Python View

This node allows executing a Python script in a local Python 3 environment. The script can read data from the input ports and must provide an output view.

Use the settings menu in the top right of the editor window to configure which Python environment to use. This environment can either be the one configured in the KNIME preferences under File → Preferences → KNIME → Python or be specified via a flow variable, as described in the KNIME Python Integration Guide. By default, the Python environment that is bundled with KNIME will be used so that you can get started right away.

Multiple input ports of various types can dynamically be added to the node and used in the script. See sections Ports and Options → Script → Editor below for a description of all available inputs, and how they can be accessed in the Python script. Make sure to set the knio.output_view variable to a view created with any of the knio.view functions. You can see a preview of the generated view in Output preview tab of the panel right of the editor. Furthermore, the node allows to import Jupyter notebooks as Python modules via the knime.scripting.jupyter module that can be imported in the script. See the API Documentation for a detailed description of the full API.

The node brings significant performance improvements over the nodes of the legacy KNIME Python Integration and enables working with larger-than-memory data. More details on how to transition to the node's new scripting API can be found here. There are also example workflows that further illustrate its use here as well as example templates on the Templates tab of the node.

AI Code Assistance (Labs):

If the KNIME AI Assistant (Labs) extension is installed, the button on the lower left of the code editor provides access to AI assisted code generation and modification. Here you can enter commands in natural language and our code assistant will replace or modify your current code according to your prompt. The more precisely you describe what results you expect, the better the results will be. The AI will suggest modifications and show them to you in a diff-view (differences between your current code on the left and the suggested code on the right) which you can apply or query another response by entering a new or refined prompt.

Please consider the following prerequisites before using this node:

The packages py4j and pyarrow, are required to be installed in your Python 3 environment in specific versions. We recommend to create a new Conda environment that contains these packages by running conda create -n new_environment -c knime -c conda-forge knime-python-scripting or via File → Preferences → KNIME → Python as described in the KNIME Python Integration Guide.
The Columnar Backend should be enabled for best performance.

Options

Script

Editor: The editor allows to configure the Python script that will later be run when this node is executed. In the script, use the knime.scripting.io module (imported as knio by default) to access the node's input data and populate its output view as described in the Ports and Views sections below. Use the knio.flow_variables dictionary to access input flow variables by name and to add new output flow variables by name.

The buttons Run all and Run selected lines allow to execute the script, or selected parts of it, right in the dialog without having to execute the entire node. The outputs of an execution can be inspected via the temporary variables view on the right side of the editor. Please note that while Run all always runs the script from scratch, clicking Run selected lines uses status of the last run as starting point and thus the temporary variables are accessible when running the selected lines. You can use Reset temporary variables to reset the current state before an execution if you want to make it independent of all previous executions. Executions of the entire node, outside of the dialog, are always independent of one another.
Temporary Variables: The pane on the right side of the editor panel lists the currently defined identifiers in the running Python instance. Reset temporary variables removes the outputs of all previous script executions.
Output Preview: This tab in the pane on the right side of the editor panel shows the output view that was generated by the last script execution that was triggered with Run all or Run selected lines.
Console: The console below the editor panel displays the standard output and standard error of the running Python instance as well as other information.

Settings Menu

Set Python executable

Allows to choose whether to obtain the Python 3 executable from the KNIME Preferences or from flow variable.

Python environment selected in KNIME preferences: use the Python executable configured under File → Preferences → KNIME → Python to execute the node.
Conda environment with Conda Environment Propagation node: use the Conda environment propagated by the selected Conda flow variable to execute the node. The propagated environment must be a Python 3 environment.

Input Ports

: No description for this port available.
…: An input object. Input objects are automatically unpickled for you. You can access them via knio.input_objects. For example, the first input object can be accessed like this: knio.input_objects[0].
…: An input table. You can access input tables via knio.input_tables. For example, the first input table can be accessed like this: knio.input_tables[0]. Each table is an instance of type knime.api.table.Table. Before being able to work with the table, you have to convert it into, for example, a pandas.DataFrame or a pyarrow.Table. Please refer to the KNIME Python Integration Guide to learn how to do this, or take a look at one of the templates on the Templates tab of the node.

Output Ports

…: An output image. The image can be populated via knio.output_images[0] in your script. To populate the output image, assign it a value like this knio.output_images[0] = <value>. The assigned value must be a bytes-like object encoding a PNG or SVG image. If no value is assigned to knio.output_images[0] the node will try to generate an SVG or PNG image from knio.output_view.

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Views

Output: The output view created by the Python script. Use knio.view(obj) function to create a view and assign to knio.output_view.

Workflows

Developers

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Installation

To use this node in KNIME, install the extension KNIME Python Integration from the below update site following our NodePit Product and Node Installation Guide:

v5.3

A zipped version of the software site can be downloaded here.

Plugin provider: KNIME AG, Zurich, Switzerland

Plugin version: 5.3.2.v202409031127

On NodePit since: 2024-07-09

Last update: 2024-10-17

KNIME versions: Since v5.3

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