When drag-and-drop hits its limit, code picks up the slack.
Today’s world is rapidly moving towards data, and you require both powerful and user-friendly tools. KNIME features a clear visual interface that enables users to automate complex tasks with minimal coding. What happens when your data needs are so demanding to be cutting-edge or so specialized that your logic extends beyond KNIME nodes? This is where Python, the superstar of advanced analytics and machine learning, comes in handy.
The true strength of data work today is the ability to combine the low-code ease of KNIME with the flexibility of Python. This combination makes it easy to transform data, construct models, and create insightful visualizations.
In this blog, we will demonstrate the benefits of using Python on the KNIME platform, how to configure and use it, and some of the major troubleshooting points. Get ready to bridge the gap between visual simplicity and programmatic power, accelerating your data-driven success.
Why Combine Python and KNIME?
The combination of Python in the KNIME Analytics Platform provides an attractive mix of productivity and expandability, supporting a variety of technical requirements across data teams.
The Value of Low-Code + Code-Based Flexibility
This interoperability is a game-changer. Python developers can package their complex libraries into KNIME components that can be used by no-code users, essentially productizing their code. This brings enhanced features throughout an organization, enabling collaboration, breaking down team barriers, and rapidly implementing data solutions.
What this looks like in real life
Custom Logic: Healthcare teams validating patient records using ICD rules in Python.
Seamless Model Deployment: Finance teams deploy regression models and instantly visualize results.
Unleash Advanced Visualizations: Data analysts are releasing sophisticated visualizations through Matplotlib, Seaborn, and Plotly, beyond anything native KNIME charts can do.
Enhanced Marketing Practices: The marketing team uses an API in Python to enhance their data in KNIME.
Where phData sees impact in client projects
At phData, we have built an extensive track record of providing high value to clients through integrating KNIME and Python. This mixed methodology allows quick prototyping and custom Python code in case of complicated requirements. We use KNIME componentization and Python integration to make scalable solutions. This guarantees effective, intuitive workflows that leverage data impact for a wide variety of teams.
Getting Started: Prerequisites and Setup
Installing the KNIME Python Integration Extension
First, you must install the extension of KNIME Python Integration within the KNIME Analytics Platform. This is done by going to Menu > Install Extensions… and searching for Python Integration. The extension can be subsequently installed by dragging it into the KNIME Hub, the workbench, or by choosing it in the list and following the installation steps. The installation usually requires restarting the KNIME Analytics Platform to complete the process.
Configuring Python Environments in KNIME
Bundled Environment
For immediate usage, we can use the bundled Python environment, which allows for a quick start without extra installation. However, it may lack specific packages for advanced tasks.
Conda Environments (Recommended)
Conda (e.g., Miniconda) is highly recommended for different packages and managing versions. Install Conda separately, then configure its path in KNIME (Preferences > KNIME > Python). From there, you can select Conda and select the available environment from the drop-down, click Apply, and close to create new Conda environments, open Anaconda prompts, and create new environments.
Manual Configuration (Without Conda)
To obtain maximum control, manually configure a Python environment by pointing KNIME to a specific script. This involves tedious manual installation of the necessary packages such as py4j, pyarrow, numpy, pandas, knime-python-base, and knime-python-scripting.
Setup Method | Pros | Cons | Best Use Case |
|---|---|---|---|
Bundled | Quick start, no external setup required | Limited pre-installed packages; may not meet all project needs
| Rapid prototyping, basic scripting, and initial exploration |
Conda | Robust package and environment management, easy dependency propagation | Requires external Conda installation and initial configuration | Complex projects, reproducible workflows, and managing specific library versions |
Manual | Full control over Python installation and packages | Requires manual installation of all dependencies, more prone to configuration errors | Specific enterprise restrictions, highly customized environments |
Example: Setting up a Virtual Environment for Use in KNIME (Conda)
Install Miniconda: From the official Anaconda website, download Miniconda according to your operating system, which is a minimal installer of Conda.
Create a new Conda environment: Open Anaconda Prompt and run the following command to create a new environment with KNIME recommended packages and also specify Python version (e.g., Python 3.11):
conda create --name myknime_env -c knime -c conda-forge knime-python-scripting=5.3 python=3.11
Activate the environment and install additional packages: After the environment is created, you can activate it and install any Python libraries required for your project (e.g,
pandas,matplotlib,scikit-learn).
conda activate myknime_env
pip install pandas matplotlib scikit-learn
Configure KNIME to use this environment: Open KNIME and navigate to
Preferences > KNIME > Python and select Conda as the Python environment configuration type and provide the path to your Miniconda installation directory. From the dropdown list, select the newly createdmyknime_env.
Using Python Nodes in KNIME
KNIME has specialized nodes that are capable of providing more powerful interfaces to Python code integration:
Python Script Node: The most general node to run arbitrary Python code, which takes inputs and produces transformed tables, objects, or images. It has an integrated Script Editor with auto-completion and Tooltips.
Python View Node: Designed for interactive visualisations or HTML views, it supports Matplotlib, Seaborn, Plotly, and other libraries to create customised charts and rich content, with a live Output Preview.
Legacy Nodes (Python Learner, Python Predictor, Python Source): These nodes were previously used for model training/prediction and data generation/reading.
For the new workflows, prefer the newer Python Script and Python View nodes. Thanks to Apache Arrow, performance and data transfer efficiency are enhanced, making it more suitable for working with large datasets.
Best practices for writing and maintaining reusable scripts
Modularity and Components: Enclose Python scripts within reusable KNIME components and metanodes to bundle logic and to make complex functionalities accessible to all users.
Environment Propagation: Utilize the Conda Environment Propagation node to manage and propagate Python environment dependencies, ensuring consistent package availability and reproducibility.
Clear Inputs/Outputs: Define explicit input and output ports using the knio module for clear data flow, enhancing readability and predictability.
Error Handling & Logging: Implement robust error handling and leverage the KNIME console for logging informative messages, aiding in effective debugging.
AI-Assisted Coding: Leverage the Ask K-AI feature in the Python Script node for AI-assisted code generation, accelerating development and democratizing scripting
Real-World Use Cases at phData
At phData, the combination of KNIME and Python has made a bigger difference for our clients.
Automating data quality checks: For a healthcare provider, we used Python and KNIME to validate medical codes, catch lab values that were out of range, and flag inconsistencies in patient records.
Embedding machine learning models: For a fintech company, we embed custom Python-trained ML models, including deep learning, into KNIME workflows. This enables seamless deployment for real-time predictions and actionable insights
Custom ETL steps: Python enables custom ETL steps for complex parsing, API integration, and data restructuring. This extends KNIME’s native capabilities for tailored data preparation.
Snowflake Integration: We integrate Python with Snowflake for advanced interactions like asynchronous queries and custom data governance. This optimizes data pipelines within the Snowflake ecosystem.
Common Challenges and Troubleshooting Tips
Environment Conflicts: To solve this, use Conda environments exclusively for KNIME and avoid global installs that could create version mismatch.
Debugging Pain: To solve this, first use
print()orlogger.info()to inspect values at key steps, so that debugging can be done faster and before debugging inside the KNIME test script outside Knime, and then troubleshooting.
Performance Bottlenecks: For large datasets, consider in-database processing and handle only the required columns and rows at each step. Optimizing performance requires a holistic approach, considering Python code efficiency, KNIME settings, and underlying infrastructure for optimal resource utilization.
Conclusion
Combining Python with KNIME gives your workflows the flexibility and precision that helps to handle real-world complexity. From building machine learning models to visualizing data and automating custom logic, Python fills the gaps where native KNIME nodes end.
Let’s build smarter workflows together.
At phData, we specialize in integrating Python with KNIME, Snowflake, and modern cloud platforms to drive efficiency and insight.
FAQs
Can I write Python once and reuse it across workflows in KNIME?
Yes! In KNIME, we can create either meta-nodes or components. We wrap our Python nodes into a component and save them. Then, it can be reused in other workflows, just like a function.
Is Python mandatory to learn for KNIME users?
Not at all. KNIME is fully functional without Python. But basic Python knowledge can unlock advanced use cases and give you a huge edge in customization, modeling, and automation
What are the system requirements to use Python in KNIME?
If your system can run KNIME smoothly, it can almost certainly handle the Python integration. The main system requirement for using Python with KNIME is a correctly configured software environment, not specific hardware. You need a Python installation with key libraries like pandas and pyarrow, preferably within a dedicated virtual environment (conda or venv). Finally, you must point KNIME to your environment’s executable path in Preferences > KNIME > Python
Which Versions of Python Are Compatible with KNIME?
Python compatibility depends entirely on your specific KNIME Analytics Platform version. Generally, modern KNIME versions support Python 3.8 through 3.11, but you must verify this for your system. To find the correct compatibility list, check your KNIME version under Help > About KNIME and consult the official KNIME Python Integration documentation online.
