How do I use custom labels for ticks in Bokeh?

Question

I understand how you specify specific ticks to show in Bokeh, but my question is if there is a way to assign a specific label to show versus the position. So for example

plot.xaxis[0].ticker=FixedTicker(ticks=[0,1])

will only show the x-axis labels at 0 and 1, but what if instead of showing 0 and 1 I wanted to show Apple and Orange. Something like

plot.xaxis[0].ticker=FixedTicker(ticks=[0,1], labels=['Apple', 'Orange'])

A histogram won't work for the data I am plotting. Is there anyway to use custom labels in Bokeh like this?

Answer 1

Label for x ticks which do not fall on the multiple(s) of ten may not be visible. It might be as a result of data points not been accurate or manipulated in a certain way. If it happens to you,

Hack on bokeh 3.1.1 for that is to use padding to spread the label for ticks at multiples of 10. My figure has x range of 50 and y range of 25 with min_height of 800 and min_width of 1600.

See below (Note the spaces given deliberately in label text) -

    label_dict = { 0: "g (1-7)", 1: "", 2: "", 3: "", 4: "", 5: "", 6: "", 7: "", 8: "", 9: "", 10: "O (8-13)", 11: "", 12: "", 13: "", 14: "", 15: "", 16: "", 17: "", 18: "", 19: "", 20: "E (14-18)            I (19-24)", 21: "", 22: "", 23: "", 24: "", 25: "", 26: "", 27: "", 28: "", 29: "", 30: "C (25-35)                ", 31: "", 32: "", 33: "", 34: "", 35: "", 36: "", 37: "", 38: "", 39: "", 40: "λ(36) k(37)      A / L (38-40)", 41: "", 42: "", 43: "", 44: "", 45: "", 46: "", 47: "", 48: "", 49: ""}

    fig.xaxis.major_label_overrides = label_dict

Answer 2

EDIT: Updated for Bokeh 0.12.5 but also see simpler method in the other answer.

This worked for me:

import pandas as pd
from bokeh.charts import Bar, output_file, show
from bokeh.models import TickFormatter
from bokeh.core.properties import Dict, Int, String

class FixedTickFormatter(TickFormatter):
    """
    Class used to allow custom axis tick labels on a bokeh chart
    Extends bokeh.model.formatters.TickFormatte
    """

    JS_CODE =  """
        import {Model} from "model"
        import * as p from "core/properties"

        export class FixedTickFormatter extends Model
          type: 'FixedTickFormatter'
          doFormat: (ticks) ->
            labels = @get("labels")
            return (labels[tick] ? "" for tick in ticks)
          @define {
            labels: [ p.Any ]
          }
    """

    labels = Dict(Int, String, help="""
    A mapping of integer ticks values to their labels.
    """)

    __implementation__ = JS_CODE

skills_list = ['cheese making', 'squanching', 'leaving harsh criticisms']
pct_counts = [25, 40, 1]
df = pd.DataFrame({'skill':skills_list, 'pct jobs with skill':pct_counts})
p = Bar(df, 'index', values='pct jobs with skill', title="Top skills for ___ jobs", legend=False)
label_dict = {}
for i, s in enumerate(skills_list):
    label_dict[i] = s

p.xaxis[0].formatter = FixedTickFormatter(labels=label_dict)
output_file("bar.html")
show(p)

Answer 3

This can be dealt with as categorical data, see bokeh documentation.

from bokeh.plotting import figure, show

categories = ['A', 'B','C' ]

p = figure(x_range=categories)
p.circle(x=categories, y=[4, 6, 5], size=20)

show(p)

Answer 4

Fixed ticks can just be passed directly as the "ticker" value, and major label overrides can be provided to explicitly supply custom labels for specific values:

from bokeh.plotting import figure, output_file, show
    
p = figure()
p.circle(x=[1,2,3], y=[4,6,5], size=20)
    
p.xaxis.ticker = [1, 2, 3]
p.xaxis.major_label_overrides = {1: 'A', 2: 'B', 3: 'C'}
    
output_file("test.html")
    
show(p)