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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

Recent Enhancements

2021 July 6: Extensive examples for Jupyter Notebooks

Hundreds of chart examples based on the CanvasXpress site examples are now available in at:


2021 June 30: Example code generation utility

CanvasXpress for Python now includes, which includes functions for creating example Python code to define and display charts. generate_canvasxpress_code_from_json will accept a reproducible research JSON str and generate the corresponding Python code, and generate_canvasxpress_code_from_json_file will accept a file path reference to a reproducible JSON to achieve the same thing. Find the functions at

2021 June 30: Support for raw text data

CanvasXPress for Javascript can accept text data. CXTextData has been added as a means by which a str value can be directly provided to the CanvasXpress for Javascript constructor, and along with that value all assumptions about transformation or error handling.

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 June 11: afterRender support

CanvasXpress objects accept the afterRender property, which defines a list of functions and parameters for each function. This list is executed once the canvas element has been updated by the creation Javascript. CanvasXpress for Python now supports this with the addition of the after_render property.

2021 June 11: Jupyter rendering supports bundled charts

CanvasXpress supports data broadcasting, which permits charts on the same Web page with the same data references to synchronize data selections and refreshes with no or minimal work on the developer's part. When used with Jupyter Notebooks, CanvasXpress for Python rendered each chart in its own Web container, which prevented broadcasting from working. With this release, CXNoteBook now permits multiple charts to be included in the same Web container.

2021 June 9: Network, Genome, and Raw JSON data now supported

The CanvasXpress JSON data format allows for network diagram specific data. CXNetworkProfile has been added to support network JSON data. A minimum verification of data type (only key-pair is permitted) is provided for a Python tier sanity check. Multiple network data formats are allowed by the Javascript library and enhanced validation will be built over time.

The CanvasXPress JSON data format allows for genome diagram specific data. CXGenomeProfile has been added to support genome JSON data. Only key-pair data is permitted, and it is verified for the tracks and type attributes in terms of presence and data type. Variations for track elements are permitted by the Javascript library and enhanced validation will be built over time.

CXProfiledData defaults to using no profile, and upon being used in the CanvasXpress render_to_html() function are assigned a profile if none is associated per the graphType configuration if present. A CXData object with no profile provides its dict-form of data as-is. To avoid profile assignment, the CXRawProfile can be assigned to data objects. This profile will pass-through the dict-form unmodified and avoid default profile assignment by the CanvasXpress object. This allows advanced data formatting or support for unknown data formats by advanced users or development releases of the CanvasXpress Javascript library.

2021 June 7: Venn JSON data now supported

The CanvasXpress JSON data format allows for venn diagram specific data. CXVennProfile has been added to support venn JSON data.

Additionally, render_to_html_parts now checks as to whether a profile has been assigned to data objects, and if not then per the graphType config parameter assigns a render profile.

2021 June 3: x, z, and cors JSON data now supported

The CanvasXpress JSON data format allows for x and z attributes to provide metadata for columns and rows, respectively. Correlation diagrams also accept the y[cors] attribute for pre-calculated correlation data.

The CXStandardProfile now explicity supports x and z attributes, and will provide essential verification for alignment with respective y components.

The CXStandardProfile now explicity supports y[cors] data in addition to y[data] and will handle metadata defaults for y[vars] and y[smps] accordingly. This brings cxStandardProfile into full compliance with typical JSON data objects.

Note that correlation data is not calculated for matrix data types, but CanvasXpress for Javasxcript will calculate those values for the referenced matrix in the JSON data if a correlation chart is indicated.

2021 May 28: width, height, and canvas properties

The CanvasXpress object now accepts dedicated width, height, canvas properties.

width and height replace the now-deprecated element_width and element_height properties, and these are expected to be the final names used for each. Values for each are used in the <canvas> element generated for use in HTML, and they affect the render container sizes when used in conjunction with contexts such as Jupyter Notebooks.

canvas tracks CXConfig values that become attributes of the generated <canvas> element. In this manner attributes such as class or style can be calculated and managed at the Python tier.

See the documentation and examples for detailed usage.

2021 May 28: dict and tuple values now supported for CXConfigs

The CanvasXpress class uses CXConfigs to track configuration parameters for the chart and <canvas> element. These now accept dict and tuple values for more convenient initialization of the CanvasXpress object.

See the documentation and examples for detailed usage.

2021 May 21: CXUrlData added

CanvasXpress accepts URL references to files or endpoints with properly formatted JSON data. CXUrlData has been added to support URL passthrough to the CanvasXpress Javascript, along with some validation ability at the Python tier.

2021 May 18: CXDataProfile added

CanvasXpress has specific requirements for data organization within a JSON so that it can be properly rendered in a chart. See the CanvasXpress documentation for additional information.

Data generated or provided at the Python tier might not satisfy those requirements, especially where matrix data is concerned. CXDataProfile has been added as a component to facilitate proper JSON formatting when providing the data to the rendered CanvasXpress Javascript. CXData has been enhanced to make use of CXDataProfile.

The CanvasXpress for Python documentation discusses profiles in detail, but in summary:

  • Each CXData object is provided with a CXStandardProfile that understands how to pass through or add y vars, smps, and data attributes as proper.

  • Default values for vars and smps are autogenerated if missing, provided that sufficient information in the data is available. This is especially handy for common matrix data sources typical in Python applications.

  • Validation is supported for affirming that rows and columns in data align with provided vars and smps attributes, respectively.

As such, raw data can be passed on to CanvasXpress via key-pair structures so as to keep mapping between Javascript and Python sources simple; however, custom or default formatting is now supported at the Python tier to ease integration and exploration where y attributes are not present in the source data.

Additional functionality will be added soon, such as to support x and z CanvasXpress JSON data profiles. Expect rapid enhancements in this area.

2021 May 17: Adjusted property names

To better align with CanvasXpress for Javascript, or otherwise avoid confusion, some property names were changed in the CanvasXpress class:

  • target_id has been replaced with render_to to align with the JS renderTo attribute. Functionality is identical. target_id will remain as a deprecated property through May, after which it will be removed per team convenience.

  • configs has been replaced with config to align with the JS config attribute. Functionality is identical. configs will remain as a deprecated property through May, after which it will be removed per team convenience.

  • chart_width and chart_height are replaced with element_width and element_height, respectively. These properties dictate the container size at the Python tier in application, such as the render window provided for Jupyter Notebooks. The actual CanvasXpress chart continues to use the width and height attributes per the JS documentation, and if these values result in a rendered chart larger than the element window then depending on the application context aspects such as scrollbars might appear to provide full access. The distinction is maintained for applications with non-reactive user interfaces, such as what might be typical of QT apps.