5 Best Ways to Use Bokeh to Create Step Line Plot in Python

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πŸ’‘ Problem Formulation: In data visualization, creating step line plots is essential for showing discrete changes across intervals, which can be more informative than traditional line plots for certain datasets. This article addresses the problem of using Bokeh, a powerful Python visualization library, to generate step line plots that accurately represent such data. Suppose our input is a sequence of data points and we desire a step plot that connects these points with vertical and horizontal lines, reflecting immediate changes in data.

Method 1: Using the step() Function

Bokeh provides a straightforward function called step() specifically for creating step line plots. This method is excellent for quickly generating a basic step plot with a default vertical stepping behavior. The function’s parameters allow customization of line properties such as color, width, and dash patterns.

Here’s an example:

from bokeh.plotting import figure, show, output_file

x = [1, 2, 3, 4, 5]
y = [6, 7, 2, 4, 5]

p = figure(title="Step Line Plot Example", plot_width=300, plot_height=300)
p.step(x, y, line_width=2)

output_file("step_plot.html")
show(p)

The output displays a step line plot in a separate HTML page, connecting the data points with right-angled lines.

This code snippet creates a Bokeh figure object, assigns x and y values to the step function creating the plot, and finally, the output_file and show functions render the plot to an HTML file.

Method 2: Customizing Steps with mode Parameter

The step() function in Bokeh comes with a mode parameter that allows users to customize the stepping direction. It can be set to ‘before’, ‘after’, or ‘center’ to control how the steps transition between data points.

Here’s an example:

from bokeh.plotting import figure, show, output_file

x = [1, 2, 3, 4, 5]
y = [6, 7, 2, 4, 5]

p = figure(title="Custom Step Mode", plot_width=300, plot_height=300)
p.step(x, y, line_width=2, mode="center")

output_file("custom_step_mode.html")
show(p)

The output is a step line plot with steps centered on each data point.

In this snippet, we specified the mode through the p.step() function which affects the rendering of our line, demonstrating how the steps are centered on the data points.

Method 3: Combination with Other Glyphs

Bokeh’s flexibility allows for a combination of step line plots with other glyphs, like circles or squares, to emphasize data points. This hybrid approach can enhance the visualization and comprehension of the underlying data.

Here’s an example:

from bokeh.plotting import figure, show, output_file

x = [1, 2, 3, 4, 5]
y = [6, 7, 2, 4, 5]

p = figure(title="Step Line with Circles", plot_width=300, plot_height=300)
p.step(x, y, line_width=2)
p.circle(x, y, size=8)

output_file("step_with_circles.html")
show(p)

The output combines a step line plot with circles accentuating each step point.

This code creates a Bokeh figure and uses both the step() and circle() functions to draw a step line plot with circles at each data point, merging two different glyphs for a more informative plot.

Method 4: Interactive Step Line Plots

Bokeh excels in creating interactive plots. Adding widgets like sliders or buttons can allow users to adjust the step line plot dynamically, making the visualization more engaging and useful for exploring the data.

Here’s an example:

from bokeh.plotting import figure, show, output_file
from bokeh.layouts import column
from bokeh.models import Slider, CustomJS

x = [1, 2, 3, 4, 5]
y = [6, 7, 2, 4, 5]

p = figure(title="Interactive Step Plot", plot_width=300, plot_height=300)
line = p.step(x, y, line_width=2)

slider = Slider(start=1, end=4, value=2, step=1, title="Line Width")
slider.js_on_change("value", CustomJS(args=dict(line=line), code="""
    line.glyph.line_width = cb_obj.value;
"""))

layout = column(slider, p)

output_file("interactive_step.html")
show(layout) 

The output is an HTML document with a step line plot and a slider that controls the line width.

By integrating a Slider widget with a CustomJS callback, this code snippet creates an interactive plot where the user can adjust the line width in real-time, offering an interactive experience.

Bonus One-Liner Method 5: Streamlined Creation with show()

If simplicity is key, Bokeh allows for a concise one-liner to quickly display a step line plot. This method provides limited customization but is unbeatable for speed and ease of use.

Here’s an example:

from bokeh.io import show
from bokeh.models import ColumnDataSource
from bokeh.plotting import figure

source = ColumnDataSource(data=dict(x=[1, 2, 3, 4, 5], y=[6, 7, 2, 4, 5]))
show(figure().step('x', 'y', source=source, line_width=2))

The output is a simple step line plot rendered directly in the browser.

This one-liner uses ColumnDataSource and passes the data along with line width directly to the step() function within the figure() and show() chaining, demonstrating Bokeh’s ability for rapid inline plotting.

Summary/Discussion

  • Method 1: Using the step() Function. Strengths: Intuitive and straightforward. Weaknesses: Limited customization options within the function’s parameters.
  • Method 2: Customizing Steps with mode Parameter. Strengths: Allows control over step transitions. Weaknesses: Still relies on the default step function behavior.
  • Method 3: Combination with Other Glyphs. Strengths: Enhances data representation. Weaknesses: May become cluttered with too many glyphs.
  • Method 4: Interactive Step Line Plots. Strengths: Enables dynamic data exploration. Weaknesses: More complex to set up and requires Javascript for interactivity.
  • Method 5: Streamlined Creation with show(). Strengths: Quick and easy for simple plots. Weaknesses: Least flexible, hardest to customize further.