Dear dataiku community,
question from a non-developer: there is a processor to transform rows into columns. The thing is that I would like to do the opposite, as openrefine allows to do it. I have not found a processsor that fit that need.
@fclavert , I will admit that I don't know of a simple way to do this with a visual recipe (though one may exist). While you wait for others to respond here is a very simple python recipe that should give you what you are after.
# -*- coding: utf-8 -*- import dataiku # Read recipe inputs input_dataset = dataiku.Dataset("input_dataset") input_dataset_df = input_dataset.get_dataframe() # Write recipe outputs transposed = dataiku.Dataset("transposed") transposed.write_with_schema(input_dataset_df.transpose())
Here, "input_dataset" should be changed to match the name of your actual dataset. You might want to try with a small-ish dataset before throwing a large dataset at it.
@fclavert so first off, I feel like a complete dolt. Not having used the Transpose recipe I jumped straight to finding a solution. It just dawned on me that transpose rows to columns should be the same as columns to row. (transpose means to make rows columns and vice-versa)
Using the Transpose calculation is not particularly intuitive (at least to me). But I found the following would allow me to transpose my data.
1. Use the "Transpose Rows into Columns" but Specify a NONEXISTENT column (something like fake_column). That gives me a warning but will make all the columns rows and vice versa.
2. Delete and rename columns as you see fit.