5 Best Ways to Use Bokeh Library to Generate Line Graphs in Python

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πŸ’‘ Problem Formulation: In data visualization, creating interactive line graphs can enhance the interpretability of data trends and patterns. The Bokeh library in Python allows developers to generate rich, interactive visualizations easily. This article provides solutions for users seeking to plot line graphs using Bokeh, with data inputs such as time series or discrete points and an expectation of a clear visual output showing trends or relationships.

Method 1: Basic Line Plot

Creating a basic line plot in Bokeh is a simple task that can be accomplished using the figure function to initialize the plot, and the line method to add the line glyph to the figure. By passing x and y data to the line method, users can create a straightforward line graph that is easily customizable.

Here’s an example:

from bokeh.plotting import figure, show

# Prepare some data
x = [1, 2, 3, 4, 5]
y = [6, 7, 2, 4, 5]

# Create a new plot with title and axis labels
p = figure(title="Basic Line Plot", x_axis_label='X-Axis', y_axis_label='Y-Axis')

# Add a line renderer with legend and line thickness
p.line(x, y, legend_label="Temp.", line_width=2)

# Show the results
show(p)

Output: This code produces an interactive line graph in a web browser.

This code example demonstrates the simplicity of creating a basic line graph with Bokeh. By defining data points for the x and y axis, initializing the plot with the figure function, and then drawing the line with line, we can visualize the data in a few lines of code. The legend_label and line_width arguments add customization to the graph, enhancing its readability.

Method 2: Multiple Lines

To compare various data sets side-by-side, Bokeh allows the addition of multiple line glyphs on the same figure. This method requires separately providing each set of data points and calling the line method for each data set you wish to appear on the graph. Different colors and legends help distinguish between the data sets.

Here’s an example:

from bokeh.plotting import figure, show

# Prepare some data
x = [1, 2, 3, 4, 5]
y1 = [6, 7, 2, 4, 5]
y2 = [2, 4, 7, 5, 3]

# Create a new plot
p = figure(title="Multiple Lines Example", x_axis_label='X-Axis', y_axis_label='Y-Axis')

# Add multiple line renderers
p.line(x, y1, legend_label="Data Set 1", line_color="blue", line_width=2)
p.line(x, y2, legend_label="Data Set 2", line_color="green", line_width=2)

# Show the results
show(p)

Output: This code results in an interactive graph with two distinct lines.

In this code snippet, x coordinates are shared between two y data sets, allowing for a clear comparison. Different attributes such as line_color customize the appearance of each line, making the multiple lines distinct and the graph significantly more informative compared to a single line graph.

Method 3: Interactive Tools

Interactivity enhances the user experience by allowing them to explore the data in greater depth. Bokeh comes with built-in tools for pan, zoom, save, and reset, among others. Users can explicitly define which tools to include by passing a list of tool names to the tools argument in the figure function.

Here’s an example:

from bokeh.plotting import figure, show

# Prepare some data
x = [1, 2, 3, 4, 5]
y = [6, 7, 2, 4, 5]

# Define tools
toolset = "pan,box_zoom,reset,save"

# Create a new plot, specifying the tools
p = figure(title="Interactive Tools Example", x_axis_label='X-Axis', y_axis_label='Y-Axis', tools=toolset)

# Add a line renderer
p.line(x, y, legend_label="Data", line_width=2)

# Show the results
show(p)

Output: An interactive graph with pan, zoom, save, and reset capabilities.

By including a specific toolset, the graph is now interactive, allowing users to focus on specific parts of the data by panning and zooming, or to save the state of their analysis. This is particularly useful for larger, more complex data sets or when presenting the data to an audience that may wish to conduct their own exploration of the figures.

Method 4: Adding Hover Tool

For deeper data exploration, the hover tool can be added to the plot, which displays a tooltip showing data values whenever the cursor hovers over the graph’s line. The HoverTool class from Bokeh provides this feature and can be customized with a format for the tooltip that displays various data properties.

Here’s an example:

from bokeh.models import HoverTool
from bokeh.plotting import figure, show

# Prepare some data
x = [1, 2, 3, 4, 5]
y = [6, 7, 2, 4, 5]

# Create a new plot
p = figure(title="Hover Tool Example", x_axis_label='X-Axis', y_axis_label='Y-Axis')

# Add a line renderer
p.line(x, y, legend_label="Data", line_width=2)

# Create a hover tool
hover = HoverTool(tooltips=[("X value", "@x"), ("Y value", "@y")])

# Add the hover tool to the plot
p.add_tools(hover)

# Show the results
show(p)

Output: A line graph with an interactive hover tooltip that displays x and y data values.

Adding a hover tool significantly increases the value of a graph by providing immediate feedback on data points as the user moves their cursor over the plot. The customization of the tooltip allows for the display of relevant information, thus enhancing data comprehension without adding clutter to the visualization itself.

Bonus One-Liner Method 5: Streamlined Line Plot Creation

Bokeh also supports a streamlined process for quick and easy line plot creation using the show() function and method chaining to configure the plot all in one line, providing a concise way to generate a line graph with minimal code.

Here’s an example:

from bokeh.plotting import show, figure

# One-liner to create a simple line graph and open in a browser
show(figure(title="Streamlined Line Plot Example", x_axis_label='X-Axis', y_axis_label='Y-Axis').line([1, 2, 3], [4, 6, 5], legend_label="Data", line_width=2))

Output: A simple interactive line graph.

This one-liner demonstrates the power and simplicity of Bokeh for quick visualization tasks. A full-featured graph is generated and displayed in just one line of code, maintaining readability and convenience. This method is particularly suited for quick data analysis and prototyping visualizations.

Summary/Discussion

  • Method 1: Basic Line Plot. Quick to implement. Does not include advanced interactive features.
  • Method 2: Multiple Lines. Great for comparing different data sets. May become cluttered with too many lines.
  • Method 3: Interactive Tools. Allows in-depth exploration of data. Can become overwhelming with irrelevant tools.
  • Method 4: Adding Hover Tool. Excellent for detailed data examination. Requires additional setup for tooltips.
  • Bonus Method 5: Streamlined Line Plot Creation. Fast and efficient for simple graphs. Limited customization and interactivity.