How to use Bokeh to build interactive data visualization applications

How to use Bokeh to build interactive data visualization applications

Introduction:
In today's big data era, data visualization is very important. Through visualization technology, we can display data in the form of graphics to better understand the characteristics and trends of the data. Bokeh is a powerful Python library that provides a rich set of tools and functions for building interactive data visualization applications. This article describes how to use Bokeh to build interactive data visualization applications, along with code examples.

1. Install Bokeh
First, we need to install the Bokeh library. Open the command line window and enter the following command:

pip install bokeh

2. Basic concepts
Before we start, we need to understand some basic concepts. Bokeh provides two basic forms of interfaces: low-level and high-level interfaces. Low-level interfaces are the basic building blocks of the Bokeh library, through which users can build custom visualization components; while high-level interfaces create common visualizations in a more convenient and faster way. This article mainly introduces the high-level interface.

Bokeh is based on plot objects, which can be charts, icons, tables, or more complex combinations. We can create and modify these drawing objects using Bokeh's high-level interface. To display these objects we need an output mode, there are several options to choose from including displaying in the browser, saving to a file or generating a static image.

3. Quick Start
Next, let us implement a simple interactive data visualization application. We take the iris data set as an example, visualize it as a scatter plot, and implement some interactive functions.

First, we need to import the required libraries and modules:

import pandas as pd
from bokeh.plotting import figure, show
from bokeh.io import output_notebook, output_file
from bokeh.models import ColumnDataSource, CategoricalColorMapper, HoverTool

Then, we load the iris data set and create a Bokeh drawing object:

# 加载鸢尾花数据集
iris = pd.read_csv('iris.csv')

# 创建绘图对象
plot = figure(title='鸢尾花数据集', 
              x_axis_label='花瓣长度', y_axis_label='花瓣宽度',
              plot_width=600, plot_height=400)

Continue Next, we plot the data in the dataset into a scatter plot and use colors to represent flower types:

# 创建颜色映射器
color_mapper = CategoricalColorMapper(factors=['setosa', 'versicolor', 'virginica'], 
                                      palette=['red', 'green', 'blue'])

# 添加散点图
plot.circle(x='petal_length', y='petal_width', 
            color={'field': 'species', 'transform': color_mapper},
            size=10, alpha=0.5, source=ColumnDataSource(iris))

Using the above code, we plot a scatter plot where the x-axis represents the petal length and the y-axis represents the petal length. The axis indicates petal width, and colors are used to indicate flower species.

Next, we add some interactive functions, such as displaying data when the mouse hovers:

# 添加悬停工具
hover = HoverTool(tooltips=[('花的种类', '@species'),
                            ('花瓣长度', '@petal_length'),
                            ('花瓣宽度', '@petal_width')])
plot.add_tools(hover)

Using the above code, when the mouse hovers over the scatter point, the type of flower, Information on petal length and petal width.

Finally, we select the specific output mode and display the drawing object:

# 在浏览器中显示
output_notebook()

# 显示绘图对象
show(plot)

Through the above steps, we successfully implemented a simple interactive data visualization application, which can be used to hover over the See the details of each data point on the scatter plot.

Conclusion:
Bokeh is a very powerful Python library that can help us build interactive data visualization applications. This article briefly introduces the basic steps to build interactive data visualization applications using Bokeh's high-level interface, and attaches code examples. I hope that readers can understand the basic usage of Bokeh through the introduction of this article, and use it flexibly in practice to build more advanced and complex data visualization applications.

The above is the detailed content of How to use Bokeh to build interactive data visualization applications. For more information, please follow other related articles on the PHP Chinese website!