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

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Highcharts Maps for Python

High-end data and map visualization for the Python ecosystem

Home <self> Quickstart: Patterns and Best Practices <quickstart> Supported Visualizations <visualizations> FAQ <faq> Toolkit Components and Roadmap <toolkit> Using Highcharts for Python <using> API Reference <api> Error Reference <errors> Getting Help <support> Contributor Guide <contributing> Testing Reference <testing> Release History <history> Glossary <glossary> License <license>

Version Compatibility

Highcharts Maps for Python is designed to be compatible with:

  • Python 3.10 or higher,
  • Highcharts JS 10.2 or higher,
  • Highcharts Core for Python 1.0.0 or higher,
  • Jupyter Notebook 6.4 or higher,
  • IPython 8.10 or higher,
  • Pandas 1.3 or higher
  • PySpark 3.3 or higher
  • topojson 1.5 or higher
  • geojson 2.5 or higher
  • GeoPandas 0.11 or higher
  • PyShp 2.3.1 or higher

Highcharts Maps for Python is an extension to the Highcharts for Python library, and provides a Python wrapper for the fantastic Highcharts Maps JavaScript data visualization library. Highcharts Maps for Python also supports

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

In order to integrate Highcharts Maps for Python into the Python ecosystem, the library features native integration 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 <map geometry>.
  • Geojson. Automatically visualizes GeoJSON map geometries <map geometry>.

Installation

Dependencies

Why Highcharts for Python?

Odds are you are aware of Highcharts Maps. If not, why not? It is the world's most popular, most powerful, category-defining JavaScript data visualization library and - in particular - for map/GIS data.

If you are building a web or mobile app/dashboard that will be visualizing data in a geographic context, you should absolutely take a look at the Highcharts suite of solutions. Just take a look at some of their fantastic Highcharts Maps demo visualizations.

Highcharts Maps is a JavaScript library, and is an extension of the Highcharts JS JavaScript library. It is written in JavaScript, and is specifically used to configure and render data visualizations in a web browser (or other JavaScript-executing, like mobile app) environment. As a JavaScript library, its audience is JavaScript developers. But what about the broader ecosystem of Python developers and data scientists?

Python is increasingly used as the technology of choice for data science and for the backends of leading enterprise-grade applications. In other words, 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 <JavaScript Object Literal Notation> 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 I wrote the Highcharts for Python toolkit to bridge that gap, and Highcharts Maps for Python to provide full support for the Highcharts Maps library extension.

Highcharts Maps for Python provides support for the Highcharts Maps extension, which is designed to provide extensive time series data visualization capabilities optimized for GIS (Geographic Information System) 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 <python: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 JS and the four core map visualizations available in Highcharts Maps, with full support for the rich JavaScript formatter (JS callback functions <callback function>) capabilities that are often needed to get the most out of Highcharts' visualization and interaction capabilities.

    • Supported Visualizations <visualizations>
  • 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 <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 <map geometry> to highly-efficient TopoJSON topologies <topology> 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

Hello World, and Basic Usage

1. Import Highcharts Maps for Python

2. Create Your Chart

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

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

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

# from a GeoPandas GeoDataFrame
my_chart = highcharts.Chart.from_geopandas(gdf,
                                           property_map = {
                                               'z': 'caseCount',
                                               'id': 'id',
                                           },
                                           series_type = 'mapbubble')

# from a Pandas dataframe
my_chart = highcharts.Chart.from_pandas(df,
                                        property_map = {
                                            'x': 'transactionDate',
                                            'y': 'invoiceAmt',
                                            'id': 'id'
                                        },
                                        series_type = 'line')

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

# from a CSV
my_chart = highcharts.Chart.from_csv('/some_file_location/filename.csv'
                                     column_property_map = {
                                        'x': 0,
                                        'y': 4,
                                        'id': 14
                                     },
                                     series_type = 'line')

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

# from a Series configuration
my_chart = highcharts.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.highcharts.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.highcharts.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')

Getting Help/Support

Contributing

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

Testing

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 <testing>.

Indices and tables

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