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


Highcharts Core for Python

High-end data visualization for the Python ecosystem

Highcharts Core for Python is a Python library that provides a Python wrapper for the Highcharts Core JavaScript data visualization library, with full integration into the robust Python ecosystem, including:

  • 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 even more use-case specific integrations across the broader toolkit.

The Highcharts for Python Toolkit

The Highcharts Core for Python library is - as the name suggests - the core library in the broader Highcharts for Python Toolkit, which together provides comprehensive support across the entire Highcharts suite of data visualization libraries:

Python Library

JavaScript Library

Description

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 for Python

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

Installation

To install Highcharts Core for Python, just execute:

$ pip install highcharts-core

Dependencies

Note

Highcharts Core 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

Warning

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

$ pip install highcharts-core

  • Highcharts Core v.10.2 or higher

    Note

    Not technically a Python dependency, but obviously Highcharts Core for Python will not work properly if your rendering layer does not leverage Highcharts Core (JS).

  • esprima-python v.4.0 or higher

  • requests v.2.31 or higher

  • validator-collection v.1.5 or higher

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 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.

Key Highcharts 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 the Highcharts for Python Toolkit. Highcharts Core for Python includes support for the over 70 data visualization types supported by Highcharts Core and while other libraries in the toolkit support the 50+ technical indicator visualizations available in Highcharts Stock.

    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.

    See also

  • 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 the Highcharts for Python toolkit applies Pythonic syntax with automatic conversion between Pythonic snake_case notation and JavaScript camelCase styles.


Highcharts for Python vs Alternatives

Since Highcharts Core (JS) is such a popular high-end data visualization library for JavaScript, there are a variety of ways to work with it in Python. We have an obvious bias towards the Highcharts for Python Toolkit, but it may be useful to compare that toolkit 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.

Tip

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 Core for Python

Tip

Best Practice!

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

# Import classes using precise module indications. For example:
from highcharts_core.chart import Chart
from highcharts_core.global_options.shared_options import SharedOptions
from highcharts_core.options import HighchartsOptions
from highcharts_core.options.plot_options.bar import BarOptions
from highcharts_core.options.series.bar import BarSeries

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 SharedOptions
from highcharts_core.global_options.shared_options import SharedOptions

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

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

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

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

4. Configure Your Chart / Global Settings

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

# EXAMPLE 1.
# 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'
}

# EXAMPLE 2.
# Using direct objects
from highcharts_core.options.title import Title
from highcharts_core.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

my_chart.display()

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. In particular, you can:

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

Contributing

We welcome contributions and pull requests! For more information, please see the Contributor Guide. 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.

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