Hello,
I am trying to figure out the best way to cleanse or parse emails for their login details.
The emails are presented as "jc638@ntrs.com" and I want to erase the "@ntrs.com" and keep the rest.
I am having issues because what I am keeping and erasing are both letters.
Would I be using a data cleansing tool or something else?
@jc638 if you use the RegEx tool in parse mode, and type in the following expression, this will just keep everything before the @ symbol and remove everything else in the newly created field:
(.+)@.*
@jc638 One way of doing this with the substring function
SubString([email],0,FindString([email], '@'))
You could also try Text-to-Columns Tool with the delimiter as @. Then the Select Tool can change the name and remove the superfluous column. But you can do it in one tool with @DataNath's suggestion.
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