Please help: parsing out anything NOT listed in a column

[waqaraphoto]

Hi everyone!

Brand new to the world of data analytics using alteryx and I absolutely love it! Trying to solve a very basic issue. The sections column has many tags that I want.. But it also has a bunch of gobbledygook that Id' like to remove..  Now luckily everything is comma delimited. I have gone as far as creating a data cleanser that converts everything to lowercase. My next steps would be to remove Everything that is not in a list of the values I would like to keep as a new column called "Section New" for example. Can someone give me a simple way to do this and which tools i should be using? 

I would imagine that Id' want to input my fields that I would like to keep.. and somehow have something that says "If this value is not in this list of comma separated values..then remove it, Otherwise keep it, or something along those lines. 

An example of a value in the data below that I would like excluded from my comma separated values within Section is "Managed by pm","pm:jerry amo" from row 46..

I would imagine the regex parse tool can be used for this.. But I do not know regex.

THANK YOU Alteryx Community!

[fmvizcaino]

Hi waqaraphoto,

You are absolutely right, the regex tool can help you achieve what you want. The only concern here is to identify all the different patterns you want to remove, if there are a lot os different patterns, we would need to think about a combination of tools to achieve the result you want.

In order to help you, would you be able to share a sample of your data and some of the different patterns you would like to remove?

Best,

Fernando Vizcaino

[waqaraphoto · Accepted solution]

Hi Fernando!

Thank you for your response. I was able to fix this by using a find and replace in which i assigned a column to the tags that I wanted removed, and then another that had blank spaces in it. This took out everything I wanted gone and left me with the famous repeated commas between the blank spaces where the removed fields once lived. I used a cleanse for that and then a Regex_Replace to remove commas. Its not quite the solution I'm after as its just a reversal that'll still require cleaning up unwanted values that come in through our datalake but it'll do for now. 

Thank you for your response!

Waqar