Table of Contents


  1. Preface
  2. Introduction to Informatica Data Engineering Integration
  3. Mappings
  4. Mapping Optimization
  5. Sources
  6. Targets
  7. Transformations
  8. Python Transformation
  9. Data Preview
  10. Cluster Workflows
  11. Profiles
  12. Monitoring
  13. Hierarchical Data Processing
  14. Hierarchical Data Processing Configuration
  15. Hierarchical Data Processing with Schema Changes
  16. Intelligent Structure Models
  17. Blockchain
  18. Stateful Computing
  19. Appendix A: Connections Reference
  20. Appendix B: Data Type Reference
  21. Appendix C: Function Reference

Python Transformation Components

Python Transformation Components

The Python transformation uses resource files and the Python code to define transformation functionality.
The Python transformation contains the following components:
Resource File
A file that contains the resources that you access in the Python code.
The file can be a pre-trained model that has been trained on a larger data set outside the Developer tool. You can use the pre-trained model to classify data or make predictions based on the data that you pass to the Python transformation. You can access the pre-trained model in the Python code.
Python Code
The Python code that the Python transformation uses to process data that you pass to the transformation. When you write Python code, you might reconstruct input variables, load a pre-trained model, and define output variables.


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