: It offers three layers of abstraction: the low-level bokeh.models for full control, the mid-level bokeh.plotting for standard glyphs, and high-level integration with tools like HoloViews .
circles = p.circle('date', 'price', source=source, size=4, color="navy", alpha=0.3)
—a complete, self-contained script used to demonstrate a feature or bug. For example, version 2.3.3 users often share "full pieces" of code to troubleshoot layout regressions in the model or panels. Bokeh documentation 3. Misleading "Apk" or Video Content
callback = CustomJS(args=dict(source=source, slider=slider), code=""" const data = source.data; const multiplier = slider.value; const new_y = data['x'].map(x => x * multiplier); data['y'] = new_y; source.change.emit(); """)
: Corrected issues where column CSS classes like scrollable were ignored and fixed layout regressions in the panel component. bokeh 2.3.3
The Bokeh 2.3.3 release includes several new features, improvements, and bug fixes. Some of the key highlights include:
If you plan to migrate an older codebase from Bokeh 2.3.3 to the modern 3.x ecosystem, you should prepare for several breaking architectural shifts.
As a patch release, Bokeh 2.3.3 focused on stability. Significant fixes included:
python stock_viewer.py
from bokeh.plotting import figure, output_file, show from bokeh.models import ColumnDataSource, HoverTool # 1. Prepare the output destination output_file("interactive_chart.html") # 2. Define the dataset data = 'x_values':, 'y_values':, 'labels': ['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5', 'Day 6', 'Day 7'] # 3. Wrap data in a ColumnDataSource source = ColumnDataSource(data=data) # 4. Initialize the Figure canvas p = figure( title="Weekly Performance Metrics (Bokeh 2.3.3)", x_axis_label="Timeline", y_axis_label="Value (USD)", plot_width=800, plot_height=400, tools="pan,box_zoom,wheel_zoom,reset,save" ) # 5. Add Glyphs to the canvas # Add a trend line p.line( x='x_values', y='y_values', source=source, line_width=3, line_color="#1f77b4", legend_label="Trend" ) # Add interactive scatter points over the line p.circle( x='x_values', y='y_values', source=source, size=10, fill_color="#ff7f0e", line_color="white", legend_label="Daily Log" ) # 6. Configure Interactive Tools (Hover Tooltips) hover = HoverTool( tooltips=[ ("Day", "@labels"), ("Value", "@y_values0.0"), ] ) p.add_tools(hover) # 7. Customize Styling p.title.text_font_size = '14pt' p.legend.location = "top_left" p.legend.click_policy = "hide" # Clicking legend item hides the glyph! # 8. Render the plot show(p) Use code with caution. Key Highlights of This Code:
Bokeh needs to know where to send your visual creations. It offers two primary functions:
While it has been succeeded by the more advanced 3.x series with enhanced performance and new features, understanding 2.3.3 is important for maintaining legacy applications or comparing its behavior with newer versions. For new projects, starting with the latest stable release is recommended to benefit from ongoing improvements, security patches, and community support.
Installing Bokeh 2.3.3 is straightforward. You can use either pip or conda . : It offers three layers of abstraction: the low-level bokeh
Once you have Bokeh 2.3.3 installed, creating a simple interactive plot is remarkably easy. The following example demonstrates the bokeh.plotting interface, which is the primary interface for most users.
Bokeh is an open-source Python library designed to help data scientists and developers create interactive visualizations and dashboards. It provides a high-level interface for drawing plots, charts, and other graphical elements, making it easy to create web-based interactive plots. Bokeh's primary goal is to provide a simple and elegant way to create interactive visualizations that can be easily shared and deployed.
pip install bokeh==2.3.3
⚠️ Later Bokeh versions (3.x) have breaking changes in API and default themes. Bokeh documentation 3