Let’s build a functional, interactive chart using Bokeh 2.3.3. This example showcases a combined line and scatter plot that reads data from a ColumnDataSource .
: This patch fixed several lingering issues from 2.3.0, 2.3.1, and 2.3.2, including:
“I actually loved using Bokeh... I like attaching callbacks to the object that started the callback... I also like not having to worry about saving/loading the current state of the application.” Bokeh Discourse · 5 years ago
from bokeh.plotting import figure, show from bokeh.io import output_notebook bokeh 2.3.3
pip install bokeh==2.3.3
When you have more than one plot or widget, you'll need to organize them. Bokeh's layout module provides flexible functions like row() , column() , and gridplot() to arrange your visualization components into a coherent dashboard. These layouts are responsive, automatically adjusting to the size of the browser window.
circles = p.circle('date', 'price', source=source, size=4, color="navy", alpha=0.3) Let’s build a functional, interactive chart using Bokeh 2
For users at the time, Bokeh 2.3.3 was the recommended stable version to upgrade to if they had experienced issues with 2.3.0, 2.3.1, or 2.3.2. It represented the Bokeh team’s commitment to rapid bug-fixing after a feature release.
Fixed an issue where a plot's height could not be reduced below 600px.
source = ColumnDataSource(data= 'date': dates, 'price': prices, 'volume': volume, 'moving_avg': pd.Series(prices).rolling(10).mean() ) I like attaching callbacks to the object that
: Improved the MultiChoice widget by fixing a bug where dropdown menus were hidden, and ensured that active tabs were correctly brought into view upon rendering.
Even with a stable release, users occasionally encounter issues. Here are common ones and how to solve them: