is there a way to save bokeh data table content

It would be nice if bokeh provides a tool button for saving/exporting the data table to csv / txt / excel files. If it already does, I have not found it in the document yet.

In the mean time, a possible answer is to export the js array (that is underneath the bokeh data table) to CSV using native javascript. It has been described here and here.

ADD: bokeh has callbacks for using js. A simple description is here. still reading about it ...

EDIT: It is probably there for a while now, but I have just noticed an example on Bokeh website for saving csv from data table.

Related to my response to this stackoverflow question. Response copied below:

Here is a working example with Python 3.7.5 and Bokeh 1.4.0

public github link to this jupyter notebook:
https://github.com/surfaceowl-ai/python_visualizations/blob/master/notebooks/bokeh_save_linked_plot_data.ipynb

environment report:

virtual env python version: Python 3.7.5
virtual env ipython version: 7.9.0

watermark package reports:

bokeh 1.4.0
jupyter 1.0.0
numpy 1.17.4
pandas 0.25.3
rise 5.6.0
watermark 2.0.2

# Generate linked plots + TABLE displaying data + save button to export cvs of selected data

from random import random

from bokeh.io import output_notebook  # prevent opening separate tab with graph
from bokeh.io import show

from bokeh.layouts import row
from bokeh.layouts import grid
from bokeh.models import CustomJS, ColumnDataSource
from bokeh.models import Button  # for saving data
from bokeh.models.widgets import DataTable, DateFormatter, TableColumn
from bokeh.models import HoverTool
from bokeh.plotting import figure


# create data
x = [random() for x in range(500)]
y = [random() for y in range(500)]

# create first subplot
plot_width = 400
plot_height = 400

s1 = ColumnDataSource(data=dict(x=x, y=y))
fig01 = figure(
    plot_width=plot_width,
    plot_height=plot_height,
    tools=["lasso_select", "reset", "save"],
    title="Select Here",
)
fig01.circle("x", "y", source=s1, alpha=0.6)

# create second subplot
s2 = ColumnDataSource(data=dict(x=[], y=[]))

# demo smart error msg:  `box_zoom`, vs `BoxZoomTool`
fig02 = figure(
    plot_width=400,
    plot_height=400,
    x_range=(0, 1),
    y_range=(0, 1),
    tools=["box_zoom", "wheel_zoom", "reset", "save"],
    title="Watch Here",
)

fig02.circle("x", "y", source=s2, alpha=0.6, color="firebrick")

# create dynamic table of selected points
columns = [
    TableColumn(field="x", title="X axis"),
    TableColumn(field="y", title="Y axis"),
]

table = DataTable(
    source=s2,
    columns=columns,
    width=400,
    height=600,
    sortable=True,
    selectable=True,
    editable=True,
)

# fancy javascript to link subplots
# js pushes selected points into ColumnDataSource of 2nd plot
# inspiration for this from a few sources:
# credit: https://stackoverflow.com/users/1097752/iolsmit via: https://stackoverflow.com/questions/48982260/bokeh-lasso-select-to-table-update
# credit: https://stackoverflow.com/users/8412027/joris via: https://stackoverflow.com/questions/34164587/get-selected-data-contained-within-box-select-tool-in-bokeh

s1.selected.js_on_change(
    "indices",
    CustomJS(
        args=dict(s1=s1, s2=s2, table=table),
        code="""
        var inds = cb_obj.indices;
        var d1 = s1.data;
        var d2 = s2.data;
        d2['x'] = []
        d2['y'] = []
        for (var i = 0; i < inds.length; i++) {
            d2['x'].push(d1['x'][inds[i]])
            d2['y'].push(d1['y'][inds[i]])
        }
        s2.change.emit();
        table.change.emit();

        var inds = source_data.selected.indices;
        var data = source_data.data;
        var out = "x, y\\n";
        for (i = 0; i < inds.length; i++) {
            out += data['x'][inds[i]] + "," + data['y'][inds[i]] + "\\n";
        }
        var file = new Blob([out], {type: 'text/plain'});

    """,
    ),
)

# create save button - saves selected datapoints to text file onbutton
# inspriation for this code:
# credit:  https://stackoverflow.com/questions/31824124/is-there-a-way-to-save-bokeh-data-table-content
# note: savebutton line `var out = "x, y\\n";` defines the header of the exported file, helpful to have a header for downstream processing

savebutton = Button(label="Save", button_type="success")
savebutton.callback = CustomJS(
    args=dict(source_data=s1),
    code="""
        var inds = source_data.selected.indices;
        var data = source_data.data;
        var out = "x, y\\n";
        for (i = 0; i < inds.length; i++) {
            out += data['x'][inds[i]] + "," + data['y'][inds[i]] + "\\n";
        }
        var file = new Blob([out], {type: 'text/plain'});
        var elem = window.document.createElement('a');
        elem.href = window.URL.createObjectURL(file);
        elem.download = 'selected-data.txt';
        document.body.appendChild(elem);
        elem.click();
        document.body.removeChild(elem);
        """,
)

# add Hover tool
# define what is displayed in the tooltip
tooltips = [
    ("X:", "@x"),
    ("Y:", "@y"),
    ("static text", "static text"),
]

fig02.add_tools(HoverTool(tooltips=tooltips))

# display results
# demo linked plots
# demo zooms and reset
# demo hover tool
# demo table
# demo save selected results to file

layout = grid([fig01, fig02, table, savebutton], ncols=3)

output_notebook()
show(layout)

# things to try:
# select random shape of blue dots with lasso tool in 'Select Here' graph
# only selected points appear as red dots in 'Watch Here' graph -- try zooming, saving that graph separately
# selected points also appear in the table, which is sortable
# click the 'Save' button to export a csv

# TODO:  export from Bokeh to pandas dataframe

2021 Update: adjusted code that works in python 3.8 and bokeh 2.2.3

For those who have trouble adjusting or finding the example on the bokeh website or are just very lazy, the below code does the minimal job:

from bokeh.models import ColumnDataSource, CustomJS
from bokeh.models.widgets import Button
from bokeh.io import show
import os

source = ColumnDataSource({'list1':[0,1,2,3],'list2':[4,5,6,7]})
button = Button(label="Download", button_type="success")
button.js_on_click(CustomJS(args=dict(source=source),code=open(os.path.join(os.path.dirname(__file__),"download.js")).read()))
show(button)

And the file download.js:

function table_to_csv(source) {
    const columns = Object.keys(source.data)
    const nrows = source.get_length()
    const lines = [columns.join(',')]

    for (let i = 0; i < nrows; i++) {
        let row = [];
        for (let j = 0; j < columns.length; j++) {
            const column = columns[j]
            row.push(source.data[column][i].toString())
        }
        lines.push(row.join(','))
    }
    return lines.join('\n').concat('\n')
}


const filename = 'data_result.csv'
const filetext = table_to_csv(source)
const blob = new Blob([filetext], { type: 'text/csv;charset=utf-8;' })

//addresses IE
if (navigator.msSaveBlob) {
    navigator.msSaveBlob(blob, filename)
} else {
    const link = document.createElement('a')
    link.href = URL.createObjectURL(blob)
    link.download = filename
    link.target = '_blank'
    link.style.visibility = 'hidden'
    link.dispatchEvent(new MouseEvent('click'))
}