High-end data visualization for the Python ecosystem

The Highcharts for Python Toolkit is a set of Python libraries that provide a Python wrapper for the Highcharts suite of JavaScript data visualization libraries, with full integration across the Python ecosystem.

The full toolkit includes support for:

The toolkit features native integrations with:

• Jupyter Labs/Notebook. You can now produce high-end and interactive plots and renders using the full suite of Highcharts visualization capabilities.
• Pandas. Automatically produce data visualizations from your Pandas dataframes
• PySpark. Automatically produce data visualizations from data in a PySpark dataframe.
• ...and more library-specific integrations, including GeoPandas, PyShp, Topjson, Geojson, Asana, and more

COMPLETE DOCUMENTATION: http://core-docs.highchartspython.com/en/latest/index.html

To install Highcharts Core for Python, just execute:

$ pip install highcharts-core

To install Highcharts Stock for Python, just execute:

$ pip install highcharts-stock

To install Highcharts Maps for Python, just execute:

$ pip install highcharts-maps

To install Highcharts Gantt for Python, just execute:

$ pip install highcharts-gantt

Highcharts is the world's most popular, most powerful, category-defining JavaScript data visualization library. If you are building a web or mobile app/dashboard that will be visualizing data in some fashion, you should absolutely take a look at the Highcharts suite of solutions. Take a peak at some fantastic demo visualizations.

As a suite of JavaScript libraries, Highcharts is written in JavaScript, and is used to configure and render data visualizations in a web browser (or other JavaScript-executing) environment. As a set of JavaScript libraries, its audience is JavaScript developers. But what about the broader ecosystem of Python developers and data scientists?

Given Python's increasing adoption as the technology of choice for data science and for the backends of leading enterprise-grade applications, Python is often the backend that delivers data and content to the front-end...which then renders it using JavaScript and HTML.

There are numerous Python frameworks (Django, Flask, Tornado, etc.) with specific capabilities to simplify integration with Javascript frontend frameworks (React, Angular, VueJS, etc.). But facilitating that with Highcharts has historically been very difficult. Part of this difficulty is because the Highcharts JavaScript suite - while supporting JSON as a serialization/deserialization format - leverages JavaScript object literals to expose the full power and interactivity of its data visualizations. And while it's easy to serialize JSON from Python, serializing and deserializing to/from JavaScript object literal notation is much more complicated.

This means that Python developers looking to integrate with Highcharts typically had to either invest a lot of effort, or were only able to leverage a small portion of Highcharts' rich functionality.

So we wrote the Highcharts for Python toolkit to bridge that gap.

Highcharts for Python provides Python object representation for all of the JavaScript objects defined in the Highcharts (JavaScript) API. It provides automatic data validation, and exposes simple and standardized methods for serializing those Python objects back-and-forth to JavaScript object literal notation.

• Clean and consistent API. No reliance on "hacky" code, dict and JSON serialization, or impossible to maintain / copy-pasted "spaghetti code".

• Comprehensive Highcharts Support. Every single Highcharts chart type and every single configuration option is supported in the Highcharts for Python toolkit. This includes the over 70 data visualization types supported by Highcharts Core and the 50+ technical indicator visualizations available in Highcharts Stock, with full support for the rich JavaScript formatter (JS callback function) capabilities that are often needed to get the most out of Highcharts' visualization and interaction capabilities.

  See Also

  • Supported Visualizations

• Simple JavaScript Code Generation. With one method call, produce production-ready JavaScript code to render your interactive visualizations using Highcharts' rich capabilities.

• Easy and Robust Chart Download. With one method call, produce high-end static visualizations that can be downloaded or shared as files with your audience. Produce static charts using the Highsoft-provided Highcharts Export Server, or using your own private export server as needed.

• Integration with Pandas and PySpark. With two lines of code, produce a high-end interactive visualization of your Pandas or PySpark dataframe.

• Consistent code style. For Python developers, switching between Pythonic code conventions and JavaScript code conventions can be...annoying. So Highcharts for Python applies Pythonic syntax with automatic conversion between Pythonic snake_case notation and JavaScript camelCase styles.

For a discussion of Highcharts for Python in comparison to alternatives, please see the COMPLETE DOCUMENTATION: http://core-docs.highchartspython.com/en/latest/index.html

Use the following links to learn more about each of the tools in the Highcharts for Python Toolkit:

• Highcharts Core for Python
• Highcharts Stock for Python
• Highcharts Maps for Python
• Highcharts Gantt for Python

The Highcharts for Python Toolkit comes with all of the great support that you are used to from working with the Highcharts JavaScript libraries. When you license the toolkit, you are welcome to use any of the following channels to get help using the toolkit. In particular, you can:

• Use the Highcharts Forums
• Use Stack Overflow with the highcharts-for-python tag
• Report bugs or request features in one of our Github repositories
• File a support ticket with us, or
• Schedule a live chat or video call with us

FOR MORE INFORMATION: https://www.highchartspython.com/get-help