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.