Trying the Highcharts for Python Toolkit

You are welcome to try the Highcharts for Python toolkit at your convenience and at no cost. You can do so by installing the libraries you need very simple:

To install Highcharts Core for Python, just execute:

$ pip install highcharts-core

If you are evaluating the Highcharts for Python Toolkit, you are welcome to install this library and use it free of charge.

However, if you are using it for professional purposes - either to use Highcharts for your work, or to build an application that integrates the library - then you have to pay for both Highcharts Core (JS) itself and for your right to use the Highcharts for Python Toolkit.

You can purchase licenses for both from Highsoft A/S at:

Demonstrating the Highcharts for Python Toolkit

We have prepared an extensive set of demos showcasing much of the code and functionality of the Highcharts for Python toolkit. To see the demos in action, we recommend that you click the following badge:

Binder: Highcharts for Python Demos

This will clone the Highcharts for Python Demos source repository within a Docker image, and launch Jupyter Lab within that Docker container. This will then let you browse, edit, and run any of the Jupyter Notebooks contained within the Highcharts for Python Demos repo.

How the Demos are Organized

Once you have launched the Binder, you can browse the folders and Notebooks in the Jupyter Lab environment. You’ll notice that the Jupyter Lab environment has one folder for each of the core Highcharts for Python libraries, respectively: highcharts-core, highcharts-stock, highcharts-maps, and highcharts-gantt. Within each of these folders, you will find sub-folders labeled in a way to describe their contents.

For example:

  • the sub-folder line-charts would contain various Notebooks that demonstrate the generation of various Line Charts using the Highcharts for Python toolkit

  • the sub-folder python-features would contain various Notebooks that demonstrate the use of Python-specific features like the .from_pandas() convenience methods

  • etc.

Navigating the demos should be fairly intuitive - just pay attention to the folder names and the filenames of the Jupyter Notebooks.

Executing a Demo

Once you are reviewing the demos, you can run a demo either by stepping through the cells in the Notebook, or by running all the cells in sequence.

Running Demos Locally


You can run the demos locally by following instructions in the Highcharts for Python Demos Github repo’s README.