💡 Problem Formulation: When working with data visualization in Python, one commonly encountered problem is the need to visually represent multiple datasets or aspects of data concurrently. Python’s Bokeh library allows for the creation of sophisticated, interactive plots. However, users might be unsure how to effectively use multiple glyphs—like lines, circles, and squares—to represent different data points or series within a single plot. Take, for instance, financial data where you want to present both the stock prices (lines) and transaction volumes (bars) on the same plot.
Method 1: Adding Multiple Glyphs by Layering
Layering in Bokeh involves sequentially adding glyphs atop one another to a single figure. It’s like painting on a canvas; you start with one element and continue adding. This method preserves context since each glyph adheres to the same coordinate system and scales accordingly.
Here’s an example:
from bokeh.plotting import figure, output_file, show
# Output to static HTML file
output_file("multiple_glyphs.html")
# Create a new plot with a title and axis labels
p = figure(title="Multiple Glyphs Example", x_axis_label='x', y_axis_label='y')
# Add a line renderer
p.line(x=[1, 2, 3, 4, 5], y=[6, 7, 2, 4, 5], line_width=2)
# Add a circle renderer
p.circle(x=[1, 2, 3, 4, 5], y=[6, 7, 2, 4, 5], size=10, color="navy", alpha=0.5)
# Show the results
show(p)
The output is an HTML file containing a plot with both a line and semi-transparent circles overlaying each data point of the line.
This code snippet creates a plot with a title and axis labels, then it adds a line glyph by calling p.line() with x and y coordinates, followed by a circle glyph to mark each datapoint using p.circle() with a specified size, color, and transparency. Finally, it outputs the plot to an HTML file to view in the browser.
Method 2: Combining Different Types of Glyphs
Bokeh’s flexibility allows for combining various glyph types to represent different data dimensions. For instance, lines can depict trends while squares indicate data intensity. This distinction enhances the plot’s information richness.
Here’s an example:
from bokeh.plotting import figure, output_file, show
output_file("combined_glyphs.html")
p = figure(title="Combined Glyphs Example")
p.line([1, 2, 3, 4, 5], [2, 5, 8, 2, 7], line_width=2)
p.square([1, 2, 3, 4, 5], [2, 5, 8, 2, 7], size=20, color="olive", alpha=0.5)
show(p)
The output is an HTML file featuring a Bokeh plot that combines lines and squares.
The code snippet creates a plot with combined glyphs—a line and squares located at the same data points. Here, p.square() is used to add squares at given x and y coordinates, also specifying size, color, and alpha. Similar to the line, these squares offer additional markers for the data points, all within a single plot.
Method 3: Utilizing Glyph Methods for Additional Data Series
Bokeh’s glyph methods can be combined to show multiple, distinct data series. For instance, different call methods such as quad(), triangle(), and x() can represent additional dimensions, such as categories or time intervals.
Here’s an example:
from bokeh.plotting import figure, output_file, show
output_file("additional_series.html")
p = figure(title="Additional Data Series")
p.triangle(x=[2, 3, 7, 5, 8], y=[6, 9, 2, 4, 1], size=15, color="firebrick", alpha=0.6)
p.x(x=[1, 4, 3, 6, 5], y=[5, 7, 2, 4, 9], size=15, color="green", alpha=0.7)
show(p)
The output is an HTML file with a plot illustrating different data series with triangles and xs.
In this example, triangles and xs are added to the plot employing p.triangle() and p.x(), which accept coordinate lists, size, color, and transparency. These distinct shapes serve as visual identifiers for different data series or categories within the same plot.
Method 4: Advanced Use of Multi-Lined Glyphs
Bokeh’s multi_line() function provides the capability to create complex, multifaceted plots. It’s ideal for visualizing datasets where each entry consists of a series of points, such as multiple time-series data or parallel coordinates plots.
Here’s an example:
from bokeh.plotting import figure, output_file, show
output_file("multi_line_glyphs.html")
p = figure(title="Multi-line Glyphs Example")
p.multi_line([[1, 3, 2], [3, 4, 6, 6]], [[2, 1, 4], [4, 7, 8, 5]],
color=["firebrick", "navy"], alpha=[0.8, 0.3], line_width=4)
show(p)
The output is an HTML file with a plot containing multi-lines in different colors and transparencies.
The p.multi_line() method displays multiple connected lines within a single invocation. Coordinate pairs are passed in as a list of lists, allowing each entry to represent a discrete line. We set different colors and transparencies for each line to distinguish them within the plot.
Bonus One-Liner Method 5: Streamlined Multi-Glyph Plotting
For a quick and concise approach, Bokeh’s patch method allows encapsulating complex shapes with minimal code. It is highly efficient when you want to render areas or enclosed shapes defined by lists of x and y coordinates.
Here’s an example:
from bokeh.plotting import figure, output_file, show
output_file("patch_glyphs.html")
p = figure(title="Streamlined Patch Glyph Example")
p.patch(x=[1, 2, 3, 2, 1], y=[2, 3, 2, 1, 0], alpha=0.5, line_width=2)
show(p)
The output is an HTML file with an elegant patch shape on the plot.
This succinct code example uses p.patch() to draw a glyph defined by x and y coordinates that form a closed shape. Set to a translucent fill and visible line width, the patch method encapsulates an area on the plot in just one line of code.
Summary/Discussion
- Method 1: Layering Glyphs. Strengths: Intuitive and straightforward for adding glyphs. Weaknesses: Can get cluttered with too many glyphs.
- Method 2: Combining Glyph Types. Strengths: Utilizes the diverse range of Bokeh glyphs for rich data representation. Weaknesses: Requires careful design to avoid visual confusion.
- Method 3: Additional Data Series. Strengths: Facilitates the representation of multiple datasets. Weaknesses: Potentially challenging to distinguish between series with similar colors or shapes.
- Method 4: Multi-Lined Glyphs. Strengths: Ideal for complex datasets and time-series. Weaknesses: Can lead to a visually dense plot which may be hard to read.
- Bonus Method 5: Patch Glyphs. Strengths: Efficient for creating quick shapes. Weaknesses: Limited to closed, area-based representations.