Released in 2021, Bokeh 2.3.3 is a specific maintenance patch in the 2.x release cycle of the popular Python interactive visualization library. While the Bokeh ecosystem has since advanced to version 3.x and beyond, version 2.3.3 remains a critical reference point for developers maintaining legacy enterprise dashboards, working within constrained environments (like specific AWS Lambda layers or older Anaconda distributions), or migrating older codebases.
Let's also add a circle scatter glyph to highlight each data point.
This command will output version information and other useful data, confirming that Bokeh 2.3.3 is correctly installed and accessible.
from bokeh.plotting import figure, output_file, show from bokeh.models import HoverTool bokeh 2.3.3
Improved TypeScript definitions within the bokehjs source, which benefited developers writing custom extensions for Bokeh. 4. Practical Implementation: Building a Plot in Bokeh 2.3.3
The HoverTool is a powerful passive tool that displays tooltips when a user hovers their mouse over a glyph. This is invaluable for exploring detailed data points. You can customize the tooltips to show specific columns from your ColumnDataSource, allowing users to inspect data values interactively.
When you run this script, a new browser tab will open, displaying your fully interactive plot with pan, zoom, save, and hover functionality. Released in 2021, Bokeh 2
Defines data structures, column data sources ( CDS ), glyph properties, and event actions. JSON / WebSockets Protocols
Bokeh is strict about data formats. For example, all columns in a ColumnDataSource must be the same length. Ensure that any data you pass as lists, arrays, or DataFrames is clean and consistent. Missing values (NaNs) can cause rendering issues or unexpected behavior with tools like HoverTool . Preprocess your data to handle or remove missing values before feeding it to Bokeh.
If you are working within an environment restricted to Bokeh 2.3.3, the syntax relies heavily on the bokeh.plotting API. Below is a comprehensive example demonstrating how to build an interactive scatter plot with tooltips, custom tools, and a linked data table. Step 1: Environment Setup To install this specific legacy version, use pip: pip install bokeh==2.3.3 Use code with caution. Step 2: Code Implementation This command will output version information and other
import numpy as np from bokeh.layouts import column from bokeh.models import CustomJS, Slider, ColumnDataSource from bokeh.plotting import figure, show, output_file output_file("slider_callback.html") # Generate initial wave data x = np.linspace(0, 10, 500) y = np.sin(x) source = ColumnDataSource(data=dict(x=x, y=y)) # Build the plot plot = figure(plot_width=600, plot_height=300, y_range=(-2, 2)) plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6) # Create a Slider widget slider = Slider(start=0.1, end=5.0, value=1.0, step=0.1, title="Frequency") # JavaScript callback code to execute in the browser callback = CustomJS(args=dict(source=source, slider=slider), codeblock=""" const data = source.data; const f = slider.value; const x = data['x']; const y = data['y']; for (let i = 0; i < x.length; i++) y[i] = Math.sin(f * x[i]); source.change.emit(); """) # Link callback to widget action slider.js_on_change('value', callback) # Arrange layouts cleanly into a vertical column layout = column(slider, plot) show(layout) Use code with caution. 6. Layout Management and Themes
# Add a line glyph p.line(x, y, legend_label="sin(x)", line_width=2)
Bokeh offers pre-configured aesthetic themes to transform your plots from a default grey background to sleek layouts like dark_minimal , light_minimal , or caliber . To apply a global theme in version 2.3.3: