Python Integration

The Python integration lets you execute scripts within KNIME workflows, providing direct access to the Python ecosystem for data manipulation, analysis, and visualization.

Tutorials

These guides provide end-to-end implementations so you can learn by building functional examples.

• Python Scripting Quickstart: Install the extension and run your first script using a temporary environment.
• Data App Development Using Plotly: Build interactive Plotly visualizations and deploy them to KNIME Hub.

Data Processing and Scripting

The API of the Python Integration can be found in the Python Script API reference.

• K-AI Scripting Assistant: Generate, explain, and debug code using the integrated AI assistant.
• Data Type Conversion: Map data between KNIME tables and Pandas or PyArrow formats.
• Batch Processing: Process large datasets that exceed your available memory.
• Jupyter Integration: Load and execute functions from your external .ipynb files.

Environment Management

KNIME uses Pixi for environment isolation. Use these resources to configure and manage your local setup.

• Pixi Configuration: Initialize the Python Environment Provider and create portable environments.
• Dependency Management: Install and manage the specific Python libraries your project requires.
• Environment Persistence: Configure permanent storage to avoid re-downloading packages.
• Conda Migration: Move your legacy "Conda Environment Propagation" nodes to the Pixi-based provider.

Architecture and Concepts

Understand the internal logic behind backend execution and filesystem organization.

• Pixi Backend Architecture: Learn how lock-file systems differ from traditional Conda environments.
• Storage Logic: How KNIME manages directory structures and OS-specific paths.
• Python Environments on Executors: Understand the three environment types available when running workflows on KNIME Executors.

Alternative Configurations

Options for legacy environments, manual installations, and third-party managers.

• Conda and Miniforge Setup: Configure workspace-wide environments via Conda.
• Conda Environment Propagation: Bundle Conda environments directly within your workflows.
• Manual Python Configuration: Use local Python binaries or your own custom start scripts.
• Legacy Node Migration: Port workflows from older Python nodes to the current framework.

Reference and Troubleshooting

Technical specifications and fixes for common configuration issues.

• Python Scripting API: Full reference for the knio and knjupyter modules.
• Bundled Packages: Review the libraries included in your default environment.
• Executor and Hub Configuration: Set knime.ini flags, preferences, and REST API parameters.
• Troubleshooting Guide: Find solutions for SSL errors, path limits, and execution failures.