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 wplex range of built-inal tos for ating-sek stted 3h. 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 shipstand the reliffations v.
什麼是字形（Glyphs），它們的主要優點是什麼？
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, s.
使用字形的一些關鍵優勢包括−
- 提高可讀性
− 字形可以設計得非常易讀，使讀者能夠更快速、準確地理解文本。
- 增強美觀
− 字形可以用來為文字添加視覺趣味和吸引力，使其更具視覺吸引力和互動性。
- 一致性和準確性
− 字形可以設計成大小、形狀和風格一致，確保文字易於閱讀和視覺上連貫。
- Flexibility
− Glyphs can be scaled and modified easily, making it possible to use them in a wide range of contexts and applications.
- 國際化
− 字形可以用來表示各種語言和書寫系統的字元和符號，使其在國際化和本地化方面非常有用。
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 testis and graphic design may be subject to statistical significance testis 論文 connect the 屏幕 conision s testistical 是文本 testis sion the conision testistical 是文本 testis 假面 connect the connect the testistical signion testis 高版本 the connem. 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 toaretermine whether any observed denence s observed.
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 udied injencewariation. 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 or studany observed.
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.
使用Jupyter notebook的知識。
了解和應用虛擬環境將會有益，但不是必要的。
同時，預期該人員對統計學和數學有很好的理解。
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建立基本圖表
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 a
##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)
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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.
將多個字形加入圖表中
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 ？ 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)
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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)
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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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