lets_plot.mapping.as_discrete

lets_plot.mapping.as_discrete(variable, label=None, order_by=None, order=None, levels=None)

The function is used to annotate a numeric data series as categorical data with the possibility of its ordering for the purposes of given visualization.

Parameters:

variablestr

The name of the variable.

labelstr

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

order_bystr

The variable name to order by.

orderint

The ordering direction. 1 for ascending, -1 for descending.

levelslist

The list of values that defines a specific order of categories.

Returns:

MappingMeta or list

Variable meta information.

Notes

The plot will use a discrete scale for the aesthetic mapping. It is similar to the factor() function from R but works differently - there is no data transformation.

To enable ordering mode, at least one ordering parameter (order_by or order) should be specified. By the default, it will use descending direction and ordering by eigenvalues. You cannot specify different order settings for the same variable. But if these settings don’t contradict each other, they will be combined.

Examples

import numpy as np
from lets_plot import *
from lets_plot.mapping import as_discrete
LetsPlot.setup_html()
n = 100
np.random.seed(42)
data = {
    'x': np.random.normal(size=n),
    'y': np.random.normal(size=n),
    'c': np.random.randint(5, size=n),
}
ggplot(data, aes('x', 'y')) + \
    geom_point(aes(color=as_discrete('c')))

import numpy as np
from lets_plot import *
from lets_plot.mapping import as_discrete
LetsPlot.setup_html()
n = 100
np.random.seed(42)
data = {
    'x': np.random.uniform(size=100),
    'c': np.random.choice(list('abcde'), size=100),
}
ggplot(data) + \
    geom_boxplot(aes(as_discrete('c', label='class', order=1), 'x'))

import numpy as np
from lets_plot import *
from lets_plot.mapping import as_discrete
LetsPlot.setup_html()
n = 100
np.random.seed(42)
data = {
    'x': np.random.normal(size=n),
    'c': np.random.choice(list('abcde'), size=n),
    'i': np.random.randint(3, size=n),
}
ggplot(data) + \
    geom_bar(aes(as_discrete('c', order=1, order_by='..count..'), 'x', \
                 fill=as_discrete('i', order=1, order_by='..count..')))