The main idea is to collect all line renderes in a list and pass this list to the CustomJS. There you can loop over this list again and apply your changes. Minimal Example import pandas as pd from bokeh.plotting import figure, show, output_notebook from bokeh.models import CheckboxGroup, CustomJS from bokeh.layouts import row output_notebook() df = … Read more
I was having the same problem. I found this reference useful. The tooltip for Sales would use the generic @height, e.g.: hover.tooltips = [(‘Sales’, ‘@height’)] Similarly, replacing @height with @y would give you the total sales for each year. I haven’t figured out how to use the tooltip to access the stacked categories or how … Read more
As of Bokeh 0.12.4 (earlier, actually but I forget the exact release) the hover tool supports mutli_line: from collections import defaultdict import numpy as np from scipy.stats import norm from bokeh.plotting import show, figure from bokeh.models import ColumnDataSource, HoverTool from bokeh.palettes import Viridis6 RT_x = np.linspace(118, 123, num=50) mass_spec = defaultdict(list) for scale, mz in … Read more
I don’t know if there is anything pre-defined that does this, but it’s possible to construct your own using groupby and overlapping pie plots. I constructed the following script to take your data and get something at least similar to what you specified. Note that the groupby calls (which are used to calculate the totals … Read more
You can use the ImageURL glyph (image_url plot method)to load images locally or from the web. from bokeh.plotting import figure, show, output_file output_file(‘image.html’) p = figure(x_range=(0,1), y_range=(0,1)) p.image_url(url=[‘tree.png’], x=0, y=1, w=0.8, h=0.6) ## could also leave out keywords # p.image_url([‘tree.png’], 0, 1, 0.8, h=0.6) show(p) One gotcha – if you graph only an image (and … Read more
I spent a few days to build a more general solution, PyWaffle. You can install it through pip install pywaffle The source code: https://github.com/gyli/PyWaffle PyWaffle does not use matshow() method, but builds those squares one by one. That makes it easier for customization. Besides, what it provides is a custom Figure class, which returns a … Read more
Using the Embedding Bokeh Plots documentation example as suggested by Fabio Pliger, one can do this in Django: in the views.py file, we put: from django.shortcuts import render from bokeh.plotting import figure from bokeh.resources import CDN from bokeh.embed import components def simple_chart(request): plot = figure() plot.circle([1,2], [3,4]) script, div = components(plot, CDN) return render(request, “simple_chart.html”, … Read more
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)