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Here’s a helpful reference paper for Bokeh 2.3.3 — structured as a quick-start + cheat sheet for users who need to work with this specific version.
These examples demonstrate the simplicity and flexibility of Bokeh 2.3.3. With its powerful features and extensive customization options, Bokeh is an ideal choice for creating interactive visualizations and dashboards. bokeh 2.3.3
: This version is typically used with Python 3.6 through 3.9. Check your environment if you encounter installation errors. Documentation Warning : Ensure you are looking at the /en/2.3.3/ path in the docs; the Here’s a helpful reference paper for Bokeh 2
3. Core Concepts (2.3.3 style)
| Concept | Description |
|---------|-------------|
| figure() | Creates a new plot with default tools, axes, grids. |
| ColumnDataSource | Central data object (like a DataFrame wrapper). |
| Glyphs | Visual marks (lines, circles, bars). |
| Layout | row, column, gridplot for arranging plots. |
| Widgets | Sliders, buttons, dropdowns (from bokeh.models). |
| Callback | Python (CustomJS) or server-side callbacks. | : This version is typically used with Python 3
Bokeh 2.3.3 can be used in a variety of scenarios, including:
This code creates a simple line plot using Bokeh 2.3.3.
Dependency Compatibility: Bokeh 2.3.3 runs smoothly on Python 3.6 to 3.9 (with limited support for 3.10). It does not require the latest versions of Jinja2, PyYAML, or Pillow, making it ideal for environments with strict dependency pinning.