lets_plot.scale_identity

lets_plot.scale_identity(aesthetic, *, name=None, breaks=None, labels=None, limits=None, na_value=None, guide='none', format=None, **other)

Use this scale when your data has already been scaled. I.e. it already represents aesthetic values that the library can handle directly. This will not produce a legend unless you also supply the breaks and labels.

Parameters

aestheticstr or list

The name(s) of the aesthetic(s) that this scale works with.

namestr

The name of the scale - used as the axis label or the legend title.

breakslist of float

A vector specifying values to display as ticks on axis.

labelslist of str

A vector of labels (on ticks).

limitslist

Continuous scale: a numeric vector of length two providing limits of the scale. Discrete scale: 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, default=’none’

Guide to use for this scale.

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 https://lets-plot.org/pages/formats.html.

Returns

FeatureSpec or FeatureSpecArray

Scales specification.

Examples

import numpy as np
from lets_plot import *
LetsPlot.setup_html()
n = 50
np.random.seed(42)
c = np.random.choice(['#e41a1c', '#377eb8', '#4daf4a'], size=n)
v = np.random.normal(size=n)
ggplot({'c': c, 'v': v}, aes(x='c', y='v')) + \
    geom_boxplot(aes(color='c', fill='c'), size=2) + \
    scale_identity(aesthetic=['color', 'fill'])