Bokeh 2.3.3 is a powerful data visualization library that offers a wide range of tools and features for creating interactive and web-based visualizations. With its high-level interface and extensive customization options, Bokeh is an excellent choice for data scientists and web developers. Whether you're creating simple plots or complex dashboards, Bokeh 2.3.3 has got you covered.
from bokeh.layouts import column from bokeh.models import Slider, CustomJS, ColumnDataSource from bokeh.plotting import figure, show
: Fixed a bug where dropdown menus were hidden in multi-choice components and ensured active tabs were correctly in view upon rendering.
This architecture allows data scientists to build complex, JavaScript-infused dashboards without writing a single line of web code. Sticking to version 2.3.3 ensures compatibility with specific versions of pandas, NumPy, and Jupyter Notebooks often found in older AWS, Azure, or internal corporate environments. Core Features of Bokeh 2.3.3
# We'll use a simplified aggregation for the box plot glyphs manually for this example # In a real scenario, you might use boxplot mod, but let's build it manually for the story effect q1_2019, q2_2019, q3_2019 = np.percentile(data_2019, [25, 50, 75]) q1_2021, q2_2021, q3_2021 = np.percentile(data_2021, [25, 50, 75])
p = figure(title="Bokeh 2.3.3 Example", x_axis_label="X", y_axis_label="Y")
Many models that were imported via bokeh.models in 2.3.3 have stricter pathways or properties in newer iterations. 8. Summary
Finally, we use show() to open the plot in your default web browser.
Glyphs are the visual shapes that represent your data. Bokeh provides a rich library of glyphs, including lines, circles, bars, patches, and many others. You add glyphs to a Figure object by calling methods like p.line() , p.circle() , or p.rect() . Each glyph method takes the data for its x and y coordinates, along with styling parameters like color, size, and transparency.
timeouts or layout shifts in newer builds, rolling back to 2.3.3 might just be the fix you need. [Source: Bokeh Discourse ] #DataViz #Python #BokehJS" Option 2: For Photographers (The Aesthetic)
Released as a critical maintenance update, version 2.3.3 focuses heavily on stabilizing the 2.x release cycle. It addresses memory leaks, refines layout layout engine performance, and ensures seamless compatibility with underlying data science tools like PyData, Pandas, and NumPy. 2. Key Features of Bokeh 2.3.3
# Add a line glyph p.line(x, y, legend_label="sin(x)", line_width=2)
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