Automatically mapping markers to category

[ahuang11]

I'd like to map category without manually setting marker

import numpy as np
import holoviews as hv
from holoviews import dim, opts

hv.extension('bokeh')

data = {
    'x values': [0, 1, 0.5],
    'y values': [1, 0, 0.5],
    'alpha': [0.5, 1, 0.3],
    'color': ['red', 'blue', 'green'],
    'marker': ['circle', 'triangle', 'diamond'],
    'size': [15, 25, 40],
    'category': ['a', 'b', 'c']
}

opts.defaults(opts.Points(size=8, line_color='black'))

hv.Points(data, kdims=['x values', 'y values'], vdims=["category"]).opts(marker="category")

Like color does:

import numpy as np
import holoviews as hv
from holoviews import dim, opts

hv.extension('bokeh')

data = {
    'x values': [0, 1, 0.5],
    'y values': [1, 0, 0.5],
    'alpha': [0.5, 1, 0.3],
    'color': ['red', 'blue', 'green'],
    'marker': ['circle', 'triangle', 'diamond'],
    'size': [15, 25, 40],
    'category': ['a', 'b', 'c']
}

opts.defaults(opts.Points(size=8, line_color='black'))

hv.Points(data, kdims=['x values', 'y values'], vdims=["category"]).opts(color="y_values")

[hoxbro]

Likely some custom logic is needed like what is done with colors here:

holoviews/holoviews/plotting/bokeh/element.py

Line 2844 in b729e34

def _get_cmapper_opts(self, low, high, factors, colors):

Not sure how it should be done.

To get something working for your example you could do this hv.Points(data, kdims=["x values", "y values"], vdims=["category", "marker"]).opts(marker="marker"):