Product Updates

Highcharts for Python v.1.4.0 Released

by

Mustapha Mekhatria

3 minutes read

Picture of Highcharts Python v.1.4.0 Release

We are pleased to announce the release of v.1.4.0 of the Highcharts for Python Toolkit, which includes Highcharts Core for Python, Highcharts Stock for Python, Highcharts Maps for Python, and Highcharts Gantt for Python). While technically this is a minor release since everything remains backwards compatible and new functionality is purely additive, it still brings a ton of significant improvements across all libraries in the toolkit:

Performance Improvements

50 – 90% faster when serializing a chart configuration from Python to JS object literal notation
30 – 90% faster when serializing a chart configuration from Python to JSON

Both major performance improvements depend somewhat on the chart configuration, but in any case it should be quite significant.

Usability / Quality of Life Improvements

Support for NumPy
Now we can create charts and data series directly from NumPy arrays.
Simpler API / Reduced Verbosity
While the toolkit still supports the full power of Highcharts (JS), the Python toolkit now supports “naive” usage and smart defaults. The toolkit will attempt to assemble charts and data series for you as best it can based on your data, even without an explicit configuration. Great for quick-and-dirty experimentation!
Python to JavaScript Conversion
Now we can write our Highcharts formatter or callback functions in Python, rather than JavaScript. With one method call, we can convert a Python callable/function into its JavaScript equivalent. This relies on integration with either OpenAI’s GPT models or Anthropic’s Claude model, so you will need to have an account with one (or both) of them to use the functionality. Because AI is generating the JavaScript code, best practice is to review the generated JS code before including it in any production application, but for quick data science work, or to streamline the development / configuration of visualizations, it can be super useful. For more information, check out our tutorial on this functionality.
Series-first Visualization
We no longer have to combine series objects and charts to produce a visualization. Now, we can visualize individual series directly with one method call, no need to assemble them into a chart object
Data and Property Propagation
When configuring our data points, we no longer have to adjust each data point individually. To set the same property value on all data points, just set the property on the series and it will get automatically propagated across all data points.
Series Type Conversion
We can now convert one series to a different series type with one method call.

Bug Fixes

Fixed a bug causing a conflict in certain circumstances where Jupyter Notebook uses RequireJS.
Fixed a bug preventing certain chart-specific required Highcharts (JS) modules from loading correctly in Jupyter Notebook/Labs.

We’re already hard at work on the next release, with more improvements coming, but while we work on it, if you’re looking for high-end data visualization you’ll find the Highcharts for Python Toolkit useful.
Here are all the more detailed links: