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CanvasXpress for Python

Project description

CanvasXpress Python Library

About CanvasXpress for Python

CanvasXpress was developed as the core visualization component for bioinformatics and systems biology analysis at Bristol-Myers Squibb. It supports a large number of visualizations to display scientific and non-scientific data. CanvasXpress also includes a simple and unobtrusive user interface to explore complex data sets, a sophisticated and unique mechanism to keep track of all user customization for Reproducible Research purposes, as well as an 'out of the box' broadcasting capability to synchronize selected data points across all CanvasXpress plots in a page. Data can be easily sorted, grouped, transposed, transformed or clustered dynamically. The fully customizable mouse events as well as the zooming, panning and drag-and-drop capabilities are features that make this library unique in its class.

CanvasXpress can be now be used within Python for native integration into IPython and Web environments, such as:

Complete examples using the CanvasXpress library including the mouse events, zooming, and broadcasting capabilities are included in this package. This CanvasXpress Python package was created by Dr. Todd C. Brett, with support from Aggregate Genius Inc., in cooperation with the CanvasXpress team.

The maintainer of the Python edition of this package is Dr. Todd C. Brett.

Project Status

Topic Status
Version and Platform Release Compatibility Implementations
Popularity PyPI - Downloads
Status docinfosci Documentation Status Coverage Status Requirements Status Activity


  1. This project uses older versions of mkdoc and pydoc-markdown due to a conflict between these packages. Once the conflict is resolved they will be upgraded to current release editions. This only affects doc builds.

Recent Enhancements

2021 June 21: Support for reproducable research JSON

CanvasXpress for Javascript can save JSON representations of a rendered chart. CanvasXpress now offers from_reproducible_json, which accepts such a JSON and creates a new CanvasXpress object with all properties updated with the relevant portions of the JSON.

2021 June 21: CanvasXPress repr now available for example code

The CanvasXpress __repr__ method now produces example Python code for all properties of the object. The Python built-ineval method can read the provided string to reconstruct the CanvasXpress object when the proper imports for the CanvasXpress packages/modules are included in the file.

2021 June 16: CXNoteBook accepts file paths for output

The CXNoteBook class now accepts a file path at which output rendered in Jupyter will also be captured for viewing in later sessions. Until now a temporary file had be used, which remains the default behaviour.

2021 June 16: CXConfigs now accepts lists of values

The CXConfigs class can now be initialized using lists of CXConfig objects or their list/tuple equivalents (e.g., ["label", "value"]). The add method supports the same formats. Similarly, wherever the CanvasXpress class accepts a CXConfigs object during initialization or assigment a list of CXConfig or equivalent objects can be provided. This is in additon to the already supported dict collections of CXConfig value equivalents.

2021 June 16: direct DataFrame support for

The CanvasXpress class already supported None, CXData, and dict data assignments. Now raw DataFrame is supported on initialization or use of the data property.

2021 June 16: pop-up browser support

One or more charts can now be displayed in a new Web page using the default browser for the host system, assuming it is graphical (e.g., MacOS X or Windows). The A Quick Script/Console Example below illustrates the use.

2021 May, June Prior Release Summary

  • All JSON data formats now generally supported:
    1. URL:
      • Data path with standard URI formatting (e.g., http, sftp, etc.)
    2. Standard:
      • Matrix and key-pair data
      • x, y, and z attributes
      • Missing attributes calculated for easier data integration
    3. Venn:
      • Matrix and key-pair data
      • venn and legend attributes
      • Missing attributes calculated for easier data integration
    4. Network
      • key-pair data
      • node attributes and general dict/list structure
    5. Genome
      • key-pair data
      • tracks and type attributes and general dict/list structure
    6. RAW / Passthrough
      • Matrix and key-pair
      • Beyond conversion of data to JSON, effective pass-through of data
  • Python dict data direct reference in parameter with auto-coonversion to CXDictData.
  • Introduction of CXDataProfile and profiled formatting of incomplete data per graphType configuration.
  • Minor bug fixes and expansion of automated tests.
  • Jupyter CXNoteBook can now render multiple charts in the same group so as to support broadcasting.
  • CanvasXpress now supports the after_render property and initialization value to enable the afterRender attribute of CanvasXpress for Javascript objects (includes DOE dashboard support).


This package is actively maintained and developed. Our focus for 2021 is:

Immediate Focus

  • Detailed documentation and working examples of all Python functionality

General Focus

  • Embedded CanvasXpress for JS libraries (etc.) for offline work
  • Integraton with dashboard frameworks for easier applet creation
  • Continued alignment with the CanvasXpress Javascript library
  • Continued stability and security, if/as needed

Getting Started


The documentation site contains complete examples and API documentation. There is also a wealth of additional information, including full Javascript API documentation, at

A Quick Script/Console Example

Charts can be defined in scripts or a console session and then displayed using the default browser, assuming that a graphical browser with Javascript support is available on the host system.

from canvasxpress.canvas import CanvasXpress
from canvasxpress.config.collection import CXConfigs
from canvasxpress.config.type import CXGraphType, CXGraphTypeOptions
from import CXDictData
from canvasxpress.render.popup import CXBrowserPopup

if __name__ == "__main__":
    # Define a CX bar chart with some basic data
    chart: CanvasXpress = CanvasXpress(
                "y": {
                    "vars": ["Gene1"],
                    "smps": ["Smp1", "Smp2", "Smp3"],
                    "data": [[10, 35, 88]]
    # Display the chart in its own Web page
    browser = CXBrowserPopup(chart)

Upon running the example the following chart will be displayed on systems such as MacOS X, Windows, and Linux with graphical systems:

A Quick Flask Example

Flask is a popular lean Web development framework for Python based applications. Flask applications can serve Web pages, RESTful APIs, and similar backend service concepts. This example shows how to create a basic Flask application that provides a basic Web page with a CanvasXpress chart composed using Python in the backend.

The concepts in this example equally apply to other frameworks that can serve Web pages, such as Django and Tornado.

Create a Basic Flask App

A basic Flask app provides a means by which:

  1. A local development server can be started
  2. A function can respond to a URL

First install Flask and CanvasXpress for Python:

pip install -U Flask canvasxpress

Then create a demo file, such as, and insert:

# save this as
from flask import Flask

app = Flask(__name__)

def canvasxpress_example():
    return "Hello!"

On the command line, execute:

flask run

And output similar to the following will be provided:

Running on (Press CTRL+C to quit)

Browsing to will result in a page with the text Hello!.

Add a Chart

CanvasXpress for Python can be used to define a chart with various attributes and then generate the necessary HTML and Javascript for proper display in the browser.

Add a templates directory to the same location as the file, and inside add a file called canvasxpress_example.html. Inside the file add:

        <meta charset="UTF-8">
        <title>Flask CanvasXpress Example</title>
        <!-- 2. Include the CanvasXpress library -->
        <!-- 3. Include script to initialize object -->
        <script type="text/javascript">
            onReady(function () {
        <!-- 1. DOM element where the visualization will be displayed -->

The HTML file, which uses Jinja syntax achieves three things:

  1. Provides a location for a <div> element that marks where the chart will be placed.
  2. References the CanvasXpress CSS and JS files needed to illustrate and operate the charts.
  3. Provides a location for the Javascript that will replace the chart <div> with a working element on page load.

Going back to our Flask app, we can add a basic chart definition with some data to our example function:

from flask import Flask, render_template

from canvasxpress.canvas import CanvasXpress
from canvasxpress.config.collection import CXConfigs
from canvasxpress.config.type import CXGraphType, CXGraphTypeOptions
from import CXDictData

app = Flask(__name__)

def canvasxpress_example():
    # Define a CX bar chart with some basic data
    chart: CanvasXpress = CanvasXpress(
                "y": {
                    "vars": ["Gene1"],
                    "smps": ["Smp1", "Smp2", "Smp3"],
                    "data": [[10, 35, 88]]

    # Get the HTML parts for use in our Web page:
    html_parts: dict = chart.render_to_html_parts()

    # Return a Web page based on canvasxpress_example.html and our HTML parts
    return render_template(

Rerun the flask app on the command line and browse to the indicated IP and URL. A page similar to the following will be displayed:

Congratulations! You have created your first Python-driven CanvasXpress app!

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