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 library supports Highcharts (JS) v.10.2 and higher, including Highcharts (JS) v.11.4.0.
COMPLETE DOCUMENTATION: https://core-docs.highchartspython.com/en/latest/index.html
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 |
To install Highcharts Core for Python, just execute:
$ pip install highcharts-coreBefore you install, please be aware of the following "hard" dependencies:
- Python 3.10 or higher
- Highcharts Core (JS) v.10.2 or higher (not technically a Python dependency, but it won't work with earlier versions of Highcharts)
- esprima-python v.4.0 or higher
- requests v.2.31 or higher
- validator-collection v.1.5 or higher
You can find more information about soft and development dependencies in the complete documentation.
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.
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 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.
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_casenotation and JavaScriptcamelCasestyles.
For a discussion of Highcharts for Python in comparison to alternatives, please see the COMPLETE DOCUMENTATION: https://core-docs.highchartspython.com/en/latest/index.html
# PRECISE-LOCATION PATTERN: 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
# CATCH-ALL PATTERN
# This method of importing Highcharts for Python classes has relatively slow
# performance because it imports hundreds of different classes from across the entire
# library. This performance impact may be acceptable to you in your use-case, but
# do use at your own risk.
# Import objects from the catch-all ".highcharts" module.
from highcharts_core import highcharts
# You can now access specific classes without individual import statements.
highcharts.Chart
highcharts.SharedOptions
highcharts.HighchartsOptions
highcharts.BarOptions
highcharts.BarSeries# 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)
# 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)
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 # EXAMPLE 3. # Pandas with time series import pandas as pd import datetime as dt import numpy as np df = pd.DataFrame([ {"ref_date": dt.date(2024, 1, 1), "data": 1}, {"ref_date": dt.date(2024, 1, 2), "data": 5}, {"ref_date": dt.date(2024, 1, 3), "data": None}, {"ref_date": dt.date(2024, 1, 4), "data": 4}, {"ref_date": dt.date(2024, 1, 5), "data": None}, ]) df['ref_date'] = pd.to_datetime(df['ref_date']) df.set_index('ref_date', inplace=True) df.index = (df.index.astype(np.int64) / 10**6).astype(np.int64) # Correcting nanoseconds to epoch, which is crucial for javascript rendering, # See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Date/now # for more information on this behaviour from highcharts_core.chart import Chart chart = Chart.from_pandas( df=df.reset_index(), series_type='line', property_map={ 'x': df.index.name, 'y': df.columns.to_list() } ) chart.options.x_axis = { 'type': 'datetime' } chart.display()
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:
# EXAMPLE 1. # as a string js_as_str = my_chart.to_js_literal() # EXAMPLE 2. # 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')
my_chart.display()
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:
- Use the Highcharts Forums
- Use Stack Overflow with the
highcharts-for-pythontag- Report bugs or request features in the library's Github repository
- File a support ticket with us
- Schedule a live chat or video call with us
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.