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Parse email addresses

The Parse email addresses (beta) step separates columns of email addresses into broken down, smaller category parts.


To start, your input data should include a column of email addresses.

Your output will include your original data coupled with four newly appended columns: "Original User", "Domain", "Cleaned User", and "Cleaned Email".

Default settings

To begin using this beta step, connect your table to the Do this first step at beginning of the snippet. There, you will rename your email address column by selecting it in the Column dropdown.

No further action is needed, and your output of the final step in that snippet will have your cleaned, parsed email addresses. Let's briefly review how we get there.

The next step of that snippet, being Remove spaces, finds any spaces in your email address data and removes them for you automatically.

Next, the Get Domain step splits out the domain by identifying the @ symbol in the  email address data and logs the domain portion in a new "Domain" column.

Similarly, the Get user step performs the inverse action, where it grabs all characters before the @ symbol and places them in a new "Original User" column.

The Clean User step looks for any email addresses that use +tags and removes them. If a +tag is found tied to the "Original User" column data, a +tag will be temporarily recorded in the "User2" column.

The Merge User step combines the "User2" column with the "Original User" column to create one "Cleaned User" column, where all of the user names from your email addresses are listed (minus +tags).

The Select columns step removes the temporary "User2" column.

The final step of this beta step snippet is the Rebuild email step which takes your "Cleaned User" column and combines it with an @ symbol and the original "Domain" column, giving you not only a parsing of the email address, but a clean, rebuilt, and complete email address as well.