The 365 Data Science team is proud to invite you to our own community forum. A very well built system to support your queries, questions and give the chance to show your knowledge and help others in their path of becoming Data Science specialists.
Anybody can ask a question
Anybody can answer
The best answers are voted up and moderated by our team

Power BI Final Project Part 2

Power BI Final Project Part 2


In the Power BI Final Project Part 2 (when in the Power Query Editor), we remove all the attribute nulls from the data set but the whole column is null.
What do you click if you only want to remove only records with null in a specific column? like the middle name column.

At @ 3 minutes and 29 seconds.
I’m sure it’s easy, I just don’t know what to click.



One more concept question – is there any rule of thumb to key you into when You use the Power Query Editor and when you should build a measure in the report editor.  I know there is a question of speed and size of data, but if that is not an issue.  Is there any conceptual difference in how you decide?  Thanks!

1 Answer

365 Team

Hey Christina,
You definitely can filter out any rows with Blank columns. Using Power Query is a bit harder than using DAX so I will answer your question using DAX. The solution would require creating additional table inside the model. So create a new table using the New Table option and write the following expression:

ExpressionName = FILTER('Table Name', 'Table Name'[ColumnName] <> BLANK())
This will create a new table excluding the blank rows. Then you can hide the original table and connect this new table to your model.

Power Query Editor should be viewed like a cookie cutter for your data. It can do things like Unpivoting, removing blank rows, concatenating columns, etc. Then you import the modeled data into Power BI. Measures are just formulas that can be executed on the go. Instead of writing SUM(SamesAmount) every time you want to use it, you call out the measure and slice it by some dimensions such as customer, date, etc. I hope this makes things more clear 🙂

It does help. Thank you.

9 months
Complete Data Science Education
Get 50% OFF