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 ggplot2 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

Guide to use for this scale. Defaults to ‘none’.

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'])