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+A very useful Python library, particularly for creating interactive visualizations in web browsers, is `Bokeh`. Bokeh enables the building of complex statistical plots quickly and through simple commands. It's highly versatile, allowing for the creation of interactive plots, dashboards, and data applications with rich, web-based visualization capabilities. Below is a concise reference guide for common use cases with `Bokeh`, formatted in Markdown syntax:
+
+# `Bokeh` Reference Guide
+
+## Installation
+```
+pip install bokeh
+```
+
+## Basic Plotting
+
+### Importing Bokeh
+```python
+from bokeh.plotting import figure, show, output_file
+```
+
+### Creating a Simple Line Plot
+```python
+# Prepare some data
+x = [1, 2, 3, 4, 5]
+y = [6, 7, 2, 4, 5]
+
+# Output to static HTML file (opens in browser)
+output_file("lines.html")
+
+# Create a new plot with a title and axis labels
+p = figure(title="simple line example", x_axis_label='x', y_axis_label='y')
+
+# Add a line renderer with legend and line thickness
+p.line(x, y, legend_label="Temp.", line_width=2)
+
+# Show the results
+show(p)
+```
+
+## Interactive Plots
+
+### Adding Tools
+```python
+from bokeh.models import PanTool, ResetTool, HoverTool
+
+# Adding pan, reset functionality
+p.add_tools(PanTool(), ResetTool())
+
+# Adding hover tool
+hover = HoverTool()
+hover.tooltips=[
+    ("Index", "$index"),
+    ("(x,y)", "($x, $y)"),
+]
+p.add_tools(hover)
+```
+
+### Widgets and Interactivity
+```python
+from bokeh.layouts import column
+from bokeh.models import Slider, Button
+from bokeh.io import curdoc
+
+# Create some widgets
+slider = Slider(start=0, end=10, value=1, step=.1, title="Stuff")
+button = Button(label="Press me")
+
+# Update function
+def update():
+    # Update function for widgets
+    ...
+
+# Button click event
+button.on_click(update)
+
+# Arrange widgets and plot into a layout
+layout = column(slider, button, p)
+
+# Add the layout to the current document
+curdoc().add_root(layout)
+```
+
+## Embedding and Linking Plots
+
+### Linking Plots Together
+```python
+from bokeh.layouts import gridplot
+
+# Linking axes
+p1 = figure(width=250, height=250)
+p2 = figure(width=250, height=250, x_range=p1.x_range, y_range=p1.y_range)
+p3 = figure(width=250, height=250, x_range=p1.x_range)
+
+# Arrange plots in a grid
+grid = gridplot([[p1, p2], [None, p3]])
+
+show(grid)
+```
+
+### Embedding Bokeh Plots in HTML
+```python
+from bokeh.embed import components
+
+script, div = components(p)
+
+# `script` is a <script> tag that contains the data for your plot
+# `div` is a <div> tag that the plot view is loaded into
+```
+
+## Server Applications
+
+### Running a Bokeh Server
+```shell
+# Save your script as, e.g., `myapp.py`
+# Run the Bokeh server
+bokeh serve --show myapp.py
+```
+
+```python
+# Inside myapp.py, use curdoc to add your layout, figures, etc.
+from bokeh.io import curdoc
+# Define plots and widgets here
+curdoc().add_root(layout)
+```
+
+`Bokeh` excels in creating interactive and visually appealing plots that can be easily embedded into web pages or displayed as part of a Python notebook. Its capability to handle large, dynamic data sets makes it suitable for real-time data applications, and its interactivity features engage users in exploring the data more deeply. This guide introduces the basics, but `Bokeh`'s comprehensive features enable much more complex data visualization and interactive applications.
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