Fixed an issue where the Column layout model ignored the scrollable CSS class, preventing the correct behavior of long lists and overflow UI elements.
Bokeh 2.3.x was the last major series before Bokeh 2.4.0 (released November 2021), which added significant features like multi_handles for legends and new DataTable sorting. After that, Bokeh 3.0 (late 2022) dropped Python 2.7 and 3.6 support and introduced major breaking changes (e.g., removing bokeh.charts and old-style callbacks).
Or via conda:
: Unlike Matplotlib, Bokeh is designed for large or streaming datasets and supports complex interactive elements like linked brushing and hover tools.
x = [1,2,3,4,5] y = [2,4,6,8,10] source = ColumnDataSource(data=dict(x=x, y=y))
: Bokeh 2.3.3 excels at creating complex, high-performance dashboards and plots that handle large or streaming datasets.
While the Bokeh project has since moved to 3.x, certain situations still mandate using the legacy 2.3.3 version: Recommendation
Fixed an issue where the Column layout model ignored the scrollable CSS class, preventing the correct behavior of long lists and overflow UI elements.
Bokeh 2.3.x was the last major series before Bokeh 2.4.0 (released November 2021), which added significant features like multi_handles for legends and new DataTable sorting. After that, Bokeh 3.0 (late 2022) dropped Python 2.7 and 3.6 support and introduced major breaking changes (e.g., removing bokeh.charts and old-style callbacks).
Or via conda:
: Unlike Matplotlib, Bokeh is designed for large or streaming datasets and supports complex interactive elements like linked brushing and hover tools.
x = [1,2,3,4,5] y = [2,4,6,8,10] source = ColumnDataSource(data=dict(x=x, y=y))
: Bokeh 2.3.3 excels at creating complex, high-performance dashboards and plots that handle large or streaming datasets.
While the Bokeh project has since moved to 3.x, certain situations still mandate using the legacy 2.3.3 version: Recommendation
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