Highcharts Core for Python: Getting Started

We’re so glad you want to use Highcharts Core for Python! This tutorial will help you get started, and give you a jumping off point to dive into all the great features of the Highcharts visualization suite. Before you really get going, we suggest you take a look at all of the great visualization types you can create using Highcharts for Python and Highcharts (JS).

Installation

First things first, to use Highcharts for Python the first step is to install the library (likely to a virtual environment). That’s pretty straightforward:

To install Highcharts Stock for Python, just execute:

$ pip install highcharts-stock

Importing Highcharts Core for Python

Once you’ve installed Highcharts Core for Python, you can import it into your project in two different ways:

Tip

Best Practice!

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

# Import classes using precise module indications. For example:
from highcharts_stock.chart import Chart
from highcharts_stock.global_options.shared_options import SharedStockOptions
from highcharts_stock.options import HighchartsStockOptions
from highcharts_stock.options.plot_options.bar import BarOptions
from highcharts_stock.options.series.bar import BarSeries

Wrangle Your Data

Since you want to use Highcharts Core for Python to visualize some data, first you’re going to have to wrangle the data into a form you can work with. How you do this really depends on the data you are working with and the other tools you are using in your tech stack.

The Highcharts for Python toolkit works with most of the “usual suspects” in the Python ecosystem, including:

For the sake of simplicity, we’ll work with Python iterables to show how you can quickly get started. Let’s say we have a simple 2-dimensional set of x/y values that we want to plot:

my_iterable = [
    [0, 123],
    [1, 456],
    [2, 789],
    [3, 987],
    [4, 654],
    [5, 321]
]

That’s all I need to wrangle my data! Highcharts for Python can work with my_iterable directly and easily, wherever data is referenced.

Tip

Different Highcharts series types support different structures of iterable.

Please review the detailed series documentation for series type-specific details of relevant iterable/array structures.

Alternatively, we can convert my_iterable into a pandas.DataFrame, numpy.ndarray, or Python dict:

# As a Pandas DataFrame
df = pandas.DataFrame(my_iterable, columns=['x', 'y'])

# As a Numpy ndarray
as_ndarray = numpy.asarray(my_iterable)

# As a Python dict
as_dict = {'x': x[0], 'y': x[1] for x in my_iterable}

Now, we can consider our data “wrangled” and ready for visualization.

Assembling a Chart

With our data wrangled, we can construct a chart with one line of code:

my_chart = Chart(data = my_iterable, series_type = 'line')

This will create a Chart instance with one series of type line (represented as a LineSeries instance).

Depending on how we’ve wrangled our data, we can similarly produce a chart from a pandas.DataFrame, numpy.ndarray, or Python dict:

# From a Pandas DataFrame

my_chart = Chart.from_pandas(df, series_type = 'line')

# From a Numpy ndarray

my_chart = Chart.from_array(as_ndarray, series_type = 'line')

# From a Python dict

my_chart = Chart(data = as_dict, series_type = 'line')

All of these lines of code are equivalent, and should produce an identical my_chart.

Configuring the Chart

Highcharts (JS) sets the standard for data visualization because it supports a huge number of easy-to-understand configuration options. Highcharts for Python makes it easy to configure any of those options directly within Python.

To do that, we can use the Chart.options property. Having assembled our chart following the instructions above, my_chart already contains a HighchartsOptions instance in the Chart.options property. You can access the LineSeries we created at Chart.options.series, and you can set any other options you need on Chart.options.

For example, let’s say we want to set the chart’s title to “My First Chart”. To do that, we can configure the Chart.options.title property using either a Title instance, or a dict:

# as a Title instance

from highcharts_stock.options.title import Title

my_chart.options.title = Title(text = 'My First Chart')

# as a dict

my_chart.options.title = {'text': 'My First Chart'}

Either way, the chart’s title will now be set to “My First Chart”.

See also

Rendering the Chart

Once we’ve assembled and configured our chart, we can render it. How we want to render it depends on our exact needs. We can:

  • Display the chart in a Jupyter Notebook

  • Save the chart as a static image

  • Generate the JavaScript that will render the chart in your users’ web browser.

Displaying in Jupyter Notebook / Jupyter Lab

If you’re using Highcharts Core for Python in a Jupyter Notebook or Jupyter Lab, you can display the chart right in your notebook. Doing so is super simple - just call the .display() method on my_chart:

my_chart.display()

That’s it! The chart will now render in the output of the cell.

See also

  • Using Highcharts Core for Python with Jupyter

Saving the Chart as a Static Image

If you want to save the chart as a static image, you can do so by calling the .download_chart() method:

my_chart.download_chart(filename = 'my_chart.png')

If you need it in a different format, you can specify the format using the format parameter. Highcharts for Python supports PNG (the default), JPEG, PDF, and SVG. For example, to save the chart as a PDF, you can do:

my_chart.download_chart(filename = 'my_chart.pdf', format = 'pdf')

And that’s it!

Rendering in the Web Browser

If you want to render your chart in your user’s web browser, then you can use Highcharts for Python to automatically generate the JavaScript code you will need. The best way to do this is to call the .to_js_literal() method on my_chart.

This will produce a string (or write to a file) containing the JS literal form of your chart and its configuration. If the code contained in this string gets executed in your user’s browser (within a set of <script></script> tags), it will render your chart.

So the way to get the JS literal is very straightforward:

# EXAMPLE 1.
# Storing the JS literal in a string.

my_js_literal = my_chart.to_js_literal()

# EXAMPLE 2.
# Saving the JS literal to a file named
# "my-js-literal.js"

my_chart.to_js_literal('my-js-literal.js')

Now the way to render this chart will ultimately depend on how your application is architected. We see three - typical - patterns employed:

  1. If your Python code is responsible for preparing the client-side HTML + JavaScript, then you can include my_js_literal in your template file. This pattern works for practically every Python web framework, including Django, and Flask.

  2. If your Python code exposes RESTful or GraphQL APIs that are consumed by your client-side application, then you can return the JS literal object as a string via your API. This can then be evaluated in your client-side application using JavaScript’s new Function() feature.

    Caution

    DO NOT USE JAVASCRIPT’S eval() FUNCTION.

    It is deprecated, and for good reason:

    It represents a major security risk. When using new Function(), we recommend balancing the need for simplicity against the need for security. You can secure your code by applying whitelisting techniques, sandboxing the scope of your new Function() context, adding additional layers of M2M signed encryption, or employing sanitization techniques on the content of the JS literal returned by your API.

    Which specific techniques make sense will depend on your application and your use case.

  3. If the data in your front-end application is generated on a periodic / batch basis, then you can save your JS literal to a static file (saved where consumable by your front-end application) and have your front-end application simply load it as-needed.