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Highcharts Stock for Python - High-end Time-series Data Visualization for the Python Ecosystem



Highcharts Stock for Python

High-end time series data visualization for the Python ecosystem

.. toctree::
  :hidden:
  :maxdepth: 3
  :caption: Contents

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

Version Compatibility

Latest Highcharts (JS) version supported: v.11.4.0

Highcharts Stock 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 or higher,
  • Jupyter Notebook 6.4 or higher,
  • IPython 8.10 or higher,
  • NumPy 1.19 or higher,
  • Pandas 1.3 or higher
  • PySpark 3.3 or higher

Highcharts Stock for Python is an extension to the Highcharts Core for Python library, providing a Python wrapper for the Highcharts Stock JavaScript data visualization library.

Highcharts Stock for Python also supports

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

Highcharts Stock for Python is fully integrated with the broader Python ecosystem, in particular:

  • 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.

Contents

The Highcharts for Python Toolkit

The Highcharts Stock 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

Installation

Dependencies

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.

Highcharts Stock for Python in particular provides support for the Highcharts Stock extension, which is designed to provide rich time series data visualization capabilities optimized for asset (e.g. stock) price data visualization, with extensive technical indicators and robust interactivity. For ease of use, it also includes the full functionality of Highcharts Core for Python as well.

Key Highcharts Stock 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 Stock for Python. This includes the over 70 data visualization types supported by Highcharts Core, the specialisted chart types and 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.

    .. 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 :term:`Highcharts Export Server <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 Stock for Python vs Alternatives

Hello World, and Basic Usage

1. Import Highcharts Stock for Python

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 SharedStockOptions
from highcharts_stock.global_options.shared_options import SharedStockOptions

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

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

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

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

4. Configure Your Chart / Global Settings

from highcharts_stock.options.title import Title
from highcharts_stock.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_stock.options.title import Title
from highcharts_stock.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

Contributing

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:

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 :doc:`Testing Reference <testing>`.

Indices and tables