Hub
Pricing About
WorkflowWorkflow

Python Transform Components Example

Python TransformationPythonPython NormalisationPython ComponentPython Production
+1
mpattadkal profile image
Draft Latest edits on 
May 3, 2021 10:18 PM
Drag & drop
Like
Download workflow
Workflow preview
This workflow is a simple example of using the Components Python Transform and Python Transform (Apply) The Component Python Transform uses KNIME Python Integration for normalisation of columns in trainset and outputs them along with the pickled file containing the preprocessing object. The Component Python Transform (Apply) can be used to perform the normalisation on the test set as per the parameters passed on by the pickled object The pickled file can also be written to specific location and used in Production workflow by loading the pickled file using the Python Transform(Load) Component which can further be connected to Python Transform(Apply) Component to get transformed data.

External resources

  • How to Set Up the Python Extension
Loading deploymentsLoading ad hoc jobs

Used extensions & nodes

Created with KNIME Analytics Platform version 4.4.0 Note: Not all extensions may be displayed.
  • Go to item
    KNIME Base nodesTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

    knime
  • Go to item
    KNIME JavasnippetTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

    knime
  • Go to item
    KNIME Python Integration

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

    knime
  • Go to item
    KNIME Quick FormsTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

    knime

Legal

By using or downloading the workflow, you agree to our terms and conditions.

KNIME
Open for Innovation

KNIME AG
Talacker 50
8001 Zurich, Switzerland
  • Software
  • Getting started
  • Documentation
  • Courses + Certification
  • Solutions
  • KNIME Hub
  • KNIME Forum
  • Blog
  • Events
  • Partner
  • Developers
  • KNIME Home
  • Careers
  • Contact us
Download KNIME Analytics Platform Read more about KNIME Business Hub
© 2025 KNIME AG. All rights reserved.
  • Trademarks
  • Imprint
  • Privacy
  • Terms & Conditions
  • Data Processing Agreement
  • Credits