• 14 March 2024
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FME Hub user haider just uploaded a new transformer to the FME Hub.

Transposes attributes so that the values in any selected Column Attribute are transposed into their own individual columns. The corresponding selected Row Attribute(s) will also be transposed in accordance with the selected column and its attributes. Aggregation works by grouping the results into desired Group-By categories. This transformer also allows you to add a suffix to the end of the newly transposed column names in order to maintain clarity and distinction between previously defined data.

### Examples

The table below shows the original input features:

- Column Attribute -> Allows you to choose one column to tranpose their values into individual columns. (REQUIRED)

- Row Attribute(s) -> Allows you to choose multiple columns so that their values can be transposed under the new column headers after transposition. (REQUIRED)

- Group By Attribute(s) -> Allows you to specify the columns that need to be aggregated. (OPTIONAL)

- Column Name Suffix -> Allows you to add a suffic to the end of the newly transposed column names. (OPTIONAL)

![Example Tables](

### Notes

- The _AttributeTransposer_ creates attributes for _every_ feature that passes through it. Therefore, please use this transformer with great care.

- The transformer will not preserve geometry. It only works on tabular data.

- New attributes are created dynamically and should be exposed using an [AttributeExposer](, for example.

- All original attributes will still be exposed on the output feature, but will not have a value, except for the _Columns Attribute_. This attribute will store all the row attributes (i.e. "row headers") in the order that the user has selected.

- This transformer has been tested on Python 2.7 and 3.4.

- If you notice a bug or desire a new feature, please [contact me]( or make a [pull request](!

- Released under [GNU General Public License v3.0](

Would you like to know more? Click here to find out more details!

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