scale_size_area#

scale_size_area(max_size=None, name=None, breaks=None, labels=None, lablim=None, limits=None, na_value=None, guide=None, trans=None, format=None)#

Continuous scale for size that maps 0 to 0.

Parameters:
max_sizefloat

The max size that is mapped to.

namestr

The name of the scale - used as the axis label or the legend title. If None, the default, the name of the scale is taken from the first mapping used for that aesthetic.

breakslist or dict

A list of data values specifying the positions of ticks, or a dictionary which maps the tick labels to the breaks values.

labelslist of str or dict

A list of labels on ticks, or a dictionary which maps the breaks values to the tick labels.

lablimint, default=None

The maximum label length (in characters) before trimming is applied.

limitslist

A vector specifying the data range for the scale and the default order of their display in guides.

na_value

Missing values will be replaced with this value.

guide

A result returned by guide_legend() function or ‘none’ to hide the guide.

trans{‘identity’, ‘log10’, ‘log2’, ‘symlog’, ‘sqrt’, ‘reverse’}

Name of built-in transformation.

formatstr

Define the format for labels on the scale. The syntax resembles Python’s:

  • ‘.2f’ -> ‘12.45’

  • ‘Num {}’ -> ‘Num 12.456789’

  • ‘TTL: {.2f}$’ -> ‘TTL: 12.45$’

For more info see Formatting.

Returns:
FeatureSpec

Scale specification.

Notes

This method maps 0 data to 0 size. Useful in some stats such as count.

Examples

 1import numpy as np
 2from lets_plot import *
 3LetsPlot.setup_html()
 4np.random.seed(100)
 5n = 50
 6x = np.random.rand(n)
 7y = np.random.rand(n)
 8area = np.power(np.random.uniform(30, size=n), 2)
 9ggplot() + geom_point(aes(x, y, size=area), alpha=0.7) + \
10    scale_size_area(max_size=15)
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