zeros(2)
 6cov = [[1, .5],
 7       [.5, 1]]
 8x, y = np.random.multivariate_normal(mean, cov, 500).T
 9ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + \
10    geom_bin2d(aes(alpha='..count..'), bins=[20, 20], \
11               fill='darkgreen') + \
12    geom_point(size=1.5, shape=21, color='white', \
13               fill='darkgreen') + \
14    ggsize(600, 450)


1import numpy as np
2from lets_plot import *
3LetsPlot.setup_html()
4np.random.seed(42)
5x, y = np.random.multivariate_normal(mean=[-98, 39], cov=[[100, 0], [0, 10]], size=100).T
6ggplot() + geom_livemap() + \
7    geom_bin2d(aes(x, y, fill='..density..'), \
8               bins=[10, 5], alpha=.5, show_legend=False)
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