Bokeh 2.3.3 !new! [LATEST]

: It works seamlessly with the broader PyData stack, including Pandas dataframes and NumPy arrays. The Role of Version 2.3.3

Some users complain that hover tooltips show ??? for special characters. : Explicitly set a css_classes property and define font-family in your own CSS file.

As of its release in mid-2021, Bokeh 2.3.3 was the most stable patch in the 2.x series. Today, it remains a critical reference point for several reasons:

Released as part of the stable 2.x roadmap, Bokeh 2.3.3 is a maintenance release that balances powerful interactive features with enterprise-grade stability. bokeh 2.3.3

hover = HoverTool(renderers=[circles], tooltips=[ ("Date", "@date%F"), ("Price", "@price0.00"), ("Volume", "@volume0,0") ], formatters="@date": "datetime") p.add_tools(hover)

: Introduced "star" markers and vectorized support for arrowheads and line-dash patterns.

Bokeh 3.x introduced a rewritten layout engine powered directly by CSS grid/flexbox. Version 2.3.3 uses a custom internal layout algorithm wrapped in absolute positioning, meaning complex nested rows/columns require precise padding configurations to prevent overlapping text elements. : It works seamlessly with the broader PyData

p = figure() p.circle(x="x", y="y", color="color", size=10, source=source) show(p)

To verify that the installation was successful and you are using the correct version, run this quick Python snippet:

show(p)

Bokeh 2.3.3 seamlessly translates Python data structures into JSON, which is rendered in the browser using BokehJS.

In a world chasing the newest features, Bokeh 2.3.3 stands as a testament to the value of stability. It offers a mature, bug-free interactive visualization engine that has been battle-tested in thousands of production dashboards, financial applications, and scientific research tools. For anyone maintaining systems that rely on the Bokeh 2.x API, this version is the definitive upgrade—the final polished gem before the paradigm shift of Bokeh 3.0.

dates = pd.date_range('2023-01-01', periods=200) prices = 100 + np.cumsum(np.random.randn(200).cumsum()) volume = np.random.randint(1000, 10000, 200) : Explicitly set a css_classes property and define