> For the complete documentation index, see [llms.txt](https://docs-v3.toucantoco.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs-v3.toucantoco.com/data-management-in-datahub/datasets-in-toucan/preparing-data/overview-of-youprep-tm/column-header/convert-columns-data-types.md). # Convert columns data types The Convert columns data types allows to cast column data types. ### Step parameters * `Convert columns` **column(array)\***: the columns to convert * `To data type` **type(string)**: the data type to convert into, either `integer`, `float`, `text`, `date` or `boolean` ### Example **Input**

Column header - convert input

**Configuration** ```json { "columns": ["id", "boolean_column"] "data_type": "text" } ``` **Output**

Column header - convert output

{% hint style="info" %} In a effort to harmonize as much as possible the conversion behaviors, for some cases, in NativeSql our implementation casting works otherwise than the CAST AS method. Precisely, when casting float to integer, the default behavior rounds the result, other languages truncate it. That’s why the use of `TRUNCATE` was implemented when converting float to int. The same implementation was done when converting strings to int (for date represented as string). As for the conversion of date to int, we handled it by assuming the dataset’s timestamp is in `TIMESTAMP_NTZ` format. {% endhint %} --- # 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-v3.toucantoco.com/data-management-in-datahub/datasets-in-toucan/preparing-data/overview-of-youprep-tm/column-header/convert-columns-data-types.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.