jupyter

**Bokeh: Interactive Visualization for Modern Web Browsers** **Overview** Bokeh is a Python library for creating interactive visualizations for modern web browsers. It creates versatile, data-driven graphics with high-performance interactivity over large or streaming datasets. **Key Differentiators** **1. Server-Side Callbacks** Unlike Plotly (which is mostly client-side JS), Bokeh has a powerful **Python Server**. - Setup: User clicks a button in the browser. - Action: Request sent to Python server. - Server: Python calculates a complex simulation. - Browser: Updates the graph. This allows for building heavy-duty data applications entirely in Python. **2. Large Data** Bokeh can use WebGL for high-performance rendering of thousands of points. **3. Linking Plots** You can link the behavior of multiple plots. Selection on one scatter plot can highlight the corresponding data in a table or another plot. **Example** ```python from bokeh.plotting import figure, show p = figure(title="Simple Line", x_axis_label='x', y_axis_label='y') p.line([1, 2, 3, 4, 5], [6, 7, 2, 4, 5], legend_label="Temp.", line_width=2) show(p) # Opens an HTML file ``` **Bokeh vs Plotly** - **Plotly**: Easier AP (Express), better for standard charts. - **Bokeh**: Better for building complex custom dashboard applications with Python callbacks. Bokeh is often used in scientific/engineering contexts where custom interaction is required.