So, in the end, Pyecharts and Echarts were my choices to do this demo. įurthermore, all of these libraries (excluding QtChart) face the same problems when it comes to PyQt5 packaging by using pyinstaller. If want to know more about the differences among those visualization tools, please have a look at my previous post. I found out that Pyecharts and Echarts are the most attractive, and the most practical, but there are fewer people are using them. Personally, I’ve only recently worked on a few visualization tools such as Plotly, Boken, Matplotlib, Seaborn, Pyecharts, Echarts, and so forth. There are many data visualization tools that can integrate with PyQt5 such as QtChart, PyQtGraph, Plotly, of course including Pyecharts and Echarts, and so on. Implementation ∘ Combination of PyQt5 and Pyecharts (Not work when Pyinstaller Packaging) ∘ Get Stock Data ∘ Create AppMainWindow ∘ Full Code Snippets: ∘ Combination of PyQt5 and Echarts (Working when Pyinstaller Packaging) ∘ Full Code Snippets: īefore starting our codings, let’s talk about the data visualization of PyQt5.This article mainly talks about how to build your own Data Visualization Desktop App with the combination of PyQt5 and Echarts in 10 Minutes.

I won’t go into detail because there is a lot of information regarding ECharts on the Internet.

It is a very attractive, user-friendly, and widely used data visualization tool. ECharts is an open-source data visualization tool from Baidu, abbreviated from Enterprise Charts, commercial-level data charts.

Best data visualization tools desktop