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This article introduces the knowledge of "what are the common Python data visualization libraries". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

Common Python data visualization library, Python code scripts are constantly reused, constantly deal with updated data; call rich tool libraries to solve the needs of making maps, interactions, and dynamic; Python can access the data interface to retrieve data in real time.

As an important language of data analysis, Python provides a lot of libraries for every aspect of data analysis. Common data visualization libraries include matplotib, seaborm, ggplot, bokeh, pygal, pyecharts and so on.

1 、 Matplotlib

Matplotlib is the originator of many data visualization libraries in Python. Its design style is very close to the commercial programming language MATLAB designed in the 1980s, and has many powerful and complex visualization functions. Matplotlib contains many types of API, and you can draw and customize charts in a variety of ways.

2 、 Seaborn

Seaborn is a visualization library for advanced encapsulation based on Matplotlib. It supports an interactive interface, which makes the function of drawing charts easier, the colors of charts more attractive, and can draw a variety of statistical charts.

3 、 ggplot

Ggplot is a library based on Matplotlib and designed to improve the visual appeal of Matplotlib in a simple way. It draws graphics in the form of overlay layers. For example, first draw the layer of the axis, then draw the layer of the point, and finally draw the layer of the line, but it is not suitable for customized graphics. In addition, ggplot2 provides an interface for the R language, some of which API, although not suitable for Python, are applicable to the R language and are very powerful.

4 、 Bokeh

Bokeh is an interactive visualization library that supports presentation in Web browsers and provides a quick and easy way to convert large datasets into high-performance, interactive, simple-structured charts.

5 、 Pygal

Pygal is a scalable vector chart library, which is used to generate SVG (ScalableVectorGraphics) format charts that can be opened in a browser. This kind of chart can be automatically scaled on different proportions of the screen to facilitate user interaction.

6 、 Pyecharts

Pyecharts is a library that generates ECharts (EnterpriseCharts Commercial Product Chart). The generated ECharts has been recognized by many developers because of its good interaction and exquisite design.

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