> 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 by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.pentaho.com/pdia-data-integration/10.2-data-integration/pdi-transformation-steps-reference-overview/python-executor.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. 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.