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 Ports

Python Transformation Ports

A Python transformation can have input and output ports.
To create and edit ports for a non-reusable Python transformation, use the
tab in the editor. To create and edit ports for a reusable Python transformation, use the
view in the editor. After you add ports to the transformation, you can use the port names as variables in the Python code.
Use the following guidelines to create input and output ports:
  • The port name can contain only ASCII characters.
  • The port name cannot be a Python keyword. For example, do not use port names such as
    import, global
  • The port name cannot be
  • You cannot configure user-defined default values in output ports in the Python transformation. Set user-defined default values in the Python code.
    For example, you can write
    output_port = 'value'
    to set the default value
    for the output port
You can enable one or more input rows as partition keys. The Data Integration Service uses the partition keys to repartition the data before the code runs. If you do not select an input row as a partition key, the data is processed using its default partitioning scheme.


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