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This article mainly introduces "what is the Python data visualization library". In the daily operation, I believe that many people have doubts about the Python data visualization library. The editor consulted all kinds of materials and sorted out simple and easy-to-use operation methods. I hope it will be helpful to answer the doubts about "what is the Python data visualization library?" Next, please follow the editor to study!

1. Matplotlib

Matplotlib is the OG of the Python data visualization library. Although it has a history of more than a decade, it is still the most widely used drawing library in the Python community. Its design is very similar to MATLAB, a proprietary programming language developed in the 1980s.

2. Seaborn

Seaborn takes advantage of the power of matplotlib to create beautiful charts in just a few lines of code. The key difference is that the default style and palette design of Seaborn are more beautiful and modern. Because Seaborn is built on top of matplotlib, you also need to know about matplotlib in order to adjust the default values for Seaborn.

3. Ggplot

Ggplot is based on ggplot2, an R language drawing system, and the concept of The Grammar of Graphics. Ggplot works differently from matplotlib: it allows you to layer components to create a complete drawing. For example, you can start with the axis, then add points, then lines, trend lines, and so on. Although graphic syntax is called the "intuitive" method of drawing, experienced matplotlib users may need time to adapt to this new approach.

4. Bokeh

Like ggplot, Bokeh is also based on The Grammar of Graphics, but unlike ggplot, it is native to Python, not ported from the R language. Its advantage is the ability to create interactive site maps, which can be easily exported to JSON objects, HTML, or interactive Web applications. Bokeh also supports streaming and real-time data.

5. Pygal

Like Bokeh and Plotly, pygal provides interactive diagrams that can be embedded in Web browsers. The main difference is the ability to output charts to SVG format. If you use a smaller dataset, an image in SVG format is fine. But if you make a chart that contains hundreds of thousands of data points, they will be difficult to render and become unresponsive.

6. Plotly

You may know that Plotly is an online platform for data visualization, but do you also know that its features can be used from Python laptops? Like Bokeh, Plotly's strengths are creating interactive graphs, but it provides charts that are not available in most libraries, such as contours, trees, and 3D charts.

7. Geoplotlib

Geoplotlib is a tool library for creating maps and drawing geographic data. You can use it to create various map types, such as contours, heat maps, and point density maps. You must install Pyglet (object-oriented programming interface) to use geoplotlib. However, since most Python data visualization libraries do not provide map types, it is possible to have a special library.

8. Gleam

Gleam is inspired by the Shiny package of the R language. It allows you to convert analysis results into interactive Web applications using only Python scripts, so you don't have to know any other languages, such as HTML,CSS or JavaScript. Gleam is suitable for any Python data visualization library. After you create a drawing, you can add fields to it so that users can filter and sort the data.

9. Missingno

It is painful to deal with missing data. Missingno allows you to use visual summaries to quickly evaluate the integrity of data sets, rather than through large tables. You can filter and sort the data according to the completion of the heat map or tree or the relevance of the points.

10. Leather

As Christopher Groskopf, creator of Leather, said: "Leather is a Python chart library for people who now need charts and don't care whether they are perfect or not." It applies to all data types and generates charts as SVG, which can be scaled without losing image quality. Because this library is relatively new, some documentation is still in progress. You can make very basic charts-but this is what you want.

11. Chartify

Chartify is a Python library that makes it easy for data scientists to create charts.

Why use Chartify?

Consistent input data format: takes less time to convert data. All drawing functions use a consistent and neat data format.

Smart default style: create a beautiful chart that requires only a few custom variables.

Simple API: make API as intuitive and easy to learn as possible.

Flexibility: Chartify is built on Bokeh, and if you need more styles, you can use Bokeh's API at any time.

12. Altair

Altair is a declarative statistics (declarative statistical) visual python library based on Vega-lite. The declaration means that you only need to provide links between the data column and the coding channel, such as x-axis, y-axis, color, etc., and it will automatically handle the rest of the drawing details. The declaration makes Altair simple, friendly and consistent. Using Altair, you can easily design effective and beautiful visual code.

At this point, the study on "what are the Python data visualization libraries" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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