High-end Gantt chart visualization for the Python ecosystem
.. toctree:: :hidden: :maxdepth: 3 :caption: Contents Home <self> Quickstart: Patterns and Best Practices <quickstart> Supported Visualizations <visualizations> FAQ <faq> Toolkit Components and Roadmap <toolkit> Using Highcharts Gantt 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 Gantt for Python is designed to be compatible with:
- Python 3.10 or higher
- Highcharts JS 10.2 or higher
- Highcharts Stock for Python 1.0 or higher
- Highcharts Core for Python 1.0 or higher
- Jupyter Notebook 6.4 or higher
- IPython 8.10 or higher
- Pandas 1.3 or higher
- PySpark 3.3 or higher
- Asana 3.0 or higher
- Monday 1.3 or higher
- Python-Jira 3.4 or higher
Highcharts Gantt for Python is an extension to the Highcharts Stock for Python library, providing a Python wrapper for the Highcharts Gantt JavaScript data visualization library.
Highcharts Gantt for Python also supports
- Highcharts Core (JS) - the core Highcharts data visualization library
- Highcharts Stock (JS) - the Highcharts extension providing time series and asset price data visualization
- 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 also features native integration with:
- Asana. Automatically generate :term:`Gantt charts <Gantt chart>` 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.
Contents
The Highcharts Gantt 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 | 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 |
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 Highcharts Gantt 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 :term:`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 we wrote the Highcharts for Python Toolkit to bridge that gap.
The Highcharts for Python Toolkit 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.
Highcharts Gantt for Python provides support for the Highcharts Gantt extension, which is designed to provide extensive capabilities optimized for project, time, and resource allocation data visualization with robust interactivity. For ease of use, it also includes the full functionality of Highcharts Stock for Python and Highcharts Core for Python.
Clean and consistent API. No reliance on "hacky" code,
dictand 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,
- the 50+ visualizations supported by Highcharts Stock
- the multiple Gantt visualizations available in Highcharts Gantt
all with full support for the rich JavaScript formatter (JS :term:`callback functions <callback function>`) capabilities that are often needed to get the most out of Highcharts' visualization and interaction capabilities.
.. seealso:: * :doc:`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, 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, or Atlassian 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_casenotation and JavaScriptcamelCasestyles.
# 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)
# 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)
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
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')
# 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')
# 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')
We welcome contributions and pull requests! For more information, please see the :doc:`Contributor Guide <contributing>`. 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 :doc:`Testing Reference <testing>`.