![Extract usernames from E-mail IDs [using LEFT and FIND formulas in Excel]](https://chandoo.org/img/f/username-from-email-id-excel-formula.png)
Today we will learn to use Excel’s LEFT and FIND formulas. But what fun it is to learn a new formula on a Tuesday?
So, we will actually learn to use these formulas to solve the problem: “extract the username from an email ID”
How is an email ID structured?
Any email ID contains 2 parts – user name and domain name.
For eg. in my email id – chandoo.d@gmail.com – chandoo.d is user name and gmail.com is domain.
So how do we get the user name out?
As you can see, username always starts at left and goes up to the symbol “@”. So, If we write a formula to fetch all the characters up to “@” symbol, it will get us the user name.
This is where LEFT() and FIND() formulas enter the scene.
What does Excel LEFT formula do?
Excel LEFT formula will let you cut a portion of text from left. For eg. =LEFT("Long",2) will give you Lo. (syntax and examples)
So, to get the email username, we need to get all the letters in the left of email ID up to the location of “@” symbol. And how do we find the position of a symbol in a text?
We use FIND formula.
FIND formula gives the location of one text in another. For eg. =FIND("do", "chandoo") will give us 5 (the location of “do” in “chandoo”).
FIND will throw an error (#VALUE!) if the text you are trying to find is not available. For eg. =FIND("peace", "world") will throw #VALUE!
Armed with these 2 formulas, now let us get that user name out of email ID
Assuming cell A1 has the email id, the formula for getting user name is =LEFT(A1,FIND("@",A1)-1)
We have to use -1 as find actually tells the position of “@” and we need all the letters up to “@”, but not “@”.
This is how it works:
Your homework:
- How would you extract the domain out of email ID? (Hint: there is a right formula for everything)
Use comments to write your answers. Don’t cheat.
Learn more excel formulas:
- 51 Excel Formulas in Plain English – Syntax, Examples and Explanation
- Excel Formulas & Working with Text
- Excel Formula Examples & Tutorials
- Learn Excel Formulas using my e-book – it is in a fun format & easy to understand
One Response to “SQL vs. Power Query – The Ultimate Comparison”
Enjoyed your SQL / Power Query podcast (A LOT). I've used SQL a little longer than Chandoo. Power Query not so much.
Today I still use SQL & VBA for my "go to" applications. While I don't pull billions of rows, I do pull millions. I agree with Chandoo about Power Query (PQ) lack of performance. I've tried to benchmark PQ to SQL and I find that a well written SQL will work much faster. Like mentioned in the podcast, my similar conclusion is that SQL is doing the filtering on the server while PQ is pulling data into the local computer and then filtering the data. I've heard about PQ query folding but I still prefer SQL.
My typical excel application will use SQL to pull data from an Enterprise DB. I load data into Structured Tables and/or Excel Power Pivot (especially if there's lot of data).
I like to have a Control Worksheet to enter parameters, display error messages and have user buttons to execute VBA. I use VBA to build/edit parameters used in the SQL. Sometimes I use parameter-based SQL. Sometimes I create a custom SQL String in a hidden worksheet that I then pull into VBA code (these may build a string of comma separated values that's used with a SQL include). Another SQL trick I like to do is tag my data with a YY-MM, YY-QTR, or YY-Week field constructed form a Transaction Date.
In an application, I like to create a dashboard(s) that may contain hyperlinks that allow the end-user to drill into data. Sometimes the hyperlink will point to worksheet and sometimes to a supporting workbook. In some cases, I use a double click VBA Macro that will pull additional data and direct the user to a supplemental worksheet or pivot table.
In recent years I like Dynamic Formulas & Lambda Functions. I find this preferable to pivot tales and slicers. I like to use a Lambda in conjunction with a cube formula to pull data from a power pivot data model. I.E. a Lambda using a cube formula to aggregate Accounting Data by a general ledger account and financial period. Rather than present info in a power pivot table, you can use this combination to easily build financial reports in a format that's familiar to Accounting Professionals.
One thing that PQ does very well is consolidating data from separate files. In the old days this was always a pain.
I've found that using SQL can be very trying (even for someone with experience). It's largely an iterative process. Start simple then use Xlookup (old days Match/Index). Once you get the relationships correct you can then use SQL joins to construct a well behaved SQL statement.
Most professional enterprise systems offer a schema that's very valuable for constructing SQL statements. For any given enterprise system there's often a community of users that will share SQL. I.E. MS Great Plains was a great source (but I haven't used them in years).
Hope this long reply has value - keep up the good work.