Create drawings with multiple glyphs using Python Bokeh

Bokeh is a powerful data visualization library in Python that helps to create interactive and unique visualizations for the web. Bokeh supports various rendering techniques and provides a wide range of built-in tools for creating complex visualizations with multiple glyphs. This document will guide you through the process of creating a plot with multiple glyphs using Bokeh. This plot combines different glyphs to display multiple data series in a single plot that provides a more efficient way to understand the relationship between different variables.

What are glyphs and what are their main advantages?

Glyphs are graphical representations of characters, symbols, or icons used in typography and graphic design. They are often used in the design and layout of text, and can include letters, numbers, punctuation marks, and other symbols.

Some key advantages of using glyphs include −

• Improve readability− Glyphs can be designed to be very readable, allowing readers to understand the text more quickly and accurately.

• Enhance aesthetics − Glyphs can be used to add visual interest and appeal to text, making it more visually appealing and interactive.

• Consistency and Accuracy − Glyphs can be designed to be consistent in size, shape and style, ensuring text is easy to read and visually coherent.

• Flexibility − Glyphs can be scaled and modified easily, making it possible to use them in a wide range of contexts and applications.

• Internationalization − Glyphs can be used to represent characters and symbols in a variety of languages ​​and writing systems, making them very useful in internationalization and localization.

Overall, glyphs are a powerful tool for typography and graphic design, and can help improve the legibility, aesthetics, consistency, and flexibility of text.

Statistical Significance of these

Glyphs themselves are not subject to statistical significance tests since they are not statistical data. However, the use of glyphs in typography and graphic design may be subject to statistical significance tests if they are used in the context of an experiment or study that involves statistical analysis. For example, if a study is examining the effects of different fonts on reading speed or comprehension, statistical tests may be used to determine whether any observed differences between the fonts are statistically significant.

In general, statistical significance tests are used to determine whether observed differences or effects are likely to be due to chance or random variation, or whether they are likely to reflect a true difference or effect in the population being studied. The specific test used depends on the research question, the type of data being analyzed, and the assumptions made about the data and population.

Therefore, while glyphs themselves are not subject to statistical significance tests, they may be used in the context of experiments or studies that are subject to statistical analysis to determine whether any observed differences or effects are statistically significant.

Prerequisites

Before we dive into the task few things should is expected to be installed onto your system −

List of recommended settings −

• pip install pandas, bokeh

• It is expected that the user will have access to any standalone IDE such as VS-Code, PyCharm, Atom or Sublime text.

• Even online Python compilers can also be used such as Kaggle.com, Google Cloud platform or any other will do.

• Updated version of Python. At the time of writing the article I have used 3.10.9 version.

• Knowledge of using Jupyter notebook.

• Understanding and applying virtual environments will be beneficial, but not required.

• Also, the individual is expected to have a good understanding of statistics and mathematics.

Create basic chart

To create a plot, we first need to import the necessary modules, such as `Figure`, `ColumnDataSource`, and the desired glyphs. Here's an example code snippet that creates a line plot with a single glyph using Bokeh −

Syntax

from bokeh.plotting import figure, output_file, show
output_file("line.html")
p = figure(title="Line Plot", x_axis_label="X", y_axis_label="Y")

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

p.line(x, y, line_width=2)

show(p)

Output

This code will create a line plot with x-axis labeled as "X", y-axis labeled as "Y", and a title "Line Plot". The line plot will display five data points with their corresponding x and y values.

Add multiple glyphs to the chart

To add multiple glyphs to the plot, we need to use the `Figure` object's `multi_line()` function. The `multi_line()` function takes multiple sequences of x and y values ​​and creates a line glyph for each of them. Here's an example code snippet to create a line plot with multiple glyphs −

from bokeh.plotting import figure, output_file, show
from bokeh.models import ColumnDataSource

output_file("multi_line.html")

p = figure(title="Multiple Glyphs", x_axis_label="X", y_axis_label="Y")

x1 = [1, 2, 3, 4, 5]
y1 = [6, 7, 2, 4, 5]

x2 = [1, 2, 3, 4, 5]
y2 = [2, 4, 6, 8, 10]

source = ColumnDataSource(data=dict(x1=x1, y1=y1, x2=x2, y2=y2))
p.multi_line(xs=[source.data["x1"], source.data["x2"]],
   ys=[source.data["y1"], source.data["y2"]],
   line_color=["red", "blue"], line_width=[2, 2])
show(p)

Output

Here, we created two sets of x and y values and stored them in a `ColumnDataSource` object. We then passed the two sequences of x and y values to the `multi_line()` function, along with the colors and line widths of the two glyphs. This will create a line plot with two glyphs, one in red color and one in blue color, each with their corresponding x and y values.

Final Program, Code

# Basic plot

from bokeh.plotting import figure, output_file, show
output_file("line.html")

p = figure(title="Line Plot", x_axis_label="X", y_axis_label="Y")

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

p.line(x, y, line_width=2)

show(p)

# Multiple graphs

from bokeh.plotting import figure, output_file, show
from bokeh.models import ColumnDataSource

output_file("multi_line.html")

p = figure(title="Multiple Glyphs", x_axis_label="X", y_axis_label="Y")

x1 = [1, 2, 3, 4, 5]
y1 = [6, 7, 2, 4, 5]

x2 = [1, 2, 3, 4, 5]
y2 = [2, 4, 6, 8, 10]

source = ColumnDataSource(data=dict(x1=x1, y1=y1, x2=x2, y2=y2))

p.multi_line(xs=[source.data["x1"], source.data["x2"]],
   ys=[source.data["y1"], source.data["y2"]],
   line_color=["red", "blue"], line_width=[2, 2])

show(p)

Conclusion

在本文档中，我们学习了如何使用Bokeh创建具有多个图元的图表。我们首先介绍了图元，然后使用单个图元创建了一个基本的折线图。然后，我们使用`Figure`对象的`multi_line()`函数向图表中添加了多个图元。使用Bokeh，可以轻松创建交互式可视化，帮助理解不同数据点之间的关系。Bokeh允许您以最小的努力创建美观的可视化，让您专注于分析数据，而不必担心可视化。

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