# Input tab Use this tab to make selections for moving data from PDI fields to Python variables. Decide if you want to process your data **Row by row** (standard PDI behavior) or **All rows** at once. The **All rows** option is commonly used for data frames. A data frame is used for storing data tables and is composed of a list of vectors of equal length. Because data frames combine the behavior of lists and matrices, it is well-suited for the analytical needs of statistical data. For example, data scientists may want to bring in a training dataset before an actual dataset. The training dataset can contain multiple types of data which allows for a broader scope, without the need to join data ahead of time. Now the data scientist can operate with an entire set in the training data frame. Selecting the **Row by row** option limits your input to only one type of data, limiting the record of data to a specific time and to what is being read. Selecting the **All rows** option broadens the depth and scope of your dataset. --- # Agent Instructions: 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: ``` GET https://docs.pentaho.com/pdia-data-integration/10.2-data-integration/pdi-transformation-steps-reference-overview/python-executor/options-python-executor/input-tab.md?ask= ``` The question should be specific, self-contained, and written in natural language. 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.