Highcharts Gantt for Python - High-end Gantt Chart Visualization for the Python Ecosystem

Highcharts Gantt for Python

High-end Gantt chart visualization for the Python ecosystem


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

  • Highcharts Core - the core Highcharts data visualization library

  • Highcharts Stock - the time-series data visualization extension to Highcharts Core

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

In order to integrate Highcharts Gantt for Python into the Python ecosystem, the library features native integration with:

  • Asana. Automatically generate Gantt charts from your Asana projects.

  • Monday.com. Automatically generate Gantt charts from your Monday.com boards.

  • JIRA. Automatically generate Gantt charts from your Jira projects.

  • Jupyter Labs/Notebook. You can now produce high-end and interactive Gantt charts 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.


To install Highcharts Gantt for Python, just execute:

$ pip install highcharts-gantt



Highcharts Gantt 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 Gantt for Python is running, then the library will simply not work. Besides Highcharts Gantt itself, all of the other hard dependencies are automatically installed when installing Highcharts Gantt for Python using:

$ pip install highcharts-gantt

Why Highcharts for Python?

Odds are you are aware of Highcharts Gantt. If not, why not? Built as an extension to Highcharts Core, it is a powerful JavaScript data visualization library for rendering rich Gantt charts.

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

Highcharts Gantt is a JavaScript library, and is an extension of the Highcharts Core 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 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 Gantt for Python to provide full support for the Highcharts Gantt library extension.

Highcharts Gantt for Python provides support for the Highcharts Gantt extension, which is designed to Gantt and timeline data visualization capabilities optimized for project management data visualization, with extensive technical indicators and robust interactivity. For ease of use, it also includes the full functionality of Highcharts Stock for Python as well.

Key Highcharts Gantt 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 Gantt for Python. 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 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.

  • Integration with Asana, Monday.com, and Jira. Your project data likely lives in a modern project management platform like Asana, Monday.com, and Jira. You can easily pull data from those platforms into your Highcharts Gantt visualizations using one method call.

  • 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.

Highcharts Gantt for Python vs Alternatives

Since Highcharts Gantt is one of the most popular high-end Gantt visualization libraries for JavaScript, there are a variety of alternative ways of working with it in Python. We have an obvious bias towards Highcharts Gantt 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 Gantt. So you work in your Python backend to deliver the data to your front-end that your front-end will need. Simple, right? Often, more complicated than one might think.

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

  • Hands Off Approach. 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.

  • JSON Serialization. Your Python code constructs a JSON object (typically by serializing a Python dict to JSON) which gets delivered to your JavaScript front-end, which then hands the JSON code off to Highcharts to visualize.

  • Custom Serialization. The most sophisticated implementations I have seen actually replicate much of the functionality of Highcharts for Python, where they construct JavaScript object literal notation serialization and de-serialization for their robust use cases.

  • Don’t Use Highcharts. In many cases, particularly when working with data science teams who are data scientists first and software developers by necessity, the team turns to weaker data visualization packages because they are available in Python.

The first approach is very “clean” from a code architecture standpoint. It maintains good separation of concerns, and leaves the problem of visualization to where (architecturally) it should be: in your front-end code. However, in practice it is often problematic because it delegates data manipulation and (potentially) business logic handling to your front-end code. Depending on the overall design of your application, it can make your code harder to maintain. Furthermore, depending on your team composition, it may simply be impractical for your team.

The second approach is also fairly clean. JSON, after all, is easy in both Python and JavaScript. But JSON is a suboptimal transport mechanism for some of Highcharts most powerful features: callback functions. As these are native JavaScript code, they cannot be serialized securely to JSON and then executed directly by the Highcharts library. This is a big security anti-pattern, so while simple use cases can be handled through JSON serialization, many more sophisticated uses of Highcharts (which would rely on formatters, event handling, etc.) will simply not work.

The third approach is the most robust. And for the most sophisticated Highcharts + Python applications that I have seen, it has been the approach of choice because it eliminates all of the limitations of the other approaches mentioned. But to do it on a custom basis takes a huge amount of effort, because the complexity of constructing a library like Highcharts for Python is non-trivial.

And the fourth option is - in my experience - one of the most common. Even though plenty of developers coming to Python from the JavaScript ecosystem ask “Why can’t we just use Highcharts?”, many in the Python ecosystem will answer “because it’s a pain to do”. So they turn to Highcharts alternatives that are more Python-friendly, like plotly.

Highcharts for Python and its Highcharts Gantt for Python extension are designed to eliminate this pain.


When to use it?

In practice, I find that rolling my own is great when I’m doing a limited number of simple and static (non-interactive) visualizations. Then I 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 I wrote Highcharts for Python and Highcharts Gantt for Python.

Hello World, and Basic Usage

1. Import Highcharts Gantt for Python


Best Practice!

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

# Import classes using precise module indications. For example:
from highcharts_gantt.chart import Chart
from highcharts_gantt.global_options.shared_options import SharedGanttOptions
from highcharts_gantt.options import HighchartsGanttOptions
from highcharts_gantt.options.plot_options.gantt import GanttOptions
from highcharts_gantt.options.series.gantt import GanttSeries

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 Asana
my_chart = highcharts.Chart.from_asana(project_gid = MY_ASANA_PROJECT_ID)

# from Monday.com
my_chart = highcharts.Chart.from_monday(board_id = MY_MONDAY_BOARD_ID)

# from JIRA
my_chart = highcharts.Chart.from_jira(project_id = MY_JIRA_PROJECT_KEY)

# 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 SharedGanttOptions
from highcharts_gantt.global_options.shared_options import SharedGanttOptions

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

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

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

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

4. Configure Your Chart / Global Settings

from highcharts_gantt.options.title import Title
from highcharts_gantt.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_gantt.options.title import Title
from highcharts_gantt.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

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 tools to get help using the toolkit. In particular, you can:

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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