# Create a new plot with a title and axis labels p = figure(title="simple line example", x_axis_label='x', y_axis_label='y')
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.
Repaired a bug that caused incorrect formatting of y-axis labels when applying custom Bokeh themes.
If you are working with older codebases or require a mature, well-tested version of the Bokeh library, 2.3.3 provides the stability needed for reliable data storytelling. bokeh 2.3.3
The following example showcases the structural layouts patched in 2.3.3. It features customized axis formats, explicit CSS class handling, and cleanly aligned HTML descriptive nodes integrated within an interactive grid:
It's crucial to understand the scope of this vulnerability:
To verify that the installation was successful and you are using the correct version, run this quick Python snippet: # Create a new plot with a title
Certain managed data science platforms, older JupyterHub deployments, or specific versions of companion libraries (like Panel or HoloViews) require Bokeh 2.3.3 to prevent version conflicts. 3. Setting Up Your Environment
Bokeh is a powerful Python library for creating interactive visualizations, dashboards, and web applications. Released in July 2021, served as a vital patch release within the 2.3.x series, focusing on stability, layout fixes, and improved rendering behavior for complex, web-based plots.
for interactions that don't require a Python server, allowing your plots to remain interactive even as standalone HTML files. Integration : Seamlessly works with Jupyter Notebooks by calling output_notebook() Bokeh documentation ⚠️ Version-Specific Warnings Python Compatibility Missing values (NaNs) can cause rendering issues or
The following production-ready example demonstrates how to leverage the feature set of Bokeh 2.3.3—combining the hatch fill features of the 2.3 line with a highly responsive columnar layout configuration:
+-------------------------------------------------------------+ | Python Layer | | (Constructs plots via figure, ColumnDataSource, glyphs) | +-------------------------------------------------------------+ | JSON Serialization v +-------------------------------------------------------------+ | BokehJS Engine | | (Client-side rendering, layout management, tool states) | +-------------------------------------------------------------+
-axis labels were poorly formatted when using specific themes, ensuring that professional-grade charts didn't lose their aesthetic appeal [3]. Widget Reliability
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.
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