Bokeh - ColumnDataSource

Most of the plotting methods in Bokeh API are able to receive data source parameters through ColumnDatasource object. It makes sharing data between plots and DataTables.

A ColumnDatasource can be considered as a mapping between column name and list of data. A Python dict object with one or more string keys and lists or numpy arrays as values is passed to ColumnDataSource constructor.

Example - Creating a ColumnDataSource

Syntax

from bokeh.models import ColumnDataSource

data = {'x':[1, 4, 3, 2, 5],
   'y':[6, 5, 2, 4, 7]}
cds = ColumnDataSource(data = data)

This object is then used as value of source property in a glyph method. Following code generates a scatter plot using ColumnDataSource.

main.py

from bokeh.plotting import figure, output_file, show
from bokeh.models import ColumnDataSource

data = {'x':[1, 4, 3, 2, 5],
   'y':[6, 5, 2, 4, 7]}

cds = ColumnDataSource(data = data)

fig = figure()
fig.scatter(x = 'x', y = 'y',source = cds, marker = "circle", size = 20, fill_color = "grey")

show(fig)

Output

Run the code and verify the output in the browser.

(myenv) D:\bokeh\myenv>py main.py

Using Panda DataFrame to create a ColumnDataSource

Instead of assigning a Python dictionary to ColumnDataSource, we can use a Pandas DataFrame for it.

Let us use test.csv (used earlier in this section) to obtain a DataFrame and use it for getting ColumnDataSource and rendering line plot.

main.csv

from bokeh.plotting import figure, output_file, show
import pandas as pd
from bokeh.models import ColumnDataSource

df = pd.read_csv('test.csv')
cds = ColumnDataSource(df)
fig = figure(y_axis_type = 'log')
fig.line(x = 'x', y = 'pow',source = cds, line_color = "grey")

show(fig)

Output

Run the code and verify the output in the browser.

(myenv) D:\bokeh\myenv>py main.py