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This article mainly introduces the relevant knowledge of "how to draw the front-end interactive visual chart directly with Python". The editor shows you the operation process through the actual case, the operation method is simple and fast, and the practicality is strong. I hope this article "how to draw the front-end interactive visual chart directly with Python" can help you solve the problem.

Bokeh characteristics

1. Interactive and visual Python drawing library for Web browsers.

two。 You can make simple and beautiful interactive visualization effects like D3.js.

3. A stand-alone HTML document or server program.

4. Can handle large, dynamic, or data streams.

5. Support for Python (or Scala, R, Julia …) .

6. You do not need to use Javascript.

Installation

There are several ways to install Bokeh. Anaconda Python distribution is officially recommended and installed using conda.

Conda install bokeh

This will also install all dependent libraries required by Bokeh. Anaconda minimizes configuration and installation work on all platforms, including Windows.

If we have installed all the dependencies, we can also use pip installation.

Pip install bokeh

Simple example

First, you need to import the figure function from bokeh.plotting, which allows you to create various types of charts. We also import the show and ouptut_notebook functions from bokeh.io-this makes it possible to display the results inline in Notebook.

From bokeh.plotting import figurefrom bokeh.io import output_notebook, show

Then, tell Bokeh to display the chart directly in Notebook. This will result in all JavaScript and data being embedded directly into the HTML of Notebook. Bokeh can be exported directly to a HTML file, or you can start a web server.

Output_notebook ()

Then, use Numpy to create some simple data.

From numpy import cos, linspacex = linspace (- 6,6100) y = cos (x)

Call the figure function of Bokeh to create a chart p. Then, call its circle () method to render a red circle at each X and Y point.

P = figure (width=500, height=500) p.circle (x, y, size=7, color= "firebrick", alpha=0.5) show (p)

After the chart is generated, you can drag and pan and zoom. In the toolbar of the chart, you can adjust more parameters.

When rendering, we can also call save () to output the chart to a HTML.

The above is just the most basic scatter plot built into Bokeh. You can explore other kinds of diagrams of Bokeh. Some of the charts are also very interesting.

Bokeh also has a separate server module for creating interactive Web applications. You can easily dynamically connect the back-end data to the elaborate Bokeh front-end visual charts.

Bokeh server can be used to:

Calculate or query with the full capabilities of Python in response to UI and tool events generated in the browser.

Automatically push server-side data updates to the front-end chart.

Use cycle timing, timeout, and asynchronous callback functions to update front-end data.

This is the end of the introduction to "how to draw the front-end interactive visual chart directly with Python". Thank you for reading. If you want to know more about the industry, you can follow the industry information channel. The editor will update different knowledge points for you every day.

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