bokeh

Starting from Initial table , I need to highlight(color) elements as shown in either one of the table examples Ex. 1, Ex. 2, Ex. 3 . Any idea? In case someone else might bump into the same need, here are some variants that I came up with. (Thanks to Bokeh team for hints!) Variant 1: Highlight the cell where column A > column B Code: from bokeh.io import output_notebook, show output_notebook() from random import randint from bokeh.io import output_file, show from bokeh.layouts import widgetbox from bokeh.models import ColumnDataSource from bokeh.models.widgets import DataTable, DateFormatter,

I am making a plot following the example found here Unfortunately, I have 17 curves I need to display, and the legend overlaps them. I know I can create a legend object that can be displayed outside the plot area like here , but I have 17 curves so using a loop is much more convenient. Do you know how to combine both methods? Ok, I found the solution. See the code below where I have just modified the interactive legend example: import pandas as pd from bokeh.palettes import Spectral4 from bokeh.plotting import figure, output_file, show from bokeh.sampledata.stocks import AAPL, IBM, MSFT, GOOG

I am trying to use Bokeh to make an editable DataTable that updates the source data when the data is edited. I started with the standard DataTable example here , and make the editable kwarg to true. Here is where I am at: from datetime import date from random import randint from bokeh.models import ColumnDataSource, Callback from bokeh.models.widgets import DataTable, DateFormatter, TableColumn from bokeh.io import output_file, output_notebook, show, vform output_notebook() data = dict(dates=[date(2014, 3, i+1) for i in range(10)], downloads=[randint(0, 100) for i in range(10)]) source =

Something like this: There is a very good package to do it in R . In python, the best that I could figure out is this, using the squarify package (inspired by a post on how to do treemaps ): import numpy as np import pandas as pd import matplotlib as mpl import matplotlib.pyplot as plt import seaborn as sns # just to have better line color and width import squarify # for those using jupyter notebooks %matplotlib inline df = pd.DataFrame({ 'v1': np.ones(100), 'v2': np.random.randint(1, 4, 100)}) df.sort_values(by='v2', inplace=True) # color scale cmap = mpl.cm.Accent mini, maxi = df['v2'].min()

I am trying desperately to embed a working bokeh applet into flask, and can't find a proper way to do this. I looked through all the examples, but I can't find one which includes the ability to update the data (best example: the sliders_applet). If I'm not mistaken, I do need the bokeh-server to be able to change the data (with sliders etc.). Starting the applet this way works, e.g.: bokeh-server --script sliders_app.py But I can't find the proper, or at least a working way to embed the sliders_app into flask. And since it should be possible to use multiple applets, it doesn't seem clean to me