> For the complete documentation index, see [llms.txt](https://docs.pentaho.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.pentaho.com/pdia-data-integration/10.2-data-integration/pdi-transformation-steps-reference-overview/python-executor.md).

# Python Executor

The Python Executor step leverages the [Python](https://www.python.org/) programming language as part of the data integration pipeline from within PDI. This step helps developers and data scientists take advantage of the strengths of the Python's versatile programming language to develop predictive solutions using existing PDI steps. Instead of writing code to connect to relational databases and Hadoop file systems, and to join and filter data, PDI allows the developer to focus their coding efforts on the data science-driven algorithms.

This step offers several options for execution. You can choose to map upstream data from a PDI input step to generate data or have the Python script generate its own data. You can opt to send all rows to Python at once, or send rows one-by-one.

When you deliver the input row-by-row, the field values of each incoming row are mapped to separate variables containing built-in types, such as numerics, strings, and Booleans. You set the names of these field values which are then available within the Python script.

When you send all rows, the data sent is considered a dataset. Python stores the dataset in a user-specified variable that kicks off your user-defined Python script. You can use the pandas DataFrame, a NumPy array, or the Python list of dictionaries as the data structure for datasets transferred into Python.

**Note:** This step only supports the CPython runtime.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.pentaho.com/pdia-data-integration/10.2-data-integration/pdi-transformation-steps-reference-overview/python-executor.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.