Highcharts for Python - High-end Data Visualization for the Python Ecosystem

Highcharts Maps for Python

High-end data and map visualization for the Python ecosystem

Highcharts Maps for Python is an extension to the Highcharts Core for Python library, providing a Python wrapper for the Highcharts Maps JavaScript data visualization library.

Highcharts Maps for Python also supports

  • Highcharts Core (JS) - the core Highcharts data visualization library

  • The Highcharts Export Server - enabling the programmatic creation of static (downloadable) data visualizations

Highcharts Maps for Python is fully integrated with the broader Python ecosystem, offerin gnative 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.

  • GeoPandas. Automatically incorporate GIS / map visualizations with data from your GeoPandas GeoDataFrames.

  • Topojson. Automatically visualizes TopoJSON map geometries.

  • Geojson. Automatically visualizes GeoJSON map geometries.

The Highcharts for Python Toolkit

The Highcharts Maps for Python library is part of the broader Highcharts for Python Toolkit, which together provides comprehensive support across the entire Highcharts suite of data visualization libraries:

Python Library

JavaScript Library


Highcharts Core for Python

Highcharts Core (JS)

(this library) the core Highcharts data visualization library

Highcharts Stock for Python

Highcharts Stock (JS)

the time series visualization extension to Highcharts Core

Highcharts Maps for Python

Highcharts Maps (JS)

the map visualization extension to Highcharts Core

Highcharts Gantt

Highcharts Gantt (JS)

the Gantt charting extension to Highcharts Core

(all libraries in the Python toolkit)

The Highcharts Export Server

enabling the programmatic creation of static (downloadable) data visualizations


To install Highcharts Maps for Python, just execute:

$ pip install highcharts-maps



Highcharts Maps for Python has several types of dependencies:

  • hard dependencies, without which you will not be able to use the library at all,

  • soft dependencies, which will not produce errors but which may limit the value you get from the library,

  • developer dependencies that contributors will need in their local environment, and

  • documentation dependencies that are necessary if you wish to generate (this) documentation


If these hard dependencies are not available in the environment where Highcharts Maps for Python is running, then the library will simply not work. Besides Highcharts Maps itself, all of the other hard dependencies are automatically installed when installing Highcharts Stock for Python using:

$ pip install highcharts-maps

Why Highcharts for Python?

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 Maps for Python provides support for the Highcharts Maps extension, which is designed to provide extensive map and data visualization capabilities optimized for GIS data visualization, with robust interactivity. For ease of use, it also includes the full functionality of Highcharts for Python as well.

Key Highcharts Maps for Python Features

  • 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 Highcharts Maps for Python. This includes the over 70 data visualization types supported by Highcharts Core and the four core map visualizations available in Highcharts Maps.

    Every Highcharts for Python library provides full support for the rich JavaScript formatter (JS callback functions) capabilities that are often needed to get the most out of Highcharts’ visualization and interaction capabilities.

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

  • Easy 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.

  • Asynchronous Map Data Retrieval. To minimize the amount of data transferred over the wire, Highcharts Maps for Python has built-in support for the configuration of asynchronous client-side retrieval of your map data.

  • Automatic TopoJSON Optimization. To minimize the amount of data transferred over the wire, Highcharts Maps for Python automatically converts your map geometries to highly-efficient TopoJSON topologies while still allowing you to work with GeoJSON data if you choose to.

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

  • 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.

Highcharts Maps for Python vs Alternatives

Since Highcharts Maps is such a popular high-end GIS data visualization library for JavaScript, there are a variety of alternative ways of working with it in Python. We have an obvious bias towards Highcharts Maps for Python, but it may be useful to compare it against some commonly-used alternatives:

By far, this is the most common approach to integrating Highcharts into your Python code. As a developer, you know that your JavaScript front-end will be using Highcharts (JS). So you work in your Python backend to deliver the data to your front-end that your front-end will need.

There are many patterns for rolling your own Highcharts + Python implementation, but the patterns we have seen most often include:

Your Python code is “hands off” - it does not touch any data visualization configuration or manipulation. All of that gets handled in your JavaScript front-end code.

This approach is very “clean” from a code architecture standpoint. However, in practice it often problematic because it delegates data manipulation and (potentially) business logic handling to your front-end code. Depending on the overall design of your software, it can make your code harder to maintain. Furthermore, depending on your team composition, it may simply be impractical for your team.


When to use it?

In practice, we find that rolling our own solution is great when doing a limited number of simple and static (non-interactive) visualizations. Then you can just quickly use some simple JSON serialization, and rapidly get a high-quality chart visualized cleanly using Highcharts. But anything more robust than that is going to prove “hacky” and incredibly difficult to maintain.

Which is why we created the Highcharts for Python Toolkit in the first place, so that:

  • you gain the ability to use as much or as little of Highcharts rich functionality as you need

  • you get native integration with key Python ecosystem members “out of the box”

  • you don’t have to worry about maintaining the “glue” code connecting your Python implementation with Highcharts (JS)

  • you have support available when you need it

Hello World, and Basic Usage

1. Import Highcharts Maps for Python


Best Practice!

This method of importing Highcharts Maps for Python objects yields the fastest performance for the import statement. However, it is more verbose and requires you to navigate the extensive Highcharts Maps for Python API.

# Import classes using precise module indications. For example:
from highcharts_maps.chart import Chart
from highcharts_maps.global_options.shared_options import SharedMapsOptions
from highcharts_maps.options import HighchartsMapsOptions
from highcharts_maps.options.plot_options.map import MapOptions
from highcharts_maps.options.series.map import MapSeries

2. Create Your Chart

# from a primitive array, using keyword arguments
my_chart = Chart(data = [[1, 23], [2, 34], [3, 45]],
                 series_type = 'line')

# from a primitive array, using the .from_array() method
my_chart = Chart.from_array([[1, 23], [2, 34], [3, 45]],
                            series_type = 'line')

# from a Numpy ndarray, using keyword arguments
my_chart = Chart(data = numpy_array, series_type = 'line')

# from a Numpy ndarray, using the .from_array() method
my_chart = Chart.from_array(data = numpy_array, series_type = 'line')

# from a JavaScript file
my_chart = Chart.from_js_literal('my_js_literal.js')

# from a JSON file
my_chart = Chart.from_json('my_json.json')

# from a Python dict
my_chart = Chart.from_dict(my_dict_obj)

# from a Pandas dataframe
my_chart = Chart.from_pandas(df)

# from a PySpark dataframe
my_chart = Chart.from_pyspark(df,
                              property_map = {
                                  'x': 'transactionDate',
                                  'y': 'invoiceAmt',
                                  'id': 'id'
                              series_type = 'line')

# from a CSV
my_chart = Chart.from_csv('/some_file_location/filename.csv')

# from a HighchartsOptions configuration object
my_chart = Chart.from_options(my_options)

# from a Series configuration, using keyword arguments
my_chart = Chart(series = my_series)

# from a Series configuration, using .from_series()
my_chart = Chart.from_series(my_series)

3. Configure Global Settings (optional)

# Import SharedMapsOptions
from highcharts_maps.global_options.shared_options import SharedMapsOptions

# from a JavaScript file
my_global_settings = SharedMapsOptions.from_js_literal('my_js_literal.js')

# from a JSON file
my_global_settings = SharedMapsOptions.from_json('my_json.json')

# from a Python dict
my_global_settings = SharedMapsOptions.from_dict(my_dict_obj)

# from a HighchartsOptions configuration object
my_global_settings = SharedMapsOptions.from_options(my_options)

4. Configure Your Chart / Global Settings

from highcharts_maps.options.title import Title
from highcharts_maps.options.credits import Credits

# Using dicts
my_chart.title = {
    'align': 'center',
    'floating': True,
    'text': 'The Title for My Chart',
    'use_html': False,

my_chart.credits = {
    'enabled': True,
    'href': 'https://www.highchartspython.com/',
    'position': {
        'align': 'center',
        'vertical_align': 'bottom',
        'x': 123,
        'y': 456
    'style': {
        'color': '#cccccc',
        'cursor': 'pointer',
        'font_size': '9px'
    'text': 'Chris Modzelewski'

# Using direct objects
from highcharts_maps.options.title import Title
from highcharts_maps.options.credits import Credits

my_title = Title(text = 'The Title for My Chart',
                 floating = True,
                 align = 'center')
my_chart.options.title = my_title

my_credits = Credits(text = 'Chris Modzelewski',
                     enabled = True,
                     href = 'https://www.highchartspython.com')
my_chart.options.credits = my_credits

5. Generate the JavaScript Code for Your Chart

Now having configured your chart in full, you can easily generate the JavaScript code that will render the chart wherever it is you want it to go:

# as a string
js_as_str = my_chart.to_js_literal()

# to a file (and as a string)
js_as_str = my_chart.to_js_literal(filename = 'my_target_file.js')

6. Generate the JavaScript Code for Your Global Settings (optional)

# as a string
global_settings_js = my_global_settings.to_js_literal()

# to a file (and as a string)
global_settings_js = my_global_settings.to_js_literal('my_target_file.js')

7. Generate a Static Version of Your Chart

# as in-memory bytes
my_image_bytes = my_chart.download_chart(format = 'png')

# to an image file (and as in-memory bytes)
my_image_bytes = my_chart.download_chart(filename = 'my_target_file.png',
                                         format = 'png')

8. Render Your Chart in a Jupyter Notebook


Getting Help/Support

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:

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


We welcome contributions and pull requests! For more information, please see the Contributor Guide. And thanks to all those who’ve already contributed:


We use TravisCI for our build automation and ReadTheDocs for our documentation.

Detailed information about our test suite and how to run tests locally can be found in our Testing Reference.

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