Articles on: Transforms

Clean data

The Clean data step removes leading or trailing spaces and other unwanted characters (letters, numbers, or punctuation) from any rows of data you'd like.

Clean data


Our input data has a column of "Text" with various leading trailing spaces and punctuations.

By using the Clean data step, we can easily remove spaces to clean up the "Text" column.


When you first connect data into this step, Pick the dataset and columns(s) to clean and your desired options to clean up (like "Remove all spaces").

The available cleaning options are:

Remove all spaces
Remove leading spaces
Remove trailing spaces
Remove all punctuations
Remove all characters
Remove all numbers
Remove Special Characters

You can combine the cleaning options if needed.

In our above screenshots, we selected the "Text" column to apply our cleaning rules to and selected three cleaning options: Remove leading spaces , Remove trailing spaces and Remove all punctuations. As seen in the screenshot below, this cleaned up varying leading, trailing spaces and punctuations that were in the "Text" column's rows.

Updated on: 20/09/2022

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